Marine organism intrusion risk prediction method and system

Abstract

The invention discloses a marine organism intrusion risk prediction method and system, and relates to the technical field of marine environment, and the method comprises the steps: calculating the environmental suitability of each sea area through employing a maximum entropy model based on a marine environment factor data layer and invasive organism geographical distribution data; human factor data including shipping density and fishery fishing degree and biological factor data including primary productivity and species number are collected, and an intrusion risk assessment system is established in combination with environmental suitability. According to the method, the weight of each index under each data is determined through an objective weighting method, an invasion organism invasion risk function is constructed, the invasion risk is calculated, the sea area invasion risk is judged accordingly, reference is provided for related departments, monitoring, protection and other measures are taken in advance for medium and high risk areas, early warning is performed in advance, and the influence of biological invasion is reduced to the minimum.

Description

Technical Field

[0001] The present invention relates to the field of marine environment technology, and in particular to a method and system for predicting the risk of marine biological invasion. Background Art

[0002] With the advancement of science and technology and the process of globalization, human exchanges have become more frequent and close. Due to human pressure, the driving factors of marine biological invasions are becoming more and more global in nature. Invasive organisms often attach to artificial creations such as aquaculture cages, the bottom of ships, and underwater equipment, and migrate to new environments with ship transportation or ballast water. The invasion of alien marine species brought about by human activities will have a negative impact on local ecosystems, biodiversity, human health and economic activities. Invasive organisms are prone to compete with farmed organisms for living materials, seriously damaging the healthy development of industries such as fisheries and aquaculture. Some invasive organisms are toxic, which will affect human health and have an impact on local tourism. For some organisms with strong adhesion, such as ascidians, it will cause problems such as affecting ship maintenance and damaging underwater equipment, which will not only bring huge economic losses, but also damage important coastal facilities such as blocking the cooling water pipes of nuclear power plants, which is easy to cause huge safety hazards. Once a biological invasion occurs and the population expands to a certain scale, the removal of invasive organisms is a costly and ineffective task. Therefore, the measures to deal with biological invasions are mainly prevention and monitoring.

[0003] The biological invasion process mainly includes five processes: introduction, colonization, latent, transmission, and spread. These five processes are affected by many factors, including: high propagule pressure and strong human activities. Shipping and aquaculture activities provide more opportunities for sea squirt invasion and spread, increasing the risk of marine invasive biological invasion; the interaction of local species, especially biological resistance. The more local species there are, the higher the primary productivity, the more stable the ecosystem, and the stronger the ability to resist the invasion of alien species; the suitability of the environment, especially the climate compatibility between the source area of ​​invasive organisms and the research area, may be an important factor in determining whether the invasion can be successful. However, there is still a lack of comprehensive and scientific evaluation technology that can integrate these three factors. Summary of the invention

[0004] In order to solve the above problems, the purpose of the present invention is to provide a prediction technology for the invasion risk of marine invasive organisms, which aims to evaluate the invasion risk of invasive organisms in advance by comprehensively evaluating these factors, and provide scientific guidance for related industries such as marine fisheries and aquaculture, so as to reduce the economic losses and safety hazards caused by biological invasions.

[0005] In order to achieve the above technical objectives, the present application provides a method for predicting the risk of marine biological invasion, comprising the following steps:

[0006] Based on the marine environmental factor data layer and the geographical distribution data of invasive organisms, the marine environmental suitability value is obtained according to the maximum entropy principle;

[0007] Based on the marine environment suitability values, data layers are constructed according to shipping density data, fishery catch data, primary productivity data and species number data;

[0008] Based on the data layer, establish a comprehensive assessment indicator system for intrusion risk;

[0009] Based on the comprehensive assessment index system of intrusion risk, the CRITIC weighting method is used to determine the weight of each index in the system;

[0010] According to the determined weights, an invasion risk function is constructed;

[0011] Based on the invasion risk function and comprehensive indicators, the risk of marine biological invasion is predicted.

