Water area ecological environment monitoring system

Abstract

The invention belongs to the technical field of environment monitoring, and discloses a water area ecological environment monitoring system, which comprises a data acquisition unit used for acquiring aquatic vegetation coverage data, aquatic plant type data, water quality index data and biological index data of various organisms of a to-be-monitored area; the evaluation index acquisition unit is used for acquiring pollution degree evaluation index data strongly related to the to-be-detected area according to the aquatic vegetation coverage data, the biological index data, the water quality index data and the aquatic plant type data; and the evaluation monitoring unit is used for evaluating the pollution degree of the to-be-monitored area according to the pollution degree evaluation index data to obtain the pollution degree of the ecological environment of the water area, completing the monitoring of the current period, uploading a monitoring result to the cloud platform for storage, and performing the monitoring of the next period. According to the method, the limitation of traditional single index evaluation is solved, and the comprehensiveness, accuracy and timeliness of water area ecological environment monitoring are remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of environmental monitoring, and particularly relates to a water area ecological environment monitoring system. Background Art

[0002] Monitoring of the water area ecological environment is an important means to ensure the health of the water ecosystem and promote environmental protection. With the development of industrialization and urbanization, water environment problems have become increasingly prominent, and the ecological environment of water areas such as rivers, lakes, and reservoirs is facing serious threats. Traditional water area ecological environment monitoring technologies mainly include on-site detection, remote sensing technology, laboratory analysis, etc. These technologies mostly rely on single indicators (such as water quality parameters or biological indicators), making it difficult to comprehensively reflect the complexity of the water area ecological environment and unable to accurately reflect the dynamic changes of water quality in real time. Summary of the Invention

[0003] The purpose of the present invention is to provide a water area ecological environment monitoring system to solve the problems existing in the above-mentioned prior art.

[0004] To achieve the above purpose, the present invention provides a water area ecological environment monitoring system, including:

[0005] A data acquisition unit, configured to obtain data on the coverage of aquatic vegetation, data on the types of aquatic plants, water quality index data, and biological index data of various organisms in the area to be measured;

[0006] An evaluation index acquisition unit, configured to obtain pollution degree evaluation index data strongly related to the area to be measured according to the data on the coverage of aquatic vegetation, biological index data, water quality index data, and data on the types of aquatic plants;

[0007] An evaluation and monitoring unit, configured to evaluate the pollution degree of the area to be measured according to the pollution degree evaluation index data, obtain the pollution degree of the water area ecological environment, complete the monitoring of the current cycle, upload the monitoring results to the cloud platform for storage, and conduct the monitoring of the next cycle.

[0008] Optionally, the data acquisition unit specifically includes:

[0009] An aquatic plant image data acquisition module, configured to obtain aquatic plant image data in the area to be measured, where the aquatic plant image data includes submerged plant image data and non-submerged plant image data;

[0010] A remote sensing image data acquisition module, configured to obtain remote sensing image data;

[0011] A water quality index and biological index acquisition module, configured to obtain water quality index data and biological index data of various organisms;

[0012] An aquatic vegetation coverage acquisition module is used to acquire aquatic vegetation coverage data of the area to be measured based on the aquatic plant image data;

[0013] The aquatic plant species acquisition module is used to acquire the aquatic plant species data of the area to be measured based on the remote sensing image data.

[0014] Optionally, the aquatic plant image data acquisition module specifically includes:

[0015] The non-submerged plant image data acquisition submodule is used to control the drone to take the orthophoto of the area to be measured according to a preset flight path to obtain the non-submerged plant image data;

[0016] The submerged plant image data acquisition submodule is used to collect the submerged plant image data in the test area according to the dual-frequency recognition sonar.

[0017] Optionally, the aquatic vegetation coverage acquisition module specifically includes:

[0018] Aquatic plant image data preprocessing submodule, used for preprocessing the aquatic plant image data to obtain preprocessed aquatic plant image data; wherein the preprocessing includes denoising filtering processing;

[0019] The vegetation coverage calculation submodule is used to perform grayscale transformation on the preprocessed aquatic plant image data to obtain the grayscale value of each pixel; extract the critical segmentation threshold of the water background and vegetation in the aquatic plant image data based on the grayscale value of each pixel; perform binary segmentation based on the critical segmentation threshold to distinguish the water background or vegetation corresponding to each pixel; and calculate the aquatic vegetation coverage data based on the ratio of vegetation pixels in the aquatic plant image data after binary segmentation to the total pixels of the image.

