Multi-dimensional analysis-based low-efficiency sea use comprehensive evaluation method

Abstract

The invention discloses a low-efficiency sea use comprehensive evaluation method based on multi-dimensional analysis, and relates to the technical field of marine resource management and evaluation, and the method comprises the steps: collecting sea use benefit data, and processing the collected data through a superposition analysis method; performing percentage assignment on the processed sea utilization benefit data; constructing a multi-level index system through an analytic hierarchy process, and calculating a target data weight value through a Delphi method; calculating a dimension score according to the assignment and the weight value of the sea utilization benefit data; and calculating a comprehensive score according to the dimension score, and performing grading evaluation on the sea use project. According to the technical scheme, efficient and accurate low-efficiency sea use evaluation can be made for sea use projects, and the method has high application value in the aspects of enhancing sea area space management and control and promoting ocean resource conservation and utilization.

Description

Technical Field

[0001] The present invention relates to the field of marine resource management and assessment technology, and in particular to a comprehensive assessment method for low-efficiency sea use based on multi-dimensional analysis. Background Art

[0002] The ocean is the largest ecosystem on the earth's surface and an important space for human survival. With the development of social economy, the population in coastal areas has increased rapidly, marine development activities have become frequent, and people's demand for marine resources has continued to rise. While the ocean is being developed and utilized, idle waste and inefficient use of marine resources have also emerged. This situation not only wastes the vast sea space, but also makes it difficult to play the economic and social benefits and ecosystem service functions that the ocean should have. Therefore, it is particularly important to carry out conservation and utilization of existing marine space resources, revitalize stock assets and expand effective investment.

[0003] The existing sea use project evaluation system is often limited to a specific area or a specific type of sea use, and it is difficult to promote it on a large scale. In addition, most evaluation systems have a strong dependence on data, and the relevant data of sea use projects are often difficult to obtain directly, which indirectly affects the accuracy and reliability of the evaluation results. Therefore, based on this problem, this application proposes a comprehensive evaluation method for inefficient sea use based on multi-dimensional analysis. Summary of the invention

[0004] Technical Purpose In order to solve the above problems, the purpose of the present invention is to provide a comprehensive evaluation method for inefficient sea use based on multi-dimensional analysis. By establishing a set of scientific, comprehensive and operational inefficient sea use evaluation index system, efficient and accurate inefficient sea use evaluation can be made for sea use projects. This method has a wide range of application scenarios and is of great significance to promoting the conservation and utilization of marine resources, rationally allocating marine space resources, and improving the protection and utilization efficiency of marine space resources.

[0005] Technical Solution In order to achieve the above-mentioned objectives, the present invention provides a comprehensive evaluation method for inefficient sea use based on multi-dimensional analysis, which assigns percentages to sea use benefit data and constructs a multi-level indicator system through a hierarchical analysis method, calculates the weight value of target data through the Delphi method, calculates dimensional scores based on the assigned sea use benefit data and the weight value, and calculates a comprehensive score based on the dimensional scores, thereby making an efficient and accurate inefficient sea use evaluation of sea use projects.

[0006] In a first aspect, the present invention provides a comprehensive evaluation method for inefficient sea use based on multi-dimensional analysis, comprising: Collect data on sea use benefits and process the collected data through overlay analysis; Assign percentages to the processed sea use benefit data; A multi-level indicator system is constructed through the hierarchical analysis method and the weight value of target data is calculated through the Delphi method; Calculate the dimension scores based on the assigned values ​​and weight values ​​of the sea use benefit data; The comprehensive score is calculated based on the dimension scores and a graded evaluation is made for the sea use projects.

[0007] Furthermore, the sea use benefit data includes geographic information data and professional department data.

[0008] Furthermore, the geographic information data includes remote sensing image results, administrative boundary survey lines and mainland and island coastline survey data.

[0009] Furthermore, the professional department data include sea area use rights data, marine functional zoning data, ecological red line data, key bay and estuary area data and other professional department data.

