Photovoltaic plant vegetation recovery intelligent management system

Abstract

The invention relates to the technical field of ecological restoration, in particular to a photovoltaic plant vegetation restoration intelligent management system, which comprises a data acquisition module, an environment sensing module, a vegetation growth monitoring module, a restoration evaluation module, an intelligent optimization control module and a decision support module, and is characterized in that multi-source data is acquired in real time through a sensor network deployed in a photovoltaic plant; big data analysis and a machine learning technology are combined, the vegetation restoration effect is accurately evaluated, a management strategy in the restoration process is optimized, measures such as irrigation, fertilization and weeding of a photovoltaic plant area are automatically adjusted so as to improve the vegetation restoration efficiency and effect, and by dynamically adjusting a restoration scheme, optimizing resource allocation and maintaining ecological balance, the vegetation restoration efficiency and effect are improved. According to the method, environmental changes can be effectively coped with, refined and sustainable vegetation restoration management is achieved, the ecological restoration efficiency of the photovoltaic plant area can be improved, technical support can be provided for other similar ecological restoration projects, and the method has wide application prospects.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological restoration, and particularly to an intelligent management system for vegetation restoration in photovoltaic power plant areas. Background Art

[0002] With the rapid development of the photovoltaic industry, more and more photovoltaic power stations are built and put into operation in different regions. Photovoltaic power stations usually require a large amount of land resources, and these lands are often affected by soil compaction, vegetation damage, etc. during the construction process of photovoltaic power station facilities, resulting in the degradation of native vegetation and the destruction of the ecological environment. Therefore, the vegetation restoration in photovoltaic power plant areas is not only an important means to improve the ecological environment quality and promote biodiversity, but also the key to achieving sustainable development goals.

[0003] However, the existing vegetation restoration technologies face many challenges in practical applications. Traditional vegetation restoration methods usually rely on manual experience and regular observations, lacking real-time monitoring and accurate assessment of the restoration process. This method is difficult to cope with the complex and changing environmental conditions in photovoltaic power plant areas, and the management decisions during the restoration process cannot be dynamically adjusted based on real-time data and scientific analysis. In addition, the evaluation of the restoration effect in existing technologies mostly relies on static data or rough manual judgment, resulting in a long vegetation restoration cycle, low efficiency, and inability to flexibly respond to different environmental conditions, thus limiting the optimization of the restoration effect. Summary of the Invention

[0004] The present invention provides an intelligent management system for vegetation restoration in photovoltaic power plant areas, which can effectively respond to environmental changes, achieve refined and sustainable vegetation restoration management, not only improve the ecological restoration efficiency of photovoltaic power plant areas, but also provide technical support for other similar ecological restoration projects, and has a wide application prospect.

[0005] The intelligent management system for vegetation restoration in photovoltaic power plant areas includes a data acquisition module, an environment perception module, a vegetation growth monitoring module, a restoration evaluation module, an intelligent optimization control module, and a decision support module, wherein: The data acquisition module collects multi-source data of the photovoltaic power plant area in real time through a sensor network deployed in the photovoltaic power plant area, and the multi-source data includes environmental data, soil data, and plant growth data; The environment perception module uses the sensor network to perceive and monitor the environmental data and plant growth data of the photovoltaic power plant area in real time, and tracks environmental changes and soil condition changes. The environment perception module includes: Using the collected environmental data, it monitors the environmental changes in the photovoltaic power plant area in real time, including temperature fluctuations, humidity changes, and wind speed changes, and evaluates the impact of the environment on vegetation growth in combination with soil data; By monitoring plant growth data and combining it with environmental data, the impact of environmental changes on vegetation growth is evaluated, and the health status and progress of vegetation restoration in the current photovoltaic plant area are judged; According to the monitoring results of environmental data and plant growth data, calculate the suitability score of environmental factors for vegetation growth; Environmental suitability score ; Among them, is the temperature, is the humidity, is the light intensity, is the precipitation, is the weight of environmental factors; Based on real-time perception and monitoring results, output data to the intelligent optimization control module. The output data includes the evaluation result of the environment on vegetation growth and the suitability score; The vegetation growth monitoring module, based on plant growth data, uses image recognition technology and growth models to monitor the growth status of vegetation in real time and evaluate the progress and effect of vegetation restoration; The restoration evaluation module, based on big data analysis technology, combines multi-source data collected to evaluate the restoration effect and generates vegetation restoration effect evaluation data. The restoration evaluation module includes: Use the linear regression algorithm to establish a regression model between environmental factors, soil conditions and vegetation growth, and evaluate the restoration effect; Based on the evaluation result of the restoration effect, calculate the predicted value of vegetation restoration. The prediction formula is: ; Among them: is the predicted value of vegetation restoration, is the model intercept, is the regression coefficient corresponding to each input variable, is the input variable, representing the multi-source data collected; Compare the predicted value with the actually observed plant growth data, evaluate the accuracy of the prediction, and generate the evaluation data of the vegetation restoration effect; The intelligent optimization control module optimizes the management strategy in the process of vegetation restoration through machine learning algorithms according to the restoration effect evaluation data generated by the restoration evaluation module; The decision support module provides scientific decision support suggestions according to the analysis results of the restoration evaluation module and the intelligent optimization control module.