[0020] Optionally, the aquatic plant species acquisition module specifically includes:

[0021] A denoising submodule, used for performing denoising and enhancement processing on the remote sensing image data;

[0022] The aquatic plant species calculation submodule is used to register the aquatic plant image data to the enhanced remote sensing image data, and determine the aquatic vegetation species and their growth center points based on the spectrum of the registered image.

[0023] Optionally, the evaluation index acquisition unit specifically includes:

[0024] A data set construction module, used to construct a pollution degree-related evaluation index data set based on the aquatic vegetation coverage data, biological index data, water quality index data and the aquatic plant species data;

[0025] A correlation analysis module for performing correlation screening on the relevant evaluation index data to obtain pollution degree evaluation index data that is strongly correlated with the area to be measured.

[0026] Optionally, the correlation analysis module specifically includes:

[0027] A strong association rule acquisition sub-module for obtaining strong association rule data of the relevant evaluation index data according to the Apriori algorithm;

[0028] A screening sub-module for performing screening analysis on the relevant evaluation index data according to the strong association rule data to obtain pollution degree evaluation index data that is strongly correlated with the area to be measured.

[0029] Optionally, the evaluation and monitoring unit specifically includes:

[0030] A weight assignment module for dynamically assigning weights to the pollution degree evaluation index data to obtain combined weight data of the pollution degree evaluation index data;

[0031] An evaluation module for evaluating the pollution degree of the area to be measured according to the pollution degree evaluation index data and the corresponding combined weight data to obtain the pollution degree of the water area ecological environment and complete the monitoring of the current cycle;

[0032] A storage module for uploading the monitoring results to the cloud platform for storage and performing the monitoring of the next cycle.

[0033] Optionally, the weight assignment module specifically includes:

[0034] A weight acquisition sub-module for obtaining subjective weight data and objective weight data of the pollution degree evaluation index data;

[0035] A combination solution sub-module for performing combination solution on the objective weight data and the subjective weight data with the goal of minimizing the deviation between the optimal weight and the basic weight according to the game algorithm to obtain the combined weight data.

[0036] Optionally, the evaluation module specifically includes:

[0037] An evidence synthesis sub-module for taking the selected pollution degree evaluation index data as a group of evidence, calculating the basic belief assignment of each evidence for each pollution category, and then performing evidence synthesis using the evidence additive synthesis rule based on the combined weight;

[0038] A pollution degree determination sub-module for selecting the pollution category with the highest degree of trust as the pollution degree of the area to be measured according to the evidence synthesis result.

[0039] The technical effect of the present invention is:

[0040] This system synthesizes data on aquatic vegetation coverage, water quality indicators, biological indicators, and aquatic plant species to construct a comprehensive pollution degree evaluation system, solving the limitations of traditional single-index evaluations. At the same time, game theory and evidence theory are introduced to dynamically adjust weights, taking into account the randomness and fuzziness in the evaluation process, improving the scientificity and accuracy of the evaluation results. The evidence synthesis results are used for pollution category judgment and trend analysis, providing a scientific basis for ecological restoration and pollution control; through multi-source data fusion and intelligent algorithms, this system can adapt to the monitoring needs of different water environments (such as deep water, shallow water, turbid water), can provide a comprehensive analysis of pollution types, covering physical, chemical, and biological pollution, significantly enhancing the comprehensiveness, accuracy, and timeliness of water ecological environment monitoring, and providing strong technical support for water environment governance and ecological restoration. Brief Description of the Drawings

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0042] The drawings forming a part of this application are used to provide a further understanding of this application. The schematic embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation of this application. In the drawings:

[0043] Figure 1 It is a schematic diagram of the system structure in the embodiments of the present invention. Detailed Embodiments

[0044] Now, various exemplary embodiments of the present invention will be described in detail. This detailed description should not be considered as a limitation of the present invention, but rather as a more detailed description of certain aspects, characteristics, and implementation schemes of the present invention.

[0045] It should be understood that the terms described in the present invention are only for describing specific embodiments and are not used to limit the present invention. Additionally, for the numerical ranges in the present invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Each intermediate value within any stated value or stated range, as well as each smaller range between any other stated value or intermediate value within the stated range, is also included in the present invention. The upper and lower limits of these smaller ranges can be independently included or excluded from the range.

[0046] It will be apparent to those skilled in the art that various modifications and variations may be made to the specific embodiments of the present invention description without departing from the scope or spirit of the present invention. Other embodiments derived from the present invention description will be apparent to those skilled in the art. The present application description and examples are exemplary only.

