Vegetable cultivation environment optimization system and method based on big data analysis

Abstract

The invention relates to a vegetable cultivation environment optimization system and method based on big data analysis, and belongs to the field of agricultural intelligent management. Comprising the following steps: acquiring environmental data such as climate, soil and water quality in real time, and preprocessing the environmental data; the preprocessed data are stored in a classified mode, and efficient management is achieved through multi-node synchronization and data indexing; modeling and training the historical data and the real-time data by using a multiple regression model, a high-order partial differential equation and a deep learning algorithm to generate a prediction model of the environment and the crop growth; and an optimization decision is generated based on the model and is executed and adjusted in real time through an automatic control system. According to the invention, the yield and quality of vegetables are effectively improved, and the loss caused by environmental fluctuation is reduced.

Description

Technical Field

[0001] The present invention relates to the fields of agricultural planting and new generation information technology, and in particular to a vegetable cultivation environment optimization system and method based on big data analysis. Background Art

[0002] With the development of modern agriculture, vegetable cultivation is gradually shifting towards high efficiency, precision and intelligence. In traditional agricultural production, environmental factors such as climate change, soil conditions and water quality fluctuations have a direct impact on crop growth, and the fluctuations of these factors often lead to instability in crop yield and quality, and even cause serious economic losses. Therefore, how to optimize the cultivation environment through scientific means and reduce the adverse effects caused by environmental fluctuations has become a key issue in agricultural production.

[0003] In recent years, the application of big data technology in the agricultural field has become increasingly widespread. By collecting multi-dimensional data in the farmland environment in real time through sensor networks and processing these data using big data analysis methods, more accurate environmental control suggestions can be provided for the growth of crops. However, in the current vegetable cultivation process, there is still a lack of a systematic method that can effectively conduct a comprehensive analysis of various environmental factors such as climate, soil, and water quality, and perform real-time environmental optimization and control based on the analysis results to improve crop yield and quality.

[0004] Although some existing environmental monitoring and control systems can monitor and regulate a single or a few environmental parameters, they often lack the ability to comprehensively analyze multidimensional data and cannot fully reflect the impact of complex cultivation environments on crop growth. In addition, these systems have a low degree of automation and are difficult to provide effective optimization decisions in a timely manner when environmental conditions change rapidly. Therefore, there is an urgent need for an intelligent system based on big data analysis that can monitor and analyze multidimensional environmental data in real time and automatically perform optimization and regulation to meet the needs of modern vegetable cultivation. Summary of the invention

[0005] The present invention provides a vegetable cultivation environment optimization system and method based on big data analysis to solve the problem of how to optimize the climate, soil, and water quality conditions in the vegetable cultivation environment through real-time monitoring and big data analysis to improve yield and quality and reduce losses due to environmental fluctuations.

[0006] In order to solve the above problems, the present invention provides a vegetable cultivation environment optimization system and method based on big data analysis, including: A vegetable cultivation environment optimization method based on big data analysis, comprising: Collect and preprocess environmental data in real time and upload analysis results; Classify and store data, synchronize multiple nodes, and create data indexes; Modeling training, real-time data analysis, and dynamic optimization models; Based on the stored historical data and real-time data, multiple regression models, high-order partial differential equations and deep learning algorithms are used for modeling and training to generate prediction models of environment and crop growth; Analyze real-time data using the trained model to generate preliminary predictions of crop growth status, and make adjustments through dynamic prediction models; Adjust and optimize the prediction model based on the new data generated and the actual execution results to ensure that the model adapts to changes in environmental conditions.

[0007] Generate optimized decisions, automatically execute controls, and provide real-time feedback adjustments.

[0008] As a preferred solution, the vegetable cultivation environment optimization method based on big data analysis also includes: Collect environmental data through sensors deployed in farmland and send the collected raw data to edge computing devices; On the edge computing device, performing noise filtering, missing value filling and abnormal data calibration on the collected raw data; The preprocessed data is preliminarily analyzed based on edge computing devices to generate environmental change trend information, soil correlation coefficients and comprehensive water quality evaluation indicators.

