Agricultural manure soil crop interaction optimization regulation and control method and system

Abstract

The invention provides an agricultural manure soil crop interaction optimization regulation and control method and system, and the method comprises the steps: carrying out the farmland unit division of a farmland region, constructing farmland unit intelligence, and constructing a farmland unit social network which comprises a physical proximity network, a feature similarity network and a causal association network; the farmland unit intelligent agent performs social learning based on the farmland unit social network, obtains individual efficiency data of the manure regulation and control strategy, generates a candidate manure regulation and control strategy set, performs strategy radiation simulation on the candidate manure regulation and control strategy set through the causal association network and the infectious disease model, obtains strategy radiation intensity data, and sends the strategy radiation intensity data to the farmland unit intelligent agent; and the global farmland unit agent performs global simulation based on the candidate manure regulation strategy set to obtain a strategy global influence index, and optimizes the candidate manure regulation strategy set according to the individual efficiency data, the strategy radiation data and the strategy global influence index to generate an agricultural manure regulation strategy. Therefore, the overall utilization rate of manure and the stability of farmland ecology are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of agriculture, in particular to an agricultural manure soil crop interaction optimization regulation method and system. BACKGROUND

[0002] With the development of agricultural scale and precision, manure as an important source of organic nutrients, through the regulation of manure can achieve the effect of improving crop yield and protecting soil ecology. The traditional method of manure regulation in farmland mainly focuses on individual decision optimization and global effect evaluation, but it still has certain limitations, and it is often difficult to meet the local and global needs of farmland.

[0003] Specifically, there are causal relationship between farmland units, such as hydrological connection and pest transmission. Although the manure regulation strategy developed for a farmland unit can significantly solve the local problem of the farmland unit, due to the causal relationship between farmland units, the local strategy for a farmland unit will inevitably have a positive or negative impact on its neighbor unit. The traditional method often ignores these impacts, making it difficult to balance the overall benefits of farmland when solving the local problem of farmland unit, resulting in low overall utilization of manure and difficulty in ensuring the stability of farmland ecology. SUMMARY

[0004] In view of the above-mentioned problems, in combination with the first aspect of the present application, the present application provides an agricultural manure soil crop interaction optimization regulation method, which comprises: S1: dividing the farmland area into farmland units and constructing farmland unit agents; S2: constructing a farmland unit social network for the farmland unit agent, the farmland unit social network including a physical proximity network, a feature similarity network and a causal relationship network; S3: the farmland unit agent performs social learning based on the farmland unit social network, obtains individual performance data of the manure regulation strategy, and generates a candidate manure regulation strategy set; S4: performing strategy radiation simulation on the candidate manure regulation strategy set through the causal relationship network and the infectious disease model to obtain strategy radiation data; S5: performing global simulation of the farmland unit agent based on the candidate manure regulation strategy set to obtain a strategy global influence index; S6: optimizing the candidate manure regulation strategy set according to the individual performance data, the strategy radiation data and the strategy global influence index to generate an agricultural manure regulation strategy.

[0005] As a further scheme of the present application, a farmland unit social network is constructed for the farmland unit agent, the farmland unit social network comprises a physical proximity network, a feature similarity network and a causal correlation network, comprising: The geometric center point of the farmland unit corresponding to the farmland unit agent is taken as a Venn diagram seed point, a physical proximity network is constructed based on the Venn diagram seed point, and the corresponding Venn diagram seed points are linked according to a pre-set edge connection rule, and a distance attenuation function is used to obtain the edge connection weight of the corresponding edge; The farmland unit agent is taken as a feature similarity network node, feature extraction is performed on the farmland unit data set corresponding to each node through a multi-head attention mechanism and a deep residual network structure to generate a farmland unit feature vector, the feature similarity between the farmland unit feature vectors corresponding to each node is obtained, and an edge connection of the feature similarity network is established according to a pre-set feature similarity threshold, and the nodes with edge connection between each other are defined as feature similarity nodes; The farmland unit agent is taken as a causal correlation network node, and a full connection network is established for the causal correlation network node, a conditional independence test is performed on the full connection network, the edges determined as conditionally independent are removed, the causal correlation strength corresponding to each edge connection in the causal correlation network is synchronously obtained, and the causal correlation network is generated.

[0006] As a further scheme of the present application, the farmland unit agent performs social learning based on the farmland unit social network, obtains individual performance data of the manure regulation strategy, and generates a candidate manure regulation strategy set, comprising: The farmland unit agent to be regulated by manure is defined as a target farmland unit agent, the spatial proximity nodes and the feature similarity nodes of the target farmland unit agent in the farmland unit social network are obtained, a proximity similar unit set is constructed, and the corresponding correlation data set is screened in combination with the causal correlation network, and a neighbor farmland unit agent set is constructed based on the proximity similar unit set and the correlation data set; The target farmland unit agent performs historical optimal manure scheduling strategy learning on the neighbor farmland unit agent to obtain an initial candidate manure scheduling strategy set; The target farmland unit agent performs counterfactual questioning on the neighbor farmland unit agent based on the initial candidate manure scheduling strategy set, the neighbor farmland unit agent calls the local causal correlation network to generate a counterfactual estimation result and sends it to the target farmland unit agent; The counterfactual estimation result is evaluated based on an adversarial review mechanism to obtain an initial candidate manure scheduling strategy evaluation result, and a candidate manure scheduling strategy set is generated.

[0007] As a further scheme of the present application, the counter-opinion mechanism is used to evaluate the counterfactual estimation result, obtain an initial candidate manure scheduling strategy evaluation result, and generate a candidate manure scheduling strategy set, including: Obtain historical performance data corresponding to all farmland unit agents in the farmland unit social network, and the historical performance data is expressed as a deviation value between actual performance and expected performance of a historical manure scheduling strategy. A predetermined number of farmland unit agents are selected from the highest and lowest ranked historical performance data, respectively, to generate a candidate counter-opinion agent set; Obtain the intermediate centrality of the farmland unit agents in the candidate counter-opinion agent set in the farmland unit social network, and select farmland unit agents within a predetermined threshold range to generate a counter-opinion agent set; Input the counterfactual estimation result into the counter-opinion agent set for prediction evaluation to generate an initial candidate manure scheduling strategy evaluation result. Based on the initial candidate manure scheduling strategy evaluation result, a candidate manure scheduling strategy set and corresponding individual performance data are generated, and the individual performance data is expressed as the expected performance of the candidate manure scheduling strategy.

[0008] As a further scheme of the present application, the strategy radiation simulation of the candidate manure regulation strategy set is performed through the causal correlation network and the infectious disease model to obtain strategy radiation data, including: Based on the causal correlation network and the infectious disease model, the diffusion process of the candidate manure regulation strategy set in the ecosystem corresponding to the global farmland unit is simulated to obtain strategy radiation data of each candidate manure regulation strategy in the candidate manure regulation strategy set; The strategy radiation data is expressed as the radiation propagation influence degree of the candidate manure regulation strategy in the global farmland area after implementation in a certain farmland unit.

