Marine biological diversity geographic information system and ecological diversity risk assessment method

Abstract

The invention relates to the technical field of marine biological diversity assessment, and particularly discloses a marine biological diversity geographic information system and an ecological diversity risk assessment method.The marine biological diversity geographic information system comprises a water quality diversity risk assessment module, a diversity and water quality assessment comprehensive model module and an RDA analysis method screening module; according to the system and the method, marine organism data and water quality data of a sea area for many years are integrated, an interactive visual system is developed, data interconnection and intercommunication are realized, and the sea area can be evaluated through diversity investigation of multiple organisms in the sea area, water environment physical and chemical factor analysis, correlation mining and evaluation model construction. The model can assess the sea water environment condition, the water body environment quality and the biodiversity risk, provides basic data and methods for related research, powerfully promotes ocean monitoring to be converted from manual work to intelligence, promotes biodiversity data integration and ecological environment monitoring in a bay area, and tracks the biological activity and survival state.

Description

Technical Field

[0001] The present invention relates to the technical field of marine biodiversity assessment, in particular to a marine biodiversity geographic information system and an ecological diversity risk assessment method. Background Art

[0002] Biodiversity is closely linked to human well-being and is the key foundation for human survival and development. Among them, conducting marine biodiversity surveys is a prerequisite for building a marine biodiversity protection information system and monitoring mechanism. By conducting long-term dynamic monitoring of the status of important target biodiversity and the ecological environment, we will continue to accumulate and improve databases such as rare and endangered animal species, economic animal databases, ecosystem databases, genetic diversity databases, etc., and then build and optimize the information network for marine biodiversity protection, providing a basis for decision-making for the protection, utilization and scientific management of biodiversity. Strengthening the protection of biodiversity can achieve the steady preservation and appreciation of natural capital and accumulate strong momentum for the green development of the region. Relying on rich ecological assets and scientific transformation methods, we can transform resource advantages and ecological environment advantages into economic advantages such as ecological agriculture, ecological tourism, green industry and health and leisure industries, thereby releasing stable, active and sustainable ecological dividends and social benefits.

[0003] At present, my country has rich information resources on marine biodiversity, but its utilization rate is low, innovative ecological products are scarce, product models are relatively simple, and there are deficiencies in the visualization and information development of ecological products. Most of the research on the relationship between marine organisms and the water environment focuses on large-scale categories, mainly through statistical analysis of a large amount of water quality data, and the construction of a relationship model between aquatic species and water environment variables. However, there is little discussion on the comprehensive impact of the physical and chemical properties of water bodies and hydrological factors on organisms, and there is also little combination of water quality to evaluate and display the biodiversity risks of bays in the region.

[0004] To this end, we propose a marine biodiversity geographic information system and ecological diversity risk assessment method to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a marine biodiversity geographic information system and an ecological diversity risk assessment method to solve the problems raised in the above background technology.

[0006] To achieve the above-mentioned purpose, in a first aspect, the present invention provides a marine biodiversity geographic information system, including: a water quality diversity risk assessment module, a diversity and water quality assessment comprehensive model module, an RDA analysis method screening module, a marine biological polymorphism index calculation module, a biodiversity grade assessment module and a visualization map module. The diversity and water quality assessment comprehensive model module is used to comprehensively assess the water quality, make up for the shortcomings of relying solely on water quality physical and chemical indicators, indicate the health status of the water ecosystem, reflect its overall health, analyze the key environmental factors affecting organisms and water quality, and provide scientific basis and decision support for water management, protection and restoration. The RDA analysis method module mainly reveals the relationship between environmental factors and biological community structure, clarifies the environmental factors that significantly affect the distribution of biological communities, screens out key water quality indicators that affect biodiversity, deeply understands the interaction mechanism between organisms and the environment, and provides a basis and basis for the formulation of subsequent ecological assessments, protection and management strategies. The marine biodiversity index calculation module is used to quantify diversity, the biodiversity grade assessment module assesses the pros and cons, and the visualization map module intuitively displays the distribution.

