Smart agricultural method for optimizing resource allocation in agricultural product supply chain

Abstract

A smart agricultural method for optimizing resource allocation in an agricultural product supply chain, the method involving a data collection module, a data preprocessing module, an intelligent optimization module, a multi-objective optimization module, a real-time adjustment module and an execution module, wherein the data collection module collects, in real time, farmland environment data, production data, logistics status and market demand information; the data preprocessing module performs data cleaning, integration and storage; the intelligent optimization module performs optimized resource allocation by means of a genetic algorithm optimization unit, a particle swarm optimization unit and a hybrid optimization unit; the multi-objective optimization module achieves multi-objective balance by means of a cost optimization unit, a customer satisfaction optimization unit and a risk optimization unit; the real-time adjustment module dynamically adjusts the state of a supply chain on the basis of feedback from a data monitoring unit and an adjustment rule unit; and the execution module executes an optimization scheme by means of an agricultural inputs deployment unit, a logistics scheduling unit and a market regulation unit.

Description

A smart agriculture method for optimizing agricultural product supply chain resource allocation TECHNICAL FIELD

[0001] The present application belongs to the technical field of smart agriculture, and specifically relates to a smart agriculture method for optimizing agricultural product supply chain resource allocation. BACKGROUND

[0002] With the continuous development of smart agriculture, optimizing agricultural product supply chain resource allocation has become an important way to improve agricultural efficiency and reduce costs. Existing technical solutions have made certain progress in optimizing supply chain resource allocation, but there are still some deficiencies that affect the overall performance and economic benefits of the supply chain.

[0003] After searching, the patent with the publication number CN118378846B, titled "Agricultural product supply chain collaborative optimization method based on swarm intelligence algorithm", was published on November 22, 2024. This patent proposes a supply chain collaborative optimization method based on the ant colony algorithm, which realizes information sharing and collaborative work between each link of the supply chain by constructing a real-time data set of agricultural products and dynamically adjusting the supply chain strategy. This technical solution can effectively improve the adaptability and response speed of the supply chain to market demand fluctuations, seasonal changes and unexpected events. However, in this technical solution, it mainly relies on the ant colony algorithm, which is sensitive to initial conditions and parameter settings, which may lead to instability and limitations of the optimization results. In addition, the processing capacity of this method in multi-objective optimization (such as the balance between cost and customer satisfaction) still needs to be strengthened, especially in complex and variable agricultural product supply chain environments.

[0004] After searching, the patent with the publication number CN118467847B, titled "Multi-dimensional agricultural-related enterprise data intelligent matching and sorting method and system", was published on January 3, 2025. This patent proposes an intelligent matching and sorting method based on multi-dimensional agricultural-related enterprise data, which calculates the expected yield of agricultural products through a neural network model and generates a stable supply chain. This technical solution can optimize the composition and matching relationship of the supply chain, improve the efficiency and stability of the supply chain, reduce the loss and risk of agricultural products in the circulation link, and reduce the cost of the supply chain. However, in this technical solution, it mainly relies on the neural network model, which has high requirements for data quality and model training, which may lead to a decrease in the accuracy and reliability of the matching results in the case of insufficient data or insufficient model training. In addition, this method has deficiencies in real-time and dynamic adjustment, making it difficult to respond to unexpected events and rapid changes in market demand. TECHNICAL PROBLEM

[0005] The existing agricultural product supply chain optimization technology still has certain deficiencies in the stability of optimization algorithm, multi-objective optimization capability, data processing quality and real-time dynamic adjustment. Therefore, the present application provides a smart agriculture method for optimizing agricultural product supply chain resource allocation, aiming to realize efficient, stable and flexible management of the supply chain by comprehensively using various intelligent optimization algorithms and real-time data processing technologies, so as to meet the demand of modern agriculture for intelligent supply chain management. Technical solution

[0006] The present application provides a smart agriculture method for optimizing agricultural product supply chain resource allocation, aiming to realize efficient, stable and flexible management of the supply chain by comprehensively using various intelligent optimization algorithms and real-time data processing technologies. The present application solves the problems of low stability of optimization algorithm, insufficient multi-objective optimization capability, low data processing quality and insufficient real-time dynamic adjustment capability in the prior art, thereby meeting the demand of modern agriculture for intelligent supply chain management.

