High-efficiency sea use dynamic evaluation system and method based on three-dimensional right confirmation

Abstract

The invention discloses an efficient sea use dynamic evaluation system and method based on three-dimensional right confirmation, and relates to the technical field of marine resource management and evaluation, and the system comprises a data processing module which is used for collecting and processing sea use benefit data; the index assignment module is used for assigning values to the sea utilization benefit data and determining weights of all dimensions and indexes; the grading evaluation module is used for determining a final score of the sea use project according to the assignment result and the weights of the dimensions and the indexes, and performing grading evaluation; the system optimization module is used for optimizing an efficient sea use evaluation index system according to environment change, technical progress, rule update and application feedback; the weight optimization module is used for searching a weight space through a particle swarm optimization algorithm and determining an optimal weight combination; and the benefit prediction module is used for predicting the benefit rating of the marine project in a future period of time. According to the technical scheme of the invention, the method can achieve the dynamic evaluation and optimization of the benefits of a marine project, and improves the utilization efficiency of marine resources.

Description

Technical Field

[0001] The present invention relates to the technical field of marine resource management and assessment, and in particular to a high-efficiency dynamic sea utilization assessment system and method based on three-dimensional property rights confirmation. Background Art

[0002] With the rapid development of the marine economy, the application of the three-dimensional sea rights confirmation model has become an important means to improve the utilization rate of marine resources. However, how to accurately identify efficient sea use areas, optimize the allocation of marine resources, and achieve sustainable development is still a severe challenge. The existing sea use benefit identification methods have problems such as limited data processing capabilities, low discrimination accuracy, and lack of real-time performance. They are difficult to meet the needs of modern marine resource management. The emergence of these problems has seriously affected the rational development and protection of marine resources. In addition, the existing discrimination methods are still at the subjective judgment stage, lacking an objective and scientific comprehensive evaluation index system, which limits people's rational allocation of marine resource utilization.

[0003] The multi-layered, complex and multifunctional characteristics of marine resources determine that marine resources must be developed in a three-dimensional manner in the surface, middle and bottom layers. A scientific and three-dimensional development method is an inevitable choice for the development of marine resources. In addition, it is difficult to reasonably and accurately evaluate the benefits of sea use projects with a fixed sea use benefit evaluation system alone. How to make the sea use benefit evaluation system improve along with the development of social economy is the top priority in marine governance. Therefore, based on this problem, this application proposes an efficient sea use dynamic evaluation system and method based on three-dimensional right confirmation. Summary of the invention

[0004] Technical Purpose In order to solve the above problems, the purpose of the present invention is to provide an efficient dynamic evaluation system and method for sea use based on three-dimensional right confirmation, which can not only make a reasonable and accurate evaluation of sea use projects according to the benefit data of sea use projects, but also its evaluation index system can adapt to the development of social economy, and has timeliness while ensuring rationality. The system and method have a wide range of application scenarios, and can realize timely updating and optimization of the efficient sea use evaluation index system, thereby providing a more efficient and accurate sea use benefit evaluation of sea use projects.

[0005] Technical Solution In order to achieve the above-mentioned purpose, the present invention provides a dynamic evaluation system and method for efficient sea use based on three-dimensional right confirmation, which assigns sea use benefit data according to the efficient sea use evaluation index system, determines the weights of each dimension and index by combining the analytic hierarchy process and the Delphi method, determines the final score of the sea use project according to the assignment results and the weights of each dimension and index, and makes a graded evaluation based on the final score, and optimizes the efficient sea use evaluation index system according to environmental changes, technological progress, rule updates and application feedback, thereby realizing the evaluation and identification of efficient sea use projects.