[0007] The water quality diversity risk assessment module is composed of a biological monitoring station module, a biological sampling and experimental measurement module, and a biological data processing and statistics module. The biological monitoring station module is used to determine the monitoring point, the sampling and measurement module is used to obtain samples and data, and the biological data processing is used to analyze the data. The overall data obtained by the monitoring station module, the biological sampling and experimental measurement module, and the biological data processing serves as the data basis for the diversity and water quality evaluation comprehensive model module, the RDA analysis method screening module, the marine biological polymorphism index calculation module, and the biodiversity level assessment module.

[0008] Furthermore, the RDA analysis screening module is composed of an environmental factor impact assessment module on zooplankton communities and an environmental factor impact assessment module on phytoplankton communities. The environmental factor impact assessment module on zooplankton communities and the environmental factor impact assessment module on phytoplankton communities are used to clarify the relationship between environmental factors and the structure of zooplankton and phytoplankton communities, identify key influencing factors, and provide a basis for studying marine ecology and water quality.

[0009] Furthermore, the calculation formula of species abundance in the biological data processing and statistical module is CB=NB / V, wherein CB is the individual abundance of marine organisms per unit volume, NB is the number of the entire network, and V is the amount of filtered water.

[0010] In a second aspect, the present invention provides a method for assessing the risk of marine biodiversity geographic information ecological diversity, comprising the following steps:

[0011] Step 1: Data collection and integration. First, collect marine biological data from the sea area for many years, including data on phytoplankton, zooplankton, large benthic organisms, swimming organisms, birds and vegetation. At the same time, obtain water quality data, such as pH, dissolved oxygen, chlorophyll a, total inorganic nitrogen, phosphate, water temperature, salinity, etc., and integrate these data into a system to lay the foundation for subsequent analysis and evaluation.

[0012] Step 2: Develop an interactive visualization system to achieve data interconnection. Through this system, biodiversity data and water quality data can be displayed intuitively, which is convenient for integrating biodiversity and monitoring the ecological environment in the bay area, and tracking biological activity traces and survival status;

[0013] Step 3: Biodiversity survey: set up 7 monitoring stations to conduct field surveys on the biodiversity of phytoplankton, zooplankton, macrobenthic organisms, nematodes, birds and vegetation in the sea area, collect samples at different stations, and count the macrobenthic organisms, zooplankton, phytoplankton, nematodes, birds and vegetation collected;

[0014] Step 4: Analysis of physical and chemical factors of water environment: Analysis of physical and chemical factors of water environment at each station, including determination and research of physical and chemical properties of water environment in the bay, such as pH, dissolved oxygen, chlorophyll a, total inorganic nitrogen, phosphate, water temperature, salinity, etc.;

[0015] Step 5: Calculate biomass, density and species abundance. Based on the survey data, calculate the biomass, density and species abundance of each station to quantify the distribution and quantity characteristics of organisms.

[0016] Step 6: Analyze species diversity, analyze the species diversity of zooplankton and phytoplankton, and use methods such as Shannon diversity index, Simpson index and Pielou evenness index to evaluate the species diversity level;

[0017] Step 7: Biological sampling and experimental measurement: At the plankton station, water quality parameters are measured using a multi-parameter water quality instrument, and plankton samples are collected at the same time. After the samples are processed in the laboratory, species identification, quantity statistics and measurement are carried out;

[0018] Step 8: Biological data processing and statistics: analyze the species composition and abundance changes of plankton in different seasons, use the dominance (Y) formula to determine the dominant species in the community, and use the species abundance calculation formula, Shannon diversity index formula and Simpson index formula to process and statistically analyze the biological data;

[0019] Step 9: Screening water quality environmental factors that affect biodiversity. Based on the RDA analysis method, 11 environmental factors were analyzed in detail to determine their impact on the community structure of phytoplankton and zooplankton species. Since the correlation between benthic organisms and environmental factors in the sea area was not obvious, factor analysis was mainly conducted on zooplankton and phytoplankton to screen out key water quality indicators that affect biodiversity, such as temperature, chlorophyll a concentration, dissolved oxygen DO, SiO3, etc.