[0006] Furthermore, the data acquisition module includes: The sensor network includes environmental sensors, soil sensors, and plant growth sensors, which are respectively arranged at different positions in the photovoltaic plant area; The environmental sensors collect environmental data in real time, including temperature, humidity, wind speed, precipitation, and light intensity; The soil sensors collect soil data, including soil humidity, soil temperature, soil pH value, and soil nutrient content; The plant growth sensors collect plant growth data, including plant height and leaf area; The multi-source data collected is preprocessed by a preset data processing unit, including formatting, cleaning, and denoising.

[0007] Furthermore, the vegetation growth monitoring module includes: Through the high-definition cameras arranged in the photovoltaic plant area, image data of the vegetation in the photovoltaic plant area is obtained in real time. The image data includes leaf morphology and leaf color; The collected image data is processed using a convolutional neural network to extract plant features in the image; The recognition results of the convolutional neural network are fused with the plant growth data collected by the sensors to comprehensively evaluate the growth status of the vegetation.

[0008] Furthermore, the vegetation growth monitoring module also includes: According to the plant growth data collected in real time, the growth process of the plant is described by an exponential growth model; Based on the collected plant growth data, the future growth trend of the plant is predicted using the exponential growth model; According to the prediction results, a plant growth trend report is generated, including the growth status, health status, and expected recovery progress of the plant at different time nodes.

[0009] Furthermore, the recovery evaluation module specifically includes: The evaluation data generated by the recovery evaluation module is compared with the target value of vegetation recovery. The target value can be a preset vegetation growth standard or a reference value generated based on historical data; By comparing the deviation between the evaluation data and the target value, the effect of vegetation recovery is judged, and the effectiveness of the current recovery strategy is evaluated; The results of the recovery evaluation are provided to the decision support module to generate a dynamic report on vegetation recovery.

[0010] Furthermore, the intelligent optimization control module includes: Obtain the recovery effect evaluation data from the recovery evaluation module and perform feature extraction on the recovery effect evaluation data; Based on the feature extraction results, an optimization model of the vegetation restoration process is established through machine learning algorithms to predict the optimal management strategies for vegetation restoration under different environmental conditions; According to the output of the optimization model, automatically adjust the management strategy of the photovoltaic plant area.

[0011] Furthermore, the decision support module includes: Obtain and analyze the analysis results of the restoration evaluation module and the intelligent optimization control module; Comprehensively consider factors such as the effect of vegetation restoration, restoration progress, environmental and soil conditions, etc., to determine the potential problems and optimization space in the current vegetation restoration process; Based on the analysis results, generate decision support suggestions, and convert the generated decision support suggestions into decision reports, which are provided to the management personnel of the photovoltaic plant area. According to the decision support suggestions, the management personnel of the photovoltaic plant area implement the adjusted vegetation restoration plan. Advantages of the present invention: In the present invention, by combining the data of the environmental perception module and the vegetation growth monitoring module, and using advanced technologies such as regression analysis and machine learning, each link in the vegetation restoration process can be accurately monitored, and the growth status of the vegetation can be evaluated in real time. Through accurate data analysis of environmental conditions, soil status and plant growth, the system can scientifically and dynamically adjust the restoration strategy and optimize the management measures for vegetation restoration (such as irrigation, fertilization and weeding, etc.). Compared with traditional manual judgment or empirical restoration plans, the present system significantly improves the efficiency and accuracy of vegetation restoration and can achieve refined management.

