Fishery sea fishing resource evaluation method and device, electronic equipment and storage medium

Abstract

The invention discloses a fishery sea fishing resource assessment method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining multi-dimensional data information of a to-be-assessed target sea area; determining a plurality of evaluation factors in the data information of each dimension; constructing a hierarchical structure model of fishery and sea fishing resource evaluation based on the multi-dimensional data information and the corresponding evaluation factors; based on the hierarchical structure model, determining the data information of each dimension and the index weight of the corresponding evaluation factor through an analytic hierarchy process; constructing a fuzzy relation matrix through fuzzy mapping representation based on the evaluation factor in combination with a preset comment set; performing fuzzy synthesis operation according to the index weight and the fuzzy relation matrix to obtain a fuzzy comprehensive evaluation result; and determining a final evaluation result of the target sea area based on the fuzzy comprehensive evaluation result. The method can accurately realize fishery sea fishing resource evaluation, and can be widely applied to the technical field of fishery resource evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of fishery resource assessment, and particularly to a method, device, electronic device and storage medium for assessing fishery sea fishing resources. Background Art

[0002] At present, the recreational fishery in coastal areas has reached a certain scale. As a recreational fishery activity, sea fishing has developed rapidly in recent years. Accurately assessing the amount of fishery sea fishing resources is crucial for reasonably planning the sea fishing industry and protecting marine fishery resources. Traditional methods for assessing the amount of sea fishing resources have many limitations. They often rely only on single data or simple on-site visits and surveys, and it is difficult to comprehensively and accurately reflect the actual situation of sea fishing resources in the sea area. For example, relying solely on the experience of fishermen or simple sampling surveys cannot take into account the complex changes in the marine environment and the resource differences in different regions. Summary of the Invention

[0003] The present invention aims to solve the problems of related technical limitations to at least a certain extent. For this purpose, the present invention provides a method, device, electronic device and storage medium for assessing fishery sea fishing resources, which can accurately assess fishery sea fishing resources.

[0004] On the one hand, an embodiment of the present invention provides a method for assessing fishery sea fishing resources, including the following steps: Obtain multi-dimensional data of the target sea area to be evaluated; Determine multiple evaluation factors for the data in each dimension; Construct a hierarchical structure model for assessing fishery sea fishing resources based on the multi-dimensional data and their corresponding evaluation factors; Based on the hierarchical structure model, determine the index weights of the data in each dimension and their corresponding evaluation factors through the analytic hierarchy process; Based on the evaluation factors and in combination with a preset comment set, construct a fuzzy relation matrix through fuzzy mapping representation; Perform fuzzy synthesis operation according to the index weights and the fuzzy relation matrix to obtain a fuzzy comprehensive evaluation result; determine the final evaluation result of the target sea area based on the fuzzy comprehensive evaluation result.

[0005] Optionally, constructing a hierarchical structure model for assessing fishery sea fishing resources based on the multi-dimensional data and their corresponding evaluation factors includes the following steps: Take the assessment of fishery sea fishing resources as the target layer; Take the data in each dimension as the criterion layer; Take the analysis indicators corresponding to the data in each dimension as the index layer; Associate the evaluation factors with the corresponding analysis indicators in the indicator layer, and then construct a hierarchical structure model based on the target layer, criterion layer, indicator layer, and their corresponding evaluation factors.

[0006] Optionally, the hierarchical structure model includes a criterion layer and an indicator layer; the criterion layer is determined based on the data of each dimension, the indicator layer is determined based on the analysis indicators corresponding to the data of each dimension, and the evaluation factors are associated with the analysis indicators in the indicator layer; based on the hierarchical structure model, the index weights of the data of each dimension and their corresponding evaluation factors are determined by the analytic hierarchy process, including the following steps: Based on the preset expert scoring, determine the relative importance among the data of each dimension in the criterion layer and the relative importance among the corresponding evaluation factors in the indicator layer; Among them, the expert scoring is determined in advance based on the expert scoring method; Construct the judgment matrices corresponding to the criterion layer and the evaluation factors according to the relative importance; Based on the judgment matrices, determine the index weights of the data of each dimension and their corresponding evaluation factors.

[0007] Optionally, determining the index weights of the data of each dimension and their corresponding evaluation factors based on the judgment matrices includes the following steps: Based on the judgment matrices, process by the eigenvalue method to obtain the maximum eigenvalue and its corresponding eigenvector for each factor; the factors include the data of each dimension and the evaluation factors; Normalize the eigenvector to obtain the weight vector of the corresponding factor; Based on the weight vector, conduct a consistency test in combination with the maximum eigenvalue; according to the result of the consistency test, use the hierarchical recursive weighting law to obtain the weights of the lower-level indicators relative to their corresponding higher-level indicators in the hierarchical structure model.

