Estuary water ecosystem integrity evaluation method and device, storage medium and product

Abstract

The invention relates to the technical field of ecological evaluation, and discloses an estuary water ecosystem integrity evaluation method and device, a storage medium and a product, and the estuary water ecosystem integrity evaluation method comprises the steps: determining biological population candidate indexes of each biological species of a target estuary water ecosystem, the biological species comprise phytoplankton, zooplankton, benthonic animals and fishes; collecting biological population candidate index data of biological species in the plurality of target areas according to the biological population candidate indexes; screening the biological population candidate indexes by using the biological population candidate index data, and determining biological population rating index data; calculating a biocenosis integrity index of each biological species by taking the biological population rating index data of the reference region as a reference value; and performing weighted calculation on the biocenosis integrity index based on the weight of each biological species to obtain a system community integrity index, and evaluating the target estuary water ecosystem. The rationality of the evaluation result can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological assessment, and in particular to an estuary water ecosystem integrity assessment method, equipment, storage medium and product. Background Art

[0002] Biological integrity refers to the characteristics of species composition, species diversity and functional structure of biological communities in natural habitats, as well as the ability to maintain their own balance, maintain structural integrity and adapt to environmental changes. The biological integrity index evaluation method was first established by KARR et al. in 1981 when studying fish. After modification and improvement, the biological integrity index has been widely used in many fields. Among them, Weisberg (1997), Lacouture (2006), Carpenter (2006) and others have successively established the benthic biological integrity index (B-IBI), the phytoplankton biological integrity index (P-IBI), and the zooplankton biological integrity index (Z-IBI) based on the monitoring data of the Chesapeake Bay. The biological integrity index has been widely used in the health assessment of aquatic ecosystems such as rivers, lakes, reservoirs, and wetlands, and plays an important role in the monitoring and management of aquatic environments such as lakes, estuaries and offshore areas.

[0003] At present, most biological integrity assessments focus on single communities such as plankton, benthic organisms, or fish. The evaluation of aquatic ecosystems is relatively one-sided and unreliable, and it is difficult to fully reflect the integrity of aquatic ecosystems. Summary of the invention

[0004] In view of this, the present invention provides an estuarine water ecosystem integrity assessment method, equipment, storage medium and product to solve the problem that the assessment method is relatively one-sided and improve the rationality of the assessment results.

[0005] In a first aspect, the present invention provides a method for evaluating the integrity of an estuarine water ecosystem, which comprises: determining candidate biological population indicators of each biological species of a target estuarine water ecosystem, wherein the biological species include phytoplankton, zooplankton, benthic animals and fish; collecting biological population candidate indicator data of each biological species in a plurality of target areas according to the candidate biological population indicators, wherein the target areas include at least reference areas and interference areas, wherein the reference areas are areas that are undisturbed or less affected by interference, and the interference areas are areas that are affected by interference; using the candidate biological population indicator data, screening the candidate biological population indicators to determine biological population rating indicator data; using the biological population rating indicator data of the reference area as a reference base value to calculate the biological community integrity index of each biological species; weighted calculation of the biological community integrity index based on the weight of each biological species to obtain the system community integrity index, and evaluating the target estuarine water ecosystem based on the system community integrity index.

[0006] In this implementation, comprehensive candidate indicators of phytoplankton, zooplankton, benthic animals and fish are considered to solve the problem of one-sided evaluation; candidate indicators are screened according to interference areas and reference areas, so that rating indicators with greater impact on estuarine water ecosystems can be selected to improve the accuracy of indicator selection; the integrity index of each biological species is further calculated according to the reference benchmark value of the reference area, and with actual standards as a reference, it can be combined with the actual impact of the target environment, and the integrity index can reflect the real data of the current environment; the community integrity index of the entire system is calculated according to the impact weight of each biological species on the estuarine water ecosystem, and the evaluation result is more accurate.

[0007] In an optional implementation, using biological population candidate indicator data to screen biological population candidate indicators and determining biological population rating indicator data includes: using biological population candidate indicator data to screen biological population candidate indicators according to screening principles to determine biological population rating indicator data; screening principles include zero-value data proportion principle, data difference principle, distribution range analysis principle, correlation analysis principle and discrimination ability analysis principle.

[0008] In this implementation, a variety of indicator screening principles are proposed. Based on multiple screening and comprehensive factors, some indicator data with higher comprehensive quality are screened out, which reduces the amount of calculation while improving the calculation quality, and further improves the evaluation accuracy.

[0009] In an optional implementation, the screening principle is the zero-value data proportion principle, and the biological population candidate indicator data is used to screen the biological population candidate indicators according to the screening principle, and determining the biological population rating indicator data includes: calculating the proportion of target areas where the biological population candidate indicator data is not 0; if the target area proportion is greater than or equal to a preset proportion, determining the biological population candidate indicator data as the biological population rating indicator data.

[0010] In this implementation, the zero-value data ratio principle is used to screen the indicator data and select representative evaluation indicators.

[0011] In an optional embodiment, the screening principle is the distribution range analysis principle, and the biological population candidate indicator data is used to screen the biological population candidate indicators according to the screening principle, and determining the biological population rating indicator data includes: comparing the biological population candidate indicator data of the reference area with the biological population candidate indicator data of the interference area, and screening the biological population candidate indicator data that are affected unidirectionally, and the unidirectional impact includes unidirectional increase or unidirectional decrease after interference; when the biological population candidate indicator data increases unidirectionally after interference, if the biological population candidate indicator data are all greater than a first preset value, the biological population candidate indicator data is deleted; when the biological population candidate indicator data decreases unidirectionally after interference, if the biological population candidate indicator data are all less than a second preset value, the biological population candidate indicator data is deleted; when the biological population candidate indicator data are dispersedly distributed, the biological population candidate indicator data is deleted.

