Knowledge graph dynamic updating method and device based on crop growth process

Abstract

The invention provides a knowledge graph dynamic updating method and device based on a crop growth process, and the method comprises the steps: inputting environment factor data and a crop growth stage into a trained Transform model, and obtaining a time sequence feature; performing relation graph construction based on the environmental factor data and the crop growth indexes through a graph convolutional network to obtain an environmental crop growth relation graph; performing feature extraction based on the environment crop growth relation graph through a graph attention network to obtain a spatial feature matrix; performing feature extraction based on the spatial feature matrix through a spatial graph convolutional network to obtain high-dimensional spatial features; performing alignment and conversion based on the high-dimensional spatial features through a spatial transformation network to obtain geographic spatial features; performing feature fusion based on the time sequence features and the geographic space features through a deep fusion model to obtain comprehensive features; and performing incremental updating on a preset knowledge graph based on the environmental factor data, the crop growth indexes and the comprehensive characteristics.

Description

Technical Field

[0001] The present invention relates to the field of knowledge graph technology, and in particular to a method and device for dynamically updating a knowledge graph based on a crop growth process. Background Art

[0002] Knowledge graph is a knowledge management method implemented through semantic network technology, and is widely used in data organization and relationship expression in multiple fields.

[0003] Knowledge graph technology is used to build a crop growth knowledge base to help agricultural practitioners make crop management decisions. Current agricultural knowledge graph systems are usually built based on expert experience, historical data, and fixed rules.

[0004] However, building agricultural knowledge graphs based on historical data or static rules lacks consideration of the time dimension and cannot dynamically adjust and update knowledge content. Summary of the invention

[0005] The present invention provides a method and device for dynamically updating a knowledge graph based on the crop growth process, so as to solve the defects in the prior art of lack of consideration of the time dimension and inability to dynamically adjust and update the knowledge content, and realize dynamic updating of the knowledge graph in a complex and changeable environment.

[0006] The present invention provides a method for dynamically updating a knowledge graph based on a crop growth process, comprising the following steps. Acquire environmental factor data and crop growth stages; input the environmental factor data and the crop growth stages into a trained Transformer model to obtain crop growth indicators output by the trained Transformer model and corresponding to the crop growth stages; input the crop growth indicators into the trained Transformer model to obtain time series features output by the trained Transformer model; construct a relationship graph based on the environmental factor data and the crop growth indicators through a graph convolution network to obtain an environmental crop growth relationship graph; perform feature extraction based on the environmental crop growth relationship graph through a graph attention network to obtain a spatial feature matrix, wherein the spatial feature matrix is ​​used to represent the correlation between the environmental factor data and the crop growth indicators; perform feature extraction based on the spatial feature matrix through a spatial graph convolution network to obtain high-dimensional spatial features; perform alignment and conversion based on the high-dimensional spatial features through a spatial transformation network to obtain geographic spatial features; perform feature fusion based on the time series features and the geographic spatial features through a deep fusion model to obtain comprehensive features; incrementally update a preset knowledge graph based on the environmental factor data, the crop growth indicators and the comprehensive features.

[0007] According to a method for dynamically updating a knowledge graph based on a crop growth process provided by the present invention, the environmental factor data and the crop growth stage are input into a trained Transformer model to obtain crop growth indicators corresponding to the crop growth stages output by the trained Transformer model, including: inputting the environmental factor data and the crop growth stage into the trained Transformer model, wherein the environmental factor data include: temperature, humidity and light; the crop growth stage includes: sowing, germination and seedling stage; using the trained Transformer model, predicting the crop growth state based on the environmental factor data and the crop growth stage to obtain crop growth indicators corresponding to the crop growth stages, wherein the crop growth indicators include leaf area index and plant height; the crop growth indicators are used to represent the crop growth state.

[0008] According to a method for dynamically updating a knowledge graph based on a crop growth process provided by the present invention, the crop growth indicators are input into the trained Transformer model to obtain time series features output by the trained Transformer model, including: using a multi-head self-attention mechanism of the trained Transformer model, predicting based on the crop growth indicators corresponding to the crop growth stages, respectively, to obtain time series features, wherein the time series features are used to predict crop growth trends at future times.

[0009] According to a method for dynamically updating a knowledge graph based on the crop growth process provided by the present invention, before the incremental update of the preset knowledge graph based on the environmental factor data, the crop growth index and the comprehensive characteristics, the method also includes: constructing a multi-level knowledge graph based on a graph database, wherein the multi-levels include a basic layer, a rule layer and a reasoning layer.

[0010] According to a method for dynamically updating a knowledge graph based on the crop growth process provided by the present invention, the multi-level knowledge graph is constructed based on a graph database, including: a basic layer of the multi-level knowledge graph is constructed based on crop types, environmental factors, climatic conditions, soil types, crop growth status and farming operations; the basic layer is used to represent each entity and entity relationship based on a resource description framework; a rule layer of the multi-level knowledge graph is constructed based on logical rules and association patterns in the crop growth process, wherein the rule layer is used to represent crop growth laws, the relationship between the environment and crops, and the impact of farming operations; an inference layer of the multi-level knowledge graph is constructed based on the basic layer and the rule layer, wherein the inference layer is used to generate crop growth decisions and farming operation suggestions.

[0011] According to a method for dynamically updating a knowledge graph based on a crop growth process provided by the present invention, the incremental update of a preset knowledge graph based on the environmental factor data, the crop growth indicators and the comprehensive characteristics includes: embedding a graph neural network and a knowledge graph, incrementally updating the preset knowledge graph based on the environmental factor data, the crop growth indicators and the comprehensive characteristics, so as to adjust the entity classification and entity relationship in the knowledge graph.

