A method and system for evaluating and diagnosing the health status of a lake

Abstract

A method and system for evaluating and diagnosing the health status of a lake, disclosed by the present invention, include: dividing the lake into health evaluation zones; constructing an index system for lake health evaluation by using the TCI model, determining the weights of each element in the criterion layer and the index layer, and assigning scores to each index; obtaining the health evaluation results of each zone and the overall health of the lake according to the weights of each element, the assigned scores, and the constructed fuzzy comprehensive evaluation model for the health of the lake zones and the overall health fuzzy comprehensive evaluation model, wherein the fuzzy comprehensive evaluation model for the health of the lake zones is constructed by applying the multi-level fuzzy comprehensive evaluation method; the overall health fuzzy comprehensive evaluation model is constructed by using the comprehensive analysis method based on the secondary fuzzy comprehensive evaluation membership matrix in the fuzzy comprehensive evaluation model for the health of the lake zones and the water surface areas of each zone. The health evaluation of the lake in zones and as a whole is realized, and the health grading scores are obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of lake ecosystem management, and in particular to a method and system for evaluating and diagnosing the health status of lakes. Background Technique

[0002] The statements in this part only provide background technical information related to the present invention, and do not necessarily constitute prior art.

[0003] Since Schaeffer et al. first proposed the concept of "no disease" in the 1980s, some experts and scholars at home and abroad have carried out relevant research and applications on the concept of lake ecosystem health, the lake health evaluation index system, and the lake health evaluation method. Currently, Costanza's view is generally recognized, and ecosystem health is divided into five aspects: no disease, development, diversity, stability, and orderly and sound system composition. However, due to differences in climate, topography, and geomorphology in the research area, the diversity of researchers' understanding of the concept of "lake health", and the difficulty of monitoring indicators, the establishment of the lake health evaluation index system often has great subjectivity. At present, although there are evaluation guidelines and local specifications, there is no complete and comprehensive evaluation system and evaluation method, and there is a lack of spatio-temporal change analysis of the whole lake and its partitions.

[0004] Lake health evaluation methods can be divided into biological monitoring methods and multi-index comprehensive evaluation methods. Biological monitoring methods mainly reflect the health status of lakes through changes in community structure, key species, the number of rare species, and mechanism indicators within the lake ecosystem. Biological monitoring methods are common methods for studying the health of lake ecosystems, but this method has some obvious disadvantages: (1) Evaluating ecosystem health based on a single indicator is somewhat one-sided; (2) The screening criteria for indicator species are not clear, and it is difficult to determine whether the reduction of indicator species will have an important impact on the system and its role in the ecosystem; (3) Social economy and human health factors are not considered, and it is difficult to comprehensively reflect the health status of the ecosystem. The multi-index comprehensive evaluation method is a method for comprehensively evaluating each organizational level of the ecosystem on the premise of selecting taxa at different organizational levels and considering different scales. Compared with biological monitoring methods, the multi-index comprehensive evaluation method combines ecology, physiological toxicology, physical chemistry, and computer-aided means, and has become a currently commonly used method with its characteristics of comprehensiveness, integrity, and easy quantification.

[0005] Although many scholars have conducted relevant research on lake health assessment, most of them focus on the overall health status of lakes, lacking the assessment of the health status of lake sub-regions and the analysis of spatio-temporal evolution laws. Secondly, the commonly used assessment method at present is to use the weighted average of the weights of each index and the actual assigned scores as the score value of the lake health status, and judge the health status of the lake based on this value. However, since the health level grading standards of each index are not the same, the finally selected lake health level grading standard cannot fully match the grading standard of the index, which may lead to misjudgment when determining the lake health status according to the weighted average. In addition, the commonly used method to determine the index weight at present is the Analytic Hierarchy Process (AHP). The Analytic Hierarchy Process is a commonly used subjective weighting method. The core of the Analytic Hierarchy Process is the construction of the judgment matrix. In the process of constructing the judgment matrix by the traditional Analytic Hierarchy Process, the scale assignment is from "1 to 9", the scale grading is more, too detailed, and it is difficult to master the comparison scales established by different scales. There may be an inverse order contrary to the actual situation, reducing the accuracy of the judgment matrix. Moreover, there is a problem that due to the subjective judgment error of the decision maker, the consistency test of the judgment matrix fails, and it is necessary to reconstruct the judgment matrix and conduct the consistency test again. Summary of the Invention

[0006] In order to solve the above problems, the present invention proposes a method and system for evaluating and diagnosing the health status of lakes, realizing the health assessment of lakes in sub-regions and as a whole, and obtaining the health grading scores.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] In the first aspect, a method for evaluating and diagnosing the health status of lakes is proposed, including:

[0009] Carry out health assessment zoning for lakes;

[0010] Use the TCI model to construct a lake health assessment index system, determine the weights of each element in the criterion layer and the index layer, and assign scores to each index;

[0011] According to the weights and score values of each element, the constructed fuzzy comprehensive evaluation model for the health of lake sub - regions, and the fuzzy comprehensive evaluation model for the overall health, the health evaluation results of each region of the lake and the overall health of the lake are obtained. Among them, the process of constructing the fuzzy comprehensive evaluation model for the health of lake sub - regions is as follows: construct the fuzzy relation matrix between each element in the index layer and each element in the criterion layer; according to the fuzzy relation matrix and the weights of each element in the index layer, obtain the membership degree matrix of the first - level index; according to the weights of each element in the criterion layer and the membership degree matrix of the first - level index, obtain the membership degree matrix of the second - level fuzzy comprehensive evaluation; according to the principle of maximum membership degree, obtain the health evaluation result from the membership degree matrix of the second - level fuzzy comprehensive evaluation; the fuzzy comprehensive evaluation model for the overall health is constructed by using the comprehensive analysis method based on the membership degree matrix of the second - level fuzzy comprehensive evaluation and the water surface area of each region.

[0012] In the second aspect, a lake health status evaluation and diagnosis system is proposed, including:

[0013] A zoning module for conducting health evaluation zoning on the lake;

[0014] A health evaluation index system construction module for constructing a lake health evaluation index system using the TCI model, determining the weights of each element in the criterion layer and the index layer, and assigning scores to each index;

[0015] A health evaluation result acquisition module for obtaining the health evaluation results of each region of the lake and the overall health of the lake according to the weights and score values of each element, the constructed fuzzy comprehensive evaluation model for the health of lake sub - regions, and the fuzzy comprehensive evaluation model for the overall health. Among them, the process of constructing the fuzzy comprehensive evaluation model for the health of lake sub - regions is as follows: construct the fuzzy relation matrix between each element in the index layer and each element in the criterion layer; according to the fuzzy relation matrix and the weights of each element in the index layer, obtain the membership degree matrix of the first - level index; according to the weights of each element in the criterion layer and the membership degree matrix of the first - level index, obtain the membership degree matrix of the second - level fuzzy comprehensive evaluation; according to the principle of maximum membership degree, obtain the health evaluation result from the membership degree matrix of the second - level fuzzy comprehensive evaluation; the fuzzy comprehensive evaluation model for the overall health is constructed by using the comprehensive analysis method based on the membership degree matrix of the second - level fuzzy comprehensive evaluation and the water surface area of each region.

