GIS data visualization management method and system based on digital twin

Abstract

This application discloses a digital twin-based GIS data visualization management method and system, relating to the field of visualization management. This method, applied to a smart agriculture management platform, includes the following steps: obtaining soil parameter data for the monitored planting area; obtaining crop images of the monitored planting area, performing image recognition on the crop images, and obtaining basic crop data; constructing a soil-crop association model using the soil parameter data and basic crop data; obtaining environmental data for the monitored planting area and constructing an environmental model; fusing the environmental model with the soil-crop association model to obtain a microclimate environment model, and determining the correlation between soil properties, environmental factors, and crop status; determining a planning strategy for the monitored planting area based on the correlation; and visually displaying the planning strategy in a pre-constructed digital twin model of the monitored planting area. This application can effectively address the problem of inaccurate manual monitoring.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of visualization management, and in particular to a GIS data visualization management method and system based on digital twins. Background Art

[0002] Greenhouse cultivation is a method of agricultural production conducted in a closed or semi-closed environment. It primarily regulates environmental factors such as light, temperature, and humidity to provide optimal growing conditions for crops. Regularly monitoring crop growth within greenhouses can help farmers understand changes in the greenhouse environment and take timely measures to adjust the environment to ensure normal crop growth and development. This can also reduce the use of pesticides and fertilizers, lowering costs and improving economic benefits.

[0003] At present, the monitoring of crop growth conditions in greenhouses is mainly carried out through manual monitoring. The growth of crops is judged based on the experience of farmers, and whether the crops need to be fertilized or watered is determined based on experience. However, using manual experience for monitoring will result in insufficient artificial agricultural production knowledge reserves, resulting in low accuracy in crop growth monitoring, causing misjudgment of crop growth, affecting the growth status of crops, and causing not only economic losses to farmers but also pollution to the environment.

[0004] There is currently no better solution to the above problems. Summary of the Invention

[0005] The embodiments of the present application provide a GIS data visualization management method and system based on digital twins to solve the problem of inaccurate manual monitoring.

[0006] To achieve the above objectives, the embodiments of the present application adopt the following technical solutions:

[0007] In a first aspect, a GIS data visualization management method based on digital twins is provided, which is characterized by comprising:

[0008] Obtain soil parameter data of the planting area to be monitored in the greenhouse;

[0009] Acquire crop images of the planting area to be monitored, and perform image recognition on the crop images to obtain basic crop data;

[0010] Constructing a soil-crop association model based on the soil parameter data and the crop basic data;

[0011] Acquiring environmental data of the planting area to be monitored, and constructing an environmental model based on the environmental data;

[0012] Fusing the environmental model with the soil-crop correlation model to obtain a microclimate environmental model, and determining correlations among soil properties, environmental factors, and crop status;

[0013] Determining a planning strategy for the planting area to be monitored based on the association relationship;

[0014] The planning strategy is visualized in a pre-built digital twin model of the planting area to be monitored.

[0015] In another possible implementation of the first aspect, acquiring a crop image of the to-be-monitored planting area and performing image recognition on the crop image to obtain basic crop data includes:

[0016] Using a drone to capture images of crops in the to-be-monitored planting area, thereby obtaining crop images;

[0017] performing image segmentation on the crop image to obtain a plurality of sub-images;

[0018] performing image recognition on each of the sub-images to obtain at least one crop leaf feature;

[0019] Basic crop data is determined based on at least one of the crop leaf characteristics.

[0020] In another possible implementation of the first aspect, performing image recognition on each sub-image to obtain at least one crop leaf feature includes:

[0021] For any one of the sub-images, performing image recognition on the sub-image to determine the edge of a crop leaf, and performing image segmentation on the sub-image based on the edge of the crop leaf to obtain a crop leaf region;

[0022] Performing feature extraction on the crop leaf region to obtain at least one crop leaf feature, wherein the crop leaf feature includes a leaf edge feature, a leaf texture feature, and a leaf color feature;

[0023] Determining basic crop data based on at least one crop leaf feature includes:

[0024] For one of the crop leaf features, basic crop data is determined based on the leaf edge feature, the leaf texture feature, and the leaf color feature. The basic crop data includes crop nutrient data, crop disease and pest data, and crop growth stage data.

[0025] In another possible implementation of the first aspect, the constructing the soil-crop association model based on the soil parameter data and the crop basic data includes:

[0026] Obtaining a training set corresponding to the soil parameter data, wherein the training set includes pH value and organic matter;

[0027] The training set is input into the initial model to train the initial model to obtain a trained initial model, and the trained initial model is used to characterize a soil-crop association model.

[0028] In another possible implementation of the first aspect, constructing the environment model based on the environment data includes:

[0029] Obtaining a training set corresponding to the environmental data, wherein the training set includes temperature, humidity, and light intensity;

[0030] The training set is input into the initial model to train the initial model to obtain a trained initial model, and the trained initial model is used to characterize the environment model.

[0031] In another possible implementation of the first aspect, fusing the environmental model with the soil-crop association model to obtain a microclimate environmental model and determining the association between soil properties, environmental factors, and crop status includes:

[0032] Determining a first change dataset of soil properties and crop status at a future time node using the soil and crop association model;

[0033] Determining a second change data set of environmental data at a future time node using the environmental model;

[0034] performing data fitting on the first change data set and the second change data set to obtain a correlation analysis function;

[0035] A microclimate environment model is constructed based on the correlation analysis function;

[0036] The soil property data, environmental data or crop status data at the target time node are input into the association analysis function to obtain the association relationship between the soil property data, environmental data and crop status data at a preset time node in the future.

[0037] In another possible implementation of the first aspect, constructing the microclimate environment model includes:

[0038] Obtain greenhouse space data and carbon dioxide concentration data

[0039] Correcting the correlation analysis function by using the space influencing factor and the air circulation influencing factor to obtain a corrected correlation analysis function;

[0040] An optimized microclimate environment model is obtained through the corrected correlation analysis function.