[0009] As a preferred solution, the vegetable cultivation environment optimization method based on big data analysis also includes: Receive pre-processed data uploaded from the edge computing and data acquisition module, and classify and store the data according to data type; Perform multi-node synchronization of stored real-time data and historical data, and perform regular data backup operations; Based on the synchronized data, a data index structure is created to support fast data retrieval and analysis.

[0010] As a preferred solution, the vegetable cultivation environment optimization method based on big data analysis also includes: Generate environmental control optimization decisions based on the output results of the intelligent data analysis and prediction module; Transmitting the optimization decision to the farmland automation control system to perform environmental control optimization operations; After performing the optimization operation, the execution effect is monitored in real time, and the control strategy is adjusted and optimized based on the feedback mechanism to ensure continuous optimization of environmental conditions.

[0011] As a preferred solution, the environmental control optimization decision is based on the analysis results of climate data, soil data and water quality data, and is calculated through a multivariate decision-making model.

[0012] As a preferred solution, the farmland automation control system automatically executes the instruction set after receiving and decoding the optimization decision instruction.

[0013] As a preferred solution, the sensor monitors various environmental parameters in real time, compares the monitoring results with the expected targets, and calculates the errors of the execution effects.

[0014] As a preferred solution, after calculating the error between the execution effect and the expected target, the optimization decision formula is further adjusted and the control instruction set is regenerated.

[0015] As a preferred solution, the vegetable cultivation environment optimization system based on big data analysis includes: The data acquisition and preprocessing module includes multiple sensors deployed in the farmland, which are used to collect climate data, soil data and water quality data in real time, and transmit the collected raw data to the edge computing device through a wireless network for preliminary processing. The preliminary processing includes noise filtering, missing value filling and abnormal data calibration.

[0016] As a preferred solution, the vegetable cultivation environment optimization system based on big data analysis includes: The intelligent data analysis and prediction module includes a multivariate regression model, a high-order partial differential equation and a deep learning algorithm. The multivariate regression model is used to analyze the relationship between environmental data and crop growth. The high-order partial differential equation is used to describe the dynamic impact of environmental changes on crop growth. The deep learning algorithm is used to model and train historical data and real-time data to generate a prediction model of the environment and crop growth.

[0017] The key innovative features of the present invention include: (1) Combination of multiple regression model and high-order partial differential equation: The present invention uses a combination of multiple regression model and high-order partial differential equation to accurately describe the dynamic impact of environmental factors on crop growth, making the optimization of the cultivation environment more scientific and accurate.

[0018] (2) Application of deep learning algorithms: The LSTM network in deep learning can capture long-term dependencies in time series data, enabling the system to dynamically predict future environmental changes and better adapt to the complex agricultural production environment.

[0019] (3) Dynamic optimization and automatic execution module: This module monitors the execution effect in real time through a feedback mechanism and automatically adjusts the control strategy, thereby achieving continuous optimization and intelligent management of the vegetable cultivation environment and ensuring the efficiency and stability of the cultivation process.

[0020] The following are its main beneficial effects: The present invention achieves accurate modeling and dynamic optimization of the vegetable cultivation environment by introducing multiple regression models, high-order partial differential equations and deep learning algorithms. Compared with traditional cultivation methods based on experience or simple sensor control, the present invention can collect and analyze multidimensional environmental data such as climate, soil and water quality in real time, and use complex mathematical models for accurate prediction and control to ensure that crops grow under optimal environmental conditions. Through real-time feedback and dynamic adjustment mechanisms, the present invention significantly improves vegetable yield and quality and reduces the adverse effects of environmental fluctuations. In addition, through the implementation of an automated control module, the system achieves intelligent regulation of environmental factors, greatly reduces the need for manual intervention, and improves production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flow chart of a vegetable cultivation environment optimization method based on big data analysis provided in an embodiment of the present invention.