[0012] In an optional embodiment, the screening principle is the discriminative ability analysis principle, and the biological population candidate indicator data is used to screen the biological population candidate indicators according to the screening principle. Determining the biological population rating indicator data also includes: using the box plot method to analyze the overlap of the biological population candidate indicator data in the reference area and the biological population candidate indicator data in the interference area; based on the overlap, determining the overlap index; determining the biological population candidate indicator data with an overlap index greater than a preset overlap index as the biological population rating indicator data.

[0013] In this implementation, the distribution range analysis principle and the discrimination ability analysis principle are adopted to select evaluation indicators that can accurately distinguish the degree of interference.

[0014] In an optional embodiment, the biological population rating index data of the reference area is used as a reference benchmark value, and the biological community integrity index of each biological species is calculated, including: for each target area, the biological population rating index data of the reference area is used as a reference benchmark value, and the deviation of the biological population rating index data is calculated; based on the deviation, the biological population rating index data is indexed and summed to obtain the regional community integrity index of the target area; based on the weight of each target area, the regional community integrity index is weighted and calculated to obtain the biological community integrity index.

[0015] In this implementation, the degree of deviation is used as the degree of difference between the evaluation index data and the baseline value. According to a unified assignment method, the index data can be converted into the same data dimension, which can facilitate the subsequent comprehensive consideration of the data impact and the accurate calculation of the biological community integrity index.

[0016] In an optional embodiment, the biological community integrity index is weightedly calculated based on the weight of each biological species to obtain the system community integrity index, including: determining the weight of each biological species based on the species trophic level of the biological species; and weighted calculating the biological community integrity index based on the weight to obtain the system community integrity index.

[0017] In this implementation, the species trophic level of biological species is used as the impact weight to weight the biological community integrity index. The community integrity index of the entire system can be calculated based on the impact weight of each biological species on the estuarine water ecosystem, and the evaluation result is more accurate.

[0018] In a second aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the estuarine water ecosystem integrity assessment method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0019] In a third aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the estuarine water ecosystem integrity assessment method of the first aspect or any corresponding embodiment thereof.

[0020] In a fourth aspect, the present invention provides a computer program product, comprising computer instructions for causing a computer to execute the method for evaluating the integrity of an estuarine water ecosystem according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 is a flow chart of a method for evaluating the integrity of an estuarine water ecosystem according to an embodiment of the present invention;

[0023] Figure 2is a flow chart of another estuarine water ecosystem integrity assessment method according to an embodiment of the present invention;

[0024] Figure 3 is a schematic diagram of box plot analysis according to an embodiment of the present invention;

[0025] Figure 4 is a flowchart of a specific estuarine water ecosystem integrity assessment method according to an embodiment of the present invention;

[0026] Figure 5 is a schematic diagram of evaluation index screening results according to an embodiment of the present invention;

[0027] Figure 6 is a structural block diagram of an estuarine water ecosystem integrity assessment device according to an embodiment of the present invention;

[0028] Figure 7 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0030] The estuarine ecosystem refers to the whole composed of the estuarine biological community and the environment on which it depends for survival within a certain time and space range. The estuarine water ecosystem is an important part of the estuarine ecosystem and an important support for material supply and biodiversity. The estuarine ecosystem is an important ecosystem type that has attracted global attention, and it breeds rich water resources and biological resources. At the same time, estuarine areas are usually economically developed and densely populated areas, and human activities interfere and have a huge impact, so the estuarine ecosystem is also the most important ecologically sensitive area and ecologically fragile area. Due to the limitations of the understanding of the ecological functions of estuaries and the demand for economic development, most of the estuarine ecosystems in the world are gradually deteriorating. It is necessary to conduct a comprehensive evaluation and judgment study on estuarine ecosystems, especially estuarine water ecosystems, so as to take corresponding countermeasures and provide basic support for the development of river basins and estuaries.

[0031] The present invention proposes an estuarine ecological integrity assessment method that comprehensively considers plankton, benthic organisms and fish, providing a scientific basis and technical support for comprehensively assessing the status of estuarine water ecosystems.

[0032] According to an embodiment of the present invention, an embodiment of a method for evaluating the integrity of an estuarine water ecosystem is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0033] In this embodiment, a method for evaluating the integrity of an estuarine water ecosystem is provided. Figure 1 is a flow chart of a method for evaluating the integrity of an estuarine water ecosystem according to an embodiment of the present invention. It should be noted that if there are substantially the same results, this embodiment is not used as Figure 1 The process sequence shown is limited. Figure 1 As shown, the process includes the following steps:

[0034] Step S101, determining candidate biological population indicators of various biological species in the target estuary water ecosystem.

[0035] Among them, biological species in estuarine water ecosystems include phytoplankton, zooplankton, benthic animals, fish, etc.

[0036] Among them, the candidate indicator types of phytoplankton populations include species richness parameters, community structure composition parameters, and community trophic structure parameters, etc.; the candidate indicator types of zooplankton populations include species richness parameters, community structure composition parameters, etc.; the candidate indicator types of benthic animals include species richness parameters, community structure composition parameters, etc.; the candidate indicator types of fish populations include species composition and abundance, tolerance, community structure composition parameters, fish health status, etc. Each candidate indicator type of biological populations includes multiple candidate indicators of biological populations.

[0037] Step S102, collecting biological population candidate indicator data of each biological species in multiple target areas according to the biological population candidate indicators.

[0038] Multiple target areas are identified as sampling points in the target estuarine water ecosystem, and biological population candidate indicator data of each biological species in each sampling point are collected according to the biological population candidate indicators.

[0039] The target area at least includes a reference area and an interference area.

[0040] Specifically, candidate indicator data of biological populations of various biological species are collected in reference areas that are undisturbed or less affected by disturbance and in disturbed areas where the data have changed due to disturbance.

[0041] Among them, the reference area is less affected by human interference, and the biological data in the reference area can reflect the baseline level of the target estuarine water ecosystem; the interference area is more affected by human interference, and the biological data in the interference area can reflect the level of the target estuarine water ecosystem under the considered interference conditions.

[0042] Step S103, using the candidate indicator data of biological populations, screening the candidate indicators of biological populations to determine the biological population rating indicator data.

