Orchard grass growing cultivation management method and system based on multi-source environmental parameters

Abstract

The invention discloses an orchard grass-growing cultivation management method and system based on multi-source environmental parameters, and relates to the technical field of smart agriculture, and the method comprises the steps: obtaining the whole-process management data of orchard grass-growing cultivation, extracting the key management measures of grass-growing cultivation, and storing the key management measures in a database; obtaining environmental influence factors of each key management measure, constructing a data driving model corresponding to each key management measure, and establishing a mapping relation between the environmental influence factors and grass growing cultivation management; multi-source environment parameters of the target orchard are collected, and grass-growing grass seed selection and grass-growing management early warning are carried out on the target orchard based on the multi-source environment parameters and the data driving model; and associating different key management measures according to grass-growing management early warning to obtain an optimal grass-growing management scheme. According to the method, grass seed selection and planting scientific management are carried out on grass growing in the orchard according to the environmental parameters, the complete biological chain and industrial chain of the orchard are kept, and healthy growth of fruit trees is promoted.

Description

Technical Field

[0001] The present invention relates to the field of smart agricultural technology, and more specifically, to an orchard grass cultivation management method and system based on multi-source environmental parameters. Background Art

[0002] At present, many orchards have a lot of weeds, which affect the growth of trees. Soil clearing and herbicide use methods can remove weeds relatively cleanly, but it is not good for the growth of fruit trees and the sustainable development of orchards. As an advanced orchard soil management model, grass cultivation plays an important role in coordinating the sustainable development of fruit trees and the environment. It can effectively improve soil structure, increase soil fertility, and regulate the microclimate of orchards. Orchard grass is currently being widely promoted and applied. This method mainly plants grass between fruit tree rows, between plants, or in the whole orchard. Grass is mostly 1-2 year old or perennial herbaceous plants. Orchard grass has the effect of heat insulation and moisture conservation. It not only reduces the soil temperature rise caused by sun exposure during the day, but also reduces the heat dissipation and cooling of the ground at night, narrows the ground temperature difference between day and night, and makes the soil temperature change more gentle during the day. It can also effectively increase the content of soil organic matter, increase soil fertility, improve fruit quality, and increase fruit yield.

[0003] Grass cultivation can not only maintain water and soil, improve the orchard environment, and promote the healthy growth of fruit trees, but also significantly increase fruit yields, improve fruit quality, and have more obvious economic benefits. However, at this stage, the management of grass cultivation is mostly determined by humans, such as the selection of grass species, the control of grass growth, and water and fertilizer management. The inefficiency of human management often has a negative impact on the soil health and fruit tree planting in the orchard. Since the management methods of orchard grass are complicated and affected by many environmental factors, and there are correlations between the factors, how to manage the entire process of grass cultivation based on multi-source environmental parameters is a problem that needs to be solved. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes an orchard grass cultivation management method and system based on multi-source environmental parameters, with the aim of scientifically managing orchard grass according to environmental parameters, prompting the orchard to maintain a complete food chain and industrial chain, and promoting the healthy growth of fruit trees.

[0005] The first aspect of the present invention provides an orchard grass cultivation management method based on multi-source environmental parameters, comprising the following steps:

[0006] Obtain the full-process management data of orchard grass cultivation, extract the key management measures of grass cultivation, divide the full-process management data based on the key management measures, and obtain the environmental impact factors of each key management measure based on correlation analysis;

[0007] Extract environmental parameters based on the environmental influencing factors, use the growth status of grass to screen environmental parameters that meet preset requirements, use the environmental parameters to build a data-driven model corresponding to each key management measure, and establish a mapping relationship between environmental influencing factors and grass cultivation management;

[0008] Collect multi-source environmental parameters of the target orchard, and select grass species and conduct early warning of grass management for the target orchard based on the multi-source environmental parameters and a data-driven model;

[0009] According to the association of different key management measures with grass management warning, the best grass management plan is obtained, and the grass species selection information, grass management warning information and grass management plan are visualized using visualization methods.