[0041] In another possible implementation of the first aspect, determining the planning strategy for the to-be-monitored planting area based on the association relationship includes:

[0042] For one of the crop leaf features, determining a crop adjustment strategy for a target crop corresponding to the crop leaf feature based on the crop basic data;

[0043] Determine the sub-image where the target crop is located, and obtain positioning data in the planting area to be monitored corresponding to the sub-image;

[0044] Determining an adjustment area of the to-be-monitored planting area based on the positioning data;

[0045] Acquiring soil property data of the adjustment area, and inputting the soil property data into the microclimate environment model to obtain environmental data and crop status data associated with the soil property data;

[0046] Determining suitable environmental data of the target crop based on the basic crop data of the target crop, and determining a first adjustment strategy for the environmental data associated with the soil attribute data based on the suitable environmental data;

[0047] Determining, through a preset state database, standard state data corresponding to the growth stage data of the target crop, and determining, based on the standard state data, a second adjustment strategy for the crop state data associated with the soil attribute data;

[0048] The first adjustment strategy, the second adjustment strategy and the crop adjustment strategy are integrated to obtain the planning strategy for the adjustment area of the planting area to be monitored.

[0049] In a second aspect, the present application provides a machine-readable storage medium having instructions stored thereon, which are used to enable a machine to execute the above-mentioned digital twin-based GIS data visualization management method.

[0050] In a third aspect, the present application provides a GIS data visualization management system based on digital twins, comprising:

[0051] a memory configured to store instructions; and

[0052] The processor is configured to call the instructions from the memory and implement the above-mentioned digital twin-based GIS data visualization management method when executing the instructions.

[0053] The above technical solution uses image recognition to obtain basic crop data from crop images within greenhouse planting areas. This helps farmers understand changes in the greenhouse environment and take timely measures to adjust the environment, effectively addressing the problem of inaccurate manual monitoring. Soil parameter data is obtained for the monitored planting area within the greenhouse, and a soil-crop correlation model is constructed based on this soil parameter data and basic crop data. This clearly demonstrates the relationship between these two data points. Building an environmental model based on the environmental data of the monitored planting area helps to more accurately assess the relationship between these two data points. Fusion of the environmental model and the soil-crop correlation model creates a microclimate model, which better identifies the correlation between soil properties, environmental factors, and crops, helping to better predict future crop growth. Based on these correlations, a planting planning strategy is determined and displayed in a digital twin model of the monitored planting area. By simulating these planting planning strategies, resource waste in agricultural production is reduced, optimal crop growth is ensured, and agricultural production efficiency is improved.

[0054] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A flowchart of a GIS data visualization management method based on digital twins provided in an embodiment of the present application;

[0056] Figure 2 A schematic diagram of a process for determining basic crop data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0057] To make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0058] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indications will also change accordingly.

[0059] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the fact that they can be implemented by ordinary technicians in this field. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0060] Figure 1 The following schematically shows a flow chart of a GIS data visualization management method based on digital twins according to an embodiment of the present application. Figure 1 As shown, an embodiment of the present application provides a GIS data visualization management method based on digital twins, which may include the following steps.

[0061] S110, obtaining soil parameter data of the planting area to be monitored in the greenhouse;

[0062] S120, obtaining crop images of the planting area to be monitored, and performing image recognition on the crop images to obtain basic crop data;

[0063] S130, constructing a soil-crop association model based on soil parameter data and crop basic data;

[0064] S140, obtaining environmental data of the planting area to be monitored, and constructing an environmental model based on the environmental data;

[0065] S150, fusing the environmental model with the soil-crop correlation model to obtain a microclimate environmental model, and determining the correlation between soil properties, environmental factors, and crop status;

[0066] S160, determining a planning strategy for the planting area to be monitored through the association relationship;

[0067] S170: Visualize the planning strategy in a pre-built digital twin model of the planting area to be monitored.

[0068] First, the soil parameter data of the planting area to be monitored in the greenhouse is obtained. The greenhouse is a closed or semi-closed agricultural facility for cultivating plants, which can transmit light and keep heat to meet the needs of plant growth. In the present embodiment, the greenhouse is a closed space composed of plastic film and a skeleton. The planting area to be monitored is the planting area in the greenhouse, and the planting area can be determined according to actual conditions. In the present embodiment, the soil parameter data includes pH parameter data (i.e., pH value) and organic matter parameter data (i.e., organic matter) in the soil. pH parameter data refers to the acidity and alkalinity parameter data in the soil, and organic matter parameter data refers to the amount of various plant and animal residues and microorganisms contained in the soil and the organic matter decomposed and synthesized by them. The organic matter in the soil is obtained by a spectroscopic sensor, and the soil in the monitored planting area is subjected to near-infrared spectroscopy analysis by the spectroscopic sensor to obtain organic matter parameter data in the soil. The spectroscopic sensor can analyze the organic matter in the soil through near-infrared spectroscopy, and identify and quantify the organic compounds in the soil.

[0069] By acquiring crop images of the monitored planting area and performing image recognition on the crop images, basic crop data is obtained. Image recognition technology is an important field of artificial intelligence and refers to the technology of using computers to process, analyze, and understand images to identify targets and objects of various different patterns. In this embodiment, a drone can be used to take photos of the monitored planting area to obtain crop images, and the crop images can be segmented to obtain multiple sub-images. The crop leaf features in the sub-images are then identified. In this embodiment, the crop leaf features include leaf edge features, leaf texture features, and leaf color features. By identifying leaf edge features, the nutrient data required by the crop can be determined. By identifying leaf texture features, the crop disease and insect pest data can be determined. By identifying leaf color features, the crop growth stage data can be determined. The nutrient data required by the crop, the crop disease and insect pest data, and the crop growth stage data are aggregated to obtain basic crop data.

[0070] A soil-crop association model is constructed based on soil parameter data and basic crop data. In this embodiment, the soil parameter data includes soil pH and organic matter, and the basic crop data includes crop nutrient data, crop disease and pest data, and crop growth stage data. The soil-crop association model is constructed using the soil parameter data and basic crop data. Specifically, a training set corresponding to the soil parameter data is obtained, wherein the training set includes soil pH data and organic matter data. The training set corresponding to the soil parameter data is input into an initial model to train the initial model. In this embodiment, the initial model can be a pre-constructed neural network model, which is trained using a training set consisting of pH data and organic matter data. The trained neural network model is the soil-crop association model.

[0071] Acquire environmental data of the planting area to be monitored. In this embodiment, the environmental data include temperature data, humidity data and light intensity data of the planting area to be monitored, and construct an environmental model based on the environmental data. Specifically, obtain a training set corresponding to the environmental data, wherein the training set includes temperature data, humidity data and light intensity data. Input the training set corresponding to the environmental data into the initial model to train the initial model. In this embodiment, the initial model can be a pre-constructed neural network model, which is trained by a training set consisting of pH data and organic matter data. The trained neural network model is a soil-crop association model.

