Agricultural product supply chain information integration method based on big data analysis

Abstract

The invention relates to the technical field of agricultural product supply chain information integration. The invention relates to an agricultural product supply chain information integration method based on big data analysis. The method comprises the following steps: S1, establishing a supply chain port, and collecting an agricultural product region list and a sales region list; s2, extracting production-related data of the agricultural product areas, sorting the production-related data according to the acquisition time nodes, performing real weight setting on the production-related data according to an acquisition mode, and establishing a corresponding area simulation model for each agricultural product area; by establishing the supply chain port, collecting the agricultural product area list and the sales area list and integrating and managing the information of the agricultural product suppliers and the sales managers, digitization of supply chain member information is realized, different members can conveniently access and manage the information, and the information transparency and the management efficiency of the supply chain are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural product supply chain information integration, and in particular to an agricultural product supply chain information integration method based on big data analysis. Background Art

[0002] At present, information transmission between supply chain members such as agricultural product suppliers and sales managers mostly relies on manual communication and paper document records. Information from each link is difficult to share and integrate in real time. The data collection methods in the production link are relatively simple and cannot fully and accurately reflect the actual production status of agricultural products. The sales link's forecast of market demand is mostly based on experience, and there is a lack of effective use of emerging information sources such as network big data.

[0003] In such a scenario, information dispersion leads to difficulties in coordination among various links of the supply chain, making it difficult to achieve accurate production and sales planning, and unable to effectively guarantee a stable supply of agricultural products. The incomplete and inaccurate collection of production data makes production decisions lack a reliable basis, which can easily lead to waste of production resources or insufficient supply. Market demand forecasts based on experience are difficult to cope with the complex and changing market environment, affecting sales results and economic benefits. Therefore, a method for integrating agricultural product supply chain information based on big data analysis is proposed. Summary of the invention

[0004] The purpose of the present invention is to provide an agricultural product supply chain information integration method based on big data analysis to solve the problems raised in the above background technology.

[0005] To achieve the above purpose, a method for integrating agricultural product supply chain information based on big data analysis is provided, which includes the following steps: S1. Establish supply chain ports and collect lists of agricultural product regions and sales regions; S2. Extract production-related data of agricultural product areas, sort the production-related data according to the collection time nodes, set real weights for the production-related data according to the collection method, and establish a corresponding regional simulation model for each agricultural product area; S3, setting a difference threshold, and combining the production-related data after the output simulation results with the regional simulation model and the difference threshold for real analysis, and then inputting the production-related data through the real analysis into the regional simulation model for agricultural production data prediction calibration; S4, extracting sales data and network market demand data of the sales area, and combining the sales data with the network market demand data to perform demand change forecasting analysis; S5. Carry out logistics and transportation simulation of agricultural product supply for each agricultural product area in combination with the sales area, evaluate and compare the simulation results with the predicted demand of the sales area, and based on the comparison results, display the sales areas and agricultural product areas that meet the demand to each other at the supply chain port.

[0006] As a further improvement of the technical solution, S1 will establish a supply chain port and open it to agricultural product suppliers and sales managers. At the same time, when agricultural product suppliers and sales managers register their accounts, they need to fill in the information of the area they are responsible for.

[0007] As a further improvement of the technical solution, the step of S1 is as follows: S1.1. Collect agricultural product data from each agricultural product supplier, including agricultural product type and product grade, and then generate agricultural product regions based on the agricultural product data fed back by the agricultural product supplier combined with regional information, and summarize and generate a list of agricultural product regions; S1.2. Generate the corresponding sales area based on the regional information filled in by each sales manager, and summarize and generate a sales area list.

[0008] As a further improvement of the technical solution, the steps of S2 are as follows: S2.1. Extract production-related data of agricultural product areas, sort them according to the collection time nodes of production-related data, and set real weights for production-related data according to the collection method; S2.2. Repeatedly combine the production-related data to generate regional simulation models corresponding to multiple different combinations. Then calculate the multiple regional simulation models in combination with the production-related data and the corresponding real weights, and select the regional simulation model with the highest weight to represent the corresponding agricultural product area.

[0009] As a further improvement of the present technical solution, when S2.2 generates a regional simulation model, if the production-related data of the agricultural product area is insufficient to support the establishment of the regional simulation model, the simulation will not be established. Data collection information will be sent to the agricultural product suppliers corresponding to the agricultural product area based on the missing data, and analysis will be performed based on the feedback information. If the establishment is still not satisfied, the agricultural product area will not be recommended at the supply chain port, and the agricultural product suppliers will operate independently.

