Intelligent management method and system based on aquatic plant data indicators

Abstract

The present invention proposes an intelligent management method and system based on aquatic plant data indicators. It belongs to the field of ecology and environmental science and technology. The method includes: analyzing the ecological relationship between different aquatic plants in the wetland, and constructing an ecological interaction network model based on the analysis results; using a wireless sensor network to collect data of various aquatic plants in the wetland in real time, and unifying data from different sources into a data platform; based on the ecological interaction network model and the data platform, dynamically analyzing the ecological interaction network in the wetland, and formulating an aquatic plant data indicator management strategy based on the results of the network dynamic analysis. Through in-depth analysis of the ecological relationship of aquatic plants and the construction of an interaction network model, key species and ecological relationships can be identified, so that targeted protection measures can be taken to maintain the diversity and stability of the wetland ecosystem and prevent ecological imbalance.

Description

Technical Field

[0001] The invention proposes an intelligent management method and system based on aquatic plant data indicators, belonging to the technical field of ecology and environmental science. Background Art

[0002] In modern ecological environment management and protection, wetlands are ecosystems with great ecological value and rich biodiversity in nature. Their health status directly affects regional and even global climate regulation, water purification, biodiversity maintenance and other aspects. However, with the continuous increase in human activities, wetland ecosystems are facing a series of severe challenges such as water pollution, decreased species diversity, and imbalanced ecological structure. Therefore, how to scientifically and efficiently manage wetland ecosystems, especially their key component - aquatic plants, has become an important topic in the field of environmental protection.

[0003] Traditional aquatic plant management methods often rely on manual monitoring and regular surveys, which is not only time-consuming and labor-intensive, but also incomplete and poorly real-time data acquisition, making it difficult to accurately reflect the dynamic changes of wetland ecosystems. At the same time, due to the complex interactions between the various biological components in wetland ecosystems, management measures targeting a single species alone often fail to achieve the expected ecological effects and may even cause new ecological problems. Summary of the invention

[0004] The present invention provides an intelligent management method and system based on aquatic plant data indicators to solve the problems mentioned in the above background technology:

[0005] The intelligent management method based on aquatic plant data indicators proposed by the present invention comprises:

[0006] S1. Analyze the ecological interactions among various aquatic plants in wetland environments and build an ecological interaction network model based on this;

[0007] S2. Use wireless sensor equipment to monitor the status of various aquatic plants in the wetland in real time, and integrate the collected data into a unified information processing platform;

[0008] S3. Based on the established ecological interaction network model and real-time data in the information platform, dynamically evaluate the interaction patterns in the wetland ecosystem, and formulate monitoring and management standards for the health status of aquatic plants based on the evaluation results;

[0009] S4. Use machine models and prediction algorithms to predict the growth trends of aquatic plants and changes in ecological interaction networks, and continuously optimize management strategies based on the prediction results.

[0010] The present invention proposes a system for implementing any of the above-mentioned intelligent management methods based on aquatic plant data indicators, the system comprising:

[0011] Model building module: Analyze the ecological interactions between various aquatic plants in wetland environments and build an ecological interaction network model based on this;

[0012] Data integration module: Use wireless sensor equipment to monitor the status of various aquatic plants in the wetland in real time, and integrate the collected data into a unified information processing platform;

[0013] Dynamic analysis module: Based on the established ecological interaction network model and real-time data in the information platform, dynamic evaluation of the interaction patterns in the wetland ecosystem is carried out, and monitoring and management standards for the health status of aquatic plants are formulated based on the evaluation results;

[0014] Strategy Optimization Module: Use machine models and prediction algorithms to predict the growth trends of aquatic plants and changes in ecological interaction networks, and continuously optimize management strategies based on the prediction results.

[0015] Beneficial effects of the present invention: Through in-depth analysis of the ecological relationships of aquatic plants and the construction of an interaction network model, key species and ecological relationships can be identified, so that targeted protection measures can be taken to maintain the diversity and stability of wetland ecosystems and prevent ecological imbalance; real-time data collection and dynamic analysis enable managers to quickly grasp changes in the ecological status of wetlands, combine prediction algorithms to predict future trends, and formulate more scientific and reasonable management strategies. By quantifying the weights of nodes and edges in ecological interaction networks, key nodes and paths can be effectively identified, providing a basis for resource allocation and risk management. The setting of early warning thresholds can issue alarms in time when the ecosystem is threatened, take countermeasures in advance, and reduce the occurrence of ecological disasters; the adaptive management algorithm can automatically adjust management measures based on real-time data and prediction feedback, forming a closed-loop system of dynamic feedback and continuous optimization. This mechanism can quickly respond to environmental changes, optimize management strategies, and improve management efficiency and effectiveness. Although not directly mentioned in the original technical solution, the constructed visualization platform and knowledge base are not only convenient for managers to operate, but also provide the public with a window to understand wetland protection work, enhance environmental awareness and scientific education functions, and are conducive to promoting an ecological and environmental protection model of social co-governance. Through the constructed knowledge base, every adjustment of the management strategy and its effectiveness are systematically recorded, providing valuable data resources for subsequent research and promoting knowledge accumulation and technological progress in the field of ecological management. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a step diagram of the method of the present invention;

[0017] Figure 2This is a system module diagram of the present invention;

[0018] Figure 3 A diagram showing the relationship between the aquatic plants described in the present invention;

[0019] Figure 4 This is a schematic diagram of the ecological interaction network model described in the present invention. DETAILED DESCRIPTION

[0020] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0021] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. The embodiments described are only a part of the embodiments of the present invention, rather than 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.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0023] One embodiment of the present invention, as Figure 1 As shown, an intelligent management method based on aquatic plant data indicators comprises:

[0024] S1. Analyze the ecological interactions between various aquatic plants in wetland environments and build an ecological interaction network model based on this; the ecological relationships include symbiosis and competition;

[0025] S2. Use wireless sensor equipment to monitor the status of various aquatic plants in the wetland in real time, including growth data, environmental data, and interaction data between plants. Through data integration technology, the collected data is integrated into a unified information processing platform;

[0026] S3. Based on the ecological interaction network model and data platform, the ecological interaction network in the wetland is dynamically analyzed. By analyzing the state changes of nodes in the network, the weight changes of edges and other information, the interaction patterns and trend information between plants are obtained. According to the results of the network dynamic analysis, a management strategy for aquatic plant data indicators is formulated. For example, for plant combinations with strong symbiotic relationships, the planting layout is optimized to promote their growth; for plants with fierce competitive relationships, resource allocation is adjusted to reduce competitive pressure.