[0072] The environmental model and the soil-crop association model are fused to obtain a microclimate environmental model. Microclimate refers to the microclimate environment formed by spatial differences in climatic factors such as temperature, humidity, wind speed, and sunshine within a specific area. In this embodiment, the microclimate environmental model is the microclimate environmental model of the planting area to be monitored in the greenhouse. The environmental model and the soil-crop association model are fused to obtain a microclimate environmental model. Specifically, the soil-crop association model is used to determine a first change data set of soil properties and crop status at future time nodes. In this embodiment, the first change data set is a collection of soil property and crop status change data at future time nodes. The environmental model is used to determine a second change data set of environmental data at future time nodes. In this embodiment, the second change data set is a data set of environmental data changes at future time nodes.

[0073] The first change data set and the second change data set are fitted to obtain a correlation analysis function. The data set of soil properties and crop status at future time nodes and the data set of environmental data at future time nodes are fitted to obtain a correlation analysis function through a preset data fitting software (such as MATLAB mathematical modeling software), and a microclimate environmental model is constructed through the correlation analysis function. After obtaining the microclimate environmental model, the correlation relationship between soil properties, environmental factors and crop status is determined, that is, the linkage relationship between soil properties, environmental factors and crop status is determined. For example, when the temperature, humidity and light intensity in the environment change, the pH value and organic matter value in the soil also change accordingly, which will affect the change in the growth state of the crop.

[0074] After determining the linkage between soil properties, environmental factors and crop status, first, the crop leaf images are identified through image recognition technology to determine the crop leaf characteristics. Based on the crop leaf characteristics, the basic crop data is obtained. Based on the basic crop data, the crop adjustment strategy corresponding to the crop leaf characteristics is determined.

[0075] In this embodiment, the crop adjustment strategy is a strategy for adjusting the growth of crops based on the basic data of the crops. For example, the growth elements missing from the crops are determined through the basic data of the crops, and the missing growth elements of the crops are adjusted. Secondly, the sub-image where the target crop is located is determined, and the positioning data of the planting area to be monitored corresponding to the sub-image is obtained. The adjustment area of the planting area to be monitored is determined through the positioning data. In this embodiment, the adjustment area can be set according to a preset range. The soil attribute data of the adjustment area is obtained, and the soil attribute data is input into the microclimate environment model to obtain environmental data and crop status data associated with the soil attribute data. That is, the soil attribute data of the adjustment area is input into the microclimate environment model to obtain environmental data and crop status data associated with the soil attribute data. According to the basic crop data of the target crop, the suitable environmental data of the target crop is determined, and according to the suitable environmental data, the first adjustment strategy for the environmental data associated with the soil attribute data is determined. In this embodiment, the first adjustment strategy refers to a strategy for adjusting the soil attribute data and environmental data based on the suitable environmental data of the target crop. Standard state data corresponding to the growth stage data of the target crop is determined using a preset state database. Based on the standard state data, a second adjustment strategy for the crop state data associated with the soil attribute data is determined. In this embodiment, the second adjustment strategy refers to a strategy for adjusting the crop state data associated with the soil attribute data using the preset database. The first adjustment strategy, the second adjustment strategy, and the crop adjustment strategy are integrated to obtain a planning strategy for the adjustment area of the monitored planting area. Specifically, the first adjustment strategy, the second adjustment strategy, and the crop adjustment strategy are combined to determine the planning strategy for the planting area.

[0076] After determining the planning strategy for a planting area, the strategy is visualized in a pre-built digital twin model of the monitored planting area. A digital twin is a virtual representation of a physical entity, process, or system created using digital modeling technology on an information platform. Displaying the planning strategy in the pre-built digital twin model of the monitored planting area clearly simulates how the strategy will operate in a real-world environment.

[0077] Visual management of agriculture through digital twin technology and GIS data is the main way to eliminate poverty in rural areas and achieve agricultural modernization. Building a visualization platform can effectively conduct regional dynamic monitoring of the crop growth environment and crop growth, improve the comprehensive diagnosis capabilities of crop growth and disasters, provide more convenient application services for agricultural production management, and help overcome the disadvantages of traditional agriculture.

[0078] The above technical solution uses image recognition to obtain basic crop data from crop images within greenhouse planting areas. This helps farmers understand changes in the greenhouse environment and take timely measures to adjust the environment, avoiding the inaccuracies of manual monitoring. Soil parameter data is obtained for the monitored planting area within the greenhouse, and a soil-crop correlation model is constructed based on this soil parameter data and basic crop data. This clearly demonstrates the relationship between these two data points. Building an environmental model based on the environmental data of the monitored planting area helps to more accurately assess the relationship between these two data points. Fusion of the environmental model and the soil-crop correlation model creates a microclimate model, which better identifies the correlation between soil properties, environmental factors, and crops, helping to better predict future crop growth. Based on these correlations, a planting planning strategy is determined and displayed in a digital twin model of the monitored planting area. By simulating these planting planning strategies, resource waste in agricultural production is reduced, optimal crop growth is ensured, and agricultural production efficiency is improved.

[0079] In one implementation of this embodiment, a crop image of the planting area to be monitored is obtained, and image recognition is performed on the crop image to obtain basic crop data, including:

[0080] S210, capturing images of crops in the monitored planting area by a drone to obtain crop images;

[0081] S220, performing image segmentation on the crop image to obtain multiple sub-images;

[0082] S230, performing image recognition on each sub-image to obtain at least one crop leaf feature;

[0083] S240: Determine basic crop data based on at least one crop leaf feature.

[0084] Acquire crop images of the planting area to be monitored, perform image recognition on the crop images, and obtain basic crop data. First, use a drone to take images of the crops in the planting area to be monitored to obtain crop images. That is, use a drone in the greenhouse to take pictures of the planting area to be monitored in the greenhouse to obtain crop images of the planting area.

[0085] Next, after obtaining a crop image of the planting area, the crop image is segmented to obtain multiple sub-images. Image recognition is then used to segment the image and extract features from the sub-images. Image recognition technology refers to the use of computers to process, analyze, and understand images to identify various patterns of targets and objects. In this embodiment, the sub-images are images obtained after the crop image has been segmented through image recognition.

