Advances on External Machine Computing Power Focusing on Internal Personal Value: A Case Study on the New Digital Currency

Abstract

As global inflation escalates and geopolitical conflicts exacerbate, the world’s economy confronts an intensified degree of instability. In this volatile environment, blockchain currencies emerge as a potential bulwark, offering both value preservation and liquidity benefits. However, the conventional “mining” process introduces significant challenges, such as high energy consumption, data security risks, and detachment from the real economy, which potentially facilitate financial capital manipulation. This research endeavors to mitigate these issues, constructing an innovative blockchain cryptocurrency framework that integrates mining and distribution with intelligent big data. It also incorporates social contributions from individuals in domains such as health, knowledge, and ecological conservation. Consequently, the efficiency of cryptocurrency production and distribution correlates with the individual’s societal contribution. The more substantial the contribution, the higher the intrinsic value of the individual and the more efficient the access. Utilizing a comprehensive framework of mathematical modeling, computer numerical simulation, and fuzzy integrated evaluation, we propose a novel endogenous-value blockchain cryptocurrency system. We quantify and optimize variables such as individual intrinsic value, community efficiency, redistribution weights, and total monetary potential. We introduce an innovative method for accumulating time-decaying values such as knowledge contribution and establish an anti-cheating framework. Our results indicate that this pioneering approach can significantly enhance mining efficiency and optimize cryptocurrency distribution. This counters traditional criticisms of blockchain currencies and paves the way for a more sustainable, fair, and efficient model for future blockchain currency systems.

1. Introduction

1.1. An Introduction to Traditional Cryptocurrency Technologies and Their Advantages

Compared to centralized currencies issued by central banks of various countries, blockchain-based currencies, commonly known as cryptocurrencies, are decentralized digital currencies with a capped total supply. They feature immutability, security and transparency, and resistance to inflation. Cryptocurrencies facilitate international transactions with faster transaction speeds and lower fees [1]. This endows cryptocurrencies with great potential in the fields of remittances and payments on a global scale. Representative blockchain-based digital currencies include Bitcoin, Ethereum, and Litecoin, among others.

The core technological aspects of cryptocurrencies involve production, distribution, trading, security, upgrades, and governance.

In general terms, the production of blockchain-based currencies mainly occurs through the mining process. Mining is the process of solving complex mathematical problems to verify transactions and add new blocks to the blockchain [2]. Miners consolidate these transaction records into a new block, then use computational power (computational resources) to solve the puzzle of this block. Once the problem is solved, the new block is added to the blockchain, and the miner receives newly created cryptocurrency as a reward [3]. However, the production efficiency of traditional cryptocurrencies is not sustainable due to their supply constraints, as many cryptocurrencies have a fixed total supply. For example, the total supply of Bitcoin is limited to 21 million coins [2]. This means that over time, new Bitcoins will be produced at an increasingly slower rate (e.g., the halving period for Bitcoin’s production rate is approximately every four years) until the maximum supply is reached. This supply constraint is intended to mimic scarce resources (such as gold) to increase their value [3].

In existing allocation models, newly created cryptocurrencies and transaction fees are distributed as rewards to miners or validator nodes [3]. This allocation method incentivizes more participants to join the network, enhancing its security and degree of decentralization [4]. Furthermore, users can directly trade cryptocurrencies in scenarios such as cryptocurrency exchanges, wallets, and applications.

Technically, blockchain-based currencies ensure network security and decentralization through consensus mechanisms. Consensus mechanisms are rules that nodes in the network must follow when verifying transactions and adding new blocks. The most common consensus mechanisms are proof of work (PoW) and proof of stake (PoS) [5]. PoW relies on miners investing computational resources to solve complex mathematical problems, while PoS is based on the amount of cryptocurrency held by users to verify transactions and create new blocks.

Security is also reflected in transparency and decentralization. A core feature of blockchain technology is its public, transparent distributed ledger [1]. This means that all transaction records can be publicly reviewed and are difficult to tamper with. This provides a high level of security for the production and distribution of cryptocurrencies. Blockchain-based currencies are not controlled by any central institution or government [4]. This helps prevent manipulation of currency supply and value while reducing reliance on a single central node. Decentralization makes cryptocurrencies less susceptible to political factors and financial crises.

Additionally, cryptocurrency transactions usually require the payment of certain fees. These fees are paid to miners or validator nodes to compensate for the resources they invest in maintaining network security and stability [3]. Transaction fees help prevent network congestion and spam transactions.

Cryptocurrencies are not controlled by governments but are governed by their digital currency community. The ecosystem of a cryptocurrency typically consists of an active community of developers and users [4]. These community members participate in decision-making processes such as protocol upgrades, hard forks, and soft forks to improve and maintain the blockchain network. Community governance helps ensure the continuous development and innovation of cryptocurrencies.

Through the above characteristics and references, the production and distribution framework of blockchain-based currencies provides stability for their supply and value while maintaining their decentralized, transparent, and secure features.

1.2. Potential Issues with Traditional Cryptocurrencies

Since the outbreak of the epidemic, countries have implemented monetary-easing policies to rescue their economies, but these measures have triggered inflationary pressures and trade barriers. Blockchain digital currencies are highly appreciated by people around the world due to their value retention and the convenience of cross-border payments. However, the existing “mining” system represented by Bitcoin and older-generation digital currencies has many criticisms such as the following: (1) It is thought to contribute to the energy crisis: Peplow notes that bitcoin mining used 40–62 TW·h of energy in 2018, with a carbon footprint of 19–30 million tons [6]. Some studies conducted in 2018 show that the total electricity required by Bitcoin is equivalent to the electricity consumption of some developed countries and regions such as Ireland, Hong Kong, and even Austria [7]. Similarly, Professor Brian Lucey of Trinity College Dublin said, “Bitcoin by itself can consume as much electricity as a medium-sized European country. That’s a staggering amount of electricity”. (2) In one Sybil attack, the attackers compromised the reputation system of a web service by creating a large number of pseudonymous identities and using them to gain a disproportionately large amount of influence. It is named after the subject of the book Sybil, a case study of a woman diagnosed with dissociative identity disorder [8]. (3) It involves a detachment from reality: Vlasov argued that Bitcoin belongs to the next stage in the evolution of money—an electronic currency that has no connection to objects in the material world [9]. The fundamentals of the real economy are now completely “decoupled” from blockchain digital currencies. A currency that is detached from reality is like a tree without roots, and even if it can be popular for a while, it will not be able to achieve long-term development. (4) Capital manipulation is involved: The current vulnerability of virtual currencies to capital manipulation poses the risk of malicious speculation. On 14 January 2022, Tesla (TSLA.US) CEO Musk tweeted that Tesla accepts dogcoin as a payment option for some of its goods, and as a result of this news, dogcoin pulled up over 20% during the day and is now up over 14% [10] …… These problems have resulted in mining seems to have become a synonym for inactivity, with the outcasts of this era affecting the consensus on its value. However, in this paper’s view, many of these problems can be solved by developing new “value endogenous” digital currencies in the context of smart city technology and reconstructing the logic of the production side and the supply and demand side through modern big data.

