Bay ecosystem health evaluation method

Abstract

The invention relates to the technical field of ecological system health evaluation, in particular to a bay ecological system health evaluation method, which comprises a PSR model framework part and an entropy weight method-EQR calculation part, and improves the accuracy of ecological system health evaluation of different regions of a bay. According to the bay ecosystem health evaluation method, an evaluation index system of bay ecosystem health is constructed from three levels of pressure, state and response based on a PSR theoretical framework, standardized selection is performed on index selection, assignment weights of the selected indexes are calculated through an entropy weight method, and on this basis, an EQR index is used for calculating a final result, so that a bay ecosystem health evaluation result is obtained. According to the bay ecological health evaluation method, multi-category division, index selection, weight assignment and EQR index calculation with allowable deviation are performed on the bay area, bay ecological health state grade division is performed on the obtained data, and the visual and accurate bay ecological health evaluation method is formed.

Description

Technical Field

[0001] The invention relates to the technical field of ecosystem health assessment, and in particular to a bay ecosystem health assessment method. Background Art

[0002] The valuation of the Gulf ecosystem involves a comprehensive consideration of the economic and non-economic values ​​of its various ecological services and resources. Changes in the Gulf ecosystem usually occur over long time scales, and the assessment of ecosystem services needs to take these changes into account, as well as social and economic factors that change over time.

[0003] When the health of some bay ecosystems is assessed, there will be large differences in ecosystem health between adjacent intervals of the bay, and the final display of the bay ecosystem health assessment method is not intuitive enough. Therefore, a bay ecosystem health assessment method is proposed to address the above problems. Summary of the invention

[0004] The purpose of the present invention is to provide a bay ecosystem health assessment method to solve the problem that when a health assessment is conducted on a portion of the bay ecosystem, there will be large differences in ecosystem health between adjacent interval areas of the bay, and the final display of the bay ecosystem health assessment method is not intuitive enough.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A bay ecosystem health assessment method, including a PSR model framework part and an entropy weight method-EQR calculation part, divides the bay area into multiple categories such as nearshore human activity area, tourist area, wetland area, agricultural and fishing area, and shipping area, and divides the bay area into multiple categories through the provided bay ecosystem health assessment method. The PSR model framework part and the entropy weight method-EQR calculation part are used to divide the regions. The PSR model framework part is a pressure-state-response model for evaluating the health of the ecosystem. The pressure model indicators include the direct ecological pressure that causes changes in the structure and function of the marine ecosystem and the terrestrial pressure that drives changes in the marine ecosystem. The state model indicators include the water quality status of the comprehensive marine environmental status and pollution status. The response model indicators are the natural responses of the ecosystem, which mainly reflect the changes and trends of various environmental and biological indicators under stress.

[0007] S1. In the selection of the terrestrial pressure index of the above-mentioned pressure model index, the total nitrogen emission, total phosphorus emission, heavy metal emission, COD emission and the change of the distribution area of ​​coastal wetlands are selected; in the selection of the water quality state of the above-mentioned state model index, COD, dissolved inorganic nitrogen, active phosphate, Hg, Cd, petroleum, organic carbon content and sulfide content are selected; in the selection of the biological index of the above-mentioned response model index, dissolved oxygen, chlorophyll a, phytoplankton algae density, zooplankton density, benthic animal density and fish egg and larvae density are selected;

[0008] S2. Collect and screen quantifiable data for the indicators selected in the above step S1, and calibrate the standard values ​​of the above cost-based indicator data and benefit-based indicator data in the bay ecosystem;

[0009] S3. De-dimensionalize and standardize the indicator data obtained in the above step S2 so that the evaluation method is not affected by the data type, and the calculated ecological health index is a dimensionless value;

[0010] S4. Assign values ​​through the entropy weight method, using the characteristics of entropy to assign corresponding weights based on the amount of information feedback from each indicator. If the entropy value of an indicator is smaller, the amount of information it provides is greater, the weight assigned is greater, and the role it plays in the comprehensive performance evaluation is greater;

[0011] S5. Calculate the ecological security index. After the data in step S4 is assigned, use EQR calculation to calculate the cost and benefit indicators of the final result.

[0012] S6. Set the bay ecosystem health level, and analyze and evaluate the calculation results obtained in step S5.