[0007] The technical solution adopted by the present application to solve the above technical problems is: a smart agriculture method for optimizing agricultural product supply chain resource allocation, comprising a data acquisition module, a data preprocessing module, an intelligent optimization module, a multi-objective optimization module, a real-time adjustment module and an execution module. The data acquisition module is used to acquire real-time data in the agricultural product supply chain. The data preprocessing module is used to preprocess the collected data. The intelligent optimization module optimizes the resource allocation of the supply chain based on various intelligent optimization algorithms. The multi-objective optimization module is used to balance between multiple objectives. The real-time adjustment module is used to dynamically adjust according to the actual operation of the supply chain. The execution module is used to execute the optimization configuration scheme.

[0008] The data acquisition module comprises an environment sensor, a production sensor, a logistics sensor and a market sensor. The environment sensor is used to monitor farmland environment data. The production sensor is used to monitor the growth status and yield of agricultural products. The logistics sensor is used to monitor the logistics status. The market sensor is used to acquire market dynamics and demand information. The data preprocessing module comprises a data cleaning unit, a data integration unit and a data storage unit. The data cleaning unit is used to remove noise and outliers in the collected data. The data integration unit is used to integrate data from multiple data sources. The data storage unit is used to store the processed data.

[0009] The intelligent optimization module comprises a genetic algorithm optimization unit, a particle swarm optimization unit and a hybrid optimization unit. The genetic algorithm optimization unit is used to preliminarily optimize the resources according to the genetic algorithm. The particle swarm optimization unit is used to further optimize according to the particle swarm algorithm. The hybrid optimization unit is used to combine the genetic algorithm and the particle swarm algorithm to form a hybrid optimization algorithm, thereby improving the stability and robustness of the optimization algorithm.

[0010] The multi-objective optimization module includes a cost optimization unit, a customer satisfaction optimization unit, and a risk optimization unit. The cost optimization unit is used to optimize the cost of the supply chain. The customer satisfaction optimization unit is used to optimize customer satisfaction. The risk optimization unit is used to evaluate and optimize the risks in the supply chain. The multi-objective optimization module balances the multiple objectives through a weight distribution unit.

[0011] The real-time adjustment module includes a data monitoring unit, an adjustment rule unit, and an adjustment execution unit. The data monitoring unit is used to monitor the running state of the supply chain in real time. The adjustment rule unit generates adjustment instructions based on real-time data and preset rules. The adjustment execution unit is used to execute the adjustment instructions to ensure that the supply chain maintains an optimal state in actual operation.

[0012] The execution module includes an agricultural material allocation unit, a logistics scheduling unit, and a market regulation unit. The agricultural material allocation unit is used to allocate agricultural materials based on the optimized configuration scheme. The logistics scheduling unit is used to schedule logistics based on the optimized configuration scheme. The market regulation unit is used to adjust market strategies based on the optimized configuration scheme.

[0013] Preferably, the intelligent optimization module further includes an adaptive parameter adjustment system, which includes a parameter monitoring unit and a parameter adjustment unit. The parameter monitoring unit is used to monitor the running parameters of the optimization algorithm in real time. The parameter adjustment unit automatically adjusts the parameters of the optimization algorithm based on the feedback of the parameter monitoring unit, improving the accuracy and stability of the optimization results.

[0014] Preferably, the multi-objective optimization module further includes a dynamic weight adjustment system, which includes a weight monitoring unit and a weight adjustment unit. The weight monitoring unit is used to monitor the actual running situation of the supply chain in real time. The weight adjustment unit dynamically adjusts the weights of the objectives based on the feedback of the weight monitoring unit, ensuring optimal multi-objective balance under different conditions.

[0015] Preferably, the real-time adjustment module further includes an exception detection system, which includes an exception monitoring unit and an exception handling unit. The exception monitoring unit is used to monitor the abnormal situation in the supply chain in real time. The exception handling unit generates exception handling instructions based on the feedback of the exception monitoring unit, ensuring that the supply chain can quickly recover to an optimal state when encountering abnormal situations.

[0016] Preferably, the data preprocessing module further includes a data fusion system, which includes a data fusion unit and a data correction unit. The data fusion unit is used to fuse data from multiple data sources. The data correction unit corrects the data based on the data fusion results, improving the accuracy and reliability of the data.

