# Research on Optimization of Food Industry Processing Process Based on Computational Intelligence

Authors: Manli Li; Shuo Li
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

Computational intelligence refers to an algorithm inspired by the body structure of natural animals and plants and unique landscapes. It can perform simultaneous multithreaded work. In the processing process of the food industry, multiple factors must be considered at the same time to optimize various parameters simultaneously. Therefore, this research combines computational intelligence algorithms with the processing flow of the food industry, conducts research on the optimization of food processing systems and processes, and fully understands the needs of the market, the competitiveness of the company’s products, and the people facing marketing. The MA-CI model was used to optimize the entire process of food processing and production, and multiple variables were integrated. The following conclusions were drawn: (1) When thenodesize=100, calculate the optimal value of the intelligent model MA−CI=0.3196, the worstvalue=0.3120, and the averageplus−minusvariance=0.3155±4.6278×10−6, in each index. Both are better than the Greedy algorithm model and the EA model. When the nodesize=200, the optimal value of the calculation intelligence model MA−CI=0.3050, the worstvalue=0.2900, and the averageplusandminusvariance=0.2970±8.9251×10−6; when the nodesize=300, the calculation intelligence model (MA-CI) optimalvalue=0.2608, the worstvalue=0.2499, and the meanplus−minusvariance=0.2551±1.0028×10−5; when the nodesize=500, calculate the optimal value of intelligent model MA−CI=0.2857, the worstvalue=0.2812, and the meanplusandminusvariance=0.2834±2.8230×10−6. (2) In the electronic circuit network, calculate the intelligent model (MA-CI): Rc=0.3174, NMI=0.7388, ΔEc=0, and k=14.43; in the USAir network, the computational intelligence model (MA-CI) is as follows: Rc=0.3041, NMI=0.8632, ΔEc=0, and k=7.43; the computational intelligence model (the uniqueness and optimality of MA-CI) has resulted in the optimal solution for the optimization of the global machining process. (3) Using six online databases to predict and verify the MA-CI model for process optimization in the food industry, it was found that the MA-CI model for process optimization in the food industry was better than the standard value. (4) Perform performance testing on the optimized MA-CI model. It is found that under the MA-CI model, MUTAG=98.2±4.3, ENZYMES=96.2±4.3, PTC=85.2±4.3, PROTEINS=82.0±3.2, NCI1=80.2±2.0, and D&D=91.9±0.5, which are better than the other models. The optimized MA-CI model can control the profit problem in food processing and production, and it is found that the MA-CI model can make the enterprise obtain the maximum profit in the shortest period.