[0027] S4. Use machine models and prediction algorithms to predict the growth trends of aquatic plants and changes in ecological interaction networks, and continuously optimize management strategies based on the prediction results.

[0028] The working principle of the above technical solution is: to conduct an in-depth analysis of the ecological relationship between different aquatic plants in the wetland, and the ecological relationship includes the symbiotic relationship (mutual benefit) and competitive relationship (resource competition) between aquatic plants. The in-depth analysis includes analysis through field investigation, literature research and data analysis. Then, based on the analysis results, an ecological interaction network model is constructed, and the ecological interaction network model reflects the mutual influence and dependence between plants through nodes (different plants) and edges (the relationship between them); using wireless sensor networks, sensors are deployed in the wetland to collect data of various aquatic plants in real time. The data includes growth data (such as height, number of leaves), environmental data (such as soil moisture, light intensity) and interaction data between plants (such as root interaction). Through data integration technology, the data collected from different sources are uniformly stored in the data platform; based on the constructed ecological interaction network model and the integrated data, the ecological interaction network in the wetland is dynamically analyzed. Using network analysis technology, the state changes of nodes (plants) in the network, the weight changes of edges (relationships) and other information are monitored to obtain the interaction rules and trend information between plants. According to the analysis results, formulate corresponding aquatic plant data index management strategies, including optimizing planting layout to promote the growth of plant combinations with strong symbiotic relationships; adjusting resource allocation to reduce competitive pressure for plants with fierce competitive relationships; using machine models and prediction algorithms to predict the growth trends of aquatic plants and changes in ecological interaction networks. Based on the prediction results, continuously optimize management strategies.

[0029] The effect of the above technical solution is: by analyzing and dynamically monitoring the ecological relationships of aquatic plants in wetlands, refined management of plant communities can be achieved. Developing targeted management strategies based on the symbiotic and competitive relationships between plants can maximize the promotion of favorable symbiotic relationships, reduce competitive pressure, and optimize the structure of the ecosystem; based on real-time data collection and dynamic analysis of aquatic plants, it is possible to achieve a reasonable allocation of resources (such as water, nutrients, etc.), and accurately deliver them according to the differences in the needs of different plants, which can improve resource utilization efficiency and reduce costs; by predicting the growth trends of aquatic plants and changes in ecological interaction networks through prediction algorithms, it is possible to timely discover the changing trends of plant communities, and maintain ecological balance through continuous optimization of management strategies to avoid imbalance in the ecosystem; combining ecological interaction network models with real-time data to provide scientific decision-making support for wetland park managers, so that they can more accurately understand the status of plant communities in wetlands and take corresponding management measures to achieve sustainable development of wetland ecosystems; through dynamic analysis and continuous optimization of ecological interaction networks, it is possible to maximize the healthy growth of aquatic plants in wetlands, protect wetland ecological environments, maintain wetland biodiversity, and enhance the ecological landscape value of wetland parks.

[0030] In one embodiment of the present invention, the S1 includes:

[0031] S11. Collecting information on all aquatic plants in the wetland park, including species, quantity and geographical distribution; arranging data on the collected aquatic plants through an encyclopedia database; the data includes plant growth habits, ecological niches and known interspecific relationship data;

[0032] S12. Recording the field observation data of plants through field survey data; the field observation data includes growth status, health status, spatial distribution and physical interaction between plants (such as root contact degree and canopy overlap);

[0033] S13. Analyze the symbiotic relationships among plants (such as nutrient sharing, mutualism, etc.) and evaluate the competitive relationships among plants (such as resource competition, space occupation, etc.) through statistical analysis methods, identify key species and key interspecific relationships, and analyze the impact on the stability of wetland ecosystems;

[0034] S14. Based on the results of ecological relationship analysis, construct an ecological interaction network model, use graph theory and network analysis algorithms to determine the nodes (plant individuals) and edges (interspecific relationships) in the network; and quantify the weights of the edges;

[0035] S15. Verify the accuracy of the ecological interaction network model through historical data and field observation data, and adjust the model parameters and structure based on the verification results.

[0036] The working principle of the above technical solution is as follows: collect information on all aquatic plants in the wetland park, including species, quantity and geographical distribution; then, organize the collected data on aquatic plants through encyclopedia databases, including plant growth habits, ecological niches and known interspecific relationship data; conduct field surveys to record the growth status, health status, spatial distribution and physical interactions between plants, such as root contact and canopy overlap; and use statistical analysis methods to analyze the symbiotic and competitive relationships between plants (such as Figure 3 ) to evaluate the impact of plant interactions on wetland ecosystem stability, identify key species and key interspecific relationships; based on the results of ecological relationship analysis, construct an ecological interaction network model, such as Figure 4 As shown in the figure, graph theory and network analysis algorithms are used to determine the nodes (plant individuals) and edges (interspecific relationships) in the network, and to quantify the weights of the edges to reflect the intensity and direction of interactions between plants. Finally, the accuracy of the ecological interaction network model is verified through historical data and field observation data, and the model parameters and structure are adjusted according to the verification results to ensure that the model can accurately reflect the actual situation.

[0037] The effects of the above technical solutions are: through the collection and organization of aquatic plant information and the recording of field observation data, we can fully understand the types, quantity, geographical distribution, growth status, ecological niche and interaction between aquatic plants in the wetland park, and provide a detailed data basis for in-depth understanding of the wetland ecosystem; by analyzing the symbiotic and competitive relationships between plants, identifying key species and key interspecific relationships, it is helpful to scientifically guide the protection and management of the wetland park. At the same time, the construction of an ecological interaction network model can help managers better understand the structure and function of plant communities, so as to formulate more effective protection strategies; the establishment of an ecological interaction network model enables us to predict the stability and dynamic changes of plant communities, timely discover possible ecological problems and intervene, which is conducive to maintaining the health and balance of wetland ecosystems; by verifying and adjusting the ecological interaction network model, the accuracy and reliability of the model can be ensured, thereby providing scientific support for decision-making and helping managers to better formulate long-term plans and policy measures for protecting wetland ecosystems; wetland ecosystems are an important part of biodiversity and ecological balance.