[0010] Furthermore, the method of preprocessing the sea benefit data includes data extraction, spatial registration, vectorization, format conversion and coordinate unification.

[0011] Furthermore, the purpose of the overlay analysis method is to extract the type of allowed construction area of ​​another vector within the prohibited area of ​​one vector, that is, the suspected conflict area.

[0012] Furthermore, the overlay analysis method includes: analysis of the compliance of sea area use with functional zoning, analysis of the compliance of sea area use with ecological red lines, analysis of sea area use efficiency and analysis of sea area utilization efficiency.

[0013] Furthermore, during the overlay analysis, the areas with missing data are processed by interpolating the average values ​​of the neighboring areas.

[0014] Furthermore, during the overlay analysis, when conducting the coastline analysis, if the coastline is highly complex, the high tide line is used instead of the coastline as the evaluation criterion.

[0015] Furthermore, the sea use benefit survey also includes the actual survey stage.

[0016] Furthermore, the target data of the actual investigation stage includes basic project information, actual sea use conditions and project operating conditions.

[0017] Furthermore, the types of percentage assignment of sea use benefit data include economic benefit index assignment, social benefit index assignment, ecological benefit index assignment, development and utilization benefit index assignment, and management measure benefit index assignment. The raw data is converted into comparable scores through percentage assignment, so that sea use projects of different types and sizes can be compared under the same standard, ensuring the scientificity and rationality of the evaluation index system.

[0018] Furthermore, the indicators for assigning the economic benefit index include per-mu output value and investment intensity.

[0019] Furthermore, the indicators for assigning the social benefit index include project nature and employment density.

[0020] Furthermore, the indicators for assigning the ecological benefit index include the degree of change in sea area attributes and the sensitivity of the project sea area.

[0021] Furthermore, the indicators for assigning the development and utilization benefit index include land reclamation status, construction status, operation status, sea area utilization rate and sea area utilization rate.

[0022] Furthermore, the indicators for assigning the effectiveness indicators of the management measures include compliance with functional zoning, compliance with industrial standards and compliance with ecological red lines.

[0023] Furthermore, in the process of assigning percentages to sea use benefit data, the indicators with missing data were estimated by adopting the average values ​​of similar projects.

[0024] Furthermore, in the process of assigning percentages to sea use benefit data, projects spanning multiple functional zones are weighted according to their areas to calculate the project's functional zoning compliance score.

[0025] Furthermore, in the AHP, different levels use different weight coefficients and standards; The calculation formula for calculating the target data weight value by the Delphi method is as follows:

[0026] in, Indicates The weight of each indicator or dimension; Indicates Expert For Scoring of indicators or dimensions; Represents the total number of experts.

[0027] The weights determined by the Delphi method can be adjusted according to actual conditions, so that the evaluation system can adapt to different regions and different types of sea use projects.

[0028] Furthermore, the calculation formula of the dimension score is as follows:

[0029] in, express The inefficient use of sea evaluation score of the dimension; express Dimension The score of the indicator; express Dimension The weight value of the indicator; Indicates the number of indicators.

[0030] The calculation formula of the comprehensive score is as follows:

[0031] in, It represents the comprehensive evaluation score of low-efficiency sea use; express The inefficient use of sea evaluation score of the dimension; express The weight value of the dimension; Indicates the number of dimensions.

[0032] By converting each indicator into a specific score and then directly summarizing it, the evaluation process is simplified and the efficiency of the evaluation is improved.

[0033] Furthermore, the sea use projects are graded according to their comprehensive evaluation scores, and a scoring report is generated for each sea use project. The scores of each dimension and the comprehensive score are analyzed according to different sea use types and administrative regions.

[0034] By calculating the dimension scores and comprehensive scores, the performance of each sea use project in different dimensions and the overall comprehensive performance can be clearly displayed, making the evaluation results easier to understand and interpret.

[0035] Furthermore, for sea use projects with abnormal scores, the reasons for the abnormalities are analyzed based on the scoring report.