[0043] Each candidate indicator of biological population is judged and screened separately, and the candidate indicator data of biological populations in multiple target areas are tested to determine whether the data under the candidate indicator of the biological population is representative, stable, uniform, whether there is a strong correlation between the data, and whether the data changes regularly due to interference.

[0044] From the candidate biological population indicators, select indicators with strong representativeness, strong stability, uniform data, no strong correlation, and data interference and change patterns as biological population rating indicators, and further screen out the biological population rating indicator data for the corresponding target area.

[0045] Step S104, using the candidate indicator data of biological populations in the reference area as a reference benchmark value, calculate the biological community integrity index of each biological species.

[0046] The biological data in the reference area can reflect the baseline level of the target estuary water ecosystem. The candidate indicator data of the biological population in the reference area is used as the reference benchmark value and compared with the biological population rating indicator data of each biological species to determine the degree of difference in the data of each biological species.

[0047] Specifically, the smaller the difference, the greater the biome integrity index of the biological species; the greater the difference, the smaller the biome integrity index of the biological species.

[0048] In one implementation, the biome integrity index is assigned a value according to the deviation degree ratio.

[0049] In another implementation, the 1, 3, 5 assignment method is used to assign values ​​to the biome integrity index.

[0050] Step S105, weighted calculation of the biological community integrity index is performed based on the weight of each biological species to obtain the system community integrity index, and the target estuary water ecosystem is evaluated based on the system community integrity index.

[0051] The weight is determined according to the degree of impact of each biological species on the target estuarine water ecosystem, where the greater the impact, the greater the weight.

[0052] In one implementation, the biome integrity index is normalized, and the normalized biome integrity index of each target organism is weighted and summed to obtain the system community integrity index. The larger the system community integrity index, the better the target estuarine water ecosystem.

[0053] The four-point method is used to divide the evaluation criteria of the normalized community integrity index. According to the system community integrity index, it is graded from large to small as excellent, good, general, poor, and extremely poor.

[0054] The evaluation grade of the target estuary water ecosystem is obtained according to the biological community integrity index.

[0055] The estuarine water ecosystem method provided in this embodiment comprehensively considers comprehensive candidate indicators of phytoplankton, zooplankton, benthic animals and fish, and solves the problem of one-sided evaluation; by screening candidate indicators according to interference areas and reference areas, it is possible to select rating indicators that have a greater impact on estuarine water ecosystems, thereby improving the accuracy of indicator selection; further, the integrity index of each biological species is calculated based on the reference benchmark value of the reference area, and with actual standards as a reference, it can be combined with the actual impact of the target environment, and the integrity index can reflect the real data of the current environment; the community integrity index of the entire system is calculated according to the impact weight of each biological species on the estuarine water ecosystem, and the evaluation result is more accurate.

[0056] In this embodiment, a method for evaluating the integrity of an estuarine water ecosystem is provided. Figure 2 is a flow chart of another estuarine water ecosystem integrity assessment method according to an embodiment of the present invention. It should be noted that if there are substantially the same results, this embodiment is not based on Figure 2 The process sequence shown is limited. Figure 2 As shown, the process includes the following steps:

[0057] Step S201, determining candidate biological population indicators of each biological species in the target estuary water ecosystem.

[0058] Among them, biological species in estuarine water ecosystems include phytoplankton, zooplankton, benthic animals, fish, etc.

[0059] Among them, the candidate indicator types of phytoplankton species populations include species richness parameters, community structure composition parameters, and community trophic structure parameters, etc.; the candidate indicator types of zooplankton species populations include species richness parameters, community structure composition parameters, etc.; the candidate indicator types of benthic animals species populations include species richness parameters, community structure composition parameters, etc.; the candidate indicator types of fish species populations include species composition and abundance, tolerance, community structure composition parameters, etc. Each candidate indicator type of species populations includes multiple candidate indicators of species populations.

[0060] In one implementation, specifically, the species richness parameters of phytoplankton include: the number of phytoplankton taxa, the total number of phytoplankton species, the number of diatom species, the number of cyanobacteria species, the number of green algae species, the total number of non-diatom species, the phytoplankton diversity index, the phytoplankton richness index, the phytoplankton uniformity index, etc. The community structure composition parameters of phytoplankton include: the percentage of diatom density, the percentage of cyanobacteria density, the percentage of green algae density, the percentage of euglena density, the percentage of dinoflagellates density, the percentage of non-diatom density, the density ratio of the largest dominant species, etc. The community nutritional structure parameters of phytoplankton include: chlorophyll a, the average weight of phytoplankton, the average size of phytoplankton, etc.

[0061] Specifically, the species richness parameters of zooplankton include: the number of zooplankton species, zooplankton diversity index, zooplankton richness index, zooplankton evenness index, total zooplankton density, total zooplankton biomass, etc. The community structure composition parameters of zooplankton include: copepod density, chaetognath density, jellyfish density, zooplankton dominant species 1 density, zooplankton dominant species 2 density, zooplankton dominant species 3 density, and the proportion of the largest dominant species density of zooplankton.

[0062] Specifically, the benthic animal species richness parameters include: number of benthic animal species, benthic animal diversity index, benthic animal richness index, benthic animal evenness index, total benthic animal density, total benthic animal biomass, etc. The benthic animal community structure composition parameters include: polychaete density, crustacean density, mollusk density, polychaete biomass, crustacean biomass, mollusk biomass, and the density proportion of dominant benthic animal groups.