In the past, blockchain miners used special mining machines to mine currencies in the blockchain, and the speed of mining depended on the arithmetic power of their mining machines. Mining arithmetic is the technical term for the speed of mining and the ability of a computer to generate hash collisions per second in HASH/S [11].

In the process of obtaining blockchain currency through mining, the miner needs to find its corresponding solution, but there is no fixed algorithm to follow, so the miner can only rely on the computer’s random hash collisions; the higher the arithmetic power of the miner, the higher the reward, for example, energy consumption and environmental destruction, legal loopholes, data security risks, excessive virtualization, etc.

1.3. Objectives, Thought Process, and Approach

The author’s goal is to propose a new, feasible framework for the production and distribution of cryptocurrencies, addressing the shortcomings of traditional blockchain mining. The principles for mining improvement are as follows: firstly, to establish a strong correlation between cryptocurrency and the real economy; secondly, to make the mining process no longer an energy competition based on private “equipment computational power”; thirdly, to strive for the mining process to have a positive impact on promoting social civilization; and finally, to deviate from the traditional rate decay in mining and reduce the monopolistic influence of early participants on cryptocurrencies. It should be noted that the research in this paper is theoretical, aiming to comprehensively consider the feasibility and sustainability of the new model from a theoretical perspective. Moreover, the author illustrates the new model using typical simulation-based cases, comparing it with the old model through case studies.

The solution of this article is to combine the acquisition of blockchain currency with the intrinsic value of individuals under big data and to link the mining efficiency and transaction value of blockchain currency with people’s daily life data so that the value of “mining” is endogenous, truly reflects the social value of participants, and becomes a tool to promote civilization. Specifically, the author proposes that the total network computational power determines the total output of cryptocurrency per unit of time. As for the distribution of the total output, individual shares are determined by personal intrinsic value (the individual’s contribution to society). The higher the contribution, the more cryptocurrency is allocated; otherwise, less is allocated. Endogenous value must reflect a person’s social contribution in terms of health, environmental protection, knowledge [12], etc. With the use of smart software and big data about life, it is no longer difficult to obtain comprehensive access to big data about a person’s health. Furthermore, web records, intellectual property rights, and databases of academic results can also show an individual’s knowledge contribution. For instance, in terms of the calculation of knowledge contribution, Han Meng proposed a decay factor based on the Gaussian function for emphasizing the timeliness of knowledge value [13]. These technologies can all be applied to the measurement of individual intrinsic value.

Of course, mechanisms to prevent cheating are indispensable in the development of blockchain digital currencies. In terms of outlier detection, Zhou, H. proposed an improved LOF outlier detection algorithm [14], which has significantly improved detection accuracy and can effectively detect abnormal data.

With reference to the above methods, this paper proposes an incremental-decay calculation method for intrinsic-value knowledge contribution and conceptualizes an anti-cheating system mechanism to further improve the mining and distribution framework of intrinsic-value big data blockchain currency.

1.4. Article Structure and Main Innovations

The first section comprises the introduction, including the literature review, issues, thought process, and innovations. The second part mainly introduces the framework of the new mining model that incorporates intrinsic value, in which the allocation and total amount of cryptocurrencies differ from traditional cryptocurrency mining models. The third part inducts and quantifies the indicator system for intrinsic value. The fourth part presents a simple case of a full cryptocurrency mining network. The fifth part conducts quantitative research on the “knowledge” indicator of intrinsic value, considering the cooling and precipitation of the indicator over time. The sixth part explores the group contribution distribution mechanism. The seventh part investigates an anti-cheating mechanism. The eighth part is the conclusion.

This paper’s innovations are divided into two aspects: thought and technical implementation. The former mainly lies in incorporating intrinsic value into the production and distribution framework of blockchain-based cryptocurrencies, theoretically avoiding problems such as disconnection from the real economy, ecological damage, and financial manipulation caused by competing for computational power. The latter pertains to the establishment and quantification of the indicator system for intrinsic value, the determination of redistribution weights, and the implementation paths for group contribution distribution mechanisms and anti-cheating mechanisms.

1.5. Table of Parameter Meanings

We summarize the main parameters used in this article in

Table 1. Table of Parameter Meanings.

Symbol/ Variable	Meaning
t	The current time
Hi(t)	The self-produced mining power (or hash rate) of miner i’s device at time t
σ	The proportion of the feedback mechanism under the cryptocurrency promotion mechanism, which represents the proportional reward or benefit that miners receive for promoting the cryptocurrency or contributing to the system
Di(t)	The arithmetic contribution from the back-end promoter at time t, which represents the additional mining power or support that the back-end promoter provides to miner i
Si(t)	The intrinsic value of individual i at time t, which represents the individual’s value or contribution to the mining process
Q(t)	The mining output at time t, which represents the amount of cryptocurrency that has been successfully mined by all miners in the system up to that moment
Q1	The mining output under the traditional model, which refers to the amount of cryptocurrency that has been successfully mined by all miners using traditional mining methods
Q2	The mining output under the intrinsic-value big data contribution, which refers to the amount of cryptocurrency that has been successfully mined by all miners who have contributed their intrinsic-value data to the system
gi(t)	The instantaneous rate of mining for individual i at moment t, which represents the rate at which individual i is able to mine cryptocurrency at time t based on their intrinsic value Si(t) and the amount of cryptocurrency being mined by all miners in the system (Q1 + Q2)
n(t)	The total number of social miners in the society participating in the mining process of digital currencies at time t
S(t)	The total intrinsic value of society at time t, which represents the collective value or contribution of all social miners in the mining process
M1(t)	The total amount of potential money currently available for mining, which is obtained by multiplying the total intrinsic value of society accumulated over time (S(t)) by a constant factor
M2(t)	The potential number of future mines expected to be available at moment t, which represents the potential for additional cryptocurrency to be mined in the future
M(t)	The total amount of money expected to be available at moment t, which is the sum of the potential money currently available for mining (M1) and the potential future mines (M2(t))
ρ	The discount factor of the value with respect to time, which reflects the reduction in the value of the potential money available for mining over time
k(t)	The multiplicative factor at moment t, which is determined by the level of trading activity for the digital currency at that moment
It	The set of information available at moment t, which may include market data, mining difficulty, and other relevant information
w	The appropriate redistribution scale factor, which represents the factor by which the mining rewards or profits should be redistributed across the population to achieve a more equal distribution of rewards
t0	Initial time
α	The time-decay factor, which determines the rate at which the value of knowledge added by user i decays over time; a higher value of α corresponds to a faster decay rate
H	The gas temperature at the surface of the object, which represents the temperature of the cooling medium (e.g., air or water) in contact with the object.
Ni(t)	The temporal decay factor for weight i at time t, which represents the degree to which the weight assigned to the contribution made by user i in the past decays over time; it is a function of the time difference between t and t0
V1	Base allocation arithmetic, which represents the initial allocation of mining rewards or profits to each member of a group based on their headcount score (hc_score) and the average intrinsic-value score (ave_score) of the group
V2	Real-time redistribution of arithmetic, which represents the redistribution of mining rewards or profits in real-time based on factors such as changes in the headcount score, average intrinsic-value score, or other relevant factors
hc_score	Headcount score, which represents the number of members in a group participating in the mining process; the higher the headcount score, the greater the share of mining rewards or profits allocated to the group as a whole
ave_score	Average intrinsic-value score of the group, which represents the average value or contribution of all members of the group to the mining process; the higher the average intrinsic-value score, the greater the share of mining rewards or profits allocated to each individual member of the group

.