[0013] Preferably, the index data is calculated in the steps S3 and S4;

[0014] (1) Standardization of variables;

[0015] In the comparison of statistical data, for data of different dimensions, this study uses the normalization method to perform dimensionless processing on various indicators. Assuming that k indicators X1, X2, …, Xk are given, where Xi = {x1, x2, …, xn}, and assuming that the standardized value of each indicator is R i , the original value x of the ijth index ij The corresponding high value level is m max , the low level is m min , cost-based indicators adopt positive standardization, and benefit-based indicators adopt reverse standardization. The calculation formula is as follows:

[0016] Positive normalization of indicators:

[0017]

[0018] Denormalization of indicators:

[0019]

[0020] (2) Determine weights using entropy weight method

[0021] The entropy weight method is used to determine the weight of indicators. After all data are standardized and converted into dimensionless values ​​between [0,1], the ratio of each indicator under each scheme is calculated. Suppose there are m indicators and n years of data have been obtained, V ij is the data matrix after the original data matrix is ​​standardized, P ij The calculation formula is as follows:

[0022]

[0023] Find the information entropy of each indicator. According to the definition of information entropy in information theory, the information entropy of a set of data is (k is the Boltzmann constant):

[0024]

[0025] According to the calculation formula of information entropy, the information entropy of each indicator is obtained, and then the weight w of each indicator is calculated by information entropy i , and satisfy 0≤w j ≤1, and ∑w j =1:

[0026]

[0027] The essence of the entropy method is still to determine the weight value of each evaluation indicator based on its discreteness. The greater the discreteness, the smaller the entropy value, but the greater the entropy weight. The entropy method requires that the indicators involved in the evaluation have a certain amount of data samples before they can be used.

[0028] Preferably, in step S5, the index data after the assignment is calculated:

[0029] (1) The concept and calculation method of EQR index;

[0030] The EQR index is used to measure the health of the bay ecosystem. For a given water body, the EQR index can reveal the relationship between the actual monitored values ​​of each environmental parameter and the reference standard applicable to the water body. In order to quantify the health of the ecosystem, the EQR index is expressed as a value between 0 and 1, where 1 indicates that the ecosystem is in good health and 0 indicates that the ecosystem is in poor health. The calculation formula is:

[0031] EQR=∑W i EQR i (6)

[0032] EQR i is the ecological quality index of the i-th evaluation index, W i is the evaluation weight of the i-th evaluation parameter;

[0033] The calculation method of the evaluation index EQR index divides the selected evaluation indicators into two categories: cost-type indicators and benefit-type indicators. Cost-type indicators refer to the process of ecological health deterioration, and their monitoring values ​​gradually increase; on the contrary, they are called benefit-type indicators;

[0034] For cost-based indicators, its EQR i for;

[0035] EQR i =Re fcon / AcStat (7)

[0036] 0≤EQR i ≤1 (8)

[0037] Among them, Refcon is the reference standard value of the i-th evaluation index, AcStat is the actual monitoring value of the i-th evaluation index, and for cost-based indicators, the maximum value of its EQR is set to 1, that is, when the actual monitoring value is less than the reference standard, the EQR value is set to 1. Since the reference standard is a fixed value, for cost-based indicators, the larger the monitoring value, the smaller the EQR index, and the worse the ecological health status reflected;

[0038] For benefit indicators, its EQR j for;

[0039] EQR j =AcStat / Refcon (9)

[0040] 0≤EQR j ≤1 (10)

[0041] Among them, Refcon is the reference standard value of the j-th evaluation indicator, AcStat is the actual monitoring value of the j-th evaluation indicator. For benefit-type indicators, the maximum value of its EQR is set to 1, that is, when the reference value is less than the actual monitoring value, the EQR value is set to 1. For benefit-type indicators, the smaller the monitoring value, the smaller the EQR index, and the worse the ecological health status reflected.

[0042] Preferably, in the steps S5 and S6, EQR evaluation calculation is performed on the cost-type indicator data and the benefit-type indicator data;

[0043] Determination of the thresholds of the bay ecological assessment indicators: For cost-based indicators, +50% is usually given as the permissible deviation from the reference standard, that is, if the monitoring value exceeds the reference standard by less than 50% of the reference standard, it is considered acceptable; and for benefit-based indicators, -25% is usually given as the permissible deviation from the reference standard;

[0044] For cost-based indicator data and benefit-based indicator data, the allowable deviation (AcDev) is usually given as different reference standards. The actual monitoring value is rewritten as formula (11). Then, substitute formula (11) into formula (7) to obtain formula (12);

