# An Improved Optimization Algorithm Based on Density Grid for Green Storage Monitoring System

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## Abstract

**:**

## 1. Introduction

#### 1.1. Status of Research on Corn Storage Monitoring Data

- (1)
- Moisture content is an important indicator of corn quality.

- (2)
- The fatty acid value of corn is also an important indicator for quality testing.

- (3)
- Mold is another quality testing standard.

_{2}gas. The experiments showed that Aspergillus flavus can grow and produce toxins in maize of differing initial quality. The production of AFB1 was two to eight times higher in the group where Aspergillus flavus was the original dominant fungus than in the other test groups, and they all exhibited an increased rate of CO

_{2}production. The rate of mold growth and storage temperature affected the production of CO

_{2}and AFB1 in stored maize. The accelerated rate of CO

_{2}gas production was observed in all of the AFB1-producing maize. Comparing the change in the rate of CO

_{2}gas production in stored corn with the time of monitoring out AFB1, it was found that monitoring CO

_{2}gas could be more than 6 days earlier. Therefore, the characteristics of fungal CO

_{2}production in maize storage can be used to predict aflatoxin contamination in advance [17]. Hui C Y applied hyperspectral techniques to study and construct a monitoring method for aflatoxin B1 (AFB1) and zearalenone (ZEN) content in moldy maize. By creating a prediction model for these two toxins in moldy maize, timely, efficient, and accurate determination of the degree of moldiness of maize was achieved [18].

#### 1.2. Current Status of Research Applications of Data Processing Algorithms

## 2. Materials and Methods

#### Relevant Definitions of the Improved Algorithm

**Definition**

**1.**

**Definition**

**2.**

**Definition**

**3.**

**Definition**

**4.**

**Definition**

**5.**

**Definition**

**6.**

**Definition**

**7.**

**Proof.**

## 3. Correlation Modeling of the Improved Algorithm

#### 3.1. Adjacent Grid Determination

#### 3.2. Determination of a Boundary Mesh

Algorithm 1 Steps for determining the boundary grid | |

Input: Feature vector of the grid to be determined, summary storage mechanism W_List | |

Output: Bounding mesh identifier Bound | |

1: | if (adjacent mesh of input mesh does not exist in W_List) |

2: | return the grid is a boundary grid |

3: | else if (the density of adjacent grid cells of this grid are above the sparsity threshold Ds or the density of adjacent grid neighboring subgrids is above the subgrid sparsity threshold D _{s_little}) |

4: | return the grid is an interior grid |

5: | else{ |

6: | return the grid |

7: | }//end else if |

#### 3.3. Detection and Processing of Isolated Grid Cells

_{f}represents the time when the data last arrived.

#### 3.4. Microclustering Algorithm

Algorithm 2 Steps of microcluster clustering | |

Input: the set of internal grid cells in the dynamic grid | |

Output: set of grid clusters | |

1: | Detect all internal grid cells and update the grid cell feature vector |

2: | While (the class attributes within the set of dynamic internal grid cells no longer change) |

3: | { for(dynamic internal grid cell W1) |

4: | { if(dynamic internal grid cells of W1 exist adjacent to the internal grid W2) |

5: | compute Fac(W1,W2); |

6: | if (there exists an adjacent grid cell W2 of W1 such that Fac(W1,W2) is maximum) |

7: | group grid W2 and W1 into the same cluster. |

8: | else group grid W1 as a separate cluster and update flag Cla. |

9: | }//end for |

10: | }//end while |

#### 3.5. Clustering Algorithm for Boundary Subgrids

Algorithm3 Steps of clustering algorithm for boundary subgrid | |

Input: the boundary grid W1 and its subgrid feature vectors, the set of grid feature vectors | |

Output: the classes to which the subgrids of grid W1 belong. | |

1: | obtain information about the boundary grid and its subgrid feature vectors. |

2: | if (grid W1 adjacent grid has internal grid) |

3: | { for(internal grid W) |

4: | { for(subgrid cell Wlittle) |

5: | { if(subgrid cell density reaches subgrid density threshold Df-little) |

6: | if the subgrid adjacent grid has a large grid that has been divided into classes, calculate the density factor, group the subgrid into the class where the grid with the largest Fac is located, and update the subgrid cell feature vector. |