[0063] Specifically, the species composition and abundance of fish include: the number of fish species, fish density, fish weight, the percentage of river-sea migratory fish species, the percentage of river-sea migratory fish individuals, the percentage of estuarine resident fish species, the percentage of estuarine resident fish individuals, the percentage of marine fish species, the percentage of marine fish individuals, the percentage of freshwater fish species, the percentage of freshwater fish individuals, the number of rare and endangered fish species, the number of alien species, etc. The tolerance of fish includes: the percentage of sensitive fish species, the percentage of sensitive fish individuals, the percentage of tolerant fish species, the percentage of tolerant fish individuals, etc. The community structure composition parameters of fish include: the percentage of planktonic fish species, the percentage of planktonic fish individuals, the percentage of benthic fish species, the percentage of benthic fish individuals, the percentage of swimming fish species, the percentage of swimming fish individuals, the percentage of mixed-biological fish species, the percentage of mixed-biological fish individuals, etc. The health status of fish includes: the percentage of deformed and diseased fish individuals.

[0064] Step S202: collecting biological population candidate indicator data of each biological species in multiple target areas according to the biological population candidate indicators.

[0065] For details, please see Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0066] Step S203, using the candidate indicator data of biological populations, screening the candidate indicators of biological populations to determine the biological population rating indicator data.

[0067] Utilize the candidate indicator data of biological populations and screen the candidate indicators of biological populations according to the screening principles to judge whether the data under the candidate indicators of biological populations are representative, stable, uniform, whether there is a strong correlation between the data, whether the data changes regularly due to interference, etc., to determine the biological population rating indicator data.

[0068] Among them, the screening principles include one or more of the zero-value data proportion principle, the data difference principle, the distribution range analysis principle, the correlation analysis principle and the discrimination ability analysis principle.

[0069] According to the principle of proportion of zero-value data, candidate indicators of biological populations are screened to determine the biological population rating indicator data.

[0070] Specifically, the proportion of target areas where the candidate indicator data of biological populations is not 0 is calculated. If the proportion of target areas is greater than or equal to the preset proportion, the candidate indicator data of biological populations is determined to be the biological population rating indicator data. If a candidate indicator data of a biological population in most target areas is 0, it means that the candidate indicator data of the biological population is not representative when evaluating the target estuarine water ecosystem and is not suitable as the biological population rating indicator data.

[0071] Exemplarily, biological population rating index data corresponding to target areas where the proportion of biological population candidate index data that is not 0 is greater than or equal to 90% are selected as biological population rating index data.

[0072] According to the principle of data difference, candidate indicators of biological populations are screened to determine the biological population rating indicator data.

[0073] In one implementation, screening is performed according to the number of species index, and candidate indicator data of biological populations with a number of species greater than a preset number of species are selected as biological population rating indicator data. Specifically, if the candidate indicator data of biological populations is a number of species index, and the result of the species is less than or equal to the preset number of species, it means that the number of species in the target estuary water ecosystem is small, the species is relatively single, and the stability of the species is poor, and the index is not suitable as biological population rating indicator data.

[0074] Exemplarily, biological population rating index data with a species number index greater than 5 is selected as the biological population rating index data.

[0075] In one implementation, screening is performed according to percentage indicators, and candidate indicator data of biological populations with percentage differences greater than a preset difference value are selected as biological population rating indicator data. Specifically, if the candidate indicator data of biological populations is a percentage indicator and the difference between the indicators is small, it means that the indicator is relatively average when evaluating the target estuary water ecosystem and is not suitable as biological population rating indicator data.

[0076] Exemplarily, biological population rating indicator data with a percentage indicator difference greater than 10% are selected as biological population rating indicator data.

[0077] According to the principles of correlation analysis, candidate indicators for biological populations are screened to determine the biological population rating indicator data.

[0078] Specifically, a correlation analysis is performed between multiple biological population candidate indicators, and the correlation coefficient is calculated. The biological population candidate indicator data whose absolute value of the correlation coefficient is less than the preset correlation coefficient and the biological population candidate indicator data whose absolute value of the correlation coefficient is greater than or equal to the preset correlation coefficient and has a greater degree of influence are selected as the biological population rating indicator data.

[0079] Exemplarily, when the correlation coefficient between two biological population candidate indicators satisfies |r|<0.7, one is selected as the biological population rating indicator data; when the correlation coefficient between two biological population candidate indicators satisfies |r|≥0.7, the biological population candidate indicator with a greater influence is selected as the biological population rating indicator data.

[0080] According to the principle of distribution range analysis, candidate indicators of biological populations are screened to determine the biological population rating indicator data.

[0081] Specifically, the candidate indicator data of biological populations in the reference area are compared with the candidate indicator data of biological populations in the interference area, the candidate indicator data of biological populations affected by unidirectional influences are screened as biological population rating indicator data, and the candidate indicator data of biological populations affected by non-unidirectional influences are deleted.

[0082] Among them, the unidirectional impact includes a unidirectional increase or a unidirectional decrease after being disturbed.

[0083] For example, please refer to Tables 1-4, Table 1 is a summary table of the interference conditions of candidate indicator data for biological populations of phytoplankton, Table 2 is a summary table of the interference conditions of candidate indicator data for biological populations of phytoplankton, Table 3 is a summary table of the interference conditions of candidate indicator data for biological populations of benthic animals, and Table 4 is a summary table of the interference conditions of candidate indicator data for biological populations of fish.

[0084] Table 1 Summary of disturbance status of candidate indicator data of phytoplankton population

[0085]

[0086]

[0087] Table 2 Summary of disturbance status of candidate indicator data of phytoplankton population

[0088]

[0089] Table 3 Summary of disturbance status of candidate indicator data of benthic animal populations

[0090]

[0091] Table 4 Summary of disturbance status of candidate index data of fish biological population

[0092]

[0093] The distribution range of candidate indicator data for biological populations was further analyzed according to the unidirectional impact type.

[0094] When the candidate indicator data of biological population increases unidirectionally after being disturbed, it is judged whether the candidate indicator data of biological population is too large. When the candidate indicator data of biological population are all greater than the first preset value, it means that the value of the indicator is too large, and the variable range after being disturbed is relatively narrow. It is not easy to accurately distinguish the areas with different degrees of interference, and the candidate indicator data of biological population is deleted.