[0036] Furthermore, for large-scale sea-use projects that span multiple years, the indicators will be adjusted based on the project’s phased characteristics.

[0037] Furthermore, the method also includes a multi-source information fusion method, which processes the original sea efficiency data in a systematic detection and distributed fusion manner through the Kalman filter method on the basis of a multi-level indicator system and target data weight values. It dynamically adjusts the weight of each measurement value to reduce the impact of noise and improve the accuracy of the data; by integrating data from different sources and different levels, it provides a comprehensive perspective for decision makers, which helps to formulate more scientific and reasonable management strategies.

[0038] Furthermore, the method also includes a linear regression method, which simulates the relationship between the dependent variable and the independent variable by finding the best fitting curve on the basis of the multi-level indicator system, and predicts the low utility of the sea area according to the input characteristic indicators. It can predict the low utility of the sea use project in the future based on historical data and statistical relationships, and improve the accuracy of the prediction; the model structure is simple, easy to understand and implement, making the evaluation process more efficient and convenient for promotion and application in practical work.

[0039] In a second aspect, the present invention further provides a comprehensive evaluation system for inefficient sea use based on multi-dimensional analysis, the system being based on the method described in the first aspect, comprising: The system comprises: Data collection module, used to collect sea use benefit data; Spatial analysis module, used to process sea use benefit data through overlay analysis method; Index assignment module, used to assign percentages to sea use benefit data; The weight calculation module is used to construct a multi-level indicator system through the hierarchical analysis method and calculate the weight value of the target data through the Delphi method; A dimension score calculation module is used to calculate the dimension score according to the value and weight value of the sea use benefit data; The comprehensive score calculation module is used to calculate the comprehensive score based on the dimension scores and make a graded evaluation of the sea use projects.

[0040] In a third aspect, the present invention further provides a computer device, comprising a processor and a memory, wherein the processor is connected to the memory, the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the computer device executes and implements the aforementioned method.

[0041] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the aforementioned method when executed by a processor.

[0042] The present invention assigns percentages to the sea use benefit data and constructs a multi-level index system through the hierarchical analysis method, calculates the weight value of the target data through the Delphi method; calculates the dimension score according to the assignment and weight value of the sea use benefit data and calculates the comprehensive score according to the dimension score, so as to make a graded evaluation of the sea use project; on the basis of the multi-level index system and the weight value of the target data, the original sea use efficiency data is processed in a systematic detection and distributed fusion manner through the Kalman filtering method; the relationship between the dependent variable and the independent variable is simulated by finding the best fitting curve, and the low utility degree of the sea area is predicted according to the input characteristic index. The method has a wide range of application scenarios and is of great significance to promoting the conservation and utilization of marine resources, the rational allocation of marine space resources, and the improvement of the protection and utilization efficiency of marine space resources.

[0043] Beneficial Effects By implementing the comprehensive evaluation method for low-efficiency sea use based on multi-dimensional analysis provided by the present invention, the following technical effects are achieved: (1) The sea use benefit data are assigned percentages and a multi-level index system is constructed through the hierarchical analysis method. The weight values ​​of the target data are calculated through the Delphi method. The original data are converted into comparable scores through percentage assignment, so that sea use projects of different types and sizes can be compared under the same standard, ensuring the scientificity and rationality of the evaluation index system. The weights determined by the Delphi method can be adjusted according to actual conditions, so that the evaluation system can adapt to sea use projects of different regions and types.

[0044] (2) The dimension scores are calculated according to the assigned values ​​and weight values ​​of the sea use benefit data, and the comprehensive scores are calculated according to the dimension scores, so as to make a graded evaluation of the sea use projects; by converting each indicator into a specific score and then directly summarizing it, the evaluation process is simplified and the efficiency of the evaluation is improved; by calculating the dimension scores and comprehensive scores, the performance of each sea use project in different dimensions and the overall comprehensive performance can be clearly displayed, making the evaluation results easier to understand and explain.