[0006] In a first aspect, the present invention provides an efficient dynamic evaluation system for sea use based on three-dimensional land rights confirmation, comprising: The data processing module is used to collect sea use benefit data from multiple channels such as satellite remote sensing images, ocean monitoring stations, and data reported by sea-using enterprises, and to clean, screen, standardize and normalize the sea use benefit data; The indicator assignment module is used to quantify the sea use benefit data according to the high-efficiency sea use evaluation indicator system, and determine the weights of each dimension and indicator by combining the hierarchical analysis method and the Delphi method; A grading evaluation module is used to determine the final score of the sea use project according to the assignment results and the weights of each dimension and indicator, and to make a graded evaluation according to the final score. The evaluation grades of the sea use project include excellent, good, general, poor and extremely poor. Among them, the sea use project with an excellent evaluation grade is classified as a high-efficiency sea use project; A system optimization module is used to optimize the high-efficiency sea use evaluation index system according to environmental changes, technological progress, rule updates, and application feedback. The optimization methods of the system optimization module include regular review, response to rule changes, adaptation to technological progress, feedback on sea use practices, adjustment triggered by special events, and consideration of regional differences; The weight optimization module is used to search the weight space through the particle swarm optimization algorithm and determine the optimal weight combination.

[0007] Furthermore, the three-dimensional rights confirmation, that is, the three-dimensional layered rights allocation in the sea area, refers to the confirmation of the right of use for different sea activities according to different spatial layers such as the water surface, water body, seabed and subsoil of the sea area.

[0008] Furthermore, the types of sea use benefit data collected by the data processing module include sea area spatial data, sea use activity time series data, economic revenue and expenditure data, ecological environment monitoring data and social feedback data.

[0009] Furthermore, the data processing module conducts spatial overlay analysis on the sea area use, marine functional zoning and ecological red line data of the sea use projects through the geographic information system to obtain the functional zoning compliance, ecological red line compliance and utilization intensity data of the sea use projects, thereby determining the overlapping area and proportion of the sea use projects in different functional zones and ecological red lines.

[0010] Furthermore, the dimensions of sea use benefit assessment include space utilization, time utilization, functional utilization, economic indicators, ecological environment, social indicators, etc.

[0011] Furthermore, the types of indicators used by the system to evaluate the benefits of sea use projects include the proportion of sea use areas at different depths, the degree of overlap of sea use in three-dimensional space, the degree of conflict in sea use in three-dimensional space, the duration of sea use activities, the periodicity of sea use activities, changes in sea use efficiency in different time periods, the degree of realization of sea use functions, the quality of sea use functions, the degree of matching with sea use functions, economic output per unit area, rate of return on investment, marine ecosystem health index, pollutant emission compliance rate, resource sustainable utilization index, employment driving effect, community satisfaction, etc.

[0012] The system constructs a multi-dimensional and efficient sea use evaluation index system by converting raw sea use benefit data into more intuitive benefit evaluation scores, and refines the calculation method of evaluation scores for sea use projects, making the system more real-time.

[0013] Furthermore, the grading evaluation module determines the scores of each dimension of the sea use project according to the assignment results of the sea use benefit data and the weights of each indicator, and determines the final score of the sea use project according to the scores of each dimension of the sea use project and the weights corresponding to the dimensions.

[0014] Furthermore, the grading evaluation module calculates the score by a multi-objective linear weighted function method.

[0015] The evaluation scores of sea use projects are calculated through the multi-objective linear weighted function method, which fully considers the influence of different dimensions and indicators on the sea use benefits of sea use projects and avoids the subjectivity and one-sidedness of traditional evaluation methods.

[0016] Furthermore, in the process of searching the weight space by the weight optimization module, the particle velocity update formula is:

[0017] In the formula, For the Particles in time At the Speed ​​in dimension; For the Particles in time At the Speed ​​in dimension; is the inertia weight, which is used to control the attenuation of particle velocity; is the first acceleration coefficient, which is used to control the tendency of particles to move toward the individual optimal state, usually between 1.5 and 2.5; is the second acceleration coefficient, which is used to control the tendency of particles to move toward the global optimum, usually between 1.5 and 2.5; The third acceleration coefficient is used to control the tendency of particles to move toward the local optimum, usually between 0.5 and 1.5; , and is a random number, usually generated using a uniformly distributed random number generator; For the Particles in time At the The optimal position of an individual in dimension; is the global optimal position at time At the The value of the dimension; is the local optimal position at time At the The value of the dimension; For the Particles in time At the Current position on the dimension.