2. Framework for Mining and Distribution of Intrinsic-Value Big Data Blockchain Currencies

The new blockchain digital currency mining model closely integrates the distribution of digital currencies with real economic and life data through big data technology. Making the total amount, distribution, and value of digital currencies people-oriented means that they are presented according to the intrinsic value of people.

2.1. The Old Model—“Mining” under the Promotion Tree

In the traditional model, the currency rewarded through block calculation is directly distributed to the corresponding miners; instead of redistributing the currency generated by the whole network computing power among the miners, the cryptocurrency is allowed to spread rapidly and expand its scale among the population [15]. That is to say, many cryptocurrency mechanisms often adopt a three-level feedback mode, which feedbacks part of the computing power of level 1 and level 2 promoters to their superiors (the more level 1 and level 2 promoters, the greater the computing power) so as to encourage people to promote more miners through computing incentives and expand the number of people participating in their cryptocurrency community. Let us sketch the logic: let the self-produced arithmetic of miner i’s device be Hi(t), and given the feedback mechanism under the cryptocurrency promotion mechanism, the proportion σ and the arithmetic contribution from the back-end promoter is Di(t). (The computing power of one’s own equipment is deducted from the computing power contributed to the previous company plus the computing power contributed by the next company.) The effective arithmetic for oneself is shown below.

$$F_i(t)=(1-\sigma )H_i(t)+D_i(t)$$

(1)

The mining output at moment t is Q(t), and the instantaneous rate of individual mining in many of the popular blockchain digital currencies today is shown below.

$$g_i(t)=\frac{F_i(t)}{{\displaystyle {\sum }_i{F}_i(t)}}Q(t)$$

(2)

As Gencer said in 2018, the output of this model relies on both the computing power of the equipment and the marketing tree to gather digital currency for the front-end miners, which can easily trigger a financial monopoly of the head embrace and create inequality [16].

2.2. New Model—Intrinsic Value Participates in Arithmetic Redistribution

In the new model, the tree mechanism that facilitates marketing may still exist [17] but is diluted; meanwhile, at the same time, the intrinsic value of individuals is reinforced and linked to the production and distribution of digital currencies. The total output of digital currency remains dependent on the total arithmetic power of society-wide devices, but the data of individuals’ intellectual lives are involved in production and distribution in a blockchain model. Three models were thus designed.

2.2.1. Introduction of Three Models

In the first model, dynamic big data reflecting human values are directly coded into the production technology of blockchain digital currencies, making the intrinsic value of individuals positively correlated with arithmetic power. The advantage of this approach is that the intrinsic value is deeply correlated with the amount of money produced. The disadvantage is that an individual’s access to digital currency still depends heavily on the arithmetic power of their own device.

Second, the data that embodies the people-oriented value are not involved in the production of the currency. In this model, the total amount of network-wide currency depends on the sum of all computing power, but the currency is not distributed directly to individuals. Until then, they should be seen as a whole and then distributed according to the intrinsic value embodied in people’s big data. Table 1 briefly reflects the logic under this model.

The third model is a combination of the above two models. Big data embodying human values are involved in production and secondary distribution throughout.

2.2.2. Issues That the New Model Needs to Address

Technical complexity: Integrating dynamic big data reflecting human values into the production and distribution of blockchain digital currencies could be technically challenging. Developing the necessary infrastructure and algorithms to accurately measure and incorporate intrinsic value may prove difficult and time-consuming.

Data privacy and security concerns: The use of individuals’ intellectual life data and big data in these models raises concerns about data privacy, security, and potential misuse. Ensuring data protection and compliance with privacy regulations would be crucial in implementing these models.

Inaccurate or unfair value representation: There could be challenges in accurately capturing and quantifying the intrinsic value of individuals based on their data. This might lead to unfair distribution or misrepresentation of individual contributions, creating potential inequalities in the system.

2.2.3. Feasibility of Large-Scale Implementation

Scalability: The proposed models may face scalability issues as the number of participants in the network grows. Ensuring that the system can efficiently handle increased data processing and distribution demands would be critical for successful large-scale implementation.

Adoption and acceptance: Encouraging widespread adoption of the new models by various stakeholders including users, businesses, and governments could be challenging. Promoting awareness and understanding of the benefits of these models would be essential for fostering acceptance and facilitating large-scale implementation.

2.2.4. Costs Associated with Implementation

Infrastructure and development costs: Developing the necessary technological infrastructure to incorporate intrinsic value in the production and distribution of blockchain digital currencies could require significant investment. This might include costs for hardware, software, and skilled personnel (see

Table 2. Comparison of traditional and endogenous-value blockchain currencies in terms of “mining”, distribution, and trading.

	Total Amount Generated by “Mining”	Allocation	Transactions
Traditional blockchain currencies	Produce currency based on total network-wide computing power	Distribute based on individual device computing power	Risk of monopoly, malicious speculation, security, etc.
Big data value-endogenous blockchain currency		Allocation based on individual intrinsic value contribution	Allocation of relevant amounts according to need under the big data of life

).

Maintenance and operational costs: Implementing the proposed models may involve ongoing maintenance and operational costs, including data storage, processing, and system updates. These expenses should be considered when assessing the feasibility of the models.

2.2.5. Potential Regulatory or Legal Challenges

Compliance with data protection regulations: Using individuals’ data in the proposed models may require compliance with various data protection regulations, such as the General Data Protection Regulation (GDPR) in the European Union. Navigating these legal requirements could be challenging and require dedicated resources.

Uncertainty in cryptocurrency regulations: The legal and regulatory landscape surrounding cryptocurrencies and blockchain technology is constantly evolving. Implementing the proposed models may involve navigating uncertainties in this area, which could impact their feasibility and acceptance.

Using the second model as an example, the instantaneous rate at which individual i mines to produce currency at time t is shown below.

$$g_i(t)=\frac{S_i(t)}{{\displaystyle {\sum }_i{S}_i(t)}}Q(t)$$

(3)

In the example, S_i(t) is the intrinsic value of individual i.