[0010] In this solution, the full-process management data of orchard grass cultivation is obtained, the key management measures of grass cultivation are extracted, and the full-process management data is divided based on the key management measures, specifically:

[0011] Using a big data search engine to obtain orchard grass cultivation related knowledge and historical orchard grass cultivation management examples to construct a data set, preprocessing the data set, and sorting the data in the data set according to the time sequence corresponding to the growth stage of orchard grass, to obtain the full process management data of orchard grass cultivation;

[0012] Performing clustering processing on the full-process management data to obtain clustering results, obtaining the data volume of each cluster in the clustering results, eliminating clusters that do not meet preset data volume requirements according to the data volume, and simplifying the full-process management data;

[0013] In the remaining clusters, keywords are extracted from the management data samples in each cluster, and the corresponding grass growth stage and corresponding management measures are determined according to the keyword vector. The key management measures for each grass growth stage are obtained after redundancy removal.

[0014] Based on the key management measures, positioning is performed in the simplified full-process management data, and neighborhood data of the key management measures are extracted according to a preset time range.

[0015] In this plan, the environmental impact factors of each key management measure are obtained based on correlation analysis, specifically:

[0016] Obtaining data segments corresponding to key management measures for orchard grass cultivation, using correlation analysis to perform primary attribution of key management measures in the data segments, using the Pearson correlation coefficient to improve the maximum correlation minimum redundancy method, and obtaining environmental influencing factors that meet preset correlation conditions through the improved maximum correlation minimum redundancy method;

[0017] Obtain the environmental impact factors selected by primary attribution, extract the environmental parameters and key management measures in the data segment according to the environmental impact factors to construct a data sample, use the SHAP interpretation model to perform deep attribution analysis on the data sample, generate the Shapley value of each environmental impact factor to construct a local interpretation matrix;

[0018] For each environmental influencing factor, the average value of the Shapley value of all data samples for the environmental influencing factor is calculated according to the local explanation matrix as the global explanation, and the global explanation is used as the importance value of the environmental influencing factor. The importance value is used for sorting, and a preset number of environmental influencing factors are selected according to the sorting results to match the key management measures used.

[0019] In this solution, environmental parameters are extracted based on the environmental influencing factors, and environmental parameters that meet the preset requirements are screened using the growth status of the grass. The environmental parameters are used to construct a data-driven model corresponding to each key management measure, specifically:

[0020] Key management measures are divided according to grass species selection and grass plant management. Environmental parameters are screened based on the environmental influencing factors that match each key management measure after division. Grass growth stages and grass growth conditions are obtained in the instances corresponding to the full-process management data. Growth condition standards for different grass growth stages are generated based on grass growth data under suitable conditions.

[0021] Compare the grass growth conditions in the example with the corresponding growth condition standards, obtain environmental parameters that meet the preset requirements, and generate an environmental parameter set corresponding to grass species selection and grass plant management;

[0022] Based on the support vector machine and autoencoder network, data-driven models corresponding to grass seed selection and grass plant management were constructed respectively. The environmental parameter set was combined with the orchard grass seed selection data and the orchard grass management data to train the corresponding data-driven model. The mapping relationship between environmental influencing factors and grass cultivation management was constructed from two aspects.

[0023] In this scheme, multi-source environmental parameters of the target orchard are collected, and grass species selection and grass management early warning are performed for the target orchard based on the multi-source environmental parameters and the data-driven model. The grass species selection is specifically as follows:

[0024] Collect multi-source environmental parameters of the target orchard according to the environmental influencing factors corresponding to the selection of the grass species, use the multi-source environmental parameters as the input of the support vector machine to generate corresponding feature vectors, and use the data-driven model composed of the trained support vector machine to predict and score the feature vectors;

[0025] The gscatter function is used to obtain the data scatter plot corresponding to the feature vector, and the contour function is used to generate the decision boundaries of different grass species labels in the scatter plot. The evaluation scores of different decision boundaries are calculated, and the grass species selection results of the target orchard are generated based on the evaluation scores.