[0012] In the present invention, by integrating multi-source data collection, environmental perception, vegetation growth monitoring, restoration evaluation, intelligent optimization control and decision support modules, a highly intelligent vegetation restoration management system is constructed. The system continuously optimizes according to real-time data, continuously improves management strategies through machine learning algorithms, and automatically adjusts restoration plans and management measures. This not only reduces the dependence on manual intervention, but also can propose customized management plans according to the specific conditions of different photovoltaic plant areas (such as terrain, climate, etc.), ensuring that the vegetation restoration process is always in the optimal state.

[0013] In the present invention, the restoration evaluation module and the intelligent optimization control module comprehensively consider multiple factors such as the effect of vegetation restoration, soil conditions, climate change, etc., avoid the ecological burden brought by single measures during the restoration process, and propose sustainable restoration strategies. The decision support module puts forward scientific suggestions according to the analysis results, including restoration plan adjustment, restoration cycle extension, ecological balance maintenance, etc., which can ensure the health of the ecological system in the photovoltaic plant area, support long-term ecological restoration goals, and provide strong technical support for the ecological protection and sustainable development of the photovoltaic plant area. Description of the Drawings

[0014] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only those of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0015] Figure 1 It is a schematic diagram of the system flow of the embodiment of the present invention. Specific embodiments

[0016] The present invention will be described in detail below in conjunction with the drawings and specific embodiments. At the same time, it should be noted here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments. For some well-known technologies, those skilled in the art can also adopt other alternative methods for implementation; moreover, the drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0017] It should be noted that in the specification, references to "an embodiment", "embodiments", "exemplary embodiments", "some embodiments", etc. indicate that the described embodiments may include specific features, structures, or characteristics, but not necessarily every embodiment includes such specific features, structures, or characteristics. Additionally, when combining embodiments to describe specific features, structures, or characteristics, implementing such features, structures, or characteristics in combination with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the relevant art.

[0018] Generally, terms can be understood at least in part from their use in the context. For example, at least in part depending on the context, the term "one or more" used herein can be used to describe any feature, structure, or characteristic in a singular sense, or can be used to describe a combination of features, structures, or characteristics in a plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey a set of exclusive factors, but rather, at least in part depending on the context, can allow for the existence of other factors that may not be explicitly described.

[0019] As Figure 1 shown, the intelligent management system for vegetation restoration in a photovoltaic plant area includes a data acquisition module, an environmental perception module, a vegetation growth monitoring module, a restoration evaluation module, an intelligent optimization control module, and a decision support module, where: The data acquisition module collects multi-source data of the photovoltaic plant area in real time through a sensor network deployed in the photovoltaic plant area. The multi-source data includes environmental data, soil data, and plant growth data; The environmental perception module uses the sensor network to perceive and monitor the environmental data and plant growth data of the photovoltaic plant area in real time, tracks environmental changes and soil condition changes, and provides a decision-making basis for vegetation restoration; The vegetation growth monitoring module, based on plant growth data, uses image recognition technology and growth models to monitor the growth status of vegetation in real time and evaluate the progress and effectiveness of vegetation restoration; The restoration evaluation module, based on big data analysis technology, combines multi-source data collected to evaluate the restoration effect and generates vegetation restoration effect evaluation data; The intelligent optimization control module, according to the restoration effect evaluation data generated by the restoration evaluation module, optimizes the management strategies in the vegetation restoration process through machine learning algorithms, and adjusts irrigation, fertilization, and weeding in the photovoltaic plant area to improve the efficiency of vegetation restoration; The decision support module, based on the analysis results of the restoration evaluation module and the intelligent optimization control module, provides scientific decision support suggestions, including restoration plan adjustment, restoration cycle extension, ecological balance maintenance, etc., for reference by management personnel.