[0009] As a further scheme of the present application, the method further includes: Based on the infectious disease model, the nodes in the causal correlation network are divided into an infection period, a latent period, and an immune period; The infection period is expressed as the adoption and implementation of the candidate manure regulation strategy by the farmland unit, the latent period is expressed as the adoption but non-implementation of the candidate manure regulation strategy by the farmland unit, and the immune period is expressed as the implementation of the candidate manure regulation strategy by the farmland unit without expected performance, and the farmland unit no longer accepts the candidate manure regulation strategy within a preset immune period; Randomly select multiple causally related network nodes as infected nodes in the infectious stage in the infectious disease model, set the transmission rate as the conversion probability to infect the uninfected nodes, so that the uninfected nodes enter the latent period, and define the nodes entering the latent period as latent nodes, set the uninfected nodes to convert into infected nodes in the infectious stage with the lesion rate as the conversion probability, and set the infected nodes to convert into immune nodes in the immune stage with the immunity rate as the conversion probability; Based on the Monte Carlo algorithm, the strategy radiation simulation is carried out, and the strategy radiation degree data of the farmland unit corresponding to the causally related network node is generated.

[0010] As a further scheme of the present application, the global farmland unit agent performs global simulation simulation based on the candidate manure regulation strategy set, and obtains a strategy global influence index, including: The global farmland unit agent matches the corresponding candidate manure regulation strategy from the candidate manure regulation strategy set, and obtains the strategy global influence index through simulation simulation, and the strategy global influence index at least includes absorption efficiency index, environmental impact index and economic benefit index, and the strategy global influence index is obtained based on the strategy global influence index; The absorption efficiency index is used to quantify the absorption efficiency of soil nutrients in the global farmland unit after the global farmland unit agent executes the corresponding candidate manure regulation strategy; The environmental impact index is used to quantify the influence rate of the global farmland on the environment after the global farmland unit agent executes the corresponding candidate manure regulation strategy; The economic benefit index is used to quantify the fluctuation of the expected income of the global farmland crops after the global farmland unit agent executes the corresponding candidate manure regulation strategy.

[0011] As a further scheme of the present application, the candidate manure regulation strategy set is optimized according to the individual performance data, the strategy radiation degree data and the strategy global influence index, and the agricultural manure regulation strategy is generated, including: Based on the strategy radiation degree data, the individual performance data of each candidate manure regulation strategy in the candidate manure regulation strategy set and the strategy global influence index of the candidate manure regulation strategy set are coordinated, and the agricultural manure regulation strategy is generated.

[0012] As a further scheme of the present application, the farmland area is divided into farmland units, and the farmland unit agent is constructed, including: Based on the geographic information system and the clustering algorithm, the continuous farmland area is divided into multiple farmland units with the same size, multiple source data of the multiple farmland units are collected, and the collected original data are subjected to outlier rejection, missing value filling and data standardization, and the farmland unit data set corresponding to each farmland unit is obtained, and the farmland unit data set at least includes farmland soil data, crop growth data, weather data and farmland management data; constructing a farmland unit intelligent agent based on the farmland unit data set and preset social learning rules and strategy triggering rules, wherein the social learning rules are used for social learning of the farmland unit and its neighboring farmland units that are physically adjacent or similar in farmland features, and the strategy triggering rules are used for triggering the manure regulation strategy generation when the farmland unit reaches a predetermined condition.

[0013] In still another aspect, the embodiment of the present application further provides an agricultural manure soil crop interaction optimization regulation system, comprising: a unit division module, which is configured to divide a farmland area into farmland units and construct farmland unit intelligent agents; a network construction module, which is configured to construct a farmland unit social network including a physical proximity network, a feature similarity network and a causal correlation network for the farmland unit intelligent agents; a social learning module, which is configured to perform social learning by the farmland unit intelligent agents, acquire individual performance data of the manure regulation strategy, and generate a candidate manure regulation strategy set; a radiation simulation module, which is configured to perform strategy radiation simulation on the candidate manure regulation strategy set according to the causal correlation network and an infectious disease model, and generate strategy radiation degree data; a global simulation module, which is configured to perform global simulation and simulation according to the candidate manure regulation strategy set by the global farmland unit intelligent agents, and acquire a strategy global influence index; a strategy generation module, which is configured to optimize the candidate manure regulation strategy set according to the individual performance data, the strategy radiation degree data and the strategy global influence index, and generate an agricultural manure regulation strategy.

[0014] Based on the above aspects, the embodiment of the present application realizes the division of a farmland area into farmland units, the construction of farmland unit intelligent agents, and the construction of a farmland unit social network for the farmland unit intelligent agents. The farmland unit social network includes a physical proximity network, a feature similarity network and a causal correlation network. Through the construction of the physical proximity network, the feature similarity network and the causal correlation network, the spatial proximity, the feature similarity and the causal correlation among farmlands are analyzed and quantified from multiple dimensions. The farmland unit social network provides data support for subsequent comprehensive manure regulation strategies. The farmland unit intelligent agents perform social learning based on the farmland unit social network, acquire individual performance data of the manure regulation strategy, and generate a candidate manure regulation strategy set. Through the social learning, the historical efficient strategies are learned, and the candidate manure regulation strategy set is generated in combination with the actual situation of the farmland unit itself, thereby providing a data basis for the subsequent generation of the manure regulation strategy. The candidate fecal fertilizer regulation strategy set is simulated by the causal correlation network and the infectious disease model, strategy radiation data is obtained, the influence of each farmland unit in the global farmland area is obtained by performing strategy radiation simulation, and the influence provides a basis for coordinating the local demand and global benefit of the fecal fertilizer regulation strategy in the sequel. The global farmland unit agent performs global simulation based on the candidate fecal fertilizer regulation strategy set, obtains a strategy global influence index, and quantifies the global benefit of the farmland after each farmland unit executes the corresponding local optimal strategy without considering the global benefit, which provides a basis for coordinating the local demand and global benefit of the fecal fertilizer regulation strategy in the sequel. According to the individual performance data, the strategy radiation data and the strategy global influence index, the candidate fecal fertilizer regulation strategy set is optimized to generate an agricultural fecal fertilizer regulation strategy, the individual performance data and the strategy global influence index of the fecal fertilizer regulation strategy are continuously optimized based on the strategy radiation data, so that the finally generated agricultural fecal fertilizer regulation strategy can consider the local demand and global benefit of the farmland, thereby improving the overall utilization rate of the fecal fertilizer and the stability of the farmland ecology. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 is an execution flow schematic diagram of an agricultural fecal fertilizer soil crop interaction optimization regulation method provided by an embodiment of the present application; Figure 2 is a schematic diagram of a farmland unit social network in an agricultural fecal fertilizer soil crop interaction optimization regulation method provided by an embodiment of the present application; Figure 3 is a schematic diagram of an agricultural fecal fertilizer soil crop interaction optimization regulation system provided by an embodiment of the present application. DETAILED DESCRIPTION

[0016] The present application will be described in detail below with reference to the accompanying drawings, Figure 1 is an execution flow schematic diagram of an agricultural fecal fertilizer soil crop interaction optimization regulation method provided by an embodiment of the present application, Figure 2 is a schematic diagram of a farmland unit social network in an agricultural fecal fertilizer soil crop interaction optimization regulation method provided by an embodiment of the present application, which will be described in detail below.

[0017] Step S1, the farmland area is divided into farmland units, and the farmland unit agent is constructed.

[0018] Specifically, the continuous farmland region is divided into multiple farmland units of consistent size based on a geographic information system and a clustering algorithm, multi-source data of the multiple farmland units are collected, and the collected raw data are subjected to outlier rejection, missing value filling and data standardization to obtain a farmland unit dataset corresponding to each farmland unit, the farmland unit dataset at least including farmland soil data, crop growth data, weather data and farmland management data.