[0026] In this scheme, multi-source environmental parameters of the target orchard are collected, and grass species selection and grass management warning are performed for the target orchard based on the multi-source environmental parameters and the data-driven model. The grass management warning is specifically:

[0027] Obtain the growth stage of grass in the target orchard, obtain multi-source environmental parameters corresponding to the corresponding environmental influencing factors from the environmental influencing factors corresponding to grass plant management according to the growth stage, and import the multi-source environmental parameters into the autoencoder network;

[0028] Reconstruct the data of the multi-source environmental parameters using a data-driven model formed by a trained autoencoder network, obtain estimated values ​​of the multi-source environmental parameters after the implementation of key management measures, and calculate the multi-dimensional residuals between the estimated values ​​and the measured values ​​of the multi-source environmental parameters;

[0029] Preset the residual threshold of the data-driven model corresponding to each key management measure, compare the multidimensional residual with the corresponding residual threshold, count the number of multidimensional residuals that exceed the residual threshold, and generate a grass management warning when the number reaches the preset standard.

[0030] In this plan, the best grass management plan is associated with the grass management warning in different key management measures, specifically:

[0031] Obtaining corresponding key management measures according to the grass management warning, using the key management measures and multi-source environmental parameters to perform similarity search on historical orchard grass cultivation management examples, and obtaining historical orchard grass cultivation management examples corresponding to the highest similarity according to similarity sorting;

[0032] The management measure parameters in the screened historical orchard grass cultivation management examples are extracted, and the environmental deviation between the target orchard and the example is calculated based on multi-source environmental parameters. The adjustment coefficient is generated according to the environmental deviation to adjust the management measure parameters, and the key management measures are configured using the adjusted management measure parameters to generate the optimal grass management plan.

[0033] The second aspect of the present invention provides an orchard grass cultivation management system based on multi-source environmental parameters, the system comprising an environmental influencing factor analysis unit, a multi-source environmental parameter acquisition unit, a data-driven model unit, and an orchard grass cultivation management output unit;

[0034] The environmental impact factor analysis unit uses the acquired knowledge related to orchard grass cultivation and historical orchard grass cultivation management examples to construct full-process management data, extract key management measures for grass cultivation, and obtain environmental impact factors of key management measures based on correlation analysis;

[0035] The multi-source environmental parameter acquisition unit extracts multi-source environmental parameters based on environmental influencing factors at different growth stages;

[0036] The data-driven model unit constructs data-driven models corresponding to grass seed selection and grass plant management based on support vector machines and autoencoder networks, respectively, uses environmental parameter sets combined with orchard grass seed selection data and orchard grass management data to train corresponding data-driven models, and constructs a mapping relationship between environmental influencing factors and grass cultivation management from two aspects; and performs grass seed selection and grass management early warning for the target orchard based on the multi-source environmental parameters and data-driven models of the target orchard;

[0037] The orchard grass cultivation management output unit obtains the best grass management plan based on the grass management warning and associating different key management measures, outputs the grass species selection information, grass management warning information and grass management plan, and visualizes them using a visualization method.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The present invention screens the environmental influencing factors in the orchard grass cultivation management measures through attribution analysis, constructs corresponding data-driven models in the grass seed selection and grass plant management, conducts grass seed selection and grass management early warning, and scientifically manages the orchard grass according to environmental parameters to ensure that the grass in the orchard has a positive impact on the fruit tree plants, prompts the orchard to maintain a complete food chain and industrial chain, and promotes the healthy growth of fruit trees. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments or exemplary embodiments of the present invention, the drawings required for use in the embodiments or exemplary descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained according to the drawings without paying creative work.