[0020] The data acquisition module includes: The sensor network includes environmental sensors, soil sensors, and plant growth sensors. The environmental sensors, soil sensors, and plant growth sensors are respectively arranged at different positions in the photovoltaic plant area to ensure comprehensive monitoring of various environmental factors in the photovoltaic plant area; The environmental sensors collect environmental data in real time, including temperature, humidity, wind speed, precipitation, and light intensity. The environmental sensors include thermocouple sensors, capacitive humidity sensors, vane anemometers, float rain gauges, and photodiode sensors. Among them; The thermocouple sensor measures the air temperature in real time, the capacitive humidity sensor measures the air humidity in real time, the vane anemometer measures the wind speed in real time, the float rain gauge measures the precipitation in real time, and the photodiode sensor measures the light intensity in real time; The soil sensors collect soil data, including soil humidity, soil temperature, soil pH value, and soil nutrient content. The soil sensors include resistive soil humidity sensors, thermistor sensors, glass electrode pH sensors, and spectroscopic analysis sensors. Among them; The resistive soil humidity sensor measures the water content of the soil in real time, the thermistor sensor measures the soil temperature in real time, the glass electrode pH sensor measures the soil acidity and alkalinity in real time, and the spectroscopic analysis sensor measures the content of main nutrients (such as nitrogen, phosphorus, potassium, etc.) in the soil in real time; The plant growth sensors collect plant growth data, including plant height and leaf area. The plant growth sensors include laser rangefinder sensors and leaf area sensors. Among them; The laser rangefinder sensor measures the height of the plant through laser ranging technology, and the leaf area sensor monitors the area of the leaves in real time through laser scanning technology; Preprocess the multi-source data collected through a preset data processing unit, including formatting, cleaning, and denoising, to ensure the accuracy and consistency of the data.

[0021] The environmental perception module includes: Utilize the processed environmental data to monitor the environmental changes in the photovoltaic plant area in real time, including temperature fluctuations, humidity changes, and wind speed changes, and evaluate the impact of the environment on vegetation growth in combination with soil data (such as soil humidity, soil temperature, etc.); By monitoring the plant growth data and combining with the environmental data, evaluate the degree of impact of environmental changes on vegetation growth, and judge the health status and progress of vegetation restoration in the current photovoltaic plant area; According to the monitoring results of the environmental data and plant growth data, calculate the suitability score of environmental factors for vegetation growth, specifically including: Use a weighted comprehensive evaluation model to standardize the environmental data, assign different weights to each environmental data according to its impact on vegetation growth, and the model formula is as follows: Environmental suitability score ; Wherein, is the temperature, is the humidity, is the light intensity, is the precipitation, is the weight of the environmental factor, depending on the influence of the specific vegetation type and climate conditions; Based on the real-time perception and monitoring results, output data to the intelligent optimization control module, and the output data includes the evaluation results of the environment on vegetation growth and the suitability score.

[0022] The vegetation growth monitoring module includes: Through the high-definition cameras arranged in the photovoltaic plant area, obtain the image data of the vegetation in the photovoltaic plant area in real time, and the image data includes leaf morphology and leaf color; Use a convolutional neural network to process the collected image data. The convolutional neural network extracts the plant features in the image through layer-by-layer processing of the image. The specific steps include: Image preprocessing: Perform denoising, enhancement, standardization, etc. on the collected image data to ensure that the quality of the input image data is suitable for in-depth learning analysis; Feature extraction: Process the image through convolutional layers, pooling layers, and fully connected layers to extract the growth features of plants from the image, such as leaf morphology, leaf area, plant height, etc.; Plant localization and segmentation: Use a convolutional neural network for object detection and segmentation of plants, distinguish the plant area in the image from the background, accurately locate the plants, and perform feature calibration; Fuse the recognition results of the convolutional neural network with the plant growth data collected by sensors (such as plant height, leaf area, etc.) to comprehensively evaluate the growth status of vegetation; By comparing the image recognition results with the sensor data, obtain more accurate real-time monitoring data of vegetation growth, and further estimate the progress and effect of vegetation restoration; Transmit the fused plant growth data to the restoration evaluation module to provide a basis for subsequent evaluation and decision-making on the effect of vegetation restoration.