[0019] In a possible embodiment, the farmland region is divided into multiple farmland units of consistent size and farmland characteristics based on a geographic information system and a clustering algorithm, a GIS vector surface segmentation tool is used for farmland unit division, clustering is performed according to farmland soil types, and the farmland region is divided into corresponding farmland unit clusters, such as sandy soil units and clay soil units; clustering is performed according to farmland crop types, and the farmland region is divided into corresponding farmland unit clusters, such as wheat units and corn units; wherein the area of farmland units of the same type is controlled to be [0.1, 10] hectares, and unit division across real barriers such as roads and rivers is avoided. Specifically, first, a topographic map, a soil type distribution map and a crop planting plan corresponding to the farmland region are imported, based on the soil type corresponding boundaries in the soil type distribution map and the crop planting blocks in the crop planting plan, a clustering algorithm is used to divide the global farmland region into 600 farmland units, including 250 sandy soil wheat units, 150 clay soil wheat units and 200 loam wheat units.

[0020] Farmland soil data are collected by a soil sensor, such as soil nitrogen, phosphorus and potassium contents of each farmland unit collected by a nitrogen, phosphorus and potassium sensor, and soil humidity data in the farmland unit collected by a soil humidity sensor; a preset crop growth monitoring camera is used to monitor the growth period of farmland crops and obtain corresponding crop growth data; real-time data of a weather station are used as weather data; corresponding farmland management data are generated according to historical farmland management records, such as farmland management data of xxxx year {historical fertilization strategy: application of pig manure 15 t / ha, historical yield: wheat annual yield 6.5 t / ha, farmer decision cycle: average 7 days}, and the collected data are subjected to standardization processing, such as missing value filling by a KNN algorithm, outlier rejection by a Z-score method, and mapping of the collected data to the [0, 1] interval by min-max standardization to generate a farmland unit dataset; The farm unit agent is constructed based on the farm unit dataset and preset social learning rules and strategy triggering rules. The social learning rules are used for social learning of the farm unit and its neighboring farm units that are physically adjacent or similar in farm features. A communication interface based on an MQTT protocol is used to realize information interaction between the farm unit agents. The strategy triggering rules are used to trigger generation of the manure regulation strategy when a predetermined condition is reached. For example, when the soil nitrogen content is lower than 80 mg / kg and the crop in the farm is in the tillering stage, social learning is triggered. At this time, the farm unit and its corresponding neighboring farm units perform social learning, learn the historical optimal manure regulation strategy in the neighboring farm units, and iteratively optimize the strategy in combination with the farm unit dataset of the farm unit to generate a corresponding locally optimal manure regulation strategy.

[0021] In step S2, a farm unit social network is constructed for the farm unit agent. The farm unit social network includes a physical proximity network, a feature similarity network, and a causal association network.

[0022] In this embodiment, step S2 includes: In step S21, the physical proximity network is constructed.

[0023] Specifically, the geometric center point of the farm unit corresponding to the farm unit agent is taken as a voronoi diagram seed point. The physical proximity network is constructed based on the voronoi diagram seed point. The corresponding voronoi diagram seed points are linked according to a preset edge connection rule. A distance decay function is used to obtain the corresponding edge connection weight.

[0024] In a possible embodiment, the farm unit agents of the global farm area are taken as the nodes of the physical proximity network. When the distance between the geometric center points of two farm units is lower than a preset threshold or there is a common edge between the boundaries of the two farm units, a non-directional edge is established between the two nodes to represent the spatial proximity relationship between the two nodes. A distance decay function is used to generate the corresponding weight of the edge connection in the physical proximity network. For example, the distance between the geometric center points of farm unit A and farm unit B is 2.8 kilometers, which is lower than the preset threshold of 3 kilometers. Therefore, a non-directional edge is established between farm unit A and farm unit B. An exponential decay function is used to obtain the edge weight between the two nodes.

[0025] In step S22, the feature similarity network is constructed.

[0026] Specifically, the farmland unit agent is taken as a feature similar network node, a multi-head attention mechanism and a deep residual network structure are used to extract features of a farmland unit data set corresponding to each node, a farmland unit feature vector is generated, a feature similarity between farmland unit feature vectors corresponding to each node is obtained, and an edge connection of the feature similar network is established according to a preset feature similarity threshold value, and nodes with an edge connection between them are defined as feature similar nodes.

[0027] In a possible embodiment, the farmland unit data set of the farmland unit agent is subjected to feature extraction to generate a corresponding farmland unit feature vector, a cosine similarity between farmland unit feature vectors corresponding to each node in the feature similar network is calculated to quantify the feature similarity between the nodes, a five-fold cross-validation method is used to set the feature similarity threshold value in the feature similar network, and whether to establish an edge connection between the feature similar network nodes is determined based on the feature similarity threshold value, for example, the feature similarity threshold value set by the five-fold cross-validation method is 0.75, assuming that the cosine similarity between the farmland unit feature vectors corresponding to feature similar network node A and feature similar network node B is 0.68, the cosine similarity between the farmland unit feature vectors corresponding to feature similar network node A and feature similar network node C is 0.78, and the cosine similarity between the farmland unit feature vectors corresponding to feature similar network node B and feature similar network node C is 0.85, according to the feature similarity threshold value 0.75, it is determined that an edge connection is established between feature similar network node A and feature similar network node B, and an edge connection is established between feature similar network node B and feature similar network node C.

[0028] Step S23, constructing a causal association network.

[0029] Specifically, the farmland unit agent is taken as a causal association network node, and a fully connected network is established for the causal association network node, a conditional independence test is performed on the fully connected network, edges determined to be conditionally independent are removed, the causal association strength corresponding to each edge connection in the causal association network is obtained synchronously, and the causal association network is generated.

[0030] In a possible embodiment, data extraction is performed on the field unit data set of the field unit agent to obtain strategy effect correlation data of the last three growth seasons, the strategy effect correlation data including a fertilization strategy of each field unit agent, such as a manure type, an application amount, and an application time, a strategy adoption time, and local performance after the strategy implementation, such as a yield increase rate and a manure utilization rate, a strategy adoption time of a neighboring field unit agent, for example, part of the strategy effect correlation data of the field unit A can be represented as {fertilization strategy: [pig manure, 15 t / ha, application in tillering stage], strategy adoption time: June 18, xxxx, local performance: [yield increase rate 15%, manure utilization rate 88%], strategy adoption time of a neighboring field unit: June 28, xxxx}.

[0031] The PC algorithm is used to obtain a causal correlation direction corresponding to the manure regulation strategy between the field unit agents, specifically, first, a causal variable set is defined, the causal variable set including a strategy variable X_i of the field unit agent i, such as defining the manure application amount as 15 t / ha, X_i=1, the manure application amount as 10 t / ha, X_i=0, a strategy variable X_j of the field unit agent j, a soil nutrient variable S, a meteorological data variable M, and a crop growth period variable G; the Fisher transformation is used to determine whether there is a causal correlation between the nodes in the causal correlation network, for example, it is assumed that the field unit A adopts a certain manure scheduling strategy on June 18, xxxx, and the neighboring field unit B adopts the same manure scheduling strategy ten days later, after controlling S, M, and G corresponding to the field unit A and the field unit B, the significance value of the field unit A and the field unit B is obtained as 0.02 by the Pearson correlation coefficient significance test method, which is lower than the statistical significance judgment standard 0.05, and since the strategy adoption time of the field unit A is earlier than that of the field unit B, it is determined that there is a causal correlation between the field unit A and the field unit B, and the corresponding causal correlation direction is that the field unit A points to the field unit B.