[0008] Optionally, constructing a fuzzy relation matrix by means of fuzzy mapping representation based on the evaluation factors in combination with the preset comment set includes the following steps: Determine the evaluation factor set according to the evaluation factors; the evaluation factors in the evaluation factor set correspond to each evaluation factor; the comment set includes multiple preset comment levels; Based on the preset index value range and the measured index numbers corresponding to each level of evaluation factors, conduct statistical analysis through the trapezoidal fuzzy membership function to obtain the membership degree of each evaluation factor to each comment level, and then construct a fuzzy relation matrix; Among them, the index value range includes the ideal index value and the lowest index value; the trapezoidal fuzzy membership function includes the semi-ascending trapezoidal fuzzy membership function and the semi-descending trapezoidal fuzzy membership function.

[0009] Optionally, the comment set includes a preset plurality of comment levels; performing a fuzzy synthesis operation according to the index weights and the fuzzy relation matrix to obtain a fuzzy comprehensive evaluation result, including the following steps: Based on the index weights and the fuzzy relation matrix, perform a fuzzy synthesis operation using a weighted average type synthesis operator to obtain a fuzzy comprehensive evaluation result; Among them, the expression of the fuzzy comprehensive evaluation result is: ; In the formula, represents the fuzzy comprehensive evaluation result; represents the index weight; represents the fuzzy relation matrix, , represents the number of indexes, represents the number of comment level types; , , .

[0010] Optionally, determining the final evaluation result of the target sea area based on the fuzzy comprehensive evaluation result, including the following steps: Perform a normalization process on the fuzzy comprehensive evaluation result to obtain the final evaluation result of the target sea area.

[0011] On the other hand, an embodiment of the present invention provides a fishery sea fishing resource evaluation device, including: The first module is used to obtain multi-dimensional data of the target sea area to be evaluated; The second module is used to determine a plurality of evaluation factors in the data of each dimension; The third module is used to construct a hierarchical structure model for fishery sea fishing resource evaluation based on the multi-dimensional data and its corresponding evaluation factors; The fourth module is used to determine the index weights of the data in each dimension and its corresponding evaluation factors based on the hierarchical structure model through the analytic hierarchy process; The fifth module is used to construct a fuzzy relation matrix based on the evaluation factors in combination with a preset comment set through fuzzy mapping representation; The sixth module is used to perform a fuzzy synthesis operation according to the index weights and the fuzzy relation matrix to obtain a fuzzy comprehensive evaluation result; determine the final evaluation result of the target sea area based on the fuzzy comprehensive evaluation result.

[0012] On the other hand, an embodiment of the present invention provides an electronic device, including: a processor and a memory; the memory is used to store a program; the processor executes the program to implement the above-mentioned fishery sea fishing resource evaluation method.

[0013] On the other hand, an embodiment of the present invention provides a computer storage medium, which stores a program executable by a processor. The program executable by the processor is used to implement the above-mentioned fishery sea fishing resource assessment method when executed by the processor.

[0014] In an embodiment of the present invention, multi-dimensional data of a target sea area to be evaluated is obtained; a plurality of evaluation factors are determined for the data in each dimension; a hierarchical structure model for fishery sea fishing resource assessment is constructed based on the multi-dimensional data and its corresponding evaluation factors; based on the hierarchical structure model, the index weights of the data in each dimension and its corresponding evaluation factors are determined by the analytic hierarchy process; a fuzzy relation matrix is constructed by means of fuzzy mapping representation based on the evaluation factors and a preset comment set; a fuzzy synthesis operation is performed according to the index weights and the fuzzy relation matrix to obtain a fuzzy comprehensive evaluation result; and a final evaluation result of the target sea area is determined based on the fuzzy comprehensive evaluation result. The embodiment of the present invention provides a high-precision and comprehensive method for evaluating fishery sea fishing resources, solves the problems of single data and poor accuracy in the existing evaluation methods, makes full use of multi-source data, and provides a scientific basis for the development of the recreational fishery sea fishing industry and the protection of fishery resources through model operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The drawings are used to provide a further understanding of the technical solutions of the present invention, and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the technical solutions of the present invention, and do not constitute a limitation to the technical solutions of the present invention.

[0016] Figure 1 is a schematic diagram of an implementation environment for the fishery sea fishing resource assessment method provided by an embodiment of the present invention; Figure 2 is a schematic flowchart of a fishery sea fishing resource assessment method provided by an embodiment of the present invention; Figure 3 is an expanded flowchart of step S300 provided by an embodiment of the present invention; Figure 4 is an expanded flowchart of step S400 provided by an embodiment of the present invention; Figure 5 is an expanded flowchart of step S403 provided by an embodiment of the present invention; Figure 6 is an expanded flowchart of step S500 provided by an embodiment of the present invention; Figure 7 is an expanded flowchart of step S600 provided by an embodiment of the present invention; Figure 8 is a schematic diagram of an overall process example of the fishery sea fishing resource assessment method provided by an embodiment of the present invention; Figure 9Schematic diagram of a fishery sea fishing resource evaluation device provided by an embodiment of the present invention; Figure 10 Schematic diagram of an electronic device provided by an embodiment of the present invention. Detailed implementation manners

[0017] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

[0018] It should be noted that although functional module division is performed in the system schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order from the module division in the system or the flowchart in the flowchart. Terms such as "first / S100" and "second / S200" in the specification, claims and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence.