[0012] The present invention also provides a knowledge graph dynamic updating device based on the crop growth process, comprising the following modules: an acquisition module, used to acquire environmental factor data and crop growth stages; a Transformer module, used to input the environmental factor data and the crop growth stages into a trained Transformer model, and obtain crop growth indicators output by the trained Transformer model and corresponding to the crop growth stages; the Transformer module is also used to input the crop growth indicators into the trained Transformer model, and obtain time series features output by the trained Transformer model; a construction module is used to construct a relationship graph based on the environmental factor data and the crop growth indicators through a graph convolutional network. An environment-crop growth relationship diagram is obtained; a feature extraction module is used to perform feature extraction based on the environment-crop growth relationship diagram through a graph attention network to obtain a spatial feature matrix, wherein the spatial feature matrix is ​​used to represent the correlation between the environmental factor data and the crop growth index; the feature extraction module is also used to perform feature extraction based on the spatial feature matrix through a spatial graph convolution network to obtain high-dimensional spatial features; a transformation module is used to perform alignment and transformation based on the high-dimensional spatial features through a spatial transformation network to obtain geographic spatial features; a fusion module is used to perform feature fusion based on the time series features and the geographic spatial features through a deep fusion model to obtain comprehensive features; an update module is used to incrementally update a preset knowledge graph based on the environmental factor data, the crop growth index and the comprehensive features.

[0013] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for dynamically updating a knowledge graph based on the crop growth process as described above is implemented.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described methods for dynamically updating a knowledge graph based on a crop growth process.

[0015] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for dynamically updating a knowledge graph based on a crop growth process.

[0016] The method and device for dynamically updating the knowledge graph based on the crop growth process provided by the present invention can predict the corresponding crop growth indicators according to the environmental factor data and the crop growth stage through the trained Transformer model, thereby capturing the long-term dependency in the data; the crop growth indicators are input into the Transformer model again, and the time series features can be extracted to obtain the change law of crop growth over time; the environment-crop growth relationship diagram constructed by the graph convolution network can show the complex relationship between environmental factors and crop growth indicators; the spatial feature matrix is ​​obtained by performing feature extraction based on the environment-crop growth relationship diagram through the graph attention network, which can represent the correlation between environmental factor data and crop growth indicators. Through the processing of spatial graph convolutional network and spatial transformation network, geographic spatial features are obtained, which can reflect the differences of crop growth in different geographic spatial locations; through the deep fusion model, time series features and geographic spatial features are integrated to obtain comprehensive features, which can comprehensively reflect the comprehensive influence of time, space and environmental factors in the process of crop growth; based on environmental factor data, crop growth indicators and comprehensive features, the preset knowledge graph is incrementally updated. The incremental update mechanism adds the newly added environmental factors, crop growth indicators and related relationship dynamics (i.e., comprehensive features) to the preset knowledge graph, which ensures the accuracy and timeliness of the graph structure, thereby solving the defects of the existing technology that lacks consideration of the time dimension and cannot dynamically adjust and update the knowledge content. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced one by one below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a flow chart of the method for dynamically updating the knowledge graph based on the crop growth process provided by the present invention.

[0019] Figure 2 It is a flow chart of the dynamic knowledge expression and graph construction of the crop growth process provided by the present invention.

[0020] Figure 3 It is a schematic diagram of the time and space multi-dimensional feature extraction process provided by the present invention.

[0021] Figure 4 It is a structural schematic diagram of a knowledge graph dynamic updating device based on the crop growth process provided by the present invention.

[0022] Figure 5 It is a schematic diagram of the physical structure of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] With the continuous development of modern agricultural technology, the demand for refined and intelligent crop production management is increasing. The growth process of crops is affected by many factors such as climate, soil conditions, pests and diseases, and the dynamic changes of these factors have a direct impact on the different stages of crop growth. These factors not only have different effects on crop growth at different time points, but may also show significant dynamic changes due to differences in regions and crop varieties. Therefore, real-time monitoring and scientific decision-making of crop growth processes in agricultural management have become the key to improving production efficiency and yield. Knowledge graph is a knowledge management method implemented through semantic web technology, which is widely used in data organization and relationship expression in multiple fields. It expresses data in the form of a graph, uses nodes to represent entities, and edges to represent the relationship between entities, thereby realizing the expression, reasoning and query functions of complex knowledge. In the field of agriculture, knowledge graph technology is used to build a crop growth knowledge base to help agricultural practitioners make crop management decisions. Current agricultural knowledge graph systems are usually built based on expert experience, historical data and fixed rules. These static maps collect and integrate knowledge from different sources, such as agricultural literature, expert experience and field data, to form a systematic expression of specific crops, pest and disease control, planting techniques, etc., providing reference and support for agricultural production management.

[0025] In view of the significant inadequacy of traditional agricultural knowledge graph systems in the face of complex and changeable crop growth environments in actual production, the present invention proposes a knowledge graph dynamic update method and device based on the crop growth process. By introducing dynamic modeling, multi-source data fusion and real-time update mechanisms, agricultural managers can make scientific and timely decisions in complex environments, thereby improving crop growth efficiency and yield.

[0026] Existing knowledge graph construction methods mainly focus on static storage and query of knowledge, and have at least one of the following defects.

[0027] Static: Traditional knowledge graphs are usually built based on historical data or static rules, lack consideration of the time dimension, and cannot dynamically adjust and update knowledge content. This static nature shows significant limitations when facing dynamic changes in the actual crop growth process.

[0028] Singleness: In existing technologies, knowledge expression models are mostly single-dimensional, usually covering only a specific aspect of crop growth, such as meteorological data or soil conditions, and lacking comprehensive consideration of the entire process of crop growth. This singleness cannot meet the needs of multi-dimensional information fusion in agricultural management.

[0029] Lag: Since the knowledge graph lacks a dynamic update mechanism, the decision support information it provides often lags behind the actual production situation and cannot respond in time to sudden changes in the crop growth process, such as sudden pests and diseases, abnormal climate, etc.

[0030] The above technical defects show that the existing knowledge graph construction methods have significant shortcomings when facing the complex and changeable crop growth process in agricultural production, especially in actual production, they cannot provide accurate and efficient real-time decision support, which seriously restricts the implementation effect of modern agricultural intelligent management.