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## Body

## 1. Introduction

Artificial intelligence (AI) is the definition and command of machines through human coding algorithms, and intelligent machines replace humans to complete certain systematic tasks. Under normal circumstances, the realized function is related to human intelligence, such as the judgment of right or wrong of some things, the grasp of time and logic, the learning, understanding, and thinking of new knowledge, and the recognition and perception of unknown things. Artificial intelligence is an important branch of computer science that involves the research, design, and application of intelligent machines. Computational intelligence (CI) refers to a general term for an algorithm inspired by the body structure of some natural animals and plants or the unique landscape of nature [1–3]. In the relationship between the two, the definition of computational intelligence algorithm is a part of artificial intelligence. In the era of computational intelligence, innovative modeling is based on the dynamic, heterogeneous, and large-scale nature of drug data sets. The method of computational intelligence is used to classify and calculate the choice of drug use. Since there is a certain error tolerance rate in the choice of drug use, a deep learning model based on computational intelligence is introduced. To accurately predict and judge the use of drugs, the method of computational intelligence also provides a certain solution to the evaluation of the safety and effectiveness of drugs in the screening of drugs on big data. Machine intelligence can realize the mining of new data and the analysis and prediction of the management team and managers and provide a good foundation for the early experiments of the intelligent model. A unique algorithm is created in the feature hiding of test objects and the classification of hidden features [4]. Computational intelligence applications in medical data can interpret and output complex and heterogeneous data sets and noisy medical data sets to users. Computational intelligence is still in a society related to intellectual property rights, data sources, regulatory issues, economics, and responsibilities. Problems also play an important role. Image-based computational intelligence algorithms can solve visual-spatial intelligence tests and classify different images, coding different solutions and methods [5, 6]. Then the model was tested. Image-based computational intelligence algorithms can obtain knowledge systems and reasoning processes from experience learning and supplement and update knowledge networks. In the prediction of the application of black-box data [7–10], it is always due to the incomplete understanding of the black-box data that the predicted results differ greatly from the actual ones. The understanding and interpretation of black-box data are very important. The original logic and general logic of the data, the evaluation and prediction model of the data, and the initial differences will all affect the interpretation of the data. Computational intelligence can optimize regular data, achieve good continuity of data, and balance the complex network relationship between data. The success of a product depends not only on the quality of the product itself and the quality of various parameters but also on the control of the product cycle process and the optimization of the production product system is also the focus of competition between enterprises. Whether the competition is successful or not, the optimization of systems and processes occupies a large part. In the entire design process of the production process, multiple variables are integrated, including the needs of the market, the competitiveness of the company’s products, the people facing the marketing, and market research. Only consider these important factors at the same time [11–13]. Only by optimizing various parameters simultaneously can the expected success be obtained. The processing process of the food industry plays a decisive role in the loss of food nutrients and the waste of raw materials. The concept of the food matrix is unified with food health and food nutrition. The food matrix distinguishes the unused components and nutrients of different fruits and vegetables. The nutrient components will interact with each other physically, and the functions produced by the free state and the combined state of the nutrient components are different. The discussion of the food matrix can improve the taste and flavor of food and promote the absorption and digestion of nutrients in the gastrointestinal tract. In recent years, innovative food processing technology has been extensively studied in food processing research. These technologies provide key advantages for advancing the preservation and quality of traditional foods, coping with the increasingly severe challenges brought about by globalization, increasing competitive pressure and diversified consumer demands. However, high-tech food industry processing procedures need to be introduced to ensure that the food industry makes more use of these technologies. At the industry level, the technical capabilities, scale, market share, and absorptive capacity of individual companies will be optimized for food industry processing. The cost, related risks, and relative advantages involved in its development and commercialization, as well as the level of complexity and compatibility, are used to optimize the processing flow of the food industry. An in-depth understanding of the development and application of process optimization in the food industry and the factors that affect its acceptance is essential to ensure its adoption in the food industry. Food consumption trends have long shifted from the need for simple calories and essential nutrients to support basic human functions to the need for a balanced supply of nutrients to achieve optimal health. In the processing of food vitamins, they are often lost or destroyed before they reach consumers because they are easily degraded by environmental factors. Computational smart microencapsulation technology is a technology designed to protect sensitive compounds from environmental factors. The use of computationally intelligent carbohydrate microencapsulation technology to optimize food processing processes can solve such problems. Optimize the logistics and transportation in the food processing process [14, 15]. It can also save costs to a certain extent and bring profits. Transporting a large number of raw materials from many suppliers to designated factories to meet production needs, inbound logistics, and outbound logistics has the same important role, because it will determine the price of raw materials purchased by the company from each supplier. By measuring the company’s new inventory holding policy and transportation strategy, the inventory management and network distribution design parts are optimized and designed.


## 2. Food Processing Flow Analysis

### 2.1. Classification of Workflow Scheduling Methods

The workflow scheduling method is composed of a single-objective optimization algorithm, a multiobjective optimization algorithm, and a heuristic algorithm, as shown in Figure1. The responsibility of single-objective optimization scheduling is to optimize the decision-making problems in the process flow. In the food processing process, there may be multiple parameters that restrict each other and conflict. We need to make a decision to find the optimal solution in the process to achieve the result of improving the performance. The single-objective optimization in the food processing process is to compare different schemes, which is simple to implement and can quickly obtain experimental results. The multiobjective optimization algorithm is a unified optimization of the parameter problems in multiple steps, which can realize the use of the smallest resource consumption and the shortest time to complete the same output, which can achieve cost control. Multiobjective algorithms for food processing are divided into QoS constraint algorithms and QoS optimization algorithms. Heuristic algorithms are often referred to as search algorithms, because the heuristic algorithm is to find all the adjustment methods of the technological process as much as possible and then obtain the value range of the objective function through comparative experiments. Among them are the maximum value of food quality and the minimum value of food quality [16–18].Figure 1
Classification of workflow scheduling methods.


### 2.2. Food Supply Chain Scheduling

In the process of optimizing the food processing process, the supply chain is an indispensable part of the food supply chain scheduling shown in Figure2. As the upstream of food processing, the optimization of the material supply chain and sales supply chain is the focus of the entire process. Only by carrying out reasonable scheduling can the cost be minimized, the efficiency problems in the transportation process can be controlled, the overall layout can be scaled at a macrolevel, and the resources of each warehouse can be rationally transferred. The core issue of supply chain management in the food processing process is to coordinate the relationship between the supply chains of different stages of decision-making and to coordinate the relationship between suppliers and sales personnel to achieve efficient cooperation. Supply chain management in the food processing process is to integrate and optimize different resources, and the final goal is to maximize benefits and at the same time ensure quality and efficiency [19–21].Figure 2
Food supply chain scheduling.