[0012] Preferably, in the process of obtaining the marine environment suitability value, seawater temperature, salinity, pH, dissolved oxygen concentration and phosphate concentration are used as marine environmental factors.

[0013] Preferably, in the process of constructing the comprehensive invasion risk assessment index system, the comprehensive invasion risk assessment index system is constructed based on environmental factor indicators, human factor indicators and biological factor indicators, wherein:

[0014] The environmental factor index is the marine environment suitability value;

[0015] Human factor indicators should include: shipping density, fishing intensity;

[0016] Indicators of biological factors should include: level of primary productivity, number of species;

[0017] Environmental factor indicators and human factor indicators are positive indicators;

[0018] The biological factor index is a negative indicator.

[0019] Preferably, in the process of obtaining the weight of each indicator, an evaluation matrix is ​​established based on the intrusion risk comprehensive evaluation indicator system and the data to be evaluated;

[0020] Standardize the evaluation matrix;

[0021] According to the standardized evaluation matrix, the Spearman correlation coefficient is calculated for each evaluation index of the comprehensive invasion risk evaluation index system to obtain the conflict of indicators, and the weight of each indicator is obtained by calculating the variability of the indicators.

[0022] Preferably, in the process of obtaining the conflict of indicators, the conflict of indicators is expressed as:

[0023]

[0024] In the formula, r kj represents the Spearman correlation coefficient between the k-th index and the j-th index value of the standardized matrix, and k = 1, ..., m, j = 1, ..., m; f j It indicates the conflict of indicators, and m indicates the number of evaluation indicators.

[0025] Preferably, in the process of obtaining the variability of the indicator, the variability of the indicator is expressed as:

[0026]

[0027] In the formula, represents the sample mean of the j-th index of the standardized matrix, S j represents the variability of the indicator, n represents the number of samples to be evaluated, and x′ ij is the index value of the jth item of the i-th sample in the standardized matrix, and i=1,…,n,j=1,…,m.

[0028] Preferably, in the process of obtaining the weight of each indicator, the weight of each indicator is expressed as:

[0029]

[0030] Where W j Represents the objective weight of the j-th indicator.

[0031] Preferably, in the process of constructing the intrusion risk function, the intrusion risk function is expressed as:

[0032]

[0033] In the formula, Invindex represents the intrusion risk function, x′ j Represents the value of the index of the j-th item in the standardized matrix.

[0034] The present invention discloses a prediction system for marine biological invasion risk, which is used to implement the above-mentioned prediction method for marine biological invasion risk, including:

[0035] A data processing module is used to obtain the marine environment suitability value based on the marine environmental factor data layer and the invasive biological geographical distribution data according to the maximum entropy principle;

[0036] The data layer construction module is used to construct the data layer based on the marine environment suitability value, shipping density data, fishery catch data, primary productivity data and species number data;

[0037] The evaluation index system construction module is used to establish a comprehensive evaluation index system for intrusion risk based on the data layer;

[0038] The weight determination module is used to determine the weight of each indicator of the system based on the comprehensive assessment indicator system of intrusion risk by using the CRITIC weighting method;

[0039] A risk function building module, used to build an intrusion risk function according to the determined weights;

[0040] The prediction module is used to predict the risk of marine biological invasion based on the invasion risk function and comprehensive indicators.

[0041] The present invention discloses the following technical effects:

[0042] The present invention determines a set of scientific and comprehensive marine biological invasion risk assessment methods based on the main influencing factors in the biological invasion process. It can easily produce invasion risk assessment maps for different organisms in different regions, which is easy to promote and use. It breaks through the technical bottleneck of comprehensively judging the invasion risk of marine invasive organisms based on multiple factors, and provides a decision-making basis for aquaculture departments, shipping departments and environmental protection departments to prevent biological invasions and carry out preventive monitoring activities, thereby improving the ability of relevant departments to detect and prevent biological invasions, reduce economic losses and prevent safety accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 A schematic diagram of the process steps of an embodiment of the present invention;