[0022] Figure 2 It is a structural block diagram of a vegetable cultivation environment optimization system based on big data analysis provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The present invention relates to a vegetable cultivation environment optimization system and method based on big data analysis, which belongs to the field of intelligent agricultural management. It includes: real-time collection of environmental data such as climate, soil and water quality, and preprocessing of the data; classification and storage of the preprocessed data, and efficient management through multi-node synchronization and data indexing; using multivariate regression models, high-order partial differential equations and deep learning algorithms to model and train historical data and real-time data, and generate a prediction model of environment and crop growth; based on the model, generate optimization decisions, and execute and adjust them in real time through an automated control system. The invention effectively improves vegetable yield and quality, and reduces losses caused by environmental fluctuations.

[0024] The vegetable cultivation environment optimization system and method based on big data analysis provided in the embodiments of the present invention are mainly used to solve the technical problem solved by the present invention, which is "how to optimize the climate, soil, and water quality conditions in the vegetable cultivation environment through real-time monitoring and big data analysis to improve yield and quality and reduce losses due to environmental fluctuations."

[0025] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present application. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature. Embodiment 1

[0027] Reference Figure 1 , is a flow chart of a vegetable cultivation environment optimization method based on big data analysis provided by an embodiment of the present invention. The vegetable cultivation environment optimization method based on big data analysis may at least include steps S100-S400: S100, collect and pre-process environmental data in real time, and upload analysis results.

[0028] S200, classify and store data, synchronize multiple nodes, and create data indexes.

[0029] S300, modeling training, real-time data analysis, and dynamic optimization model.

[0030] S400, the system generates optimized decisions, automatically executes control, and provides real-time feedback adjustments.

[0031] S100, real-time collection and pre-processing of environmental data, and uploading of analysis results. Step S100 includes steps S110-S130: S110: Collect environmental data through sensors deployed in the farmland, and send the collected raw data to the edge computing device. Specifically: Environmental data is collected by sensors deployed in the farmland, including climate data (such as temperature T e 、Humidity e , wind speed V e etc.), soil data (such as soil moisture H s , soil temperature T s , soil pH s etc.) and water quality data (such as water pH n 、Water temperature T n 、Oxygen content in water n The collected raw data is sent to the edge computing device. The edge computing device has the ability to process real-time data and can aggregate and transmit data from multiple sensor nodes.

[0032] S120: On the edge computing device, perform preliminary cleaning on the raw data collected in step S110. Specifically, the following operations are included: First, perform noise filtering operations and use Kalman filter or median filter algorithm to remove noise from the collected data to eliminate random interference in the sensor acquisition process.

[0033] Furthermore, missing value filling is performed to fill in the missing environmental data (T e , H s Interpolation method or mean filling method based on historical data can be used to complete the data.

[0034] Furthermore, the abnormal data can be calibrated to correct the abnormal points using the distribution characteristics of historical data and physical laws. s If the real-time collected value deviates from the standard range of historical data, correction can be made based on adjacent data points to adjust the abnormal value to a reasonable range.

[0035] S130: Perform preliminary analysis on the data pre-processed in step S120 based on the edge computing device. Specifically, the following operations are included: First, the time series analysis method is used to analyze the processed climate data T e (t), H e (t), V e (t) Perform trend analysis to generate environmental change trend information F e (t), where F e (t) represents the climate change trend function within a specific time t.

[0036] Furthermore, for the soil data H s (t), T s (t), P s (t) Carry out correlation analysis and calculate the correlation coefficient R between soil moisture and temperature s , used to judge the stability of the soil environment.

[0037] Furthermore, combined with water quality data P n (t), T n (t), O n The real-time change of (t) is used to generate the comprehensive water quality evaluation index C through the multivariate regression model. n (t), the indicator C n (t) Comprehensive evaluation value used to characterize the current water quality status.

[0038] In generating the above analysis results F e (t), R s , C n(t), the edge computing device uploads the processed data together with the pre-processed environmental data to the distributed data storage and management module. Further, in the subsequent step S210, the generated environmental change trend information F e (t) will be used to train the prediction model, soil correlation coefficient R s and water quality comprehensive evaluation index C n (t) are used to optimize the input parameters of the decision model.