### 2.3. Reinforcement Learning Model

The basic algorithm of the reinforcement learning model is the reinforcement learning algorithm. The reinforcement learning algorithm (Figure3) includes model reinforcement learning algorithms, which can be learned and upgraded according to existing algorithms, while model-free reinforcement learning algorithms perform autonomous learning and continuously call new ones from various parameters. The parameters are optimized until the most efficient operation of the model is finally reached. The processing flow system is restricted by the use of irregular terms, as well as the nonstationarity of the system, which will affect the management of the process. The abstract model algorithm will also affect the processing flow system, which may be caused by some mismatch between the abstract algorithm and the problems encountered in real life. Using reinforcement learning algorithms can use dynamic learning models to solve problems encountered in the food processing process and achieve efficient and unified decision-making, bringing great efficiency improvement for enterprises [1, 22–24].Figure 3
Reinforcement learning model.


### 2.4. Mesh Processing Flow

The grid processing process needs to consider the cross-data transmission of various data communication paths between large problems. As shown in the grid process algorithm in Figure4, the grid process algorithm mainly includes two categories: the best-effort-based processing and QoS constraint-based processing. In the deterministic model, the dependency between tasks and input/output data is known in advance; in the nondeterministic model, they are only known at runtime. Therefore, the workflow scheduling algorithm is based on the deterministic type of the abstract workflow model and is sent again in the form of a directed acyclic graph (DAG).Figure 4
Grid process algorithm.


## 2.1. Classification of Workflow Scheduling Methods

The workflow scheduling method is composed of a single-objective optimization algorithm, a multiobjective optimization algorithm, and a heuristic algorithm, as shown in Figure1. The responsibility of single-objective optimization scheduling is to optimize the decision-making problems in the process flow. In the food processing process, there may be multiple parameters that restrict each other and conflict. We need to make a decision to find the optimal solution in the process to achieve the result of improving the performance. The single-objective optimization in the food processing process is to compare different schemes, which is simple to implement and can quickly obtain experimental results. The multiobjective optimization algorithm is a unified optimization of the parameter problems in multiple steps, which can realize the use of the smallest resource consumption and the shortest time to complete the same output, which can achieve cost control. Multiobjective algorithms for food processing are divided into QoS constraint algorithms and QoS optimization algorithms. Heuristic algorithms are often referred to as search algorithms, because the heuristic algorithm is to find all the adjustment methods of the technological process as much as possible and then obtain the value range of the objective function through comparative experiments. Among them are the maximum value of food quality and the minimum value of food quality [16–18].Figure 1
Classification of workflow scheduling methods.


## 2.2. Food Supply Chain Scheduling

In the process of optimizing the food processing process, the supply chain is an indispensable part of the food supply chain scheduling shown in Figure2. As the upstream of food processing, the optimization of the material supply chain and sales supply chain is the focus of the entire process. Only by carrying out reasonable scheduling can the cost be minimized, the efficiency problems in the transportation process can be controlled, the overall layout can be scaled at a macrolevel, and the resources of each warehouse can be rationally transferred. The core issue of supply chain management in the food processing process is to coordinate the relationship between the supply chains of different stages of decision-making and to coordinate the relationship between suppliers and sales personnel to achieve efficient cooperation. Supply chain management in the food processing process is to integrate and optimize different resources, and the final goal is to maximize benefits and at the same time ensure quality and efficiency [19–21].Figure 2
Food supply chain scheduling.


## 2.3. Reinforcement Learning Model

The basic algorithm of the reinforcement learning model is the reinforcement learning algorithm. The reinforcement learning algorithm (Figure3) includes model reinforcement learning algorithms, which can be learned and upgraded according to existing algorithms, while model-free reinforcement learning algorithms perform autonomous learning and continuously call new ones from various parameters. The parameters are optimized until the most efficient operation of the model is finally reached. The processing flow system is restricted by the use of irregular terms, as well as the nonstationarity of the system, which will affect the management of the process. The abstract model algorithm will also affect the processing flow system, which may be caused by some mismatch between the abstract algorithm and the problems encountered in real life. Using reinforcement learning algorithms can use dynamic learning models to solve problems encountered in the food processing process and achieve efficient and unified decision-making, bringing great efficiency improvement for enterprises [1, 22–24].Figure 3
Reinforcement learning model.