[0045] Figure 2 This is a numerical schematic diagram of the environmental suitability of the scleractinian in the global sea area according to an embodiment of the present invention;

[0046] Figure 3 A distribution map of the risk values ​​of the invasion of the scleractinia in global waters according to an embodiment of the present invention;

[0047] Figure 4 This is a distribution map of medium and high invasion risk areas of Styracantha in global waters according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0049] The present invention provides an assessment method for predicting the risk of marine biological invasion, comprising the following steps:

[0050] S1. Based on the marine environmental factor data layer and the geographical distribution data of invasive organisms, a maximum entropy model is constructed based on the maximum entropy principle, and the marine environmental suitability is calculated through the maximum entropy model;

[0051] S2. Collect data on shipping density, fishery catch, primary productivity, and species number, and establish data layers based on the data;

[0052] S3. Establish a comprehensive assessment indicator system for invasion risk;

[0053] S4. Use the CRITIC weighting method to determine the weight of each indicator in the intrusion risk comprehensive assessment index system;

[0054] S5. constructing an intrusion risk function according to the determined weights;

[0055] S6. Based on the intrusion risk function, various indicators are comprehensively considered to determine the intrusion risk and display it visually.

[0056] In step S1, the geographical distribution data of marine invasive organisms are collected, a database of invasive organism distribution points is established, and the data is cleaned and de-redundant; an environmental variable data layer is created, and the maximum entropy model constructed based on the maximum entropy principle is used to calculate the marine environmental suitability value.

[0057] In step S1, the environmental factors include seawater temperature, salinity, pH, dissolved oxygen concentration, and phosphate concentration.

[0058] In step S3, a comprehensive invasion risk assessment index system is established based on environmental factor indicators, human factor indicators, and biological factor indicators;

[0059] The environmental factor index should be the suitability value established by integrating various marine environmental factors using the maximum entropy model in step S1.

[0060] Human factor indicators should include: shipping density, fishing intensity;

[0061] Indicators of biological factors should include: level of primary productivity, number of species;

[0062] Environmental factor indicators and human factor indicators are positive indicators, while biological factor indicators are negative indicators.

[0063] Step S4 includes the following sub-steps:

[0064] Step S4.1: Based on the intrusion risk comprehensive assessment index system and the data to be assessed, an assessment matrix X = (x ij ) n×m , where n is the number of samples to be evaluated, m is the number of evaluation indicators, and x ij is the j-th index value of the i-th sample, and i=1,…,n,j=1,…,m.

[0065] Step S4.2: For the evaluation matrix X=(x ij ) n×m Perform standardization to form a standardized matrix X′=(x′ ij ) n×m ; Different formulas are used for different evaluation indicators:

[0066] Positive indicators:

[0067] Negative indicators:

[0068] Step S4.3: Using the standardized processing results, calculate the Spearman correlation coefficient r for each evaluation index of the intrusion risk comprehensive evaluation index system. kj ,r kj is the Spearman correlation coefficient between the kth index and the jth index value of the standardized matrix, and k = 1, ..., m, j = 1, ..., m. According to the correlation coefficient, the index conflict f is obtained. j , f j is the indicator conflict of the j-th indicator;

[0069] Conflict formula:

[0070] Step S4.4: Calculate the variability S of each evaluation indicator of the comprehensive invasion risk evaluation index system j :

[0071]

[0072] In the formula, Represents the sample mean of the j-th index of the standardized matrix.

[0073] Step S4.5: According to the evaluation index variability S j Conflict with the standard j , and calculate the objective weight W of each evaluation index of the intrusion risk comprehensive evaluation index system j , represents the objective weight of the jth indicator:

[0074] In step S5, the intrusion risk index Invindex is calculated by the following formula:

[0075]

[0076] In step S6, based on the invasion risk index of each sea area, by comparing with the sea area where the invasive organisms actually exist, the invasion risk level classification is determined, and the geographic information mapping software ArcGIS is used to produce a marine invasive organism invasion risk distribution map to further determine the invasion risk of the marine invasive organisms.