[0016] In the third aspect, an electronic device is proposed, including a memory, a processor, and computer instructions stored on the memory and running on the processor. When the computer instructions are run by the processor, the steps of a lake health status evaluation and diagnosis method are completed.

[0017] In the fourth aspect, a computer - readable storage medium is proposed for storing computer instructions. When the computer instructions are executed by the processor, the steps of a lake health status evaluation and diagnosis method are completed.

[0018] Compared with the prior art, the beneficial effects of the present invention are:

[0019] 1. The present invention realizes the lake health assessment for both partitioned and overall areas and obtains the health grading scores. The results of the partitioned health assessment can clearly reflect the changes in the health level of the lake in space, which helps to quickly and accurately identify the locations of pollution sources and formulate corresponding treatment plans. The obtained health grading scores are helpful for analyzing the causes of the lake health status and judging the temporal change trend of the lake health status.

[0020] 2. From the perspective of ecosystem health management, the present invention establishes a lake health assessment method that integrates a health assessment index system and a health fuzzy comprehensive evaluation model applicable to lakes. This is beneficial to the rapid and comprehensive assessment of the lake health status and helps to quickly and accurately identify the main restrictive factors of lake health. The method adopted in the present invention is scientific and reasonable, the established model is simple and easy to understand, and the calculated results are accurate and intuitive, providing a very effective technical management tool for lake management, with prominent practical significance.

[0021] Advantages of additional aspects of the present invention will be partially given in the following description, partially become apparent from the following description, or be understood through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings forming a part of this application are used to provide a further understanding of this application. The schematic embodiments and descriptions thereof of this application are used to explain this application and do not constitute an improper limitation to this application.

[0023] Figure 1 is a flowchart of the disclosed method in Embodiment 1;

[0024] Figure 2 is a partition map of the lake health assessment of Nansi Lake;

[0025] Figure 3 is the score assigned to each partition of Nansi Lake in 2018. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0027] It should be noted that the following detailed description is illustrative and aims to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs.

[0028] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they specify the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0029] Embodiment 1

[0030] In this embodiment, a method for evaluating and diagnosing the health status of a lake is disclosed. As Figure 1 shown, it includes:

[0031] Carry out a health evaluation zoning for the lake;

[0032] Adopt the TCI model to construct a health evaluation index system for each area of the lake, determine the weights of each element in the criterion layer and the index layer, and assign scores to each index;

[0033] According to the weights of each element, the assigned scores, and the constructed fuzzy comprehensive evaluation model for the health of the lake area and the overall health fuzzy comprehensive evaluation model of the lake, obtain the health evaluation results of each area of the lake and the overall health of the lake. Among them, the process of constructing the fuzzy comprehensive evaluation model for the health of the lake area is as follows: construct a fuzzy relationship matrix between each element in the index layer and each element in the criterion layer; according to the fuzzy relationship matrix and the weights of each element in the index layer, obtain the membership degree matrix of the first-level index; according to the weights of each element in the criterion layer and the membership degree matrix of the first-level index, obtain the second-level fuzzy comprehensive evaluation membership degree matrix; according to the principle of maximum membership degree, obtain the health evaluation result from the second-level fuzzy comprehensive evaluation membership degree matrix; the overall health fuzzy comprehensive evaluation model is constructed by using the comprehensive analysis method according to the second-level fuzzy comprehensive evaluation membership degree matrix and the water surface area of each area.

[0034] Among them, according to the actual situation of the lake, considering the hydrological and hydrodynamic characteristics of the lake, the water quality status of the lake, the jurisdiction lake sections of the lake chief system around the lake, the spatial heterogeneity of the biological distribution in the lake, and the water function area zoning characteristics of the lake, a health evaluation zoning for the lake is carried out.

[0035] Referring to the relevant specifications and guidelines in the industry, according to the needs of lake management and the actual situation of the lake, a lake health assessment index system is established using the "Target-Criterion-Index" (TCI) model. The established lake health assessment index system includes a target layer, a criterion layer, and an index layer. The target layer represents the lake health status. The criterion layer reflects the attributes and levels of the lake health status from different aspects, including chemical integrity, morphological structure integrity, hydrological integrity, biological integrity, and social service function integrity. The index layer consists of the indicators corresponding to each element in the criterion layer. Among them, the indicators corresponding to the chemical integrity criterion layer are the quality of water, lake trophic status, sediment pollution status, and water self-purification ability; the indicators corresponding to the morphological structure integrity criterion layer are the lake connectivity index, the proportion of lake area shrinkage, the natural condition of the shoreline, and the degree of illegal development and utilization of the water area shoreline; the indicators corresponding to the hydrological integrity criterion layer are the satisfaction degree of the lowest ecological water level and the variation degree of the inflow into the lake; the indicators corresponding to the biological integrity criterion layer are the biological integrity index of macrozoobenthos, the fish retention index, the waterbird status, the density of phytoplankton, and the coverage of large aquatic plants; the indicators corresponding to the social service function integrity criterion layer are the flood control compliance rate, the guarantee degree of water supply volume, the compliance rate of centralized drinking water sources in lakes, the shoreline utilization management index, and the public satisfaction degree.

[0036] The scoring criteria for the above indicators refer to the "Guidelines for the Health Assessment of Rivers and Lakes (Trial)" (hereinafter referred to as the "Guidelines"). The meanings and calculation methods of each indicator are explained as follows:

[0037] (1) Quality of water

[0038] The quality of water is an important indicator for judging the health of a lake. When judging, the selection of sub-item indicators (such as total phosphorus TP, total nitrogen TN, dissolved oxygen DO, permanganate index, chemical oxygen demand, Ph) should meet the requirements of the water quality index assessment of the river and lake chief system in each region. The water quality category of the worst water quality item during the evaluation period represents the water quality category of the lake. The measured concentration values of the evaluation indicators are scored according to the "GB 3838-2002 Surface Water Environment Quality Standard" and the Guidelines. When the concentrations of multiple water quality items are all in the worst water quality category, separate scoring calculations are required, and the lowest value is taken.

[0039] (2) Lake trophic status

[0040] This indicator first needs to evaluate and calculate the lake trophic status index according to the regulations of the "SL 395-2007 Technical Regulations for the Quality Assessment of Surface Water Resources", and then determine the score of the lake trophic status according to the lake trophic status index value.

[0041] (3) Sediment pollution status

[0042] The sediment pollution status of lakes is expressed by the sediment pollution index, which is the percentage of the pollutant concentration in the sediment to the corresponding standard value. Since there is currently no industry standard for controlling the pollutant concentration in lake sediments, the standard value of lake pollutant concentration refers to the "Soil Environmental Quality - Risk Control Standards for Polluted Agricultural Land (Trial)" (GB 15618-2018). When assigning scores to the sediment pollution index, the multiple value of the pollutant with the highest exceedance concentration is selected.