[0019] The mention of "embodiment" in this article means that the specific features, structures or characteristics described in connection with the embodiment can be included in at least one embodiment of the present invention. The phrase appears in various positions 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. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0020] It can be understood that the fishery sea fishing resource evaluation method provided by the embodiment of the present invention can be applied to any computer device with data processing and computing capabilities, and this computer device can be various types of terminals or servers. When the computer device in the embodiment is a server, the server is an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network, content distribution network), and big data and artificial intelligence platforms. Optionally, the terminal is a smart phone, a tablet computer, a notebook computer, a desktop computer, etc., but it is not limited thereto.

[0021] For the convenience of understanding the technical solution of the present invention, first, the technical feature proper nouns that may appear in the embodiments of the present invention are explained: Such as Figure 1 shown, is a schematic diagram of an implementation environment provided by an embodiment of the present invention. Refer to Figure 1, the implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be network-connected by wireless or wired means to complete data transmission and exchange.

[0022] The server 101 can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0023] In addition, the server 101 can also be a node server in a blockchain network. Among them, the blockchain is a new application mode of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithms.

[0024] The terminal 102 can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited thereto. The terminal 102 and the server 101 can be directly or indirectly connected by wired or wireless communication means, and the embodiments of the present invention do not limit this here.

[0025] Exemplarily based on Figure 1 the shown implementation environment, the embodiments of the present invention provide a method for evaluating fishery sea fishing resources. Taking the application of this method for evaluating fishery sea fishing resources in the server 101 as an example for illustration, it can be understood that this method for evaluating fishery sea fishing resources can also be applied to the terminal 102.

[0026] Referring to Figure 2 , Figure 2 is a flowchart of the method for evaluating fishery sea fishing resources applied to the server provided by the embodiments of the present invention. The execution subject of this method for evaluating fishery sea fishing resources can be any of the foregoing computer devices (including the server or the terminal). Referring to Figure 2 , the method includes the following steps: S100. Obtain multi-dimensional data of the target sea area to be evaluated; Among them, the multi-dimensional data can include nautical chart data, marine sediment survey, marine satellite remote sensing, hydrological data, location distribution of POIs of recreational fishery elements, etc.; Exemplarily, in some specific implementation manners, the acquisition of multi-dimensional data can be achieved as follows: Nautical chart data: Collect nautical charts covering detailed information such as islands, marine ranches, underwater reefs, offshore platforms, shipwrecks, etc. These special geographical elements affect the habitats, reproduction, and migration paths of fish.

[0027] Marine regional geological survey: Obtain seabed topography and sediment data. Different substrate types and terrain conditions provide different living environments for marine organisms.

[0028] Hydrological data: Collect hydrological data such as water temperature, ocean current, salinity, and tide. These factors have important impacts on the growth, distribution, and behavior of fish.

[0029] Seabed disaster data: Analyze the distribution and potential impact areas of disaster factors such as seabed landslides and turbidity currents. Seabed disasters may change the marine ecological environment and thus affect the sea fishing resources.

[0030] Marine satellite remote sensing environmental information: Extract information such as sea water temperature, chlorophyll concentration, dissolved oxygen, and sea surface height for monitoring the dynamic changes of the marine ecological environment and reflecting the survival status of marine organisms.

[0031] Distribution of POIs of recreational fishery elements: Collect location information such as sea fishing spots, fishing ports, aquaculture farms, and offshore activity operations through technical means such as web data scraping, and analyze the impact of human fishery activities on sea fishing resources.

[0032] S200. Determine multiple evaluation factors for the data in each dimension; Exemplarily, in some specific embodiments, the multi-source data collected can be analyzed to preliminarily screen out the evaluation factors closely related to the sea fishing resource quantity. Specifically, it includes: selecting factors such as the area of marine ranch, seabed reef density, the number of shipwrecks and offshore platforms from the chart data; selecting factors such as terrain undulation, proportion of sediment types, and organic matter content of sediments from the marine substrate survey data; selecting factors such as average water temperature, average flow velocity, and salinity change rate from the hydrological characteristic data; selecting factors such as the proportion of disaster-affected area from the seabed disaster factors; selecting factors such as the average chlorophyll concentration from the marine satellite remote sensing environmental information; selecting factors such as sea fishing spot density and fishing port influence range from the distribution of POIs of recreational fishery elements.

[0033] S300. Construct a hierarchical structure model for the evaluation of fishery sea fishing resources based on the multi-dimensional data and their corresponding evaluation factors; It should be noted that in some embodiments, as Figure 3 shown, step S300 may include the following steps: S301. Take the evaluation of fishery sea fishing resources as the target layer; S302. Take the data in each dimension as the criterion layer; S303. Take the analysis indicators corresponding to the data in each dimension as the index layer; S304. Associate the evaluation factors with the corresponding analysis indicators in the index layer, and then construct a hierarchical structure model according to the target layer, criterion layer, index layer, and their corresponding evaluation factors.