[0017] Preferably, the execution module further comprises an intelligent feedback system, which comprises a feedback monitoring unit and a feedback processing unit. The feedback monitoring unit is used to monitor the execution effect of the execution module in real time, and the feedback processing unit generates feedback processing instructions according to the feedback of the feedback monitoring unit, so as to ensure that the execution module can maintain the optimal effect in actual operation.

[0018] The structural composition, implementation mode and operation principle of the application are as follows:

[0019] The data acquisition module: the environment sensor, the production sensor, the logistics sensor and the market sensor acquire various data in the supply chain in real time, including farmland environment data, agricultural product growth conditions and yield, logistics state and market demand information. The data cleaning unit in the data preprocessing module removes noise and outliers in the collected data, the data integration unit integrates data from multiple data sources, and the data storage unit stores the processed data.

[0020] The intelligent optimization module: the genetic algorithm optimization unit, the particle swarm optimization unit and the hybrid optimization unit optimize the configuration of the supply chain resources in turn. The genetic algorithm optimization unit performs preliminary optimization based on the genetic algorithm, the particle swarm optimization unit performs further optimization based on the particle swarm algorithm, and the hybrid optimization unit combines the genetic algorithm and the particle swarm algorithm to form a hybrid optimization algorithm, thereby improving the stability and robustness of the optimization result. The adaptive parameter adjustment system automatically adjusts the parameters of the optimization algorithm according to the feedback of the parameter monitoring unit, thereby further improving the accuracy and stability of the optimization result.

[0021] The multi-objective optimization module: the cost optimization unit, the customer satisfaction optimization unit and the risk optimization unit optimize the cost, customer satisfaction and risk in the supply chain respectively. The weight distribution unit weights each target according to the preset weight distribution rule, so as to realize the balance among multiple targets. The dynamic weight adjustment system dynamically adjusts the weight of each target according to the feedback of the weight monitoring unit, so as to ensure that the optimal multi-target balance can be achieved under different conditions.

[0022] The real-time adjustment module: the data monitoring unit monitors the running state of the supply chain in real time, the adjustment rule unit generates adjustment instructions according to real-time data and preset rules, and the adjustment execution unit executes the adjustment instructions, so as to ensure that the supply chain maintains the optimal state in actual operation. The abnormality monitoring unit in the abnormality detection system monitors the abnormality in the supply chain in real time, and the abnormality processing unit generates abnormality processing instructions according to the feedback of the abnormality monitoring unit, so as to ensure that the supply chain can quickly recover to the optimal state when encountering abnormal conditions.

[0023] The execution module: the agricultural material allocation unit, the logistics scheduling unit and the market adjustment unit adjust the agricultural material, the logistics and the market strategy according to the optimal configuration scheme. The feedback monitoring unit in the intelligent feedback system monitors the execution effect of the execution module in real time, and the feedback processing unit generates feedback processing instructions according to the feedback of the feedback monitoring unit, so that the execution module can maintain the optimal effect in actual operation.

[0024] The data fusion system: the data fusion unit fuses the data of multiple data sources, and the data correction unit corrects the data according to the data fusion result, so as to improve the accuracy and reliability of the data. Beneficial effects

[0025] The adaptive parameter adjustment system can automatically adjust the algorithm parameters in the optimization process, improve the stability and robustness of the optimization result, and ensure the optimization effect under different conditions.

[0026] The dynamic weight adjustment system can dynamically adjust the weight of each target according to the actual running situation, realize the optimal balance among multiple targets, and improve the overall performance of the supply chain.