[0095] When the candidate indicator data of biological populations decreases unidirectionally after being disturbed, it is determined whether the candidate indicator data of biological populations is too small. When the candidate indicator data of biological populations are all less than the second preset value, it means that the indicator value is too small, and the variable range after being disturbed is relatively narrow, and it is not easy to accurately distinguish areas with different degrees of interference, so the candidate indicator data of biological populations is deleted.

[0096] When the numerical distribution of candidate indicator data of biological populations is dispersed, it means that the standard deviation of the indicator is large and unstable, and the candidate indicator data of biological populations is deleted.

[0097] According to the principles of discriminant ability analysis, candidate indicators of biological populations are screened to determine the biological population rating indicator data.

[0098] Specifically, the box plot method is used to analyze the overlap of the candidate indicator data of biological populations in the reference area and the candidate indicator data of biological populations in the interference area, and the overlap index is determined based on the overlap; the candidate indicator data of biological populations with an overlap index greater than the preset overlap index are determined as the biological population rating indicator data.

[0099] For example, see Figure 3 , Figure 3 is a schematic diagram of the box plot analysis according to an embodiment of the present invention. Figure 3 As shown in the figure, the overlap of the 25% to 75% quantile range of the reference area and the interference area is compared, and different IQ values ​​are assigned to each. The box in the figure represents the distribution range of the 25% to 75% quantile value, and the small rectangle represents the median. The comparison of the two boxes will show Figure 3 There are five cases: A: no overlap, IQ is 3; B: partial overlap, but the median values ​​of each are outside the box range of the other party, IQ is 2; C and D: only one median value is within the box range of the other party, IQ is 1; E: the median values ​​of each are within the box range of the other party, IQ is 0. Delete the candidate indicators of biological populations with IQ ≥ 2 and determine the biological population rating indicator data.

[0100] In this implementation, a variety of indicator screening principles are proposed. Based on multiple screening and comprehensive factors, some indicator data with higher comprehensive quality are screened out, which reduces the amount of calculation while improving the calculation quality, and further improves the evaluation accuracy.

[0101] Step S204, using the candidate indicator data of biological populations in the reference area as a reference benchmark value, calculate the biological community integrity index of each biological species.

[0102] The biological data in the reference area can reflect the baseline level of the target estuary water ecosystem. The candidate indicator data of the biological population in the reference area is used as the reference benchmark value and compared with the biological population rating indicator data of each biological species to determine the degree of difference in the data of each biological species.

[0103] Specifically, the above step S204 includes:

[0104] Step S2041: For each target area, the biological population candidate index data of the reference area is used as a reference benchmark value to calculate the deviation of the biological population candidate index data.

[0105] The deviation calculation formula is:

[0106]

[0107] Among them, S is the deviation, Q0 is the reference value, Q i Candidate indicator data for biological populations.

[0108] Step S2042: Based on the degree of deviation, the candidate indicator data of biological populations are indexed and summed to obtain the regional community integrity index of the target area.

[0109] In one implementation, each candidate indicator data of a biological population is scored using the 1, 3, 5 scoring method.

[0110] Specifically, the degree of deviation of the biological population candidate indicator data is judged according to the deviation degree. When the deviation degree is greater than the first deviation threshold, it is considered that the biological population candidate indicator data deviates significantly from the benchmark value, and the index of the biological population candidate indicator data is assigned a value of 1; when the deviation degree is less than or equal to the first deviation threshold and greater than or equal to the second deviation threshold, it is considered that the biological population candidate indicator data deviates slightly from the benchmark value, and the index of the biological population candidate indicator data is assigned a value of 3; when the deviation degree is less than the second deviation threshold, it is considered that the biological population candidate indicator data is close to the benchmark value, and the index of the biological population candidate indicator data is assigned a value of 5.

[0111] The indexes of candidate indicator data of all biological populations in each target area are added together to obtain the regional community integrity index of the corresponding target area.

[0112] Step S2043: weighted calculation of the regional community integrity index based on the weight of each target area to obtain the biological community integrity index.

[0113] Specifically, the biome integrity index is:

[0114]

[0115] Among them, IBI j is the biome integrity index of biome j, IBI ji is the regional community integrity index of biome j in target area i.

[0116] Furthermore, the biome integrity index was normalized.

[0117] In this implementation, the degree of deviation is used as the degree of difference between the evaluation index data and the baseline value. According to a unified assignment method, the index data can be converted into the same data dimension, which can facilitate the subsequent comprehensive consideration of the data impact and the accurate calculation of the biological community integrity index.

[0118] Step S205, weighted calculation of the biological community integrity index is performed based on the weight of each biological species to obtain the system community integrity index, and the target estuary water ecosystem is evaluated based on the system community integrity index.

[0119] Specifically, the above step S205 includes:

[0120] Step S2051, determining the weight of each biological species based on the species trophic level of the biological species.

[0121] The species trophic level of biological species is obtained by searching literature and survey data, and weights are assigned to each biological species based on the species trophic level. The sum of the weights is 1.

[0122] Specifically, the weights of biological species are:

[0123]

[0124] Among them, k j is the weight of species j, F j is the species trophic level of biological species j, and F is the sum of the species trophic levels of biological species.

[0125] For example, please refer to Table 5, which is a weight assignment table for biological species.

[0126] Table 5 Weight assignment table of biological species

[0127]

[0128] Step S2052, weighted calculation of the biological community integrity index based on the weight to obtain the system community integrity index.

[0129] Specifically, the phylogeny integrity index is:

[0130]

[0131] Among them, IBI is the system community integrity index, and N is the number of biological species.

[0132] Step S2053, evaluating the target estuary water ecosystem based on the system community integrity index.

[0133] The four-point method is used to divide the evaluation criteria of the normalized community integrity index. According to the system community integrity index, it is graded from large to small as excellent, good, general, poor, and extremely poor. Please refer to Table 6, which is a table of the evaluation criteria for the community integrity index.