[0010] As a further improvement of the technical solution, the steps of S3 are as follows: S3.1. Set dynamic difference thresholds based on the latest simulation results of the regional model combined with time differences; S3.2. Calculate the time difference between the production-related data collected after the latest simulation result and the latest simulation result, then extract the corresponding dynamic difference threshold value based on the time difference, then analyze the difference rate of the production-related data in combination with the latest simulation result, and perform a true analysis on the difference rate in combination with the dynamic difference threshold value. When the difference rate is less than or equal to the dynamic difference threshold value, it is determined to be true production-related data. On the contrary, when the difference rate is greater than the dynamic difference threshold value, it is determined to be false production-related data. S3.3. Input the production-related data determined as real in S3.2 into the regional simulation model to calibrate the agricultural production data forecast, thereby regenerating the simulation results.

[0011] As a further improvement of the technical solution, the S3.3 further includes the following steps: S3.3.1. Send the latest simulation results to the agricultural product suppliers in the agricultural product area through the supply chain port, and send accurate feedback to the agricultural product suppliers at the time point corresponding to the latest simulation results; S3.3.2. Monitor the feedback results of S3.3.1. When the feedback from the agricultural product supplier is accurate, continue to monitor. Otherwise, when the feedback from the agricultural product supplier is inaccurate, collect the growth data of the agricultural product area from the agricultural product supplier, and feed back the growth data to the supply chain port for manual review, and replace the latest simulation results according to the review results.

[0012] As a further improvement of the technical solution, the step of S4 is as follows: S4.1. Extract sales data of the sales area and collect market demand data in the network; S4.2. Combine sales data with network market demand data to analyze changes in forecasted demand, obtain forecasted demand corresponding to the sales area, and then send the forecasted demand to the sales manager for modification and feedback, and determine the forecasted demand based on the feedback results.

[0013] As a further improvement of the technical solution, the step of S5 is as follows: S5.1. Carry out logistics and transportation simulation of agricultural product processes in combination with each agricultural product region and the sales region, and then evaluate and compare the simulation results with the predicted demand of the sales region. When the simulation results meet the predicted demand, they are retained. Otherwise, they are not retained. S5.2. According to the simulation results retained in S5.1, the corresponding sales areas and agricultural product areas are extracted and displayed to each other at the supply chain port.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. In this agricultural product supply chain information integration method based on big data analysis, by establishing a supply chain port, collecting agricultural product area lists and sales area lists, integrating and managing the information of agricultural product suppliers and sales managers, the digitization of supply chain member information is realized, which facilitates different members to access and manage information, and improves the information transparency and management efficiency of the supply chain.

[0015] 2. In this agricultural product supply chain information integration method based on big data analysis, by extracting production-related data from agricultural product areas, sorting and setting real weights according to the collection time nodes, establishing a regional simulation model, and supplementing and analyzing data when data is insufficient, the accuracy and reliability of the model are guaranteed. By selecting the model with the highest weight to represent the agricultural product area, the production situation of the agricultural product area can be more accurately reflected, providing strong support for agricultural production. At the same time, the logistics and transportation of agricultural product areas and sales areas are simulated, and evaluation and comparison are carried out in combination with predicted demand to display sales areas and agricultural product areas that meet demand. By simulating the transportation process, considering multiple factors, evaluating transportation efficiency and demand satisfaction, it is helpful to optimize logistics transportation routes and resource allocation, improve the overall efficiency of the supply chain, reduce costs, and improve customer satisfaction.