[0020] Step 10: Calculate the marine biodiversity indicators. According to the actual situation of the sea area, calculate the Shannon diversity index (H'), Simpson index (D') and uniformity index (J') of phytoplankton, zooplankton and macrobenthic organisms respectively to evaluate the diversity and stability of the community;

[0021] Step 11: Construct a comprehensive model for diversity and water quality assessment, determine the final assessment indicators, including water quality indicators and biodiversity indicators, use the data collected from 7 stations in the sea area in January, April, July and October, calculate the weight of each indicator through the entropy method + TOPSIS, and rank the station samples to find the best and worst stations;

[0022] Step 12: Ecosystem health risk assessment and classification. By calculating the ecosystem risk comprehensive index, the water quality and biodiversity of each sample point in the sea area are assessed. According to the calculation standard of the Ecosystem Health Comprehensive Index (EHCI), when EHCI ≥ 80, it means that the ecosystem is healthy and the water quality is good; when 80> EHCI ≥ 60, it means that the ecosystem risk is small and the water quality is good; when 60> EHCI ≥ 40, it means that the ecosystem has a medium risk and the water quality is medium. Based on the calculation results, the ecological health status of the sea area in different seasons and stations is graded and evaluated.

[0023] Furthermore, the step eight is an indicator for measuring the diversity of biological communities, which comprehensively considers the number of species and the distribution of the number of individuals of each species. The larger the value, the higher the diversity of the community.

[0024] Furthermore, the water quality indicators in step eleven specifically include dissolved oxygen, chlorophyll a, temperature, ammonia nitrogen and nitrate, and the biodiversity indicators specifically include zooplankton species diversity index, Simpson index, phytoplankton species diversity index, Simpson index.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] In the present invention, the system and method integrate the marine biological data (phytoplankton, etc.) and water quality data (pH, etc.) of the sea area for many years, develop an interactive visualization system, realize data interconnection, and through the diversity survey of various organisms in the sea area and the analysis of the physical and chemical factors of the water environment, explore the correlation and construct an evaluation model. This model can evaluate the water environment status, water environment quality and biodiversity risks of the sea area, provide basic data and methods for related research, and effectively promote the transformation of marine monitoring from manual to intelligent, promote the integration of biodiversity data and ecological environment monitoring in the bay area, and track biological activities and survival status. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a comprehensive model diagram of diversity risk and water quality assessment of the present invention;

[0028] Figure 2 This is the location map of the sea water quality monitoring stations in the present invention;

[0029] Figure 3 It is the RDA analysis ordination diagram of the zooplankton community and environmental factors in the marine area of ​​the present invention;

[0030] Figure 4 This is the RDA analysis ranking diagram of phytoplankton and environmental factors in the sea area of ​​the present invention;

[0031] Figure 5 This is a diagram of the system construction plan in the present invention. DETAILED DESCRIPTION

[0032] 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.

[0033] See also Figure 1-Figure 5In an embodiment of the present invention, a marine biodiversity geographic information system includes: a water quality diversity risk assessment module, a diversity and water quality assessment comprehensive model module, an RDA analysis method screening module, a marine biological polymorphism index calculation module, a biodiversity grade assessment module and a visualization map module. The diversity and water quality assessment comprehensive model module is used to comprehensively assess water quality, make up for the shortcomings of relying solely on water quality physical and chemical indicators, indicate the health status of the water ecosystem, reflect its overall health, analyze key environmental factors affecting organisms and water quality, and provide scientific basis and decision support for water management, protection and restoration. The RDA analysis method module mainly reveals the relationship between environmental factors and biological community structure, clarifies environmental factors that significantly affect biological community distribution, screens out key water quality indicators that affect biodiversity, deeply understands the interaction mechanism between organisms and the environment, and provides a basis and basis for the formulation of subsequent ecological assessment, protection and management strategies. The marine biodiversity index calculation module is used to quantify diversity, the biodiversity grade assessment module assesses the pros and cons, and the visualization map module intuitively displays the distribution;