[0038] In one embodiment of the present invention, the S15 includes:

[0039] Preprocessing the collected historical data and field observation data includes cleaning, removing data with duplicates, anomalies or too many missing values, screening the data based on the relevance, importance and timeliness of the data, and retaining relevant data; the relevant data includes data closely related to the assessment of wetland ecosystem stability and the construction of ecological interaction network models;

[0040] Check data from different sources for contradictions or inconsistencies;

[0041] If it exists, data tracing and verification are performed;

[0042] Clarify the goals and indicators of model validation, including the accuracy, stability and predictive ability of the model; select the validation method based on the characteristics of the model and the data situation;

[0043] Compare the model predictions with field observations, calculate the prediction error, and back-validate the model using historical data to check the model's performance under historical circumstances;

[0044] Analyze the influence of each parameter in the model on the model output, identify key parameters, and optimize and adjust the key parameters of the model based on the verification results and sensitivity analysis;

[0045] Analyze the network structure of the ecological interaction network model, identify the key nodes and key paths in the network, and adjust and optimize the network structure of the model based on the verification results and network structure analysis; such as adding or reducing nodes, changing edge weights, etc.

[0046] After adjusting the model parameters and structure, the model is verified again, and the model is further improved and optimized based on the results of iterative verification.

[0047] The working principle of the above technical solution is as follows: first, pre-process the collected historical data and field observation data, and screen them according to the relevance, importance and timeliness of the data, and retain data closely related to the wetland ecosystem stability assessment and the construction of the ecological interaction network model; check the data from different sources to ensure that there are no contradictions or inconsistencies between the data. If there are any, trace and verify the data to ensure the accuracy and consistency of the data; determine the goals and indicators of model verification, including the accuracy, stability and predictive ability of the model, and select appropriate verification methods according to the characteristics of the model and the data situation, such as cross-validation, independent sample verification, etc. Among them, the main goal of model verification is to evaluate the performance of the constructed model in practical applications, and ensure that it can accurately and stably reflect and predict the dynamic changes of the target system. For the management of aquatic plant data indicators in artificial wetland parks, the goals of model verification specifically include:

[0048] Confirm model accuracy: Ensure that the model can accurately reflect the growth status of aquatic plants, environmental conditions, and interactions between plants.

[0049] Evaluate model stability: Check whether the model performs consistently at different times and under different environmental conditions to avoid model failure due to data fluctuations or changes in the external environment.

[0050] Verify predictive ability: Evaluate the model's ability to predict future growth trends of aquatic plants and changes in ecological interaction networks, and provide a reliable basis for the formulation and optimization of management strategies.

[0051] The indicators for model validation include:

[0052] Accuracy metrics: Choose metrics that can quantify the difference between the model prediction results and the actual observations, such as mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), etc.

[0053] Stability indicators: Evaluate the performance differences of the model under different data sets or different time windows, and measure the stability of the model output by using indicators such as coefficient of variation and standard deviation.

[0054] Predictive power indicators: Select indicators that reflect the accuracy of the model's prediction of future trends, such as prediction accuracy, recall rate, F1 score, etc., in the ecosystem, and consider indicators unique to time series prediction, such as lag time, prediction confidence interval, etc.

[0055] Measures of accuracy, stability and predictive power:

[0056] A measure of accuracy is calculated by calculating the difference between the model's predictions and the actual observed values. For example, when using MSE or RMSE, lower values ​​indicate that the model's predictions are more accurate.

[0057] Stability measurement: Stability is usually measured by evaluating the consistency of the model's performance under different conditions. The coefficient of variation or standard deviation of the model's output results in different data sets or different time periods is calculated by statistical methods. A smaller coefficient of variation or standard deviation indicates a more stable model.

[0058] Metrics of predictive ability: Predictive ability is mainly tested by applying the model to future data sets. In an ecosystem, this means using historical data to train the model and then testing its predictive ability with future data. Indicators such as prediction accuracy, recall, and F1 score can quantify how accurately the model predicts future trends. In addition, analyze the model's prediction confidence interval to assess the uncertainty of the prediction results. Compare the model prediction results with field observation data, calculate the prediction error, evaluate the accuracy of the model, and use historical data to back-test the model and check the model's performance in historical situations. The specific references to the model prediction results include:

[0059] Implementation of prediction function: Although the ecological interaction network model itself describes the interaction relationship between aquatic plants, it can provide the basic structure or constraints for the prediction model. The prediction function is achieved by combining this network model with machine learning models, time series analysis or other prediction algorithms. The algorithm uses the information in the ecological interaction network model (such as symbiotic and competitive relationships between plants, niche overlap, etc.) and real-time collected data (such as growth data, environmental data, etc.) to train the prediction model.

[0060] Content of prediction results: The "prediction results" here actually refer to the prediction of the future growth status of aquatic plants, the evolution trend of ecological interaction networks, etc. through a comprehensive model (i.e. a model that combines the ecological interaction network model and the prediction algorithm). These predictions may include but are not limited to the growth rate of plants, population density, ecological niche changes, etc.

[0061] The model output refers to:

[0062] Content of model output: "Model output" refers to the results generated by the comprehensive model under given input data (such as current aquatic plant data, environmental data, etc.). These outputs can be direct prediction values ​​(such as plant population density at a certain point in the future) or prediction trends (such as the direction of structural changes in the ecological interaction network). In addition, model output may also include uncertainty analysis (such as prediction confidence intervals) or sensitivity analysis (such as the impact of different parameter changes on prediction results); analyze the degree of influence of each parameter in the model on the model output, identify key parameters, and optimize and adjust the key parameters of the model based on the verification results and sensitivity analysis to improve the prediction accuracy of the model; analyze the network structure of the ecological interaction network model, identify the key nodes and key paths in the network, and adjust and optimize the network structure of the model based on the verification results and network structure analysis to improve the stability and prediction ability of the model; after the model parameters and structure are adjusted, the model is verified again, and the model is further improved and optimized based on the results of iterative verification until the expected accuracy and stability requirements are met.

[0063] The effects of the above technical solutions are as follows: by cleaning, screening and verifying historical data and field observation data, the quality and accuracy of the data can be guaranteed, a reliable data basis can be provided for model construction, and the prediction accuracy and stability of the model can be improved; the consistency of data from different sources can be checked and verified to ensure the consistency and reliability of the data, while clarifying the goals and indicators of model verification, which is helpful to accurately evaluate the performance and effectiveness of the model; by comparing the model prediction results with the field observation data and calculating the prediction error, the accuracy of the model can be evaluated, and the performance of the model under historical conditions can be further verified by using historical data for retrospective verification, which is helpful to discover and correct problems and defects in the model; by analyzing the degree of influence of each parameter in the model on the output, identifying key parameters, and optimizing and adjusting them, the prediction accuracy of the model can be improved; at the same time, analyzing and optimizing the network structure of the ecological interaction network model can help improve the stability and prediction ability of the model; by iteratively verifying and adjusting the model parameters and structure, the model is continuously optimized until the expected accuracy and stability requirements are met, thereby improving the application effect and reliability of the model.