[0134] Table 6 Classification table of community integrity index evaluation standards

[0135] Evaluation rating excellent good generally Poor Very bad IBI 0.8～1 0.6～0.8 0.4～0.6 0.2～0.4 0～0.2

[0136] In this implementation, the species trophic level of biological species is used as the impact weight to weight the biological community integrity index. The community integrity index of the entire system can be calculated based on the impact weight of each biological species on the estuarine water ecosystem, and the evaluation result is more accurate.

[0137] For example, a specific method for evaluating the integrity of an estuarine water ecosystem is provided in this embodiment. Taking the evaluation of the integrity of the Yangtze River Estuary water ecosystem as an example, the present invention is further described in detail. Figure 4is a flowchart of a specific estuarine water ecosystem integrity assessment method according to an embodiment of the present invention. It should be noted that if there are substantially the same results, this embodiment is not based on Figure 4 The process sequence shown is limited. Figure 4 As shown, the process includes the following steps:

[0138] Step S401, determining the evaluation index of the integrity of the Yangtze River Estuary water ecosystem.

[0139] Specifically, based on relevant literature and research results, a candidate indicator set for each ecological factor integrity index is constructed. Please refer to Tables 1 to 4 for details.

[0140] Step S402, screening of indicators for evaluating the integrity of the Yangtze River Estuary water ecosystem.

[0141] Specifically, see Figure 5 , Figure 5 2 is a schematic diagram of evaluation index screening results according to an embodiment of the present invention.

[0142] Screening of evaluation indicators for phytoplankton.

[0143] Specifically, the phytoplankton survey data from 25 points in the Yangtze River Estuary and adjacent waters in May 2015 were statistically processed, and according to the statistical results, the indicators that did not meet the conditions of "90% of the sampling points had a non-zero value, the number of species was above 5, and the difference between percentage indicators was greater than 10%" were screened out. Pearson correlation analysis was performed on the remaining biological indicators to test the independence of the information reflected by each indicator. A correlation coefficient |r| between two indexes greater than 0.70 indicates that the two indexes are highly correlated. For highly correlated indicators, one of them can be selected. The candidate indicators were screened according to the method of step S203, and finally four evaluation indicators were screened out, namely P7 (phytoplankton diversity index), P8 (phytoplankton richness index), P10 (total phytoplankton density), and P17 (density ratio of the largest dominant species of phytoplankton).

[0144] Please refer to Table 7, which shows the correlation analysis results of candidate indicator data of phytoplankton populations.

[0145] Table 7 Correlation analysis results of candidate indicator data of phytoplankton population

[0146] P2 P3 P7 P8 P9 P10 P11 P17 P18 P2 1 P3 .913** 1 P7 .514* .641** 1 P8 .965** .929** .684** 1 P9 -.309 -.092 .587** -.129 1 P10 .704** .573** -.122 .531** -.670** 1 P11 .267 .551** .388 .318 .239 .036 1 P17 -.129 -.305 -.867** -.315 -.851** .406* -.249 1 P18 .407* .334 .357 .396 -.004 .145 -.065 -.211 1

[0147] Screening of evaluation indicators for zooplankton.

[0148] Specifically, the zooplankton survey data from 25 locations in the Yangtze River Estuary and adjacent waters in May 2015 were statistically processed, and candidate indicators were screened according to the method of step S203, and finally four indicators, namely Z2 (zooplankton diversity index), Z3 (zooplankton richness index), Z6 (zooplankton total biomass) and Z13 (density ratio of the largest dominant species of zooplankton), were screened out.

[0149] Please refer to Table 8, which shows the correlation analysis results of the candidate indicator data of zooplankton populations.

[0150] Table 8 Correlation analysis results of candidate index data of zooplankton population

[0151] Z1 Z2 Z3 Z4 Z5 Z6 Z13 Z1 1 Z2 .856** 1 Z3 .927** .896** 1 Z4 .336 .723** .546** 1 Z5 -.051 -.175 -.288 -.384 1 Z6 .523** .445* .447* .177 .045 1 Z13 -.633** -.909** -.710** -.862** .168 -.388 1

[0152] Screening of evaluation indicators for benthic animals.

[0153] Specifically, the benthic animal survey data from 25 locations in the Yangtze River Estuary and adjacent waters in May 2015 were statistically processed, and candidate indicators were screened according to the method of step S203, and finally four indicators, namely B2 (benthic animal diversity index), B5 (total benthic animal density), B6 ​​(total benthic animal biomass) and B7 (density ratio of benthic animal polychaetes) were screened out.

[0154] Please refer to Table 9, which shows the correlation analysis results of candidate indicator data of benthic animal populations.

[0155] Table 9 Correlation analysis results of candidate index data of benthic animal populations

[0156] B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12 B2 1 B3 .834** 1 B4 .975** .718** 1 B5 .678 .376* .785** 1 B6 .158 .185 .200 .432* 1 B7 .261 .138 .272 .041 -.112 1 B8 -.055 -.007 -.075 .028 .028 -.387* 1 B9 -.214 -.102 -.218 .030 .225 -.688** -.170 1 B10 -.039 -.090 -.049 -.129 -.312 .648** -.300 -.435* 1 B11 .017 -.059 .018 -.054 -.199 -.018 .600** -.318 -.213 1 B12 .078 .180 .083 .258 .485** -.532** -.016 .702** -.720** -.353 1

[0157] Screening of evaluation indicators for fish.

[0158] Specifically, the fishery resource survey data from 16 locations in the Yangtze River estuary and adjacent waters in May 2015 were statistically processed, and candidate indicators were screened according to the method in step S203, and finally five evaluation indicators were screened out, namely, F1 (number of fish species), F3 (fish weight resource density (kg / km2)), F6 (percentage of estuarine fish species), F19 (percentage of planktivorous fish individuals), and F24 (percentage of mixed biophagous fish species).

[0159] Please refer to Table 10, which is the correlation analysis result of the candidate indicator data of fish biological populations.