[0032] The Bayesian network is used to estimate the probability that the field unit agent j adopts a strategy when the field unit agent i adopts a strategy after controlling S, M, and G, and the probability is taken as an edge connection weight value between the field unit agents i and j, that is, w_{i,j}=P(X_j=1|X_i=1,S,M,G), for example, the edge connection weight between the field unit A and the field unit B is obtained as w_{A,B}=P(X_B=1|X_A=1,S,M,G)=0.78 by the Bayesian network.

[0033] In step S3, the field unit agent performs social learning based on the field unit social network to obtain individual performance data of the manure regulation strategy, and generates a candidate manure regulation strategy set.

[0034] In this embodiment, step S3 includes: Step S31, construct a neighbor farmland unit agent set.

[0035] Specifically, the farmland unit agent to be regulated by manure is defined as a target farmland unit agent, the corresponding spatial adjacent nodes and characteristic similar nodes of the target farmland unit agent in the farmland unit social network are obtained, a neighbor similar unit set is constructed, and the corresponding associated data set is screened in combination with the causal association network, and the neighbor farmland unit agent set is constructed based on the neighbor similar unit set and the associated data set.

[0036] In a possible embodiment, the spatial adjacent nodes connected to the target farmland unit agent and having an edge weight not lower than a preset spatial similarity threshold are selected from the physical adjacent network, the characteristic similar nodes connected to the target farmland unit agent and having an edge weight not lower than a preset characteristic similarity threshold are selected from the characteristic similar network, and the neighbor similar unit set is constructed based on the spatial adjacent nodes and the characteristic similar nodes; the causal association nodes having a causal impact on the target farmland unit agent itself and having an edge weight not lower than a preset causal association threshold are selected from the causal association network, and the associated data set is generated; and the neighbor similar unit set and the associated data set are fused and deduplicated to generate the neighbor farmland unit agent set.

[0037] For example, the farmland unit agent A selects 5 spatially adjacent nodes and 7 feature-similar nodes from the physically adjacent network and the feature-similar network, respectively, to construct a set of adjacent and similar units, removes the duplicate 3 feature-similar nodes from the set of adjacent and similar units, and finally retains 5 spatially adjacent nodes and 4 feature-similar nodes. Similarly, the associated data set and the set of adjacent and similar units are removed to generate a set of neighbor farmland unit agents. If the number of elements in the set of neighbor farmland unit agents is less than the lower limit of the preset element quantity threshold, the corresponding threshold lower limit is reduced by a preset step, and the corresponding nodes are sequentially expanded into the set of neighbor farmland unit agents according to a preset screening order. The preset screening order is represented as feature-similar node priority > causal association network node priority > spatially adjacent node priority, that is, assuming that the values of the preset spatial similarity threshold, the preset feature similarity threshold, and the preset causal association threshold are 0.8, 0.7, and 0.6, respectively, if the nodes need to be supplemented into the set of neighbor farmland unit agents, the corresponding threshold lower limit can be reduced by 5%. If the number of elements in the set of neighbor farmland unit agents still does not reach the lower limit of the preset element quantity threshold after supplementing the nodes, the threshold lower limit continues to be reduced by 5% until the number of elements in the set of neighbor farmland unit agents reaches the lower limit of the preset element quantity threshold. If the number of elements in the set of neighbor farmland unit agents exceeds the upper limit of the preset element quantity threshold, the redundant elements are removed according to the reverse order of the preset screening order, that is, the farmland unit agent corresponding to the spatially adjacent node with the lowest edge weight in the set of neighbor farmland unit agents is preferentially removed. It should be noted that the number of farmland unit agents corresponding to each type of node in the set of neighbor farmland unit agents, including spatially adjacent nodes, causal association network nodes, and feature-similar nodes, cannot be less than 2. Therefore, when the number of farmland unit agents corresponding to spatially adjacent nodes in the set of neighbor farmland unit agents is 2, the farmland unit agents corresponding to causal association network nodes need to be removed. Similarly, when the number of causal association network nodes is 2, the farmland unit agents corresponding to feature-similar nodes need to be removed.

[0038] It can be understood that when the corresponding threshold is not adjusted to the threshold lower limit by a preset step, the threshold cannot be adjusted below the preset safety boundary. For example, the initial values of the preset spatial similarity threshold, the preset feature similarity threshold, and the preset causal association threshold are 0.8, 0.7, and 0.6, respectively, and the safety boundary is set to 70% of the initial threshold, that is, the adjusted preset spatial similarity threshold, the preset feature similarity threshold, and the preset causal association threshold cannot be lower than 0.56, 0.49, and 0.42, respectively. If the number of elements in the set of neighbor farmland unit agents still does not reach the lower limit of the preset element quantity threshold after adjusting the threshold to the preset safety boundary, the farmland unit agents are screened from the set of neighbor farmland unit agents of the farmland unit agents in the set of neighbor farmland unit agents of the target farmland unit agent and supplemented into the set of neighbor farmland unit agents of the target farmland unit agent.

[0039] For example, the neighbor farmland unit agent set of the farmland unit agent A contains only 5 farmland unit agents, the preset element quantity threshold range is [8, 12], and the number of farmland unit agents contained in the neighbor farmland unit agent set is lower than the lower limit of the preset element quantity threshold, so farmland unit agents need to be supplemented into the neighbor farmland unit agent set. Assuming that the adjustment threshold reaches the preset safety boundary, the number of farmland unit agents contained in the neighbor farmland unit agent set of the farmland unit agent A is 7, at this time, the farmland unit agent B is selected from the neighbor farmland unit agent set, and the farmland unit agents are supplemented from the neighbor farmland unit agent set of the farmland unit agent B according to the preset screening order until the number of elements in the neighbor farmland unit agent set of the target farmland unit agent reaches the lower limit of the preset element quantity threshold.

[0040] In step S32, social learning is performed to generate a candidate manure scheduling strategy set.

[0041] Specifically, the target farmland unit agent learns the historical optimal manure scheduling strategy of the neighbor farmland unit agent to obtain an initial candidate manure scheduling strategy set, the target farmland unit agent asks the neighbor farmland unit agent counterfactually based on the initial candidate manure scheduling strategy set, the neighbor farmland unit agent calls the local causal association network to generate a counterfactual estimation result and sends it to the target farmland unit agent, the counterfactual estimation result is evaluated based on the adversarial review mechanism to obtain an initial candidate manure scheduling strategy evaluation result, and a candidate manure scheduling strategy set is generated.