[0031] The purpose of the present invention is to provide a method and device for dynamically updating a knowledge graph based on the crop growth process, aiming to overcome the defects of staticity, singleness and hysteresis in the prior art, realize dynamic monitoring and real-time knowledge updating of the crop growth process, and thus improve the intelligence and precision level of agricultural production management. Specifically, the present invention realizes the dynamic construction and intelligent reasoning of the knowledge graph by constructing a knowledge expression model that can dynamically adapt to changes in the crop growth environment, and combines multi-dimensional data fusion and real-time update mechanisms. This will enable agricultural production management to make more scientific and timely decisions in the face of complex and changing environments, ensuring the optimal growth state and efficient yield of crops.

[0032] Optionally, the method for dynamically updating the knowledge graph based on the crop growth process of the embodiment of the present application can be executed by a server, or by a terminal device, or jointly by a server and a terminal device, taking the example of the method for dynamically updating the knowledge graph based on the crop growth process in the present embodiment being executed by a server.

[0033] Figure 1 is a flow chart of a method for dynamically updating a knowledge graph based on a crop growth process provided by the present invention, such as Figure 1 As shown, the method includes the following steps.

[0034] Step 101, obtaining environmental factor data and crop growth stage.

[0035] In the embodiment of the present invention, IoT sensors (such as soil moisture sensors, weather stations) and remote sensing technologies (such as drones and satellite images) are used to collect real-time environmental factor data. The main data collected include: meteorological data (temperature, humidity, precipitation), soil data (humidity, nutrients), crop growth data (growth rate, leaf area index), and management measures (fertilization, irrigation, etc.).

[0036] In an embodiment of the present invention, the growth process of a crop is divided into several key stages to obtain crop growth stages, wherein the crop growth stages include sowing, germination, seedling stage, tillering stage, heading stage and maturity stage.

[0037] Step 102, inputting the environmental factor data and the crop growth stage into the trained Transformer model, and obtaining the crop growth indicators corresponding to the crop growth stages output by the trained Transformer model.

[0038] In the embodiment of the present invention, the Transformer model is used to predict the growth status of crops. The trained Transformer model is used to input environmental factors (temperature, humidity, light, etc.) to predict the growth indicators of crops at various growth stages (such as leaf area index and plant height).

[0039] The core formula of the Transformer model can be referred to the following formula (1): (1)

[0040] in, represents the attention mechanism, is the query matrix, is the key matrix, is the value matrix, is the dimension of the key matrix, represents the normalization function, Represents the transposed matrix.

[0041] Step 103, input the crop growth index into the trained Transformer model to obtain the time series features output by the trained Transformer model.

[0042] In an embodiment of the present invention, the Transformer model is used for time series prediction, which can effectively capture long-term dependencies and nonlinear changes. Combined with the multi-head self-attention mechanism, the time series data of the crop growth process is processed to obtain time series features.

[0043] Through the embodiments of the present invention, the trained Transformer model can accurately predict the corresponding crop growth indicators based on the environmental factor data and the crop growth stage, so as to capture the long-term dependencies in the data and provide high-precision prediction results; the crop growth indicators are input into the Transformer model again, and time series features can be extracted, which is helpful to understand the change pattern of crop growth over time and provide an important basis for subsequent decision-making and analysis.

[0044] Step 104, constructing a relationship graph based on the environmental factor data and the crop growth index through a graph convolutional network to obtain an environment-crop growth relationship graph.

[0045] Step 105, extracting features based on the environment-crop growth relationship graph through a graph attention network to obtain a spatial feature matrix, wherein the spatial feature matrix is ​​used to represent the correlation between environmental factor data and crop growth indicators.

[0046] In an embodiment of the present invention, the crop growth indicators (leaf area index, plant height) at each crop growth stage are input into a graph convolutional network (GCN), and a relationship diagram between environmental factors and crop growth stages is constructed through the graph convolutional network. Next, the generated environment-crop growth relationship diagram is input into a graph attention network (GAT), which further focuses on the impact of various environmental factors on different growth stages, and finally outputs a new feature matrix (i.e., a spatial feature matrix), which reveals the relevance and importance of various environmental factors to crop growth indicators at different growth stages.

[0047] Graph convolutional networks (GCN) and graph attention networks (GAT) are used to analyze the various dynamic factors that affect crop growth. GCN and GAT can process the complex relationship between environmental data and crop growth, capture the node features and relationships in the graph, and thus provide more accurate dynamic adjustment suggestions. The calculation formula (2) of the GCN model is: (2)

[0048] in, Indicates The nodes of a layer represent matrices, is the activation function, is the degree matrix, is the adjacency matrix With the identity matrix of and, is a learnable weight matrix.

[0049] The calculation formula (3) of the GCN model is: (3)

[0050] in, Representation Node The updated feature vector, represents the activation function, Representation Node The set of neighbor nodes of Representation Node To Node The attention weight, represents the weight matrix, Representation Node The feature vector of .

[0051] Through the embodiment of the present invention, a spatial feature matrix with spatial information is obtained for subsequent reasoning tasks.

[0052] Step 106, extract features based on the spatial feature matrix through a spatial graph convolutional network to obtain high-dimensional spatial features.

[0053] Step 107, aligning and transforming based on the high-dimensional spatial features through a spatial transformation network to obtain geographic spatial features.

[0054] In the embodiment of the present invention, a spatial graph convolutional network (SGCN) and a spatial transformer network (STN) are used to process geospatial data. These models can map spatial data to feature space, extract and transform spatial features, and thus achieve accurate modeling and analysis of spatial data.

[0055] In the previous paper, graph convolutional networks (GCN) and graph attention networks (GAT) were used to extract spatial features in the crop growth process. These features mainly come from the relationship graph between the environment and crop growth indicators, representing local spatial information.

[0056] Next, these extracted spatial feature matrices (such as local spatial relations) are fed as input to the Spatial Graph Convolutional Network (SGCN), which further extracts and analyzes complex spatial features associated with geographic locations, maps them into feature space, and captures broader spatial dependencies.