[0018] Simply determining the weight of each indicator by combining the analytic hierarchy process and the Delphi method may not be able to quickly, efficiently and accurately obtain the optimal weight combination due to the large number of indicators or frequent indicator updates. The addition of a weight optimization module can quickly converge to the optimal or approximately optimal solution, improve the efficiency of weight optimization, and reduce the risk of falling into local optimality by conducting a global search in the solution space. The global search capability of the system is enhanced, the actual situation of the sea use project is reflected more scientifically, and the accuracy and reliability of the evaluation results are improved. The system can adaptively adjust the weights according to the historical optimal positions of individuals and groups, making the weight optimization more flexible and accurate.

[0019] Furthermore, the system also includes a benefit prediction module for predicting the benefit rating of the sea use project in the future based on the historical benefit data of the sea use project and a multi-dimensional evaluation index system.

[0020] Furthermore, the benefit prediction module adjusts the evaluation model through a gradient boosting machine. Specifically, it predicts the efficiency of new sea use projects by learning the sea use efficiency information in historical data and identifies potential inefficient sea use areas. The core principle is to combine multiple weak learners into a strong learner in an iterative manner.

[0021] Furthermore, the updating formula of the evaluation model is:

[0022] In the formula, For the The strong learner of the next step is the accumulation of all previous weak learners; For the Step-wise strong learner; is the learning rate, used to control the contribution of each step; For the Step weak learner; To adjust the coefficients, used to balance the contributions of weak learners and geospatial models; It is a geospatial model used to capture the spatial correlation of the sea area.

[0023] The benefit prediction module comprehensively analyzes the benefits of sea use projects through historical benefit data and a multi-dimensional evaluation index system, thereby improving the accuracy of predictions on the benefits of sea use projects in the future. It helps to protect the marine ecological environment, reduce the problems of excessive consumption of marine resources and continuous deterioration of the marine environment, and promote the efficient development of the marine economy by identifying efficient sea use projects and accelerating their promotion and implementation.

[0024] Furthermore, the system also includes a remote monitoring device powered by hybrid energy. The device casing is made of anti-corrosion material, has built-in solar photovoltaic panels and micro wind turbines, and is equipped with a lithium battery energy storage module. The device integrates water quality sensors, meteorological sensors, sonar depth sounders and GPS positioning modules, and transmits data to the data processing module via satellite communications.

[0025] In a second aspect, the present invention further provides a method for dynamic evaluation of efficient sea use based on three-dimensional right confirmation, the method being based on the system described in the first aspect, comprising: The method comprises: Collect and process data on sea use benefits; The sea use benefit data are assigned values ​​according to the high-efficiency sea use evaluation index system, and the weights of each dimension and index are determined by combining the analytic hierarchy process and the Delphi method; The final score of the sea use project is determined based on the assignment results and the weights of each dimension and indicator, and a graded evaluation is made based on the final score; Optimize the evaluation index system for efficient sea use based on environmental changes, technological progress, rule updates, and application feedback; The particle swarm optimization algorithm is used to find the optimal weight combination.

[0026] In a third aspect, the present invention also 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 at least one step of the aforementioned method for dynamic evaluation of efficient sea use based on stereoscopic rights confirmation.

[0027] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, at least one step of the aforementioned method for dynamic evaluation of efficient sea use based on stereoscopic rights confirmation is implemented.