## 2.4. Mesh Processing Flow

The grid processing process needs to consider the cross-data transmission of various data communication paths between large problems. As shown in the grid process algorithm in Figure4, the grid process algorithm mainly includes two categories: the best-effort-based processing and QoS constraint-based processing. In the deterministic model, the dependency between tasks and input/output data is known in advance; in the nondeterministic model, they are only known at runtime. Therefore, the workflow scheduling algorithm is based on the deterministic type of the abstract workflow model and is sent again in the form of a directed acyclic graph (DAG).Figure 4
Grid process algorithm.


## 3. Application of Computational Intelligence in Food Processing Process

### 3.1. Greedy Algorithm Model [25]

The food processing raw material supply chain is as follows:(1)PSt+1/St=PSt+1/St,St−1,St−2,⋯,S1.The supply method is(2)δSβδβ=−2X′y+2XX′β=0.Regular equations are(3)XX′b=X′y.The split optimization of the food processing process is(4)b=XX′−1X′y,Sβ=y′y+2βX′y+β′Xβ,δS∂δ∂=−2X′y+2XX′∂=0.The split simulation of food processing flow is(5)δ2Sβ/δβδβ′β−b=2X′Xβ,q=v′v=∑iv2i.


### 3.2. EA Algorithm Model [26–28]

The relevant constraints in food processing and production are(6)totalT=min∑i∈v∑Pk∈pXiktkj,totalQ=max∑i∈w∑Pk∈pXiktkj.The processing and production sales are(7)xik∈0,1,∀i∈v,0≤k≤n,st∑k=1mxik=1,Sk=S1,S2,⋯,Sm.The food processing market research and analysis are(8)qi≤qi+1−∑k=1ntikxik0≤i≤n,x0=x01,x02,⋯,x0n.The first-order accumulation is(9)x1=x11,x12,⋯,x1n,x1k=∑i=1kx0i,k=1,2,pk=x0kx1k−1,k=2,3.The optimization model prediction is(10)x,0k=1n−k+1xk+xk+1+⋯+xn,k=1,2,x0k=x,0k.


### 3.3. MA-CI Model

The first-order linear differential equation is(11)dx1dt+ax1=u,dx1dt+a^x1=u^.The food processing process optimization model is(12)x^k+1=x10−u^a^e−a∧k,k=0,1,2,x∧0k+1=x∧1k+1.The food processing efficiency is(13)x∧0k+1=e−a−1x0n−uae−ak.The cost and profit in the processing flow are(14)x¯=1n∑k=1nx0k,ε¯=1n∑k=1nε0k,T=1n∑k=1nε0k+1n∑k=1nx0k.


## 3.1. Greedy Algorithm Model [25]

The food processing raw material supply chain is as follows:(1)PSt+1/St=PSt+1/St,St−1,St−2,⋯,S1.The supply method is(2)δSβδβ=−2X′y+2XX′β=0.Regular equations are(3)XX′b=X′y.The split optimization of the food processing process is(4)b=XX′−1X′y,Sβ=y′y+2βX′y+β′Xβ,δS∂δ∂=−2X′y+2XX′∂=0.The split simulation of food processing flow is(5)δ2Sβ/δβδβ′β−b=2X′Xβ,q=v′v=∑iv2i.


## 3.2. EA Algorithm Model [26–28]

The relevant constraints in food processing and production are(6)totalT=min∑i∈v∑Pk∈pXiktkj,totalQ=max∑i∈w∑Pk∈pXiktkj.The processing and production sales are(7)xik∈0,1,∀i∈v,0≤k≤n,st∑k=1mxik=1,Sk=S1,S2,⋯,Sm.The food processing market research and analysis are(8)qi≤qi+1−∑k=1ntikxik0≤i≤n,x0=x01,x02,⋯,x0n.The first-order accumulation is(9)x1=x11,x12,⋯,x1n,x1k=∑i=1kx0i,k=1,2,pk=x0kx1k−1,k=2,3.The optimization model prediction is(10)x,0k=1n−k+1xk+xk+1+⋯+xn,k=1,2,x0k=x,0k.


## 3.3. MA-CI Model

The first-order linear differential equation is(11)dx1dt+ax1=u,dx1dt+a^x1=u^.The food processing process optimization model is(12)x^k+1=x10−u^a^e−a∧k,k=0,1,2,x∧0k+1=x∧1k+1.The food processing efficiency is(13)x∧0k+1=e−a−1x0n−uae−ak.The cost and profit in the processing flow are(14)x¯=1n∑k=1nx0k,ε¯=1n∑k=1nε0k,T=1n∑k=1nε0k+1n∑k=1nx0k.