[0057] The high-dimensional spatial features extracted by the spatial graph convolutional network are used as input to the spatial transformer network (STN). The spatial transformer network will perform spatial transformation on the high-dimensional spatial features, perform refined spatial feature alignment and conversion, and output a geospatial feature matrix for accurate modeling and analysis.

[0058] Among them, the formula (4) of the spatial graph convolutional network model is: (4)

[0059] in, is the normalized adjacency matrix, It is The node feature matrix of the layer, is a learnable weight matrix, is the activation function.

[0060] The formula (5) of the spatial transformation network model is: (5)

[0061] in, is the spatial transformation parameter, is the input image, represents an affine function, It is the output after spatial transformation.

[0062] Here, first, preliminary spatial features are extracted through GCN / GAT; second, complex spatial dependency features are extracted through SGCN; finally, spatial transformation and alignment are performed through STN to obtain the final geospatial data features.

[0063] Step 108, performing feature fusion based on time series features and geographic space features through a deep fusion model to obtain comprehensive features.

[0064] Use IoT sensors (such as soil moisture sensors, weather stations) and remote sensing technologies (such as drones and satellite images) for real-time data collection. The main data collected include: meteorological data (temperature, humidity, precipitation), soil data (humidity, nutrients), crop growth data (growth rate, leaf area index), management measures (fertilization, irrigation, etc.).

[0065] In the embodiment of the present invention, a deep fusion model is applied to process time series data and spatial data from different sensors by combining convolutional neural networks and recurrent neural networks. Convolutional neural networks are used for feature extraction, and recurrent neural networks are used for time series modeling to achieve efficient fusion and processing of data.

[0066] A deep fusion model is applied to integrate the temporal data processed by the Transformer model and the geospatial data processed by the graph convolutional network, graph attention network, spatial graph convolutional network, and spatial transformation network.

[0067] The time series data is modeled through the Transformer model, and its multi-head self-attention mechanism is used to effectively capture the long-term dependence and nonlinear changes in the time dimension during crop growth, generating time series features.

[0068] The spatial data is used to extract spatial features through graph convolutional networks and graph attention networks to capture the spatial dependencies between environmental factors and crop growth indicators and generate a spatial feature matrix.

[0069] The spatial feature matrix is ​​input into the spatial graph convolutional network and the spatial transformation network for feature extraction, spatial feature alignment and conversion to obtain geographic spatial features.

[0070] The deep fusion model takes time series features and geographic space features as input and performs efficient fusion through the fusion layer; the fusion layer can use a fully connected layer or an attention mechanism to achieve coordinated analysis and processing of spatiotemporal features, and ultimately generate comprehensive features to improve the prediction accuracy of crop growth status.

[0071] Step 109, incrementally update the preset knowledge graph based on environmental factor data, crop growth indicators and comprehensive characteristics.

[0072] In an embodiment of the present invention, a time series-based incremental update method is adopted to dynamically input new data; the real-time stream processing framework Apache Flink and incremental learning technology are used to update model parameters and knowledge graphs in real time, supporting rapid processing of data streams and dynamic adjustment of models.

[0073] For example, environmental factors and crop growth-related data are collected in real time through a variety of sensors (such as temperature, humidity, light, etc.). Before entering the system, the data stream is first preprocessed to eliminate noise and outliers and perform normalization.

[0074] The preprocessed data is stream processed through Apache Flink. Flink can process high-throughput, low-latency real-time data streams, and supports window operations, processing real-time data in batches to meet the analysis requirements at different time granularities. The data in each time window is delivered to the model in a timely manner.

[0075] Using incremental learning technology, model parameters are dynamically updated according to the new input each time new data arrives. The incremental learning module can gradually adjust the weights and parameters of the model based on the feature differences between historical data and new data without retraining the entire model. This significantly improves computing efficiency, especially in the case of large-scale data streams, and can ensure the real-time and stability of the model.

[0076] As new data and environmental changes occur, the knowledge graph also needs to be updated in real time. The incremental update mechanism dynamically adds new environmental factors, crop growth indicators, and related relationships to the existing knowledge graph to ensure the accuracy and timeliness of the graph structure. At the same time, based on changes in the graph structure, new reasoning processes can be triggered to explore potential knowledge associations and further improve the level of intelligence.

[0077] According to a method for dynamically updating a knowledge graph based on a crop growth process provided by the present invention, environmental factor data and crop growth stages are input into a trained Transformer model to obtain crop growth indicators corresponding to the crop growth stages output by the trained Transformer model, including: Input the environmental factor data and crop growth stage into the trained Transformer model, where the environmental factor data includes temperature, humidity and light; the crop growth stage includes sowing, germination and seedling stage; Through the trained Transformer model, the crop growth status is predicted based on the environmental factor data and the crop growth stage, and the crop growth indicators corresponding to the crop growth stages are obtained. Among them, the crop growth indicators include leaf area index and plant height; the crop growth indicators are used to indicate the crop growth status.

[0078] In the embodiment of the present invention, the input environmental factor data are specifically temperature, humidity, and light. The environmental factor data and the crop growth stage (sowing, germination, and seedling stage) are used as inputs to predict the crop growth status through the trained Transformer model. The output is the growth index corresponding to each growth stage, including leaf area index and plant height, which is used to dynamically monitor the crop growth.

[0079] According to a method for dynamically updating a knowledge graph based on a crop growth process provided by the present invention, crop growth indicators are input into a trained Transformer model to obtain time series features output by the trained Transformer model, including: Through the multi-head self-attention mechanism of the trained Transformer model, predictions are made based on the crop growth indicators corresponding to the crop growth stages to obtain time series features, where the time series features are used to predict the crop growth trend in the future.

[0080] The aforementioned Transformer model is used to predict the growth indicators (leaf area index, plant height) of crops at various growth stages, and the corresponding crop growth indicators for each crop growth stage are obtained; then, the crop growth indicators are used as input, and the Transformer model is further used for time series prediction to obtain time series features. Through the multi-head self-attention mechanism of the Transformer model, the long-term dependence and nonlinear changes in the crop growth process can be captured, and the growth trend at future moments can be predicted.