[0042] It can be understood that after obtaining the initial candidate manure scheduling strategy set, the target farmland unit agent interrogates the neighbor farmland unit agent providing the initial candidate manure scheduling strategy counterfactually. Specifically, the neighbor farmland unit agent calls the corresponding farmland unit data set, synchronously combines the digital twin to construct a virtual scene without implementing the initial candidate manure scheduling strategy, and deduces the growth trajectory and yield result of the farmland crop in the virtual scene to generate a counterfactual estimation result. The counterfactual estimation result is compared with the historical farmland management data in the farmland unit data set to obtain the causal gain value between the strategy execution scene and the non-execution strategy scene. The causal gain value is output as individual performance data, which represents the expected performance of the candidate manure scheduling strategy. For example, when farmland unit agent A recommends the initial candidate manure scheduling strategy of "deeply applying 5 tons of manure before the rainy season" to farmland unit agent B, farmland unit agent B will ask farmland unit agent A to show its corresponding historical farmland management record. Assuming that farmland unit agent A implemented the strategy under the same environmental conditions in June last year, the corn yield reached 800 kg / mu, and the counterfactual simulation calculated that in the scenario without using the strategy, the corn yield was only 650 kg / mu, and the corresponding causal gain value was 150 kg / mu.

[0043] In one possible embodiment, the historical performance data corresponding to all farmland unit agents in the farmland unit social network is obtained, the historical performance data is represented as the deviation value of the actual performance and the expected performance of the historical manure scheduling strategy, and the farmland unit agents are ranked in ascending order of the historical performance data. A predetermined number of farmland unit agents are selected from the highest and lowest ranked historical performance data, respectively, to generate a candidate counter-opinion agent set. For example, the top 5 farmland unit agents with the highest historical performance data and the bottom 3 farmland unit agents with the lowest historical performance data are selected to form the candidate counter-opinion agent set. The intermediate centrality of the farmland unit agents in the candidate counter-opinion agent set in the farmland unit social network is obtained, and the farmland unit agents within a predetermined threshold range are selected to generate a counter-opinion agent set, where the intermediate centrality is represented as the number of times a node is located on the shortest path between other nodes. The more times, the more important the node is in the network. For example, the predetermined threshold range of the intermediate centrality is [0.7, 0.85]. Assuming that only the intermediate centrality of farmland unit agent B in the candidate counter-opinion agent set is 0.6, which is lower than the lower limit of the predetermined threshold range of the intermediate centrality, the candidate counter-opinion agent is removed, and the remaining candidate counter-opinion agents form the counter-opinion agent. The counter-factual estimation result is input into the counter-opinion agent set for prediction evaluation to generate an initial candidate manure scheduling strategy evaluation result. Based on the initial candidate manure scheduling strategy evaluation result and combined with the propensity score algorithm, a candidate manure scheduling strategy is selected from the initial candidate manure scheduling strategy set to generate a candidate manure scheduling strategy set. For example, when generating the candidate manure scheduling strategy set of farmland unit A, the "whether to adopt a certain strategy" is used as the dependent variable, and the farmland unit data set of farmland unit A is used as the independent variable. The propensity scores of the 6 initial candidate manure scheduling strategies obtained through social learning are calculated by Logistic regression. Assuming that the propensity scores of the 6 initial candidate manure scheduling strategies are 0.72, 0.71, 0.68, 0.64, 0.61, and 0.58, respectively, the strategies with propensity scores lower than 0.65 are removed, and the remaining initial candidate manure scheduling strategies are packaged as a candidate manure scheduling strategy set for output.

[0044] In step S4, the candidate manure regulation strategy set is simulated by the causal correlation network and the infectious disease model to obtain strategy radiation data.

[0045] Specifically, the diffusion process of the candidate manure regulation strategy set in the global farmland unit corresponding ecosystem is simulated based on the causal correlation network and the infectious disease model to obtain the strategy radiation data of each candidate manure regulation strategy in the candidate manure regulation strategy set. The strategy radiation data is represented as the radiation propagation influence degree of the candidate manure regulation strategy in the global farmland area after the implementation of the candidate manure regulation strategy in a certain farmland unit.

[0046] It can be understood that based on the infectious disease model, the nodes in the causal correlation network are divided into an infection period, a latent period and an immune period. The infection period represents that the farmland unit adopts and implements the candidate manure regulation strategy. The latent period represents that the farmland unit adopts but does not implement the candidate manure regulation strategy. The immune period represents that the farmland unit implements the candidate manure regulation strategy without expected effectiveness, and does not accept the candidate manure regulation strategy within a preset immune period.

[0047] In a possible embodiment, a plurality of nodes in the causal correlation network are randomly selected as infected nodes in the infection period in the infectious disease model. A transmission infection is performed on uninfected nodes with a transmission rate as a conversion probability, so that the uninfected nodes enter the latent period. The nodes entering the latent period are defined as latent nodes. The uninfected nodes are converted into infected nodes in the infection period with a morbidity rate as a conversion probability. The infected nodes are converted into immune nodes in the immune period with an immunity rate as a conversion probability. Strategy radiation simulation is performed based on the Monte Carlo algorithm to generate strategy radiation degree data of the farmland units corresponding to the nodes in the causal correlation network.

[0048] It can be understood that the transmission rate represents the probability that the farmland unit j also adopts the candidate manure regulation strategy after the farmland unit i adopts the candidate manure regulation strategy. The transmission rate is positively correlated with the edge weight of the causal correlation network, for example, the transmission rate = 1.2 * w_{i,j}, where 1.2 is a preset transmission rate coefficient, which is used to reduce the deviation between the transmission rate of the strategy radiation simulation and the actual transmission rate of the strategy, that is, assuming that the edge connection weight between the farmland unit i and the farmland unit j is 0.5, the corresponding transmission rates of the two are 0.6. The morbidity rate represents the probability that the farmland unit adopts the candidate manure regulation strategy from knowing the candidate manure regulation strategy. The morbidity rate is generated based on the historical farmer decision cycles included in the farmland unit data set of the farmland unit, for example, assuming that the historical average decision cycle of a farmland unit is 5 days, the morbidity rate = 1 / 5 = 0.20 / day. If the candidate manure regulation strategy involves complex operations such as mixed manure ratio, the morbidity rate is reduced to 75% of the initial value, that is, the morbidity rate corresponding to the candidate manure regulation strategy is adjusted to 0.15 / day. The immunity rate represents the probability that the farmland unit rejects the same strategy after adopting the candidate manure regulation strategy. The immunity rate is set based on the probability that the farmland unit in the farmland unit data set does not adopt the same strategy in the future 2 growth seasons when the implementation effect of the strategy does not meet the expectation, for example, after a farmland unit adopts a manure regulation strategy X, the actual implementation effect does not meet the expectation, and the maximum similarity of the manure regulation strategies adopted by the farmland unit in the subsequent 2 growth seasons to the manure regulation strategy X is 60%, then the immunity rate of the farmland unit is set to 0.6.

[0049] Specifically, the global candidate manure regulation strategy set corresponding to each farmland unit is screened according to a preset target candidate strategy screening rule, and a target candidate strategy for strategy radiation simulation is obtained. For example, according to the target candidate strategy screening rule that "a candidate manure regulation strategy is determined as a target candidate strategy if it is included in the candidate manure regulation strategy set of at least 20% of the global farmland units and the average treatment effect corresponding to the strategy is not less than 9%", the candidate manure regulation strategy set of farmland unit A is screened, and the following three target candidate strategies are screened out: target candidate strategy S1 {pig manure single application}, target candidate strategy S2 {pig manure, chicken manure and cow manure are applied in proportion}, and target candidate strategy S3 {pig manure, chicken manure and cow manure are applied in rotation}.

