Method and system for predicting illegal fishing risk in Yangtze River basin

Abstract

The invention relates to a Yangtze River basin illegal fishing risk prediction method and system, and the method comprises the steps: taking an illegal fishing activity in the Yangtze River basin as a top event based on a fault tree analysis method, decomposing the causes of the illegal fishing event, and determining the weight of each cause through an analytic hierarchy process based on a fault tree; performing cause prediction on illegal fishing in combination with the fault tree and an analytic hierarchy process result; the system is used for implementing the method. Compared with the prior art, the method has the advantages that the fault tree and the analytic hierarchy process are used for comprehensively and systematically analyzing illegal fishing from multiple levels, and the accuracy of illegal fishing prediction is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of risk prediction, and in particular to a method and system for predicting and preventing illegal fishing risks in the Yangtze River Basin. Background Art

[0002] As China's mother river, the Yangtze River Basin is one of China's most important waters. However, the Yangtze River Basin has faced serious illegal fishing problems in recent years, which has not only caused great damage to aquatic biological resources, but also threatened the ecological balance and sustainable development of the Yangtze River in my country. In recent years, the Yangtze River Basin has faced serious illegal fishing problems, which has not only caused serious damage to aquatic biological resources and the ecological diversity of the Yangtze River, but also posed a serious threat to the ecological balance and sustainable development of the basin, causing great damage to my country's fishery resources. Therefore, it is of great significance to conduct risk assessment of illegal fishing in the Yangtze River Basin. However, traditional illegal fishing monitoring and assessment methods usually rely on single indicators or empirical assessments, and cannot comprehensively and systematically analyze the complex interactive relationship of risk factors, which has a serious impact on the accuracy of predicting illegal fishing risks in the Yangtze River Basin.

[0003] Therefore, how to conduct a comprehensive and systematic analysis of risk factors to improve prediction accuracy is a problem that needs to be solved. Summary of the invention

[0004] The purpose of the present invention is to overcome the defect of the above-mentioned prior art that the prediction accuracy is low due to reliance on a single indicator and empirical evaluation, and to provide a method for predicting and preventing illegal fishing risks in the Yangtze River Basin.

[0005] Fault tree analysis has several significant advantages in risk assessment and system safety analysis, such as being intuitive and easy to understand, and being able to combine qualitative and quantitative analysis. It is widely used in the field of risk assessment and is an effective system risk analysis tool. The application of hierarchical analysis method in risk assessment can effectively evaluate and analyze the risk level of decision-making plans, help identify and reduce risks, and thus improve the robustness and reliability of decisions. Therefore, the present invention combines fault tree and hierarchical analysis method to predict illegal fishing incidents in the Yangtze River Basin.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] According to a first aspect of the present invention, a method for predicting the risk of illegal fishing in the Yangtze River Basin is provided. The method takes illegal fishing activities in the Yangtze River Basin as the top event based on the fault tree analysis method, decomposes the causes of illegal fishing incidents and determines the weight of each cause based on the fault tree using the hierarchical analysis method; and predicts the causes of illegal fishing by combining the fault tree and the hierarchical analysis results.

[0008] As a preferred technical solution, the prediction of the causes of illegal fishing risks includes the following steps:

[0009] Taking illegal fishing as the target event, obtaining an illegal fishing cause event set based on historical illegal fishing events, establishing a fault tree model based on the illegal fishing cause event set, wherein the illegal fishing cause event set is composed of a plurality of intermediate events, and the intermediate events include a plurality of basic events; calculating the structural importance of basic events based on the fault tree model;

[0010] Constructing a hierarchical analysis model according to the fault tree and determining the factors of the criterion layer and the indicator layer in the hierarchical analysis model, and constructing a judgment matrix; the factors of the indicator layer are the basic events of the fault tree model; classifying and analyzing the factors of the indicator layer to obtain the factors of the criterion layer, wherein one criterion layer factor includes at least one matching indicator layer factor;

[0011] The judgment matrix is ​​tested for compliance, and the weight values ​​of each criterion layer and indicator layer factor are calculated based on the judgment matrix that meets the standards, and the causes of illegal fishing are predicted based on the weight values.