[0045] (3) Based on the multi-level indicator system and target data weight values, the original sea efficiency data is processed by systematic detection and distributed fusion through the Kalman filtering method; by dynamically adjusting the weight of each measurement value, the impact of noise is reduced and the accuracy of the data is improved; by integrating data from different sources and different levels, a comprehensive perspective is provided for decision makers, which helps to formulate more scientific and reasonable management strategies.

[0046] (4) On the basis of the multi-level indicator system, the relationship between the dependent variable and the independent variable is simulated by finding the best fitting curve, and the low utility level of the sea area is predicted according to the input characteristic indicators. Based on historical data and statistical relationships, the low utility level of sea use projects in the future can be predicted, and the accuracy of the prediction is improved. The model structure is simple, easy to understand and implement, making the evaluation process more efficient and convenient for promotion and application in practical work. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to make the above-mentioned comprehensive evaluation method for low-efficiency sea use based on multi-dimensional analysis of the present invention more obvious and easy to understand, the following is a brief introduction to the drawings required for use in the specific implementation of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without paying creative labor.

[0048] Figure 1 A schematic diagram showing the flow of the comprehensive evaluation method for inefficient sea use; Figure 2 A schematic diagram showing the process of investigating the benefits of sea use; Figure 3 A schematic diagram showing the spatial overlay analysis framework; Figure 4 A schematic diagram showing the process of evaluating and determining inefficient sea use; Figure 5 Schematic diagram of the analytic hierarchy process model for inefficient sea use. DETAILED DESCRIPTION

[0049] Embodiment 1: A comprehensive evaluation method for inefficient sea use based on multi-dimensional analysis is provided. The process of the comprehensive evaluation method for inefficient sea use is as follows: Figure 1 As shown, the method includes: collecting sea use benefit data and processing the collected data through an overlay analysis method; assigning percentages to the processed sea use benefit data; A multi-level indicator system is constructed through the hierarchical analysis method and the weight value of the target data is calculated through the Delphi method; the dimension scores are calculated based on the assignment and weight value of the sea use benefit data; the comprehensive scores are calculated based on the dimension scores and the sea use projects are graded and evaluated. The details are as follows.

[0050] The process of investigating the benefits of using the sea is as follows: Figure 2 As shown, the sea use benefit data includes geographic information data and professional department data.

[0051] The geographic information data include remote sensing image results, administrative boundary survey lines and mainland and island coastline survey data.

[0052] The professional department data include sea area use rights data, marine functional zoning data, ecological red line data, key bay and estuary area data and other professional department data.

[0053] The method of preprocessing the sea benefit data includes data extraction, spatial registration, vectorization, format conversion and coordinate unification.

[0054] Spatial overlay analysis framework Figure 3 As shown, the purpose of the overlay analysis method is to extract the type of allowed construction area of ​​another vector within the prohibited area of ​​one vector, that is, the suspected conflict area.

[0055] The overlay analysis method specifically includes: analysis of the compliance of sea area use with functional zoning, superimposing the sea area use right data with the marine functional zoning data and calculating the overlapping area between each sea use project and each functional zone. The calculation formula for the proportion of sea use area that does not conform to the functional zoning is: non-compliance rate = sea use area that does not conform to the functional zoning / total sea use area of ​​the project × 100%; analysis of the compliance of sea area use with ecological red lines, superimposing the sea area use right data with the ecological red line data and identifying the sea use projects located within the ecological red line. The calculation formula for the proportion of the area occupied by the ecological red line is: occupation rate = sea use area that does not conform to the functional zoning / total sea use area of ​​the project × 100%; Rate = area occupied by ecological red line / total sea area used by the project × 100%; for sea area use efficiency analysis, the actual sea use scope is extracted through visual interpretation method, and the actual sea use area and approved sea use data vector are superimposed and analyzed. The sea area utilization rate is calculated as follows: Utilization rate = actual sea use area / approved sea use area × 100%; for sea area use efficiency analysis, the effective utilization scope and actual sea use scope are extracted through visual interpretation method, and the effective utilization area and actual sea use area are superimposed and analyzed. The sea area utilization rate is calculated as follows: Utilization rate = effective utilization area / actual sea use area × 100%.