[0043] (4) Water self-purification capacity

[0044] The water self-purification capacity is measured by the dissolved oxygen concentration in water. The survival of aquatic plants and animals is closely related to dissolved oxygen (DO). Dissolved oxygen is a necessary condition for the survival of aquatic animals such as fish. When the dissolved oxygen concentration is greater than 5 mg / L, it is suitable for the survival of most fish. At the same time, dissolved oxygen is also a very important factor in the water self-purification process. The lake needs to maintain good water quality and the water self-purification process under the condition of the existence of dissolved oxygen.

[0045] (5) Lake connectivity index

[0046] The evaluation is based on the smoothness of the water flow between the main inflowing and outflowing rivers around the lake being evaluated and the lake. The larger the lake connectivity index, the better the lake connectivity.

[0047] (6) Lake area shrinkage ratio

[0048] It is expressed as the ratio of the shrunk (or expanded) area of the lake water surface in the evaluation year to the lake water surface area in the historical reference year. The historical reference year generally selects a year in the late 1980s with a similar hydrological frequency to the evaluation year. For the specific calculation method and score assignment standard, please refer to the guide.

[0049] (7) Natural condition of the shoreline

[0050] The health status of the lake shoreline is evaluated by selecting the natural condition indicators of the shoreline, which includes two aspects: the stability of the lake shore and the vegetation coverage rate of the shoreline. Among them, the stability of the lake shore includes aspects such as the slope angle of the shore, the vegetation coverage rate of the shore slope, the height of the shore slope, the substrate of the shore slope, and the scouring condition of the shore slope. For the specific calculation method and score assignment standard, please refer to the guide.

[0051] (8) Degree of illegal development and utilization of the water area shoreline

[0052] The degree of illegal development and utilization of the water area shoreline comprehensively considers the standardization construction rate of the sewage outfalls into the lake, the reasonable layout degree of the sewage outfalls into the lake, and the "four chaos" situation of the lake. According to the specific situation of the evaluated lake, the score is assigned according to the guide.

[0053] (9) Degree of satisfaction with the lowest ecological water level

[0054] The lowest ecological water level is an important indicator to ensure the survival of lake organisms and the health of the lake. The lowest ecological water level of a lake is generally selected as the limit value determined in the lake planning or management documents, or is determined by methods such as the natural water level data method, the lake morphology method, and the minimum biological space requirement method.

[0055] (10) Degree of variation in the inflow to the lake

[0056] The degree of variation in the inflow to the lake reflects the impact of human activities on the inflowing runoff, and is expressed by the average deviation degree between the measured monthly runoff inflowing into the lake of the rivers around the lake and the natural monthly runoff. The greater the degree of variation in the inflow to the lake, the greater the impact of human activities and the lower the score. For the specific calculation method and scoring standard, refer to the guidelines.

[0057] (11) Biological integrity index of macroinvertebrates

[0058] The biological integrity index of macroinvertebrates (BIBI) reflects the status of aquatic organisms in the lake and is evaluated by analyzing and comparing the status of macroinvertebrates at the reference points and damaged points of the lake.

[0059] (12) Fish retention index

[0060] The fish retention index is an important indicator of the health of the lake and is expressed by the difference between the number of fish species in the evaluation year and the number of fish species in the historical reference year. Through field investigations of the lake waters, markets, and consultations with relevant personnel, residents at sampling points, anglers, restaurant operators, etc., and by referring to literature materials, etc., the number of fish species (excluding alien species) is obtained. The fish investigation and sampling monitoring can be determined according to fish investigation technical standards such as the "Specifications for Reservoir Fisheries Resources Investigation (SL 167-2014)".

[0061] (13) Status of waterbirds

[0062] Investigate and evaluate the species and numbers of birds in the lake, and use the on-site observation records as the basis for scoring. Classify and score according to the status of waterbird habitats into five levels: good, relatively good, average, relatively poor, and very poor. The scoring of the waterbird status can also adopt the reference point multiple method, with the monitoring data of the historical reference period before the major changes in the water quality and morphology of the river and lake as the basis point, and it is advisable to use the monitoring data in the 1980s or earlier.

[0063] (14) Phytoplankton density

[0064] The evaluation of phytoplankton density index can adopt the reference point multiple method or the direct judgment scoring method according to the actual situation of the evaluated lake. The reference point multiple method takes lakes with similar types in the same ecological zone or lake geographical zone that have not been affected or slightly affected by human activities, and uses the monitoring data in the historical reference period before the major changes in lake water quality and morphology as the basis point. It calculates the multiple of the phytoplankton density in the evaluation year compared with the historical basis point and assigns scores. When there is no reference point, the direct judgment scoring method can be directly used for scoring according to the phytoplankton density of the lake.

[0065] (15) Coverage of large aquatic plants

[0066] The coverage of large aquatic plants includes the total coverage of non - alien species of four types of plants, namely emergent plants, floating - leaf plants, submerged plants and floating plants in the lake - facing waters of the lakeshore zone. The reference point comparison scoring method or the direct judgment scoring method can be adopted according to the actual situation of the evaluated lake.

[0067] (16) Flood control compliance rate

[0068] The flood control compliance rate of lakes is evaluated by the flood control compliance of dikes and buildings at the lake - surrounding mouths. The flood control compliance rate of lakes should not only count the flood control compliance rate of the dike length in the evaluation year of the lake, but also evaluate the proportion of buildings at the lake - surrounding mouths that meet the design standards. When there is no relevant plan for the flood control compliance standard for the evaluated lake, it can be determined by referring to the "Code for Flood Control Standard (GB 50201 - 2014)".

[0069] (17) Degree of guarantee of water supply quantity

[0070] The degree of guarantee of water supply quantity of lakes is the percentage of the number of days when the daily water level or flow of the lake reaches the water supply guarantee level or flow within a year to the total number of days in the whole year. The specific scoring criteria can be found in the guide.

[0071] (18) Compliance rate of centralized drinking water sources in lakes

[0072] The water quality compliance rate of centralized drinking water sources in lakes refers to the percentage of the number of compliant centralized drinking water sources (surface water) in the evaluated lake to the total number of centralized drinking water sources in the evaluated lake. The selected evaluation indicators are 24 basic indicators and 5 supplementary indicators for centralized drinking water sources in the "Surface Water Environment Quality Standard (GB 3838 - 2002)".

[0073] (19) Shoreline utilization management index

[0074] The shoreline utilization management index reflects the degree of protection and integrity of the lake shoreline. The shoreline utilization management index includes the shoreline utilization rate and the integrity rate of the utilized shoreline, and scores are assigned according to the calculation results.