[0016] 3. In this agricultural product supply chain information integration method based on big data analysis, by setting a difference threshold, the production-related data after the output simulation results are truly analyzed to determine the authenticity of the data, and the real data is input into the regional simulation model for prediction and calibration, so that the simulation results are closer to the actual production situation. By sending simulation results and feedback to suppliers, and monitoring and calibrating according to the feedback, the accuracy of agricultural production simulation results is ensured, providing a reliable basis for production decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is the overall flow chart of the present invention; Figure 2 A flowchart of the present invention for summarizing and generating a sales area list; Figure 3 A flowchart for selecting the region simulation model with the highest weight to represent the corresponding agricultural product region in the present invention; Figure 4 A flowchart of setting a dynamic difference threshold for the present invention; Figure 5 A flowchart of the present invention for determining forecast demand based on feedback results; Figure 6 The present invention extracts a flowchart of corresponding sales areas and agricultural product areas for mutual display at the supply chain port. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] See also Figure 1 - Figure 6 As shown, the purpose of this embodiment is to provide an agricultural product supply chain information integration method based on big data analysis, comprising the following steps: S1. Establish supply chain ports and collect lists of agricultural product regions and sales regions; S1 will establish a supply chain port and open it to agricultural product suppliers and sales managers. At the same time, when agricultural product suppliers and sales managers register their accounts, they need to fill in the information of the area they are responsible for. The specific steps are as follows: Establish a supply chain portal: The supply chain portal is a digital platform for supply chain management, which allows different supply chain members (such as agricultural product suppliers and sales managers) to access and manage information. It has functions including allowing suppliers and sales managers to create accounts, granting different permissions based on user types (suppliers or sales managers), and users fill in the area information they are responsible for when registering (such as agricultural product suppliers fill in the area they supply, and sales managers fill in the area they sell); Agricultural product supplier registration: Agricultural product suppliers need to fill in the regional information they are responsible for during the registration process. The regional information may include the type of agricultural product, the area of ​​supply, and the product grade; Sales manager registration: When registering, the sales manager needs to fill in the sales area for which he is responsible. The sales area information includes sales channels and sales areas.

[0020] The steps of S1 are as follows: S1.1. Collect agricultural product data from each agricultural product supplier, including agricultural product type and product grade, and then generate agricultural product regions based on the agricultural product data fed back by the agricultural product supplier combined with regional information, and summarize and generate a list of agricultural product regions; Each agricultural product supplier needs to register on the supply chain port and fill in the information of the agricultural products it supplies. At the same time, the agricultural product type, product grade and supply area information provided by the supplier will be stored in the database and form a data record. The agricultural product area is then generated based on the regional information and agricultural product types provided by the supplier. According to the supplier's region and the agricultural products it supplies, the system will automatically generate the regional information of each agricultural product, and then summarize all the agricultural product regional information to generate a complete list of agricultural product regions.

[0021] S1.2. Generate the corresponding sales area according to the regional information filled in by each sales manager, and summarize and generate a sales area list; When a sales manager registers, he / she needs to fill in the sales area information for which he / she is responsible. The sales area information filled in by the sales manager will be stored in the database and corresponding records will be formed. At the same time, the sales area will be generated based on the area information provided by the sales manager. The area information filled in by the sales manager is closely related to the sales channel for which he / she is responsible. The system will generate the corresponding sales area based on this information. Finally, all the sales area information will be summarized to generate a complete sales area list.

[0022] S2. Extract production-related data of agricultural product areas, sort the production-related data according to the collection time nodes, set real weights for the production-related data according to the collection method, and establish a corresponding regional simulation model for each agricultural product area; The steps of S2 are as follows: S2.1. Extract production-related data from agricultural product areas and sort them according to the time nodes of their collection. At the same time, set real weights for the production-related data according to the collection methods. For example, satellite remote sensing can be set to 0.9, IoT device collection to 0.8, manual reporting to 0.6, and the Agricultural Bureau to 0.7. Production-related data include agricultural product types, agricultural product grades, production areas, yields, planting cycles, soil types, water source conditions and other environmental data, climate data (temperature, precipitation, humidity), data collection time nodes, and data collection methods; Data sources include manual reporting, IoT device collection, satellite remote sensing, and synchronization with local agricultural bureau systems; S2.2. Repeatedly combine the production-related data to generate regional simulation models corresponding to multiple different combinations. Then, calculate the multiple regional simulation models in combination with the production-related data and the corresponding real weights, and select the regional simulation model with the highest weight to represent the corresponding agricultural product area. The specific formula is as follows: ; Among them, v i is the key indicator value of the i-th data (such as yield, planting area, etc., which can be weighted combination), w i is the true weight of the i-th data, n is the total number of data, M is the regional simulation model, and Score (M) is the score of the regional simulation model; Then the model with the highest score is selected as the representative simulation model for the current agricultural product area.