[0012] As an optimal technical solution, the method for calculating the structural importance of basic events is: determining the minimum cut set of the fault tree based on the fault tree model; calculating the structural importance of all basic events based on the relevant valid information of the minimum cut set; the relevant valid information includes the number of minimum cut sets of the fault tree, the number of minimum cut sets containing the basic event being calculated, and the number of basic events in the minimum cut set containing the basic event being calculated.

[0013] As a preferred technical solution, the calculation method used to calculate the structural importance of the basic event is an approximate algorithm, and the structural importance is expressed in the form of a fraction.

[0014] As a preferred technical solution, the judgment matrix includes a criterion layer factor judgment matrix and an indicator layer factor judgment matrix; wherein, constructing the criterion layer factor judgment matrix includes the following steps:

[0015] The structural importance of the index layer factors is obtained through the structural importance of the basic events, and the lowest common multiple of the denominator of the structural importance is extracted;

[0016] Multiplying the least common multiple by the structural importance of each indicator layer factor to obtain the judgment factor of each indicator layer factor, that is, the first judgment factor;

[0017] Add the first judgment factors of the indicator layer factors belonging to the same criterion layer factor to obtain the second judgment factor, that is, the judgment factor of the criterion layer factor, and repeat this step until all the second judgment factors are obtained;

[0018] The criterion layer judgment matrix A is calculated based on the second judgment factor, and the expression is:

[0019]

[0020] Among them, a ij is the relative importance value of criterion level factor i relative to criterion level factor j, M i is the second judgment factor of criterion layer factor i; M j is the second judgment factor of criterion layer factor j; n is the number of criterion layer factors;

[0021] Constructing the indicator-level factor judgment matrix of criterion-level factor i includes the following steps:

[0022] Calculate the judgment matrix B based on the first judgment factor of each indicator layer factor in the criterion layer factor i i , the expression is:

[0023]

[0024] Among them, b pq is the relative importance value of the indicator layer factor p relative to the indicator layer factor q in the criterion layer factor i, X p is the first judgment factor of the index layer factor p; X p is the first judgment factor of the indicator layer factor q; m is the number of indicator layer factors.

[0025] As a preferred technical solution, the compliance inspection includes the following steps:

[0026] The consistency index is calculated based on the maximum eigenvalue of the judgment matrix and the order of the judgment matrix;

[0027] The consistency value is calculated based on the consistency index and the random consistency index. If the consistency value is less than the specified value, the judgment matrix meets the requirements, otherwise the judgment matrix does not meet the requirements.

[0028] As a preferred technical solution, the calculation of the weight values ​​of each criterion layer and indicator layer factor based on the judgment matrix of reaching the standard includes the following steps:

[0029] Obtain the criterion layer factor judgment matrix, and use the hierarchical analysis method to calculate the weight value of each criterion layer factor based on the criterion layer factor judgment matrix. The calculation formula is:

[0030]

[0031] Among them, W i is the weight value of factor i at the criterion level; a ij is the relative importance value of criterion level factor i relative to criterion level factor j;

[0032] Obtain all indicator layer factor judgment matrices, and use the hierarchical analysis method based on the indicator layer factor judgment matrix to obtain the relative weight value of each indicator layer factor, expressed as:

[0033]

[0034] Among them, V p Indicates the relative weight value of the standard layer factor p; b pq is the relative importance value of the index layer factor p relative to the index layer factor q;

[0035] The relative weight value of each indicator layer factor is multiplied by the weight value of the criterion layer factor to which it belongs to obtain the weight value of each indicator layer factor.