[0027] The abnormality detection system can monitor and handle the abnormal situation in the supply chain in real time, ensure that the supply chain can quickly recover to the optimal state when encountering unexpected events, and improve the adaptability and response speed of the supply chain. BRIEF DESCRIPTION OF DRAWINGS

[0028] Fig. 1 is a whole system architecture diagram of the present application;

[0029] Fig. 2 is a structural schematic diagram of the data acquisition module;

[0030] Fig. 3 is a structural schematic diagram of the data preprocessing module;

[0031] Fig. 4 is a structural schematic diagram of the intelligent optimization module;

[0032] Fig. 5 is a structural schematic diagram of the multi-objective optimization module;

[0033] Fig. 6 is a structural schematic diagram of the real-time adjustment module;

[0034] Fig. 7 is a structural schematic diagram of the execution module;

[0035] Fig. 8 is a structural schematic diagram of the data fusion system;

[0036] The reference signs are as follows:

[0037] 1, data acquisition module; 11, environmental sensor; 12, production sensor; 13, logistics sensor; 14, market sensor; 2, data preprocessing module; 21, data cleaning unit; 22, data integration unit; 23, data storage unit; 24, data fusion unit; 25, data correction unit; 3, intelligent optimization module; 31, genetic algorithm optimization unit; 32, particle swarm optimization unit; 33, hybrid optimization unit; 34, adaptive parameter adjustment system; 341, parameter monitoring unit; 342, parameter adjustment unit; 4, multi-objective optimization module; 41, cost optimization unit; 42, customer satisfaction optimization unit; 43, risk optimization unit; 44, weight distribution unit; 45, dynamic weight adjustment system; 451, weight monitoring unit; 452, weight adjustment unit; 5, real-time adjustment module; 51, data monitoring unit; 52, adjustment rule unit; 53, adjustment execution unit; 54, anomaly detection system; 541, anomaly monitoring unit; 542, anomaly processing unit; 6, execution module; 61, agricultural material allocation unit; 62, logistics scheduling unit; 63, market regulation unit; 64, intelligent feedback system; 641, feedback monitoring unit; 642, feedback processing unit. Embodiments of the present application

[0038] In order to make the technical means, creative features, purposes and effects of the present application easy to understand, the present application will be further described below in combination with specific embodiments.

[0039] The embodiment of the present application provides a smart agriculture method for optimizing agricultural product supply chain resource configuration, solves the problems of low stability of optimization algorithm, insufficient multi-objective optimization capability, low data processing quality and insufficient real-time dynamic adjustment capability in the prior art, and meets the demand of modern agriculture for intelligent supply chain management.

[0040] Referring to FIGS. 1-8, a smart agriculture method for optimizing agricultural product supply chain resource configuration includes a data acquisition module 1, a data preprocessing module 2, an intelligent optimization module 3, a multi-objective optimization module 4, a real-time adjustment module 5 and an execution module 6.

[0041] Data acquisition module 1

[0042] The data acquisition module 1 includes an environmental sensor 11, a production sensor 12, a logistics sensor 13 and a market sensor 14. The environmental sensor 11 is used to monitor farmland environmental data such as temperature, humidity, illumination, etc.; the production sensor 12 is used to monitor the growth conditions and yield of agricultural products; the logistics sensor 13 is used to monitor the logistics status such as the position and speed of transport vehicles; and the market sensor 14 is used to obtain market dynamics and demand information such as market price and demand volume. These sensors transmit data in real time to the data preprocessing module 2 through wireless communication technology.

[0043] Data preprocessing module 2

[0044] The data preprocessing module 2 includes a data cleaning unit 21, a data integration unit 22, a data storage unit 23, a data fusion unit 24, and a data correction unit 25. The data cleaning unit 21 is used to remove noise and outliers in the collected data, ensuring the accuracy of the data; the data integration unit 22 integrates data from multiple data sources to form a unified data format; the data storage unit 23 is used to store the processed data for subsequent analysis and use; the data fusion unit 24 fuses data from multiple data sources to improve the integrity and reliability of the data; and the data correction unit 25 corrects the data according to the data fusion results, further improving the accuracy of the data.

[0045] Intelligent optimization module 3

[0046] The intelligent optimization module 3 includes a genetic algorithm optimization unit 31, a particle swarm optimization unit 32, a hybrid optimization unit 33, and an adaptive parameter adjustment system 34. The genetic algorithm optimization unit 31 performs preliminary resource optimization based on the genetic algorithm, the particle swarm optimization unit 32 performs further optimization based on the particle swarm algorithm, and the hybrid optimization unit 33 combines the genetic algorithm and the particle swarm algorithm to form a hybrid optimization algorithm, improving the stability and robustness of the optimization results. The adaptive parameter adjustment system 34 includes a parameter monitoring unit 341 and a parameter adjustment unit 342. The parameter monitoring unit 341 is used to monitor the running parameters of the optimization algorithm in real time, and the parameter adjustment unit 342 automatically adjusts the parameters of the optimization algorithm according to the feedback of the parameter monitoring unit 341, improving the accuracy and stability of the optimization results.