[0056] During the overlay analysis, areas with missing data are handled by interpolating the mean values ​​of neighboring areas.

[0057] During the overlay analysis, when conducting coastline analysis, if the coastline is highly complex, the high tide line is used instead of the coastline as the evaluation criterion.

[0058] The sea use benefits survey also includes the actual survey stage.

[0059] The target data of the actual investigation stage include basic information of the project (project name, type of sea use, approved area, etc.), actual sea use situation (actual sea use scope, construction status, utilization status, etc.) and project operating status (operating time, output value, number of employees, etc.).

[0060] The evaluation and determination process of inefficient sea use is as follows: Figure 4 shown.

[0061] The types of percentage assignment of sea use benefit data include: assignment of economic benefit indicators, which uses the economic benefit indicators of industrial enterprises above a certain scale in the study area as a reference, and the relevant economic indicators / industrial and mining land area as the benchmark value, and is assigned according to the evaluation criteria; assignment of social benefit indicators, which is assigned according to the importance and nature of the sea use projects; assignment of ecological benefit indicators, which is assigned according to the degree of change in the attributes of the sea area caused by the use of the sea by different projects; assignment of development and utilization benefit indicators, which is assigned according to the reclamation status, construction status, and operation status of different projects; and assignment of management measure benefit indicators, which is assigned according to the compliance with functional zoning.

[0062] The indicators for assigning the economic benefit index include per-mu output value and investment intensity.

[0063] The indicators for assigning the social benefit index include project nature and employment density.

[0064] The indicators for assigning the ecological benefit index include the degree of change in sea area attributes and the sensitivity of the project sea area.

[0065] The indicators for assigning the development and utilization benefit index include land reclamation status, construction status, operation status, sea area utilization rate and sea area utilization rate.

[0066] The indicators for assigning the effectiveness indicators of the management measures include compliance with functional zoning, compliance with industrial standards and compliance with ecological red lines.

[0067] In the process of assigning percentages to sea use benefit data, the indicators with missing data are estimated by adopting the average value of similar projects.

[0068] In the process of assigning percentages to sea use benefit data, projects spanning multiple functional zones are weighted according to their areas to calculate the project's functional zoning compliance score.

[0069] In the process of assigning percentages to sea use benefit data, per-mu output value represents the output value per unit area in a normal year within the project's sea use range, in units of 10,000 yuan / hectare. It is calculated based on the project's annual financial statements and assigned values ​​against the assignment table. The calculation formula is Score = f(V / A), where V is the output value in a normal year and A is the total area of ​​sea use for the project. The evaluation criteria for per-mu output value (E1) include: taking the total industrial output value of industrial enterprises above designated size in the study area / industrial and mining land area as the benchmark value Am, assigning points to enterprises according to the evaluation criteria, and directly assigning 0 points to enterprises that are not operating normally (enterprises that are not operating normally are enterprises that have not been put into production, are suspended, or are being transferred).

[0070] Investment intensity refers to the fixed asset investment per unit area within the project's sea area, in units of 10,000 yuan / hectare. It is calculated based on the total project investment and the sea area, and assigned values ​​according to the assignment table. The calculation formula is Score = f(I / A), where I is the total investment and A is the total sea area of ​​the project. The evaluation criteria for investment intensity (E2) include: taking the total assets of industrial enterprises above designated size in the study area / the area of ​​industrial and mining land as the benchmark value At, grading and assigning points to enterprises according to the evaluation criteria, and directly assigning 0 points to enterprises that are not operating normally (enterprises that are not operating normally are enterprises that have not been put into production, are suspended or are being transferred).

[0071] The nature of the project refers to the authority that approves the sea use project, reflecting the importance of the project’s sea use. It is determined based on the project approval documents and directly assigned a value by referring to the assignment table.