[0047] The words “include,” “including,” “have,” “contain,” etc. used in this article are open-ended terms, meaning including but not limited to.

[0048] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0049] like Figure 1 As shown, this embodiment provides a water ecological environment monitoring system, including:

[0050] A data collection unit is used to obtain aquatic vegetation coverage data, aquatic plant species data, water quality index data and biological index data of various organisms in the area to be tested;

[0051] An evaluation index acquisition unit, used for acquiring pollution degree evaluation index data strongly correlated with the area to be tested based on the aquatic vegetation coverage data, biological index data, water quality index data and aquatic plant species data;

[0052] The evaluation monitoring unit is used to evaluate the pollution level of the area to be tested according to the pollution level evaluation index data, obtain the pollution level of the water ecological environment, complete the monitoring of the current cycle, upload the monitoring results to the cloud platform for storage, and conduct monitoring of the next cycle.

[0053] The water ecological environment monitoring system proposed in this embodiment integrates aquatic vegetation coverage data, biological indicator data, water quality indicator data and aquatic plant species data to construct a comprehensive and in-depth pollution-related evaluation indicator data set. This innovative method effectively overcomes the limitation of traditional technology that relies on only a single indicator for evaluation, and realizes the diversification and refinement of the evaluation system.

[0054] Through in-depth strong correlation analysis of the above indicators, the most closely correlated indicator system was selected, and the game theory was further applied to scientifically and rationally determine the combined weights of these indicators. This weight setting method fully considers the randomness and fuzziness in the evaluation process, not only avoiding the defect of too rigid boundary division of indicator level intervals in traditional fuzzy comprehensive evaluation, but also solving the problem of lack of unified standards for membership function selection, thereby greatly improving the accuracy and credibility of the evaluation results.

[0055] On this basis, the system also introduces the evidence theory for comprehensive judgment of the pollution degree. This method can efficiently handle the ambiguity and uncertainty existing in the pollution degree evaluation, making the evaluation conclusion closer to the actual situation. Given the diversity of water pollution types, involving multiple complex fields such as physics, chemistry, and biology, in this embodiment, through a comprehensive and in-depth analysis of the pollution types of the ecosystem and the properties and sources of its pollutants, the accurate quantification of the water area ecological environment pollution level is achieved, providing a strong scientific basis for formulating and implementing targeted treatment measures.

[0056] The water area ecological environment monitoring system of this embodiment not only significantly improves the accuracy and comprehensiveness of monitoring, but also effectively promotes the pertinence and effectiveness of environmental treatment measures, making important contributions to the protection and improvement of the water area ecological environment.

[0057] This embodiment provides a water area ecological environment monitoring system, which mainly includes the following functional units:

[0058] Data acquisition unit: responsible for obtaining the data of aquatic vegetation coverage, aquatic plant species, water quality indicators, and biological indicators in the area to be measured.

[0059] Evaluation index acquisition unit: based on the collected data, screen the evaluation indexes that are strongly correlated with the pollution degree.

[0060] Evaluation and monitoring unit: evaluate the pollution degree and upload the results to the cloud platform to support periodic monitoring.

[0061] Data acquisition unit:

[0062] Non-submerged plant image data acquisition: based on the set flight path, control the drone to take the orthophoto of the area to be measured;

[0063] Submerged plant image data acquisition: use a dual-frequency identification sonar to collect submerged plant image data;

[0064] Preprocess the collected image data (denoising and filtering), perform gray-scale transformation, extract the critical segmentation threshold, perform binary segmentation, and finally calculate the vegetation coverage.

[0065] Vegetation coverage refers to the ratio of the vertical projection area of plants in a certain area to the area of that area, expressed as a percentage. Vegetation coverage is an important quantitative evaluation factor for studying the ecological environment changes in a region. As the main producer in the aquatic ecosystem, aquatic vegetation has a very important impact on maintaining the biodiversity of the aquatic ecosystem and promoting the self-purification of water bodies. Understanding and mastering the detailed situation of aquatic vegetation is of great significance for a series of activities such as the evaluation of the aquatic ecosystem, the analysis of the impact of human activities on the water environment, and the formulation of countermeasures to control invasive aquatic plants.

[0066] Data collection of aquatic plant species: De-noise and enhance the remote sensing images, and align the drone images with the remote sensing images (geometric correction is performed using the quadratic polynomial method). The remote sensing image resolution is better than 0.5 meters and contains at least four bands: red, green, blue, and near-infrared.