[0047] Multi-objective optimization module 4

[0048] The multi-objective optimization module 4 includes a cost optimization unit 41, a customer satisfaction optimization unit 42, a risk optimization unit 43, a weight distribution unit 44, and a dynamic weight adjustment system 45. The cost optimization unit 41 is used to optimize the cost of the supply chain, the customer satisfaction optimization unit 42 is used to optimize customer satisfaction, and the risk optimization unit 43 is used to evaluate and optimize the risks in the supply chain. The weight distribution unit 44 weights each target according to the pre-set weight distribution rule, achieving balance between multiple objectives. The dynamic weight adjustment system 45 includes a weight monitoring unit 451 and a weight adjustment unit 452. The weight monitoring unit 451 is used to monitor the actual operation of the supply chain in real time, and the weight adjustment unit 452 dynamically adjusts the weight of each target according to the feedback of the weight monitoring unit 451, ensuring that the optimal multi-objective balance is achieved under different circumstances.

[0049] Real-time adjustment module 5

[0050] The real-time adjustment module 5 includes a data monitoring unit 51, an adjustment rule unit 52, an adjustment execution unit 53, and an anomaly detection system 54. The data monitoring unit 51 is used to monitor the running state of the supply chain in real time, the adjustment rule unit 52 generates adjustment instructions according to real-time data and preset rules, the adjustment execution unit 53 executes the adjustment instructions to ensure that the supply chain maintains the optimal state in actual operation. The anomaly detection system 54 includes an anomaly monitoring unit 541 and an anomaly handling unit 542. The anomaly monitoring unit 541 is used to monitor abnormal situations in the supply chain in real time, and the anomaly handling unit 542 generates anomaly handling instructions according to the feedback of the anomaly monitoring unit 541 to ensure that the supply chain can quickly recover to the optimal state when encountering abnormal situations.

[0051] The execution module 6

[0052] The execution module 6 includes an agricultural material allocation unit 61, a logistics scheduling unit 62, a market regulation unit 63, and an intelligent feedback system 64. The agricultural material allocation unit 61 is used to allocate agricultural materials according to the optimized configuration scheme, the logistics scheduling unit 62 is used to schedule logistics according to the optimized configuration scheme, and the market regulation unit 63 is used to adjust market strategies according to the optimized configuration scheme. The intelligent feedback system 64 includes a feedback monitoring unit 641 and a feedback handling unit 642. The feedback monitoring unit 641 is used to monitor the execution effect of the execution module 6 in real time, and the feedback handling unit 642 generates feedback handling instructions according to the feedback of the feedback monitoring unit 641 to ensure that the execution module 6 can maintain the optimal effect in actual operation.

[0053] Specific operation principle and operation process

[0054] Taking a specific agricultural product supply chain as an example, it is assumed that the supply chain involves multiple links such as farmland environment monitoring, agricultural product production, logistics transportation, and market demand.

[0055] Data collection: The environmental sensor 11 monitors the temperature, humidity, and other environmental data of the farmland in real time; the production sensor 12 monitors the growth status and yield of agricultural products; the logistics sensor 13 monitors the position and speed of the transportation vehicle; and the market sensor 14 obtains market price and demand information. These data are transmitted to the data preprocessing module 2 through wireless communication technology.

[0056] Data preprocessing: The data cleaning unit 21 removes noise and outliers in the collected data; the data integration unit 22 integrates data from multiple data sources; the data storage unit 23 stores the processed data; the data fusion unit 24 fuses data from multiple data sources; and the data correction unit 25 corrects the data according to the data fusion results.

[0057] Intelligent optimization: the genetic algorithm optimization unit 31 performs preliminary resource optimization based on the genetic algorithm; the particle swarm optimization unit 32 performs further optimization based on the particle swarm algorithm; the hybrid optimization unit 33 combines the genetic algorithm and the particle swarm algorithm to form a hybrid optimization algorithm. The adaptive parameter adjustment system 34 automatically adjusts the parameters of the optimization algorithm according to the feedback of the parameter monitoring unit 341.