[0072] Employment density refers to the number of employees per unit area within the project's sea area, in units of person / hectare. It is calculated based on the number of jobs provided by the project and the sea area, and is assigned a value according to the assignment table. The assignment formula is: Score = f(E / A), where E is the number of employees and A is the sea area. The evaluation criteria for employment density (S2) include: taking the average number of employees of industrial enterprises above designated size in the study area / the area of ​​industrial and mining land as the benchmark value Aj, and grading and assigning points to enterprises according to the evaluation criteria. Enterprises that are not operating normally are directly assigned 0 points (enterprises that are not operating normally are enterprises that have not been put into production, are suspended or are being transferred).

[0073] The degree of change in sea area attributes refers to the degree of change in the sea area attributes caused by different projects' sea use methods. Among them, land reclamation and impermeable structures have the greatest impact, while open and other sea use methods have the smallest impact. It is assigned based on the area ratio of the corresponding sea use methods, reference evaluation standards, and comparison with the assignment table.

[0074] The sensitivity of the project sea area refers to the ecological sensitivity of the sea area where the project is located, specifically the degree of occupation of wetlands or important estuaries, bays and important natural ecological space selection areas. It is determined by checking the project sea area, wetland / estuary / bay and other spatial locations, and assigning values ​​according to the assignment table.

[0075] The reclamation status, construction status and operation status are directly assigned values ​​according to the remote sensing interpretation and field investigation results, and compared with the assignment table.

[0076] The sea area use rate and sea area utilization rate are calculated based on the spatial analysis results and assigned according to the assignment table. The assignment formula is Score = f(A_actual / A_approved), where A_actual is the actual use / utilization area and A_approved is the approved / actual sea area.

[0077] Functional zoning conformity is directly assigned according to the spatial analysis results and the assignment table.

[0078] Industry standard compliance is directly assigned according to the project type and the assignment table.

[0079] The ecological red line compliance is directly assigned according to the spatial analysis results and the assignment table.

[0080] For indicators with missing data, the average value of similar items was used for estimation.

[0081] For projects that span multiple functional zones, the functional zoning compliance score is calculated weighted by area.

[0082] The hierarchical table of low-efficiency sea use evaluation indicators is shown in Table 1.

[0083] Table 1. Evaluation index assignment level table for low-efficiency sea use

[0084] In the analytic hierarchy process, different levels use different weight coefficients and standards.

[0085] The AHP model includes a target layer, a criterion layer and an indicator layer. The AHP model of low-efficiency sea use is as follows: Figure 5 The target layer includes comprehensive evaluation of low-efficiency sea use; the criterion layer includes benefits of sea area development and utilization, benefits of implementation of management measures, social benefits of sea area use, economic benefits of sea area use and ecological benefits of sea area use; the indicator layer includes 14 important indicators.

[0086] The calculation formula for calculating the target data weight value by the Delphi method is as follows:

[0087] in, Indicates The weight of each indicator or dimension; Indicates Expert For Scoring of indicators or dimensions; Represents the total number of experts.

[0088] The evaluation index system for inefficient sea use is shown in Table 2.

[0089] Table 2. Evaluation index system for low-efficiency sea use

[0090] The score of each dimension is calculated by weighted average method, that is, the score of the indicator layer under it is multiplied by its respective weight and then summed up. The score of the target layer is obtained by multiplying the score of each dimension under it by its respective weight and then summing up.

[0091] The calculation formula of the dimension score is as follows:

[0092] in, express The inefficient use of sea evaluation score of the dimension; express Dimension The score of the indicator; express Dimension The weight value of the indicator; Indicates the number of indicators.

[0093] The calculation formula of the comprehensive score is as follows:

[0094] in, It represents the comprehensive evaluation score of low-efficiency sea use; express The inefficient use of sea evaluation score of the dimension; express The weight value of the dimension; Indicates the number of dimensions.

[0095] The sea use projects are graded according to their comprehensive evaluation scores, and a scoring report is generated for each sea use project. The scores of each dimension and the comprehensive score are analyzed according to different sea use types and administrative regions.