[0045] AcStat=(1+AcDev)×Refcon (11)

[0046] EQR 良 / 中 =1 / (1+AcDev) (12)

[0047] Combined with the given allowable deviation, the boundary between good / medium evaluation index can be determined. For the boundary between poor / inferior, formula (13) is used to calculate:

[0048] 1-EQR 良 / 中 =EQR 良 / 中 -EQR 差 / 劣 (13)

[0049] The excellent / good and medium / poor boundaries can be calculated using formula (14) and formula (15):

[0050] EQR 优 / 良 =(1+EQR 良 / 中 ) / 2 (14)

[0051] EQR 中 / 差 =(EQR 良 / 中 +EQR 差 / 劣 ) / 2 (15)

[0052] Since the cost-type indicator data and the benefit-type indicator data are usually given with allowable deviations (AcDev) as different reference standards, the cost-type indicator data and the benefit-type indicator data correspond to different evaluation boundaries.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] In the present invention, the bay area is divided into multiple categories, including nearshore human activity areas, tourist areas, wetland areas, agricultural and fishing areas, and shipping areas. The PSR model framework part and the entropy weight method-EQR calculation part of the bay ecosystem health evaluation method provided are used to improve the accuracy of the ecosystem health evaluation of different areas of the bay. The evaluation method of the bay ecosystem health is based on the PSR theoretical framework to construct an evaluation index system for the bay ecological health from three levels: pressure, state, and response. The index selection is standardized, and the assignment weights of the selected indicators are calculated by the entropy weight method. On this basis, the final result is calculated using the EQR index. Through the above settings, the bay area is first divided into multiple categories, and the indicators are selected, the weights are assigned, and the EQR index is calculated with an allowable deviation. The obtained data is divided into the bay ecological health status level, forming an intuitive and accurate evaluation method for the bay ecological health. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a schematic diagram of the bay ecosystem health assessment process of the present invention;

[0056] Figure 2 A schematic diagram of the zones for the bay zone evaluation of the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] See also Figure 1 , Figure 2 , the present invention provides a technical solution:

[0059] A bay ecosystem health assessment method, including a PSR model framework part and an entropy weight method-EQR calculation part, divides the bay area into multiple categories such as nearshore human activity area, tourist area, wetland area, agricultural and fishing area, and shipping area, and divides the bay area into multiple categories through the provided bay ecosystem health assessment method. The PSR model framework part and the entropy weight method-EQR calculation part are used to divide the regions. The PSR model framework part is a pressure-state-response model for evaluating the health of the ecosystem. The pressure model indicators include the direct ecological pressure that causes changes in the structure and function of the marine ecosystem and the terrestrial pressure that drives changes in the marine ecosystem. The state model indicators include the water quality status of the comprehensive marine environmental status and pollution status. The response model indicators are the natural responses of the ecosystem, which mainly reflect the changes and trends of various environmental and biological indicators under stress.

[0060] S1. In the selection of the terrestrial pressure index of the above-mentioned pressure model index, the total nitrogen emission, total phosphorus emission, heavy metal emission, COD emission and the change of the distribution area of ​​coastal wetlands are selected; in the selection of the water quality state of the above-mentioned state model index, COD, dissolved inorganic nitrogen, active phosphate, Hg, Cd, petroleum, organic carbon content and sulfide content are selected; in the selection of the biological index of the above-mentioned response model index, dissolved oxygen, chlorophyll a, phytoplankton algae density, zooplankton density, benthic animal density and fish egg and larvae density are selected;

[0061] S2. Collect and screen quantifiable data for the indicators selected in the above step S1, and calibrate the standard values ​​of the above cost-based indicator data and benefit-based indicator data in the bay ecosystem;

[0062] S3. De-dimensionalize and standardize the indicator data obtained in the above step S2 so that the evaluation method is not affected by the data type, and the calculated ecological health index is a dimensionless value;

[0063] S4. Assign values ​​through the entropy weight method, using the characteristics of entropy to assign corresponding weights based on the amount of information feedback from each indicator. If the entropy value of an indicator is smaller, the amount of information it provides is greater, the weight assigned is greater, and the role it plays in the comprehensive performance evaluation is greater;

[0064] S5. Calculate the ecological security index. After the data in step S4 is assigned, use EQR calculation to calculate the cost and benefit indicators of the final result.