[0058] Multi-objective optimization: the cost optimization unit 41 optimizes the cost of the supply chain; the customer satisfaction optimization unit 42 optimizes customer satisfaction; the risk optimization unit 43 assesses and optimizes the risks in the supply chain. The weight distribution unit 44 weights each target according to the pre-set weight distribution rules. The dynamic weight adjustment system 45 dynamically adjusts the weight of each target according to the feedback of the weight monitoring unit 451.

[0059] Real-time adjustment: the data monitoring unit 51 monitors the running state of the supply chain in real time; the adjustment rule unit 52 generates adjustment instructions according to real-time data and pre-set rules; the adjustment execution unit 53 executes the adjustment instructions. The exception monitoring unit 541 in the exception detection system 54 monitors the exception in the supply chain in real time; the exception processing unit 542 generates exception processing instructions according to the feedback of the exception monitoring unit 541.

[0060] Execution: the agricultural material allocation unit 61 allocates agricultural materials according to the optimized configuration scheme; the logistics scheduling unit 62 schedules logistics according to the optimized configuration scheme; the market adjustment unit 63 adjusts market strategies according to the optimized configuration scheme. The feedback monitoring unit 641 in the intelligent feedback system 64 monitors the execution effect of the execution module 6 in real time; the feedback processing unit 642 generates feedback processing instructions according to the feedback of the feedback monitoring unit 641.

[0061] Through the above steps, the present application can realize efficient, stable and flexible management of agricultural product supply chain resources, and improve the overall performance and response speed of the supply chain.

[0062] The basic principles, main features and advantages of the present application are shown and described above. It should be understood by those skilled in the art that the present application is not limited by the above embodiments, and the above embodiments and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

[0063] Certain terminology can also be used in the summary or detailed description of the present application for the purpose of brevity but are intended to be in no way limiting of the application. For example, the terms "right," "left," "rear," "front," "up," "down," "under" and the like as can be referenced herein can mean such terms only in the context of the particular figure to which the terminology is introduced and are not intended to more generally apply.

[0064] It is to be understood that the terminology "including", "containing", or any other variation thereof, is intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements is not necessarily limited to those elements, but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0065] The above description illustrates and describes the only preferred embodiments of the present application. However, it is to be understood that the application is not limited to the above-described and illustrated embodiments, and that modifications and variations are possible without departing from the spirit and scope of the application as defined by the appended claims.

Claims

1. A smart agriculture method for optimizing resource allocation in the agricultural product supply chain, characterized in that, Includes the following steps: The data acquisition module (1) is used to acquire real-time data in the agricultural product supply chain. The data acquisition module (1) includes an environmental sensor (11), a production sensor (12), a logistics sensor (13), and a market sensor (14). The data preprocessing module (2) is used to preprocess the acquired data. The data preprocessing module (2) includes a data cleaning unit (21), a data integration unit (22), and a data storage unit (23). The intelligent optimization module (3) optimizes the allocation of supply chain resources based on a variety of intelligent optimization algorithms. The intelligent optimization module (3) includes a genetic algorithm optimization unit (31), a particle swarm optimization unit (32), and a hybrid algorithm. The system includes a cost optimization unit (33); a multi-objective optimization module (4) for balancing multiple objectives, which includes a cost optimization unit (41), a customer satisfaction optimization unit (42), and a risk optimization unit (43); a real-time adjustment module (5) for dynamically adjusting the system based on the actual operation of the supply chain, which includes a data monitoring unit (51), an adjustment rule unit (52), and an adjustment execution unit (53); and an execution module (6) for executing the optimization configuration scheme, which includes an agricultural input allocation unit (61), a logistics scheduling unit (62), and a market adjustment unit (63).