[0064] In one embodiment of the present invention, the S2 includes:

[0065] S21. Select sensors according to the terrain and plant distribution of the wetland park, deploy the selected sensors, and design the wireless sensor network architecture;

[0066] S22, collect aquatic plant data of plants through wireless sensor networks, including growth data (such as plant height, leaf area, biomass, etc.), environmental data (such as water temperature, water quality, light, soil moisture, etc.), and interaction data between plants (such as root interweaving degree, space occupancy, etc.); and pre-process the collected aquatic plant data; the pre-processing includes cleaning, denoising and outlier processing, etc., and uniformly format and standardize the pre-processed data;

[0067] S23. Integrate aquatic plant data based on database technology and build a unified data platform, and design data table structure and index based on the constructed data platform;

[0068] S24. Regularly check and verify data quality using preset data quality standards and verification methods, and correct errors and outliers.

[0069] The working principle of the above technical solution is: according to the terrain and plant distribution of the wetland park, suitable sensors are selected and deployed in appropriate locations. These sensors can include sensors that measure environmental parameters such as water quality, water temperature, soil moisture, and light, as well as sensors that measure plant growth status and interactions, such as:

[0070] Plant height:

[0071] Ultrasonic sensor: Ultrasonic sensor can indirectly measure plant height by emitting ultrasonic waves and measuring the time it takes for them to reflect back from a target (such as a plant stem) to calculate the distance. This method is non-contact, suitable for aquatic plants, and will not cause damage to the plants.

[0072] Laser Distance Sensors: Similar to ultrasonic sensors, laser distance sensors use a laser beam to measure distance, typically with greater accuracy and a longer measurement range.

[0073] Leaf area:

[0074] Image sensor (camera): Combined with image processing technology, a camera can be used to capture images of plant leaves and calculate leaf area through software algorithms (such as image segmentation, edge detection, etc.).

[0075] Optical sensors: Some specially designed optical sensors can estimate leaf area by measuring the degree to which leaves block light, which is usually faster and easier to automate.

[0076] Biomass:

[0077] Direct measurement followed by input: Biomass (i.e., the total organic matter content of a plant) is often difficult to measure directly with sensors because it relates to the overall mass and composition of the plant. In practice, biomass is often estimated by periodically harvesting and weighing plant samples, which are then manually input into the system. However, some research is exploring the use of techniques such as near-infrared spectroscopy to estimate biomass non-destructively.

[0078] Root interweaving degree:

[0079] X-ray imaging or CT scanning: To study the degree of root interweaving, more complex equipment is usually required, such as X-ray imaging systems or computed tomography (CT) machines. These devices can produce detailed images of plant roots to analyze the degree of interweaving. However, these devices are not suitable for use in the field or in large-scale wireless sensor networks.

[0080] Dedicated root monitoring sensors: In recent years, some research institutions and enterprises have developed sensors specifically for monitoring root growth, which usually combine technologies such as image recognition or conductivity measurement to assess the growth and degree of interweaving of roots. However, the application of such sensors is relatively limited and the cost is relatively high.

[0081] Space occupancy:

[0082] Stereo vision sensors or LiDAR: These sensors can assess spatial occupancy of plants by measuring the three-dimensional position of objects in space. LiDAR emits laser beams and measures the time it takes for them to reflect back, thereby building a 3D model of the surrounding environment. Stereo vision sensors calculate depth information by taking the difference in images from two or more cameras. These technologies can be used to assess spatial distribution and occupancy between plants.

[0083] The growth data, environmental data and interaction data of aquatic plants are collected in real time through wireless sensor networks. The collected data are pre-processed, including cleaning, denoising and processing of outliers, to ensure the accuracy and reliability of the data, and the data are uniformly formatted and standardized for subsequent data integration and analysis; based on database technology, the collected aquatic plant data are integrated and a unified data platform is built. The data table structure and index are designed on the data platform to efficiently store, manage and query data, providing support for subsequent data analysis and application; according to the preset data quality standards and verification methods, the data is regularly checked and verified. By checking the integrity, consistency and accuracy of the data, errors and outliers can be discovered and corrected in a timely manner.

[0084] The effects of the above technical solutions are as follows: through the wireless sensor network, real-time monitoring and collection of growth data, environmental data and interaction data of aquatic plants are realized, so that the status and changes of the wetland ecosystem can be understood in time; through preprocessing and data quality inspection, it is ensured that the collected data are cleaned, denoised and processed for outliers, and the data quality is high, and regular quality inspection and verification are carried out to ensure the accuracy and reliability of the data; a unified data platform is built based on database technology, which integrates a large amount of aquatic plant data, provides a platform for sharing and querying data for relevant researchers and decision makers, and promotes information sharing and resource utilization efficiency; through the integration and analysis of aquatic plant data, scientific data support is provided for the management and protection of wetland parks, which can help to formulate more reasonable protection measures and management strategies; through the monitoring and analysis of plant growth data and environmental data, it is helpful to timely discover problems in the ecological environment, and provide an important basis for the protection and restoration of the wetland ecological environment.

[0085] In one embodiment of the present invention, the S24 includes:

[0086] Define the required and optional items for data fields, and set accuracy ranges for each data field based on historical data and professional knowledge, such as the normal fluctuation range of growth data such as plant height and leaf area. Ensure that data from different sources or at different time points are logically consistent, such as the consistency of light intensity and plant growth status.

[0087] Use scripts to check whether there are null values ​​or missing fields in the data table. If so, mark or automatically fill in the missing data; use statistical methods (such as mean, standard deviation, median, etc.) to detect anomalies in data points and identify data that exceeds the normal fluctuation range; identify and correct inconsistent data by comparing the correlation or dependency between different data sets;

[0088] According to the importance and update frequency of the data, set the inspection cycle and perform automated inspections, such as daily, weekly or monthly, and present the inspection results in a visual way;

[0089] For errors or abnormal values ​​that cannot be accurately determined by automated inspection, they are fed back to humans for review and correction. For predictable data fields (such as data with seasonal changes), historical data or trend prediction models are used for correction.

[0090] Re-verify the corrected data and provide feedback to the data producer or relevant personnel. Based on the verification results and feedback, continuously optimize the data quality standards and verification methods.