[0081] Environmental factor data and crop growth stages are taken as input and passed into the trained Transformer model. The Transformer model will predict crop growth indicators at different crop growth stages based on these environmental factor data. The Transformer model is mainly used here to capture the time series characteristics of the growth state and help predict the changes in indicators at different growth stages under specific environmental conditions.

[0082] According to a method for dynamically updating a knowledge graph based on a crop growth process provided by the present invention, before incrementally updating a preset knowledge graph based on environmental factor data, crop growth indicators and comprehensive characteristics, the method further includes: A multi-level knowledge graph is constructed based on the graph database, where the multiple levels include the basic layer, the rule layer, and the reasoning layer.

[0083] In the embodiment of the present invention, a multi-level knowledge graph is designed, including a basic layer (crop types, environmental factors), a rule layer (crop growth rules), and a reasoning layer (reasoning logic and decision support). The basic layer uses the Resource Description Framework (RDF) to represent data, and the rule layer uses the SPARQL query language to define rules.

[0084] Use the graph database Neo4j to store knowledge graphs and dynamically adjust the nodes and relationships in the knowledge graph. Combined with graph neural network GNN and graph embedding technology, the graph is dynamically updated to reflect the knowledge in new data in real time.

[0085] According to a method for dynamically updating a knowledge graph based on a crop growth process provided by the present invention, a multi-level knowledge graph is constructed based on a graph database, including: The basic layer of the multi-level knowledge graph is constructed based on crop types, environmental factors, climate conditions, soil types, crop growth status, and farming operations; the basic layer is used to represent various entities and entity relationships based on the resource description framework; The rule layer of a multi-level knowledge graph is constructed based on the logical rules and association patterns in the crop growth process. The rule layer is used to represent the crop growth law, the relationship between the environment and crops, and the impact of farming operations. A multi-level knowledge graph reasoning layer is constructed based on the basic layer and the rule layer, where the reasoning layer is used to generate crop growth decisions and agricultural operation suggestions.

[0086] In an embodiment of the present invention, the base layer (Data Layer) is the bottom layer of the knowledge graph, which contains basic information related to crop growth, including crop types, environmental factors (such as temperature, humidity, light, etc.), climatic conditions, soil types, crop growth status (such as leaf area index, plant height, etc.) and agricultural operations (seeding, irrigation, fertilization, etc.).

[0087] Representation: The base layer uses RDF (Resource Description Framework) to represent entities and their relationships. The RDF triple structure (subject-predicate-object) is used to describe crops and their related environmental factors. For example: Example of a triple: Corn - Suitable temperature -20°C

[0088] Wheat - Suitable light - 10 hours

[0089] Soil moisture - impact - crop growth rate

[0090] The basic layer data uses a unified namespace and standardized description method to ensure that data from different sources can be uniformly mapped to the knowledge graph, supporting upper-layer rules and reasoning operations.

[0091] The Rule Layer is used to define the logical rules and association patterns in the crop growth process, ensuring that the system can make reasonable judgments and inferences based on the dynamic changes in crop growth. The Rule Layer includes the crop growth rules, the relationship between the environment and crops, and the impact of farming operations.

[0092] The rule layer uses SPARQL (SPARQL Protocol and RDF Query Language) query language to define various rules. SPARQL can query, operate and reason based on the RDF data of the base layer. Rules can set specific actions and results based on conditions (such as temperature range, light duration, etc.).

[0093] Example of SPARQL rules: sparql

[0094] SELECT ?crop WHERE {

[0095] ?croprdf:type:crop.

[0096] ?Crops: Suitable temperature?Temperature.

[0097] FILTER (?temperature>= 15&&?temperature<= 25)

[0098] }

[0099] This rule can filter the types of crops suitable for the current climate conditions based on the temperature range.

[0100] The rule layer is closely connected to the base layer, extracting data from the base layer through SPARQL and making conditional judgments based on the set rules. It also provides data support for the reasoning layer as the basis for reasoning.

[0101] The inference layer is the decision support layer of the knowledge graph. It derives new knowledge and decision suggestions based on the data of the basic layer and the logic of the rule layer. The inference layer is mainly responsible for intelligent reasoning from existing data to generate crop growth decisions, agricultural operation suggestions, etc.

[0102] The reasoning layer uses OWL (Web Ontology Language) to represent knowledge and define reasoning logic. OWL-based reasoning can determine new knowledge relationships through semantic features such as classes, attributes, and relationships.

[0103] At the same time, the reasoning layer integrates the rule engine (such as Drools), uses reasoning rules to perform reasoning analysis on the input data, and combines environmental changes, crop growth status and historical data to provide support for decision-making.

[0104] An example of the reasoning process: Input: Current temperature is 22°C, humidity is 60%, and daylight duration is 12 hours.

[0105] Reasoning rule: If the temperature is between 15-25°C, the humidity is between 50-70%, and the light duration is more than 10 hours, it is recommended to increase irrigation to promote crop growth.

[0106] Output: The system outputs a recommendation based on the rule: "The current environment is suitable for irrigation. It is recommended to water 50 liters per acre."

[0107] The reasoning layer obtains the conditions provided by the rule layer through SPARQL queries, and infers the data through the rule engine to generate new decision suggestions and knowledge. The reasoning results will be updated to the basic layer and the rule layer to form dynamic feedback.

[0108] The connection between the basic layer and the rule layer is that the basic layer provides data such as crops, environment, and farming operations, and the rule layer uses SPARQL to query this data and apply the set logical rules.

[0109] The connection between the rule layer and the reasoning layer is that the rules defined in the rule layer provide a basis for the reasoning layer to make judgments, and the reasoning layer makes intelligent decisions based on the information in the rule layer.

[0110] The connection between the reasoning layer and the base layer is that the output of the reasoning layer (such as agricultural operation suggestions and decision results) will be fed back to the base layer to form new triple data and further improve the knowledge graph.

[0111] The connection between the reasoning layer and the rule layer is that the output of the reasoning layer (such as agricultural operation suggestions and decision results) will be fed back to the basic layer to form new triple data and further improve the knowledge graph.