[0160] Table 10 Correlation analysis results of candidate index data of fish species

[0161] M1 M2 M3 M4 M5 M6 M7 M14 M15 M16 M17 M18 M19 M20 M21 M22 M23 M1 1 M2 .384 1 M3 .334 .048 1 M4 .275 -.150 <![CDATA[-.683 * ]]> 1 M5 <![CDATA[-.742 ** ]]> .229 -.372 -.310 1 M6 -.277 .155 <![CDATA[.681 * ]]> <![CDATA[-1.000 * * ]]> .314 1 M7 <![CDATA[.742 ** ]]> -.229 .372 .310 <![CDATA[-1.000 * * ]]> -.314 1 M14 <![CDATA[.721 * ]]> .297 -.339 <![CDATA[.751 ** ]]> -.399 <![CDATA[-.753 ** ]]> .399 1 M15 -.168 .519 -.375 -.086 .555 .088 -.555 -.008 1 M16 <![CDATA[-.721 * ]]> -.297 .339 <![CDATA[-.751 ** ]]> .399 <![CDATA[.753 ** ]]> -.399 <![CDATA[-1.000 * * ]]> .008 1 M17 .168 -.519 .375 .086 -.555 -.088 .555 .008 <![CDATA[-1.000 * * ]]> -.008 1 M18 -.508 .202 <![CDATA[-.682 * ]]> .035 <![CDATA[.662 * ]]> -.036 <![CDATA[-.662 * ]]> -.146 <![CDATA[.734 * ]]> .146 <![CDATA[-.734 * ]]> 1 M19 -.171 .362 -.261 -.071 .590 .072 -.590 .211 .270 -.211 -.270 .291 1 M20 -.587 -.270 .491 <![CDATA[-.704 * ]]> .347 <![CDATA[.709 * ]]> -.347 <![CDATA[-.862 ** ]]> -.094 <![CDATA[.862 * * ]]> .094 -.191 -.089 1 M21 -.232 -.448 <![CDATA[.615 * ]]> -.438 -.247 .440 .247 <![CDATA[-.689 * ]]> -.547 <![CDATA[.689 * ]]> .547 -.499 <![CDATA[-.603 * ]]> <![CDATA[.740 * * ]]> 1 M22 .393 -.219 .348 .124 <![CDATA[-.725 * ]]> -.128 <![CDATA[.725 * ]]> .093 <![CDATA[-.810 ** ]]> -.093 <![CDATA[.810 * * ]]> -.541 -.442 -.178 .453 1 M23 -.288 -.401 -.193 .152 -.013 -.152 .013 -.255 -.254 .255 .254 .102 <![CDATA[-.624 * ]]> .017 .335 .224 1

[0162] Step S403, assigning values ​​to the Yangtze River Estuary water ecosystem integrity assessment indicators.

[0163] Specifically, please refer to Table 11, which is the evaluation index assignment table of the water ecosystem integrity of the Yangtze River Estuary.

[0164] Table 11 The evaluation index table of the integrity of the Yangtze River Estuary water ecosystem

[0165]

[0166]

[0167] The survey site areas of historical survey data are not exactly the same. Referring to the above-mentioned evaluation and scoring standards, each indicator of each site area is scored and the completeness index is calculated.

[0168] Please refer to Tables 12 to 15. Table 12 is the integrity index scoring table for phytoplankton, Table 13 is the integrity index scoring table for zooplankton, Table 14 is the integrity index scoring table for benthic animals, and Table 15 is the integrity index scoring table for fish.

[0169] Table 12 Phytoplankton integrity index score table

[0170]

[0171]

[0172] Table 13 Scoring table of zooplankton integrity index

[0173] Position Score Normalization Position Score Normalization 1 12 0.60 A2 8 0.40 2 4 0.20 A3 8 0.40 3 8 0.40 A5 8 0.40 4 8 0.40 A6 8 0.40 6 6 0.30 A7 6 0.30 7 4 0.20 A9 6 0.30 11 6 0.30 A18 8 0.40 A1 8 0.40 Completeness Index 0.36

[0174] Table 14 Integrity index score table of benthic animals

[0175] Position Score Normalization Position Score Normalization 2 8 0.40 8 6 0.30 3 6 0.30 9 8 0.40 4 4 0.20 10 4 0.20 5 8 0.40 11 4 0.20 6 4 0.20 14 4 0.20 7 4 0.20 15 6 0.30 Completeness Index 0.28

[0176] Table 15 Fish integrity index score table

[0177] Position Score Normalization Position Score Normalization A01 7 0.28 A11 7 0.28 A02 7 0.28 A13 5 0.20 A03 7 0.28 A14 7 0.28 A04 7 0.28 A15 5 0.20 A05 7 0.28 S11 7 0.28 A06 5 0.20 S6 5 0.20 A08 7 0.28 S7 5 0.20 A10 5 0.20 S8 5 0.20 Completeness Index 0.25

[0178] Step S404, determining the weights of the Yangtze River Estuary water ecosystem integrity assessment indicators.

[0179] The structural integrity assessment system of the Yangtze River Estuary ecosystem is constructed by integrating the phytoplankton integrity index, zooplankton integrity index, benthic animal integrity index and fish integrity index of the Yangtze River Estuary ecosystem.

[0180] Specifically, refer to Table 5 to determine the weights.

[0181] Step S405, calculation and evaluation of the Yangtze River Estuary water ecosystem integrity index.

[0182] The Yangtze River Estuary water ecosystem integrity index is calculated as:

[0183] IBI=0.42*0.11+0.36*0.22+0.28*0.28+0.25*0.38=0.30.

[0184] According to the calculation results of the Yangtze River Estuary Water Ecosystem Integrity Index and reference to Table 6, the integrity of the Yangtze River Estuary water ecosystem is determined to be “poor”.

[0185] In this embodiment, a device for evaluating the integrity of an estuarine water ecosystem is also provided, and the device is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0186] This embodiment provides a device for evaluating the integrity of an estuarine water ecosystem. Figure 6 is a structural block diagram of an estuarine water ecosystem integrity assessment device according to an embodiment of the present invention. Figure 6 As shown in the figure, the estuarine water ecosystem integrity assessment device includes:

[0187] The determination module 601 is used to determine candidate population indicators of various biological species in the target estuary water ecosystem.