[0036] According to a second aspect of the present invention, there is provided a prediction system for illegal fishing risks in the Yangtze River Basin, the prediction system for illegal fishing risks in the Yangtze River Basin comprising:

[0037] Fault tree analysis module: used to obtain an illegal fishing cause event set, the illegal fishing cause event set is composed of multiple basic events, and calculate the structural importance of the basic events;

[0038] Hierarchical analysis module: used to receive basic event information and structural importance values ​​of the basic event information in the fault tree analysis module, and calculate weight values ​​based on the structural importance values;

[0039] Analysis module: sort the weight values, predict the causes of illegal fishing based on the sorting results and output the prediction results.

[0040] According to a third aspect of the present invention, there is provided an electronic device, comprising a memory and a processor, wherein a computer program is stored in the memory, and the method described above is implemented when the processor executes the program.

[0041] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, wherein the program implements the method described when executed by a processor.

[0042] Compared with the prior art, the present invention uses the fault tree analysis method to analyze the causes of illegal fishing as the top event, uses the obtained causes as evaluation indicators and quantifies their impact on illegal fishing with the importance of each evaluation indicator, builds a hierarchical analysis model based on the fault tree results, calculates the weight values ​​of factors at each level that cause illegal fishing, and makes an intuitive, comprehensive and systematic prediction of illegal fishing events based on the weight values. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1This is a flow chart of the method for predicting illegal fishing risks in the Yangtze River Basin of the present invention;

[0044] Figure 2 It is a fault tree structure diagram of illegal fishing incidents of the present invention;

[0045] Figure 3 This is the hierarchical analysis structure diagram of illegal fishing in the present invention. DETAILED DESCRIPTION

[0046] 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 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 should fall within the scope of protection of the present invention.

[0047] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent.

[0048] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0049] Embodiment 1:

[0050] This embodiment provides a method for predicting illegal fishing risks in the Yangtze River Basin. Figure 1 The detailed steps are as follows:

[0051] S1. Construct fault tree model:

[0052] S11. Taking illegal fishing in the Yangtze River Basin as the target event, the illegal fishing cause event set is obtained based on historical illegal fishing events. The cause event set includes 10 intermediate events as shown in Table 1:

[0053] Table 1

[0054] serial number Intermediate Events serial number Intermediate Events <![CDATA[M1]]> Inadequate supervision by relevant departments <![CDATA[M6]]> Joint law enforcement mechanism fails <![CDATA[M2]]> Individuals engaging in illegal fishing activities <![CDATA[M7]]> Lack of awareness of fishing ban <![CDATA[M3]]> Family members gather together to fish illegally <![CDATA[M8]]> Lack of independent judgment <![CDATA[M4]]> The geographical location is ideal for illegal fishing <![CDATA[M9]]> The waters are rich in aquatic resources <![CDATA[M5]]> Relevant departments lack awareness of banning fishing <![CDATA[M 10 ]]> Good fishing conditions in the waters

[0055] S12. Further analysis of the intermediate events was conducted to obtain the basic events that led to illegal fishing in the Yangtze River Basin, including 17 basic events as shown in Table 2;

[0056] Table 2

[0057]

[0058]

[0059] S13, based on the events in Table 1 and Table 2, the following Figure 2 The fault tree model shown, except for the green rectangle representing illegal fishing events, the remaining rectangles represent intermediate events, and the circles represent basic events;

[0060] S14. Calculate the importance of basic events:

[0061] S141. Determine the minimum cut set of the fault tree based on the fault tree model as shown in Table 3;

[0062] Table 3

[0063]

[0064]

[0065] S142. Calculate the structural importance of all basic events using an approximate algorithm based on relevant valid information of the minimum cut set. The calculation formula is as follows:

[0066]

[0067] Among them, I φ(i) is the structural importance of the ith basic event; K is the number of minimum cut sets of the fault tree; m is the number of minimum cut sets containing the ith basic event; n j is the total number of basic events in the jth minimum cut set where the i-th basic event is located;

[0068] According to S14 calculation, the structural importance of each basic event is shown in Table 4:

[0069] Table 4

[0070]

[0071]

[0072] S2. Constructing the hierarchical analysis model:

[0073] S21. According to the basic events in Table 2, the indicator layer factors in the hierarchical analysis model are established, and the indicator layer factors are classified and analyzed to obtain the criterion layer factors of the hierarchical analysis model, as shown in Table 5:

[0074] Table 5

[0075]

[0076]

[0077] S22, draw Figure 3 The AHP analysis model;

[0078] S23, construct the criterion layer factor judgment matrix and the indicator layer factor judgment matrix respectively:

[0079] Among them, constructing the criterion layer factor judgment matrix includes the following steps:

[0080] S231, obtain the structural importance of the indicator layer factors through the structural importance of the basic events, and extract the lowest common multiple of the denominator of the structural importance, 120;

[0081] S323. Multiply the least common multiple by the structural importance of each indicator layer factor to obtain the judgment factor of each indicator layer factor, that is, the first judgment factor. The judgment factors of each indicator layer factor are shown in Table 6:

[0082] Table 6

[0083] Criteria-level factors Judgment Factor Criteria Level Factors Judgment Factor <![CDATA[X1]]> 4 <![CDATA[X 10 ]]> 4 <![CDATA[X2]]> 4 <![CDATA[X 11 ]]> 12 <![CDATA[X3]]> 4 <![CDATA[X 12 ]]> 12 <![CDATA[X4]]> 6 <![CDATA[X 13 ]]> 12 <![CDATA[X5]]> 6 <![CDATA[X 14 ]]> 3 <![CDATA[X6]]> 4 <![CDATA[X 15 ]]> 3 <![CDATA[X7]]> 4 <![CDATA[X 16 ]]> 3 <![CDATA[X8]]> 4 <![CDATA[X 17 ]]> 3 <![CDATA[X9]]> 4

[0084] S324, add the first judgment factors of the indicator layer factors belonging to the same criterion layer factor to obtain the second judgment factor, that is, the judgment factor of the criterion layer factor, and repeat this step until all the second judgment factors are obtained, as shown in Table 7:

[0085] Table 7

[0086] Indicator level factors Judgment Factor Indicator level factors Judgment Factor <![CDATA[M1]]> 24 <![CDATA[M3]]> 36 <![CDATA[M2]]> 20 <![CDATA[M4]]> 12

[0087] S325. Calculate the criterion layer judgment matrix based on the second judgment factor, as shown in Table 8:

[0088] Table 8

[0089] Criteria Layer <![CDATA[M1]]> <![CDATA[M2]]> <![CDATA[M3]]> <![CDATA[M4]]> <![CDATA[M1]]> 1 6 / 5 2 / 3 2 <![CDATA[M2]]> 5 / 6 1 5 / 9 5 / 3 <![CDATA[M3]]> 3 / 2 9 / 5 1 3 <![CDATA[M4]]> 1 / 2 3 / 5 1 / 3 1

[0090] Constructing the indicator-level factor judgment matrix of criterion-level factor i includes the following steps:

[0091] S326, calculate the judgment matrix B based on the first judgment factor of each indicator layer factor in the criterion layer factor i i , where B1 is shown in Table 9:

[0092] Table 9

[0093] <![CDATA[Criterion layer M1]]> <![CDATA[X1]]> <![CDATA[X2]]> <![CDATA[X3]]> <![CDATA[X4]]> <![CDATA[X5]]> <![CDATA[X1]]> 1 1 1 2 / 3 2 / 3 <![CDATA[X2]]> 1 1 1 2 / 3 2 / 3 <![CDATA[X3]]> 1 1 1 2 / 3 2 / 3 <![CDATA[X4]]> 3 / 2 3 / 2 3 / 2 1 1 <![CDATA[X5]]> 3 / 2 3 / 2 3 / 2 1 1

[0094] B2 is shown in Table 10:

[0095] Table 10

[0096] <![CDATA[Criterion layer M2]]> <![CDATA[X6]]> <![CDATA[X7]]> <![CDATA[X8]]> <![CDATA[X9]]> <![CDATA[X 10 ]]> <![CDATA[X6]]> 1 1 1 1 1 <![CDATA[X7]]> 1 1 1 1 1 <![CDATA[X8]]> 1 1 1 1 1 <![CDATA[X9]]> 1 1 1 1 1 <![CDATA[X 10 ]]> 1 1 1 1 1

[0097] B3 is shown in Table 11:

[0098] Table 11

[0099]

[0100]

[0101] B4 is shown in Table 12:

[0102] Table 12

[0103] <![CDATA[Criterion layer M2]]> <![CDATA[X 14 ]]> <![CDATA[X 15 ]]> <![CDATA[X 16 ]]> <![CDATA[X 17 ]]> <![CDATA[X 14 ]]> 1 1 1 1 <![CDATA[X 15 ]]> 1 1 1 1 <![CDATA[X 16 ]]> 1 1 1 1 <![CDATA[X 17 ]]> 1 1 1 1

[0104] S3. Calculate the weight of each criterion layer and indicator layer factor, and predict illegal fishing based on the weight value:

[0105] S31, compliance inspection:

[0106] S311. Calculate the consistency index based on the maximum eigenvalue of the judgment matrix and the order of the judgment matrix. The calculation formula is as follows:

[0107] I C =(λ max -m) / (n-1);

[0108] R C =I C / I R ;

[0109] Among them, I C is the consistency index of the judgment matrix; max is the maximum eigenvalue of the constructed judgment matrix; n is the order of the judgment matrix; I R is the random consistency index of the judgment matrix.

[0110] S312. After calculation, it can be obtained that the consistency index value of the criterion layer judgment matrix is ​​0.0224; the consistency index value of the judgment matrix B1 does not meet the standard; the consistency index values ​​of the judgment matrices B2, B3 and B4 are all 0; therefore, the regulatory factor M1 and its corresponding indicator layer factors will not be used as prediction indicators.

[0111] S32. Weight calculation:

[0112] S321, obtaining a criterion layer factor judgment matrix, and using the analytic hierarchy process based on the criterion layer factor judgment matrix according to the formula: Calculate the weight value of each criterion layer factor, where W i is the weight value of factor i at the criterion level; a ijis the relative importance value of criterion layer factor i relative to criterion layer factor j, and the weight values ​​of each criterion layer factor are shown in the following table:

[0113] Table 13

[0114] Criteria Level Factors <![CDATA[M2]]> <![CDATA[M3]]> <![CDATA[M4]]> Weight value 0.217 0.391 0.130

[0115] S322, obtain all indicator layer factor judgment matrices, and use the analytic hierarchy process based on the indicator layer factor judgment matrix according to the formula Among them, V p Indicates the relative weight value of the standard layer factor p; b pq is the relative weight value of the indicator layer factor p relative to the indicator layer factor q, and the weight value of the indicator layer factor is calculated based on the relative weight value; the relative weight value and weight value of each element in B2 are shown in the following table:

[0116] Table 14

[0117] <![CDATA[B2]]> <![CDATA[X6]]> <![CDATA[X7]]> <![CDATA[X8]]> <![CDATA[X9]]> <![CDATA[X 10 ]]> Relative weight value 0.2 0.2 0.2 0.2 0.2 Weight value 0.0434 0.0434 0.0434 0.0434 0.0434

[0118] The relative weights of the various elements in B3 are shown in the following table:

[0119] Table 15

[0120] <![CDATA[B3]]> <![CDATA[X 11 ]]> <![CDATA[X 12 ]]> <![CDATA[X 13 ]]> Relative weight value 0.33 0.33 0.33 Weight value 0.129 0.129 0.129

[0121] The relative weights of the elements in B4 are shown in the following table:

[0122] Table 16

[0123] <![CDATA[B4]]> <![CDATA[X 14 ]]> <![CDATA[X 15 ]]> <![CDATA[X 16 ]]> <![CDATA[X 17 ]]> Relative weight value 0.25 0.25 0.25 0.25 Weight value 0.0325 0.0325 0.0325 0.0325