## 4. Simulation Experiment

### 4.1. Experimental Results on BA Scale-Free Network

The Greedy algorithm model, EA model, and MA-CI model with different nodes are used for experiments in the research on the optimization of processing procedures in the food industry. In the analysis of the complexity of the computational intelligence model (CI), it is found that compared with the other two models (Greedy algorithm model and EA model), the computational intelligence model (CI) algorithm occupies relatively less time resources and memory resources. The experimental results are shown in Table1 (community robustness indicators of the three algorithms on networks of different node sizes), using the optimal value, the worst value, and the average plus or minus variance for judgment. The results show that when the nodesize=100, the optimal value of the computational intelligence model MA−CI=0.3196, the worstvalue=0.3120, and the averageplusandminusvariance=0.3155±4.6278×10−6; all indicators are better than Greedy algorithm model and EA model. When the nodesize=200, the optimal value of the calculation intelligence model MA−CI=0.3050, the worstvalue=0.2900, and the averageplusandminusvariance=0.2970±8.9251×10−6; when the node size =300, the calculation intelligence model (MA-CI) optimalvalue=0.2608, the worstvalue=0.2499, and the meanplus−minusvariance=0.2551±1.0028×10−5; when the nodesize=500, calculate the optimal value of intelligent model MA−CI=0.2857, the worstvalue=0.2812, and the meanplusandminusvariance=0.2834±2.8230×10−6. The data visualization is shown in Figure 5.Table 1
Community robustness indicators of the three algorithms on different node scale networks.
Node sizeAlgorithmThe optimal valueWorst valueMean±variance100Greedy algorithm0.32310.30550.3139±3.0533×10−5EA0.31900.28790.3080±6.9963×10−5MA-CI0.31960.31200.3155±4.6278×10−6200Greedy algorithm0.30300.27920.2877±3.5374×10−5EA0.28780.26150.2762±4.8277×10−5MA-CI0.30500.29000.2970±8.9251×10−6300Greedy algorithm0.30360.27810.2874±3.6404×10−5EA0.29740.27390.2874±4.2040×105MA-CI0.31190.30080.3054±5.6058×10−6500Greedy algorithm0.26080.24990.2551±1.0028×10−5EA0.27130.25140.2630±4.3180×10−5MA-CI0.28570.28120.2834±2.8230×10−6Figure 5
Community robustness indicators of the three algorithms on networks with different node scales.


### 4.2. Experimental Results on the Real Network

In the experiments on the real network in the research of the food industry processing process optimization, the parametersRc, NMI, ΔEc, and k will have an important impact on the community robustness index of the model and then affect the performance of the experiment, divided into electronic circuit network and USAir network (as shown in Table 2). In the electronic circuit network, the computational intelligence model (MA-CI) is as follows: Rc=0.3174, NMI=0.7388, ΔEc=0, and k=14.43; in the USAir network, the computational intelligence model (MA-CI) is as follows: Rc=0.3041, NMI=0.8632, ΔEc=0, and k=7.43; the uniqueness and optimality of the computational intelligence model (MA-CI) are verified in these two models, and the optimal solution for global processing optimization is obtained.Table 2
Experimental results on the real network.
NetworkAlgorithmRcNMIΔEckElectronic circuitOriginal0.22721013.00Greedy algorithm0.30750.7972014.75EA0.29510.7702014.63MA-CI0.31740.7388014.43USAirOriginal0.2436099.00Greedy algorithm0.28830.803107.40EA0.29400.845307.47MA-CI0.30410.863207.43


### 4.3. Processing Flow Split Prediction

The flow in the single-layered type and the flow in the multilayer type are, respectively, predicted (as shown in Table3). Use the DecpWalk (avg) model, DecpWalk (onca) model, LINE (avg) model, LINE (coneat) model, nodc2vec (avg) model, node2vec (concat) model, mctapath2vec model, and OhmNct (avg) in single-layered type. The model predicts the indexes of Lazega, CKM, C.elegans, H.genetic, PPI, and Twitter, respectively. The same indicators are also predicted in the multilayer type of food processing process. As shown in Figure 6, the best performance is predicted.Table 3
Split prediction of the processing flow.
TypeModelLazegaCKMC.elegansH.geneticPPITwitterSingle-layeredDecpWalk (avg)0.7960.7640.7030.6180.6890.586DecpWalk (onca)0.8020.7730.7160.6230.6960.593LINE (avg)0.7030.6340.6210.5580.6640.516LINE (coneat)0.6920.6260.6250.5740.6700.527nodc2vec (avg)0.7980.7810.7250.6470.7260.581node2vec (concat)0.8050.7870.7420.6520.7300.597mctapath2 vec0.8060.7750.7220.6250.6920.595MultilayerOhmNct (avg)0.7620.7400.7730.7120.6950.556OhmNct (concat)0.7640.7430.7850.7160.7040.563MNE (avg)0.8280.8670.8210.7700.6970.604MNE (concat)0.8330.8710.8250.7780.7020.617MVE0.8150.9120.8280.8260.7220.645HMNE0.8220.9230.8350.8430.7470.653Figure 6
Comparison of best performance.