[0023] S2.2 When generating a regional simulation model, if the production-related data of the agricultural product area is insufficient to support the establishment of the regional simulation model, the simulation will not be established. Data collection information will be sent to the agricultural product supplier corresponding to the agricultural product area based on the missing data, and the establishment and analysis will be carried out based on the feedback information. If the establishment is still not satisfied, the agricultural product area will not be recommended at the supply chain port, and the agricultural product supplier will operate independently. The steps are as follows: Check the integrity of production data: Before establishing a regional simulation model, first confirm whether the production data of the agricultural product area is complete, that is, whether it meets the most basic data requirements of the model. Check indicators include whether key production data is missing (such as planting area, yield, soil type, climate, etc.), whether the collected data meets the expected standards, and whether there is no abnormal fluctuation; If the data is insufficient, the supplier should be requested to provide additional data. The specific content of the data requested should include the type of missing data (yield, climate, soil type, etc.), data collection method recommendations (remote sensing, sensor reporting, manual reporting, etc.), and the time point of the data request (to ensure timeliness). The supplier should provide additional data within the specified time. Analyze and establish based on the supplementary data fed back by the supplier: the supplier returns the required supplementary data within the specified time, and the integrity of the data is checked again. If the supplementary data is still insufficient, proceed to the next step (i.e. refuse to establish the simulation model). On the contrary, if the data meets the requirements, continue to build the regional simulation model, and conduct scoring and selection.

[0024] S3, setting a difference threshold, and combining the production-related data after the output simulation results with the regional simulation model and the difference threshold for real analysis, and then inputting the production-related data through the real analysis into the regional simulation model for agricultural production data prediction calibration; The steps for S3 are as follows: S3.1. Set dynamic difference thresholds based on the latest simulation results of the regional model combined with time differences; S3.2. Calculate the time difference between the production-related data collected after the latest simulation result and the latest simulation result, then extract the corresponding dynamic difference threshold value based on the time difference, then analyze the difference rate of the production-related data in combination with the latest simulation result, and perform a true analysis on the difference rate in combination with the dynamic difference threshold value. When the difference rate is less than or equal to the dynamic difference threshold value, it is determined to be true production-related data. On the contrary, when the difference rate is greater than the dynamic difference threshold value, it is determined to be false production-related data. The specific steps are as follows: Get the latest regional simulation results: Get the latest results of regional simulation models, which usually include forecast values ​​of various production-related data, such as yield, planted area, etc. Collect new production-related data: Collect the latest production-related data at the current time point. This data may come from on-site collection, sensors, supplier feedback and other channels. Compare the timestamp of the latest simulation result with the collection timestamp of the new data, and calculate the time difference between the two. Set dynamic difference threshold: The dynamic difference threshold is calculated based on the time difference. The setting of the dynamic difference threshold can be determined by a preset rule, which is usually related to the time difference: as the time difference increases, the threshold changes. The formula is as follows: ; Where E(t) is the dynamic difference threshold, t is the time difference, e is the initial threshold, and α is the attenuation factor; Perform difference rate analysis: Calculate the difference rate between the new production data and the latest simulation results. The difference rate can be calculated by the ratio or difference between the two. If the difference rate is less than or equal to the dynamic difference threshold, the data is considered to be true. Conversely, if the difference rate is greater than the dynamic difference threshold, the data is considered to be false. Feedback and adjustment: Based on the judgment results, feedback is automatically sent to the supply chain port. If the data is judged to be false, the supplier can be asked to re-verify or adjust the data. If it is judged to be true data, it can be included in the new regional model adjustment to further improve the forecast.

[0025] S3.3. Input the production-related data determined as real in S3.2 into the regional simulation model to calibrate the agricultural production data forecast, thereby regenerating the simulation results.

[0026] The calibrated regional simulation model will generate new agricultural production forecast data. The model will correct the forecast results based on the real input data to make it closer to the actual production situation. The calibrated agricultural production data can be used for further decision support, production planning, supply chain adjustment, etc.

[0027] S3.3 also includes the following steps: S3.3.1. Send the latest simulation results to the agricultural product suppliers in the agricultural product area through the supply chain port, and send accurate feedback to the agricultural product suppliers at the time point corresponding to the latest simulation results; S3.3.2. Monitor the feedback results of S3.3.1. When the feedback from the agricultural product supplier is accurate, continue to monitor. Otherwise, when the feedback from the agricultural product supplier is inaccurate, collect the growth data of the agricultural product area from the agricultural product supplier and feed the growth data back to the supply chain port for manual review. Replace the latest simulation results according to the review results. The latest simulation results can be sent to suppliers, and the model will be continuously monitored and calibrated based on the supplier's feedback. When the feedback is inaccurate, the system will collect new growth data and conduct manual review, and finally replace the simulation data based on the review results, which can ensure that the simulation results of agricultural production are always accurate and provide strong support for production decisions.