[0124] S33. The causes of illegal fishing incidents were predicted based on the weight values ​​of the criterion layer factors and the indicator layer factors, among which M3>M2>M1. It can be seen that the main factors causing illegal fishing incidents come from family environmental factors. Illegal fishing is chosen due to family survival difficulties, and livelihood needs, economic conditions and member structure account for the same proportion; the second is caused by personal factors, including the cultural level and legal concepts of the people involved in illegal fishing incidents, as well as the temptation of huge profits and the herd mentality from the outside world; the last is the natural environment factor.

[0125] Embodiment 2:

[0126] The above is an introduction to the method embodiment. The following provides a prediction system for illegal fishing risks in the Yangtze River Basin to further illustrate the scheme of the present invention.

[0127] The system includes:

[0128] Fault tree analysis module: used to obtain the set of illegal fishing cause events, which consists of multiple intermediate events, and the intermediate events include multiple basic events; and calculate the structural importance of the basic events;

[0129] Hierarchical analysis module: used to receive basic event information and structural importance values ​​of basic event information in the fault tree analysis module, and calculate weight values ​​based on the structural importance values;

[0130] Analysis module: Sort the weight values, predict the causes of illegal fishing based on the sorting results and output the predicted results.

[0131] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0132] Embodiment 3:

[0133] The electronic device of the present invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The CPU, ROM and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0134] Multiple components in the device are connected to the I / O interface, including: input units, such as keyboards, mice, etc.; output units, such as various types of displays, speakers, etc.; storage units, such as disks, optical disks, etc.; and communication units, such as network cards, modems, wireless communication transceivers, etc. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunication networks.

[0135] The processing unit performs the various methods and processes described above, such as methods S1 to S3. For example, in some embodiments, methods S1 to S3 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via a ROM and / or a communication unit. When the computer program is loaded into the RAM and executed by the CPU, one or more steps of methods S1 to S3 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute methods S1 to S3 in any other appropriate manner (e.g., by means of firmware).

[0136] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0137] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0138] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0139] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for predicting illegal fishing risks in the Yangtze River Basin, characterized in that: Based on the fault tree analysis method, this method takes illegal fishing activities in the Yangtze River Basin as the top event, decomposes the causes of illegal fishing incidents and determines the weight of each cause using the hierarchical analysis method based on the fault tree; the causes of illegal fishing are predicted by combining the fault tree and hierarchical analysis results.

2. The method for predicting illegal fishing risk in the Yangtze River Basin according to claim 1, characterized in that: The aforementioned prediction of the causes of illegal fishing risks includes the following steps: Taking illegal fishing as the target event, obtaining an illegal fishing cause event set based on historical illegal fishing events, establishing a fault tree model based on the illegal fishing cause event set, wherein the illegal fishing cause event set is composed of a plurality of intermediate events, and the intermediate events include a plurality of basic events; calculating the structural importance of basic events based on the fault tree model; Constructing a hierarchical analysis model according to the fault tree and determining the factors of the criterion layer and the indicator layer in the hierarchical analysis model, and constructing a judgment matrix; the factors of the indicator layer are the basic events of the fault tree model; classifying and analyzing the factors of the indicator layer to obtain the factors of the criterion layer, wherein one criterion layer factor includes at least one matching indicator layer factor; The judgment matrix is ​​tested for compliance, and the weight values ​​of each criterion layer and indicator layer factor are calculated based on the judgment matrix that meets the standards, and the causes of illegal fishing are predicted based on the weight values.

3. The method for predicting illegal fishing risk in the Yangtze River Basin according to claim 2, characterized in that: The method for calculating the structural importance of basic events is: determining the minimum cut set of the fault tree based on the fault tree model; calculating the structural importance of all basic events according to the relevant effective information of the minimum cut set; the relevant effective information includes the number of minimum cut sets of the fault tree, the number of minimum cut sets containing the basic event being calculated, and the number of basic events in the minimum cut set containing the basic event being calculated.