[0096] The comprehensive evaluation table of inefficient sea use is shown in Table 3.

[0097] Table 3. Comprehensive evaluation of inefficient use of seawater

[0098] For sea use projects with abnormal scores, the reasons for the abnormalities shall be analyzed based on the scoring report.

[0099] For large-scale sea-use projects spanning multiple years, indicators will be adjusted based on the project’s phased characteristics.

[0100] The method also includes a multi-source information fusion method. Based on a multi-level indicator system and target data weight values, the original sea use efficiency data is processed in a systematic detection and distributed fusion manner through a Kalman filtering method. Specifically, the method includes: collecting sea use efficiency data and directly processing the collected original data through a Kalman filtering method; classifying multi-source data and sorting them from low to high fusion levels to achieve feature acquisition; for data of the same or similar type, eliminating environmental interference and prediction errors through correlation analysis.

[0101] Among them, the Kalman gain formula is as follows:

[0102] in, represents the Kalman gain, which is used to determine the weight between the predicted state and the actual measurement value; represents the error covariance prediction matrix; represents the observation matrix, which is used to map the state to the measurement space; represents the observation noise covariance matrix, which is used to describe the uncertainty in the measurement process; represents the transpose of the observation matrix; It represents an adaptive adjustment item based on the situation of the sea use project. It reduces the impact of noise and improves the accuracy of data by dynamically adjusting the weight of each measurement value; it provides a comprehensive perspective for decision makers by integrating data from different sources and different levels, which helps to formulate more scientific and reasonable management strategies.

[0103] Embodiment 2: On the basis of the above-mentioned embodiment, the method adds a linear regression method, and on the basis of a multi-level indicator system, the relationship between the dependent variable and the independent variable is simulated by finding the best fitting curve, and the low utility degree of the sea area is predicted according to the input characteristic indicators.

[0104] First, feature transformation is performed on the sea use benefit data as needed to help the model better capture patterns in the data, and the data set is divided.

[0105] The weights and bias terms are assigned randomly selected initial values ​​and the mean squared error is used as the loss function to measure the difference between the model's predicted values ​​and the actual values.

[0106] During each iteration of the model, the loss function is calculated while updating the values ​​of the weights and bias terms.

[0107] The generalization ability of the model is enhanced by ridge regression. The loss function formula of ridge regression is as follows:

[0108] in, Represents the regression coefficient vector, including all characteristic coefficients; Indicates The target variable value of samples; represents the intercept term, that is, the expected value of the target variable when all eigenvalues ​​are 0; Indicates The sample eigenvalues; represents the regularization parameter, which is used to prevent overfitting; represents the total number of features; Represents the total number of samples.

[0109] By inputting the features of the data into the trained model to obtain the output value to predict the data, Explain the effect of the independent variables on the dependent variable by looking at the values ​​of the weights and bias terms.

[0110] The importance of different features is taken into account through weighted prediction. The weighted prediction formula is as follows:

[0111] in, represents the predicted value; represents the intercept term; Indicates The regression coefficient of each feature; Indicates The regression coefficient of each feature; Indicates The value of a feature; Represents the total number of features.

[0112] For example, there is the following dataset: Dataset 1: economic benefit 80, social benefit 70, ecological benefit 60, development and utilization benefit 85, management measure benefit 75, inefficient use of the sea 30; Dataset 2: economic benefit 90, social benefit 80, ecological benefit 70, development and utilization benefit 90, management measure benefit 80, inefficient use of the sea 40; Dataset 3: Economic benefit 75, social benefit 65, ecological benefit 55, development and utilization benefit 80, management measure benefit 70, inefficient sea use degree 46; Dataset 4: economic benefits 85, social benefits 75, ecological benefits 68, development and utilization benefits 88, management measures benefits 78, and inefficient use of the sea 54; Assumptions , , ; Assuming the regression coefficient , , , , , ; Take sample 1 as an example:

[0113] Assume there is a new sample with economic benefits of 82, social benefits of 78, ecological benefits of 62, development and utilization benefits of 87, and management measures benefits of 76:

[0114] The results of the linear regression method are shown in Table 4.