7: | else ignore this sub-grid, i.e., the clustering grid density requirement is not met. |

8: | }//end for |

9: | }//end for |

10: | }//end if |

## 4. Application of the Improved Algorithm in the Corn Storage Monitoring System

Algorithm4 Steps to improve algorithms in monitoring systems | |

1: | Initialize the division grid, initialize the grid maintenance cell W_List, current time t; |

2: | While (data flow is not finished) |

3: | { read in data item Y, preprocess the data, map to the corresponding grid, update the grid tuple and put it in the data structure information table W_List |

4: | if (t = T)//i.e. time reaches the first interval |

5: | adjudicate the boundary grid and form the initial grid according to the microcluster clustering algorithm. |

6: | if (t > T and t mod T = 0)//that is, the time to reach the second and subsequent intervals |

7: | { decay the grid density according to the decay function and update the grid tuple. |

8: | detection and processing of isolated meshes. |

9: | adjusting the boundary grid and internal grid information to the density grid microclusters. |

10: | starting a new thread to invoke the offline algorithm. |

11: | Offline clustering. |

12: | Matching analysis results. |

13: | Snapshot storage of summary information. |

14: | WebSocket message pushing. |

15: | }//end if |

16: | }//end While |

#### 4.1. Analysis of the Operational Effect of the Improved Algorithm

#### 4.1.1. Operation of the Improved Algorithm

#### 4.1.2. Comparative Analysis of the Improved Algorithms

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 8.**Comparison of processing time of Clu-Stream algorithm, D-Stream algorithm, and improved algorithm.

**Figure 9.**Comparison of processing time of D-Stream algorithm (length/2) and improved algorithm (length).

**Figure 10.**Comparison of clustering accuracy of Clu-Stream algorithm, D-Stream algorithm, and improved algorithm.

**Figure 12.**Comparison of the running time of the improved algorithm under single-node and multi-node conditions.

Monitoring Point | Molded Grain % | Moisture Content % | Capacity Weight g/L |
---|---|---|---|

1 | 0.0 | 10.9 | 741 |

2 | 0.0 | 11.1 | 738 |

3 | 0.1 | 11.0 | 742 |

4 | 0.0 | 12.7 | 551 |

5 | 0.0 | 12.7 | 756 |

6 | 1.2 | 12.2 | 753 |

7 | 0.0 | 12.0 | 732 |

8 | 0.6 | 15.1 | 743 |

9 | 3.0 | 12.8 | 620 |

10 | 1.2 | 17.5 | 638 |

11 | 0.2 | 13.6 | 560 |

12 | 4.0 | 10.9 | 652 |

13 | 6.0 | 10.6 | 667 |

14 | 5.5 | 10.4 | 785 |

15 | 0.0 | 9.4 | 757 |

16 | 1.0 | 22.9 | 687 |

17 | 1.5 | 21.3 | 705 |

18 | 0.2 | 22.0 | 700 |

19 | 0.0 | 12.9 | 769 |

20 | 0.0 | 13.3 | 746 |

Grade | Weight Capacity g/L | Mildew Grain Content % | Moisture Content % |
---|---|---|---|

1 | $\ge $720 | $\le $2.0 | $\le $14.0 |

2 | $\ge $690 | ||

3 | $\ge $660 | ||

4 | $\ge $630 | ||

5 | $\ge $600 | ||

Other | $<$600 |

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**MDPI and ACS Style**

Zhang, Y.; Zhu, Z.; Ning, W.; Fathollahi-Fard, A.M.
An Improved Optimization Algorithm Based on Density Grid for Green Storage Monitoring System. *Sustainability* **2022**, *14*, 10822.
https://doi.org/10.3390/su141710822

**AMA Style**

Zhang Y, Zhu Z, Ning W, Fathollahi-Fard AM.
An Improved Optimization Algorithm Based on Density Grid for Green Storage Monitoring System. *Sustainability*. 2022; 14(17):10822.
https://doi.org/10.3390/su141710822

**Chicago/Turabian Style**

Zhang, Yanting, Zhe Zhu, Wei Ning, and Amir M. Fathollahi-Fard.
2022. "An Improved Optimization Algorithm Based on Density Grid for Green Storage Monitoring System" *Sustainability* 14, no. 17: 10822.
https://doi.org/10.3390/su141710822