[0112] In the embodiment of the present invention, a graph database technology (such as Neo4j) is used to create and update the knowledge graph in real time. The nodes include crop growth status, environmental factors, and management measures, and the relationship edges represent the influence between different nodes.

[0113] Using graph database technology, these data are converted into graph structures to create nodes (crop types, environmental factors, management measures) and relationship edges (indicating the impact between different nodes). On this basis, new data inputs are monitored in real time, and the nodes and relationships in the knowledge graph are dynamically adjusted through an incremental update mechanism to ensure that the knowledge graph always reflects the latest crop growth status and environmental changes.

[0114] According to a method for dynamically updating a knowledge graph based on a crop growth process provided by the present invention, a preset knowledge graph is incrementally updated based on environmental factor data, crop growth indicators and comprehensive characteristics, including: Through graph neural network and knowledge graph embedding, the preset knowledge graph is incrementally updated based on environmental factor data, crop growth indicators and comprehensive characteristics to adjust the entity classification and entity relationship in the knowledge graph.

[0115] For example, continuously collect new environmental factor data, crop growth indicators, and comprehensive feature data. Preprocess the new data to ensure that it is consistent with the data structure in the knowledge graph. Use the graph neural network model to analyze the new data, identify new entity classifications and relationships, and obtain analysis results. Based on the analysis results, dynamically adjust the entity classifications and relationships in the knowledge graph, perform incremental update operations, and add new entities, attributes, and relationships to the knowledge graph. Ensure the efficiency and accuracy of the update process to avoid damage to the original knowledge graph.

[0116] In the embodiment of the present invention, the graph neural network GNN and the knowledge graph embedding technology TransE are used to perform node classification and relationship prediction, and combined with the rule-based reasoning engine Drools, the reasoning and application of knowledge are realized.

[0117] Combining historical data and real-time information, using reinforcement learning Deep Q-Learning and decision tree algorithms, we can generate targeted agricultural management recommendations, such as the best time to fertilize and pest control measures. We can also optimize decision-making strategies based on feedback mechanisms.

[0118] By integrating historical data (such as crop growth records, fertilization and pest control measures) with real-time information (such as current weather conditions and soil moisture), a multi-dimensional data set is constructed. Then, the Deep Q-Learning algorithm in reinforcement learning is used to establish an intelligent agent model, which takes actions (such as fertilization, irrigation or pest control) according to the environmental status (current crop growth stage, environmental factors, etc.) and evaluates the effectiveness of the action through a reward mechanism (such as an increase in crop yield or growth rate).

[0119] At the same time, combined with the decision tree algorithm, historical data is analyzed to generate targeted agricultural management recommendations. This process includes feature selection, data segmentation and decision tree construction, and ultimately forms clear decision rules to guide agricultural management strategies.

[0120] During the implementation process, the feedback mechanism is used to monitor the actual effects of management measures and adjust decision-making strategies based on the results. Through continuous learning and optimization, the system can more accurately recommend the best fertilization time, pest and disease control measures, etc., to improve the efficiency and sustainability of agricultural production.

[0121] In some embodiments, the present invention provides a knowledge graph dynamic update system based on the crop growth process, which specifically includes the following modules.

[0122] Data acquisition module: includes sensor data acquisition, remote sensing data processing, edge computing processing, data preprocessing and storage sub-modules.

[0123] Dynamic knowledge expression module: includes dynamic model construction, deep fusion, and time series prediction sub-modules.

[0124] Knowledge graph construction and update module: includes graph construction, graph update, and knowledge management sub-modules.

[0125] Intelligent reasoning and decision support module: includes reasoning engine, decision support system and user interface module.

[0126] User interface module: provides visual display and user interaction functions, including data visualization, report generation, and real-time feedback sub-modules.

[0127] Through the above embodiments provided by the present invention, the Transformer model and multi-head self-attention mechanism are used to accurately capture the changes in time series data during crop growth, thereby improving the accuracy of predicting the state of each growth stage of the crop. The combination of GCN and GAT further enhances the ability to handle the complex relationship between environmental factors and crop growth, and optimizes the adaptability of dynamic factors.

[0128] The use of deep fusion models combined with CNN and RNN can efficiently fuse data from different sensors to ensure data integrity and accuracy. Dynamic update technology Apache Flink and incremental learning technology support real-time update of models and knowledge graphs, ensuring that the system can quickly adapt to dynamic changes in actual production.

[0129] The construction and update of dynamic knowledge graphs are realized through RDF and graph database (Neo4j), which can accurately reflect the latest information on crop growth and environmental factors. Combining GNN and knowledge graph embedding technology TransE and reinforcement learning, the system can provide accurate agricultural management suggestions and optimization strategies, and improve the intelligent level of decision support.

[0130] The present invention achieves accurate prediction and dynamic response capabilities for the crop growth process through a dynamic knowledge expression model and an efficient data fusion and real-time update mechanism. It can accurately capture the state of crops at different growth stages, adapt to environmental changes in a timely manner, and provide optimized agricultural management suggestions through intelligent reasoning. This not only improves the efficiency and accuracy of crop production, but also enhances the intelligent level of agricultural management, significantly improving the scientificity and practicality of production decisions.

[0131] refer to Figure 2 , Figure 2 It is a flow chart of the dynamic knowledge expression and graph construction of the crop growth process provided by the present invention, which includes data collection, knowledge expression model, dynamic knowledge graph construction and intelligent reasoning.

[0132] Data collection includes: sensing devices, Ethernet, mobile Internet, Internet of Things and cloud storage.

[0133] The knowledge expression models include: dynamic modeling of growth stages and the combination of time and space dimensions; input data include varieties, physiological growth and environment; specific models include: spatial graph convolutional network (SGCN), spatial transformer network (STN), Transformer model, graph convolutional network (GCN) and graph attention network (GAT).

[0134] The construction of dynamic knowledge graphs includes: research on weakly supervised learning, dynamic update strategies, and internal update mechanisms; for example, crops planted in the field, crop diseases, disease characteristics, and corresponding (solution) methods; collaborative completion and association rule mining.