Taking the third model as an example, the total monetary output at moment t is divided into two parts, i.e., Q₁ and Q₂, the former being the mining output under the traditional model and the latter being the mining output under the intrinsic-value big data contribution. Then, the instantaneous rate of mining for individual i at moment t is as follows:

$$g_i(t)=\frac{F_i(t)}{{\displaystyle {\sum }_i{F}_i(t)}}{Q}_1(t)+\frac{S_i(t)}{{\displaystyle {\sum }_i{S}_i(t)}}{Q}_2(t)$$

(4)

After clarifying the logical system, we specify below the algorithmic logic of the individual intrinsic value S_i(t).

The total amount of digital currency in the new model is different from the total amount of set fixed currency such as Bitcoin. The new model needs to guarantee a certain scarcity but also effectively prevent inflation and link to the intrinsic value of the society. The traditional blockchain digital currency has a certain total amount, the mining output has a half-life, and the mining efficiency of the first participants is much greater than that of the later participants, which is not enough incentive for the later participants to participate and therefore needs to be amended. However, it cannot make the total amount of currency infinite but should correspond to the total intrinsic value of the society. Suppose the total number of social miners, which refers to individuals or entities who participate in the mining process of digital currencies, is n(t), and the total intrinsic value of society is as given below:

$$S(t)={\displaystyle {\sum }_{i=1}^{n(t)}{S}_i(t)}$$

(5)

The total intrinsic value of society accumulated over time is then multiplied by some constant to obtain the total amount of potential money currently available for mining:

$$M_1(t)={\displaystyle {\int }_0^{t}k(\tau )S(\tau )}{e}^{-\rho (\tau -t)}d\tau$$

(6)

where ρ is the discount factor of the value with respect to time, and k(t) is the multiplicative factor at moment t. It is determined by how intensely people are trading that digital currency at moment t. If the transaction demand is large, k(t) is larger, and if the transaction demand is small, k(t) becomes smaller. In this way, the mineable blockchain digital currency always changes in concert with the transaction demand. Therefore, M₁ is not a fixed number but follows the demand for digital currency to adjust or develop naturally. The current mining method is to take M₁ as the total potential currency and carry on according to the mining model with a half-life, but as the intrinsic value of society increases, the number of participants increases, and the total potential amount also increases. Compared with the decaying speed of traditional mining such as Bitcoin, the decaying speed of the new model is much slower and even increases when the total intrinsic value of society grows faster. The total amount of money thus generated will not cause serious inflation because the total amount is always linked to the total physical value.

At moment t, we write down the set of information as I_t. The potential number of future mines is expected to be as follows:

$$M_2(t)= {\displaystyle {\int }_t^{\infty }E[k(t)|I_t]}k(t)E[S(\tau )|I_t]e^{-\rho (\tau -t)}d\tau$$

(7)

The total amount of money is expected to be as follows:

$$M(t)=M_1(t)+M_2(t)$$

(8)

3. Structure of the Indicators of the Individual’s Intrinsic Value

Intrinsic value is the result of a comprehensive evaluation of several aspects, and therefore, S_i(t) is calculated by weighting a system of indicators. This paper puts forth that in modern society, the value of the individual is mainly reflected in four dimensions: “health, knowledge, environmental protection, and other factors”. According to Shiva Johri, blockchain technology has shown tremendous potential in the fields of accounting and auditing. It can simplify financial reporting and auditing processes by providing an integrated, long-term accounting record structure and encrypted, secure distributed ledger [18]. This also applies to our proposed framework for endogenous-value blockchain currency. By utilizing the distributed storage feature of blockchain technology, we can ensure the security and reliability of various types of personal value data such as health, knowledge, and environmental protection.

Table 3. Indicators and weights for each level of individual intrinsic value.

	Total Amount Generated by “Mining”	Allocation
Health contribution X1 W1	Physical fitness X11 W12 Quality of movement X12 W12 Sleep quality X13 W13 Quality of diet X14 W14	Body mass index (BMI), age, illness, genderItem, intensity, pattern Time, regularity, depth Variety, quantity, habits
Knowledge contribution X2 W2	Declarative knowledge X21 W21 Procedural knowledge X22 W22 Innovative knowledge X23 W23	Teaching, media, science, etc. / Publications, academic papers, patented inventions
Ecological contribution X3 W3	Ecological footprint reduction (also available as carbon footprint) X31 W31 Ecological carrying capacity contribution X32 W32	Arable land, building land, forests, fossil energy, grassland, sea Resources such as land, water, forests, and minerals
Other contributions X4 W4	Entertainment and socializing X41 W41 Charity record X42 W42 Morality X43 W43	Project, amount / Literature, art

shows the composition and weighting of the lower-level indicators of the intrinsic value of the individual S_i(t). These indicators include both quantitative and textual data, which require the use of common fuzzy mathematical or big data techniques. Moreover, the weights can be determined using hierarchical analysis [19], entropy weighting, topsis method [20], etc.

$$S_i(t)={\displaystyle {\sum }_j{X}_j{W}_j}={\displaystyle {\sum }_j{W}_j{\displaystyle {\sum }_s{W}_{js}}{X}_{js}}$$

(9)

3.1. Health Contribution

In a smart city, the mining of blockchain currency leaves the miner itself and is instead linked to health. The health of each person is analyzed by big data records, and the mining arithmetic of each person is calculated according to different health factors. In this paper, two directions were chosen to combine with blockchain currencies.

(i)

Basic health direction: This is done through a complete physical examination test, including height, weight, body organs, etc., which in turn forms an objective assessment of each person’s own physical condition. This process is known as “mining” at a basic level, where each person’s level determines his or her “mining” power;

(ii)

Active health direction: In addition to the daily health assessment, people can also improve their health calculations through their own efforts. Examples include stepn and strava, which try to influence people’s lifestyles with various exercises such as running and cycling. People can develop a healthy lifestyle through exercise, receive rewards from the system, and exchange ideas with other members of the community. Furthermore, people can use their health bracelets to record real-time data such as sleep and diet and synchronize these data with their mobile phones, tablets, or other electronic devices [21] and upload them to the big data processing center in time for scientific processing and analysis, which dynamically affects the amount of arithmetic power.