[0086] In this embodiment, image recognition can be used to identify each sub-image and determine at least one crop leaf feature in each sub-image. In this embodiment, the crop leaf features include leaf edge features, leaf texture features, and leaf color features. Leaf edge features refer to the shape features of the leaf edge, such as a serrated feature, where the leaf edge has obvious serrations, and a lobed feature, where the leaf edge has shallow cracks. Leaf texture features refer to the pattern features on the leaf surface, such as features such as vinyl patterns, vein patterns, and bird's beak patterns. Leaf color features refer to the color of the leaf surface, which is primarily determined by the pigments within it. For example, yellowing of leaves may indicate disease. By identifying the sub-images, at least one of the crop leaf features, including leaf edge features, leaf texture features, and leaf color features, is determined.

[0087] By determining the characteristics of crop leaves, basic crop data is determined. In this embodiment, the basic crop data includes crop nutrient data, crop disease and insect pest data, and crop growth stage data. By determining the characteristics of crop leaves, basic crop data is determined. That is, the crop nutrient data, crop disease and insect pest data, and crop growth stage data are determined based on the leaf edge characteristics, leaf texture characteristics, and leaf color characteristics. For example, when the crop is deficient in nitrogen, the leaf color characteristics will become light green or yellow-white. When the crop is deficient in calcium, the leaf edge characteristics will curl up into a fishhook shape. When the leaf texture characteristics are curled, the crop will develop disease. When the leaf color characteristics of the crop are different, the crop growth stage is also different. For example, the leaf color characteristics of tea leaves in the early growth stage are light green, and the color in the later growth stage is dark green. The basic crop data is determined based on different crop leaf characteristics.

[0088] By taking crop images with drones and performing feature analysis on the crop images, we can obtain the crop leaf features of the sub-images. Based on the crop leaf features, we can obtain basic crop data. Based on the crop leaf features, we can accurately grasp the current growth status of the crops and make timely adjustments to the crop growth status to ensure the optimal growth state of the crops.

[0089] In one implementation of this embodiment, image recognition is performed on each sub-image to obtain at least one crop leaf feature, including:

[0090] S310: for any sub-image, perform image recognition on the sub-image to determine the edge of the crop leaf, and perform image segmentation on the sub-image based on the edge of the crop leaf to obtain a crop leaf region;

[0091] S320: Extract features from the crop leaf region to obtain at least one crop leaf feature, wherein the crop leaf feature includes a leaf edge feature, a leaf texture feature, and a leaf color feature;

[0092] Determine basic crop data based on at least one crop leaf feature, including:

[0093] S330: For a crop leaf feature, determine the crop basic data based on the leaf edge feature, leaf texture feature, and leaf color feature. The crop basic data includes crop nutrient data, crop disease and pest data, and crop growth stage data.

[0094] Image recognition is performed on each sub-image to obtain at least one crop leaf feature. First, for any sub-image, image recognition is performed on the sub-image to determine the crop leaf edge. Specifically, the edge of the crop leaf image is detected. Edge detection is based on the grayscale changes between the local area and the surrounding area in the image. When the brightness or grayscale value of a region in the image changes significantly, these changes typically correspond to the outline of an object, the boundary between different regions, etc. Edge detection algorithms identify edges in the image by analyzing these changes, dividing pixels near the edge into different regions. The pixels of the local features of the edge image of the crop leaf image are discontinuous with the pixels of other parts. Therefore, detection is usually performed based on sudden changes in the image grayscale value, which can effectively separate different regions. After identifying the pixels of the crop leaf edge image, the leaf image is determined and segmented from the background image. The leaf image is extracted to obtain the crop leaf region.

[0095] After obtaining the image of the crop leaf area, feature extraction is performed on the crop leaf area. In this embodiment, the crop leaf features are leaf edge features, leaf texture features, and leaf color features. The crop leaf features are extracted mainly through image recognition to extract the leaf edge features, leaf texture features, and leaf color features of the crop leaf area. In this embodiment, the leaf edge feature refers to the shape feature of the leaf edge, such as the serrated feature. The leaf texture feature refers to the texture feature of the leaf surface, such as the presence of vinyl pattern, vein pattern, and bird's beak pattern. The leaf surface color feature refers to the color of the leaf surface. By extracting the leaf features of the crop leaf area, at least one leaf edge shape feature, leaf surface texture feature, and leaf surface color feature are obtained.

[0096] After obtaining at least one crop leaf feature, basic crop data is determined based on the at least one crop leaf feature. Specifically, image recognition is performed to obtain leaf edge features, leaf texture features, and leaf color features of the crop leaf region. The basic crop data is then determined based on the leaf edge features, leaf texture features, and leaf color features. In this embodiment, the basic crop data includes crop nutrient requirements, crop pest and disease data, and crop growth stage data. Determining the basic crop data based on the leaf features can be performed by querying a preset database for the basic crop data corresponding to the leaf features, thereby obtaining the basic crop data. For example, if the leaf edge feature is curled, the leaf texture feature is a rayon pattern, and the leaf color feature is yellow, these three features can be entered into the preset database to obtain the corresponding basic crop data. For example, if the leaf edge feature is curled, the leaf texture feature is a rayon pattern, and the leaf color feature is yellow, the crop nutrient requirements data is nitrogen fertilizer, and the crop disease data indicates that aphids, spider mites, and the like absorb leaf sap, causing the leaves to lose their green color and turn yellow. Furthermore, the determination of whether the crop growth stage data is normal can be performed.

[0097] Figure 2 A schematic diagram of a process for determining basic crop data according to an embodiment of the present application is shown schematically. Figure 2 As shown, determine whether the leaf color feature is the preset feature; if so, determine whether the color feature is uniform; if not, determine whether the leaf texture feature is the preset feature; if the texture is not obvious, determine whether the edge feature is the preset feature, if not, determine whether irregular pixel areas appear, and comprehensively determine the nutrients, pests and diseases, and growth stage required by the crop.

[0098] In another embodiment, the implementation method for determining the basic data of a crop based on the leaf edge features, leaf texture features, and leaf color features may be, first, determining whether the leaf color feature is a preset color feature, such as the preset color feature is light green, and judging whether the color of the currently extracted leaf color feature is consistent with the preset color feature. If the colors are consistent, such as both are light green, then it is determined whether the color features of the crop leaves are uniform. The determination of whether the color features are uniform is mainly based on the pixel values of the crop leaves. When the pixel values of the crop leaves are within the preset pixel value range, it is determined that the color features of the crop leaves are uniform. When it is determined that the color features of the crop leaves are uniform and the leaf color features meet the preset features, it can be preliminarily determined that the current crop is free of pests and diseases and lacks the elements required for crop growth.