[0039] S200, classify and store data, synchronize multiple nodes, and create data indexes. Step S200 includes steps S210-S230: S210, receiving pre-processed data uploaded from the edge computing and data acquisition module, and classifying and storing the data according to data type.

[0040] The preprocessed data includes climate data F e (t), soil correlation coefficient R s , Comprehensive evaluation index of water quality C n (t) and basic environmental data (T e , H e 、V e , H s 、T s , P s , P n 、T n , O n Specifically, the system classifies and stores the data according to the data type, and the classification storage includes the following steps: The climate data F e (t) is stored in the climate data repository, which is used to store the climate parameters collected in real time and provide an access interface for subsequent data analysis; the soil correlation coefficient R s And the processed soil basic data (H s 、T s , P s ) is stored in the soil data repository, which manages various soil parameters by category to ensure data consistency and integrity in the subsequent analysis process; the comprehensive water quality evaluation index C n (t) and basic water quality data (P n 、T n , O n ) to the water quality data repository. The data repository further supports the rapid retrieval and call of water quality data.

[0041] In step S210, the F calculated in step S130 e (t), R s and Cn (t) are all key parameters, which ensure the effectiveness of classified storage and lay the foundation for the efficient operation of the entire system.

[0042] S220: Perform multi-node synchronization on the real-time data and historical data stored in step S210, and perform regular data backup operations. Specifically, the following operations are included: The F in the climate data repository is synchronized through the distributed database system. e (t) and other climate parameters are synchronized between multiple nodes to ensure high data availability. This step includes master-slave node data synchronization and data consistency verification after failure recovery.

[0043] Furthermore, the R s , H s , T s , P s Data also needs to be synchronized between multiple nodes. This operation is achieved through a distributed hash table (DHT) to achieve fast retrieval and distributed storage, and the consistency of soil data between different nodes in the system.

[0044] C in Water Quality Data Repository n (t) and basic water quality data (P n , T n , O n ) is also synchronized through a multi-node synchronization mechanism. To improve the fault tolerance of data, erasure coding technology is introduced in this step for redundant storage and backup.

[0045] S230: Create a data index structure based on the data synchronized in step S220. Specifically, the following operations are included: is the climate data F e (t) and related climate basic data to create a time series index to support the subsequent rapid retrieval and analysis of climate change trends. This index structure is optimized based on the B+ tree structure, ensuring large-scale storage and rapid query of time series data; for soil data R s , H s , T s , P s Create an index structure based on multidimensional space. The index structure is based on the R-tree algorithm and supports multidimensional query and correlation analysis of soil parameters. n (t) and basic water quality data (P n , T n , O n) Create a query structure based on an inverted index to support complex multi-condition queries. This index structure enables the system to quickly locate and retrieve required data when processing large-scale water quality data. The index structure created in step S230 directly serves the subsequent intelligent data analysis and prediction module (S300), ensuring the efficiency and accuracy of data retrieval in the case of large amounts of data.

[0046] S300, modeling training, real-time data analysis, dynamic optimization model. Step S300 includes steps S310-S330: S310: Based on the stored historical data and real-time data, the data is modeled and trained to generate a prediction model of the environment and crop growth. Specifically: First, for the climate data T e (t), soil data H s (t), water quality data P n (t) and other basic environmental data, and construct a multiple regression equation, which is expressed as:

[0047] Among them, Y(t) is the predicted crop growth status, Input data for the environment at different points in time (e.g. climate, soil, water quality, etc.), is the regression coefficient, The multiple regression model generates a preliminary environmental response prediction model by combining the correlations among the input data.

[0048] Based on the preliminary model, the dynamic characteristics of environmental changes are further considered, and the influence of environmental parameters on crop growth is described by high-order partial differential equations (PDE). , its influence function u( ,t) can be expressed as:

[0049] in, is the Laplace operator, c is the propagation speed constant, is the impact function of the environment on crop growth. By solving this set of partial differential equations, we can get the predicted value of the impact of environmental changes on crop growth.