[0188] The collection module 602 is used to collect candidate biological population index data of each biological species in multiple target areas according to the candidate biological population index, and the target area at least includes a reference area and a disturbance area, the reference area is an area that is not disturbed or less affected by disturbance, and the disturbance area is an area that is affected by disturbance and changes;

[0189] A screening module 603 is used to screen the candidate indicators of biological populations using the candidate indicator data of biological populations and determine the rating indicator data of biological populations;

[0190] The first calculation module 604 is used to calculate the biological community integrity index of each biological species by using the biological population rating index data of the reference area as a reference benchmark value;

[0191] The second calculation module 605 is used to perform weighted calculation on the biological community integrity index based on the weight of each biological species to obtain the system community integrity index, and evaluate the target estuarine water ecosystem based on the system community integrity index.

[0192] The further functional description of each of the above modules is the same as that of the above corresponding embodiments and will not be repeated here.

[0193] The estuarine water ecosystem integrity assessment device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0194] The embodiment of the present invention also provides a computer device having the above Figure 6 The estuarine water ecosystem integrity assessment device shown.

[0195] See also Figure 7 , Figure 7 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 7 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 7 A processor 10 is taken as an example.

[0196] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0197] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0198] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0199] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0200] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 7 The example of connecting through bus is taken in the following.

[0201] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0202] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0203] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0204] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for evaluating the integrity of an estuarine water ecosystem, characterized in that: The method comprises: Determine candidate biological population indicators for each biological species of the target estuarine water ecosystem, including phytoplankton, zooplankton, benthic animals and fish; According to the candidate biological population index, data of candidate biological population indexes of each biological species in multiple target areas are collected, wherein the target areas at least include reference areas and interference areas, wherein the reference areas are areas that are not disturbed or are less affected by interference, and the interference areas are areas that are affected by interference and change; Using the biological population candidate indicator data, screening the biological population candidate indicators to determine biological population rating indicator data; The biological community integrity index of each biological species is calculated using the biological population rating index data of the reference area as a reference baseline value; The biological community integrity index is weighted and calculated based on the weight of each biological species to obtain a system community integrity index, and the target estuarine water ecosystem is evaluated based on the system community integrity index.

2. The method for evaluating the integrity of an estuarine water ecosystem according to claim 1, characterized in that: The method of using the candidate indicator data of biological populations to screen the candidate indicators of biological populations and determine the biological population rating indicator data includes: Using the candidate indicator data of biological populations, screening the candidate indicators of biological populations according to screening principles to determine biological population rating indicator data; The screening principles include the zero-value data proportion principle, the data difference principle, the distribution range analysis principle, the correlation analysis principle and the discrimination ability analysis principle.

3. The method for evaluating the integrity of an estuarine water ecosystem according to claim 2, characterized in that: The screening principle is the zero-value data ratio principle. The biological population candidate indicator data is screened according to the screening principle to determine the biological population rating indicator data, including: Calculate the proportion of target areas where the candidate indicator data of the biological population is not 0; If the target area ratio is greater than or equal to the preset ratio, the candidate indicator data for biological populations is determined to be the biological population rating indicator data.

4. The method for evaluating the integrity of an estuarine water ecosystem according to claim 2, characterized in that: The screening principle is the distribution range analysis principle, and the use of the candidate indicator data of biological populations to screen the candidate indicators of biological populations according to the screening principle to determine the biological population rating indicator data includes: Comparing the candidate indicator data of biological populations in the reference area with the candidate indicator data of biological populations in the interference area, and screening the candidate indicator data of biological populations affected in a unidirectional manner, wherein the unidirectional influence includes a unidirectional increase or a unidirectional decrease after being disturbed; When the candidate indicator data of the biological population increases unidirectionally after being disturbed, if the candidate indicator data of the biological population are all greater than a first preset value, the candidate indicator data of the biological population are deleted; When the candidate indicator data of the biological population decreases unidirectionally after being disturbed, if the candidate indicator data of the biological population are all less than a second preset value, the candidate indicator data of the biological population are deleted; When the values ​​of the candidate indicator data of biological populations are dispersed, the candidate indicator data of biological populations are deleted.

5. The method for evaluating the integrity of an estuarine water ecosystem according to claim 4, characterized in that: The screening principle is the discrimination ability analysis principle, and the use of the candidate indicator data of biological populations to screen the candidate indicators of biological populations according to the screening principle to determine the biological population rating indicator data also includes: Analyzing the overlap between the candidate indicator data of biological populations in the reference area and the candidate indicator data of biological populations in the interference area using a box plot method; Based on the overlap situation, determining an overlap index; The candidate indicator data of biological populations whose coincidence index is greater than a preset coincidence index are determined as the biological population rating indicator data.

6. The method for evaluating the integrity of an estuarine water ecosystem according to claim 2, characterized in that: The calculation of the biological community integrity index of each biological species using the biological population rating index data of the reference area as a reference benchmark value includes: For each of the target areas, the biological population rating index data of the reference area is used as a reference benchmark value to calculate the deviation of the biological population rating index data; Based on the deviation, the biological population rating index data is indexed and summed to obtain a regional community integrity index of the target area; The regional community integrity index is weighted and calculated based on the weight of each target area to obtain the biological community integrity index.

7. The method for evaluating the integrity of an estuarine water ecosystem according to claim 2, characterized in that: The biological community integrity index is weighted and calculated based on the weight of each biological species to obtain the system community integrity index, which includes: Determining a weight for each biological species based on the species trophic level of the biological species; The biological community integrity index is weighted and calculated based on the weight to obtain the system community integrity index.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the estuarine water ecosystem integrity assessment method according to any one of claims 1 to 7 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the estuarine water ecosystem integrity assessment method according to any one of claims 1 to 7.

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the estuarine water ecosystem integrity assessment method according to any one of claims 1 to 7.

Images