[0099] Secondly, when the preset color feature is light green, the extracted leaf color feature is yellow and light yellow. At this time, the leaf color feature is not the preset color feature. When judging whether the current crop leaf color feature is uniform, if it is not uniform, then judge whether the leaf texture feature is the preset texture feature. For example, the preset texture feature is obvious texture. If the current leaf texture feature is not obvious texture, after the current leaf texture feature is inconsistent with the preset texture feature, judge whether the edge feature meets the preset edge feature. For example, the preset edge feature is charred, brown feature, and irregular edge. Judge whether the current edge feature has charred, brown feature, and irregular edge. The current edge feature is not charred, brown feature, and irregular edge. Then, through the leaf color feature, leaf texture feature and leaf edge feature, comprehensively determine whether nutrients need to be supplemented, whether it is in the disease and pest stage, and whether the current crop growth stage meets the growth stage, thereby comprehensively determining the basic data of the crop.

[0100] By performing image recognition on sub-images and determining the characteristics of crop leaves, we can obtain basic crop data, accurately obtain basic crop information, and obtain the growth elements, pests and diseases, and growth stages required for the next step of the crop. We can adjust the basic crop growth data in a timely manner to provide a suitable growth environment for the crop.

[0101] In one implementation of this embodiment, a soil-crop association model is constructed based on soil parameter data and crop basic data, including:

[0102] S410, obtaining a training set corresponding to soil parameter data, the training set including pH value and organic matter;

[0103] S420: Input the training set into the initial model to train the initial model to obtain a trained initial model, and the trained initial model is used to characterize the soil-crop association model.

[0104] A soil-crop association model is constructed based on soil parameter data and basic crop data. Specifically, first, a training set corresponding to the soil parameter data is obtained. The training set refers to a data set used to establish and train the model. The main function of the training set is to adjust the model parameters by analyzing sample data so that the model can accurately predict or classify new data. The initial model is trained by obtaining a training set of soil parameter data. In this embodiment, the training set includes pH value and organic matter. pH value refers to the acidity and alkalinity data in the soil, and organic matter refers to the amount of various plant and animal residues and microorganisms contained in the soil and the organic matter synthesized by their decomposition. The initial model is trained using the pH value and organic matter in the soil to obtain a soil-crop association model.

[0105] After obtaining the training set corresponding to the soil parameter data, the training set is input into the initial model. In this embodiment, the initial model is a preset neural network model. The training set is input into the initial model to train the initial model to obtain a trained initial model. The initial model is trained with a large amount of soil parameter data. The trained initial model can be directly used in the analysis of the relationship between soil and crops, thereby obtaining a soil-crop relationship model.

[0106] By training the initial model with the training set corresponding to the soil parameter data, a soil-crop association model is obtained. This can not only improve the model performance and make the data analysis between soil and crops more accurate, but also effectively promote crop growth, increase crop yield and quality, and reduce the occurrence of pests and diseases, thus providing a solid foundation for the sustainable development of agriculture.

[0107] In one implementation of this embodiment, building an environment model based on environment data includes:

[0108] S510, obtaining a training set corresponding to environmental data, the training set including temperature, humidity, and light intensity;

[0109] S520: Input the training set into the initial model to train the initial model to obtain a trained initial model, and the trained initial model is used to represent the environment model.

[0110] An environmental model is constructed based on environmental data. Specifically, first, a training set corresponding to the environmental data is obtained. A training set refers to a data set used to build and train the model. The main function of the training set is to analyze sample data and adjust the model's parameters so that the model can accurately predict or classify new data. By obtaining a training set corresponding to the environmental data, the initial model is trained. In this embodiment, the training set includes temperature, humidity, and light intensity. Temperature refers to the optimal temperature for plant growth, humidity refers to the optimal humidity for plant growth, mainly referring to the optimal moisture content for crop growth, and light intensity refers to the optimal light intensity for crop growth. Light is crucial to plant growth. Light not only provides energy for plants and promotes photosynthesis, but also regulates plant growth and development. Appropriate light intensity and duration contribute to the normal growth and development of plants. The initial model is trained using the temperature, humidity, and light intensity in the environment to obtain an environmental model.

[0111] After obtaining the training set corresponding to the environmental data, the training set is input into the initial model. In this embodiment, the initial model is a preset initial environmental model. The training set is input into the initial model to train the initial model and obtain a trained initial model. The initial model is trained with a large amount of environmental data, and the trained initial model can be directly used in the analysis of the environmental data, thereby obtaining an environmental model.

[0112] By training the initial model with the training set corresponding to the environmental data, an environmental model is obtained. This can not only improve the model performance and make the environmental data analysis more accurate, but also effectively promote crop growth, increase crop yield and quality, and reduce the occurrence of pests and diseases, thus providing a solid foundation for the sustainable development of agriculture.

[0113] In one implementation of this embodiment, the environmental model and the soil-crop association model are integrated to obtain a microclimate environment model, and the correlation between soil properties, environmental factors and crop status is determined, including:

[0114] S610: Determine a first change dataset of soil properties and crop status at a future time node using a soil and crop association model;

[0115] S620: Determine a second changed data set of environmental data at a future time node using the environmental model;

[0116] S630, performing data fitting on the first change data set and the second change data set to obtain a correlation analysis function;

[0117] S640, constructing a microclimate environment model based on the correlation analysis function;

[0118] S650: Input the soil property data, environmental data or crop status data at the target time node into the association analysis function to obtain the association relationship between the soil property data, environmental data and crop status data at a preset time node in the future.

[0119] The environmental model and the soil-crop association model are integrated to obtain a microclimate environmental model. Specifically, first, the first change data set of soil properties and crop status at future time nodes is determined through the soil-crop association model. That is, the historical soil parameter data is input into the soil-crop association model to obtain the first change data set of soil properties and crop status at future time nodes. In this embodiment, the first change data set refers to a collection of soil property and crop status change data at future time nodes. The first change data set is composed of multiple soil property and crop status change data at future time nodes. For example, soil parameter data lasting three years is input into the soil-crop association model. Based on the soil parameter data lasting three years, soil and crop association data in the next three years are obtained, such as the soil parameter changes and crop growth status in the next three years. The data of soil properties and crop status constitute a data set to obtain the first change data set.