[0041] Figure 1 A flow chart of an orchard grass cultivation management method based on multi-source environmental parameters is shown;

[0042] Figure 2 A flow chart showing the environmental impact factors for each key management measure is shown;

[0043] Figure 3A flowchart of constructing a data-driven model corresponding to each key management measure is shown;

[0044] Figure 4 The block diagram of the orchard grass cultivation management system based on multi-source environmental parameters is shown. DETAILED DESCRIPTION

[0045] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0046] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0047] like Figure 1 As shown, in the first embodiment of the present invention, a method for orchard grass cultivation management based on multi-source environmental parameters is provided, comprising:

[0048] S102, obtaining full-process management data of orchard grass cultivation, extracting key management measures of grass cultivation, dividing the full-process management data based on the key management measures, and obtaining environmental impact factors of each key management measure based on correlation analysis;

[0049] S104, extracting environmental parameters based on the environmental influencing factors, screening environmental parameters that meet preset requirements using the growth status of grass, using the environmental parameters to construct a data-driven model corresponding to each key management measure, and establishing a mapping relationship between environmental influencing factors and grass cultivation management;

[0050] S106, collecting multi-source environmental parameters of the target orchard, and performing grass species selection and grass management early warning for the target orchard based on the multi-source environmental parameters and a data-driven model;

[0051] S108, obtaining the best grass management plan according to the grass management warning and associating different key management measures, and visually displaying the grass species selection information, grass management warning information and grass management plan by using a visualization method.

[0052] It should be noted that a data set is constructed by using a big data search engine to obtain relevant knowledge on orchard grass cultivation and historical orchard grass cultivation management examples, and the data set is preprocessed by data cleaning, dimensionality reduction, normalization, etc., and the data is sorted in the data set according to the time series corresponding to the growth stage of orchard grass, so as to obtain the full-process management data of orchard grass cultivation; the full-process management data is clustered to obtain clustering results, and the data volume of each cluster cluster in the clustering results is obtained. According to the data volume, clusters that do not meet the preset data volume requirements are eliminated, and the full-process management data is simplified; when the data volume of a cluster is too small, it means that the corresponding management data is not universal enough and does not belong to matters that require special attention in grass cultivation management. In the remaining clusters, the management data samples in each cluster are subjected to keyword extraction, and the corresponding grass growth stage and corresponding management measures are determined according to the keyword vector, such as orchard drainage and clearing of weeds and bad grasses in the seedling stage to prevent malicious competition with grass. The key management measures for each grass growth stage are obtained by removing redundancy from similar expressions of management measures, such as sowing management, seedling management, water and fertilizer management, mowing management, etc. The key management measures are located in the simplified full-process management data based on the key management measures, and the neighborhood data of the key management measures are extracted according to the preset time range.

[0053] Figure 2 A flow chart for obtaining environmental impact factors for each key management measure is shown.

[0054] According to an embodiment of the present invention, the environmental impact factors of each key management measure are obtained based on correlation analysis, specifically:

[0055] S202, obtaining data segments corresponding to key management measures for orchard grass cultivation, performing primary attribution of key management measures in the data segments using correlation analysis, improving the maximum correlation minimum redundancy method using the Pearson correlation coefficient, and obtaining environmental influencing factors that meet preset correlation conditions through the improved maximum correlation minimum redundancy method;

[0056] S204, obtaining the environmental impact factors selected by primary attribution, extracting environmental parameters and key management measures in the data segment according to the environmental impact factors to construct a data sample, performing deep attribution analysis on the data sample using the SHAP interpretation model, generating the Shapley value of each environmental impact factor to construct a local interpretation matrix;

[0057] S206: For each environmental influencing factor, the average value of the Shapley values ​​of all data samples for the environmental influencing factor is calculated according to the local explanation matrix as the global explanation, the global explanation is used as the importance value of the environmental influencing factor, and the importance value is used for sorting. According to the sorting result, a preset number of environmental influencing factors are selected to match the key management measures to be used.