[0023] The vegetation growth monitoring module also includes: According to the plant growth data collected in real time (such as plant height, leaf area, etc.), describe the plant growth process through an exponential growth model. The exponential growth model is used to simulate the rapid growth stage of plants under suitable environmental conditions. The specific model expression is as follows: ; where, is the growth amount of the plant at time (such as plant height or leaf area), is the initial growth amount of the plant (such as plant height or leaf area at the time of planting), is the growth rate of the plant (which can be adjusted according to the specific environment of the photovoltaic plant area), is the time; Based on the collected plant growth data (such as plant height, leaf area, etc.), use the exponential growth model to predict the future growth trend of plants. The specific steps include: Parameter fitting: According to the collected growth data, estimate the parameters in the model (such as the initial growth amount of the plant and the growth rate of the plant ) through the least squares method or other fitting methods.

[0024] Growth prediction: Use the fitted model parameters to predict the growth in the future for a period of time to predict growth indicators such as the height and leaf area of the plant; According to the prediction results, generate a plant growth trend report, including the growth status, health status, and expected restoration progress of the plant at different time nodes. These prediction results will provide decision support for the restoration evaluation module and provide a basis for the intelligent optimization control module to develop an optimization management strategy.

[0025] The restoration evaluation module includes: Based on regression analysis technology, combine the collected multi-source data to evaluate the restoration effect, specifically including: Use the linear regression algorithm to establish a regression model between environmental factors, soil conditions, and vegetation growth; Standardize the input variables in regression analysis (such as environmental data, soil data) to ensure that different variables have the same scale for model training; Calculate the expected values of vegetation restoration based on the parameters of the regression model and the training set data, including vegetation growth rate, leaf area growth, and plant height.

[0026] Based on the evaluation results of the restoration effect, calculate the predicted value of vegetation restoration. The prediction formula is: ; Where: is the predicted value of vegetation restoration (such as predicted plant height, leaf area, etc.), is the model intercept, are the regression coefficients corresponding to each input variable, is the input variable, representing the multi-source data collected, such as is the temperature, is the air humidity, : soil humidity, : light intensity, is the precipitation.

[0027] The restoration evaluation module specifically includes: Compare the evaluation data generated by the restoration evaluation module with the target value of vegetation restoration. The target value can be a preset vegetation growth standard or a reference value generated based on historical data; By comparing the deviation between the evaluation data and the target value, judge the effect of vegetation restoration and evaluate the effectiveness of the current restoration strategy, specifically including: Use mathematical methods (such as mean square error, relative error, etc.) to calculate the deviation between the restoration effect and the target value; Evaluate the progress of vegetation restoration based on the deviation value, judge whether the current management measures are effective, and what adjustments are needed; Provide the results of the restoration evaluation to the decision support module to generate a dynamic report on vegetation restoration. The report includes the current status of vegetation restoration, the deviation from the target value, and suggestions for future improvement for subsequent optimization and management decisions.

[0028] The intelligent optimization control module includes: Obtain the restoration effect evaluation data from the restoration evaluation module. The restoration effect evaluation data includes the current state of vegetation restoration, the restoration progress, the expected value of the restoration effect, and the trend of vegetation growth under relevant environmental conditions; Extract features from the data for evaluating the restoration effect. By analyzing the features of the data for evaluating the restoration effect, identify the key factors affecting vegetation restoration, including environmental factors (such as temperature, humidity, light intensity, precipitation, etc.), soil conditions (such as soil humidity, soil temperature, soil pH value, etc.), and plant growth data (such as plant height, leaf area, etc.); Based on the feature extraction results, establish an optimization model for the vegetation restoration process through machine learning algorithms. The optimization model uses historical vegetation restoration data and environmental condition data as the training data set and is trained using the support vector machine algorithm to predict the optimal management strategy for vegetation restoration under different environmental conditions; According to the output of the optimization model, automatically adjust the management strategy of the PV plant area, specifically including: Adjust the irrigation strategy: According to the predicted vegetation restoration needs and current soil humidity, meteorological data and other information, optimize the irrigation frequency and irrigation volume to ensure that plants obtain appropriate moisture; Adjust the fertilization strategy: Automatically adjust the fertilization amount and fertilization time according to the needs of plant growth and soil nutrient conditions to promote the healthy growth of plants; Adjust the weeding strategy: According to the vegetation growth status and the weed growth situation in the PV plant area, formulate a weeding plan to avoid the negative impact of weeds on the vegetation restoration process; The intelligent optimization control module continuously collects new environmental data, soil data and plant growth data, and compares them with the previously optimized management strategy to adjust the management plan in real time to ensure the continuous optimization of the vegetation restoration process.