2. A smart agriculture method for optimizing resource allocation in the agricultural product supply chain, characterized in that, Includes the following steps: The data acquisition module (1) is used to acquire real-time data in the agricultural product supply chain. The data acquisition module (1) includes an environmental sensor (11), a production sensor (12), a logistics sensor (13), and a market sensor (14). The data preprocessing module (2) is used to preprocess the acquired data. The data preprocessing module (2) includes a data cleaning unit (21), a data integration unit (22), and a data storage unit (23). The intelligent optimization module (3) optimizes the allocation of supply chain resources based on multiple intelligent optimization algorithms. The intelligent optimization module (3) includes a genetic algorithm optimization unit (31), a particle swarm optimization unit (32), and a hybrid optimization unit (33). The multi-objective optimization module (4) is used to balance multiple objectives. The multi-objective optimization module (4) includes a cost optimization unit (41), a customer satisfaction optimization unit (42), and a risk optimization unit (43). Real-time adjustment Module (5) is used to make dynamic adjustments based on the actual operation of the supply chain. The real-time adjustment module (5) includes a data monitoring unit (51), an adjustment rule unit (52), and an adjustment execution unit (53). Execution module (6) is used to execute the optimization configuration scheme. The execution module (6) includes an agricultural input allocation unit (61), a logistics scheduling unit (62), and a market adjustment unit (63). According to the smart agriculture method for optimizing the allocation of agricultural product supply chain resources as described in claim 1, the intelligent optimization module (3) further includes an adaptive parameter adjustment system (34). The adaptive parameter adjustment system (34) includes a parameter monitoring unit (341) and a parameter adjustment unit (342). The parameter monitoring unit (341) is used to monitor the running parameters of the optimization algorithm in real time. The parameter adjustment unit (342) is used to automatically adjust the parameters of the optimization algorithm based on the feedback from the parameter monitoring unit (341).

3. The smart agriculture method for optimizing the allocation of agricultural product supply chain resources according to claim 1, characterized in that, The multi-objective optimization module (4) further includes a weight allocation unit (44) and a dynamic weight adjustment system (45). The weight allocation unit (44) is used to weight each objective. The dynamic weight adjustment system (45) includes a weight monitoring unit (451) and a weight adjustment unit (452). The weight monitoring unit (451) is used to monitor the actual operation of the supply chain in real time. The weight adjustment unit (452) is used to dynamically adjust the weight of each objective based on the feedback from the weight monitoring unit (451).

4. The smart agriculture method for optimizing the allocation of agricultural product supply chain resources according to claim 1, characterized in that, The real-time adjustment module (5) further includes an anomaly detection system (54), which includes an anomaly monitoring unit (541) and an anomaly processing unit (542). The anomaly monitoring unit (541) is used to monitor anomalies in the supply chain in real time, and the anomaly processing unit (542) is used to generate anomaly processing instructions based on the feedback from the anomaly monitoring unit (541).

5. The smart agriculture method for optimizing the allocation of agricultural product supply chain resources according to claim 1, characterized in that, The execution module (6) further includes an intelligent feedback system (64), which includes a feedback monitoring unit (641) and a feedback processing unit (642). The feedback monitoring unit (641) is used to monitor the execution effect of the execution module (6) in real time, and the feedback processing unit (642) is used to generate feedback processing instructions based on the feedback from the feedback monitoring unit (641).

6. The smart agriculture method for optimizing the allocation of agricultural product supply chain resources according to claim 1, characterized in that, The environmental sensor (11) is used to monitor farmland environmental data, the production sensor (12) is used to monitor the growth status and yield of agricultural products, the logistics sensor (13) is used to monitor logistics status, and the market sensor (14) is used to obtain market dynamics and demand information.

7. The smart agriculture method for optimizing the allocation of agricultural product supply chain resources according to claim 1, characterized in that, The data cleaning unit (21) is used to remove noise and outliers from the collected data, the data integration unit (22) is used to integrate data from multiple data sources, and the data storage unit (23) is used to store the processed data.

8. The smart agriculture method for optimizing the allocation of agricultural product supply chain resources according to claim 1, characterized in that, The genetic algorithm optimization unit (31) is used to perform preliminary resource optimization based on the genetic algorithm, the particle swarm optimization unit (32) is used to perform further optimization based on the particle swarm algorithm, and the hybrid optimization unit (33) is used to combine the genetic algorithm and the particle swarm algorithm to form a hybrid optimization algorithm.

9. The smart agriculture method for optimizing the allocation of agricultural product supply chain resources according to claim 1, characterized in that, The cost optimization unit (41) is used to optimize the cost of the supply chain, the customer satisfaction optimization unit (42) is used to optimize customer satisfaction, and the risk optimization unit (43) is used to assess and optimize the risks in the supply chain.

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