[0034] Exemplarily, in some specific embodiments, as shown in Table 1 below, an evaluation index system is designed by constructing a hierarchical structure model. The evaluation target is divided into an objective layer (evaluation of fishery sea fishing resources), a criterion layer (constraint information related to fish habitat growth and human activities such as chart data, marine sediment survey, hydrological characteristics, submarine disaster factors, marine satellite remote sensing environmental information, and distribution of POIs of recreational fishery elements), and an index layer (index layer and specific evaluation factors). Through this hierarchical structure, the mutual relationship between various factors is clearly shown, facilitating subsequent analysis and calculation.

[0035] Table 1

[0036] S400. Based on the hierarchical structure model, determine the index weights of the data materials in each dimension and their corresponding evaluation factors through the analytic hierarchy process; It should be noted that the hierarchical structure model includes a criterion layer and an index layer; the criterion layer is determined based on the data materials in each dimension, the index layer is determined based on the analysis indexes corresponding to the data materials in each dimension, and the evaluation factors are associated with the analysis indexes in the index layer; in some embodiments, as Figure 4 shown, step S400 may include the following steps: S401. Determine the relative importance between the data materials in each dimension in the criterion layer and the relative importance between the corresponding evaluation factors in the index layer based on a preset expert score; wherein, the expert score is determined in advance based on the expert scoring method; S402. Construct a judgment matrix corresponding to the criterion layer and the evaluation factors according to the relative importance; S403. Determine the index weights of the data materials in each dimension and their corresponding evaluation factors based on the judgment matrix.

[0037] Exemplarily, in some specific embodiments, construct a judgment matrix: for the criterion layer and the index layer, compare the relative importance of each factor pairwise through the expert scoring method. For example, as shown in Table 2 below, a 1-9 level comparison scale method can be used to measure the relative importance of each factor and construct a judgment matrix. For example, for the criterion layer, compare the importance of chart data and marine sediment survey data for the evaluation of sea fishing resources, and so on, to construct the judgment matrix where represents the importance degree of the i-th factor relative to the j-th factor, and satisfies .

[0038] Table 2

[0039] wherein, in some embodiments, as Figure 5As shown, determining the data materials of each dimension and the index weights of their corresponding evaluation factors based on the judgment matrix may include the following steps: S4031. Based on the judgment matrix, obtain the maximum eigenvalue and its corresponding eigenvector for each factor through eigenvalue method processing; the factors include the data materials of each dimension and the evaluation factors; S4032. Normalize the eigenvector to obtain the weight vector of the corresponding factor; S4033. Based on the weight vector, perform consistency test in combination with the maximum eigenvalue; according to the result of the consistency test, use the hierarchical recursive weighting law to obtain the weight of the lower-level index relative to its corresponding higher-level index in the hierarchical structure model.

[0040] Exemplarily, in some specific embodiments, as shown in Table 3 below, calculate the weight vector: use the eigenvalue method to determine the maximum eigenvalue of the judgment matrix and the corresponding eigenvector W， whose eigenvalue and eigenvector satisfy the equation W, where is the eigenvalue of matrix A, and W is the eigenvector of matrix A corresponding to the eigenvalue , and solve the eigenvalue from the characteristic equation | I - A| = 0, where I is the n-order identity matrix The characteristic equation | I - A| = |A| = 0, obtaining n eigenvalues 1 , 1 , 3 ..., n . Substitute = max into the equation ( max I - A) X = 0 to obtain the fundamental solution system, and after normalization, obtain the eigenvector W of max ; normalize the eigenvector W to obtain the weight vector of each factor = , ), where . The weight vector reflects the relative importance of each factor in the evaluation, and the normalized = ( , ,..., ). Calculation of single-level weight vector , is the weight of the factor and is the number of factors

[0041] Consistency check: Calculate the maximum eigenvalue of the matrix , the consistency index , and find the corresponding average random consistency index RI (determined according to the matrix order ). Calculate the consistency ratio , when CR < 0.1, it is considered that the consistency of the judgment matrix is acceptable; otherwise, the judgment matrix needs to be adjusted until the consistency requirement is met. Using the hierarchical recursive weighting law, obtain the weights of the lower-level indicators relative to the higher-level indicators

[0042] For the index layer, calculate the comprehensive hierarchical weight vector and conduct a consistency check. According to the formula obtain the weights of each element in the index layer