[0075] (20) Public satisfaction

[0076] The public satisfaction refers to the degree of satisfaction of the public with aspects such as the water environment, water quality and quantity, and water-related landscapes of the evaluated lakes. It is evaluated by the public survey method, and generally the degree of satisfaction of the public is obtained by distributing questionnaires. Index scores are assigned according to the public satisfaction of the public participating in the survey within the evaluation area.

[0077] Based on the established lake health evaluation index system, determine the health evaluation grading standards for each index, and determine the weights of each element in the index layer and criterion layer.

[0078] Specifically, the improved analytic hierarchy process is used to determine the weights of each element in the index layer. The process includes:

[0079] Establish a comparison matrix between each element in the index layer;

[0080] According to the comparison matrix, obtain an indirect judgment matrix;

[0081] According to the indirect judgment matrix, obtain a transfer matrix;

[0082] According to the transfer matrix, obtain an optimal transfer matrix;

[0083] According to the optimal transfer matrix, obtain a quasi-optimal consistent transfer matrix;

[0084] Solve the quasi-optimal consistent transfer matrix to obtain the eigenvector corresponding to the maximum eigenvalue, which is the weight of each element in the index layer.

[0085] The process of using the improved analytic hierarchy process to determine the weights of each element in the index layer is discussed in detail. The specific process is as follows:

[0086] (1) Establish a hierarchical structure model

[0087] Based on the mutual relationship as the basis for stratification, divide the decision-making goal, consideration factors and decision-making object into the highest layer, middle layer and lowest layer, namely the goal layer, criterion layer and index layer.

[0088] (2) Establish a comparison matrix

[0089] Make pairwise comparisons between each element in the index layer, and invite experts in the water conservancy industry to scale using the "0-2" three-scale method, and then establish a comparison matrix A.

[0090]

[0091] In the formula:

[0092]

[0093] (3) Obtain the indirect judgment matrix

[0094] r iThe sorting index in the comparison matrix, and the sorting index of the importance degree of each element can be calculated by the following formula:

[0095]

[0096] r max represents the maximum sorting index, and r min represents the minimum sorting index.

[0097] The elements of the indirect judgment matrix C can be determined by the following formula, where c ij is the value of the i-th row and j-th column of the indirect judgment matrix.

[0098]

[0099] In the formula: represents the importance degree given according to a certain scale when r max is compared with r min .

[0100] (4) Construct the transfer matrix

[0101] Construct the transfer matrix and let it be D. The elements d ij in the matrix follow the following formula:

[0102] d ij =lgc ij (i, j = 1, 2, 3,..., n)

[0103] (5) Construct the optimal transfer matrix

[0104] Let the optimal transfer matrix be V. The conversion calculation formula is as follows:

[0105]

[0106] In the formula: d ik represents the element of the i-th row and k-th column in the transfer matrix D, and d jk represents the element of the j-th row and k-th column in the transfer matrix D. k represents the serial number of the index in the index layer corresponding to this criterion layer.

[0107] (6) Construct the quasi-optimal consistent transfer matrix

[0108] Let the quasi-optimal consistent transfer matrix of the indirect judgment matrix C be C'. The elements c' ij in the matrix C' follow the following formula:

[0109]

[0110] (7) Determine the weights of each element in the index layer

[0111] To determine the weights of each element in the index layer, understood from a mathematical essence, it is to solve the maximum eigenvalue and the corresponding eigenvector of the quasi-optimal consistent transfer matrix C':

[0112] C' n×n W = λ max W

[0113] In the formula: C' n×n is a quasi-optimal consistent transfer matrix of order n×n; λ max is the maximum eigenvalue of the quasi-optimal consistent transfer matrix; W is the eigenvector corresponding to the maximum eigenvalue; the obtained W is the required weight vector.

[0114] Determine the weights of each element in the criterion layer by referring to the norms and guidelines in the industry. For cases without reference basis, the weights of each element in the criterion layer can also be determined by the improved analytic hierarchy process. The process includes:

[0115] Establish a comparison matrix between each element in the criterion layer;

[0116] Obtain an indirect judgment matrix based on the comparison matrix;

[0117] Obtain a transfer matrix based on the indirect judgment matrix;

[0118] Obtain an optimal transfer matrix based on the transfer matrix;

[0119] Obtain a quasi-optimal consistent transfer matrix based on the optimal transfer matrix;

[0120] Solve the quasi-optimal consistent transfer matrix to obtain the eigenvector corresponding to the maximum eigenvalue, which is the weight of each element in the criterion layer.

[0121] The process of constructing a fuzzy comprehensive evaluation model for lake sub-region health is as follows: construct a fuzzy relationship matrix between each element in the index layer and each element in the criterion layer; obtain the membership degree matrix of the first-level index according to the fuzzy relationship matrix and the weights of each element in the index layer; obtain the second-level fuzzy comprehensive evaluation membership degree matrix according to the weights of each element in the criterion layer and the membership degree matrix of the first-level index; obtain the health evaluation result from the second-level fuzzy comprehensive evaluation membership degree matrix according to the maximum membership degree principle, specifically including:

[0122] (1) Establish a factor set

[0123] The factor set is a set composed of various factors that affect the evaluation object.

[0124] U = {u1, u2, …, u j}, u j = {u j1 , u j2 , …, u jn}

[0125] In the formula: U is the factor set affecting the target layer, that is, the criterion layer set; since each criterion layer contains different n indicators, an indicator set u is established for each indicator in the criterion layer j , u jn Determined according to the number of indicators under the j criterion layer.

[0126] That is: put the criterion layer factors in the lake health assessment index system into the criterion layer factor set, and put the index layer factors into the index layer factor set.

[0127] (2) Construct the weight set

[0128] Construct the weight sets of each element, including the criterion layer weight set W and the index layer weight set W j , as shown in the following formula.

[0129] W = {w1, w2, …, w j},W j = {w j1 , w j2 , …, w jn}

[0130] Among them, w j represents the weight of the j element in the criterion layer, and w jn represents the weight of the nth indicator corresponding to the j element in the criterion layer.

[0131] That is: put the weights of each element in the criterion layer into the criterion layer weight set W, and put the weights of each element in the index layer into the index layer weight set W j .

[0132] (3) Determine the fuzzy relation matrix R ij

[0133] Put the target layer factors into the evaluation set to establish the evaluation set V n = {v 1n , v 2n , v 3n , v 4n , v 5n},In the formula, v 1n to v 5n respectively represent the scoring standard values of the five health levels of "very healthy", "healthy", "sub-healthy", "unhealthy" and "poor state" of the lake corresponding to the nth indicator.

[0134] Construct a formula applicable to the larger-the-better type indicators, calculate the membership degree according to the following formula, where x represents the score value of each indicator. Construct the fuzzy relation matrix R between the evaluation set and each element in the criterion layer factor set ij , that is, the membership degree matrix of the i-th partition and the j-th criterion layer.