### 4.4. Using Data and Making Predictions

Using six online databases, MUTAG, ENZYMES, PTC, PROTEINS, NCI1, and D&D1 to predict and verify the MA-CI model of the food industry process optimization, we found that the food industry process optimization MA-CI model is better than the standard value (as shown in Table4).Table 4
Data set prediction.
Data setNumber of picturesNumber of graph categoriesAverage number of nodesAverage number of sidesNumber of node categoriesMUTAG188217.9319.797ENZYMES600632.6364.143PTC344214.2914.6919PROTEINS1,113239.0672.823NCI14,110229.8732.337D&D1,1782284.32715.6681


### 4.5. Performance Test

As shown in Table5 and Figure 7, the optimized MA-CI model is tested for performance. It is found that under the MA-CI model, MUTAG=98.2±4.3, ENZYMES=96.2±4.3, PTC=85.2±4.3, PROTEINS=82.0±3.2, NCI1=80.2±2.0, and D&D=91.9±0.5, which are better than the other models.Table 5
Performance test.
MethodMUTAGENZYMESPTCPROTEINSNCI1D&DKernelGRAPHLET81.7±2.141.0±1.054.7±1.471.7±0.662.3±0.374.9±1.1SHORTEST-PATH81.7±2.542.3±1.558.9±2.476.4±0.574.5±0.278.9±0.8MA-CICI98.2±4.396.2±4.385.2±4.382.0±3.280.2±2.091.9±0.5GraphSAGE85.1±7.554.25±6.063.9±7.775.9±3.277.7±1.575.4±3.0PATCHY-SAN92.6±4.2−60.0±4.875.9±2.878.6±1.976.3±2.4SORTPOOL85.8±1.757.1±2.058.6±2.575.5±0.974.4±0.579.4±0.9GAT89.4±6.1−66.7±5.174.7±2.275.2±3.3−DIFFPOOL−62.5±5.6−76.3±3.5−80.6±3.5GIN89.0±6.053.3±4.763.7±8.275.9±3.882.7±1.680.9±2.7OursLPD-GCN++ (NoLFR)95.2±3.963.5±4.871.5±6.378.7±3.281.0±1.681.3±1.9LPD-GCN++ (NoDC)94.5±5.865.3±7.273.2±6.279.0±3.081.8±5.582.6±4.0LPD-GCN++ (NoGCA)94.2±4.361.7±5.171.7±4.578.3±2.278.7±1.682.1±2.5Figure 7
Data set prediction.For the cost and profit forecast, the optimized MA-CI model can control the profit problem in food processing and production and bring the maximum profit to the enterprise. The input data set in the process is abstracted, as shown in Table6. It is concluded that the MA-CI model can make the enterprise obtain the maximum profit in the shortest period.Table 6
Control of cost and profit.
AttributesTimet (unit: day)Costc (unit: ten thousand Yuan)Quality parameterp11349750.880.910.93p21244210.810.830.86p325—65—0.820.85—p446—44—0.90.91—p53563210.930.950.96p6234320.80.920.930.94p723—21—0.910.920.94p834—74—0.820.85—