[0077] Example: Figure 1-4 As shown, this embodiment provides an assessment method for predicting the risk of marine biological invasion. This embodiment takes the invasive organism Ascidianus truncatus as an example. Figure 1 As shown, the following steps are included

[0078] S1. Based on the marine environmental factor data layer and the geographical distribution data of invasive organisms, a maximum entropy model is constructed based on the maximum entropy principle, and the marine environmental suitability is calculated through the maximum entropy model;

[0079] Collect geographical distribution information of stalked tunicates, search the Global Biodiversity Information Facility (GBIF) and the Ocean Biogeographic Information System (OBIS) database, consult the full-text database of journals, the (VIP) Chinese Science and Technology Journal Database and other literature databases, and establish a global marine stalked tunicate distribution point database.

[0080] We obtained eight environmental variable data layers of four environmental factors, including seawater temperature, salinity, dissolved oxygen content, and phosphate concentration, and used ENMtools to remove redundancy from the global distribution point data of Scidia stygophora in combination with the resolution of the environmental data layer. There are a total of 1,230 global distribution points of Scidia stygophora, and after redundancy removal, there are a total of 644 actual application points.

[0081] The maximum entropy model of the global ocean distribution of sclerotinia was constructed using the maximum entropy principle. The R package Kuenm was used to optimize the maximum entropy model parameters to obtain the optimal model parameters. The optimal parameters were distribution type: Linear, Product, and regularization multiplier: 1.9. The model was used to calculate the environmental suitability values ​​of sclerotinia worldwide. The environmental suitability values ​​of sclerotinia worldwide are as follows: Figure 2 shown.

[0082] S2. Collect data on shipping density, fishery catch, primary productivity, and species number, and establish data layers based on the data;

[0083] Retrieve the Global Maritime Traffic Density Service (GMTDS) to obtain the shipping density data layer, retrieve the FAO sustainable fishing percentage data in different sea areas to produce the fishery catch data layer, retrieve the Global Marine Environment Datasets (GMED) to obtain primary productivity data, and retrieve the marine species number data to draw the species data layer.

[0084] S3. Establish a comprehensive assessment index system for invasion risk as shown in Table 1;

[0085] Table 1

[0086]

[0087] S4. Use the CRITIC weighting method to determine the weight of each indicator in the intrusion risk comprehensive assessment index system;

[0088] The CRITIC weighting method is used to determine the weights of various indicators in the intrusion risk assessment system mainly through the following sub-steps:

[0089] Step S4.1: Based on the intrusion risk comprehensive assessment index system and the data to be assessed, establish an assessment matrix X = (x ij ) n×m , where n is the number of samples to be evaluated, m is the number of evaluation indicators, and x′ ij is the jth index value of the i-th sample, and i=1,…,n, j=1,…,m. The established evaluation matrix is ​​shown in Table 2;

[0090] Table 2

[0091]

[0092] Step S4.2: For the evaluation matrix X=(x ij ) n×m The matrix X′=(x′ ij )n×m ; Different formulas are used for different evaluation indicators:

[0093] Positive indicators:

[0094] Negative indicators:

[0095] The standardized results are shown in Table 3;

[0096] Table 3

[0097]

[0098] Step S4.3: Using the standardized processing results, the Spearman correlation coefficient r is calculated for each evaluation index of the intrusion risk comprehensive evaluation index system. kj ,r kj is the Spearman correlation coefficient between the kth indicator and the jth indicator, and k = 1, ..., m, j = 1, ..., m. According to the correlation coefficient, the indicator conflict f is obtained. j , f j is the indicator conflict of the j-th indicator;

[0099] Conflict formula:

[0100] The correlation coefficients of the intrusion risk assessment indicators are shown in Table 4;