[0034] The water quality diversity risk assessment module is composed of a biological monitoring station module, a biological sampling and experimental measurement module, and a biological data processing and statistics module. The biological monitoring station module is used to determine the monitoring point, the sampling and measurement module is used to obtain samples and data, and the biological data processing is used to analyze the data. The overall data obtained by the monitoring station module, the biological sampling and experimental measurement module, and the biological data processing serves as the data basis for the diversity and water quality evaluation comprehensive model module, the RDA analysis method screening module, the marine biological polymorphism index calculation module, and the biodiversity level assessment module.

[0035] The RDA analysis screening module is composed of an environmental factor impact assessment module on zooplankton community and an environmental factor impact assessment module on phytoplankton community. The environmental factor impact assessment module on zooplankton community and the environmental factor impact assessment module on phytoplankton community are used to clarify the relationship between environmental factors and the community structure of zooplankton and phytoplankton, find out the key influencing factors, and provide a basis for studying marine ecology and water quality. The calculation formula of species abundance in the biological data processing and statistics module is CB=NB / V, wherein CB is the individual abundance of seawater organisms per unit volume, NB is the number of the entire network, and V is the amount of filtered water.

[0036] See also Figure 1-Figure 5 , a marine biodiversity geographic information system, comprising the following steps:

[0037] Step 1: Data collection and integration. First, collect marine biological data from the sea area for many years, including data on phytoplankton, zooplankton, large benthic organisms, swimming organisms, birds and vegetation. At the same time, obtain water quality data, such as pH, dissolved oxygen, chlorophyll a, total inorganic nitrogen, phosphate, water temperature, salinity, etc., and integrate these data into a system to lay the foundation for subsequent analysis and evaluation.

[0038] Step 2: Develop an interactive visualization system to achieve data interconnection. Through this system, biodiversity data and water quality data can be displayed intuitively, which is convenient for integrating biodiversity and monitoring the ecological environment in the bay area, and tracking biological activity traces and survival status;

[0039] Step 3: Biodiversity survey: set up 7 monitoring stations to conduct field surveys on the biodiversity of phytoplankton, zooplankton, macrobenthic organisms, nematodes, birds and vegetation in the sea area, collect samples at different stations, and count the macrobenthic organisms, zooplankton, phytoplankton, nematodes, birds and vegetation collected;

[0040] Step 4: Analysis of physical and chemical factors of water environment: Analysis of physical and chemical factors of water environment at each station, including determination and research of physical and chemical properties of water environment in the bay, such as pH, dissolved oxygen, chlorophyll a, total inorganic nitrogen, phosphate, water temperature, salinity, etc.;

[0041] Step 5: Calculate biomass, density and species abundance. Based on the survey data, calculate the biomass, density and species abundance of each station to quantify the distribution and quantity characteristics of organisms.

[0042] Step 6: Analyze species diversity, analyze the species diversity of zooplankton and phytoplankton, and use methods such as Shannon diversity index, Simpson index and Pielou evenness index to evaluate the species diversity level;

[0043] Shannon diversity index (H) uses Shannon-Weiner index, which is used to describe the disorder and uncertainty of individual species. The higher the uncertainty, the more unknown factors there are in the community, that is, the higher the diversity. The calculation formula is: The larger the Simpson index (D), the higher the diversity. It is a scalar of α-diversity. In the Simpson index, rare species play a smaller role, while common species play a larger role. Its calculation formula is:

[0044] In the above formula, S is the number of species, N is the total number of individuals of the species, and Ni is the number of individuals of the i-th species.

[0045] Step 7: Biological sampling and experimental measurement: At the plankton station, water quality parameters are measured using a multi-parameter water quality instrument, and plankton samples are collected at the same time. After the samples are processed in the laboratory, species identification, quantity statistics and measurement are carried out;

[0046] Step 8: Biological data processing and statistics, analyze the species composition and abundance changes of plankton in different seasons, use the dominance formula to determine the dominant species in the community, and use the species abundance calculation formula, Shannon diversity index formula and Simpson index formula to process and statistically analyze the biological data. Shannon is an indicator used to measure the diversity of biological communities. It comprehensively considers the number of species and the distribution of the number of individuals of each species. The larger the value, the higher the diversity of the community.