[0050] The state space of the strategy radiation simulation is initialized, and simulation is performed for each target candidate strategy. First, the original unit corresponding to each target candidate strategy is selected, which represents the farmland unit that generates the strategy earliest and has the highest average treatment effect. Then, the direct influence unit of the original unit in the causal correlation network is obtained, which represents the farmland unit that has an edge connection with the node corresponding to the original unit in the causal correlation network and has an edge weight not less than a preset threshold. For example, the farmland unit corresponding to the causal correlation node with an edge connection weight not less than 0.6 with the node corresponding to the original unit can be regarded as a direct influence unit. A predetermined number of global farmland units are randomly selected, and the state of the selected farmland units is initialized to the infection period. The farmland units that are not initialized to enter the infection period are set to the susceptible state, which represents that the farmland unit has not received any candidate manure regulation strategy.

[0051] Further, multi-time step iterative simulation is adopted. In each time step, the farmland unit in the infection period infects its neighbor farmland unit with the transmission rate corresponding to the farmland unit and its neighbor farmland unit as the conversion probability, so that the neighbor farmland unit enters the latent period. The farmland unit in the latent period enters the infection period with the disease rate as the conversion probability. For the farmland unit in the infection period, the strategy execution state of the current farmland unit is randomly determined to be "strategy failure, not as expected" by a predetermined rule. For the farmland unit determined to be strategy failure, the state of the farmland unit is converted to the immune period with the immune rate corresponding to the farmland unit as the conversion probability. For example, the strategy execution state of the farmland unit is randomly determined by taking the historical failure rate average corresponding to the currently executed candidate manure regulation strategy as the determination rule. The above simulation process is repeated until each farmland unit completes at least one simulation process, and the basic reproduction number, the final propagation size and the average propagation delay of each farmland unit after the simulation are obtained.

[0052] generate a global radiation influence heat map based on the strategy radiation index, and output the radiation influence heat map as strategy radiation data. Specifically, the basic reproductive number, the final size of the propagation, and the average propagation delay are weighted and summed according to a predetermined weight distribution ratio to generate the strategy radiation index corresponding to each farmland unit. For example, assuming that the basic reproductive number, the final size of the propagation, and the average propagation delay obtained by farmland unit A after simulation are 2.0, 0.8, and 0.6 respectively, and the strategy radiation index obtained after weighted summation according to a ratio of 4:3:3 is 1.22.

[0053] It can be understood that the basic reproductive number can be represented as At this time, the basic reproductive number means that when farmland unit i is taken as the initial infectious period node, the average number of newly infected farmland units that can be activated by a single infectious period farmland unit in the simulation process, and when , it indicates that the candidate manure regulation strategy corresponding to unit i has sustainable propagation, The greater the value, the higher the propagation radiation intensity of the strategy and the greater the influence of the farmland unit in the global farmland area. For example, when the of farmland unit A is 2.5, it means that when each farmland unit in the infectious period adopts the candidate manure regulation strategy provided by farmland unit A, an average of 2.5 farmland units can be infected to enter the infectious period; the final size of the propagation can be represented as At this time, the final size of the propagation means that after the simulation ends, the total number of farmland units in the strategy propagation chain of farmland unit i that enter the infectious period accounts for the proportion of the global farmland units. The greater the value of the final size of the propagation, the greater the radiation range of the candidate manure regulation strategy corresponding to the farmland unit; the average propagation delay can be represented as At this time, the average propagation delay means the average time for the candidate manure regulation strategy corresponding to farmland unit i to propagate to all farmland units in the infectious period from activation, and the average propagation delay is standardized. For example, the average propagation delay of the candidate manure regulation strategy of farmland unit E is 10 days, and the maximum delay time of the global is 20 days, and the standardized value = 1-10 / 20 = 0.5.

[0054] In step S5, the global farmland unit agent performs global simulation based on the set of candidate manure regulation strategies to obtain a global influence index of the strategy.

[0055] Specifically, the global farmland unit agent matches a corresponding candidate manure regulation strategy from the candidate manure regulation strategy set, and obtains a strategy global influence index based on a strategy global influence indicator, the strategy global influence indicator at least including an absorption efficiency indicator, an environmental impact indicator, and an economic benefit indicator.

[0056] In a possible embodiment, each farmland unit agent selects a locally optimal strategy from the candidate manure regulation strategy set of itself as a simulation execution strategy, the locally optimal strategy being represented as a strategy with the highest average treatment effect, each farmland unit implements the locally optimal strategy in a pre-constructed simulation environment to simulate a complete natural growing season, and obtains an absorption efficiency indicator, an environmental impact indicator, and an economic benefit indicator of each farmland unit when implementing the corresponding locally optimal strategy, and generates a strategy global influence index according to the absorption efficiency indicator, the environmental impact indicator, and the economic benefit indicator.

[0057] It can be understood that the absorption efficiency indicator is used to quantify the absorption efficiency of soil nutrients in the global farmland unit after the global farmland unit agent executes the corresponding candidate manure regulation strategy; the environmental impact indicator is used to quantify the influence rate of the global farmland on the environment after the global farmland unit agent executes the corresponding candidate manure regulation strategy; and the economic benefit indicator is used to quantify the fluctuation of the expected yield of crops in the global farmland after the global farmland unit agent executes the corresponding candidate manure regulation strategy.

[0058] For example, the global farmland unit includes three farmland units, farmland unit A, farmland unit B and farmland unit C, and the corresponding local optimal strategies are strategy A, strategy B and strategy C respectively. Through a complete natural growth season simulation, assuming that the total nitrogen application amount of manure is 50t and the total nitrogen absorption amount of crops is 45t, the corresponding absorption efficiency is 90%, and the nitrogen absorption efficiency is output as the absorption efficiency index; assuming that the soil types of farmland unit A, farmland unit B and farmland unit C are only sandy soil and clay soil, the average water loss amount of the sandy soil unit and the clay soil unit is compared with the preset natural loss standard, and the water loss rates of the sandy soil unit and the clay soil unit are obtained as 80% and 60% respectively, the water loss rate is weighted and summed to obtain the final water loss rate of 70%, and the water loss rate is output as the environmental impact index; assuming that the average yield of the three farmland units A, B and C is increased by 20% compared with the historical yield after adopting the corresponding local optimal strategy, the value of the economic benefit index is 0.2; the values of the absorption efficiency index, the environmental impact index and the economic benefit index obtained by the preset weight ratio are weighted and summed to obtain the strategy global influence index of farmland unit A, farmland unit B and farmland unit C, which is 0.9*0.3+0.7*0.4+0.2*0.3=0.61 (here, only the acquisition of the strategy global influence index is exemplified, and the specific determination needs to be combined with the actual situation).

[0059] Step S6, according to the individual performance data, the strategy radiation data and the strategy global influence index, the candidate manure regulation strategy set is optimized to generate an agricultural manure regulation strategy.

[0060] Specifically, based on the strategy radiation data, the individual performance data of each candidate manure regulation strategy in the candidate manure regulation strategy set and the strategy global influence index of the candidate manure regulation strategy set are coordinated to generate an agricultural manure regulation strategy.