[0043] Table 3

[0044] S500. Based on the evaluation factors and in combination with the preset comment set, construct a fuzzy relation matrix through fuzzy mapping representation It should be noted that in some embodiments, as Figure 6 shown, step S500 may include the following steps: S501. Determine the evaluation factor set according to the evaluation factors; the evaluation factors in the evaluation factor set correspond to each evaluation factor; the comment set includes multiple preset comment levels; S502. Based on the preset index value range and in combination with the measured index values corresponding to each level of evaluation factors, conduct statistical analysis through the trapezoidal fuzzy membership function to obtain the membership degree of each evaluation factor to each comment level, and then construct a fuzzy relation matrix; wherein, the index value range includes the ideal index value and the lowest index value; the trapezoidal fuzzy membership function includes the semi-ascending trapezoidal fuzzy membership function and the semi-descending trapezoidal fuzzy membership function

[0045] Exemplarily, in some specific embodiments, determine the evaluation factor set = , ), where are each of the above-mentioned evaluation factors in the index layer. Determine the comment set = , ), and determine the comment level to which the sea fishing resource quantity level belongs according to the interval division of the statistical data value, and set it as ={"First-level fishing ground (abundant resources)", "Second-level fishing ground (relatively abundant resources)", "Third-level fishing ground (average resources)", "Fourth-level fishing ground (scarce resources)"}。

[0046] By statistically analyzing the monitoring indicators at all levels, each evaluation factor is determined For each comment Membership degree For example, for the evaluation factor "marine ranch area", by analyzing the statistical data of the abundance of sea fishing resources corresponding to different area scales, its membership degree to each comment is determined.

[0047] When the evaluation factor set And the comment set Are determined, the fuzzy relationship between the evaluation influencing factors and the evaluation criteria is represented through a fuzzy mapping. The fuzzy relationship matrix is set as (R), and the secondary fuzzy matrices are set as (R 1 ), (R 2 ), (R 3 ). The expression is , , , ; For the positive and reverse nature of the evaluation indicators on the evaluation results, different methods are used to calculate the membership degree of evaluation. For positive indicators, the semi-rising trapezoidal fuzzy membership function is selected:

[0048] For reverse indicators, the semi-falling trapezoidal fuzzy membership function is selected:

[0049] Represents the actual value of the indicator, Represents the membership degree of the indicator, that is, the standardized value of the indicator in the evaluation system; among them, for positive indicators: Represents the ideal value of the indicator, Represents the lowest value of the indicator; for reverse indicators Represents the ideal value of the indicator, Represents the lowest value of the indicator.

[0050] S600. Perform a fuzzy synthesis operation based on the indicator weights and the fuzzy relationship matrix to obtain the fuzzy comprehensive evaluation result; determine the final evaluation result of the target sea area based on the fuzzy comprehensive evaluation result.

[0051] It should be noted that the comment set includes multiple preset comment levels; in some embodiments, as Figure 7 Shown, step S600 may include the following steps: Specifically, according to the index weights and the fuzzy relation matrix, a fuzzy composition operation is performed to obtain the fuzzy comprehensive evaluation result, which may include the following steps: S601. Based on the index weights and the fuzzy relation matrix, a weighted average type composition operator is used to perform a fuzzy composition operation to obtain the fuzzy comprehensive evaluation result; Among them, the expression of the fuzzy comprehensive evaluation result is: ; In the formula, represents the fuzzy comprehensive evaluation result; represents the index weight; represents the fuzzy relation matrix, , represents the number of indexes, represents the number of comment levels; , , .

[0052] Exemplarily, in some specific embodiments, the fuzzy composition operation is performed: the determined weight vector and the fuzzy relation matrix can be used to perform the fuzzy composition operation to obtain the fuzzy comprehensive evaluation result vector , and its fuzzy relation matrix , ,( represents the number of indexes, represents the number of evaluation levels, represents the membership degree of the factor to the fuzzy subset ( , and it is normalized so that the sum of the matrix is 1), (here, a weighted average type composition operator is adopted to more comprehensively consider the influence of each factor). According to the sizes of the elements in, determine the comment level to which the sea fishing resource amount belongs, and complete the evaluation of the sea fishing resource amount. represents the final decision set, which contains the result of the fuzzy comprehensive evaluation; represents the fuzzy weight vector (i.e., the index weight); represents the fuzzy comprehensive evaluation matrix.

[0053] Specifically, based on the fuzzy comprehensive evaluation result, determining the final evaluation result of the target sea area may include the following steps: S602. Perform a normalization process on the fuzzy comprehensive evaluation result to obtain the final evaluation result of the target sea area.

[0054] Exemplarily, in some specific embodiments, the evaluation result of the first-level index can be used as the factor set to realize the evaluation of each second-level index, and Standardize the data to obtain the final evaluation result.

[0055] To explain the principle of the technical solution of the present invention in detail, the overall process of the present invention will be described below in conjunction with some specific embodiments. It is easy to understand that the following is an explanation of the technical principle of the present invention and should not be regarded as a limitation of the present invention.