3.2. Knowledge Contribution

Apart from health, the combination of mining and the knowledge dimension cannot be ignored. The American scholar Machlup defined knowledge from a philosophical–epistemological perspective as “an objective interpretation based on what has been known” [22]. However, such a definition cannot reflect the phenomenon of knowledge proliferation—the dynamic and developmental nature of knowledge. According to the author, knowledge can be divided into declarative knowledge (mainly teaching and learning), procedural knowledge (mainly engineering industry), and innovative knowledge (mainly scientific research). Declarative knowledge encompasses symbolic representations, concepts, and propositions; procedural knowledge is the application of declarative knowledge, and its basic structure is active or generative. Innovative knowledge is primarily the in-depth study and exploration of unknown areas. The combination of knowledge contribution and the arithmetic of mining here is never simply based on the number of papers published but requires a multi-dimensional nature and must be well established as well as a self-learning evaluation system and intelligent system. In the case of the first type of knowledge, teaching is the transmission of existing knowledge, whether general knowledge or specialist knowledge, and the executive is judged according to its depth, breadth, and impact; the higher the score, the more arithmetic people are rewarded. The second type is the translation of existing knowledge. It may take a long time to translate a result, and there are many different people applying the same theory, but in the blockchain, the rate of translation of each person’s result can correspond to the proportion of the reward for mining. The third type of knowledge is a combination of the first two, which can be either a complement and innovation of declarative knowledge or a completely new application of procedural knowledge. For innovation, it is an important step in the materialization of mining. Previously, many scientific innovations were not completely understood due to the limitations of the times, such as who was the first to propose them and who was the first to apply them and similar issues. It may be modified, improved, or even overturned in subsequent blocks, but this does not affect the blockchain’s record of its initial value. In a smart city, everyone’s intellectual property can be protected to the greatest extent possible, and everyone can be rewarded for their knowledge. Relying on the support of big data, people rely on their knowledge and contributions to obtain the corresponding blockchain currency and wealth. Such measures should theoretically bring full potential to smart cities and inject a constant stream of energy into technological innovation and knowledge dissemination.

3.3. Ecological Contribution

As an indispensable part of a smart city, environmental protection is inseparable from our lives, e.g., waste sorting, water and electricity saving, low-carbon travel, etc. Often, people may have understood the importance of environmental protection, but their concrete implementation has not been satisfactory. This is where blockchain currency augmented by big data can play a deeply motivating role. First of all, from the perspective of individuals, each person’s recorded environmental behavior can be converted into a corresponding environmental factor, and the more environmental factors people have in the same period of time, the higher their blockchain mining arithmetic power, thus earning more blockchain currency. For example, statistics on people’s use of computers, lights, washing machines, TVs, underfloor heating, air conditioning, and other equipment in a day can be converted into carbon dioxide emissions and recorded in the blockchain; the lower the carbon emissions, the more arithmetic power the whole system rewards. This is a two-way interactive incentive mechanism that can effectively promote urban environmental protection to break the “last metre”. Secondly, in terms of smart cities as a whole, each smart city is allocated a certain amount of total CO₂ emissions at the beginning of each phase depending on the unique industrial base and natural environment of each city. Above a specified amount, a portion of the city’s hash rate can be used to purchase the corresponding amount of carbon emissions from the state or other cities, or the “air-purifying function” of an ecological forest of a certain duration, size, and species can be purchased from the owner of the ecological forest. What is purchased from the forest owner is not the possession of the forest but the carbon dioxide absorbed by the forest [23]. Through such a carbon-trading mechanism, we can effectively reduce the original cost of environmental protection and help to “green” the smart city.

3.4. Other Contributions

The endogenous value of a complete blockchain currency is not limited to the three basic areas of health, knowledge, and environmental protection but should be fully integrated with the smart city and people’s social behavior, including entertainment and social interaction, charitable giving, ethics, literature, and arts [24]. In this way, people’s daily lives will always reflect the changes in mining speed. If their behavior can have a positive effect on society, the mining speed will be increased; on the contrary, those who impair the interests of others and society will be punished by the system by reducing the mining speed or even imposing a fine.

4. Case Calculations for the Basic Framework of the New Model

4.1. Simplified Comparison of Traditional and Novel Models

Today, there are two blockchain digital currencies, namely A and B. A is the traditional blockchain digital currency, and B is the endogenous-value-based blockchain digital currency that this paper sets up.

For the purpose of simplification, this article studies a four-tier blockchain cryptocurrency network with only seven people and agrees that coin B will be endogenously redistributed in value by drawing 60% of the arithmetic power of model A. The seven-person relationship network is shown in Figure 1, with Andy as the first tier; Bob and Elon as the second tier, i.e., part of the direct influence by Andy; Lucy and Jim as the third tier, indirectly influenced by Joe’s influence and directly influenced by Andy; and Lee as the fourth echelon, directly influenced by Jim.

Table 4. Rules for the production and distribution of the two currencies.

Currency	The Total Amount of “Mining” Produced	The Miner’s Immediate Computing Power
A	Money is produced based on the whole network	Each person receives 20% of the computational power from their first-level referrals in the promotion chain and 10% of the computational power from their second-level referrals
B		According to the calculation power of A mode, 60% is drawn out for endogenous value redistribution

presents the mining rules for traditional cryptocurrency A and new cryptocurrency B under the new model. Both cryptocurrencies are generated by the total network computational power, but the allocation of effective computational power and currency distribution models differs. For currency A, each person receives 20% of the computational power from their first-level referrals and 10% from their second-level referrals in the promotion chain. In a typical case involving seven members, the computational power of each member’s device, the transferred computational power, and the contributed computational power are all listed in

Table 5. The status of seven people mining A coins at time.

Name	Equipment Computing Power	Feedback	Transfer Out	Net Computing Power	Gain
Joe	500 K	730 K	0	1230 K	24.6
Andy	1000 K	160 K	200 K	960 K	19.2
Bob	1400 K	0	280 K	1120 K	22.4
Elon	1000 K	0	200 K	800 K	16
Lucy	200 K	0	60 K	140 K	2.8
Jim	300 K	120 K	90 K	330 K	6.6
Lee	600 K	0	180 K	420 K	8.4
Total	5000 K	1010	1010	5000 K	100

. Furthermore, in Table 5, we calculate the total network computational power and the amount of currency allocated to each member based on their effective computational power (net computing power).

In

Table 6. The status of seven people mining B coins at time.

Name	Raw Net Computing Power	Health	Knowledge	Ecology	Other	Value	Gain
		Weight 0.3	Weight 0.3	Weight 0.3	Weight 0.1
Joe	1230 K	−2	9	3	4	3.4	21.2
Andy	960 K	1	4	2	0	2.1	14.8
Bob	1120 L	6	0	−4	7	1.3	13.3
Elon	800 K	4	2	−10	0	−1.2	2.4
Lucy	140 K	0	0	8	1	2.5	9.5
Jim	330 K	−1	1	10	8	3.8	15.4
Lee	420 K	6	10	4	0	6	23.5
sum	5000 K	14	26	13	20	17.9	100

, the algorithm for currency is shown below:

$$g_i(t)=\frac{F_i(t)}{{\displaystyle {\sum }_i{F}_i(t)}}\frac{2}{5}Q(t)+\frac{S_i(t)}{{\displaystyle {\sum }_i{S}_i(t)}}\frac{3}{5}Q(t)$$

(10)

Jim and Lee, who are environmental- and health-conscious, have a low device count and are at the end of the spectrum, but because of their relatively high personal value, the amount of currency acquired is larger. This is something that cannot be achieved with traditional blockchain digital currencies.