[0091] The working principle of the above technical solution is as follows: define the fields in the data table to determine which fields are required and which are optional to ensure the integrity of the key fields in the data table; set the accuracy range for each data field based on historical data and professional knowledge, such as the normal fluctuation range of growth data such as plant height and leaf area, so that judgment and correction can be made in subsequent data inspections; check whether there are null values ​​or missing fields in the data table by writing scripts. If so, mark or automatically fill in the missing data to ensure the integrity and availability of the data; use statistical methods (such as mean, standard deviation, median, etc.) to detect anomalies of data points, identify data that exceeds the normal fluctuation range, and mark or correct it; identify and correct inconsistent data by comparing the correlation or dependency between different data sets. Check the data to ensure that it is logically consistent; set an inspection cycle and perform automated inspections, such as daily, weekly or monthly, based on the importance and update frequency of the data, and present the inspection results in a visual manner, such as reports, charts or dashboards; errors or abnormal values ​​that cannot be accurately judged by automated inspections need to be fed back to manual review and correction to ensure the accuracy of the data; for predictable data fields (such as data with seasonal changes), historical data or trend prediction models can be used to correct them to improve the accuracy of the data; the corrected data is re-verified to ensure that the data quality is improved and no new errors are introduced, and the correction results are fed back to the data producer or relevant personnel so that they can understand the data quality issues and improvement measures; based on the verification results and feedback, the data quality standards and verification methods are continuously optimized.

[0092] The effects of the above technical solutions are as follows: by defining mandatory items and accuracy ranges, as well as processing null values ​​and abnormal data, the integrity and accuracy of key fields in the data table can be ensured, and data quality can be improved; by comparing the correlation or dependency between different data sets, data from different sources or at different time points can be ensured to be logically consistent, thereby avoiding contradictions or inconsistencies between data; setting inspection cycles and performing automated inspections, and presenting the inspection results in a visual manner, data quality problems can be discovered and corrected in a timely manner, thereby improving the efficiency and timeliness of data quality monitoring; combining manual review with the use of historical data or predictive models for correction, it is possible to give full play to the advantages of professionals and technical means, and improve the accuracy and comprehensiveness of data correction; the correction results are fed back to data producers or relevant personnel, and data quality standards and verification methods are continuously optimized based on verification results and feedback, so as to achieve the goal of continuous improvement and optimization of data quality management; improving the accuracy and completeness of data can enhance the credibility and application value of data, provide stronger support for decision-making and analysis, and thus improve business efficiency and decision-making quality.

[0093] In one embodiment of the present invention, S3 includes:

[0094] S31. Real-time monitoring of node status changes (such as growth rate, health status, etc.) in the ecological interaction network, and analysis of edge weight changes, and based on the analysis results, evaluation of changes in the intensity of interactions between plants;

[0095] S32. Analyze the dynamic change patterns and trends of the network over time using a time series analysis algorithm, and identify key nodes and key paths in the network based on the analysis results;

[0096] S33. Evaluate the impact and risk of different management strategies on the stability of ecological interaction networks, set warning thresholds, and issue warnings when the network status reaches or exceeds the threshold;

[0097] S34. Based on the results of network dynamic analysis and risk assessment, formulate management strategies, convert management strategies into specific operational measures, and implement them.

[0098] The working principle of the above technical solution is: by real-time monitoring of node status changes in the ecological interaction network, such as plant growth rate, health status, etc., and analyzing the weight changes of the edges. Data is obtained through sensors, monitoring equipment and other means, and data analysis methods are used to evaluate the node status and interaction strength; the dynamic change law and trend of the network over time are analyzed using time series analysis algorithms, key nodes and key paths are identified, and their impact on network stability is analyzed. Time series analysis is performed through historical data and real-time monitoring data to identify the dynamic characteristics and key nodes of the network; the impact and risk of different management strategies on the stability of the ecological interaction network are evaluated, and early warning thresholds are set, and early warnings are issued when the network status reaches or exceeds the threshold. Different management strategies are evaluated based on models and experimental data, and appropriate early warning mechanisms are set; management strategies are formulated based on the results of network dynamic analysis and risk assessment, considering the impact of seasonal changes and climate conditions on plant growth, seasonal management strategies are formulated, and emergency response plans are formulated. Management strategies are converted into specific operational measures and implemented.

[0099] The effects of the above technical solutions are: by real-time monitoring of the node state changes and edge weight changes of the ecological interaction network, it is possible to timely discover changes in plant growth rate, health status, etc., so as to more accurately evaluate the changes in the intensity of interactions between plants, which is helpful to achieve fine management of the ecosystem; by using the time series analysis algorithm to analyze the dynamic changes of the network, it is possible to accurately identify key nodes and key paths, and then evaluate their impact on network stability. This helps to focus on and manage nodes and paths that have an important impact on system stability, and improve the stability and anti-interference ability of the ecosystem; by evaluating the impact and risk of different management strategies on the stability of the ecological interaction network, it is possible to optimize the management strategy and set a suitable early warning threshold. When the network status reaches or exceeds the early warning threshold, it can issue an early warning in time, which helps to take targeted measures to prevent potential risks and maintain the health of the ecosystem; considering the impact of seasonal changes and climatic conditions on plant growth, formulating seasonal management strategies, and formulating emergency response plans can better adapt to changes in the external environment and improve the adaptability of the ecosystem to external interference. Transforming management strategies into specific operational measures and implementing them can ensure the stability and sustainable development of the ecosystem.

[0100] In one embodiment of the present invention, the S33 includes:

[0101] Based on historical data and professional knowledge, determine the baseline values ​​of the network under normal conditions, including growth rate and health status; identify the range of changes in key nodes and key paths in the network under normal conditions as preliminary thresholds for early warning;

[0102] By simulating the impact of different management strategies on the network status, the sensitivity of the network to different management strategies is evaluated, and the preliminary threshold is dynamically adjusted according to the sensitivity analysis results;

[0103] Use probabilistic models and simulation methods to assess the risk level of network stability under different management strategies, and further optimize the warning threshold based on the risk assessment results;

[0104] In actual applications, the accuracy and effectiveness of the warning threshold are verified by real-time monitoring of changes in network status, and the warning threshold is regularly updated based on monitoring results and feedback;

[0105] When the network status reaches or exceeds the warning threshold, the warning mechanism is automatically triggered and warning information is sent to the management personnel, who then formulate specific response strategies based on the warning information.