[0115] Table 4. Summary of the effects of linear regression methods

[0116] As shown in Table 4, the prediction error of the comprehensive evaluation method for inefficient sea use is significantly reduced after optimization by the linear regression method, which indicates that the linear regression method can more accurately predict the degree of inefficiency of sea use projects in the future.

[0117] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable non-transient storage media containing computer-usable program code.

[0118] The present invention can provide computer program instructions to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce means for implementing the system.

[0119] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions of the system.

[0120] These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions of the system.

Claims

1. A comprehensive evaluation method for inefficient sea use based on multi-dimensional analysis, characterized by: include: Collect data on sea use benefits and process the collected data through overlay analysis; Assign percentages to the processed sea use benefit data; A multi-level indicator system is constructed through the hierarchical analysis method and the weight value of the target data is calculated through the Delphi method; Calculate the dimension scores based on the assigned values ​​and weight values ​​of the sea use benefit data; The comprehensive score is calculated based on the dimension scores and a graded evaluation is made for the sea use projects.

2. The method according to claim 1, characterized in that: The overlay analysis method includes: sea area use and functional zoning compliance analysis, sea area use and ecological red line compliance analysis, sea area use efficiency analysis and sea area utilization efficiency analysis.

3. The method according to claim 1, characterized in that: The types of percentage assignment of sea use benefit data include economic benefit index assignment, social benefit index assignment, ecological benefit index assignment, development and utilization benefit index assignment and management measure benefit index assignment.

4. The method according to claim 1, characterized in that: In the AHP, different levels use different weight coefficients and standards; The calculation formula for calculating the target data weight value by the Delphi method is as follows: in, Indicates The weight of each indicator or dimension; Indicates Expert For Scoring of indicators or dimensions; Represents the total number of experts.

5. The method according to claim 1 or 4, characterized in that: The calculation formula for the dimension score is as follows: in, express The inefficient use of sea evaluation score of the dimension; express Dimension The score of the indicator; express Dimension The weight value of the indicator; Indicates the number of indicators; The calculation formula of the comprehensive score is as follows: in, It represents the comprehensive evaluation score of low-efficiency sea use; express The inefficient use of sea evaluation score of the dimension; express The weight value of the dimension; Indicates the number of dimensions.

6. The method according to claim 1, characterized in that: The method also includes a multi-source information fusion method, which processes the original sea efficiency data in a systematic detection and distributed fusion manner through a Kalman filtering method based on a multi-level indicator system and target data weight values.

7. The method according to claim 1, characterized in that: The method also includes a linear regression method, which simulates the relationship between the dependent variable and the independent variable by finding the best fitting curve based on the multi-level indicator system, and predicts the low utility degree of the sea area according to the input characteristic indicators.

8. A comprehensive evaluation system for inefficient sea use based on multi-dimensional analysis, characterized by: The implementation of the system is based on the method described in any one of claims 1 to 7: The system comprises: Data collection module, used to collect sea use benefit data; Spatial analysis module, used to process sea use benefit data through overlay analysis method; Index assignment module, used to assign percentages to sea use benefit data; The weight calculation module is used to construct a multi-level indicator system through the hierarchical analysis method and calculate the weight value of the target data through the Delphi method; A dimension score calculation module is used to calculate the dimension score according to the assigned value and weight value of the sea use benefit data; The comprehensive score calculation module is used to calculate the comprehensive score based on the dimension scores and make a graded evaluation of the sea use projects.

9. A computer device comprising a processor and a memory, wherein the processor is connected to the memory, and the memory is used to store a computer program, wherein: The processor is configured to execute the computer program stored in the memory, so that the computer device performs at least one step in the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, characterized in that: When the computer program is executed, at least one step of the method according to any one of claims 1 to 7 is implemented.

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