[0065] S6. Set the bay ecosystem health level, and analyze and evaluate the calculation results obtained in step S5.

[0066] The present invention takes Jiaozhou Bay as an example:

[0067] Table 1 Evaluation criteria for terrigenous pressure index

[0068]

[0069] Note: (+) indicates cost-based indicators, (-) indicates benefit-based indicators

[0070] As shown in Table 1 , the selected total nitrogen emissions, total phosphorus emissions, heavy metal emissions, COD emissions, and changes in coastal wetland distribution area were quantitatively evaluated;

[0071] Table 2 Evaluation criteria for water quality and sedimentation status indicators

[0072]

[0073] Note: (+) indicates cost-based indicators, (-) indicates benefit-based indicators

[0074] As shown in Table 2 , COD, dissolved inorganic nitrogen, active phosphate, Hg, Cd, petroleum, organic carbon content, and sulfide content were selected for evaluation standard quantification;

[0075] Table 3 Evaluation criteria for biological response indicators

[0076]

[0077] Note: (+) indicates cost-based indicators, (-) indicates benefit-based indicators

[0078] As shown in Table 3, dissolved oxygen, chlorophyll a, phytoplankton algae density, zooplankton density, benthic zoodensity, and fish egg and larvae density were selected for quantitative evaluation criteria;

[0079] Comparing the statistical data over the years, this project uses normalization method to make various indicators dimensionless for data of different dimensions;

[0080] Calculate the indicator data of statistical data;

[0081] (1) Standardization of variables;

[0082] In the comparison of statistical data, for data of different dimensions, this study uses the normalization method to perform dimensionless processing on various indicators. Assuming that k indicators X1, X2, …, Xk are given, where Xi = {x1, x2, …, xn}, and assuming that the standardized value of each indicator is R i , the original value x of the ijth index ij The corresponding high value level is m max , the low level is m min , cost-based indicators adopt positive standardization, and benefit-based indicators adopt reverse standardization. The calculation formula is as follows:

[0083] Positive normalization of indicators:

[0084]

[0085] Denormalization of indicators:

[0086]

[0087] The standardized values ​​of comprehensive health level indicators of Jiaozhou Bay ecosystem were obtained;

[0088] Table 4 Standardized values ​​of comprehensive health indicators of Jiaozhou Bay ecosystem from 2013 to 2022

[0089]

[0090]

[0091]

[0092]

[0093] Note: Since there are many missing years for the data on the changes in the distribution area of ​​coastal wetlands in the past decade, they are not included in the health assessment data for the time being;

[0094] As shown in Table 4, the standardized values ​​of the comprehensive health level indicators of the Jiaozhou Bay ecosystem from 2013 to 2022 are used as the reference examples of the target standard values. After all data are standardized, according to the calculation formula of the weights;

[0095] (2) Determine the weights using the entropy weight method;

[0096] The entropy weight method is used to determine the weight of indicators. After all data are standardized and converted into dimensionless values ​​between [0,1], the ratio of each indicator under each scheme is calculated. Suppose there are m indicators and n years of data have been obtained, V ij is the data matrix after the original data matrix is ​​standardized, P ij The calculation formula is as follows:

[0097]

[0098] Find the information entropy of each indicator. According to the definition of information entropy in information theory, the information entropy of a set of data is (k is the Boltzmann constant):

[0099]

[0100] According to the calculation formula of information entropy, the information entropy of each indicator is obtained, and then the weight w of each indicator is calculated by information entropy i , and satisfy 0≤w j ≤1, and ∑w j =1:

[0101]

[0102] The essence of the entropy method is still to determine the weight value of each evaluation index by its discrete degree. The greater the discrete degree, the smaller the entropy value, but the greater the entropy weight. The entropy method requires that the indexes involved in the evaluation have a certain amount of data samples before they can be used.