[0106] The working principle of the above technical solution is as follows: through historical data and professional knowledge, the baseline value of the network in normal state is determined, including indicators such as growth rate and health status. At the same time, the change range of key nodes and key paths in normal state is identified as the preliminary threshold of early warning; the impact of different management strategies on network status is evaluated by simulation method, and the sensitivity of the network to different management strategies is analyzed. According to the results of sensitivity analysis, the preliminary threshold is dynamically adjusted to adapt to the changes in network status under different management strategies; further, the risk level of network stability under different management strategies is evaluated by using probability model and simulation method. According to the results of risk assessment, the early warning threshold is optimized to ensure that the threshold can accurately reflect the risk level of network stability; in actual application, the accuracy and effectiveness of the early warning threshold are verified by real-time monitoring of changes in network status. According to the monitoring results and feedback, the early warning threshold is regularly updated to adapt to changes in network status and adjustments to management strategies; when the network status reaches or exceeds the early warning threshold, the early warning mechanism is automatically triggered to send early warning information to the management personnel. According to the early warning information, a specific response strategy is formulated, including adjusting the management strategy, taking emergency measures, etc., to maintain the stability of the network.

[0107] The effects of the above technical solutions are as follows: by determining the network baseline value through historical data and professional knowledge, and dynamically adjusting the warning threshold, it can accurately reflect the changes in network status, timely discover abnormal situations and trigger the warning mechanism, thereby ensuring the accuracy and real-time nature of the warning system; by simulating the impact of different management strategies on the network status, evaluating the sensitivity of the network to different management strategies, and optimizing the warning threshold, the warning system can adapt to the changes in network status under different management strategies, and has strong flexibility and adaptability; by using probability models and simulation methods to evaluate the risk level of network stability, and optimizing the warning threshold according to the risk assessment results, it can more accurately reflect the risk level of network stability, and improve the prediction ability and pertinence of the warning system; by verifying the accuracy and effectiveness of the warning threshold through real-time monitoring, and regularly updating the warning threshold according to the monitoring results and feedback, the warning system can continuously learn and improve and maintain consistency with the actual situation; when the network status reaches or exceeds the warning threshold, the warning mechanism is automatically triggered, and warning information is sent to the management personnel, and at the same time, specific response strategies are formulated, including adjusting the management strategy, taking emergency measures, etc., to maintain the stability of the network and minimize possible losses.

[0108] In one embodiment of the present invention, the S4 includes:

[0109] S41. Use machine learning models to make rolling predictions on the future growth status of aquatic plants and the potential evolution trend of ecological interaction networks.

[0110] S42. Based on the prediction results, use optimization algorithms (such as genetic algorithms and simulated annealing algorithms) to dynamically adjust management strategies. Through adaptive management algorithms, the intensity and direction of management measures can be automatically adjusted according to real-time monitoring data and prediction feedback; such as dynamically adjusting water resource scheduling and nutrient replenishment strategies, or timely intervening in biological manipulation to balance interspecies competition.

[0111] S43. Through the effect evaluation and feedback mechanism, the actual operation results are compared and analyzed with the prediction model prediction results, the model deviation is identified and feedback is corrected to form a closed-loop system of management-prediction-adjustment;

[0112] S44. Build a knowledge base to record the reasons, processes and results of each strategy adjustment.

[0113] The working principle of the above technical solution is: the growth status of aquatic plants and the evolution trend of ecological interaction networks are predicted using machine learning models. Based on historical data and ecological knowledge, the model continuously monitors and predicts the future state of the ecosystem by continuously acquiring new data, updating model parameters, and re-predicting. This rolling prediction can maintain the timeliness and accuracy of the prediction model, helping managers to adjust strategies in a timely manner; according to the results of the rolling prediction, the management strategy is dynamically adjusted using optimization algorithms. Optimization algorithms such as genetic algorithms and simulated annealing algorithms can help managers find the optimal management strategy while considering multiple factors (such as cost-effectiveness, ecosystem sustainability, long-term impact, etc.). Through adaptive management algorithms, the intensity and direction of management measures are automatically adjusted according to real-time monitoring data and prediction feedback to respond to changes in the ecosystem; the actual operation results are compared and analyzed with the prediction results of the prediction model, the model deviation is identified and feedback is corrected. By continuously evaluating the actual effect and correcting the deviation of the prediction model in a timely manner, a closed-loop system of management-prediction-adjustment is formed to ensure the effectiveness and continuous improvement of the management strategy; the reasons, processes and results of each strategy adjustment are recorded to provide case references for similar situations in the future. By building a knowledge base, we can accumulate experiences and lessons learned in wetland ecosystem management, continuously improve management effectiveness, and provide more reference and support for future decision-making.

[0114] The effects of the above technical solutions are: through rolling prediction, constantly updating the model and re-predicting, the changing trends of aquatic plant growth status and ecological interaction networks can be captured in time, which improves the timeliness and accuracy of the prediction and helps to take effective management measures in time; using optimization algorithms, such as genetic algorithms and simulated annealing algorithms, dynamically adjust management strategies, consider multiple factors and find the optimal solution. This intelligent management enables managers to respond to complex ecosystem changes more efficiently and maximize the sustainable development of ecosystems; through effect evaluation and feedback mechanisms, the actual operation results are compared and analyzed with the prediction results of the prediction model, and model deviations are identified and corrected in time. The real-time adjustment and feedback mechanism helps to maintain the flexibility and effectiveness of management strategies and keep management measures synchronized with ecosystem changes; build a knowledge base to record the reasons, processes and results of each strategy adjustment, and provide case references for similar situations in the future. It helps to gradually accumulate wisdom and experience in wetland ecosystem management and improve management efficiency, and also provides valuable reference materials for other wetland managers.

[0115] In one embodiment of the present invention, the S43 includes:

[0116] Compare the actual operation results with the predicted results output by the prediction model, analyze the differences between the two, and set a series of indicators, such as growth rate deviation rate, health status index difference, etc., to quantify the deviation between the actual results and the predicted results;

[0117] According to the nature and size of the deviation, the deviation is divided into types, including systematic deviation and random deviation;

[0118] Analyze the deviation data, identify the main sources of deviation, and adjust the parameters of the prediction model based on the deviation analysis results;

[0119] To address data quality issues, optimize data collection and processing processes. If the deviation is large and cannot be resolved by parameter adjustment, update the prediction model and introduce a new algorithm.

[0120] Integrate the steps of comparative analysis, deviation identification, feedback correction, etc. into an automated process. Use real-time monitoring data to evaluate the prediction model in real time. Once a large deviation is found, immediately start the feedback correction process.

[0121] Set deviation tolerance based on management needs and ecosystem characteristics, and formulate corresponding risk management strategies for possible larger deviations or risks.