[0101] Table 4

[0102] Data Types Number of species Fishing rate Environmental suitability Shipping density Primary productivity Number of species 1.000 0.090 0.053 -0.012 0.011 Fishing rate 0.090 1.000 -0.037 0.011 -0.097 Environmental suitability 0.053 -0.037 1.000 0.069 -0.330 Shipping density -0.012 0.011 0.069 1.000 -0.100 Primary productivity 0.011 -0.097 -0.330 -0.100 1.000

[0103] The conflict of indicators is shown in Table 5;

[0104] Table 5

[0105] Number of species Fishing rate Environmental suitability Shipping density Primary productivity Conflict 4.772 4.713 4.391 4.985 4.362

[0106] Step S4.4: Calculate the variability S of each evaluation indicator of the comprehensive invasion risk evaluation index system j ;

[0107]

[0108] In the formula, represents the sample mean of the j-th index of the standardized matrix;

[0109] The variability of each indicator is shown in Table 6;

[0110] Table 6

[0111] Number of species Fishing rate Environmental suitability Shipping density Primary productivity Variability 0.1229 0.2507 0.1498 0.184 0.0375

[0112] Step S4.5: According to the evaluation index variability S j Conflict with the standard j , calculate the objective weight W of each evaluation index of the intrusion risk comprehensive evaluation index system j , represents the objective weight of the jth indicator;

[0113]

[0114] The weights of each indicator are shown in Table 7;

[0115] Table 7

[0116] Number of species Fishing rate Environmental suitability Shipping density Primary productivity Weight 16％ 33％ 20％ 26％ 5％

[0117] According to step S5, the intrusion risk index Invindex is calculated by the following formula:

[0118]

[0119] The invasion risk index of each region is shown in Table 8;

[0120] Table 8

[0121]

[0122] According to step S6, based on the invasion risk function, the invasion risk is determined by integrating various indicators, and the invasion risk distribution map of the stylated tunicate is produced using the geographic information mapping software ArcGIS, such as Figure 3 As shown;

[0123] According to the real distribution of Sclerotium in the Yellow Sea and Bohai Sea, the invasion risk at this location is ≥0.65, so the reclassification tool of ArcGIS, a geographic information mapping software, is used to divide the suitable habitat into: low invasion risk area <0.65, 0.65≤medium invasion risk area≤0.8, 0.8<high invasion risk area. Figure 4 shown.

[0124] The maximum entropy model (Maxent) is a species distribution model based on the maximum entropy theory. The maximum entropy principle is a criterion for selecting the probability distribution of random variables that best conforms to the objective situation. It has been widely used in the fields of statistical thermodynamics and machine learning in information science; it estimates the probability distribution based on the concept of entropy. In statistics, it is used to describe the distribution of possibilities in data.

[0125] Objective weighting is a method used to determine the importance of variables or features in data analysis. It does not rely on subjective judgment or expert opinion, but calculates weights based on the characteristics of the data itself. The Critic method is a specific objective weighting method that determines the weight of each variable by analyzing the correlation and difference between variables.

[0126] The method of the present invention is applied, based on the environmental factor data layer and the geographical distribution data of the stalked tunicate, combined with human factors and biological factors, and the objective weighting method CRITIC is used to comprehensively evaluate the invasion risk of the stalked tunicate in different regions. In the example, the final result and the geographical distribution of the tunicate also verify its accuracy; the method of the present invention can divide different levels of attention for preventing biological invasions. Once a biological invasion is large-scale, it is difficult to solve and consumes a lot of manpower and material resources. Through the present invention, corresponding monitoring and prevention measures are taken in advance in medium and high-risk areas, and the biological invasion can be responded to in advance before it occurs or in the early stage of occurrence, reducing the prevention cost of biological invasion, and preventing economic losses and safety hazards after the biological invasion occurs. This also shows that the method of the present invention has a high application value.