[0029] The decision support module includes: Obtain and analyze the analysis results of the restoration evaluation module and the intelligent optimization control module. The restoration evaluation module provides data for evaluating the vegetation restoration effect, and the intelligent optimization control module provides data for adjusting the management strategy; Comprehensively consider factors such as the effect of vegetation restoration, restoration progress, and environmental and soil conditions to determine the potential problems and optimization space in the current vegetation restoration process; Based on the analysis results, generate decision support suggestions, specifically including: Adjust the restoration plan: According to the progress of vegetation restoration and environmental changes, adjust the original restoration plan and propose new restoration measures (such as increasing fertilization, adjusting irrigation volume, optimizing vegetation planting density, etc.); Extend the restoration period: When the vegetation restoration effect is not ideal or the environmental conditions change greatly, it is recommended to extend the restoration period and reset the restoration goal and phased tasks; Maintain ecological balance: Evaluate the balance of the ecological environment in the restoration process and propose suggestions for maintaining or improving the ecological balance, such as controlling excessive fertilization, reducing artificial intervention and other measures to promote the healthy development of the ecosystem; Dynamic recovery plan: Dynamically adjust the recovery strategy based on real-time data and long-term trends to ensure the achievability and efficiency of the recovery goal and reduce resource waste; The decision support module converts the generated decision support suggestions into decision reports and provides them to the management personnel of the photovoltaic plant area. The reports include specific suggestions for adjusting the recovery plan, reasons for extending the recovery period, measures for maintaining ecological balance, etc., to help the management personnel make scientific decisions. According to the decision support suggestions, the management personnel of the photovoltaic plant area implement the adjusted vegetation recovery plan.

[0030] This invention covers any alternatives, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To enable the public to have a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments of this invention. However, those skilled in the art can fully understand this invention without the description of these details. Additionally, well-known methods, processes, procedures, components, and circuits, etc., are not described in detail to avoid unnecessary confusion to the essence of this invention.

[0031] The above are only the preferred embodiments of this invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of this invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of this invention.

Claims

1. An intelligent management system for vegetation restoration in a photovoltaic plant area, characterized in that, It includes a data acquisition module, an environmental perception module, a vegetation growth monitoring module, a restoration evaluation module, an intelligent optimization control module, and a decision support module, where: The data acquisition module collects multi-source data of the PV plant area in real time through a sensor network deployed in the PV plant area. The multi-source data includes environmental data, soil data, and plant growth data; The environmental perception module uses the sensor network to perceive and monitor the environmental data and plant growth data of the PV plant area in real time, tracking environmental changes and soil condition changes. The environmental perception module includes: Using the collected environmental data, it monitors the environmental changes in the PV plant area in real time, including temperature fluctuations, humidity changes, and wind speed changes, and evaluates the impact of the environment on vegetation growth in combination with soil data; By monitoring plant growth data and combining environmental data, it evaluates the degree of impact of environmental changes on vegetation growth, and judges the health status and progress of vegetation restoration in the current PV plant area; According to the monitoring results of environmental data and plant growth data, it calculates the suitability score of environmental factors for vegetation growth; Environmental suitability score ; wherein, is the temperature, is the humidity, is the light intensity, is the precipitation, is the weight of environmental factors; Based on the real-time perception and monitoring results, it outputs data to the intelligent optimization control module. The output data includes the evaluation result of the environment on vegetation growth and the suitability score; The vegetation growth monitoring module, based on plant growth data, uses image recognition technology and growth models to monitor the growth status of vegetation in real time, and evaluates the progress and effect of vegetation restoration; The restoration evaluation module, based on big data analysis technology, combines the collected multi-source data to evaluate the restoration effect, and generates vegetation restoration effect evaluation data. The restoration evaluation module includes: Using a linear regression algorithm, it establishes a regression model between environmental factors, soil conditions, and vegetation growth to evaluate the restoration effect; Based on the evaluation result of the restoration effect, it calculates the predicted value of vegetation restoration. The prediction formula is: ； Wherein: is the predicted value of vegetation restoration, is the model intercept, are the regression coefficients corresponding to each input variable, are the input variables, representing the multi-source data collected; It compares the predicted value with the actually observed plant growth data, evaluates the accuracy of the prediction, and generates evaluation data on the vegetation restoration effect; The intelligent optimization control module optimizes the management strategy during the vegetation restoration process through machine learning algorithms according to the restoration effect evaluation data generated by the restoration evaluation module; The decision support module provides scientific decision support suggestions based on the analysis results of the restoration evaluation module and the intelligent optimization control module.