[0135] The membership function f(x) belonging to the first level (very healthy) is

[0136]

[0137] The membership function f(x) belonging to the second level (healthy) is

[0138]

[0139] The membership function f(x) belonging to the third level (sub-healthy) is

[0140]

[0141] The membership function f(x) belonging to the fourth level (unhealthy) is

[0142]

[0143] The membership function f(x) belonging to the fifth level (poor state) is

[0144]

[0145] Construct the fuzzy relation matrix R according to the membership function ij .

[0146]

[0147] In the formula, r is the membership degree calculated by the membership function, r ij11 represents the membership degree of the first item of the i-th partition, the j-th criterion layer to the first level (very healthy) calculated according to the membership function f(x), r ij12 represents the membership degree of the first item of the i-th partition, the j-th criterion layer to the second level (healthy) calculated according to the membership function f(x), and so on; ijn represents that the number of indicators in the j-th criterion layer of the i-th partition is n.

[0148] (4) Conduct hierarchical fuzzy evaluation by partition

[0149] The first-level fuzzy evaluation of the index layer to the criterion layer, that is: according to the fuzzy relation matrix R ij and the index layer weight set W j , construct the membership degree matrix of the first-level index.

[0150] Use the following formula for the first-level fuzzy evaluation to obtain the membership degree matrix B of the first-level index ij , that is, the membership degree matrix of the j-th criterion layer of the i-th partition.

[0151]

[0152] Where: b ijn is the membership degree of the j-th criterion layer in the i-th partition to the lake health level, and n is an integer from 1 to 5.

[0153] The secondary fuzzy evaluation of the criterion layer to the target layer, that is: according to the criterion layer weight set W and the membership degree matrix B of the primary index ij , obtain the secondary fuzzy comprehensive evaluation membership degree matrix B i , including:

[0154] Construct the secondary fuzzy evaluation relation matrix as R i , as shown in the following formula.

[0155]

[0156] Obtain the secondary fuzzy comprehensive evaluation membership degree matrix B according to the following formula i , that is, the membership degree matrix of the lake health level in the i-th partition.

[0157]

[0158] According to the principle of maximum membership degree, obtain the health evaluation result from the secondary fuzzy comprehensive evaluation membership degree matrix, that is, select The corresponding health level is the evaluation result of the final lake partition.

[0159] The overall health fuzzy comprehensive evaluation model adopts the comprehensive analysis method according to the secondary fuzzy comprehensive evaluation membership degree matrix B i and the water surface area W i ' of each area to construct and obtain.

[0160] The overall health evaluation of the lake is based on the results of the partition health evaluation. According to the comprehensive analysis method, an overall health fuzzy comprehensive evaluation model is established, as shown in the following formula.

[0161]

[0162] Where: B is the overall health fuzzy evaluation set of the lake, B i is the health evaluation fuzzy evaluation set of each partition, W i ' is the water surface area of each partition, and m is the number of lake partitions.

[0163] (5) Conduct a comprehensive analysis and evaluation of lake health

[0164] In addition, according to the maximum membership degree of each partition and the whole, the health status of the lake partition and the whole can be judged, but the change differences within the same health level of the lake cannot be specifically reflected. In order to facilitate the comparison of the changes in each partition and each criterion layer, this embodiment also constructs a comprehensive analysis and evaluation model of lake health, and obtains the health grading scores of each area and the whole of the lake according to the comprehensive analysis and evaluation model of lake health.

[0165] The comprehensive analysis and evaluation model for lake health is as follows:

[0166]

[0167] Among them, μ j is the lake health score of the j criterion layer, μ is the overall lake health score, and W e is the score matrix, that is, the health classification score matrix.

[0168] The lake partition health fuzzy comprehensive evaluation model, the overall health fuzzy comprehensive evaluation model and the comprehensive analysis and evaluation model for lake health constitute the lake health evaluation model. Enter the weights and assigned scores of each element into the lake health evaluation model, and conduct lake health evaluation through the calculation results output by the evaluation model.

[0169] The method disclosed in this embodiment has the following advantages:

[0170] (1) Conduct the lake health evaluation of the partition and the whole

[0171] Divide the healthy areas of the lake, and at the same time establish the lake partition health fuzzy comprehensive evaluation model and the overall health fuzzy comprehensive evaluation model to complete the lake health evaluation of the partition and the whole. The results of the partition health evaluation can clearly reflect the changes in the health level of the lake in space, which helps to quickly and accurately identify the location of pollution sources and formulate corresponding treatment plans.

[0172] (2) Use the improved analytic hierarchy process to determine the weights of each element in the index layer

[0173] Aiming at the problems of possible subjective judgment errors of decision-makers and the failure to pass the consistency test of the judgment matrix in the traditional AHP method, the improved analytic hierarchy process uses the "0-2" three-scale method to construct the comparison matrix, which better overcomes the influence of the fuzziness of subjective judgment on decision-making; and uses the concept of the optimal transfer matrix to more accurately construct the judgment matrix, avoiding the consistency test and adjustment of the judgment matrix. Construct the optimal transfer matrix and the quasi-optimal consistent transfer matrix of the initial judgment matrix, and directly obtain the relative weight of a certain level factor with respect to a certain factor in the upper level after solving the eigenvector of the quasi-optimal consistent transfer matrix. Since the quasi-optimal consistent matrix meets the consistency requirements of the matrix, there is no need to perform steps such as the consistency test on the initial judgment matrix, avoiding the cumbersome calculation process in the traditional AHP method. At the same time, introducing the "three-scale" method can reduce the subjectivity of determining weights using the expert scoring method to a certain extent and improve the accuracy of the lake health evaluation results.

[0174] (3) Use the fuzzy comprehensive evaluation method to calculate the health status of the lake partition

[0175] To avoid the possible misjudgment that may occur in the method of determining the lake health status based on the weighted average, the fuzzy comprehensive evaluation method is adopted in the calculation of the zonal health evaluation in this method. By means of the fuzzy relation matrix, the membership degrees of each index in the five health levels (very healthy, healthy, sub-healthy, unhealthy, poor state) are judged. According to the principle of the maximum membership degree, the health level corresponding to the maximum membership degree is selected as the final evaluation result. In this way, the judgment deviation caused by the different grading standards of the health levels of the indexes can be avoided.

[0176] (4) Score and comparative analysis of the final evaluation result

[0177] According to the maximum membership degree of each zone and the lake, the health status of the lake zones and the whole can be judged, but the change differences within the same health level of the lake cannot be specifically reflected. To facilitate the comparison of the changes in each zone and each criterion layer, a grading score matrix of the health level is established, and the health evaluation scores of each criterion layer, lake zones and the whole of the lake can be calculated. Based on this, it helps to analyze the causes of the lake health status and judge the changing trend of the lake health status over time.