As seen in Figure 2, we can see that traditional blockchain digital currencies show a decreasing trend as the mining tier decreases, while endogenous-value-based blockchain digital currencies vary in value obtained depending on the weighting of different areas. The former developers and the first group of people to hold the currency have the most to gain, as the asymmetry of information creates a monopoly of capital that is detrimental to social development. The latter, on the other hand, allows each individual to reap more rewards in the areas that they are good at or interested in and less digital currency in their less-proficient areas, allowing for a form of resource allocation in society through a combination with big data.

4.2. Practical Steps for the Proposed Goal of an Endogenous-Value-Based Blockchain Digital Currency

This passage highlights the differences between traditional blockchain digital currencies and endogenous-value-based blockchain digital currencies. It explains how the latter allows for more equitable distribution of rewards based on individual strengths and interests, while the former benefits early adopters and developers disproportionately.

In order to achieve the proposed goal of implementing endogenous-value-based blockchain digital currencies, this paper outlines the practical steps that connect to the concepts discussed in the passage:

• Identifying key value areas: To ensure that the endogenous-value-based blockchain digital currency system effectively rewards individuals based on their strengths and interests, it is crucial to identify the key value areas that will be weighted in the system;
• Developing a value-weighting mechanism: The system must create a mechanism that fairly and accurately assigns value to individual contributions in the identified areas. This mechanism should be transparent, well documented, and adjustable to account for changes in societal values and priorities;
• Integrating with big data: In order to allocate resources effectively, the system should be able to access and analyze relevant big data sources. This will help the system assess an individual’s performance in their area of expertise or interest and allocate digital currency rewards accordingly;
• Ensuring security and privacy: As the system will be dealing with personal information and financial transactions, it is essential to implement strong security measures and protect user privacy;
• Piloting the system: The endogenous-value-based blockchain digital currency system must be tested in a controlled environment to evaluate its performance, assess potential issues, and gather user feedback;
• Refining and scaling up: Based on the results of the pilot study, we must make any necessary refinements to the system and gradually scale up its implementation to a wider audience.

By following this roadmap, this paper establishes a connection between the concepts discussed in the passage and the practical steps needed to achieve the goal of implementing an endogenous-value-based blockchain digital currency system. This system will allow for more equitable distribution of resources and foster social development.

4.3. Optimisation of Redistribution Ratio and Realisation Scheme

In (4), the total monetary output at moment t is divided into two parts Q₁ and Q₂, with the former being the mined output under the traditional model and the latter being the mined output under the intrinsic-value big data contribution. This section explores the proportion of currency involved in redistribution in coin A.

$$g_i(t)=\frac{F_i(t)}{{\displaystyle {\sum }_i{F}_i(t)}}\frac{2}{5}Q(t)+\frac{S_i(t)}{{\displaystyle {\sum }_i{S}_i(t)}}\frac{3}{5}Q(t)$$

(11)

To optimize the algorithm, the results are derived in this paper in the form of control variables on a guaranteed ${\mathit{w}}_{\mathbf{1}}+{\mathit{w}}_{\mathbf{2}}$ $=$ 1 constraint to find the appropriate redistribution scale factor, and the results are shown in Figure 2.

As can be seen from Figure 2, when ${\mathit{w}}_{\mathbf{1}}$ = 1.0, the distribution method fully inherits the traditional way of distribution by arithmetic power, which cannot bring out the endogenous value of blockchain digital currencies; when ${\mathit{w}}_{\mathbf{1}}$ < 0.3, some users have a negative return, which damages people’s own interests and can discourage users from mining.

An algorithm has to be continually updated to achieve longevity. Therefore, while keeping the fundamentals intact, the goal to improve the intrinsic-value algorithm by regularly fine-tuning the strategy based on feedback from real data to ensure that everyone can successfully engage in mining and that those who contribute more to the intrinsic value obtain something out of it, creating a technical climate where good money drives out bad money.

For each user, we need to ensure the following:

$$g_i(t)=w_1\frac{F_i\left(t\right)}{{\sum }_i{F}_i\left(t\right)}Q(t)+w_2\frac{S_i\left(t\right)}{{\sum }_i{S}_i\left(t\right)}Q(t)$$

(12)

This paper will therefore determine the w₁ and w₂ that exactly make g i = 0 and finalize the necessary equation:

$$w_1^{\prime }=w_1+\epsilon$$

(13)

$$w_2^{\prime }=w_2-\epsilon$$

(14)

where ε is a small number greater than zero.

Specifically, according to Formula (4), at time t, the total monetary output from mining is divided into two parts, i.e., Q₁ and Q₂, with Q₁ being the output under the traditional mining model and Q₂ being the output based on the intrinsic-value big data contribution. This section aims to explore the proportion of currency involved in redistribution in coin A.

To optimize the algorithm, this paper uses the method of control variables and presents the results in the form of a guaranteed constraint of 1 to find the appropriate redistribution scale factor. As shown in Figure 2, when w₁ = 1.0, the distribution method fully inherits the traditional way of distribution by arithmetic power, which cannot bring out the endogenous value of blockchain digital currencies. When w₁ < 0.3, some users may have negative returns, which can damage their own interests and discourage users from mining.

To ensure that everyone can successfully engage in mining and that those who contribute more to the intrinsic value obtain more out of it, creating a technical climate where good money drives out bad money, the algorithm needs to be continually updated to achieve longevity. Therefore, the goal of this paper is to improve the intrinsic-value algorithm by regularly fine-tuning the strategy based on feedback from real data while keeping the fundamentals intact.

5. The Expansion of the Knowledge Dimension

The new model aims to incorporate individual intrinsic values such as health, environmental protection, and knowledge into the mining and distribution process of blockchain digital currencies. By doing so, the model can better reflect the social contributions and real-world value of participants.

In this context, the expansion of the knowledge dimension highlights the developmental nature of knowledge as an intrinsic value. Unlike health and environmental dimensions, which have more fixed or measurable values, knowledge can continue to grow and develop over time. People can benefit from their knowledge contributions by continually adding to their expertise and skills.

The time-decay factor is relevant because it considers the timeliness and relevance of an individual’s knowledge contribution. As knowledge evolves, and new information becomes available, older contributions may lose value or relevance. By incorporating a time-decay factor in the new blockchain model, the system can adjust and reward participants based on their continuous contributions and the current relevance of their knowledge. By incorporating the time-decay factor into the proposed new blockchain model, the system can dynamically adjust the intrinsic value of an individual’s knowledge contributions, ensuring that the mining and distribution of digital currencies are more closely linked to the real-world impact and relevance of participants’ expertise. This section will explore the intrinsic value of knowledge in relation to the time-decay factor.