## 4.1. Experimental Results on BA Scale-Free Network

The Greedy algorithm model, EA model, and MA-CI model with different nodes are used for experiments in the research on the optimization of processing procedures in the food industry. In the analysis of the complexity of the computational intelligence model (CI), it is found that compared with the other two models (Greedy algorithm model and EA model), the computational intelligence model (CI) algorithm occupies relatively less time resources and memory resources. The experimental results are shown in Table1 (community robustness indicators of the three algorithms on networks of different node sizes), using the optimal value, the worst value, and the average plus or minus variance for judgment. The results show that when the nodesize=100, the optimal value of the computational intelligence model MA−CI=0.3196, the worstvalue=0.3120, and the averageplusandminusvariance=0.3155±4.6278×10−6; all indicators are better than Greedy algorithm model and EA model. When the nodesize=200, the optimal value of the calculation intelligence model MA−CI=0.3050, the worstvalue=0.2900, and the averageplusandminusvariance=0.2970±8.9251×10−6; when the node size =300, the calculation intelligence model (MA-CI) optimalvalue=0.2608, the worstvalue=0.2499, and the meanplus−minusvariance=0.2551±1.0028×10−5; when the nodesize=500, calculate the optimal value of intelligent model MA−CI=0.2857, the worstvalue=0.2812, and the meanplusandminusvariance=0.2834±2.8230×10−6. The data visualization is shown in Figure 5.Table 1
Community robustness indicators of the three algorithms on different node scale networks.
Node sizeAlgorithmThe optimal valueWorst valueMean±variance100Greedy algorithm0.32310.30550.3139±3.0533×10−5EA0.31900.28790.3080±6.9963×10−5MA-CI0.31960.31200.3155±4.6278×10−6200Greedy algorithm0.30300.27920.2877±3.5374×10−5EA0.28780.26150.2762±4.8277×10−5MA-CI0.30500.29000.2970±8.9251×10−6300Greedy algorithm0.30360.27810.2874±3.6404×10−5EA0.29740.27390.2874±4.2040×105MA-CI0.31190.30080.3054±5.6058×10−6500Greedy algorithm0.26080.24990.2551±1.0028×10−5EA0.27130.25140.2630±4.3180×10−5MA-CI0.28570.28120.2834±2.8230×10−6Figure 5
Community robustness indicators of the three algorithms on networks with different node scales.


## 4.2. Experimental Results on the Real Network

In the experiments on the real network in the research of the food industry processing process optimization, the parametersRc, NMI, ΔEc, and k will have an important impact on the community robustness index of the model and then affect the performance of the experiment, divided into electronic circuit network and USAir network (as shown in Table 2). In the electronic circuit network, the computational intelligence model (MA-CI) is as follows: Rc=0.3174, NMI=0.7388, ΔEc=0, and k=14.43; in the USAir network, the computational intelligence model (MA-CI) is as follows: Rc=0.3041, NMI=0.8632, ΔEc=0, and k=7.43; the uniqueness and optimality of the computational intelligence model (MA-CI) are verified in these two models, and the optimal solution for global processing optimization is obtained.Table 2
Experimental results on the real network.
NetworkAlgorithmRcNMIΔEckElectronic circuitOriginal0.22721013.00Greedy algorithm0.30750.7972014.75EA0.29510.7702014.63MA-CI0.31740.7388014.43USAirOriginal0.2436099.00Greedy algorithm0.28830.803107.40EA0.29400.845307.47MA-CI0.30410.863207.43


## 4.3. Processing Flow Split Prediction

The flow in the single-layered type and the flow in the multilayer type are, respectively, predicted (as shown in Table3). Use the DecpWalk (avg) model, DecpWalk (onca) model, LINE (avg) model, LINE (coneat) model, nodc2vec (avg) model, node2vec (concat) model, mctapath2vec model, and OhmNct (avg) in single-layered type. The model predicts the indexes of Lazega, CKM, C.elegans, H.genetic, PPI, and Twitter, respectively. The same indicators are also predicted in the multilayer type of food processing process. As shown in Figure 6, the best performance is predicted.Table 3
Split prediction of the processing flow.
TypeModelLazegaCKMC.elegansH.geneticPPITwitterSingle-layeredDecpWalk (avg)0.7960.7640.7030.6180.6890.586DecpWalk (onca)0.8020.7730.7160.6230.6960.593LINE (avg)0.7030.6340.6210.5580.6640.516LINE (coneat)0.6920.6260.6250.5740.6700.527nodc2vec (avg)0.7980.7810.7250.6470.7260.581node2vec (concat)0.8050.7870.7420.6520.7300.597mctapath2 vec0.8060.7750.7220.6250.6920.595MultilayerOhmNct (avg)0.7620.7400.7730.7120.6950.556OhmNct (concat)0.7640.7430.7850.7160.7040.563MNE (avg)0.8280.8670.8210.7700.6970.604MNE (concat)0.8330.8710.8250.7780.7020.617MVE0.8150.9120.8280.8260.7220.645HMNE0.8220.9230.8350.8430.7470.653Figure 6
Comparison of best performance.