[0103] The weights of each indicator of Jiaozhou Bay ecosystem health assessment were obtained, and the weights of each indicator data of Jiaozhou Bay were assigned as shown in Table 5;

[0104] Table 5 Evaluation index weights

[0105]

[0106]

[0107] Note: (+) indicates cost-based indicators, (-) indicates benefit-based indicators

[0108] In step S5, the index data after assignment is calculated:

[0109] (1) The concept and calculation method of EQR index;

[0110] The EQR index is used to measure the health of the bay ecosystem. For a given water body, the EQR index can reveal the relationship between the actual monitored values ​​of each environmental parameter and the reference standard applicable to the water body. In order to quantify the health of the ecosystem, the EQR index is expressed as a value between 0 and 1, where 1 indicates that the ecosystem is in good health and 0 indicates that the ecosystem is in poor health. The calculation formula is:

[0111] EQR=∑W i EQR i (6)

[0112] EQR i is the ecological quality index of the i-th evaluation index, W i is the evaluation weight of the i-th evaluation parameter;

[0113] The calculation method of the evaluation index EQR index divides the selected evaluation indicators into two categories: cost-type indicators and benefit-type indicators. Cost-type indicators refer to the process of ecological health deterioration, and their monitoring values ​​gradually increase; on the contrary, they are called benefit-type indicators;

[0114] For cost-based indicators, its EQR i for;

[0115] EQR i =Re fcon / AcStat (7)

[0116] 0≤EQR i ≤1 (8)

[0117] Among them, Refcon is the reference standard value of the i-th evaluation index, AcStat is the actual monitoring value of the i-th evaluation index, and for cost-based indicators, the maximum value of its EQR is set to 1, that is, when the actual monitoring value is less than the reference standard, the EQR value is set to 1. Since the reference standard is a fixed value, for cost-based indicators, the larger the monitoring value, the smaller the EQR index, and the worse the ecological health status reflected;

[0118] For benefit indicators, its EQR j for;

[0119] EQR j =AcStat / Refcon (9)

[0120] 0≤EQR j ≤1 (10)

[0121] Among them, Refcon is the reference standard value of the j-th evaluation indicator, AcStat is the actual monitoring value of the j-th evaluation indicator. For benefit-type indicators, the maximum value of its EQR is set to 1, that is, when the reference value is less than the actual monitoring value, the EQR value is set to 1. For benefit-type indicators, the smaller the monitoring value, the smaller the EQR index, and the worse the ecological health status reflected.

[0122] In steps S5 and S6, EqR evaluation calculation is performed on the cost-type indicator data and benefit-type indicator data;

[0123] Determination of the thresholds of the bay ecological assessment indicators: For cost-based indicators, +50% is usually given as the permissible deviation from the reference standard, that is, if the monitoring value exceeds the reference standard by less than 50% of the reference standard, it is considered acceptable; and for benefit-based indicators, -25% is usually given as the permissible deviation from the reference standard;

[0124] For cost-based indicator data and benefit-based indicator data, the allowable deviation (AcDev) is usually given as different reference standards. The actual monitoring value is rewritten as formula (11). Then, substitute formula (11) into formula (7) to obtain formula (12);

[0125] AcStat=(1+AcDev)×Refcon (11)

[0126] EQR 良 / 中 =1 / (1+AcDev) (12)

[0127] Combined with the given allowable deviation, the boundary between good / medium evaluation index can be determined. For the boundary between poor / inferior, formula (13) is used to calculate:

[0128] 1-EQR 良 / 中 =EQR 良 / 中 -EQR 差 / 劣 (13)

[0129] The excellent / good and medium / poor boundaries can be calculated using formula (14) and formula (15):

[0130] EQR 优 / 良 =(1+EQR 良 / 中) / 2 (14)

[0131] EQR 中 / 差 =(EQR 良 / 中 +EQR 差 / 劣 ) / 2 (15)

[0132] Since the cost-type indicator data and the benefit-type indicator data are usually given with allowable deviations (AcDev) as different reference standards, the cost-type indicator data and the benefit-type indicator data correspond to different evaluation boundaries.

[0133] The obtained EQR data are summarized in Table 5 below.

[0134] Table 5 Schematic diagram of EQR and allowable deviation of cost-based indicators and benefit-based indicators

[0135]

[0136] Table 6 EQR index evaluation level classification standards

[0137]

[0138] As shown in Table 6, the meaning of each level of health level is described as follows:

[0139] Healthy (0.83≤N<1): It means that the ecosystem maintains its natural attributes, biodiversity and ecosystem structure remain stable, the main service functions of the ecosystem function normally, and the ecological pressures such as human activities and natural activities are within the carrying capacity of the ecosystem;

[0140] Basic health (0.67≤N<0.83): It means that the ecosystem basically maintains its natural attributes, biodiversity and ecosystem structure remain basically stable, the main service functions of the ecosystem can basically function normally, and the ecological pressures such as human activities and natural activities are basically within the carrying capacity of the ecosystem;