[0067] Water quality and biological indicator data collection:

[0068] Collection content: water quality index data (such as pH, dissolved oxygen, chemical oxygen demand, etc.) and biological index data (such as biodiversity index, bioaccumulation factor, etc.).

[0069] Evaluation index acquisition unit:

[0070] Correlation screening: input aquatic vegetation coverage data, biological indicator data, water quality indicator data, and aquatic plant species data, obtain strong association rule data based on the Apriori algorithm, screen evaluation indicators that are strongly correlated with the degree of pollution, and output a strongly correlated pollution degree evaluation indicator data set.

[0071] Dynamic empowerment:

[0072] Subjective weight: Subjective weight is obtained based on the decision laboratory method (DEMATEL).

[0073] Objective weight: Obtain objective weight based on anti-entropy weight method.

[0074] Combined weight: Through the game algorithm, the subjective weight and the objective weight are combined and solved with the goal of minimizing the deviation between the optimal weight and the basic weight, and the combined weight data of the pollution degree evaluation index is output.

[0075] Evaluation and monitoring unit:

[0076] Pollution degree evaluation: Apply the evidence theory, take the pollution degree evaluation index data as a set of evidence, and calculate the basic trust distribution of each piece of evidence for different pollution categories. Based on the combined weight, use the evidence additive synthesis rule to synthesize evidence, and select the pollution category with the highest trust as the pollution degree of the area to be tested.

[0077] Data upload and periodic monitoring: Upload the evaluation results to the cloud platform for storage and automatically start the next cycle of monitoring. The periodic monitoring results are stored in the cloud platform to support long-term ecological monitoring and trend analysis.

[0078] In this embodiment, by integrating data on aquatic vegetation coverage, plant species, water quality, and biological indicators, a comprehensive pollution level evaluation system is constructed. The problem of unreasonable weight allocation in traditional evaluation methods is solved by combining subjective and objective weights through a game algorithm. This embodiment can effectively handle the ambiguity and uncertainty in pollution evaluation, improve the reliability and precision of evaluation results. In addition, this embodiment supports long-term ecological monitoring, facilitating trend analysis and the formulation of treatment measures.

[0079] The pollution category analysis results of this embodiment include:

[0080] Sensory pollution: color change, turbidity change, foam, and odor.

[0081] Chemical pollution: inorganic harmful substances (acids, alkalis, inorganic salts), inorganic toxic substances (heavy metals, cyanides), organic harmful substances (oxygen-consuming organic substances), organic toxic substances (phenols, pesticides, polycyclic aromatic hydrocarbons).

[0082] Physical pollution: oil pollution, thermal pollution.

[0083] Biological pollution: pollution caused by bacteria, viruses, protozoa, algae, etc.

[0084] The specific implementation process of this embodiment includes:

[0085] Data collection stage: Set the flight path of the drone and control the drone to take orthophotos of the area to be measured. Preprocess the acquired image data (denoising and filtering), perform gray-scale transformation on the preprocessed image to obtain the gray-scale value of each pixel; extract the critical segmentation threshold of the water area background and vegetation based on the gray-scale value; use the critical segmentation threshold for binary segmentation to distinguish the water area background and vegetation; calculate the proportion of vegetation pixels in the total pixels to obtain the aquatic vegetation coverage data of non-submerged plants.

[0086] Acquisition of submerged plant image data: Use a dual-frequency identification sonar to collect submerged plant image data of the area to be measured. Similar to the processing of non-submerged plant image data, calculate the aquatic vegetation coverage of submerged plants through steps such as preprocessing, gray-scale transformation, and binary segmentation.

[0087] Collection of aquatic plant species data:

[0088] Remote sensing image data processing: Obtain remote sensing image data of the area to be measured with a resolution better than 0.5 meters, including four bands of red, green, blue, and near-infrared. Perform denoising and enhancement processing on the remote sensing image, register the non-submerged plant image taken by the drone with the remote sensing image, select obvious common feature points, and perform geometric correction through the quadratic polynomial method to make the two basically overlap. Based on the spectral information of the registered image, determine the aquatic plant species and their growth center points.

[0089] Water quality and biological indicator data collection: Collect water samples in the area to be measured and detect water quality indicators (such as pH, dissolved oxygen, chemical oxygen demand, ammonia nitrogen, etc.). At the same time, collect biological samples and analyze biological indicators (such as biodiversity index, biological enrichment factor, etc.).