[0056] First of all, it should be noted that multi-source data such as the spatial distribution of nautical chart data, marine sediment surveys, marine satellite remote sensing, and the location distribution of fishery element POIs contain rich marine ecological and fishery resource information. Extracting marine geographical environment information that affects the habitat, foraging, and migration of fish, effectively integrating and utilizing it, and analyzing it using scientific mathematical models will greatly improve the accuracy and reliability of the assessment of sea fishing resource quantity, provide a basis for the location selection and delimitation of sea fishing grounds, and provide strong support for the sustainable development of the recreational fishery industry.

[0057] In some specific application scenarios, such as Figure 8 shown, the specific implementation manner of the embodiment of the present invention can be achieved through the following process: S1. Data collection: Taking a well-known sea fishing area as an example, use professional nautical chart mapping software to obtain high-resolution nautical chart data, conduct marine sediment surveys through equipment such as multi-beam echosounders and seabed samplers, obtain hydrological characteristic data in real time from marine monitoring stations, collect marine satellite remote sensing environmental information with the help of a satellite data receiving system, and obtain fishery element POI distribution data using geographic information system tools and on-site investigations.

[0058] S2. Preliminary selection of evaluation factors: Organize fishery experts and marine scholars to deeply analyze the collected data, and initially screen out 26 evaluation factors closely related to the sea fishing resource quantity according to experience and relevant research results, covering various data sources.

[0059] S3. Construction of the hierarchical structure model: Construct a hierarchical structure model according to the structure of the target layer, criterion layer, and index layer. Take the evaluation of fishery sea fishing resource quantity as the target layer, six data source categories as the criterion layer, and 20 evaluation factors as the index layer, and clarify the logical relationship between each layer.

[0060] S4. Determine the index weights of each data based on the analytic hierarchy process: Invite 10 senior experts to score the factors in the criterion layer and index layer pairwise to construct a judgment matrix. Taking the judgment matrix of the criterion layer as an example, the maximum eigenvalue is obtained through calculation, and then the weight vector and the consistency ratio CR are calculated. After multiple adjustments and tests, ensure that the CR of all judgment matrices is less than 0.1 to obtain the final weights of each factor.

[0061] S5. Fuzzy comprehensive evaluation model operation: Determine the evaluation factor set and the comment set . Through expert scoring and statistical analysis of historical data, construct a fuzzy relation matrix . Utilize the determined weight vector and the fuzzy relation matrix to perform fuzzy composition operation to obtain the fuzzy comprehensive evaluation result vector . According to the element sizes in, judge that the sea fishing resource quantity in this sea area belongs to the "second-level fishing ground (relatively rich in resources)" level. At the same time, conduct a sensitivity analysis on the evaluation results to study the influence of the weight changes of each factor on the evaluation results, and further verify the reliability of the evaluation method

[0062] In summary, the purpose of the present invention is to provide a high-precision and comprehensive evaluation method for fishery sea fishing resource quantity, solve the problems of single data and poor accuracy in the existing evaluation methods, make full use of multi-source data, and provide a scientific basis for the development of the recreational fishery sea fishing industry and fishery resource protection through model operation

[0063] Compared with the prior art, the present invention has at least the following beneficial effects: ① The present invention proposes a recreational fishery sea fishing resource evaluation method in view of the deficiencies of the existing evaluation methods. By integrating multi-source data, various factors affecting the sea fishing resource quantity are comprehensively considered, including the marine geographical environment, ecological environment, human activities, etc., making the evaluation results more comprehensive and accurate. ② Use the analytic hierarchy process to determine the weights of each factor, which reasonably reflects the relative importance of different factors to the sea fishing resource quantity; adopt the fuzzy comprehensive evaluation model to handle the fuzziness and uncertainty in the evaluation process, improving the scientificity and reliability of the evaluation. ③ The evaluation method provided by the present invention has strong operability and practicability, can provide a scientific decision-making basis for fishery management departments and sea fishing industry practitioners, and promote the sustainable development of the sea fishing industry

[0064] On the other hand, as Figure 9 shown, the embodiment of the present invention provides a fishery sea fishing resource evaluation device 900, which may include: The first module 901 is used to obtain multi-dimensional data materials of the target sea area to be evaluated The second module 902 is used to determine multiple evaluation factors in the data materials of each dimension The third module 903 is used to construct a hierarchical structure model for fishery sea fishing resource evaluation based on the multi-dimensional data materials and their corresponding evaluation factors The fourth module 904 is used to determine the index weights of the data materials of each dimension and their corresponding evaluation factors based on the hierarchical structure model through the analytic hierarchy process The fifth module 905 is used to construct a fuzzy relation matrix through fuzzy mapping representation based on evaluation factors in combination with a preset comment set. The sixth module 906 is used to perform fuzzy composition operations according to the index weights and the fuzzy relation matrix to obtain a fuzzy comprehensive evaluation result; and determine the final evaluation result of the target sea area based on the fuzzy comprehensive evaluation result.

[0065] The content of the method embodiment of the present invention is applicable to the device embodiment. The functions specifically implemented by the device embodiment are the same as those of the above method embodiment, and the beneficial effects achieved are also the same as those of the above method.