[0058] It should be noted that the SHAP explanation model calculates the contribution of each feature to the prediction result based on the marginal contribution of each feature in the instance based on cooperative game theory and local explanation, and realizes the explanation of each instance. Using the SHAP explanation model, for each data sample, the local explanation will calculate the corresponding value, i.e., the Shapley value, for each environmental influencing factor in it, characterizing the contribution value to the prediction result. For any environmental influencing factor, the weighted sum of all possible feature value combinations is obtained to obtain the Shapley value of the environmental influencing factor; during the training process, the Shapley value of each environmental influencing factor and the Shapley mean of all data samples are obtained. When the Shapley value of the environmental influencing factor is greater than the Shapley mean, a positive impact is generated, otherwise a negative impact is generated. For a certain characteristic variable, the Shapley value corresponding to the environmental influencing factor in all data samples is calculated, and their average value is used as the importance value of the environmental influencing factor, so as to obtain a global explanation. Preferably, in the selection of grass species, the environmental influencing factors include soil physical and chemical properties, climatic conditions, orchard weed characteristics, orchard related pests and diseases characteristics, etc.

[0059] Figure 3 A flowchart for constructing a data-driven model corresponding to each key management measure is shown.

[0060] According to an embodiment of the present invention, a data-driven model corresponding to each key management measure is constructed to establish a mapping relationship between environmental influencing factors and grass cultivation management, specifically:

[0061] S302, dividing the key management measures according to grass species selection and grass plant management, screening environmental parameters based on the environmental influencing factors matched by each key management measure after division, obtaining the grass growth stage and grass growth status in the instance corresponding to the full-process management data, and generating growth status standards for different grass growth stages based on the grass growth data under suitable conditions;

[0062] S304, comparing the grass growth condition in the example with the corresponding growth condition standard, obtaining environmental parameters that meet the preset requirements, and generating an environmental parameter set corresponding to grass species selection and grass plant management;

[0063] S306, based on the support vector machine and the autoencoder network, respectively construct the data-driven models corresponding to the grass seed selection and grass plant management, use the environmental parameter set combined with the orchard grass seed selection data and the orchard grass management data to train the corresponding data-driven model, and construct the mapping relationship between environmental influencing factors and grass cultivation management from two aspects.

[0064] It should be noted that the multi-source environmental parameters of the target orchard are collected, including but not limited to soil conditions, climate conditions, orchard weed conditions, grass plant parameters, etc. Grass species selection and grass management warning are carried out for the target orchard, wherein grass species selection is specifically: multi-source environmental parameters of the target orchard are collected according to the environmental influencing factors corresponding to the grass species selection. Support vector machine (SVM) is a supervised learning model for classification and regression analysis, which helps to find the best hyperplane in classification problems to help distinguish data points of different categories. The multi-source environmental parameters are used as the input of a support vector machine to generate corresponding feature vectors, and the feature vectors are predicted and scored using a data-driven model composed of a trained support vector machine. During the training process, a classification space is constructed using a set of environmental parameters combined with orchard grass seed selection data, and orchard grass seed selection is aggregated and positioned in the classification space according to the corresponding grass seed labels. The gscatter function is used to obtain a data scatter plot corresponding to the feature vector, and the contour function is used to generate decision boundaries of different grass seed labels in the scatter plot. Evaluation scores of different decision boundaries are calculated based on the distribution of feature vectors falling into the decision areas corresponding to different grass seed labels, and the grass seed selection results of the target orchard are generated based on the evaluation scores.