[0141] Sub-health (0.5≤N<0.67): It means that the ecosystem maintains its natural attributes to a certain extent, biodiversity and ecosystem structure change to a certain extent, but the ecosystem can still play its main service function, and the ecological pressure of human activities and natural activities exceeds the carrying capacity of the ecosystem, but as long as the pressure of human activities is relieved or weakened, the ecosystem can still recover by itself;

[0142] Unhealthy (0.33≤N<0.5): It means that the ecosystem cannot maintain its natural attributes, biodiversity and ecosystem structure have changed significantly, the main service functions of the ecosystem have been seriously degraded or lost, and the ecological pressures such as human activities and natural activities are too great to exceed the carrying capacity of the ecosystem, making it difficult for the ecosystem to recover on its own.

[0143] Extremely unhealthy (0≤N<0.33): It means that the ecosystem cannot maintain its natural attributes, biodiversity and ecosystem structure have changed greatly, the main service functions of the ecosystem have been lost, and ecological pressures such as human activities and natural activities far exceed the carrying capacity of the ecosystem. Even if this pressure is relieved, the system must rely on external capabilities and it will take some time to recover.

[0144] The evaluation method for the health of the bay ecosystem provided by the present invention is used to ensure the effectiveness and reliability of the evaluation method for the health of the bay ecosystem by analyzing the parameter calculation results of the Jiaozhou Bay.

[0145] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A bay ecosystem health assessment method, including a PSR model framework and an entropy weight method-EQR calculation part, characterized by: The bay area is divided into multiple categories, such as nearshore human activity area, tourist area, wetland area, agricultural and fishing area, and shipping area. The PSR model framework part and the entropy weight method-EQR calculation part of the bay ecosystem health assessment method are provided. The PSR model framework part is a pressure-state-response model for ecosystem health assessment. The pressure model indicators include direct ecological pressure that causes changes in the structure and function of the marine ecosystem and terrestrial pressure that drives changes in the marine ecosystem. The state model indicators include water quality status that integrates the marine environment status and pollution status. The response model indicators are the natural response of the ecosystem, which mainly reflects the changes and trends of various environmental and biological indicators under stress. S1. In the selection of the terrestrial pressure index of the above-mentioned pressure model index, the total nitrogen emission, total phosphorus emission, heavy metal emission, COD emission and the change of the distribution area of ​​coastal wetlands are selected; in the selection of the water quality state of the above-mentioned state model index, COD, dissolved inorganic nitrogen, active phosphate, Hg, Cd, petroleum, organic carbon content and sulfide content are selected; in the selection of the biological index of the above-mentioned response model index, dissolved oxygen, chlorophyll a, phytoplankton algae density, zooplankton density, benthic animal density and fish egg and larvae density are selected; S2. Collect and screen quantifiable data for the indicators selected in the above step S1, and calibrate the standard values ​​of the above cost-based indicator data and benefit-based indicator data in the bay ecosystem; S3. De-dimensionalize and standardize the indicator data obtained in the above step S2 so that the evaluation method is not affected by the data type, and the calculated ecological health index is a dimensionless value; S4. Assign values ​​through the entropy weight method, using the characteristics of entropy to assign corresponding weights based on the amount of information feedback from each indicator. If the entropy value of an indicator is smaller, the amount of information it provides is greater, the weight assigned is greater, and the role it plays in the comprehensive performance evaluation is greater; S5. Calculate the ecological security index. After the data in step S4 is assigned, use EQR calculation to calculate the cost and benefit indicators of the final result. S6. Set the health level of the bay ecosystem and analyze the calculation results obtained in step S5. and evaluation.

2. A bay ecosystem health assessment method according to claim 1, characterized in that: In the steps S3 and S4, the index data are calculated; (1) Standardization of variables; In the comparison of statistical data, for data of different dimensions, this study uses the normalization method to perform dimensionless processing on various indicators. Assuming that k indicators X1, X2, …, Xk are given, where Xi = {x1, x2, …, xn}, and assuming that the standardized value of each indicator is R i , the original value x of the ijth index ij The corresponding high level is m max , the low level is m min , cost-based indicators adopt positive standardization, and benefit-based indicators adopt reverse standardization. The calculation formula is as follows: Positive normalization of indicators: Denormalization of indicators: (2) Determine weights using entropy weight method The entropy weight method is used to determine the weight of indicators. After all data are standardized and converted into dimensionless values ​​between [0,1], the ratio of each indicator under each scheme is calculated. Suppose there are m indicators and n years of data have been obtained, V ij is the data matrix after the original data matrix is ​​standardized, P ij The calculation formula is as follows: Find the information entropy of each indicator. According to the definition of information entropy in information theory, the information entropy of a set of data is (k is the Boltzmann constant): in. According to the calculation formula of information entropy, the information entropy of each indicator is obtained, and then the weight w of each indicator is calculated by information entropy i , and satisfy 0≤w j ≤1, and ∑w j =1: The essence of the entropy method is still to determine the weight value of each evaluation indicator based on its discreteness. The greater the discreteness, the smaller the entropy value, but the greater the entropy weight. The entropy method requires that the indicators involved in the evaluation have a certain amount of data samples before they can be used.