[0066] On the other hand, the embodiment of the present invention also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the above-mentioned fishery sea fishing resource evaluation method. The electronic device can be any intelligent terminal including a tablet computer, a vehicle-mounted computer, etc.

[0067] It can be understood that the content in the above method embodiment is applicable to the device embodiment of the present invention. The functions specifically implemented by the device embodiment are the same as those of the above method embodiment, and the beneficial effects achieved are also the same as those of the above method embodiment.

[0068] As Figure 10 shown, Figure 10 schematically shows the hardware structure of an electronic device 1000 in another embodiment. The electronic device 1000 includes: A processor 1001, which can be implemented in ways such as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided by the embodiments of the present invention; A memory 1002, which can be implemented in forms such as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1002 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1002 and are called by the processor 1001 to execute the network node population optimization method of the embodiments of the present invention; An input / output interface 1003, which is used to implement information input and output; A communication interface 1004, which is used to implement communication and interaction between this device and other devices. Communication can be achieved through a wired method (such as USB, network cable, etc.) or through a wireless method (such as mobile network, WIFI, Bluetooth, etc.); A bus 1005, which transmits information between various components of the device (such as a processor 1001, a memory 1002, an input / output interface 1003, and a communication interface 1004); Among them, the processor 1001, the memory 1002, the input / output interface 1003, and the communication interface 1004 achieve communication connections with each other inside the device through the bus 1005.

[0069] The electronic device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0070] The content of the method embodiments of the present invention is applicable to the electronic device embodiments of the present invention. The functions specifically implemented by the electronic device embodiments of the present invention are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those of the above method.

[0071] Another aspect of the embodiments of the present invention also provides a computer-readable storage medium. The storage medium stores a program, and the program is executed by a processor to implement the previous method.

[0072] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the above two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, in which computer-readable program code is carried. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, and this computer-readable medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted by any suitable medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0073] The content of the method embodiments of the present invention is applicable to the embodiments of this computer-readable storage medium. The functions specifically implemented by the embodiments of this computer-readable storage medium are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those of the above method.

[0074] The embodiments of the present invention also disclose a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method described above.

[0075] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that, in some alternative implementations, the functions noted in the blocks may occur in a different order than that noted in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and combinations of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0076] It should be noted that although several modules of devices for action execution are mentioned in the above detailed description, such a division is not mandatory. In fact, according to the embodiments of the present invention, the features and functions of two or more of the above-described modules or units can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0077] From the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software, or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present invention.

[0078] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order mentioned in the operation diagrams. For example, depending on the functions / operations involved, two consecutive blocks shown may actually be executed substantially simultaneously or the blocks can sometimes be executed in the reverse order. In addition, the embodiments presented and described in the flowcharts of the present invention are provided by way of example for the purpose of providing a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logical flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of a larger operation are executed independently.

[0079] In addition, although the present invention has been described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the functions and / or features may be integrated in a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It should also be understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. Rather, considering the attributes, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the modules will be understood within the ordinary skills of an engineer. Thus, those skilled in the art can implement the present invention as set forth in the claims without undue experimentation. It should also be understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0080] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0081] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution device, apparatus, or equipment (such as a computer-based device, a device including a processor, or other devices that can fetch and execute instructions from the instruction execution device, apparatus, or equipment), or in combination with these instruction execution devices, apparatus, or equipment. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in combination with an instruction execution device, apparatus, or equipment.

[0082] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which a program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing it as appropriate, and then storing it in a computer memory.

[0083] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution device. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well-known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0084] In the description of this specification, the description with reference to terms such as "an embodiment", "some embodiments", "example", "specific example", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0085] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the claims and their equivalents.

[0086] The above has specifically described the preferred embodiments of the present invention, but the present invention is not limited to the embodiments. Those skilled in the art can also make various equivalent deformations or substitutions without departing from the spirit of the present invention, and these equivalent deformations or substitutions are all included within the scope defined by the claims of the present invention.

Claims

1. A method for assessing marine fishing resources, characterized in that: The following steps are involved: Obtain multi-dimensional data on the target sea area to be assessed; Determining a plurality of evaluation factors in the data of each dimension; A hierarchical model for evaluating fishery resources is constructed based on the multi-dimensional data and the corresponding evaluation factors; Based on the hierarchical structure model, determining the data of each dimension and the indicator weight of the corresponding evaluation factor through hierarchical analysis method; Based on the evaluation factors combined with a preset comment set, a fuzzy relationship matrix is ​​constructed through fuzzy mapping representation; Perform fuzzy synthesis operation according to the indicator weights and the fuzzy relationship matrix to obtain a fuzzy comprehensive evaluation result; The final evaluation result of the target sea area is determined based on the fuzzy comprehensive evaluation result.