[0047] Step 9: Screening water quality environmental factors that affect biodiversity. Based on the RDA analysis method, 11 environmental factors were analyzed in detail to determine their impact on the community structure of phytoplankton and zooplankton species. Since the correlation between benthic organisms and environmental factors in the sea area was not obvious, factor analysis was mainly conducted on zooplankton and phytoplankton to screen out key water quality indicators that affect biodiversity, such as temperature, chlorophyll a concentration, dissolved oxygen DO, SiO3, etc.

[0048] Step 10: Calculate the marine biodiversity indicators. According to the actual situation of the sea area, calculate the Shannon diversity index (H'), Simpson index (D') and uniformity index (J') of phytoplankton, zooplankton and macrobenthic organisms respectively to evaluate the diversity and stability of the community;

[0049] Step 11: Construct a comprehensive model for diversity and water quality assessment, determine the final assessment indicators, including water quality indicators and biodiversity indicators, use the data collected from 7 stations in the sea area in January, April, July and October, calculate the weight of each indicator by entropy method + TOPSIS and rank the station samples to find the best and worst stations. The water quality indicators in step 11 specifically include dissolved oxygen, chlorophyll a, temperature, ammonia nitrogen and nitrate, and the biodiversity indicators specifically include zooplankton species diversity index, Simpson index, phytoplankton species diversity index, Simpson index;

[0050] Calculate the index weights based on entropy method + topisis and build a ranking model for the comprehensive water ecological situation of the station

[0051] 1. Data indicator classification

[0052] According to the above analysis, the nine water quality factors, including dissolved oxygen (DO), chlorophyll a, temperature, ammonia nitrogen, nitrate, zooplankton species diversity index (FD-H'), Simpson index (FD-D'), phytoplankton species diversity index (FZ-H'), and Simpson index (FZ-D'), were divided into positive factors and negative factors according to the month, and a 9*7 original matrix was constructed, covering the data information of four seasons and seven stations.

[0053] 2. Data standardization

[0054] The original matrix data is standardized according to dimensionless processing methods such as MMS (forwarding) and NMMS (inverseing).

[0055] Positive indicator: (X-Min) / (Max-Min) (Positive MMS)

[0056] Negative indicator: (Max-X) / (Max-Min) (reverse NMMS)

[0057] 3. Calculate the weight pij, entropy value ej and information redundancy value d;

[0058]

[0059] d j =1-e j

[0060] 4. Determine the weight

[0061]

[0062] According to the entropy weight formula, the entropy values ​​and entropy weights of different indicators in the sea area in winter (January), spring (April), summer (July) and autumn (October) were calculated. The following table shows the results for July. It can be seen that among the selected indicators, the indicators with the greatest impact on the health of the Meishan Bay marine ecosystem in July are ammonia nitrogen, phytoplankton species richness index (FZ-D') and zooplankton species diversity index (FD-H'), and their entropy weights are: 0.130242, 0.161259, 0.149109 respectively;

[0063] Table 1 shows the entropy weights of the marine biodiversity assessment in July.

[0064]

[0065] 5. Construct weighted matrix

[0066]

[0067] 6. Calculate the gap between each evaluation index and the best and worst vectors

[0068]

[0069] 7. Evaluate the closeness of the object to the optimal solution and rank them

[0070]

[0071] Sorting is done according to the size of Ci. The larger the Ci, the closer the evaluation object is to the optimal value. According to the calculation, the ranking results of the comprehensive situation of the station ecosystem are shown in the following table (taking July as an example). It can be seen that among the 7 selected sampling stations, B3, B4, and B5 stations are ranked at the bottom, and the comprehensive situation of the ecosystem is relatively poor;

[0072] Table 2 Ranking of comprehensive status of station ecosystems

[0073]

[0074]

[0075] Evaluation of biodiversity level based on comprehensive ecosystem risk index

[0076] This study adopts the evaluation method of ecosystem health structure function indicator system, and the comprehensive index formula of ecosystem health is as follows:

[0077]

[0078] Where:

[0079] EHCI—comprehensive index value of ecosystem health;

[0080] W i —The entropy weight of the evaluation index in the comprehensive evaluation index system;

[0081] X i —The normalized value of the evaluation index, which is between 0 and 1.