[0122] The working principle of the above technical solution is as follows: compare the actual operation results with the predicted results output by the prediction model, and quantify the deviation between the two by setting a series of indicators, such as the growth rate deviation rate, the difference in health status index, etc.; according to the nature and size of the deviation, classify the deviation into types, including systematic deviation and random deviation; analyze the deviation data to identify the main sources of deviation, such as improper model parameter settings, low input data quality, changes in external environmental factors, etc.; according to the deviation analysis results, adjust the parameters of the prediction model to improve the prediction accuracy of the model. If the deviation is large and cannot be solved by parameter adjustment, consider updating the prediction model and introducing a new algorithm; for data quality issues, optimize the data collection and processing process, improve data quality, and ensure the accuracy and completeness of the model input data; integrate the comparative analysis, deviation identification, feedback correction and other steps into an automated process to reduce human intervention and improve efficiency. Use real-time monitoring data to evaluate the prediction model in real time, and once a large deviation is found, immediately start the feedback correction process; set the deviation tolerance according to management needs and ecosystem characteristics to avoid instability caused by over-correction. In response to possible major deviations or risks, formulate corresponding risk management strategies to ensure the stability and sustainability of the ecosystem.

[0123] The effects of the above technical solutions are as follows: through comparative analysis and deviation identification, the deviation between the prediction model and the actual results can be discovered in time, and the model parameters can be adjusted or updated in a targeted manner, thereby improving the accuracy of the prediction; the solution optimizes data quality issues, and by optimizing the data collection and processing process, the data quality is improved, which helps to improve the prediction accuracy of the model; the steps of comparative analysis, deviation identification and feedback correction are integrated into an automated process, which reduces human intervention and improves efficiency. At the same time, the use of real-time monitoring data to evaluate the prediction model in real time can timely discover deviations and initiate the correction process, further improving the immediacy and accuracy of management; by setting deviation tolerance and formulating risk management strategies, system instability caused by over-correction can be avoided, ensuring the stability and sustainability of the ecosystem; through automated processes and real-time monitoring, human resource investment is reduced, and management efficiency and accuracy are improved, thus bringing significant cost-effectiveness.

[0124] One embodiment of the present invention, as Figure 2 As shown, an intelligent management system based on aquatic plant data indicators, the system includes:

[0125] Model building module: Analyze the ecological interactions between various aquatic plants in wetland environments and build an ecological interaction network model based on this;

[0126] Data integration module: Use wireless sensor equipment to monitor the status of various aquatic plants in the wetland in real time, and integrate the collected data into a unified information processing platform;

[0127] Dynamic analysis module: Based on the established ecological interaction network model and real-time data in the information platform, dynamic evaluation of the interaction patterns in the wetland ecosystem is carried out, and monitoring and management standards for the health status of aquatic plants are formulated based on the evaluation results;

[0128] Strategy Optimization Module: Use machine models and prediction algorithms to predict the growth trends of aquatic plants and changes in ecological interaction networks, and continuously optimize management strategies based on the prediction results.

[0129] The working principle of the above technical solution is: to conduct an in-depth analysis of the ecological relationship between different aquatic plants in the wetland, including the symbiotic relationship (mutual benefit) and competitive relationship (resource competition) between them. This may involve methods such as field surveys, literature research and data analysis. Then, based on the analysis results, an ecological interaction network model is constructed, which can reflect the mutual influence and dependence between plants through nodes (different plants) and edges (the relationship between them); using wireless sensor networks, sensors are deployed in the wetland to collect data of various aquatic plants in real time. These data may include growth data (such as height, number of leaves), environmental data (such as soil moisture, light intensity) and interaction data between plants (such as root interaction). Through data integration technology, the data collected from different sources are uniformly stored in the data platform for subsequent analysis and management; based on the constructed ecological interaction network model and integrated data, the ecological interaction network in the wetland is dynamically analyzed. This may involve the use of network analysis technology to monitor the state changes of nodes (plants) in the network, the weight changes of edges (relationships) and other information to obtain the interaction rules and trend information between plants. According to the analysis results, corresponding aquatic plant data indicator management strategies are formulated, such as optimizing the planting layout to promote the growth of plant combinations with strong symbiotic relationships; adjusting resource allocation to reduce competitive pressure for plants with fierce competitive relationships; using machine models and prediction algorithms to predict the growth trends of aquatic plants and changes in ecological interaction networks. Based on the prediction results, management strategies are continuously optimized to ensure the healthy development of aquatic plant communities and ecological balance.

[0130] The effect of the above technical solution is: by analyzing and dynamically monitoring the ecological relationships of aquatic plants in wetlands, refined management of plant communities can be achieved. According to the symbiotic and competitive relationships between plants, targeted management strategies are formulated to maximize the promotion of favorable symbiotic relationships, reduce competitive pressure, and optimize the ecosystem structure; based on real-time data collection and dynamic analysis of aquatic plants, it is possible to achieve a reasonable allocation of resources (such as water, nutrients, etc.), accurately deploy resources according to the differences in the needs of different plants, improve resource utilization efficiency, and reduce costs; by predicting the growth trends of aquatic plants and changes in ecological interaction networks through prediction algorithms, it is possible to timely discover the changing trends of plant communities, and maintain ecological balance through continuous optimization of management strategies to avoid imbalance in the ecosystem; combining ecological interaction network models with real-time data to provide scientific decision-making support for wetland park managers, so that they can more accurately understand the status of plant communities in wetlands and take corresponding management measures to achieve sustainable development of wetland ecosystems; through dynamic analysis and continuous optimization of ecological interaction networks, it is possible to maximize the healthy growth of aquatic plants in wetlands, protect wetland ecological environments, maintain wetland biodiversity, and enhance the ecological landscape value of wetland parks.