[0178] (5) Advantage analysis of the combined application of the analytic hierarchy process and the fuzzy comprehensive evaluation method

[0179] The analytic hierarchy process is a method for weight analysis. The need for element weights in the application of the fuzzy comprehensive evaluation method provides a basis for the combined use of these two methods. Both the analytic hierarchy process and the fuzzy comprehensive evaluation method are applicable to the evaluation of multi-factor complex problems with a hierarchical structure. The combination of the two methods can better analyze and evaluate abstract and fuzzy problems. In addition, both the analytic hierarchy process and the fuzzy comprehensive evaluation method are methods that combine qualitative and quantitative analysis. They can not only evaluate the research object by mathematical methods, but also give full play to human experience, making the evaluation results more in line with the objective reality.

[0180] The method disclosed in this embodiment is based on the perspective of ecosystem health management, and establishes a lake health evaluation method that combines an evaluation index system and a health evaluation model applicable to lakes, which is conducive to achieving a rapid and comprehensive assessment of the lake health status, and at the same time helps to quickly and accurately identify the main restrictive factors of lake health. The method adopted by the present invention is scientific and reasonable, the established model is simple and easy to understand, and the calculated results are accurate and intuitive, providing a very effective technical management tool for lake management, and the practical significance is very prominent.

[0181] Taking the Nansi Lake in Shandong Province as an example, using the relevant data of this lake in 2018, the specific implementation method of the present invention will be specifically described.

[0182] (1) Conduct health evaluation zoning according to the actual situation of the Nansi Lake

[0183] According to the "Guidelines for the Health Assessment of Rivers and Lakes (Trial)" promulgated by the Department of River and Lake Management of the Ministry of Water Resources in August 2020 and the actual situation of the Nansi Lake, a health assessment zoning for the Nansi Lake was carried out. The zoning principles are as follows:

[0184] 1) The Nansi Lake consists of four lakes, namely, the Weishan Lake, Zhaoyang Lake, Dushan Lake, and Nanyang Lake. Consider the hydrological and hydrodynamic characteristics of each lake, the water quality status of the lakes, and the lake sections under the jurisdiction of the lake chief system around the Nansi Lake;

[0185] 2) Consider the zoning characteristics of the water function areas of the lakes;

[0186] 3) Considering that the biological indicators of the four lakes in the Nansi Lake, such as the density of phytoplankton, the coverage of large aquatic plants, and the integrity index of macrozoobenthos, have spatial heterogeneity, the biological zoning is carried out according to the four lakes, namely, the Weishan Lake zoning, Zhaoyang Lake zoning, Dushan Lake zoning, and Nanyang Lake zoning.

[0187] According to the above principles, the Nansi Lake is divided into 14 health assessment zones. For the specific zoning, see Figure 2 .

[0188] (2) Establish a health assessment index system for the Nansi Lake

[0189] Considering the differences in each zone of the Nansi Lake, a criterion layer for the assessment system is established from five aspects: chemical integrity, morphological structure integrity, hydrological integrity, biological integrity, and social service function integrity. Based on the principles for establishing the index system and combining the actual situation of the Nansi Lake, the frequency statistics method is used to select the lake health assessment indicators with a relatively high usage frequency, and a health assessment index system for the Nansi Lake is established, as shown in Table 1.

[0190] Table 1 Health assessment index system for the Nansi Lake

[0191]

[0192]

[0193] (3) According to the established lake health assessment index system, determine the health assessment grading standards for each index, and determine the weights of each element in the index layer and criterion layer.

[0194] The index grading standards refer to the industry standards and specifications issued locally, and the health assessment grading standards for the lake health assessment indicators as shown in Table 2 are established.

[0195] Table 2 Health assessment grading standards for lake health assessment indicators

[0196]

[0197] To further improve the accuracy of the evaluation index system, the weights are determined by combining experience and theory. That is, the weights of each element in the criterion layer are determined by referring to the norms and guidelines in the industry, and the weights of each element in the index layer are determined by using the improved analytic hierarchy process.

[0198] The weights of each element in the criterion layer are determined by referring to the "Guidelines for the Evaluation of River and Lake Health (Trial)", that is, the weight of B to A is shown in Table 3.

[0199] Table 3 Weights of the Criterion Layer for Lake Health Evaluation

[0200]

[0201] The weights of each element in the index layer are determined by using the improved analytic hierarchy process. According to the scoring of 10 experts in the water conservancy industry using the "0-2" scale method, a comparison matrix is established, and an indirect judgment matrix is obtained through formula calculation. The indirect judgment matrices of the corresponding indicators for each element in the criterion layer are shown in Tables 4-8.

[0202] Table 4 Indirect Judgment Matrix between Indicators Corresponding to B1

[0203]

[0204] Table 5 Indirect Judgment Matrix between Indicators Corresponding to B2

[0205]

[0206] Table 6 Indirect Judgment Matrix between Indicators Corresponding to B3

[0207]

[0208] Table 7 Indirect Judgment Matrix between Indicators Corresponding to B4

[0209]

[0210]

[0211] Table 8 Indirect Judgment Matrix between Indicators Corresponding to B5

[0212]

[0213] According to the indirect judgment matrix, a transfer matrix, an optimal transfer matrix, and a quasi-optimal consistent transfer matrix are constructed using the model formula. The eigenvalues and their corresponding eigenvectors of the quasi-optimal consistent transfer matrix are solved, and the obtained eigenvector is the weight corresponding to each index, as shown in Table 9.

[0214] Table 9 Weights of the Elements for the Health Evaluation of the Nansi Lake

[0215]

[0216]

[0217] According to the scoring criteria of the reference indicators and the actual situation of the South Four Lakes, collect the evaluation index data for the evaluation year, assign scores to each index of the 14 sub - regions. The assigned scores of each sub - region of the South Four Lakes in 2018 are as Figure 3 shown.

[0218] (5) Based on the assigned scores of each sub - region of the South Four Lakes as the data basis, apply the multi - level fuzzy comprehensive evaluation method to construct a fuzzy comprehensive evaluation model for the health of lake sub - regions and a fuzzy comprehensive evaluation model for the overall health, and conduct evaluation calculations on the health status of the sub - regions and the overall situation of the South Four Lakes.

[0219] 1) According to the formula calculation, the membership degree matrix of Sub - region 1 of the South Four Lakes in 2018 is as follows:

[0220]

[0221]

[0222]

[0223]

[0224]

[0225] 2) First - level fuzzy evaluation of the index layer to the criterion layer

[0226]

[0227]

[0228]

[0229]

[0230]

[0231] 3) Second - level fuzzy evaluation of the criterion layer to the target layer

[0232] According to the first - level fuzzy evaluation set, the weights of each element in the criterion layer and the above formula, conduct a second - level fuzzy evaluation on the target layer to obtain the second - level fuzzy evaluation membership degree matrix B1.

[0233]

[0234] According to the maximum membership degree principle, in the second - level fuzzy evaluation membership degree matrix of the South Four Lakes, the maximum value is 0.3501, which is in a healthy state. Therefore, the lake health state of Sub - region 1 of the South Four Lakes is in a healthy state and belongs to Class II lakes.

[0235] 4) Zoning health assessment results

[0236] Similarly, calculate the health assessment status of the other 13 zones of Nansi Lake in 2018. According to the principle of maximum membership degree, obtain the health status grading of each zone of Nansi Lake, as shown in Table 10.