[0028] The present invention assigns values ​​to sea use benefit data according to the high-efficiency sea use evaluation index system, determines the weights of each dimension and index by combining the analytic hierarchy process and the Delphi method, determines the final score of the sea use project according to the assignment results and the weights of each dimension and index, and makes a graded evaluation according to the final score; optimizes the high-efficiency sea use evaluation index system according to environmental changes, technological progress, rule updates, and application feedback; finds the optimal weight combination through the particle swarm optimization algorithm, thereby optimizing the weights of each dimension and index in the high-efficiency sea use evaluation index system; predicts the benefit rating of the sea use project in the future based on the historical benefit data of the sea use project and the multi-dimensional evaluation index system. The system and method have a wide range of application scenarios, and can realize the timely update and optimization of the high-efficiency sea use evaluation index system, thereby providing a more efficient and accurate sea use benefit evaluation of sea use projects.

[0029] Beneficial Effects By implementing the above-mentioned high-efficiency sea use dynamic evaluation system and method based on three-dimensional right confirmation provided by the present invention, the following technical effects are achieved: (1) The present invention assigns values ​​to the sea use benefit data according to the high-efficiency sea use evaluation index system, determines the weights of each dimension and index by combining the analytic hierarchy process and the Delphi method, determines the final score of the sea use project according to the assignment results and the weights of each dimension and index, and makes a graded evaluation according to the final score. By converting the original sea use benefit data into a more intuitive benefit evaluation score, a multi-dimensional high-efficiency sea use evaluation index system is constructed, and the evaluation score calculation method of the sea use project is refined, so that the system can have higher real-time performance; the evaluation score of the sea use project is calculated by the multi-objective linear weighted function method, which fully considers the influence of different dimensions and indicators on the sea use benefit of the sea use project, and avoids the subjectivity and one-sidedness of the traditional evaluation method.

[0030] (2) Optimize the efficient use of the sea evaluation index system based on environmental changes, technological progress, rule updates, and application feedback. By identifying and adjusting unreasonable indicator values ​​and weights, we can ensure that the efficient use of the sea evaluation index system can be optimized and updated in a timely manner, ensure that the evaluation system always matches the current environment and technological development level, and improve the adaptability and flexibility of the system; by optimizing the evaluation system, we can more effectively identify and evaluate efficient use of the sea projects, promote the efficient and intensive use of marine resources, and achieve sustainable use of marine resources.

[0031] (3) The particle swarm optimization algorithm is used to find the optimal weight combination, thereby optimizing the weights of each dimension and indicator in the efficient sea use evaluation index system. It can quickly converge to the optimal or approximately optimal solution, improve the efficiency of weight optimization, and reduce the risk of falling into the local optimum by conducting a global search in the solution space. It enhances the global search capability of the system, more scientifically reflects the actual situation of the sea use project, and improves the accuracy and reliability of the evaluation results. It enables the system to adaptively adjust the weights according to the historical optimal positions of individuals and groups, making weight optimization more flexible and accurate.

[0032] (4) Predict the benefit rating of sea-use projects in the future based on the historical benefit data of sea-use projects and the multi-dimensional evaluation index system. By comprehensively analyzing the benefits of sea-use projects through historical benefit data and the multi-dimensional evaluation index system, the accuracy of the prediction of the benefits of sea-use projects in the future is improved; it helps to protect the marine ecological environment, reduce the problems of excessive consumption of marine resources and the continuous deterioration of the marine environment; by identifying efficient sea-use projects and accelerating their promotion and implementation, it can promote the efficient development of the marine economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to make the above-mentioned efficient sea utilization dynamic evaluation system and method based on three-dimensional property rights confirmation of the present invention more obvious and easy to understand, the drawings required for use in the specific implementation methods of the present invention will be briefly introduced below. 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 creative work.

[0034] Figure 1 A schematic diagram showing the process of a dynamic evaluation method for efficient sea use based on three-dimensional property rights confirmation; Figure 2 A schematic diagram showing the process of investigating the sea use project situation; Figure 3 Schematic diagram of the AHP model for high-efficiency sea utilization. DETAILED DESCRIPTION

[0035] Embodiment 1: A system and method for dynamic evaluation of efficient sea use based on three-dimensional right confirmation is provided. The flow chart of the method for dynamic evaluation of efficient sea use based on three-dimensional right confirmation is as follows: Figure 1 As shown, the dynamic evaluation system for efficient sea use based on three-dimensional property rights confirmation includes: a data processing module, an indicator assignment module, a grading evaluation module, a system optimization module and a weight optimization module, specifically the dynamic evaluation system and method for efficient sea use based on three-dimensional property rights confirmation as described in the following steps.