2. The intelligent management system for vegetation restoration in a photovoltaic plant area according to claim 1, characterized in that, The data acquisition module includes: The sensor network includes environmental sensors, soil sensors, and plant growth sensors. The environmental sensors, soil sensors, and plant growth sensors are respectively arranged at different positions in the PV plant area; The environmental sensors collect environmental data in real time, including temperature, humidity, wind speed, precipitation, and light intensity; The soil sensors collect soil data, including soil humidity, soil temperature, soil pH value, and soil nutrient content; The plant growth sensors collect plant growth data, including plant height and leaf area; The collected multi-source data is preprocessed through a preset data processing unit, including formatting, cleaning, and denoising.

3. The intelligent management system for vegetation restoration in a photovoltaic plant area according to claim 1, wherein The vegetation growth monitoring module includes: Through high-definition cameras arranged in the PV plant area, it obtains image data of the vegetation in the PV plant area in real time. The image data includes leaf morphology and leaf color; Process the collected image data using a convolutional neural network to extract plant features in the images; Fuse the recognition results of the convolutional neural network with the plant growth data collected by the sensors to comprehensively evaluate the growth status of the vegetation.

4. The intelligent management system for vegetation restoration in a photovoltaic plant area according to claim 3, characterized in that, The vegetation growth monitoring module further includes: Describe the growth process of the plants through an exponential growth model based on the real-time collected plant growth data; Predict the future growth trend of the plants using the exponential growth model based on the collected plant growth data; Generate a plant growth trend report according to the prediction results, including the growth status, health condition, and expected recovery progress of the plants at different time nodes.

5. The intelligent management system for vegetation restoration in a photovoltaic plant area according to claim 1, characterized in that The recovery evaluation module specifically includes: Compare the evaluation data generated by the recovery evaluation module with the target value of vegetation recovery, where the target value can be a preset vegetation growth standard or a reference value generated based on historical data; Judge the effect of vegetation recovery and evaluate the effectiveness of the current recovery strategy by comparing the deviation between the evaluation data and the target value; Provide the result of the recovery evaluation to the decision support module to generate a dynamic report on vegetation recovery.

6. The intelligent management system for vegetation restoration in a photovoltaic plant area according to claim 4, wherein, The intelligent optimization control module includes: Obtain the recovery effect evaluation data from the recovery evaluation module and perform feature extraction on the recovery effect evaluation data; Based on the feature extraction results, establish an optimization model for the vegetation recovery process through machine learning algorithms to predict the optimal management strategy for vegetation recovery under different environmental conditions; Automatically adjust the management strategy of the photovoltaic plant area according to the output of the optimization model.

7. The intelligent management system for vegetation restoration in a photovoltaic plant area according to claim 6, characterized in that, The decision support module includes: Obtain and analyze the analysis results of the recovery evaluation module and the intelligent optimization control module; Comprehensively consider factors such as the effect of vegetation recovery, recovery progress, and environmental and soil conditions to determine the potential problems and optimization space in the current vegetation recovery process; Generate decision support suggestions based on the analysis results, and convert the generated decision support suggestions into a decision report, which is provided to the management personnel of the photovoltaic plant area. According to the decision support suggestions, the management personnel of the photovoltaic plant area execute the adjusted vegetation recovery plan.

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