[0237] Table 10 Health assessment results of each zone of Nansi Lake in 2018

[0238]

[0239] 5) Overall health assessment results

[0240] According to the health assessment results of each zone in the zoning health status of Nansi Lake, calculate the overall health status of Nansi Lake in 2018 based on the overall health comprehensive analysis model of the lake. The results are shown in Table 11.

[0241] Table 11 Overall health assessment results of Nansi Lake

[0242]

[0243] (6) In order to facilitate the comparison of the changes in each zone and each criterion layer, establish an overall health comprehensive analysis and evaluation model for the lake.

[0244] Taking into account various factors, take (100, 90, 75, 60, 40) as the grading score matrix of the lake health level, and the health scores of each criterion layer of the lake and the overall health of the lake can be calculated. The overall health score of the lake is:

[0245] 100×0.3806 + 90×0.4174 + 75×0.1450 + 60×0.0254 + 40×0.0316 = 89.29

[0246] The health assessment score results of each criterion layer of Nansi Lake in 2018 are shown in Table 12.

[0247] Table 12 Health assessment scores of each criterion layer of Nansi Lake in 2018

[0248]

[0249]

[0250] (7) Health assessment results and countermeasure analysis

[0251] According to the calculation results of the above lake health assessment model, the overall health status of Nansi Lake in 2018 is in a healthy state, with an overall score of 89.29 points.

[0252] From the perspective of the criterion layer, the score of the chemical integrity criterion layer B1 is 89.89 points, the score of the morphological structure integrity criterion layer B2 is 92.18 points, the score of the hydrological integrity criterion layer B3 is 85.79 points, the score of the biological integrity criterion layer B4 is 89.09 points, and the score of the social service function integrity criterion layer B5 is 88.94 points. In comparison, the score of the lake hydrological integrity criterion layer B3 is relatively low overall. It is recommended that managers start from this aspect, formulate corresponding countermeasures, minimize the impact of human activities on the rivers flowing into the lake, and maintain the natural connection state between the rivers and the lake. For the landscape rivers in the basin, a certain ecological flow of the river should be ensured to maintain the ecological balance of the river.

[0253] From the perspective of the sub-region, sub-regions 5, 7, 12, and 14 are in a very healthy state, and the rest of the sub-regions are in a healthy state. From the scoring situation of the sub-region criterion layer, it can be seen that the four sub-regions in a very healthy state have obtained relatively high scores in the chemical integrity criterion layer B1, the morphological structure integrity criterion layer B2, and the biological integrity criterion layer B4. This indicates that the governance of other healthy sub-regions can start from the above three aspects. At the same time, in order to formulate more specific countermeasures, it is possible to start from the indicators corresponding to the above criterion layer. In the chemical integrity criterion layer, corresponding measures need to be taken to control the situation of high total nitrogen and total phosphorus concentrations. The pollution source can be controlled by building additional rural sewage treatment plants to reduce the discharge of untreated domestic sewage; at the same time, green agriculture is advocated to reduce the use of agricultural fertilizers and pesticides in the Nansi Lake Basin, and strengthen the ammonification and nitrification capabilities of the water body. In the morphological structure criterion layer, the amount of shoreline vegetation planting should be appropriately increased to increase the shoreline vegetation coverage rate. In accordance with the method of "mainly natural restoration and combined with artificial restoration", measures such as returning forests and grasslands, withdrawing from farming and banning grazing, and afforestation are taken to reduce the proportion of shoreline hardening and improve the ecological environment of the lake shoreline. In the biological integrity criterion layer, methods such as adding algaecides can be used to reduce the density of phytoplankton, and at the same time, submerged plants, floating-leaved plants, emergent plants, etc. are planted to increase the coverage of large aquatic plants. An excellent lake ecology will bring a good habitat environment for birds, fish, and benthic organisms, which is conducive to the survival and reproduction of lake organisms and the healthy development of the ecosystem.

[0254] For other problems, it is recommended that the management department introduce corresponding management measures. The scale of cage aquaculture in the lake area should be strictly controlled to avoid polluting the lake water due to over-farming and eutrophication, and practical measures should be taken to timely treat the polluted waters. In addition, through national and local subsidy and support policies, the residents in the lake can be relocated to reduce the impact of human activities in the lake area, but corresponding resettlement measures are required for the relocation of farmers in the lake area. Illegal coal mining and coal digging behaviors are prohibited, the sunken roads are repaired, and illegal operations on the lake surface are prohibited.

[0255] Example 2

[0256] In this embodiment, a lake health status evaluation and diagnosis system is disclosed, including:

[0257] A zoning module for performing health evaluation zoning on the lake;

[0258] A health evaluation index system construction module for constructing a lake health evaluation index system using the TCI model, determining the weights of each element in the criterion layer and the index layer, and assigning scores to each index;

[0259] A health evaluation result acquisition module for obtaining the health evaluation results of each area and the overall health of the lake according to the weights of each element, the assigned score values, and the constructed lake area health fuzzy comprehensive evaluation model and overall health fuzzy comprehensive evaluation model. Among them, the process of constructing the lake area health fuzzy comprehensive evaluation model is as follows: constructing a fuzzy relationship matrix between each element in the index layer and each element in the criterion layer; obtaining the membership degree matrix of the first-level index according to the fuzzy relationship matrix and the weights of each element in the index layer; obtaining the second-level fuzzy comprehensive evaluation membership degree matrix according to the weights of each element in the criterion layer and the membership degree matrix of the first-level index; obtaining the health evaluation result from the second-level fuzzy comprehensive evaluation membership degree matrix according to the maximum membership degree principle; the overall health fuzzy comprehensive evaluation model is constructed using the comprehensive analysis method according to the second-level fuzzy comprehensive evaluation membership degree matrix and the water surface area of each area.

[0260] Embodiment 3

[0261] In this embodiment, an electronic device is disclosed, including a memory, a processor, and computer instructions stored on the memory and running on the processor. When the computer instructions are run by the processor, the steps of a lake health status evaluation and diagnosis method disclosed in Embodiment 1 are completed.

[0262] Embodiment 4

[0263] In this embodiment, a computer-readable storage medium is disclosed for storing computer instructions. When the computer instructions are executed by the processor, the steps of a lake health status evaluation and diagnosis method disclosed in Embodiment 1 are completed.