[0061] In a possible embodiment, the global farmland is divided into a high radiation area, a medium radiation area and a low radiation area based on the policy radiation data, the weight ratio of the divided areas is adaptively adjusted, and the corresponding policy comprehensive score is calculated according to the adjusted weight ratio. For example, the farmland units in the area where the policy radiation index in the radiation influence thermal map is not less than 0.8 are divided into the high radiation area, the farmland units in the area where the policy radiation index is in [0.6, 0.8] are divided into the medium radiation area, and the farmland units in the area where the policy radiation index is less than 0.6 are divided into the low radiation area. The corresponding weight is set by using the analytic hierarchy process combined with the characteristics of the divided areas. For example, in the high radiation area, the driving effect of policy propagation on the global is focused, and the weight ratio of the individual performance data, the policy radiation data and the policy global influence index is 1:2:1. In the medium radiation area, the balance of the three indexes of the individual performance data, the policy radiation data and the policy global influence index is focused, and the weight ratio of the individual performance data, the policy radiation data and the policy global influence index is 1:1:1. In the low radiation area, the combination of local benefits and global benefits of the farmland units is emphasized, and the weight ratio of the individual performance data, the policy radiation data and the policy global influence index is 2:1:2.

[0062] The three candidate manure regulation strategies with the highest policy comprehensive score in the candidate manure regulation strategy set of each farmland unit are obtained. If the number of candidate manure regulation strategies is less than three, all the candidate manure regulation strategies are directly called. The non-dominated sorting genetic algorithm is used, and the sum of the policy comprehensive scores of all farmland units is used as the optimization objective function to obtain the global Pareto optimal solution set of the farmland. The normalized values of the absorption efficiency index, the environmental impact index and the economic benefit index corresponding to each strategy in the global Pareto optimal solution set of the farmland are obtained, and the normalized values are defined as the membership degrees. The normalization method is the actual value of the index / the maximum value of the index. The closer the value of the membership degree is to 1, the better the strategy performs in the corresponding index dimension. The comprehensive membership degrees of the corresponding regions are obtained by combining the weight ratios of the high radiation area, the medium radiation area and the low radiation area. The solution with the highest comprehensive membership degree in the corresponding region is selected as the global comprehensive optimal strategy, and the global comprehensive optimal strategy is output as the agricultural manure regulation strategy.

[0063] Figure 3 is a schematic diagram of an agricultural manure soil crop interaction optimization regulation system provided by an embodiment of the present application.

[0064] Specifically, an agricultural manure soil crop interaction optimization regulation system comprises: A unit division module is configured to divide the farmland area into farmland units and construct farmland unit agents.

[0065] a network construction module, configured to construct a farmland unit social network including a physical proximity network, a feature similarity network and a causal correlation network for the farmland unit agent.

[0066] a social learning module, configured to perform social learning by the farmland unit agent, acquire individual performance data of the manure regulation strategy, and generate a candidate manure regulation strategy set.

[0067] a radiation simulation module, configured to perform strategy radiation simulation on the candidate manure regulation strategy set according to the causal correlation network and the infectious disease model, and generate strategy radiation degree data.

[0068] a global simulation module, configured to perform global simulation on the farmland unit agent according to the candidate manure regulation strategy set, and acquire a strategy global influence index.

[0069] a strategy generation module, configured to optimize the candidate manure regulation strategy set according to the individual performance data, the strategy radiation degree data and the strategy global influence index, and generate an agricultural manure regulation strategy.

[0070] The specific use and role of the embodiment are described as follows: First, the farmland area is divided into farmland units, farmland unit agents are constructed, and a farmland unit social network is constructed for the farmland unit agents. The farmland unit social network includes a physical proximity network, a feature similarity network and a causal correlation network. Through the construction of the physical proximity network, the feature similarity network and the causal correlation network, the spatial proximity, the feature similarity and the causal correlation among farmlands are analyzed and quantified from multiple dimensions. The farmland unit social network provides a data basis for subsequent comprehensive manure regulation strategies. Then, the farmland unit agent performs social learning based on the farmland unit social network, acquires individual performance data of the manure regulation strategy, and generates a candidate manure regulation strategy set. The candidate manure regulation strategy set is generated by learning the historical efficient strategy through social learning and combining the actual situation of the farmland unit itself, which provides data support for measuring the local demand and benefit of the farmland unit for subsequent generation of the manure regulation strategy. Then, the candidate manure regulation strategy set is subjected to strategy radiation simulation through the causal correlation network and the infectious disease model, and strategy radiation degree data are acquired. The influence of each farmland unit in the global farmland area is acquired through the strategy radiation simulation, which provides a connecting bridge for coordinating the local demand and global benefit of the manure regulation strategy. Then, the global farmland unit agent performs global simulation based on the candidate manure regulation strategy set to obtain a strategy global influence index, which quantifies the global benefit of the farmland after each farmland unit executes the corresponding local optimal strategy without considering the global benefit, thereby providing a basis for measuring the global effect of the farmland for subsequent coordination of the local demand and global benefit of the manure regulation strategy. Finally, the candidate manure regulation strategy set is optimized according to the individual performance data, the strategy radiation degree data, and the strategy global influence index to generate an agricultural manure regulation strategy, and the individual performance data and the strategy global influence index of the manure regulation strategy are continuously optimized according to the strategy radiation degree data, so that the finally generated agricultural manure regulation strategy can balance the local demand and global benefit of the farmland, thereby improving the overall utilization rate of manure and the stability of the farmland ecology.

[0071] It should be understood that the term "and / or" herein merely describes the association relationship of the associated objects, and indicates that there can be three relationships, for example, A and / or B, which can represent three cases of existence of A alone, existence of A and B together, and existence of B alone, where A and B can be singular or plural. In addition, the character " / " herein generally represents an "or" relationship between the front and rear associated objects, but can also represent an "and / or" relationship. The specific meaning can be understood according to the context before and after.

[0072] It should be understood that in the embodiments of the present application, the size of the serial number of the above processes does not mean the order of execution, and the execution order of the processes should be determined according to their functions and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0073] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. An agricultural manure soil crop interaction optimization regulation method, characterized in that, The method comprises: S1: dividing the farmland area into farmland units, and constructing a farmland unit agent; S2: constructing a farmland unit social network for the farmland unit agent, wherein the farmland unit social network comprises a physical proximity network, a feature similarity network, and a causal correlation network; S3: the farmland unit agent performs social learning based on the farmland unit social network, obtains individual performance data of the manure regulation strategy, and generates a candidate manure regulation strategy set; S4: performing strategy radiation simulation on the candidate manure regulation strategy set through the causal correlation network and the infectious disease model to obtain strategy radiation degree data; S5: the global farmland unit agent performs global simulation on the candidate manure regulation strategy set to obtain a strategy global influence index; S6: optimizing the candidate manure regulation strategy set according to the individual performance data, the strategy radiation degree data, and the strategy global influence index to generate an agricultural manure regulation strategy.

2. The method according to claim 1, wherein the method is characterized by, The farmland unit social network for the farmland unit agent comprises a physical proximity network, a feature similarity network, and a causal correlation network, which comprises: Taking the geometric center point of the farmland unit corresponding to the farmland unit agent as a voronoi diagram seed point, constructing a physical proximity network based on the voronoi diagram seed point, linking the corresponding voronoi diagram seed points according to a pre-set edge connection rule, and using a distance decay function to obtain the edge connection weight; Taking the farmland unit agent as a feature similarity network node, extracting features of the farmland unit data set corresponding to each node through a multi-head attention mechanism and a deep residual network structure to generate a farmland unit feature vector, obtaining the feature similarity between the farmland unit feature vectors corresponding to each node, and establishing an edge connection of the feature similarity network according to a pre-set feature similarity threshold, defining the nodes with edge connections as feature similarity nodes; Taking the farmland unit agent as a causal correlation network node, establishing a fully connected network for the causal correlation network node, performing conditional independence test on the fully connected network, removing the edges determined as conditionally independent, synchronously obtaining the causal correlation strength of each edge connection in the causal correlation network, and generating a causal correlation network.