[0065] In addition, a grass management warning is carried out during the growth process of grass in the orchard, specifically as follows: the growth stage of grass in the target orchard is obtained according to the recorded data of grass plants, and the multi-source environmental parameters corresponding to the corresponding environmental influencing factors are obtained from the environmental influencing factors corresponding to the grass plant management according to the growth stage, and the multi-source environmental parameters are imported into the autoencoder network, the input is the measured values ​​of the multi-source environmental parameters during the growth process of grass in the target orchard, and the output is the estimated values ​​of the multi-source environmental parameters under the implementation of cultivation management; the data-driven model composed of the trained autoencoder network is used to reconstruct the multi-source environmental parameters, and the estimated values ​​of the multi-source environmental parameters after the implementation of key management measures are obtained, and the multi-dimensional residuals between the estimated values ​​and the measured values ​​of the multi-source environmental parameters are calculated; the residual threshold of the data-driven model corresponding to each key management measure is preset, and the multi-dimensional residual is compared with the corresponding residual threshold, and it is easy to judge whether management is needed according to the multi-dimensional residual data, and the number of multi-dimensional residuals exceeding the residual threshold is counted, and when the number reaches the preset standard, a grass management warning is generated. Preferably, in the grass seedling stage, in order to promote the rapid growth of grass seedlings, chemical fertilizers or organic fertilizers should be appropriately added. In case of drought, the orchard should be irrigated in time, and the measured values ​​of water and fertilizer characteristics of the target orchard soil are imported into the autoencoder network to obtain the estimated values ​​of water and fertilizer characteristics after the implementation of water and fertilizer management. When the deviation between the estimated values ​​of water and fertilizer characteristics and the measured values ​​of water and fertilizer characteristics is too large, it means that the grass in the target orchard needs water and fertilizer management.

[0066] It should be noted that, according to the grass management warning, the corresponding key management measures are obtained. When there are multiple grass management warnings, the corresponding key management measures are integrated, repeated management steps are removed, and a sequence priority is set for them. The key management measures and multi-source environmental parameters are used to perform similarity retrieval in the historical orchard grass cultivation management instances, and the historical orchard grass cultivation management instances corresponding to the highest similarity are obtained according to the similarity sorting; the management measure parameters in the selected historical orchard grass cultivation management instances are extracted, and the environmental deviation between the target orchard and the instance is calculated based on the multi-source environmental parameters, and the management measure parameters are adjusted according to the environmental deviation. The adjusted management measure parameters are used to configure the key management measures to generate the best grass management plan. In addition, the grass species selection information, grass management warning information and grass management plan are output and visualized according to the preset visualization equipment and sending method to ensure that orchard managers can carry out orchard grass management in a timely manner to increase the soil organic matter content, increase soil fertility, improve fruit quality, and increase fruit yield.

[0067] Figure 4 The block diagram of the orchard grass cultivation management system based on multi-source environmental parameters is shown.

[0068] The second aspect of the present invention provides an orchard grass cultivation management system 4 based on multi-source environmental parameters, the system comprising an environmental influencing factor analysis unit 401, a multi-source environmental parameter acquisition unit 402, a data-driven model unit 403, and an orchard grass cultivation management output unit 404;

[0069] The environmental impact factor analysis unit uses the acquired knowledge related to orchard grass cultivation and historical orchard grass cultivation management examples to construct full-process management data, extract key management measures for grass cultivation, and obtain environmental impact factors of key management measures based on correlation analysis;

[0070] The multi-source environmental parameter acquisition unit extracts multi-source environmental parameters based on environmental influencing factors at different growth stages;

[0071] The data-driven model unit constructs data-driven models corresponding to grass seed selection and grass plant management based on support vector machines and autoencoder networks, respectively, uses environmental parameter sets combined with orchard grass seed selection data and orchard grass management data to train corresponding data-driven models, and constructs a mapping relationship between environmental influencing factors and grass cultivation management from two aspects; and performs grass seed selection and grass management early warning for the target orchard based on the multi-source environmental parameters and data-driven models of the target orchard;

[0072] The orchard grass cultivation management output unit obtains the best grass management plan based on the grass management warning and associating different key management measures, outputs the grass species selection information, grass management warning information and grass management plan, and visualizes them using a visualization method.

[0073] The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms. In addition, the functional units in the embodiments of the present invention can be all integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the above-mentioned integrated units can be implemented in the form of hardware or in the form of hardware plus software functional units.