[0135] Intelligent reasoning includes: obtaining data on environmental factors, such as weather, soil, diseases, and varieties, and making decisions based on reasoning algorithms through GNN and TransE to perform growth forecasts, disease and pest diagnosis, water and fertilizer irrigation, and yield estimation.

[0136] refer to Figure 3 , Figure 3 It is a schematic diagram of the time and space multi-dimensional feature extraction process provided by the present invention.

[0137] like Figure 3 As shown in the figure, environmental factor data are obtained, where the environmental factor data include weather, soil, diseases, and varieties. Spatial features are extracted through the spatial graph convolutional network (SGCN), and then temporal features are extracted through Transformer, and edge learning is performed to obtain multiple time series features and geographic spatial features to facilitate subsequent knowledge fusion.

[0138] The following is a description of the knowledge graph dynamic updating device based on the crop growth process provided by the present invention. The knowledge graph dynamic updating device based on the crop growth process described below and the knowledge graph dynamic updating method based on the crop growth process described above can be referenced to each other.

[0139] refer to Figure 4 , Figure 4 It is a structural schematic diagram of the knowledge graph dynamic update device based on the crop growth process provided by the present invention, which includes an acquisition module 401, a Transformer module 402, a construction module 403, a feature extraction module 404, a transformation module 405, a fusion module 406 and an update module 407.

[0140] An acquisition module 401 is used to acquire environmental factor data and crop growth stages; Transformer module 402, used to input environmental factor data and crop growth stages into the trained Transformer model, and obtain crop growth indicators corresponding to the crop growth stages output by the trained Transformer model; The Transformer module 402 is also used to input the crop growth index into the trained Transformer model to obtain the time series features output by the trained Transformer model; A construction module 403 is used to construct a relationship graph based on environmental factor data and crop growth indicators through a graph convolution network to obtain an environment-crop growth relationship graph; A feature extraction module 404 is used to extract features based on the environment-crop growth relationship graph through a graph attention network to obtain a spatial feature matrix, wherein the spatial feature matrix is ​​used to represent the correlation between environmental factor data and crop growth indicators; The feature extraction module 404 is further used to extract features based on the spatial feature matrix through a spatial graph convolutional network to obtain high-dimensional spatial features; Transformation module 405, used for aligning and transforming based on high-dimensional spatial features through a spatial transformation network to obtain geographic spatial features; A fusion module 406 is used to perform feature fusion based on time series features and geographic space features through a deep fusion model to obtain comprehensive features; The updating module 407 is used to incrementally update the preset knowledge graph based on environmental factor data, crop growth indicators and comprehensive characteristics.

[0141] Specifically, the above-mentioned knowledge graph dynamic update device based on the crop growth process provided by the present invention can implement all the method steps implemented by the above-mentioned knowledge graph dynamic update method embodiment based on the crop growth process, and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as the method embodiment will not be described in detail here.

[0142] Figure 5 is a schematic diagram of the physical structure of the electronic device provided by the present invention, such as Figure 5As shown, the electronic device may include: a processor (processor) 510, a communication interface (Communications Interface) 520, a memory (memory) 530 and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other through the communication bus 540. The processor 510 can call the logic instructions in the memory 530 to execute a method for dynamically updating a knowledge graph based on the crop growth process, the method comprising: obtaining environmental factor data and crop growth stages; inputting the environmental factor data and crop growth stages into the trained Transformer model to obtain crop growth indicators corresponding to the crop growth stages output by the trained Transformer model; inputting the crop growth indicators into the trained Transformer model to obtain time series features output by the trained Transformer model; constructing a relationship graph based on the environmental factor data and the crop growth indicators through a graph convolution network to obtain an environment-crop growth relationship graph; extracting features based on the environment-crop growth relationship graph through a graph attention network to obtain a spatial feature matrix, wherein the spatial feature matrix is ​​used to represent the correlation between the environmental factor data and the crop growth indicators; extracting features based on the spatial feature matrix through a spatial graph convolution network to obtain high-dimensional spatial features; aligning and transforming based on the high-dimensional spatial features through a spatial transformation network to obtain geographic spatial features; fusing features based on time series features and geographic spatial features through a deep fusion model to obtain comprehensive features; and incrementally updating a preset knowledge graph based on the environmental factor data, the crop growth indicators, and the comprehensive features.

[0143] In addition, the logic instructions in the above-mentioned memory 530 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0144] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the knowledge graph dynamic update method based on the crop growth process provided by the above methods, and the method includes: obtaining environmental factor data and crop growth stage; inputting the environmental factor data and crop growth stage into the trained Transformer model to obtain crop growth indicators corresponding to the crop growth stages output by the trained Transformer model; inputting the crop growth indicators into the trained Transformer model to obtain the trained Transformer model. Output time series features; construct a relationship graph based on environmental factor data and crop growth indicators through a graph convolution network to obtain an environment-crop growth relationship graph; extract features based on the environment-crop growth relationship graph through a graph attention network to obtain a spatial feature matrix, wherein the spatial feature matrix is ​​used to represent the correlation between environmental factor data and crop growth indicators; extract features based on the spatial feature matrix through a spatial graph convolution network to obtain high-dimensional spatial features; align and transform based on high-dimensional spatial features through a spatial transformation network to obtain geographic spatial features; fuse features based on time series features and geographic spatial features through a deep fusion model to obtain comprehensive features; incrementally update the preset knowledge graph based on environmental factor data, crop growth indicators and comprehensive features.