[0028] S4, extracting sales data and network market demand data of the sales area, and combining the sales data with the network market demand data to perform demand change forecasting analysis; The steps of S4 are as follows: S4.1. Extract sales data of the sales area and collect market demand data in the network; S4.2. Combine sales data with network market demand data to predict demand changes and obtain the corresponding forecast demand for the sales area. Then send the forecast demand to the sales manager for modification and feedback. Determine the forecast demand based on the feedback results. The specific steps are as follows: Extract sales data of sales area: Extract historical sales data of sales area from database or sales management system. These data usually include time, sales volume, sales amount, product category, etc. Market demand data collection: Collect market demand data through the Internet, including user behavior, social media trends, search engine query volume, consumer reviews, etc.; Combine sales data and market demand data for forecasting analysis: Use machine learning algorithms to combine sales data and market demand data to generate forecasts for future demand. The specific formula is as follows: ; Among them, D x For the predicted demand, S x is the historical sales data, A x is the market demand data, β and ε are weight coefficients, indicating the importance of sales data and market demand data; Send forecast demand to sales manager for modification feedback: Send the generated forecast demand to sales manager, who will review and adjust it. The feedback from sales manager may be based on market strategy, production capacity, inventory and other factors; Determine the final forecast demand based on the feedback results: According to the feedback from the sales management side, adjust the forecast demand model and finally determine the sales demand that needs to be executed.

[0029] S5. Carry out logistics and transportation simulation of agricultural product supply for each agricultural product area in combination with the sales area, evaluate and compare the simulation results with the predicted demand of the sales area, and based on the comparison results, display the sales areas and agricultural product areas that meet the demand to each other at the supply chain port.

[0030] The steps of S5 are as follows: S5.1. Carry out logistics and transportation simulation of agricultural product processes in combination with each agricultural product region and the sales region, and then evaluate and compare the simulation results with the predicted demand of the sales region. When the simulation results meet the predicted demand, they are retained. Otherwise, they are not retained. S5.2. According to the simulation results retained in S5.1, the corresponding sales areas and agricultural product areas are extracted and displayed to each other at the supply chain port. The specific steps are as follows: Combine agricultural product areas with sales areas to simulate logistics and transportation: Based on the process and characteristics of each agricultural product, simulate the transportation process from the production site (agricultural product area) to the sales area. Factors that need to be considered include transportation methods, time, cost, weather, transportation restrictions, etc. The demand of each sales area and the corresponding agricultural product area may have different logistics routes and times. The simulation results will help evaluate the transportation efficiency of each area; Combined with the forecasted demand of the sales area: The forecasted demand of the sales area is compared with the transportation simulation results. The simulation result can be a transportation plan, including the transportation time and product arrival volume of different sales areas. These are compared with the demand of the sales area to evaluate whether the demand can be met; Evaluate whether the simulation results meet the forecast demand: set a threshold. When the degree of satisfaction of the transportation simulation results is not lower than the threshold, the simulation results are retained; when the results are lower than the threshold, they are not retained. The evaluation criteria can be factors such as transportation time, product integrity, transportation cost, and demand coverage. Extract and display the results of the supply chain port: Extract the corresponding sales area and agricultural product area data from the retained simulation results and display them on the supply chain port (such as the supply chain management system, visualization panel, etc.), providing an interactive display of data such as the logistics transportation process, demand satisfaction, cost, time, etc., so that managers can make more targeted decisions.

[0031] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for integrating agricultural product supply chain information based on big data analysis, characterized in that: The following steps are involved: S1. Establish supply chain ports and collect lists of agricultural product regions and sales regions; S2. Extract production-related data of agricultural product areas, sort the production-related data according to the collection time nodes, set real weights for the production-related data according to the collection method, and establish a corresponding regional simulation model for each agricultural product area; S3, setting a difference threshold, and combining the production-related data after the output simulation results with the regional simulation model and the difference threshold for real analysis, and then inputting the production-related data through the real analysis into the regional simulation model for agricultural production data prediction calibration; S4, extracting sales data and network market demand data of the sales area, and combining the sales data with the network market demand data to perform demand change forecasting analysis; S5. Carry out logistics and transportation simulation of agricultural product supply for each agricultural product area in combination with the sales area, evaluate and compare the simulation results with the predicted demand of the sales area, and based on the comparison results, display the sales areas and agricultural product areas that meet the demand to each other at the supply chain port.