[0131] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An intelligent management method based on aquatic plant data indicators, characterized in that: The method comprises: S1. Analyze the ecological interactions among various aquatic plants in wetland environments and build an ecological interaction network model based on this; S2. Use wireless sensor equipment to monitor the status of various aquatic plants in the wetland in real time, and integrate the collected data into a unified information processing platform; S3. Based on the established ecological interaction network model and real-time data in the information platform, dynamically evaluate the interaction patterns in the wetland ecosystem, and formulate monitoring and management standards for the health status of aquatic plants based on the evaluation results; S4. Use machine models and prediction algorithms to predict the growth trends of aquatic plants and changes in ecological interaction networks, and continuously optimize management strategies based on the prediction results; The S4 comprises: S41. Use machine learning models to make rolling predictions on the future growth status of aquatic plants and the potential evolution trend of ecological interaction networks. S42. Based on the prediction results, the management strategy is dynamically adjusted using the optimization algorithm. The intensity and direction of the management measures are automatically adjusted based on the real-time monitoring data and prediction feedback through the adaptive management algorithm; S43. Through the effect evaluation and feedback mechanism, the actual operation results are compared and analyzed with the prediction model prediction results, the model deviation is identified and feedback is corrected to form a closed-loop system of management-prediction-adjustment; S44. Build a knowledge base to record the reasons, processes and results of each strategy adjustment; Said S1 comprises: S11. Collect information on all aquatic plants in the wetland park and organize the collected information on aquatic plants through the encyclopedia database; S12. Record the field observation data of plants through field survey data; S13. Analyze the symbiotic relationships among plants through statistical analysis, evaluate the competitive relationships among plants, identify key species and key interspecific relationships, and analyze the impact on the stability of wetland ecosystems; S14. Based on the results of ecological relationship analysis, construct an ecological interaction network model and use graph theory and network analysis algorithms to determine the nodes and edges in the network; S15. Verify the accuracy of the ecological interaction network model through historical data and field observation data, and adjust the model parameters and structure according to the verification results; The S3 includes: S31. Real-time monitoring of node status changes in the ecological interaction network, and analysis of edge weight changes, and evaluation of changes in the intensity of plant interactions based on the analysis results; S32. Analyze the dynamic change patterns and trends of the network over time using a time series analysis algorithm, and identify key nodes and key paths in the network based on the analysis results; S33. Evaluate the impact and risk of different management strategies on the stability of ecological interaction networks, set warning thresholds, and issue warnings when the network status reaches or exceeds the threshold; S34. Formulate management strategies based on the results of network dynamic analysis and risk assessment, convert the management strategies into specific operational measures, and implement them; The S33 comprises: Based on historical data and professional knowledge, determine the baseline value of the network under normal conditions, and identify the range of changes in key nodes and key paths in the network under normal conditions as preliminary thresholds for early warning; By simulating the impact of different management strategies on the network status, the sensitivity of the network to different management strategies is evaluated, and the preliminary threshold is dynamically adjusted according to the sensitivity analysis results; Use probabilistic models and simulation methods to assess the risk level of network stability under different management strategies, and further optimize the warning threshold based on the risk assessment results; Verify the accuracy and effectiveness of the warning threshold by monitoring changes in network status in real time, and regularly update the warning threshold based on monitoring results and feedback; When the network status reaches or exceeds the warning threshold, the warning mechanism is automatically triggered and warning information is sent to the management personnel, who then formulate specific response strategies based on the warning information.

2. The intelligent management method based on aquatic plant data indicators according to claim 1 is characterized in that: The S15 comprises: Pre-process the collected historical data and field observation data, and screen the data based on their relevance, importance, and timeliness; Checking data from different sources; If there are any contradictions or inconsistencies, data tracing and verification will be carried out; Clarify the goals and indicators of model verification, and select verification methods based on the characteristics of the model and data conditions; Compare the model predictions with field observations, calculate the prediction error, and back-validate the model using historical data to check the model's performance under historical circumstances; Analyze the influence of each parameter in the model on the model output, identify key parameters, and optimize and adjust the key parameters of the model based on the verification results and sensitivity analysis; Analyze the network structure of the ecological interaction network model, identify the key nodes and key paths in the network, and adjust and optimize the network structure of the model based on the verification results and network structure analysis; After adjusting the model parameters and structure, the model is verified again, and the model is further improved and optimized based on the results of iterative verification.

3. The intelligent management method based on aquatic plant data indicators according to claim 1 is characterized in that: The S2 comprises: S21. Select sensors according to the terrain and plant distribution of the wetland park, deploy the selected sensors, and design the wireless sensor network architecture; S22, collecting aquatic plant data of plants through a wireless sensor network; preprocessing the collected aquatic plant data; and uniformly formatting and standardizing the preprocessed data; S23. Integrate aquatic plant data based on database technology and build a unified data platform, and design data table structure and index based on the constructed data platform; S24. Regularly check and verify data quality using preset data quality standards and verification methods, and correct errors and outliers.

4. The intelligent management method based on aquatic plant data indicators according to claim 3 is characterized in that: The S24 comprises: Define required and optional fields for data, and set accuracy ranges for each field based on historical data and expertise; Use scripts to check whether there are null values ​​or missing fields in the data table. If so, mark or automatically fill in the missing data. Use statistical methods to detect anomalies in data points and identify data that exceeds the normal fluctuation range. By comparing the correlation or dependency between different data sets, inconsistent data can be identified and corrected; According to the importance and update frequency of the data, set the inspection cycle and perform automated inspections, and present the inspection results in a visual way; For errors or abnormal values ​​that cannot be accurately determined by automated inspection, they are fed back to humans for review and correction. For predictable data fields, historical data or trend prediction models are used to make corrections. The corrected data shall be verified again and the correction results shall be fed back to the data producer or relevant personnel. Based on the verification results and feedback, the data quality standards and verification methods shall be continuously optimized.

5. The intelligent management method based on aquatic plant data indicators according to claim 1 is characterized in that: The S43 includes: Compare the actual operation results with the predicted results output by the forecast model, analyze the differences between the two, set indicators, and quantify the deviation between the actual results and the predicted results; According to the nature and size of the deviation, the deviation is divided into types, including systematic deviation and random deviation; Analyze the deviation data, identify the main sources of deviation, and adjust the parameters of the prediction model based on the deviation analysis results; Optimize data collection and processing processes to address data quality issues; Integrate the above steps into an automated process, use real-time monitoring data to evaluate the prediction model in real time, and immediately initiate the feedback correction process if there is any deviation.

6. A system for implementing the intelligent management method based on aquatic plant data indicators as described in any one of claims 1 to 5, characterized in that: The system comprises: Model building module: Analyze the ecological interactions between various aquatic plants in wetland environments and build an ecological interaction network model based on this; Data integration module: Use wireless sensor equipment to monitor the status of various aquatic plants in the wetland in real time, and integrate the collected data into a unified information processing platform; Dynamic analysis module: Based on the established ecological interaction network model and real-time data in the information platform, dynamic evaluation of the interaction patterns in the wetland ecosystem is carried out, and monitoring and management standards for the health status of aquatic plants are formulated based on the evaluation results; Strategy Optimization Module: Use machine models and prediction algorithms to predict the growth trends of aquatic plants and changes in ecological interaction networks, and continuously optimize management strategies based on the prediction results.

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