[0036] The so-called three-dimensional rights confirmation, that is, the three-dimensional layered rights establishment in the sea area, refers to the confirmation of the right of use for different sea activities according to different spatial layers such as the water surface, water body, seabed and subsoil of the sea area.

[0037] The data processing module is used to collect sea use benefit data from multiple channels such as satellite remote sensing images, ocean monitoring stations, and data reported by sea-using enterprises, and to clean, screen, standardize and normalize the sea use benefit data. The sea use project situation investigation process is as follows: Figure 2 shown.

[0038] The types of sea use benefit data collected by the data processing module include sea area spatial data, sea use activity time series data, economic revenue and expenditure data, ecological environment monitoring data and social feedback data.

[0039] The specific method of cleaning and screening the sea use efficiency data by the data processing module includes removing repeated, erroneous and obviously abnormal data points in the data, and using linear interpolation and mean filling methods to repair the missing parts.

[0040] The specific method of the data processing module to standardize and normalize the sea use benefit data includes standardizing data of different dimensions and magnitudes.

[0041] The data processing module uses the geographic information system to perform spatial overlay analysis on the sea area use, marine functional zoning and ecological red line data of the sea use projects to obtain the functional zoning compliance, ecological red line compliance and utilization intensity data of the sea use projects, thereby determining the overlapping area and proportion of the sea use projects in different functional zones and ecological red lines.

[0042] In the process of performing spatial overlay analysis, the data processing module generates a spatial utilization situation layer including a distribution map of sea area used at different depths, a spatial distribution map of sea use activities, and a comparison map between functional zoning and actual sea use.

[0043] The types of indicators used by the system to evaluate the benefits of sea use projects include the proportion of sea use areas at different depths, the degree of overlap of sea use in three-dimensional space, the degree of conflict in sea use in three-dimensional space, the duration of sea use activities, the periodicity of sea use activities, changes in sea use efficiency in different time periods, the degree of realization of sea use functions, the quality of sea use functions, the degree of matching with sea use functions, economic output per unit area, rate of return on investment, marine ecosystem health index, pollutant emission compliance rate, resource sustainable utilization index, employment driving effect, community satisfaction, etc.

[0044] The data processing module also processes data by converting scattered raw data into organized, easy-to-analyze structured information. The dimensions and indicators involved in the structured information are shown in Table 1.

[0045] Table 1. Structured information table Dimensions index Required data Space Utilization Proportion of sea area used at different depths Total available area and actual sea area in each depth interval Space Utilization Overlapping degree of three-dimensional space Three-dimensional spatial coordinates and scope of various sea use activities Time utilization Duration of sea use activities The start and end time of each sea use project Functional Utilization Degree of realization of sea function Expected functional objectives and actual implementation of various sea use projects Economic indicators Economic output per unit area Annual economic benefits of each sea-use project and actual sea-use area Ecological Environment Marine Ecosystem Health Index Water quality monitoring indicators, biodiversity indicators, ecosystem assessment results The data processing module divides the survey data according to the data association matrix, and the division results are shown in Table 2.

[0046] Table 2. Survey data classification table

[0047] The index assignment module is used to quantify the sea use benefit data according to the high-efficiency sea use evaluation index system, and determine the weights of each dimension and index by combining the hierarchical analysis method and the Delphi method. The high-efficiency sea use hierarchical analysis method model is as follows: Figure 3 shown.

[0048] The dimensions of sea use benefit assessment include space utilization, time utilization, functional utilization, economic indicators, ecological environment, social indicators, etc.

[0049] The method and standard for indicator assignment module to perform indicator assignment are shown in Table 3.