5.1. The Intrinsic-Value Function in the Context of the Time-Decay Factor

In the model described in the previous section, we define the value of knowledge as ${\mathit{U}}_{\mathsf{\tau },\mathit{i}}$ added by user i in year τ.

$$U_{\mathsf{\tau },i}\left(0\right)=\sum _s{W}_{2s}{X}_{2s}$$

(15)

The previous model leads to the problem that if a user has already made a very significant contribution at the knowledge level, he or she will enjoy near-permanent dividends without additional effort, which inevitably leads to a poor allocation of resources. Therefore, this article has tried and compared several different decay algorithms as follows.

5.1.1. Fractional Linear Time-Decay Function

First, we consider one of the simplest fractional linear time-decay functions [25], as shown in Formula (12).

$$f(|t-t_0|)=\frac{1}{1+\alpha |t-t_0|}$$

(16)

where α represents the time-decay factor, which determines the decay rate of f (|t − t₀|); t represents the current time, and t₀ represents the time when the user last made a knowledge contribution; T − t represents the time difference, and f (|t − t₀|) decreases as the time difference increases, taking values in the range (0, 1).

Using the fractional linear time-decay function, we can construct the following expressions:

$$N_i\left(t\right)=\frac{0.8}{1+\alpha t}+0.2$$

(17)

where the constant term 0.2 represents the portion retained permanently, α represents the time-decay factor, which varies from system to system depending on the actual situation, and t is the time elapsed since the user made the knowledge contribution.

5.1.2. The Ebbinghaus Forgetting Curve

The famous German psychologist Hermann Ebbinghaus experimentally studied the changes in human memory forgetting, as shown in

Table 7. Ebbinghaus’s law of forgetting.

Time Interval	Memory Retention/%
Just finished memorizing	100
After 20 min	58.2
After 1 h	44.2
After 8–9 h	35 8
1 day later	33.7
2 days later	27.8

. The table shows that most human memories are forgotten within the first day, after which the rate of forgetting begins to level off, and eventually, only a small portion is retained [26].

The tabular data were plotted as an Ebbinghaus forgetting curve, as shown in Figure 3.

It can be seen that the portion of memory retention decays gradually with time, but the trend of decay is faster and then slower. Therefore, this paper can achieve a similar forgetting effect by fitting an exponential function with the parameters shown in Formula (14).

$$W_t=A{e}^{Bt}+C{e}^{Dt}$$

(18)

The final fitted function for the Ebbinghaus forgetting curve is as follows:

$$W_t=65.79e^{-61.32t}+34.21e^{-0.0181t}$$

(19)

The results of the fit are shown in Figure 4.

In conjunction with the Ebbinghaus forgetting curve, this article considers a time-decay method to highlight the importance of the values contributed in the most recent period. For each weight, we multiply by a time-decay factor x:

$$N_i\left(t\right)=0.8(65.79e^{-61.32t}+34.21e^{-0.0181t})+0.2$$

(20)

Bringing in W_t gives the following:

$$N_i\left(t\right)=0.8 \mathrm{\ast } W_t+0.2$$

(21)

5.1.3. Newton’s Law of Cooling

Newton’s law of cooling refers to the transfer of heat from an object to the surrounding medium to cool itself when its temperature is higher than the ambient temperature [27]. Newton’s law of cooling does not take into account the nature of the object and is not a physical equation; it is a mathematical differential equation about the temperature of an abstract object that simply decreases with time, as shown in Formula (18). The Newtonian Leibniz formula can be used to calculate the definite integral of a function by finding the indefinite integral, which is obtained by solving the definite integral according to the formula for the density of heat flow on the surface of the object and the Newtonian Leibniz formula for the degree of time decay, as shown in Formula (19).

$$\frac{dT\left(t\right)}{dt}=-\alpha (T(t)-H)$$

(22)

$$T(t)=T_0 \ast e^{-\alpha (t-t_0)}$$

(23)

where the minus sign represents the cooling process, H is the gas temperature at the surface of the object, α is the cooling factor, t₀ is the time at which the user has contributed knowledge in the most recent time, and T(t) is the surface temperature of the object, which is T₀ at time t₀.

With reference to the cooling process of the object, we consider a time-decay method to highlight the importance of the value contributed in the most recent period. For each weight, we multiply by a temporal decay factor N_i(t):

$$N_i\left(t\right)=0.8 \ast N_0{e}^{-\mathsf{\alpha }\left(t+l\right)}+0.2$$

(24)

where α > 0 is the exponential decay constant.

As can be seen from the function, at time lim t → ∞, N_i(t) → 0.2. This partly protects the permanent historical contribution of the user.

Considering that under normalization, N_i(t) decays with contribution creation time from 1 and decays to 0.3 after 30 years (the number of years the state protects patents), we obtain the following:

$$0.8 \ast N_0{e}^{-\mathsf{\alpha }l}+0.2=1$$

(25)

$$0.8 \ast N_0{e}^{-\mathsf{\alpha }\left(20+l\right)}+0.2=0.3$$

(26)

We can thus obtained the following:

$$\mathsf{\alpha }=\frac{1}{20}ln\left(\frac{1}{0.125}\right)$$

(27)

$$l=\frac{1}{\mathsf{\alpha }}ln\left(\frac{N_0}{1}\right)$$

(28)

Let $N_0=1,\mathrm{s}\mathrm{o} \mathsf{\alpha }=0.104,l=0,N_i\left(t\right)=0.8 \ast e^{-0.104t}+0.2$

The image of the function is shown in Figure 5.

Thus, it obtains the intrinsic value created in year zero, corrected for the addition of a time-decay factor, decaying over year t as $U_{0,i}\left(t\right)$.

$$U_{0,i}\left(t\right)=\left(0.8 \ast e^{-0.104t}+0.2\right)U_{0,i}\left(0\right)$$

(29)

Therefore, the cumulative value $S_i\left(t\right)$ in year t is as given below:

$$S_i\left(t\right)=\sum _{\mathsf{\tau }}^t{U}_{\mathsf{\tau },\mathrm{i}}\left(t\right)N_i(t-\mathsf{\tau })$$

(30)

5.2. Calculation of the Case with the Addition of Time Cooling

To simplify the model, for the time being, only the intrinsic knowledge value gains of Joe, Andy, and Bob are considered. For comparison purposes, the total value of the three individuals over the last 30 years is assumed to be equal, as follows in

Table 8. The value of knowledge created by three people in 30 years.

Name	Year 1 Value	Year 2 Value	Year… Value	Year 29 Value	Year 30 Value	Total
Joe	3.1	0.1	0.1	0.1	0.1	6
Andy	0.1	0.1	0.1	0.1	3.1	6
Bob	0.2	0.2	0.2	0.2	0.2	6

.