[0120] Secondly, the second change data set of environmental data at future time nodes is determined through the environmental model. That is to say, the historical environmental data is input into the environmental model to obtain the environmental change data at the future time nodes. In this embodiment, the second change data set is a data set of environmental data changes at future time nodes. Multiple environmental change data sets at future time nodes constitute the second change data set. For example, the environmental data within the past three years are input into the environmental model. Based on the environmental data within the past three years, the environmental data within the next three years are obtained, such as the environmental conditions and crop growth status within the next three years. The data on soil properties and crop status constitute a data set, thereby obtaining the second change data set.

[0121] Data fitting is performed on the first change data set and the second change data set to obtain a correlation analysis function. Specifically, by fitting the first change data set and the second change data set, first, a first reference sequence and a second reference sequence are determined. In this embodiment, the first reference sequence is the first change data set, and the second reference sequence is the second change data set.

[0122] Secondly, the first and second reference series are initialized and dimensionless. The main purpose of dimensionless data is to eliminate the dimensional effects between different features or indicators, making the data comparable for subsequent analysis. The first and second reference series are fitted using the fitting formula. The data fitting formula is as follows:

[0123]

[0124] Among them, min j is the minimum value of the second reference sequence, min t is the minimum value of time, y i (t) is the first reference sequence, x j (t) is the second reference sequence, ρ is the resolution coefficient, and the value range is usually (0,1). In this embodiment, ρ can be 0.5, and the max j is the maximum value of the first reference sequence, max t is the maximum value of time.

[0125] The first reference sequence and the second reference sequence are fitted by a fitting formula to obtain a correlation analysis function. In this embodiment, the correlation analysis function is a function obtained by fitting the first change data set and the second change data set using the fitting formula.

[0126] After obtaining the correlation analysis function, a microclimate model is constructed based on the correlation analysis function. A microclimate model refers to the microclimate environment formed by spatial differences in climatic factors such as temperature, humidity, wind speed, and sunshine within a specific area. In this embodiment, the microclimate model is the microclimate model of the monitored planting area within a greenhouse. The environmental model and the soil-crop correlation model are fused to obtain the correlation analysis function, thereby obtaining the microclimate model.

[0127] The soil attribute data, environmental data or crop status data of the target time node are input into the association analysis function to obtain the association relationship between the soil attribute data, environmental data and crop status data at the preset time node in the future. In this embodiment, the target time node can be determined according to the actual situation. That is, the soil attribute data, environmental data or crop status data of the target time node are input into the association analysis function. For example, if the target time node is three years, the pH value and organic matter in the soil within three years, the temperature, humidity, light intensity or crop status data in the environmental data are input into the association analysis function to obtain the association relationship between the soil attribute data, environmental data and crop status data at the preset time node in the future. For example, after the corresponding data is input into the association analysis function, the change of the crop status data and the relationship between their changes when the soil pH value and organic matter and the temperature, humidity and light intensity change can be obtained.

[0128] By fitting the first change data set and the second change data set to obtain the correlation analysis function, and constructing a microclimate environment model based on the correlation analysis function, the microclimate environment conditions in the greenhouse can be clearly displayed, which helps to effectively regulate the microclimate environment in the greenhouse, ensure the best growth environment for crops, and protect farmers' economic income.

[0129] In one implementation of this embodiment, constructing a microclimate environment model includes:

[0130] S710, obtaining greenhouse space data and carbon dioxide concentration data;

[0131] S720. Correct the correlation analysis function based on the spatial influencing factors and the air circulation influencing factors to obtain a corrected correlation analysis function.

[0132] S730. Obtain an optimized microclimate environment model through the corrected correlation analysis function.

[0133] Construct a microclimate environment model. Specifically, first, obtain greenhouse space data and carbon dioxide concentration data. The carbon dioxide concentration data is mainly obtained by detecting the carbon dioxide concentration in the greenhouse through a sensor, thereby obtaining carbon dioxide concentration data. Carbon dioxide concentration plays a vital role in the growth process of plants, mainly affecting photosynthesis efficiency and plant growth rate. In this embodiment, carbon dioxide concentration is used to characterize the air circulation conditions in the greenhouse.

[0134] After obtaining the greenhouse spatial data and CO2 concentration data, the correlation analysis function is calibrated using spatial and air circulation factors. This means that the greenhouse spatial data and CO2 concentration data will affect the correlation analysis function, so it is necessary to calibrate the correlation analysis function based on these data. Specifically, the greenhouse spatial data and CO2 concentration data are first used as the third and fourth reference series, respectively.

[0135] Among them, the first reference sequence is Y i ={Y i (t)}; the second reference sequence is X j ={X j (t)};

[0136] The third reference sequence is Z i ={Z i (t)}; the fourth reference sequence is K j ={K j (t)};

[0137] First correction sequence = first reference sequence + preset weight × greenhouse space data;

[0138] First calibration sequence = second reference sequence + preset weight × carbon dioxide concentration data;

[0139] The corrected correlation analysis function formula is as follows:

[0140]

[0141] Among them, the first correction sequence is α i (t); the second correction sequence β i (t), the minimum value of the first correction sequence is min i , the minimum value of the second correction sequence is min j , the maximum value of the first correction sequence is max j , the maximum value of the second correction sequence is max i , ρ is the resolution coefficient, which usually takes a value in the range of (0,1). In this embodiment, ρ can be 0.5.

[0142] The correlation analysis function is corrected by combining the first and second correction series to obtain a corrected correlation analysis function. Using the corrected correlation analysis function, an optimized microclimate environment model is obtained. Microclimate refers to the microclimate environment formed by spatial differences in climatic factors such as temperature, humidity, wind speed, and sunshine within a specific area. In this embodiment, the microclimate environment model is a microclimate environment model of the monitored planting area within a greenhouse. After using the corrected correlation analysis function, the corrected correlation analysis function formula can be fitted to a model using preset software to obtain the optimized microclimate environment model.

[0143] By correcting the correlation analysis function and obtaining the optimized microclimate environment model, not only can an accurate microclimate environment model be obtained, but also by analyzing the optimized microclimate environment model, the relationship between the microclimate environment and the crop growth status can be determined, which helps to build the best crop growth environment and provide crop growth status.