[0145] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the processor executes the method for dynamically updating the knowledge graph based on the crop growth process provided by the above methods, the method comprising: obtaining environmental factor data and crop growth stage; inputting the environmental factor data and crop growth stage into a trained Transformer model to obtain crop growth indicators corresponding to the crop growth stages output by the trained Transformer model; inputting the crop growth indicators into the trained Transformer model to obtain time series features output by the trained Transformer model; and The convolution network constructs a relationship graph based on environmental factor data and crop growth indicators to obtain an environment-crop growth relationship graph; the graph attention network is used to extract features based on the environment-crop growth relationship graph to obtain a spatial feature matrix, wherein the spatial feature matrix is ​​used to represent the correlation between environmental factor data and crop growth indicators; the spatial graph convolution network is used to extract features based on the spatial feature matrix to obtain high-dimensional spatial features; the spatial transformation network is used to align and transform high-dimensional spatial features to obtain geographic spatial features; the deep fusion model is used to fuse features based on time series features and geographic spatial features to obtain comprehensive features; the preset knowledge graph is incrementally updated based on environmental factor data, crop growth indicators and comprehensive features.

[0146] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0147] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for dynamically updating a knowledge graph based on the crop growth process, characterized in that: include: Obtain data on environmental factors and crop growth stages; Inputting the environmental factor data and the crop growth stage into the trained Transformer model to obtain crop growth indicators output by the trained Transformer model that correspond to the crop growth stages respectively; Inputting the crop growth index into the trained Transformer model to obtain a time series feature output by the trained Transformer model; Constructing a relationship graph based on the environmental factor data and the crop growth index through a graph convolution network to obtain an environment-crop growth relationship graph; Extracting features based on the environment-crop growth relationship graph through a graph attention network to obtain a spatial feature matrix, wherein the spatial feature matrix is ​​used to represent the correlation between the environmental factor data and the crop growth index; Extracting features based on the spatial feature matrix through a spatial graph convolutional network to obtain high-dimensional spatial features; Aligning and transforming the high-dimensional spatial features through a spatial transformation network to obtain geographic spatial features; Performing feature fusion based on the time series features and the geographic space features through a deep fusion model to obtain comprehensive features; The preset knowledge graph is incrementally updated based on the environmental factor data, the crop growth index and the comprehensive characteristics.

2. The method for dynamically updating the knowledge graph based on the crop growth process according to claim 1, characterized in that: The step of inputting the environmental factor data and the crop growth stage into the trained Transformer model to obtain crop growth indicators output by the trained Transformer model and corresponding to the crop growth stages, respectively, includes: Inputting the environmental factor data and the crop growth stage into the trained Transformer model, wherein the environmental factor data includes: temperature, humidity and light; the crop growth stage includes: sowing, germination and seedling stage; The trained Transformer model is used to predict crop growth status based on the environmental factor data and the crop growth stage, so as to obtain crop growth indicators corresponding to the crop growth stages, wherein the crop growth indicators include leaf area index and plant height; and the crop growth indicators are used to represent the crop growth status.

3. The method for dynamically updating the knowledge graph based on the crop growth process according to claim 2 is characterized in that: The step of inputting the crop growth index into the trained Transformer model to obtain the time series features output by the trained Transformer model includes: Through the multi-head self-attention mechanism of the trained Transformer model, prediction is performed based on the crop growth indicators corresponding to the crop growth stages, thereby obtaining time series features, wherein the time series features are used to predict the crop growth trend at future moments.

4. The method for dynamically updating the knowledge graph based on the crop growth process according to claim 1, characterized in that: Before incrementally updating the preset knowledge graph based on the environmental factor data, the crop growth index and the comprehensive features, the method further includes: A multi-level knowledge graph is constructed based on a graph database, wherein the multiple levels include a basic layer, a rule layer, and a reasoning layer.

5. The method for dynamically updating the knowledge graph based on the crop growth process according to claim 4 is characterized in that: The multi-level knowledge graph is constructed based on the graph database, including: A basic layer of a multi-level knowledge graph is constructed based on crop types, environmental factors, climate conditions, soil types, crop growth status, and farming operations; the basic layer is used to represent various entities and entity relationships based on a resource description framework; Constructing a rule layer of the multi-level knowledge graph based on logical rules and association patterns in the crop growth process, wherein the rule layer is used to represent crop growth laws, the relationship between the environment and crops, and the impact of farming operations; The reasoning layer of the multi-level knowledge graph is constructed based on the basic layer and the rule layer, wherein the reasoning layer is used to generate crop growth decisions and farming operation suggestions.

6. The method for dynamically updating the knowledge graph based on the crop growth process according to claim 1, characterized in that: The incremental updating of the preset knowledge graph based on the environmental factor data, the crop growth index and the comprehensive features includes: By embedding graph neural network and knowledge graph, the preset knowledge graph is incrementally updated based on the environmental factor data, the crop growth indicators and the comprehensive characteristics to adjust the entity classification and entity relationship in the knowledge graph.

7. A knowledge graph dynamic updating device based on crop growth process, characterized in that: include: An acquisition module is used to obtain environmental factor data and crop growth stages; A Transformer module, used for inputting the environmental factor data and the crop growth stage into a trained Transformer model to obtain crop growth indicators output by the trained Transformer model and corresponding to the crop growth stages respectively; The Transformer module is further used to input the crop growth index into the trained Transformer model to obtain the time series characteristics output by the trained Transformer model; A construction module, used to construct a relationship graph based on the environmental factor data and the crop growth index through a graph convolution network to obtain an environment-crop growth relationship graph; A feature extraction module, used for performing feature extraction based on the environment-crop growth relationship graph through a graph attention network to obtain a spatial feature matrix, wherein the spatial feature matrix is ​​used to represent the correlation between the environmental factor data and the crop growth index; The feature extraction module is further used to extract features based on the spatial feature matrix through a spatial graph convolutional network to obtain high-dimensional spatial features; A transformation module, used for aligning and transforming the high-dimensional spatial features through a spatial transformation network to obtain geographic spatial features; A fusion module, used for performing feature fusion based on the time series feature and the geographic space feature through a deep fusion model to obtain a comprehensive feature; An updating module is used to incrementally update a preset knowledge graph based on the environmental factor data, the crop growth index and the comprehensive characteristics.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for dynamically updating the knowledge graph based on the crop growth process as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for dynamically updating the knowledge graph based on the crop growth process as described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for dynamically updating the knowledge graph based on the crop growth process as described in any one of claims 1 to 6 is implemented.

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