[0264] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: the specific implementation manners of the present invention can still be modified or equivalently replaced, and any modification or equivalent replacement without departing from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A method for evaluating and diagnosing the health status of a lake, characterized in that: include: Conduct health assessment zoning for lakes; The TCI model was used to construct a lake health evaluation index system, determine the weights of each element in the criterion layer and indicator layer, and assign points to each indicator; According to the weights and scores of each element and the constructed lake zoning health fuzzy comprehensive evaluation model and overall health fuzzy comprehensive evaluation model, the health evaluation results of each lake zone and the lake as a whole are obtained. The process of constructing the lake zoning health fuzzy comprehensive evaluation model is as follows: constructing the fuzzy relationship matrix between each element in the indicator layer and each element in the criterion layer; obtaining the membership matrix of the first-level indicator according to the fuzzy relationship matrix and the weight of each element in the indicator layer; obtaining the second-level fuzzy comprehensive evaluation membership matrix according to the weight of each element in the criterion layer and the membership matrix of the first-level indicator; obtaining the health evaluation results from the second-level fuzzy comprehensive evaluation membership matrix according to the maximum membership principle; the overall health fuzzy comprehensive evaluation model is constructed using the comprehensive analysis method based on the second-level fuzzy comprehensive evaluation membership matrix and the water surface area of each zone; The overall health fuzzy comprehensive evaluation model is constructed using a comprehensive analysis method based on the secondary fuzzy comprehensive evaluation membership matrix and the water surface area of each district. Specifically: Where: is the fuzzy evaluation set of the overall health of the lake, is the membership matrix of the first-level indicators of each partition, is the water surface area of each zone, The number of lake zones; Based on the membership matrix of the primary indicators, the overall lake health evaluation results, and the comprehensive analysis and evaluation model for lake health, the health grading scores for each lake area and the entire lake were obtained. The comprehensive analysis and evaluation model for lake health is as follows: in, for Criteria-level lake health score, For overall lake health score, is the score matrix, that is, the health grading score matrix, is the membership matrix of the first-level indicators, It is the fuzzy evaluation set of the overall health of the lake.

2. A lake health assessment and diagnosis method according to claim 1, characterized in that: The health evaluation index system includes the target layer, the criterion layer and the indicator layer. The target layer represents the health status of the lake. The criterion layer reflects the attributes and levels of the health status of the lake from different aspects. The indicator layer is composed of indicators corresponding to each element in the criterion layer.

3. A lake health assessment and diagnosis method according to claim 2, characterized in that: The criterion layer includes chemical integrity, morphological and structural integrity, hydrological integrity, biological integrity and social service function integrity; among them, the indicators corresponding to the chemical integrity criterion layer are the quality of water quality, lake nutrient status, sediment pollution status and water body self-purification capacity; the indicators corresponding to the morphological and structural integrity criterion layer are lake connectivity index, lake area shrinkage ratio, shoreline natural condition and the degree of illegal development and utilization of water shoreline; the indicators corresponding to the hydrological integrity criterion layer are the degree of satisfaction of the minimum ecological water level and the degree of variation of inflow into the lake; the indicators corresponding to the biological integrity criterion layer are the biological integrity index of large benthic invertebrates, fish retention index, water bird status, phytoplankton density and large aquatic plant coverage; the indicators corresponding to the social service function integrity criterion layer are flood control compliance rate, water supply volume guarantee level, lake centralized drinking water source compliance rate, shoreline utilization management index and public satisfaction.

4. A lake health assessment and diagnosis method according to claim 1, characterized in that: The process of determining the weight of each element in the indicator layer using the improved hierarchical analysis method is as follows: Establish a comparison matrix between the elements in the indicator layer; According to the comparison matrix, the indirect judgment matrix is obtained; According to the indirect judgment matrix, the transfer matrix is obtained; According to the transfer matrix, the optimal transfer matrix is obtained; According to the optimal transfer matrix, a quasi-optimal consistent transfer matrix is obtained; The quasi-optimal consistent transfer matrix is solved to obtain the eigenvector corresponding to the maximum eigenvalue, which is the weight of each element in the indicator layer.

5. A lake health assessment and diagnosis method according to claim 1, characterized in that: The construction process of the fuzzy comprehensive evaluation model for lake zoning health is as follows: The elements in the criterion layer of the lake health evaluation index system are placed in the criterion layer factor set, the elements in the indicator layer are placed in the indicator layer factor set, the weights of each element in the criterion layer are placed in the criterion layer weight set, the weights of each element in the indicator layer are placed in the indicator layer weight set, and the elements in the target layer are placed in the evaluation set; Construct the fuzzy relationship matrix between the evaluation set and the elements in the criterion layer factor set; According to the fuzzy relationship matrix and the indicator layer weight set, the membership matrix of the first-level indicators is constructed , according to the criterion layer weight set and the first-level index membership matrix, the second-level fuzzy comprehensive evaluation membership matrix is obtained ; According to the maximum membership principle, the health evaluation results are obtained from the secondary fuzzy comprehensive evaluation membership matrix.

6. A lake health status evaluation and diagnosis system, characterized in that: include: Zoning module, used to zoning lakes for health assessment; The health evaluation index system construction module is used to construct the lake health evaluation index system using the TCI model, determine the weights of each element in the criterion layer and the indicator layer, and assign points to each indicator; The health evaluation result acquisition module is used to obtain the health evaluation results of each lake district and the lake as a whole according to the weights and scores of each element and the constructed lake district health fuzzy comprehensive evaluation model and overall health fuzzy comprehensive evaluation model. The process of constructing the lake district health fuzzy comprehensive evaluation model is as follows: constructing a fuzzy relationship matrix between each element in the indicator layer and each element in the criterion layer; obtaining the membership matrix of the first-level indicator according to the fuzzy relationship matrix and the weights of each element in the indicator layer; obtaining the second-level fuzzy comprehensive evaluation membership matrix according to the weights of each element in the criterion layer and the membership matrix of the first-level indicator; obtaining the health evaluation results from the second-level fuzzy comprehensive evaluation membership matrix according to the maximum membership principle; the overall health fuzzy comprehensive evaluation model is constructed using a comprehensive analysis method based on the second-level fuzzy comprehensive evaluation membership matrix and the water surface area of each district; The overall health fuzzy comprehensive evaluation model is constructed using a comprehensive analysis method based on the secondary fuzzy comprehensive evaluation membership matrix and the water surface area of each district. Specifically: Where: is the fuzzy evaluation set of the overall health of the lake, is the membership matrix of the first-level indicators of each partition, is the water surface area of each zone, The number of lake zones; Based on the membership matrix of the primary indicators, the overall lake health evaluation results, and the comprehensive analysis and evaluation model for lake health, the health grading scores for each lake area and the entire lake were obtained. The comprehensive analysis and evaluation model for lake health is as follows: in, for Criteria-level lake health score, For overall lake health score, is the score matrix, that is, the health grading score matrix, is the membership matrix of the first-level indicators, It is the fuzzy evaluation set of the overall health of the lake.

7. An electronic device, characterized in that: The method comprises a memory and a processor, and computer instructions stored in the memory and executed on the processor. When the computer instructions are executed by the processor, the steps of a lake health status evaluation and diagnosis method as described in any one of claims 1 to 5 are completed.

8. A computer-readable storage medium, characterized in that Used to store computer instructions, which, when executed by a processor, complete the steps of a lake health status assessment and diagnosis method as described in any one of claims 1 to 5.

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