Their cumulative returns over 30 years were calculated using a fractional linear time-decay function (α = 0.1), B Ebbinghaus forgetting curve fitting function, and C faux Newtonian cooling law function, respectively, as shown in Figure 6.

In Figure 6, it can be seen that in the absence of a decay factor, Joe as an early contributor gains a great deal over time; under the fractional linear time-decay function, Joe’s early cumulative gains are huge, while Bob’s cumulative gains gradually approach Joe’s as time increases; under the Ebbinghaus forgetting curve fitting function, it is still Joe who gains the most, while Bob, who can make a steady contribution, gains under the Newton’s law of cooling function; Joe’s early cumulative returns are still large, while Bob’s cumulative returns approach Joe’s as time increases and eventually overtake him at around 28 years, which this paper encourages as an incentive for users to continue to contribute (Bob) rather than sit on dividends (Joe) or look to speculate (Andy).

6. Group Contribution Distribution Mechanism

Based on the previous mining model and considering the community governance model of cryptocurrencies, the incentive mechanism for community groups can help improve efficiency during the promotion process. Considering the new blockchain model discussed earlier, which emphasizes the inclusion of individuals’ intrinsic values in the mining and distribution process, we will now explore a group contribution distribution mechanism that specifically addresses the intrinsic value of knowledge, allowing users to spontaneously introduce new participants and actively contribute to the system’s overall endogenous value.

This paper defines a person’s neighborhood as follows: first-level subordinates, second-level subordinates, and superiors. One’s neighborhood and oneself form a group.

For the allocation of group value, this paper considers two types of allocation:

A. Base allocation arithmetic V₁: V₁ takes into account group size and average intrinsic group value, encouraging the construction of good, active groups rather than bloated but mixed or refined but lukewarm groups.

$$V1=\frac{{\mathsf{\sigma }}_1\ast hc\_score+{\mathsf{\sigma }}_2\ast ave\_score}{{\mathsf{\sigma }}_1+{\mathsf{\sigma }}_2}$$

(31)

where hc_score is the headcount score., and ave_score is the average intrinsic-value score of the group.

B. Real-time redistribution of arithmetic V₂: V₂ redistributes value according to a real-time horse-race mechanism where each group needs to PK (based on arithmetic, value) 358 against groups of the same rank.

Group contributions are allocated in the manner, as shown in Figure 7.

$$g_i=w_1\frac{F_i\left(t\right)}{{\sum }_i{F}_i\left(t\right)}Q\left(t\right)+w_2\frac{S_i\left(t\right)}{{\sum }_i{S}_i\left(t\right)}Q\left(t\right)>0$$

(32)

The algorithm flow is as follows:

(1)

Determine the initial allocation based on the base allocation V₁ and the group’s historical contribution value score;

(2)

Evaluate the data based on the group’s real-time contribution value score;

(3)

Create a real-time group ranking based on the data evaluation results;

(a)

If the assessment result is higher than the previous group in the same tier, the group moves to the next value tier while returning to 2;

(b)

If the assessment result is lower than the previous group in the same tier, the real-time reallocation of arithmetic power to that group is reduced.

For the determination of the value hierarchy, this paper can adopt the unsupervised K-means clustering algorithm for implementation:

Step 1. Obtaining populations with different initial hierarchies based on different base allocation.

Step 2. Selecting groups and randomly initializing their respective centroids.

Step 3. Classifying each group by calculating the distance between the group and each centroid and then classifying this group into the cluster of groups closest to it.

Step 4. Recalculating the group centroids based on these classification categories by taking the mean of all vectors in the group of groups.

Step 5. Repeating these steps for a set of iterations.

7. Anti-Cheating System Mechanism

Given the potential for speculation and market manipulation within the newly proposed blockchain model, it is imperative to design an effective anti-cheating mechanism to safeguard the stability and integrity of the digital currency ecosystem. As blockchain becomes increasingly integrated with daily life, computational power becomes deeply entrenched in people’s routines. The scale of this power consequently determines differing returns for each individual. Inevitably, this means that there will be attempts at market speculation, a behavior that often exacerbates market bubbles, leading to severe financial shocks and crises and, subsequently, societal instability.

Notably, anti-cheating methods have evolved in tandem with the sophistication of cheating. For instance, the International Chess Federation (FIDE) and the Association of Chess Professionals (ACP) have established a permanent Anti-Cheating Committee (ACC) in response to the risks associated with cheating in computer-assisted chess competitions. Furthermore, Kristoffer la Cour incorporated a hash function to preclude data tampering within a system, ensuring consistency between output values [28].

In 2009, Craig Gentry proposed the Homomorphic Cryptosystem, a system that leverages a private key and a public key. In this system, the public key serves as a function to perform calculations on encrypted data, while the private key is used for the encryption and decryption of values [29]. Drawing from these precedents, a robust anti-cheating system is indispensable within an endogenous-value blockchain cryptocurrency system to preempt such scenarios. The flow of the anti-cheating mechanism is depicted in Figure 8.

According to this paper, a perfect anti-cheating system needs to have the following three steps: detection, analysis, and feedback. Firstly, through the corresponding detection model, the arithmetic situation and mining results of all people are recorded in real time. Generally speaking, the data are normally distributed, but there are also some outliers, which the system will transfer to the data processing center after detection. In a second step, the data central analyzes the data and retraces it through multiple solutions such as GPS, dynamic sensing, and machine learning [30]. If the data are found to have been obtained in a reasonable manner, then the amount of power will be deducted, and the user will be given a warning; after three warnings, the user will be punished.

By establishing a perfect anti-cheating mechanism, we can effectively avoid arbitrage and cheating and ensure that everyone can obtain the arithmetic rewards and benefits they deserve in a fair and just environment, thus maintaining the healthy development of the whole blockchain community.

8. Conclusions

In the future of smart life, the mining and distribution of the new generation of blockchain currencies should be integrated with real-time big data, no longer relying on the arithmetic power and graphics cards of individual devices and avoiding immeasurable harm to the environment while instead being intertwined with all aspects of people’s lives. The blockchain digital currency, with deep involvement of smart life data, is a reflection of the materialization of mining and the return of intrinsic value. The mining system of the new generation of blockchain currency not only enables people to create corresponding material wealth but also helps people’s health, promotes the construction of a green ecological environment, avoids harming and wasting electronic devices, and sets off a wave of charity and public welfare in the society. This article enables readers to better understand the value of the existence of endogenous-value-based blockchain digital currencies through simple case calculations and solutions to problems that may arise in practical applications. Of course, we can also purposefully promote the development of different fields such as health, knowledge innovation, or environmental protection by flexibly adjusting the weights of each indicator to truly internalize value. The only way for the blockchain currency, which is not favored at the moment, to achieve intelligent interconnection with everything inside and outside people’s bodies and minds and to maximize the return of economic entities with the help of big data is to bring its proper value into play for promoting the development of social civilization.