[0050] After completing the construction of the above model, the LSTM network in deep learning is used to train the historical data. The large model combines the multi-layer perceptron (MLP) and RNN architecture to achieve long-term memory and dynamic prediction of crop growth under complex environmental conditions. The gradient descent method is used in the model training process to optimize the regression coefficients and the parameters of the partial differential equation.

[0051] S320: Analyze the real-time data using the model trained in step S310, and predict the environmental change trend and crop growth status based on the analysis results. Specifically: Real-time data input and multi-dimensional analysis: The climate data collected in real time , soil data , water quality data Input into the above trained multivariate regression model and high-order partial differential equation model for real-time calculation to generate the preliminary predicted value Y'(t) of crop growth status:

[0052] Dynamic prediction using LSTM network: The preliminary prediction value Y'(t) and real-time data are further input into the LSTM network to generate the final crop growth status prediction (t). LSTM networks can capture long-term dependencies in time series and provide accurate dynamic prediction results.

[0053] After the calculation is completed, the accuracy of the prediction model is evaluated using complex analysis and the Riemann curvature tensor, and the changing trend of each environmental parameter in multidimensional space is represented by the tensor:

[0054] in, represents the Riemann curvature tensor, which describes the spatial geometric properties and interactions between environmental variables.

[0055] S330: According to the new data generated in step S320 and the actual execution effect, the prediction model is adjusted and optimized. Specifically: The actual monitored crop growth status (t) and predicted value (t) is compared and the prediction error E(t) is calculated: E(t) = (t) - (t) Using error feedback, the Bellman-Ford algorithm is used to update the gradient and optimize the regression model. and the parameters c and f in the partial differential equation ( ,t):

[0056] in, is the learning rate, which controls the amplitude of parameter updates.

[0057] The LSTM network and high-order partial differential equations are retrained using the updated parameters, and the model is validated using the validation set to ensure that it still has high accuracy under new environmental conditions.

[0058] S400, generate optimization decisions, automatically execute control, and make real-time feedback adjustments. Step S400 at least includes steps S410-S430: S410: Generate environmental control optimization decisions based on the output results of the intelligent data analysis and prediction module. Specifically: The optimization decision includes operation instructions such as water and fertilizer adjustment, temperature control, and light management. Specifically, the steps are as follows: First, the final crop growth status prediction value obtained in step S320 is used (t) and updated environmental change prediction results , , , the optimization decision formula D(t) is generated through the multivariate decision model, which is expressed as:

[0059] in, , , is the environmental control weight coefficient, which corresponds to the influence weight of climate, soil and water quality on crop growth. This formula decomposes the generated optimization decision D(t) into a specific control instruction set, which includes water and fertilizer regulation W(t), temperature control , lighting management L(t), etc.

[0060] Based on the control instruction set, the specific parameters required for each control operation are further calculated. For example: Water and fertilizer regulation W(t) is calculated by the following formula:

[0061] in, and Respectively indicate water quality and soil moisture The influence coefficient on water and fertilizer regulation.

[0062] Temperature control Similar to the calculation of light management L(t), the final instructions are generated by combining the corresponding environmental input indicators and the optimization model.

[0063] Optimize the decision instruction set {W(t), , L(t)} are generated, these instruction sets are transmitted to step S420 for execution.

[0064] S420, transmitting the optimization decision to the farmland automation control system to execute the environmental control optimization operation. Specifically: The optimization decision instruction set {W(t), , L(t)} is transmitted to the farmland automation control system through the communication module. After receiving the command, the control system decodes the parameters and prepares to perform the corresponding operation.

[0065] According to the water and fertilizer adjustment instruction W(t), the irrigation system is started or adjusted. The specific operation includes adjusting the amount of water and fertilizer, etc. to ensure the soil moisture and water quality Reach the set optimal value.