3. A bay ecosystem health assessment method according to claim 1, characterized in that: In the step S5, the index data after the assignment is calculated: (1) The concept and calculation method of EQR index; The EQR index is used to measure the health of the bay ecosystem. For a given water body, the EQR index can reveal the relationship between the actual monitored values ​​of each environmental parameter and the reference standard applicable to the water body. In order to quantify the health of the ecosystem, the EQR index is expressed as a value between 0 and 1, where 1 indicates that the ecosystem is in good health and 0 indicates that the ecosystem is in poor health. The calculation formula is: EQR=∑W i ·EQR i (6) EQR i is the ecological quality index of the i-th evaluation index, W i is the evaluation weight of the i-th evaluation parameter; The calculation method of the evaluation index EQR index divides the selected evaluation indicators into two categories: cost-type indicators and benefit-type indicators. Cost-type indicators refer to the process of ecological health deterioration, and their monitoring values ​​gradually increase; on the contrary, they are called benefit-type indicators; For cost-based indicators, its EQR i for; EQR i =Re fcon / AcStat (7) 0≤EQR i ≤1 (8) Among them, Refcon is the reference standard value of the i-th evaluation index, AcStat is the actual monitoring value of the i-th evaluation index, and for cost-based indicators, the maximum value of its EQR is set to 1, that is, when the actual monitoring value is less than the reference standard, the EQR value is set to 1. Since the reference standard is a fixed value, for cost-based indicators, the larger the monitoring value, the smaller the EQR index, and the worse the ecological health status reflected; For benefit indicators, its EQR j for; EQR j =AcStat / Refcon (9) 0≤EQR j ≤1 (10) Among them, Refcon is the reference standard value of the j-th evaluation indicator, AcStat is the actual monitoring value of the j-th evaluation indicator. For benefit-type indicators, the maximum value of its EQR is set to 1, that is, when the reference value is less than the actual monitoring value, the EQR value is set to 1. For benefit-type indicators, the smaller the monitoring value, the smaller the EQR index, and the worse the ecological health status reflected.

4. A bay ecosystem health assessment method according to claim 1, characterized in that: In the steps S5 and S6, EqR evaluation calculation is performed on the cost-type indicator data and the benefit-type indicator data; Determination of the thresholds of the bay ecological assessment indicators: For cost-based indicators, +50% is usually given as the permissible deviation from the reference standard, that is, if the monitoring value exceeds the reference standard by less than 50% of the reference standard, it is considered acceptable; and for benefit-based indicators, -25% is usually given as the permissible deviation from the reference standard; For cost-based indicator data and benefit-based indicator data, the allowable deviation (AcDev) is usually given as different reference standards. The actual monitoring value is rewritten as formula (11). Then, substitute formula (11) into formula (7) to obtain formula (12); AcStat=(1+AcDev)×Refcon (11) EQR 良 / 中 =1 / (1+AcDev) (12) Combined with the given allowable deviation, the boundary between good / medium evaluation index can be determined. For the boundary between poor / inferior, formula (13) is used to calculate: 1-EQR 良 / 中 =EQR 良 / 中 -EQR 差 / 劣 (13) The excellent / good and medium / poor boundaries can be calculated using formula (14) and formula (15): EQR 优 / 良 =(1+EQR 良 / 中 ) / 2 (14) EQR 中 / 差 =(EQR 良 / 中 +EQR 差 / 劣 ) / 2 (15) Since the cost-type indicator data and the benefit-type indicator data are usually given with allowable deviations (AcDev) as different reference standards, the cost-type indicator data and the benefit-type indicator data correspond to different evaluation boundaries.

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