[0090] Correlation screening: Integrate the collected data on aquatic vegetation coverage, aquatic plant species, water quality indicators, and biological indicators into a dataset. Use the Apriori algorithm to analyze the dataset to obtain strong association rule data; based on the strong association rules, screen out the evaluation indicators that are strongly correlated with the pollution level.

[0091] Subjective weight acquisition: Use the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to obtain the subjective weights of each evaluation indicator.

[0092] Objective weight acquisition: Use the anti-entropy weight method to obtain the objective weights of each evaluation indicator.

[0093] Combined weight calculation: Use the game algorithm to minimize the deviation between the optimal weight and the basic weight, and solve the combination of subjective weights and objective weights to obtain the combined weights of each evaluation indicator.

[0094] Pollution level evaluation: Use the screened pollution level evaluation indicator data as a set of evidence, calculate the basic probability assignment (BPA) of each evidence to different pollution categories; use the evidence additive synthesis rule based on the combined weights (Dempster-Shafer synthesis rule) to synthesize each evidence, and select the pollution category with the highest degree of belief as the pollution level of the area to be measured according to the evidence synthesis result.

[0095] Upload the pollution level evaluation results to the cloud platform for storage, start the next cycle of monitoring, and repeat the above data collection, evaluation, and upload process.

[0096] Pollution category analysis:

[0097] According to the evaluation results, analyze the pollution categories (such as sensory pollution, chemical pollution, physical pollution, biological pollution, etc.), and analyze the changing trend of the pollution level based on the periodic monitoring data stored on the cloud platform.

[0098] The categories of water pollution can be divided from multiple perspectives. From the perspective of sensory pollution, water pollution is mainly manifested as color changes, turbidity changes, bubbles and odors, etc., which can be directly felt from aspects such as human vision, taste and smell. From a chemical perspective, water pollution can be mainly divided into four categories: inorganic harmful substances, inorganic toxic substances, organic harmful substances and organic toxic substances. Specifically, inorganic harmful substances mainly include substances such as acids, alkalis and inorganic salts; inorganic toxic substances include heavy metals, cyanides, fluorides, etc.; organic harmful substances refer to those organic substances that can consume oxygen in water; and organic toxic substances include phenolic compounds, organic pesticides, polycyclic aromatic hydrocarbons, polychlorinated biphenyls, etc. In addition, water pollution can also be classified according to the nature and source of pollutants. For example, oil pollution mainly comes from oil exploration along the coast and in estuaries, oil tanker transportation and the discharge of wastewater from the oil refining industry; thermal pollution is mainly caused by the increase in water temperature due to the discharge of industrial thermal cooling wastewater. Eutrophication pollution is caused by the discharge of a certain nutrient into the water body, resulting in excessive nutrient content in the water body and a large reproduction of aquatic plants.

[0099] Finally, from a biological perspective, water pollution also includes pollution caused by biological factors such as bacteria, viruses, protozoa, parasitic worms and algae in sewage discharge.

[0100] By conducting correlation analysis and data mining on the data of pollution degree evaluation indicators, characteristic indicators closely related to the pollution degree are obtained, and multi-parameter indicators are obtained as pollution degree evaluation indicators. On this basis, dynamic weight assignment is carried out through cooperative game, the subjective and objective weight values of the indicator system are calculated respectively, the combined weight value is obtained, and the evidence theory is used to judge the pollution degree, effectively utilizing the ambiguity and uncertainty in pollution degree evaluation. Using the evidence synthesis result, not only can the pollution category be judged, but also the analysis of the pollution change trend can be carried out.

[0101] As mentioned above, it is only the preferred specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the technical field of this application within the technical scope disclosed by this application should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A water ecological environment monitoring system, characterized in that: include: A data collection unit is used to obtain aquatic vegetation coverage data, aquatic plant species data, water quality index data and biological index data of various organisms in the area to be tested; An evaluation index acquisition unit, used for acquiring pollution degree evaluation index data strongly correlated with the area to be tested based on the aquatic vegetation coverage data, biological index data, water quality index data and aquatic plant species data; The evaluation monitoring unit is used to evaluate the pollution level of the area to be tested according to the pollution level evaluation index data, obtain the pollution level of the water ecological environment, complete the monitoring of the current cycle, upload the monitoring results to the cloud platform for storage, and conduct monitoring of the next cycle.