[0074] Those skilled in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0075] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for orchard grass cultivation and management based on multi-source environmental parameters, characterized in that: The following steps are involved: Obtain the full-process management data of orchard grass cultivation, extract the key management measures of grass cultivation, divide the full-process management data based on the key management measures, and obtain the environmental impact factors of each key management measure based on correlation analysis; Extract environmental parameters based on the environmental influencing factors, use the growth status of grass to screen environmental parameters that meet preset requirements, use the environmental parameters to build a data-driven model corresponding to each key management measure, and establish a mapping relationship between environmental influencing factors and grass cultivation management; Collect multi-source environmental parameters of the target orchard, and select grass species and conduct early warning of grass management for the target orchard based on the multi-source environmental parameters and a data-driven model; According to the association of different key management measures with grass management warning, the best grass management plan is obtained, and the grass species selection information, grass management warning information and grass management plan are visualized using visualization methods.

2. The orchard grass cultivation management method based on multi-source environmental parameters according to claim 1, characterized in that: Obtain the full-process management data of orchard grass cultivation, extract the key management measures of grass cultivation, and divide the full-process management data based on the key management measures, specifically as follows: Using a big data search engine to obtain orchard grass cultivation related knowledge and historical orchard grass cultivation management examples to construct a data set, preprocessing the data set, and sorting the data in the data set according to the time sequence corresponding to the growth stage of orchard grass, to obtain the full process management data of orchard grass cultivation; Performing clustering processing on the full-process management data to obtain clustering results, obtaining the data volume of each cluster in the clustering results, eliminating clusters that do not meet preset data volume requirements according to the data volume, and simplifying the full-process management data; In the remaining clusters, keywords are extracted from the management data samples in each cluster, and the corresponding grass growth stage and corresponding management measures are determined according to the keyword vector. The key management measures for each grass growth stage are obtained after redundancy removal. Based on the key management measures, positioning is performed in the simplified full-process management data, and neighborhood data of the key management measures are extracted according to a preset time range.

3. The orchard grass cultivation management method based on multi-source environmental parameters according to claim 1, characterized in that: Based on correlation analysis, the environmental impact factors of each key management measure are obtained, specifically: Obtaining data segments corresponding to key management measures for orchard grass cultivation, using correlation analysis to perform primary attribution of key management measures in the data segments, using the Pearson correlation coefficient to improve the maximum correlation minimum redundancy method, and obtaining environmental influencing factors that meet preset correlation conditions through the improved maximum correlation minimum redundancy method; Obtain the environmental impact factors selected by primary attribution, extract the environmental parameters and key management measures in the data segment according to the environmental impact factors to construct a data sample, use the SHAP interpretation model to perform deep attribution analysis on the data sample, generate the Shapley value of each environmental impact factor to construct a local interpretation matrix; For each environmental influencing factor, the average value of the Shapley value of all data samples for the environmental influencing factor is calculated according to the local explanation matrix as the global explanation, and the global explanation is used as the importance value of the environmental influencing factor. The importance value is used for sorting, and a preset number of environmental influencing factors are selected according to the sorting results to match the key management measures used.

4. The orchard grass cultivation management method based on multi-source environmental parameters according to claim 1, characterized in that: Based on the environmental influencing factors, environmental parameters are extracted, and the growth status of grass is used to screen environmental parameters that meet the preset requirements. The environmental parameters are used to construct a data-driven model corresponding to each key management measure, specifically: Key management measures are divided according to grass species selection and grass plant management. Environmental parameters are screened based on the environmental influencing factors that match each key management measure after division. Grass growth stages and grass growth conditions are obtained in the instances corresponding to the full-process management data. Growth condition standards for different grass growth stages are generated based on grass growth data under suitable conditions. Compare the grass growth conditions in the example with the corresponding growth condition standards, obtain environmental parameters that meet the preset requirements, and generate an environmental parameter set corresponding to grass species selection and grass plant management; Based on the support vector machine and autoencoder network, data-driven models corresponding to grass seed selection and grass plant management were constructed respectively. The environmental parameter set was combined with the orchard grass seed selection data and the orchard grass management data to train the corresponding data-driven model. The mapping relationship between environmental influencing factors and grass cultivation management was constructed from two aspects.