2. The method for assessing sea fishing resources according to claim 1, characterized in that: The hierarchical model for evaluating fishery and sea fishing resources is constructed based on the multi-dimensional data and the corresponding evaluation factors, including the following steps: Taking the fishery sea fishing resource assessment as the target layer; The data of each dimension is used as a criterion layer; The indicators to be analyzed corresponding to the data in each dimension are used as the indicator layer; The evaluation factor is associated with the corresponding indicator to be analyzed in the indicator layer, and then the hierarchical structure model is constructed according to the target layer, the criterion layer, the indicator layer and the corresponding evaluation factor.

3. The method for assessing sea fishing resources according to claim 1, characterized in that: The hierarchical structure model includes a criterion layer and an indicator layer; the criterion layer is determined based on the data of each dimension, the indicator layer is determined based on the indicators to be analyzed corresponding to the data of each dimension, and the evaluation factor is associated with the indicators to be analyzed in the indicator layer; the indicator weights of the data of each dimension and the corresponding evaluation factors are determined by the hierarchical analysis method based on the hierarchical structure model, including the following steps: Determine the relative importance of the data of each dimension in the criterion layer and the relative importance of each evaluation factor corresponding to the indicator layer based on the preset expert ratings; Wherein, the expert rating is predetermined based on the expert scoring method; Constructing a judgment matrix corresponding to the criterion layer and the evaluation factors according to the relative importance; The data of each dimension and the indicator weight of the corresponding evaluation factor are determined based on the judgment matrix.

4. The method for assessing sea fishing resources according to claim 3, characterized in that: The step of determining the data of each dimension and the corresponding indicator weight of the evaluation factor based on the judgment matrix includes the following steps: Based on the judgment matrix, the maximum eigenvalue corresponding to each factor and its corresponding eigenvector are obtained by eigenroot method; the factors include the data and the evaluation factors of each dimension; Normalizing the feature vector to obtain a weight vector of the corresponding factor; Based on the weight vector, a consistency check is performed in combination with the maximum eigenvalue; according to the result of the consistency check, the weight of the lower-level index in the hierarchical structure model relative to its corresponding higher-level index is obtained using the hierarchical weighting law.

5. The method for assessing marine fishing resources according to claim 1, characterized in that: The step of constructing a fuzzy relationship matrix based on the evaluation factors combined with a preset comment set through fuzzy mapping representation includes the following steps: Determining an evaluation factor set according to the evaluation factors; the evaluation factors in the evaluation factor set correspond to each of the evaluation factors; the comment set includes a plurality of preset comment levels; Based on the preset indicator value range and the actual measured number of indicators corresponding to the evaluation factors at each level, a statistical analysis is performed through a trapezoidal fuzzy membership function to obtain the membership of each evaluation factor to each comment level, and then the fuzzy relationship matrix is ​​constructed; Among them, the indicator value range includes the ideal value of the indicator and the minimum value of the indicator; the trapezoidal fuzzy membership function includes a half-ascending trapezoidal fuzzy membership function and a half-descending trapezoidal fuzzy membership function.

6. The method for assessing sea fishing resources according to claim 1, characterized in that: The comment set includes a plurality of preset comment levels; the fuzzy synthesis operation is performed according to the indicator weight and the fuzzy relationship matrix to obtain a fuzzy comprehensive evaluation result, including the following steps: Based on the index weights and the fuzzy relationship matrix, a weighted average synthesis operator is used to perform fuzzy synthesis operation to obtain a fuzzy comprehensive evaluation result; Among them, the expression of fuzzy comprehensive evaluation result is: ； In the formula, Indicates the fuzzy comprehensive evaluation result; Indicates the indicator weight; represents the fuzzy relationship matrix, , Indicates the number of indicators, Indicates the number of comment levels; , , .

7. The method for assessing marine fishing resources according to claim 1, characterized in that: Determining the final evaluation result of the target sea area based on the fuzzy comprehensive evaluation result comprises the following steps: The fuzzy comprehensive evaluation result is standardized to obtain the final evaluation result of the target sea area.

8. A fishery sea fishing resource assessment device, characterized in that: include: The first module is used to obtain multi-dimensional data of the target sea area to be evaluated; A second module is used to determine a plurality of evaluation factors in the data of each dimension; The third module is used to construct a hierarchical model for evaluating fishery resources based on the multi-dimensional data and the corresponding evaluation factors; The fourth module is used to determine the data of each dimension and the index weight of the corresponding evaluation factor through the hierarchical analysis method based on the hierarchical structure model; A fifth module is used to construct a fuzzy relationship matrix based on the evaluation factors combined with a preset comment set through fuzzy mapping representation; The sixth module is used to perform fuzzy synthesis operation according to the indicator weight and the fuzzy relationship matrix to obtain a fuzzy comprehensive evaluation result; The final evaluation result of the target sea area is determined based on the fuzzy comprehensive evaluation result.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1 to 7.

10. A computer storage medium storing a program executable by a processor, characterized in that: The program executable by the processor is used to implement the method according to any one of claims 1 to 7 when executed by the processor.

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