[0082] Table 3 Ecological health comprehensive index classification

[0083]

[0084]

[0085] Step 12: Ecosystem health risk assessment and classification. By calculating the ecosystem risk comprehensive index, the water quality and biodiversity of each sample point in the sea area are assessed. According to the calculation standard of the Ecosystem Health Comprehensive Index (EHCI), when EHCI ≥ 80, it means that the ecosystem is healthy and the water quality is good; when 80> EHCI ≥ 60, it means that the ecosystem risk is small and the water quality is good; when 60> EHCI ≥ 40, it means that the ecosystem has a medium risk and the water quality is medium. Based on the calculation results, the ecological health status of the sea area in different seasons and stations is graded and evaluated.

[0086] According to the calculation standard of the Ecosystem Health Index (EHCI), when EHCI ≥ 80, it means that the ecosystem is healthy and the water quality is good; when 80> EHCI ≥ 60, it means that the ecosystem risk is small and the water quality is good; when 60> EHCI ≥ 40, it means that the ecosystem has medium risk and the water quality is medium. EHCI is expressed with 100 as the benchmark, thus obtaining the comprehensive biodiversity risk index of Meishan Bay under different water quality conditions in different seasons.

[0087] According to the EHCI calculation formula, the comprehensive index values ​​and ecological health risk classification of each station in the Meishan Bay waters in July were obtained (see Table 3). The results show that the Meishan Bay waters were in a medium health state as a whole in July, the ecosystem was at moderate risk, and the water quality was medium. The water quality at stations B3 and B5 was poor, and the biodiversity risk was relatively high. This is consistent with the results of the TOPSIS ranking analysis, which indirectly proves the scientificity and feasibility of this method;

[0088] Table 4 Risk assessment of comprehensive ecological health index

[0089]

[0090] The working principle and advantages of the present invention are:

[0091] This system and method integrates marine biological data (such as phytoplankton, zooplankton, large benthic organisms, swimming organisms, birds and vegetation) and water quality data (such as pH, dissolved oxygen, chlorophyll a, total inorganic nitrogen, phosphate, water temperature, salinity, etc.) in the sea area over many years, and develops an interactive visualization system to achieve data interconnection and interoperability, facilitate biodiversity data integration and ecological environment monitoring in the bay area, and track biological activity traces and survival status. Through the investigation of biodiversity such as phytoplankton, zooplankton, large benthic organisms, swimming organisms, birds and vegetation in the sea area and the analysis of physical and chemical factors of water environment at relevant stations, their correlation is explored, and a diversity and water quality risk assessment model is constructed to evaluate the water environment status of the sea area, analyze its water environment quality and biodiversity risks, provide basic data and methods for marine water quality evaluation and diversity risk assessment research, and promote the transformation of marine monitoring from "manual monitoring" to "smart monitoring";

[0092] System construction plan;

[0093] Production of sample points, sample lines and sample quadrat vectors in the region based on arcGis;

[0094] Create a biodiversity vector file based on the quarterly data of marine life in the bay, including survey points of phytoplankton, zooplankton, macrobenthic organisms and swimming organisms, bird activity routes and vegetation distribution areas;

[0095] Biological / water quality database construction based on MySQL;

[0096] Using the marine biological survey data from each quarter, a biological database is constructed, covering the main types, species abundance, total number of individuals and number of species of phytoplankton, zooplankton, large benthic organisms and swimming organisms, as well as diversity data such as the number, morphology and migration characteristics of birds, water quality data and picture information.