5. The orchard grass cultivation management method based on multi-source environmental parameters according to claim 1, characterized in that: Collect multi-source environmental parameters of the target orchard, and select grass species and conduct grass management early warning for the target orchard based on the multi-source environmental parameters and the data-driven model, wherein the grass species selection is specifically as follows: Collect multi-source environmental parameters of the target orchard according to the environmental influencing factors corresponding to the selection of the grass species, use the multi-source environmental parameters as the input of the support vector machine to generate corresponding feature vectors, and use the data-driven model composed of the trained support vector machine to predict and score the feature vectors; The gscatter function is used to obtain the data scatter plot corresponding to the feature vector, and the contour function is used to generate the decision boundaries of different grass species labels in the scatter plot. The evaluation scores of different decision boundaries are calculated, and the grass species selection results of the target orchard are generated based on the evaluation scores.

6. The orchard grass cultivation management method based on multi-source environmental parameters according to claim 1, characterized in that: Collect multi-source environmental parameters of the target orchard, and select grass species and conduct grass management warning for the target orchard based on the multi-source environmental parameters and the data-driven model. The grass management warning is specifically: Obtain the growth stage of grass in the target orchard, obtain multi-source environmental parameters corresponding to the corresponding environmental influencing factors from the environmental influencing factors corresponding to grass plant management according to the growth stage, and import the multi-source environmental parameters into the autoencoder network; Reconstruct the data of the multi-source environmental parameters using a data-driven model formed by a trained autoencoder network, obtain estimated values ​​of the multi-source environmental parameters after the implementation of key management measures, and calculate the multi-dimensional residuals between the estimated values ​​and the measured values ​​of the multi-source environmental parameters; Preset the residual threshold of the data-driven model corresponding to each key management measure, compare the multidimensional residual with the corresponding residual threshold, count the number of multidimensional residuals that exceed the residual threshold, and generate a grass management warning when the number reaches the preset standard.

7. The orchard grass cultivation management method based on multi-source environmental parameters according to claim 1, characterized in that: According to the grass management warning in different key management measures, the best grass management plan is associated with the following: Obtaining corresponding key management measures according to the grass management warning, using the key management measures and multi-source environmental parameters to perform similarity search on historical orchard grass cultivation management examples, and obtaining historical orchard grass cultivation management examples corresponding to the highest similarity according to similarity sorting; The management measure parameters in the screened historical orchard grass cultivation management examples are extracted, and the environmental deviation between the target orchard and the example is calculated based on multi-source environmental parameters. The adjustment coefficient is generated according to the environmental deviation to adjust the management measure parameters, and the key management measures are configured using the adjusted management measure parameters to generate the optimal grass management plan.

8. An orchard grass cultivation management system based on multi-source environmental parameters, characterized in that: Implementing an orchard grass cultivation management method based on multi-source environmental parameters as claimed in any one of claims 1 to 7, the system comprises an environmental influencing factor analysis unit, a multi-source environmental parameter acquisition unit, a data-driven model unit, and an orchard grass cultivation management output unit; The environmental impact factor analysis unit uses the acquired knowledge related to orchard grass cultivation and historical orchard grass cultivation management examples to construct full-process management data, extract key management measures for grass cultivation, and obtain environmental impact factors of key management measures based on correlation analysis; The multi-source environmental parameter acquisition unit extracts multi-source environmental parameters based on environmental influencing factors at different growth stages; The data-driven model unit constructs data-driven models corresponding to grass seed selection and grass plant management based on support vector machines and autoencoder networks, respectively, uses environmental parameter sets combined with orchard grass seed selection data and orchard grass management data to train corresponding data-driven models, and constructs a mapping relationship between environmental influencing factors and grass cultivation management from two aspects; and performs grass seed selection and grass management early warning for the target orchard based on the multi-source environmental parameters and data-driven models of the target orchard; The orchard grass cultivation management output unit obtains the best grass management plan based on the grass management warning and associating different key management measures, outputs the grass species selection information, grass management warning information and grass management plan, and visualizes them using a visualization method.

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