[0127] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0128] In the description of the present invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0129] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for predicting the risk of marine biological invasion, characterized in that: The following steps are involved: Based on the marine environmental factor data layer and the geographical distribution data of invasive organisms, the marine environmental suitability value is obtained according to the maximum entropy principle; Based on the marine environment suitability values, a data layer is constructed according to shipping density data, fishery catch data, primary productivity data and species number data; Based on the data layer, a comprehensive intrusion risk assessment indicator system is established; Based on the comprehensive intrusion risk assessment index system, the CRITIC weighting method is used to determine the weight of each index in the system; According to the determined weights, an invasion risk function is constructed; Based on the invasion risk function, various indicators are combined to predict the risk of marine biological invasion.

2. A method for predicting the risk of marine biological invasion according to claim 1, characterized in that: In the process of obtaining the marine environment suitability value, seawater temperature, salinity, pH, dissolved oxygen concentration and phosphate concentration are used as marine environmental factors.

3. A method for predicting the risk of marine biological invasion according to claim 2, characterized in that: In the process of constructing the comprehensive assessment index system of invasion risk, the comprehensive assessment index system of invasion risk is constructed according to environmental factor index, human factor index and biological factor index, wherein: The environmental factor index is the marine environment suitability value; The human factor indicators should include: shipping density, fishing intensity; The biological factor indicators should include: primary productivity level, number of species; The environmental factor index and the human factor index are positive indicators; The biological factor index is a negative indicator.

4. A method for predicting the risk of marine biological invasion according to claim 3, characterized in that: In the process of obtaining the weight of each indicator, an evaluation matrix is ​​established based on the comprehensive evaluation indicator system of invasion risk and the data to be evaluated; Standardizing the evaluation matrix; According to the standardized evaluation matrix, the Spearman correlation coefficient is calculated for each evaluation index of the intrusion risk comprehensive evaluation index system in pairs to obtain the conflict of the index, and the weight of each index is obtained by calculating the variability of the index.

5. A method for predicting the risk of marine biological invasion according to claim 4, characterized in that: In the process of obtaining the conflict of indicators, the conflict of indicators is expressed as: In the formula, r kj represents the Spearman correlation coefficient between the k-th index and the j-th index value of the standardized matrix, and k = 1, ..., m, j = 1, ..., m; f j It indicates the conflict of indicators, and m indicates the number of evaluation indicators.

6. A method for predicting the risk of marine biological invasion according to claim 5, characterized in that: In the process of obtaining the variability of an indicator, the variability of the indicator is expressed as: In the formula, represents the sample mean of the j-th index of the standardized matrix, S j represents the variability of the indicator, n represents the number of samples to be evaluated, and x′ ij is the index value of the jth item of the i-th sample in the standardized matrix, and i=1,…,n,j=1,…,m.

7. A method for predicting the risk of marine biological invasion according to claim 6, characterized in that: In the process of obtaining the weight of each indicator, the weight of each indicator is expressed as: Where W j Represents the objective weight of the j-th indicator.

8. A method for predicting the risk of marine biological invasion according to claim 7, characterized in that: In the process of constructing the intrusion risk function, the intrusion risk function is expressed as: In the formula, Invindex represents the intrusion risk function, x′ j Represents the value of the index of the j-th item in the standardized matrix.

9. A prediction system for marine biological invasion risk, used to implement a prediction method for marine biological invasion risk as claimed in any one of claims 1 to 8, characterized in that: include: A data processing module is used to obtain the marine environment suitability value based on the marine environmental factor data layer and the invasive biological geographical distribution data according to the maximum entropy principle; A data layer construction module is used to construct a data layer based on the marine environment suitability value, shipping density data, fishery catch data, primary productivity data and species number data; An evaluation index system building module is used to establish an intrusion risk comprehensive evaluation index system based on the data layer; A weight determination module, used to determine the weight of each indicator of the system by using the CRITIC weighting method based on the intrusion risk comprehensive assessment indicator system; A risk function building module, used to build an intrusion risk function according to the determined weights; The prediction module is used to predict the risk of marine biological invasion based on the invasion risk function and comprehensive indicators.

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