[0066] According to the temperature control instruction And light management instructions L(t) to regulate the temperature and light conditions of the greenhouse. Temperature regulation involves adjusting the heating or ventilation system, while light regulation involves switching the light source on and off and controlling the brightness.

[0067] During the above execution process, each operation is performed according to the requirements of the instruction set, and the execution status is monitored in real time.

[0068] S430: After executing the optimization operation, monitor the execution effect in real time, adjust and optimize the control strategy based on the feedback mechanism, and continuously optimize the environmental conditions. Specifically: Use sensors to monitor various environmental parameters in real time , , etc., to obtain the environment status data after execution By comparison With expected parameters , evaluate the implementation effect.

[0069] Calculate the difference between the actual performance and the expected performance : = -

[0070] like If the preset threshold is exceeded, the feedback mechanism is activated for further adjustment. , , etc. control parameters, and update the optimization decision formula D(t).

[0071] According to the optimization decision formula D'(t) adjusted by feedback, the instruction set {W(t), , L(t)}, and repeat the execution process in step S420 to ensure that the environmental conditions are continuously optimized. Embodiment 2

[0072] Figure 2 1 is a structural block diagram of a vegetable cultivation environment optimization system based on big data analysis provided by an embodiment of the present invention. Figure 2 As shown, the system may include: The data acquisition and preprocessing module 10 is used to collect environmental data in real time from the sensor network deployed in the farmland, and the environmental data includes climate data (temperature, humidity, wind speed, etc.), soil data (humidity, temperature, pH value, etc.) and water quality data (pH value, temperature, oxygen content, etc.). This module further performs preprocessing operations on the collected raw data, including noise filtering, missing value filling and abnormal data calibration, to ensure the quality and consistency of the data.

[0073] The distributed data storage and management module 20 is used to receive and store the pre-processed data from the data acquisition and pre-processing module. The data is classified and stored according to its type, including climate data, soil data and water quality data. This module supports multi-node synchronization and data backup operations to ensure the integrity and security of the data, and supports efficient data retrieval and management by creating a data index structure.

[0074] The intelligent data analysis and prediction module 30 uses the original multivariate regression model, high-order partial differential equation and deep learning algorithm to model and train the environmental data based on the stored historical data and real-time data, and generates a prediction model for the environment and crop growth. This module can analyze the real-time data and predict the environmental change trend and crop growth status based on the analysis results. At the same time, it can dynamically optimize and adjust the prediction model according to the actual execution effect to adapt to the changing environmental conditions.

[0075] The dynamic optimization and automatic execution module 40 generates environmental control optimization decisions based on the output results of the intelligent data analysis and prediction module, and the optimization decisions include operation instructions such as water and fertilizer regulation, temperature control, and light management. This module transmits the optimization decisions to the farmland automation control system, executes environmental control optimization operations, and monitors the execution effects in real time, adjusts and optimizes the control strategy based on the feedback mechanism, and ensures the continuous optimization of the cultivation environment.

[0076] The key innovative features of the present invention include: (1) Combination of multiple regression model and high-order partial differential equation: The present invention uses a combination of multiple regression model and high-order partial differential equation to accurately describe the dynamic impact of environmental factors on crop growth, making the optimization of the cultivation environment more scientific and accurate.

[0077] (2) Application of deep learning algorithms: The LSTM network in deep learning can capture long-term dependencies in time series data, enabling the system to dynamically predict future environmental changes and better adapt to the complex agricultural production environment.

[0078] (3) Dynamic optimization and automatic execution module: This module monitors the execution effect in real time through a feedback mechanism and automatically adjusts the control strategy, thereby achieving continuous optimization and intelligent management of the vegetable cultivation environment and ensuring the efficiency and stability of the cultivation process.