[0097] Construct a comprehensive model for biodiversity risk and water quality assessment based on the biodiversity database;

[0098] Based on the established biodiversity and water quality database, a diversity and water quality evaluation model is constructed with reference to diversity survey and water quality evaluation methods, and the calculation results are integrated into the system background database for visual display.

[0099] Based on vue+MySQL+nodejs full stack project function implementation;

[0100] This project uses technologies such as Node.js and MySQL, is developed through the Express framework, and provides an API interface that complies with the RESTful specification. The front end of the system uses the Vue framework and the ElementUI library, and all codes are developed using the latest HTML, CSS, and JavaScript standards.

[0101] Publishing web visualization map services based on GeoServer;

[0102] Based on the open source map server software GeoServer, we optimized and published the bay biological distribution vector data, designed and implemented an automatic publishing module for vector map services, and ultimately applied it to the marine biodiversity digital system to achieve data interconnection, biodiversity visualization, and comprehensive assessment of water quality and diversity risks.

Claims

1. A marine biodiversity geographic information system, characterized in that: include: Water quality diversity risk assessment module, diversity and water quality assessment comprehensive model module, RDA analysis method screening module, marine biological polymorphism index calculation module, biodiversity level assessment module and visualization map module. The diversity and water quality assessment comprehensive model module is used to comprehensively assess water quality, make up for the shortcomings of relying solely on water quality physical and chemical indicators, indicate the health status of water ecosystems, reflect its overall health, analyze key environmental factors affecting organisms and water quality, and provide scientific basis and decision-making support for water management, protection and restoration. The main function of the RDA analysis method module is to reveal the relationship between environmental factors and biological community structure, clarify the environmental factors that have a significant impact on the distribution of biological communities, screen out key water quality indicators that affect biodiversity, deeply understand the interaction mechanism between organisms and the environment, and provide a basis and basis for the formulation of subsequent ecological assessments, protection and management strategies. The marine biodiversity index calculation module is used to quantify diversity, the biodiversity level assessment module evaluates the pros and cons, and the visualization map module intuitively displays the distribution. The water quality diversity risk assessment module is composed of a biological monitoring station module, a biological sampling and experimental measurement module, and a biological data processing and statistics module. The biological monitoring station module is used to determine the monitoring point, the sampling and measurement module is used to obtain samples and data, and the biological data processing is used to analyze the data. The overall data obtained by the monitoring station module, the biological sampling and experimental measurement module, and the biological data processing serves as the data basis for the diversity and water quality evaluation comprehensive model module, the RDA analysis method screening module, the marine biological polymorphism index calculation module, and the biodiversity level assessment module.

2. A marine biodiversity geographic information system according to claim 1, characterized in that: The RDA analysis screening module is composed of an environmental factor impact assessment module on zooplankton community and an environmental factor impact assessment module on phytoplankton community. The environmental factor impact assessment module on zooplankton community and the environmental factor impact assessment module on phytoplankton community are used to clarify the relationship between environmental factors and the community structure of zooplankton and phytoplankton, find out the key influencing factors, and provide a basis for studying marine ecology and water quality.

3. A marine biodiversity geographic information system according to claim 2, characterized in that: The calculation formula of species abundance in the biological data processing and statistical module is CB=NB / V, where CB is the individual abundance of marine organisms per unit volume, NB is the number of the entire network, and V is the amount of filtered water.