4. The method for predicting illegal fishing risk in the Yangtze River Basin according to claim 3, characterized in that: The calculation method used to calculate the structural importance of the basic event is an approximate algorithm, and the structural importance is expressed in the form of a fraction.

5. The method for predicting illegal fishing risk in the Yangtze River Basin according to claim 2, characterized in that: The judgment matrix includes a criterion layer factor judgment matrix and an indicator layer factor judgment matrix; Constructing the criterion layer factor judgment matrix includes the following steps: The structural importance of the index layer factors is obtained through the structural importance of the basic events, and the lowest common multiple of the denominator of the structural importance is extracted; Multiplying the least common multiple by the structural importance of each indicator layer factor to obtain the judgment factor of each indicator layer factor, that is, the first judgment factor; Add the first judgment factors of the indicator layer factors belonging to the same criterion layer factor to obtain the second judgment factor, that is, the judgment factor of the criterion layer factor, and repeat this step until all the second judgment factors are obtained; The criterion layer judgment matrix A is calculated based on the second judgment factor, and the expression is: Among them, a ij is the relative importance value of criterion level factor i relative to criterion level factor j, M i is the second judgment factor of criterion layer factor i; M j is the second judgment factor of criterion layer factor j; n is the number of criterion layer factors; Constructing the indicator-level factor judgment matrix of criterion-level factor i includes the following steps: Calculate the judgment matrix B based on the first judgment factor of each indicator layer factor in the criterion layer factor i i , the expression is: Among them, b pq is the relative importance value of the indicator layer factor p relative to the indicator layer factor q in the criterion layer factor i, X p is the first judgment factor of the index layer factor p; X p is the first judgment factor of the indicator layer factor q; m is the number of indicator layer factors.

6. The method for predicting illegal fishing risks in the Yangtze River Basin according to claim 2, characterized in that: The compliance inspection comprises the following steps: The consistency index is calculated based on the maximum eigenvalue of the judgment matrix and the order of the judgment matrix; The consistency value is calculated based on the consistency index and the random consistency index. If the consistency value is less than the specified value, the judgment matrix meets the requirements, otherwise the judgment matrix does not meet the requirements.

7. The method for predicting illegal fishing risks in the Yangtze River Basin according to claim 2, characterized in that: The calculation of the weight values ​​of each criterion layer and indicator layer factor based on the judgment matrix of reaching the standard includes the following steps: Obtain the criterion layer factor judgment matrix, and use the hierarchical analysis method to calculate the weight value of each criterion layer factor based on the criterion layer factor judgment matrix. The calculation formula is: Among them, W i is the weight value of factor i at the criterion level; a ij is the relative importance value of criterion level factor i relative to criterion level factor j; Obtain all indicator layer factor judgment matrices, and use the hierarchical analysis method based on the indicator layer factor judgment matrix to obtain the relative weight value of each indicator layer factor, expressed as: Among them, V p Indicates the relative weight value of the standard layer factor p; b pq is the relative importance value of the index layer factor p relative to the index layer factor q; The relative weight value of each indicator layer factor is multiplied by the weight value of the criterion layer factor to which it belongs to obtain the weight value of each indicator layer factor.

8. A prediction system for illegal fishing risks in the Yangtze River Basin, characterized in that: The prediction system for illegal fishing risks in the Yangtze River Basin includes: Fault tree analysis module: used to obtain an illegal fishing cause event set, wherein the illegal fishing cause event set is composed of multiple intermediate events, and the intermediate events include multiple basic events; and calculate the structural importance of the basic events; Hierarchical analysis module: used to receive basic event information and structural importance values ​​of the basic event information in the fault tree analysis module, and calculate weight values ​​based on the structural importance values; Analysis module: sort the weight values, predict the causes of illegal fishing based on the sorting results and output the prediction results.

9. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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