[0079] The following are its main beneficial effects: The present invention achieves accurate modeling and dynamic optimization of the vegetable cultivation environment by introducing multiple regression models, high-order partial differential equations and deep learning algorithms. Compared with traditional cultivation methods based on experience or simple sensor control, the present invention can collect and analyze multidimensional environmental data such as climate, soil and water quality in real time, and use complex mathematical models for accurate prediction and control to ensure that crops grow under optimal environmental conditions. Through real-time feedback and dynamic adjustment mechanisms, the present invention significantly improves vegetable yield and quality and reduces the adverse effects of environmental fluctuations. In addition, through the implementation of an automated control module, the system achieves intelligent regulation of environmental factors, greatly reduces the need for manual intervention, and improves production efficiency.

[0080] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various changes or substitutions within the technical scope disclosed in the present application, and the above should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A vegetable cultivation environment optimization method based on big data analysis, characterized in that: The method comprises: S100, collect and pre-process environmental data in real time and upload analysis results; S200, classify and store data, synchronize multiple nodes, and create data indexes; S300, modeling training, real-time data analysis, dynamic optimization model; Based on the stored historical data and real-time data, multiple regression models, high-order partial differential equations and deep learning algorithms are used for modeling and training to generate prediction models of environment and crop growth; Analyze real-time data using the trained model to generate preliminary predictions of crop growth status, and make adjustments through dynamic prediction models; Adjust and optimize the prediction model based on the new data generated and the actual execution results to ensure that the model adapts to changes in environmental conditions; S400, generate optimization decisions, automatically execute control, and make real-time feedback adjustments.

2. The method according to claim 1, characterized in that Step S100 includes: S110, collecting environmental data through sensors deployed in the farmland, and sending the collected raw data to the edge computing device; S120, performing noise filtering, missing value filling, and abnormal data calibration on the original data collected in step S110 on the edge computing device; S130: Perform preliminary analysis on the data preprocessed in step S120 based on the edge computing device to generate environmental change trend information, soil correlation coefficient and comprehensive water quality evaluation index.

3. The method according to claim 1, characterized in that Step S200 includes: S210, receiving pre-processed data uploaded from the edge computing and data acquisition module, and classifying and storing the data according to data type; S220, performing multi-node synchronization on the stored real-time data and historical data, and performing regular data backup operations; S230. Based on the data synchronized in step S220, create a data index structure to support fast data retrieval and analysis.

4. The method according to claim 1, characterized in that: Step S400 includes: S410, generating an environmental control optimization decision according to the output results of the intelligent data analysis and prediction module; S420, transmitting the optimization decision to the farmland automation control system to perform environmental control optimization operations; S430. After executing the optimization operation, monitor the execution effect in real time, adjust and optimize the control strategy based on the feedback mechanism, and ensure continuous optimization of environmental conditions.

5. The method according to claim 4, characterized in that The environmental control optimization decision generated in step S410 is based on the analysis results of climate data, soil data and water quality data, and is calculated through a multivariate decision model.

6. The method according to claim 4, characterized in that After receiving and decoding the optimization decision instruction, the farmland automation control system described in step S420 automatically executes the instruction set.

7. The method according to claim 4, characterized in that In step S430, various environmental parameters are monitored in real time by sensors, and the monitoring results are compared with the expected targets to calculate the errors of the execution effects.

8. The method according to claim 4, characterized in that After calculating the error between the execution effect and the expected target in step S430, the optimization decision formula is further adjusted and the control instruction set is regenerated.

9. A vegetable cultivation environment optimization system based on big data analysis, characterized in that: The data acquisition and preprocessing module includes multiple sensors deployed in the farmland, which are used to collect climate data, soil data and water quality data in real time, and transmit the collected raw data to the edge computing device through a wireless network for preliminary processing. The preliminary processing includes noise filtering, missing value filling and abnormal data calibration.

10. A vegetable cultivation environment optimization system based on big data analysis, characterized in that: The intelligent data analysis and prediction module includes a multivariate regression model, a high-order partial differential equation and a deep learning algorithm. The multivariate regression model is used to analyze the relationship between environmental data and crop growth. The high-order partial differential equation is used to describe the dynamic impact of environmental changes on crop growth. The deep learning algorithm is used to model and train historical data and real-time data to generate a prediction model of the environment and crop growth.

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