[0144] In one implementation of this embodiment, determining a planning strategy for a planting area to be monitored through association relationships includes:

[0145] S810: For a crop leaf feature, determine a crop adjustment strategy for a target crop corresponding to the crop leaf feature based on basic crop data;

[0146] S820: Determine the sub-image where the target crop is located, and obtain positioning data in the planting area to be monitored corresponding to the sub-image;

[0147] S830: Determine an adjustment area of the planting area to be monitored based on the positioning data;

[0148] S840: Acquire soil property data of the adjustment area, and input the soil property data into a microclimate environment model to obtain environmental data and crop status data associated with the soil property data;

[0149] S850: Determine suitable environmental data for the target crop based on the basic crop data of the target crop, and determine a first adjustment strategy for environmental data associated with the soil attribute data based on the suitable environmental data;

[0150] S860: Determine, through a preset state database, standard state data corresponding to the growth stage data of the target crop, and determine, based on the standard state data, a second adjustment strategy for the crop state data associated with the soil attribute data;

[0151] S870: Integrate the first adjustment strategy, the second adjustment strategy, and the crop adjustment strategy to obtain a planning strategy for the adjustment area of the planting area to be monitored.

[0152] The planning strategy for the monitored planting area is determined through the association relationship. Specifically, first, for a crop leaf feature, a crop adjustment strategy for the target crop corresponding to the crop leaf feature is determined based on the crop basic data. In this embodiment, the crop adjustment strategy refers to a strategy for adjusting the most suitable environment for crop growth based on the crop basic data. For example, the strategy involves determining the growth elements missing from the crop based on the crop basic data and adjusting the missing elements. To determine the crop basic data based on the crop leaf features, a preset database can be used to query the crop basic data corresponding to the crop leaf features, thereby obtaining the crop basic data. For example, if the leaf edge feature is curled, the leaf texture feature is a vinyl pattern, and the leaf color feature is yellow, these three features can be entered into the preset database to obtain the crop basic data corresponding to the three features. Based on the basic crop data, a crop adjustment strategy for the target crop corresponding to the crop leaf characteristics is determined. That is, based on the crop nutrient data, crop disease and pest data, and crop growth stage data, a crop adjustment strategy for the target crop corresponding to the crop leaf characteristics is determined. For example, by analyzing the color characteristics of the crop leaves, the nitrogen element in the nutrient data required by the crop is obtained, and the nitrogen element is supplemented to the target crop based on the nutrient data required by the crop. In this embodiment, the target crop is a crop whose basic crop data needs to be adjusted, and the crop adjustment strategy for the target crop corresponding to the crop leaf characteristics is determined based on the basic crop data.

[0153] Determine the sub-image where the target crop is located, and obtain the positioning data in the planting area to be monitored corresponding to the sub-image. In this embodiment, the sub-image is an image obtained by segmenting the crop image through image recognition. The positioning data in the planting area to be monitored is determined by the sub-image of the target crop. That is to say, based on the basic data of the crop, the target crops whose crop nutrient data, crop disease and pest data, and crop growth stage data need to be adjusted in the planting area to be monitored are judged, the positions of these target crops that need to be adjusted are obtained, and the positioning data in the planting area to be monitored is determined.

[0154] After determining the positioning data of the target crop in the monitored planting area, the adjustment area of the monitored planting area is determined based on the positioning data. That is to say, after determining the position of the target crop whose basic crop data needs to be adjusted in the monitored planting area, the adjustment area can be generated for the position of the target crop according to the preset range. In this embodiment, the preset range can be determined according to the actual situation. For example, the positioning data of the target crop whose crop disease and pest data needs to be adjusted is obtained through the characteristics of the crop leaves, and the crops within the preset range are subjected to disease and pest control according to the preset range. For example, the preset range is one meter, and a circle with a radius of n meters is drawn with the position of the target crop as the center. The crops within this circle are subjected to disease and pest control, and the circular area is used as the adjustment area of the monitored planting area. n can be set freely.

[0155] In another embodiment, the implementation method may be to obtain the soil state of the target crop by determining the positioning data of the target crop in the planting area to be monitored. In this embodiment, the soil state includes soil moisture, pH value, organic matter, and soil color. The crops with consistent soil state and located in the surrounding area are determined through the positioning data of the target crop, and the positioning data of the crop whose soil state is consistent with that of the target crop is obtained, and the adjustment area is determined through the positioning data. For example, after determining the position of the target crop, the soil state of the target crop is obtained, and an area with the same soil state as that of the target crop is found near the target crop. The area of m square meters of this area is used as the adjustment area, where the value of m can be set freely.

[0156] After determining the adjustment area of the planting area to be monitored, obtain the soil attribute data of the adjustment area of the planting area to be monitored, and input the soil attribute data into the microclimate environment model to obtain environmental data and crop status data associated with the soil attribute data. That is to say, determine the linkage relationship between soil attributes, environmental factors and crop status. For example, when the temperature, humidity and light intensity in the environment change, the pH value and organic matter value in the soil also change accordingly, which will affect the growth status of the crops. The linkage relationship between soil attributes, environmental factors and crop status is determined, and the environmental data and crop status data associated with the soil attribute data are obtained.

[0157] Based on the basic crop data of the target crop, the suitable environmental data of the target crop is determined. That is, based on the crop nutrient data, crop disease and pest data, and crop growth stage data of the target crop, it is determined whether the target crop needs to adjust the crop nutrient data, crop disease and pest data, and crop growth stage data. If the target crop needs to adjust any of the crop nutrient data, crop disease and pest data, and crop growth stage data, the basic crop data of the target crop is adjusted to determine the suitable environmental data of the target crop. In this embodiment, the suitable environmental data refers to the environmental data in a state where the basic crop data of the target crop does not need to be adjusted. Based on the suitable environmental data, the soil attribute data under the suitable environmental data is determined, and the soil attribute data is adjusted based on the environmental data associated with the soil attribute data to determine a first adjustment strategy. In this embodiment, the first adjustment strategy refers to a strategy for adjusting the soil attribute data based on the environmental data associated with the soil attribute data.