2. A water ecological environment monitoring system according to claim 1, characterized in that: The data acquisition unit specifically includes: An aquatic plant image data acquisition module, used to acquire aquatic plant image data of the area to be tested, wherein the aquatic plant image data includes submerged plant image data and non-submerged plant image data; A remote sensing image data acquisition module is used to acquire remote sensing image data; Water quality index and biological index acquisition module, used to obtain water quality index data and biological index data of various organisms; An aquatic vegetation coverage acquisition module is used to acquire aquatic vegetation coverage data of the area to be measured based on the aquatic plant image data; The aquatic plant species acquisition module is used to acquire the aquatic plant species data of the area to be measured based on the remote sensing image data.

3. A water ecological environment monitoring system according to claim 2, characterized in that: The aquatic plant image data acquisition module specifically includes: The non-submerged plant image data acquisition submodule is used to control the drone to take the orthophoto of the area to be measured according to a preset flight path to obtain the non-submerged plant image data; The submerged plant image data acquisition submodule is used to collect the submerged plant image data in the test area according to the dual-frequency recognition sonar.

4. A water ecological environment monitoring system according to claim 2, characterized in that: The aquatic vegetation coverage acquisition module specifically includes: Aquatic plant image data preprocessing submodule, used for preprocessing the aquatic plant image data to obtain preprocessed aquatic plant image data; wherein the preprocessing includes denoising filtering processing; The vegetation coverage calculation submodule is used to perform grayscale transformation on the preprocessed aquatic plant image data to obtain the grayscale value of each pixel; extract the critical segmentation threshold of the water background and vegetation in the aquatic plant image data based on the grayscale value of each pixel; perform binary segmentation based on the critical segmentation threshold to distinguish the water background or vegetation corresponding to each pixel; and calculate the aquatic vegetation coverage data based on the ratio of vegetation pixels in the aquatic plant image data after binary segmentation to the total pixels of the image.

5. A water ecological environment monitoring system according to claim 2, characterized in that: The aquatic plant species acquisition module specifically includes: A denoising submodule, used for performing denoising and enhancement processing on the remote sensing image data; The aquatic plant species calculation submodule is used to register the aquatic plant image data to the enhanced remote sensing image data, and determine the aquatic vegetation species and their growth center points based on the spectrum of the registered image.

6. A water ecological environment monitoring system according to claim 1, characterized in that: The evaluation index acquisition unit specifically includes: A data set construction module, used to construct a pollution degree-related evaluation index data set based on the aquatic vegetation coverage data, biological index data, water quality index data and the aquatic plant species data; The correlation analysis module is used to perform correlation screening on the relevant evaluation index data to obtain pollution degree evaluation index data that is strongly correlated with the area to be tested.

7. A water ecological environment monitoring system according to claim 6, characterized in that: The correlation analysis module specifically includes: A strong association rule acquisition submodule, used to acquire strong association rule data of the relevant evaluation index data according to the Apriori algorithm; The screening submodule is used to screen and analyze the relevant evaluation index data according to the strong association rule data to obtain the pollution degree evaluation index data that is strongly correlated with the area to be tested.

8. The water ecological environment monitoring system according to claim 1 is characterized in that: The evaluation and monitoring unit specifically includes: A weighting module, used for dynamically weighting the pollution degree evaluation index data to obtain combined weight data of the pollution degree evaluation index data; An evaluation module is used to evaluate the pollution degree of the area to be tested according to the pollution degree evaluation index data and the corresponding combined weight data, obtain the pollution degree of the water ecological environment, and complete the monitoring of the current cycle; The storage module is used to upload the monitoring results to the cloud platform for storage and to conduct the next cycle of monitoring.

9. A water ecological environment monitoring system according to claim 8, characterized in that: The weighting module specifically includes: A weight acquisition submodule, used to acquire subjective weight data and objective weight data of the pollution degree evaluation index data; The combined solution submodule is used to combine and solve the objective weight data and the subjective weight data according to the game algorithm with the goal of minimizing the deviation between the optimal weight and the basic weight, so as to obtain the combined weight data.

10. A water ecological environment monitoring system according to claim 8, characterized in that: The evaluation module specifically includes: The evidence synthesis submodule is used to take the selected pollution degree evaluation index data as a set of evidence, calculate the basic trust distribution of each piece of evidence for each pollution category, and then use the evidence additive synthesis rule based on the combined weight to synthesize the evidence; The pollution degree determination submodule is used to select the pollution category with the highest trust as the pollution degree of the area to be tested according to the evidence synthesis result.

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