3. The method of claim 1, wherein the method is characterized by: The farmland unit agent performs social learning based on the farmland unit social network, obtains individual performance data of the manure regulation strategy, and generates a candidate manure regulation strategy set, which comprises: Defining the farmland unit agent to be regulated by manure as a target farmland unit agent, obtaining the spatial proximity nodes and feature similarity nodes of the target farmland unit agent in the farmland unit social network, constructing a proximity similar unit set, and screening a corresponding correlation data set in combination with the causal correlation network, constructing a neighbor farmland unit agent set based on the proximity similar unit set and the correlation data set; The target farmland unit agent learns the historical optimal manure scheduling strategy of the neighbor farmland unit agent to obtain an initial candidate manure scheduling strategy set; The target farmland unit agent performs counterfactual interrogation on neighbor farmland unit agents based on an initial candidate manure scheduling strategy set, the neighbor farmland unit agent calls a local causal correlation network to generate a counterfactual estimation result and sends the counterfactual estimation result to the target farmland unit agent; The counterfactual estimation result is evaluated based on the adversarial review mechanism to obtain an initial candidate manure scheduling strategy evaluation result and generate a candidate manure scheduling strategy set.

4. The method of claim 3, wherein the method is characterized by, The counterfactual estimation result is evaluated based on the adversarial review mechanism to obtain an initial candidate manure scheduling strategy evaluation result and generate a candidate manure scheduling strategy set, including: Obtain historical performance data of all farmland unit agents in the farmland unit social network, the historical performance data is expressed as a deviation value of actual performance and expected performance of a historical manure scheduling strategy, select a predetermined number of farmland unit agents from the highest and lowest ranked historical performance data respectively, and generate a candidate adversarial review agent set; Obtain the intermediate centrality of the farmland unit agents in the candidate adversarial review agent set in the farmland unit social network, select farmland unit agents within a predetermined threshold range, and generate an adversarial review agent set; Input the counterfactual estimation result into the adversarial review agent set for prediction evaluation to generate an initial candidate manure scheduling strategy evaluation result, generate a candidate manure scheduling strategy set based on the initial candidate manure scheduling strategy evaluation result, and generate individual performance data corresponding to the candidate manure scheduling strategy set, the individual performance data is expressed as the expected performance of the candidate manure scheduling strategy.

5. The method of claim 1, wherein the method is characterized by: Strategy radiation simulation is performed on the candidate manure regulation strategy set through the causal correlation network and the infectious disease model to obtain strategy radiation data, including: The diffusion process of the candidate manure regulation strategy set in the ecosystem of the global farmland unit is simulated based on the causal correlation network and the infectious disease model to obtain strategy radiation data of each candidate manure regulation strategy in the candidate manure regulation strategy set; The strategy radiation data is expressed as the radiation propagation influence degree of the candidate manure regulation strategy in the global farmland area after implementation in a certain farmland unit.

6. The method of claim 5, wherein the method is characterized by, The method further includes: Divide the nodes in the causal correlation network into an infection period, a latent period and an immune period based on the infectious disease model; The infection period is expressed as the farmland unit adopting and implementing the candidate manure regulation strategy, the latent period is expressed as the farmland unit adopting but not implementing the candidate manure regulation strategy, and the immune period is expressed as the farmland unit implementing the candidate manure regulation strategy not reaching the expected performance, and not accepting the candidate manure regulation strategy within a preset immune period; Randomly select multiple causal correlation network nodes as infected nodes in the infection period in the infectious disease model, set the transmission rate as the conversion probability to transmit and infect the uninfected nodes, so that the uninfected nodes enter the latent period, and define the nodes entering the latent period as latent nodes, set the uninfected nodes to convert to infected nodes entering the infection period with a lesion rate as the conversion probability, and set the infected nodes to convert to immune nodes entering the immune period with an immune rate as the conversion probability; Perform strategy radiation simulation based on the Monte Carlo algorithm to generate strategy radiation data of the farmland unit corresponding to the nodes in the causal correlation network.

7. The method of claim 1, wherein the method is characterized by: The global farmland unit agent performs global simulation based on the candidate manure regulation strategy set to obtain a strategy global influence index, including: The global farmland unit agent matches the corresponding candidate manure regulation strategy from the candidate manure regulation strategy set, and obtains a strategy global influence index through simulation, wherein the strategy global influence index at least includes an absorption efficiency index, an environmental impact index, and an economic benefit index, and the strategy global influence index is obtained based on the strategy global influence index; The absorption efficiency index is used to quantify the absorption efficiency of soil nutrients in the global farmland after the global farmland unit agent executes the corresponding candidate manure regulation strategy; The environmental impact index is used to quantify the impact rate of the global farmland on the environment after the global farmland unit agent executes the corresponding candidate manure regulation strategy; The economic benefit index is used to quantify the fluctuation of the expected income of crops in the global farmland after the global farmland unit agent executes the corresponding candidate manure regulation strategy.

8. The method of claim 1, wherein the method is characterized by: The candidate manure regulation strategy set is optimized based on the individual performance data, the strategy radiation degree data, and the strategy global influence index to generate an agricultural manure regulation strategy, including: The individual performance data of each candidate manure regulation strategy in the candidate manure regulation strategy set and the strategy global influence index of the candidate manure regulation strategy set are coordinated based on the strategy radiation degree data to generate the agricultural manure regulation strategy.

9. The method of claim 1, wherein the method is characterized by: The farmland area is divided into farmland units to construct farmland unit agents, including: The continuous farmland area is divided into multiple farmland units of the same size based on a geographic information system and a clustering algorithm, multiple source data of the multiple farmland units are collected, and the collected raw data is subjected to outlier rejection, missing value filling, and data standardization to obtain a farmland unit data set corresponding to each farmland unit, wherein the farmland unit data set at least includes farmland soil data, crop growth data, weather data, and farmland management data; The farmland unit agent is constructed based on the farmland unit data set and preset social learning rules and strategy triggering rules, wherein the social learning rules are used for social learning of the farmland unit and its adjacent farmland units that are physically adjacent or similar in farmland characteristics, and the strategy triggering rules are used for triggering the generation of the manure regulation strategy when the farmland unit meets a predetermined condition.

10. An agricultural manure soil crop interaction optimization regulation system for implementing the method of any one of claims 1 to 9, characterized in that, Including: A unit division module for dividing the farmland area into farmland units to construct farmland unit agents; A network construction module for constructing a farmland unit social network including a physical proximity network, a characteristic similarity network, and a causal association network for the farmland unit agents; A social learning module for the farmland unit agents to perform social learning to obtain individual performance data of the manure regulation strategy and generate a candidate manure regulation strategy set; A radiation simulation module for performing strategy radiation simulation on the candidate manure regulation strategy set based on the causal association network and a contagion model to generate strategy radiation degree data; A global simulation module for the global farmland unit agent to perform global simulation based on the candidate manure regulation strategy set to obtain a strategy global influence index; The strategy generation module is configured to optimize the candidate set of manure regulation strategies according to the individual performance data, the strategy radiation data, and the strategy global influence index, and generate an agricultural manure regulation strategy.

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