[0158] After determining the first adjustment strategy, the preset state database is used to determine standard state data corresponding to the growth stage data of the target crop. Based on the standard state data, a second adjustment strategy for the crop state data associated with the soil property data is determined. In this embodiment, the second adjustment strategy is a strategy for adjusting the soil property data based on the standard state data corresponding to the growth stage data of the target crop. The growth stage data of the target crop is input into the preset state database to obtain the standard state data of the target crop. Based on the standard state data, soil property data under the standard state data is obtained. The current soil property data is adjusted based on the soil property data under the standard state data to obtain the second adjustment strategy, thereby ensuring optimal crop growth.

[0159] The first adjustment strategy, the second adjustment strategy and the crop adjustment strategy are integrated to make comprehensive adjustments to the adjustment area, thereby determining the planning strategy for the adjustment area of the monitored planting area. In this embodiment, the planning strategy is a strategy for adjusting the adjustment area of the monitored planting area through the first adjustment strategy, the second adjustment strategy and the crop adjustment strategy.

[0160] Comprehensive adjustment of the adjustment area through the first adjustment strategy, the second adjustment strategy and the crop adjustment strategy can not only ensure the best environment for crop growth in the monitored planting area, but also provide optimized planting time for crops, improve crop management efficiency, reduce agricultural risks, promote precision agriculture, and enhance resource management capabilities.

[0161] The present application provides a machine-readable storage medium having instructions stored thereon, which are used to enable a machine to execute the above-mentioned digital twin-based GIS data visualization management method.

[0162] This application provides a GIS data visualization management system based on digital twins, including:

[0163] a memory configured to store instructions; and

[0164] The processor is configured to call instructions from the memory and implement the above-mentioned digital twin-based GIS data visualization management method when executing the instructions.

[0165] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0166] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0167] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0168] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0169] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0170] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0171] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0172] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0173] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A GIS data visualization management method based on digital twins, characterized by: include: Obtain soil parameter data of the planting area to be monitored in the greenhouse; Acquire crop images of the planting area to be monitored, and perform image recognition on the crop images to obtain basic crop data; Constructing a soil-crop association model based on the soil parameter data and the crop basic data; Acquiring environmental data of the planting area to be monitored, and constructing an environmental model based on the environmental data; The environmental model and the soil-crop association model are integrated to obtain a microclimate environmental model, and the association relationship between soil properties, environmental factors and crop status is determined, including: determining a first change data set of soil properties and crop status at a future time node through the soil-crop association model; determining a second change data set of environmental data at a future time node through the environmental model; performing data fitting on the first change data set and the second change data set to obtain an association analysis function; constructing a microclimate environmental model based on the association analysis function; inputting soil property data, environmental data or crop status data at a target time node into the association analysis function to obtain the association relationship between soil property data, environmental data and crop status data at a preset future time node; Determining a planning strategy for the planting area to be monitored based on the association relationship; Visualizing the planning strategy in a pre-built digital twin model of the plantation area to be monitored; Constructing a microclimate environment model includes: obtaining greenhouse space data and carbon dioxide concentration data; correcting the correlation analysis function based on spatial influencing factors and air circulation influencing factors to obtain a corrected correlation analysis function; and obtaining an optimized microclimate environment model based on the corrected correlation analysis function; The planning strategy of the to-be-monitored planting area is determined through the association relationship, including: for a crop leaf feature, determining a crop adjustment strategy of a target crop corresponding to the crop leaf feature based on the basic crop data; determining a sub-image where the target crop is located, and obtaining positioning data in the to-be-monitored planting area corresponding to the sub-image; determining an adjustment area of the to-be-monitored planting area based on the positioning data; obtaining soil attribute data of the adjustment area, and inputting the soil attribute data into the microclimate environment model to obtain environmental data and crop status data associated with the soil attribute data; determining suitable environmental data of the target crop based on the basic crop data of the target crop, and determining a first adjustment strategy for the environmental data associated with the soil attribute data based on the suitable environmental data; determining standard status data corresponding to the growth stage data of the target crop through a preset status database, and determining a second adjustment strategy for the crop status data associated with the soil attribute data based on the standard status data; integrating the first adjustment strategy, the second adjustment strategy, and the crop adjustment strategy to obtain the planning strategy for the adjustment area of the to-be-monitored planting area.

2. The method according to claim 1, characterized in that The step of acquiring crop images of the to-be-monitored planting area and performing image recognition on the crop images to obtain basic crop data includes: Using a drone to capture images of crops in the to-be-monitored planting area, thereby obtaining crop images; performing image segmentation on the crop image to obtain a plurality of sub-images; performing image recognition on each of the sub-images to obtain at least one crop leaf feature; Basic crop data is determined based on at least one of the crop leaf characteristics.

3. The method according to claim 2, characterized in that The performing image recognition on each of the sub-images to obtain at least one crop leaf feature includes: For any one of the sub-images, performing image recognition on the sub-image to determine the edge of a crop leaf, and performing image segmentation on the sub-image based on the edge of the crop leaf to obtain a crop leaf region; Performing feature extraction on the crop leaf region to obtain at least one crop leaf feature, wherein the crop leaf feature includes a leaf edge feature, a leaf texture feature, and a leaf color feature; Determining basic crop data based on at least one crop leaf feature includes: For one of the crop leaf features, basic crop data is determined based on the leaf edge feature, the leaf texture feature, and the leaf color feature. The basic crop data includes crop nutrient data, crop disease and pest data, and crop growth stage data.

4. The method according to claim 1, wherein The step of constructing a soil-crop association model based on the soil parameter data and the crop basic data includes: Obtaining a training set corresponding to the soil parameter data, wherein the training set includes pH value and organic matter; The training set is input into the initial model to train the initial model to obtain a trained initial model, and the trained initial model is used to characterize a soil-crop association model.

5. The method according to claim 1, wherein The step of constructing an environment model based on the environment data includes: Obtaining a training set corresponding to the environmental data, wherein the training set includes temperature, humidity, and light intensity; The training set is input into the initial model to train the initial model to obtain a trained initial model, and the trained initial model is used to characterize the environment model.

6. A machine-readable storage medium, characterized in that The machine-readable storage medium stores instructions for enabling a machine to execute the digital twin-based GIS data visualization management method according to any one of claims 1 to 5.

7. A GIS data visualization management system based on digital twins, characterized by: include: a memory configured to store instructions; as well as A processor is configured to call the instructions from the memory and to implement the digital twin-based GIS data visualization management method according to any one of claims 1 to 5 when executing the instructions.

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