4. A marine biodiversity geographic information ecological diversity risk assessment method, using a marine biodiversity geographic information system according to any one of claims 1 to 4, characterized in that: The following steps are involved: Step 1: Data collection and integration. First, collect marine biological data from the sea area for many years, including data on phytoplankton, zooplankton, large benthic organisms, swimming organisms, birds and vegetation. At the same time, obtain water quality data, such as pH, dissolved oxygen, chlorophyll a, total inorganic nitrogen, phosphate, water temperature, salinity, etc., and integrate these data into a system to lay the foundation for subsequent analysis and evaluation. Step 2: Develop an interactive visualization system to achieve data interconnection. Through this system, biodiversity data and water quality data can be displayed intuitively, which is convenient for integrating biodiversity and monitoring the ecological environment in the bay area, and tracking biological activity traces and survival status; Step 3: Biodiversity survey: set up 7 monitoring stations to conduct field surveys on the biodiversity of phytoplankton, zooplankton, macrobenthic organisms, nematodes, birds and vegetation in the sea area, collect samples at different stations, and count the macrobenthic organisms, zooplankton, phytoplankton, nematodes, birds and vegetation collected; Step 4: Analysis of physical and chemical factors of water environment: Analysis of physical and chemical factors of water environment at each station, including determination and research of physical and chemical properties of water environment in the bay, such as pH, dissolved oxygen, chlorophyll a, total inorganic nitrogen, phosphate, water temperature, salinity, etc.; Step 5: Calculate biomass, density and species abundance. Based on the survey data, calculate the biomass, density and species abundance of each station to quantify the distribution and quantity characteristics of organisms. Step 6: Analyze species diversity, analyze the species diversity of zooplankton and phytoplankton, and use methods such as Shannon diversity index, Simpson index and Pielou evenness index to evaluate the species diversity level; Step 7: Biological sampling and experimental measurement: At the plankton station, water quality parameters are measured using a multi-parameter water quality instrument, and plankton samples are collected at the same time. After the samples are processed in the laboratory, species identification, quantity statistics and measurement are carried out; Step 8: Biological data processing and statistics: analyze the species composition and abundance changes of plankton in different seasons, use the dominance (Y) formula to determine the dominant species in the community, and use the species abundance calculation formula, Shannon diversity index formula and Simpson index formula to process and statistically analyze the biological data; Step 9: Screening water quality environmental factors that affect biodiversity. Based on the RDA analysis method, 11 environmental factors were analyzed in detail to determine their impact on the community structure of phytoplankton and zooplankton species. Since the correlation between benthic organisms and environmental factors in the sea area was not obvious, factor analysis was mainly conducted on zooplankton and phytoplankton to screen out key water quality indicators that affect biodiversity, such as temperature, chlorophyll a concentration, dissolved oxygen DO, SiO3, etc. Step 10: Calculate the marine biodiversity indicators. According to the actual situation of the sea area, calculate the Shannon diversity index (H'), Simpson index (D') and uniformity index (J') of phytoplankton, zooplankton and macrobenthic organisms respectively to evaluate the diversity and stability of the community; Step 11: Construct a comprehensive model for diversity and water quality assessment, determine the final assessment indicators, including water quality indicators and biodiversity indicators, use the data collected from 7 stations in the sea area in January, April, July and October, calculate the weight of each indicator through the entropy method + TOPSIS, and rank the station samples to find the best and worst stations; Step 12: Ecosystem health risk assessment and classification. By calculating the ecosystem risk comprehensive index, the water quality and biodiversity of each sample point in the sea area are assessed. According to the calculation standard of the Ecosystem Health Comprehensive Index (EHCI), when EHCI ≥ 80, it means that the ecosystem is healthy and the water quality is good; when 80> EHCI ≥ 60, it means that the ecosystem risk is small and the water quality is good; when 60> EHCI ≥ 40, it means that the ecosystem has a medium risk and the water quality is medium. Based on the calculation results, the ecological health status of the sea area in different seasons and stations is graded and evaluated.

5. A marine biodiversity geographic information ecological diversity risk assessment method according to claim 4, characterized in that: In step eight, Shannon is an indicator for measuring the diversity of biological communities, which comprehensively considers the number of species and the distribution of the number of individuals of each species. The larger the value, the higher the diversity of the community.

6. A marine biodiversity geographic information ecological diversity risk assessment method according to claim 4, characterized in that: The water quality indicators in step eleven specifically include dissolved oxygen, chlorophyll a, temperature, ammonia nitrogen and nitrate, and the biodiversity indicators specifically include zooplankton species diversity index, Simpson index, phytoplankton species diversity index, Simpson index.

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