2. The agricultural product supply chain information integration method based on big data analysis according to claim 1 is characterized by: The S1 will establish a supply chain port and open it to agricultural product suppliers and sales managers. At the same time, when agricultural product suppliers and sales managers register their accounts, they need to fill in the information of the area they are responsible for.

3. The agricultural product supply chain information integration method based on big data analysis according to claim 1 is characterized by: The steps of S1 are as follows: S1.

1. Collect agricultural product data from each agricultural product supplier, including agricultural product type and product grade, and then generate agricultural product regions based on the agricultural product data fed back by the agricultural product supplier combined with regional information, and summarize and generate a list of agricultural product regions; S1.

2. Generate the corresponding sales area based on the regional information filled in by each sales manager, and summarize and generate a sales area list.

4. The agricultural product supply chain information integration method based on big data analysis according to claim 1 is characterized by: The steps of S2 are as follows: S2.

1. Extract production-related data of agricultural product areas, sort them according to the collection time nodes of production-related data, and set real weights for production-related data according to the collection method; S2.

2. Repeatedly combine the production-related data to generate regional simulation models corresponding to multiple different combinations. Then calculate the multiple regional simulation models in combination with the production-related data and the corresponding real weights, and select the regional simulation model with the highest weight to represent the corresponding agricultural product area.

5. The agricultural product supply chain information integration method based on big data analysis according to claim 4 is characterized by: When generating a regional simulation model, S2.2 will not perform simulation if the production-related data of the agricultural product area is insufficient to support the establishment of the regional simulation model. Data collection information will be sent to the agricultural product suppliers corresponding to the agricultural product area based on the missing data, and analysis will be performed based on the feedback information. If the establishment is still not satisfied, the agricultural product area will not be recommended at the supply chain port, and the agricultural product suppliers will operate independently.

6. The agricultural product supply chain information integration method based on big data analysis according to claim 1 is characterized by: The steps of S3 are as follows: S3.

1. Set dynamic difference thresholds based on the latest simulation results of the regional model combined with time differences; S3.

2. Calculate the time difference between the production-related data collected after the latest simulation result and the latest simulation result, then extract the corresponding dynamic difference threshold value based on the time difference, then analyze the difference rate of the production-related data in combination with the latest simulation result, and perform a true analysis on the difference rate in combination with the dynamic difference threshold value. When the difference rate is less than or equal to the dynamic difference threshold value, it is determined to be true production-related data. On the contrary, when the difference rate is greater than the dynamic difference threshold value, it is determined to be false production-related data. S3.

3. Input the production-related data determined as real in S3.2 into the regional simulation model to calibrate the agricultural production data forecast, thereby regenerating the simulation results.

7. The agricultural product supply chain information integration method based on big data analysis according to claim 6 is characterized by: The S3.3 also includes the following steps: S3.3.

1. Send the latest simulation results to the agricultural product suppliers in the agricultural product area through the supply chain port, and send accurate feedback to the agricultural product suppliers at the time point corresponding to the latest simulation results; S3.3.

2. Monitor the feedback results of S3.3.

1. When the feedback from the agricultural product supplier is accurate, continue to monitor. Otherwise, when the feedback from the agricultural product supplier is inaccurate, collect the growth data of the agricultural product area from the agricultural product supplier, and feed back the growth data to the supply chain port for manual review, and replace the latest simulation results according to the review results.

8. The agricultural product supply chain information integration method based on big data analysis according to claim 1 is characterized by: The steps of S4 are as follows: S4.

1. Extract sales data of the sales area and collect market demand data in the network; S4.

2. Combine sales data with network market demand data to analyze changes in forecasted demand, obtain forecasted demand corresponding to the sales area, and then send the forecasted demand to the sales manager for modification and feedback, and determine the forecasted demand based on the feedback results.

9. The agricultural product supply chain information integration method based on big data analysis according to claim 1 is characterized by: The steps of S5 are as follows: S5.

1. Carry out logistics and transportation simulation of agricultural product processes in combination with each agricultural product region and the sales region, and then evaluate and compare the simulation results with the predicted demand of the sales region. When the simulation results meet the predicted demand, they are retained. Otherwise, they are not retained. S5.

2. According to the simulation results retained in S5.1, the corresponding sales areas and agricultural product areas are extracted and displayed to each other at the supply chain port.

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