## 4.4. Using Data and Making Predictions

Using six online databases, MUTAG, ENZYMES, PTC, PROTEINS, NCI1, and D&D1 to predict and verify the MA-CI model of the food industry process optimization, we found that the food industry process optimization MA-CI model is better than the standard value (as shown in Table4).Table 4
Data set prediction.
Data setNumber of picturesNumber of graph categoriesAverage number of nodesAverage number of sidesNumber of node categoriesMUTAG188217.9319.797ENZYMES600632.6364.143PTC344214.2914.6919PROTEINS1,113239.0672.823NCI14,110229.8732.337D&D1,1782284.32715.6681


## 4.5. Performance Test

As shown in Table5 and Figure 7, the optimized MA-CI model is tested for performance. It is found that under the MA-CI model, MUTAG=98.2±4.3, ENZYMES=96.2±4.3, PTC=85.2±4.3, PROTEINS=82.0±3.2, NCI1=80.2±2.0, and D&D=91.9±0.5, which are better than the other models.Table 5
Performance test.
MethodMUTAGENZYMESPTCPROTEINSNCI1D&DKernelGRAPHLET81.7±2.141.0±1.054.7±1.471.7±0.662.3±0.374.9±1.1SHORTEST-PATH81.7±2.542.3±1.558.9±2.476.4±0.574.5±0.278.9±0.8MA-CICI98.2±4.396.2±4.385.2±4.382.0±3.280.2±2.091.9±0.5GraphSAGE85.1±7.554.25±6.063.9±7.775.9±3.277.7±1.575.4±3.0PATCHY-SAN92.6±4.2−60.0±4.875.9±2.878.6±1.976.3±2.4SORTPOOL85.8±1.757.1±2.058.6±2.575.5±0.974.4±0.579.4±0.9GAT89.4±6.1−66.7±5.174.7±2.275.2±3.3−DIFFPOOL−62.5±5.6−76.3±3.5−80.6±3.5GIN89.0±6.053.3±4.763.7±8.275.9±3.882.7±1.680.9±2.7OursLPD-GCN++ (NoLFR)95.2±3.963.5±4.871.5±6.378.7±3.281.0±1.681.3±1.9LPD-GCN++ (NoDC)94.5±5.865.3±7.273.2±6.279.0±3.081.8±5.582.6±4.0LPD-GCN++ (NoGCA)94.2±4.361.7±5.171.7±4.578.3±2.278.7±1.682.1±2.5Figure 7
Data set prediction.For the cost and profit forecast, the optimized MA-CI model can control the profit problem in food processing and production and bring the maximum profit to the enterprise. The input data set in the process is abstracted, as shown in Table6. It is concluded that the MA-CI model can make the enterprise obtain the maximum profit in the shortest period.Table 6
Control of cost and profit.
AttributesTimet (unit: day)Costc (unit: ten thousand Yuan)Quality parameterp11349750.880.910.93p21244210.810.830.86p325—65—0.820.85—p446—44—0.90.91—p53563210.930.950.96p6234320.80.920.930.94p723—21—0.910.920.94p834—74—0.820.85—


## 5. Conclusion

This research conducted an investigation on the optimization of food processing systems and processes, fully understood the needs of the market, the competitiveness of the company’s products, and the people faced by marketing, and used the MA-CI model to optimize the entire process of food processing and production. A variety of variables are integrated. The following conclusions are drawn: (1) When thenodesize=100, calculate the optimal value of the intelligent model MA−CI=0.3196, the worstvalue=0.3120, and the averageplus−minusvariance=0.3155±4.6278×10−6, in each index; both are better than the Greedy algorithm model and the EA model. When the nodesize=200, the optimal value of the calculation intelligence model MA−CI=0.3050, the worstvalue=0.2900, and the averageplusandminusvariance=0.2970±8.9251×10−6; when the nodesize=300, the calculation intelligence model (MA-CI) optimalvalue=0.2608, the worstvalue=0.2499, and the meanplus−minusvariance=0.2551±1.0028×10−5; when the nodesize=500, calculate the optimal value of intelligent model MA−CI=0.2857, the worstvalue=0.2812, and the meanplusandminusvariance=0.2834±2.8230×1−6. (2) In the electronic circuit network, calculate the intelligent model (MA-CI): Rc=0.3174, NMI=0.7388, ΔEc=0, and k=14.43; in the USAir network, the computational intelligence model (MA-CI) is as follows: Rc=0.3041, NMI=0.8632, ΔEc=0, and k=7.43. The computational intelligence model (the uniqueness and optimality of MA-CI) has resulted in the optimal solution for the optimization of the global machining process. (3) Using six online databases to predict and verify the MA-CI model for process optimization in the food industry, it was found that the MA-CI model for process optimization in the food industry was better than the standard value. (4) Perform performance testing on the optimized MA-CI model. It is found that under the MA-CI model, MUTAG=98.2±4.3, ENZYMES=96.2±4.3, PTC=85.2±4.3, PROTEINS=82.0±3.2, NCI1=80.2±2.0, and D&D=91.9±0.5, which are better than other models. The optimized MA-CI model can control the profit problem in food processing and production, and it is found that the MA-CI model can make the enterprise obtain the maximum profit in the shortest period.



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*Source: 7781369-2022-03-25.xml*