[0050] Table 3. Index value assignment level table Dimensions Indicator layer meaning Evaluation instructions Calculation method Assignment criteria Space Utilization Proportion of sea area used at different depths The ratio of the actual sea area (A1) to the available area (A2) in different depth intervals The higher the proportion of sea area used, the higher the utilization rate A1 / A2 ×100% 0-20%: 1 point, 20-40%: 2 points, 40-60%: 3 points, 60-80%: 4 points, 80-100%: 5 points Overlapping degree of three-dimensional space The ratio of the overlapping area (O) of different sea use activities in three-dimensional space to the total sea use area (T) The lower the overlap, the better, to avoid resource conflicts O / T × 100% 80-100%: 1 point, 60-80%: 2 points, 40-60%: 3 points, 20-40%: 4 points, 0-20%: 5 points Degree of conflict in the use of sea in three-dimensional space The degree to which different sea-use activities interfere with and restrict each other in the same three-dimensional space (C) The less conflict, the better. Expert evaluation combined with actual situation analysis 1 point: very serious conflict, 2 points: relatively serious conflict, 3 points: moderate conflict, 4 points: relatively few conflicts, 5 points: no conflict Time utilization Duration of sea use activities The duration of a single sea use activity from the start time (S) to the end time (E) The longer the duration, the higher the economic value E - S 20% or less below the industry average: 1 point, 10%-20% below the industry average: 2 points, close to the industry average: 3 points, 10%-20% above the industry average: 4 points, more than 20% above the industry average: 5 points Periodicity of sea activities The regularity and frequency of sea-use activities that occur repeatedly over a certain period of time (P) Regular activities score higher Count the number of activities and intervals within a specific time period In line with the normal cycle law: 5 points, slightly deviated from the cycle law: 4 points, deviated from the cycle law: 3 points, deviated from the cycle law more: 2 points, seriously deviated from the cycle law: 1 point Changes in sea use efficiency in different time periods The change in the ratio of output (O2) to input (I2) of the same sea use activity in different seasons or time periods relative to the ratio of output (O1) to input (I1) in the previous period The smaller the change in efficiency, the better ((O2 / I2 -O1 / I1) / (O1 / I1)) ×100% Change less than 10%: 5 points, change 10%-20%: 4 points, change 20%-30%: 3 points, change 30%-40%: 2 points, change greater than 40%: 1 point Functional Utilization Degree of realization of sea function The degree to which actual sea use activities achieve the expected functional objectives (D) The higher the degree of functional realization, the better D / 100% 0-20%: 1 point, 20-40%: 2 points, 40-60%: 3 points, 60-80%: 4 points, 80-100%: 5 points Sea use function quality The quality level of products or services provided by marine activities (Q) The higher the quality, the better Evaluated through quality testing and user feedback 1 point: very poor quality, 2 points: poor quality, 3 points: average quality, 4 points: good quality, 5 points: excellent quality Compatibility with sea use functions The degree of consistency between the actual sea use function (F) and the initial sea use function (P) The higher the match, the better F / P × 100% 0-20%: 1 point, 20-40%: 2 points, 40-60%: 3 points, 60-80%: 4 points, 80-100%: 5 points Economic indicators Economic output per unit area Economic benefits per unit area of ​​a specific sea area (R) The higher the output, the better R / Fraction of sea area used Current value Minimum value Maximum value Minimum value Assign points based on interpolation results Return on Investment Ratio of investment benefits (G) to investment costs (C) of marine activities The higher the rate of return, the better (Benefit - Cost) / Cost × 100% Score Current value Minimum value Maximum value Minimum value Assign points based on interpolation results Ecological Environment Marine Ecosystem Health Index Comprehensive assessment of marine water quality, biodiversity and other ecological indicators (H) The higher the health index, the better Through professional monitoring and data analysis 0-20 points: 1 point, 20-40 points: 2 points, 40-60 points: 3 points, 60-80 points: 4 points, 80-100 points: 5 points Pollutant emission compliance rate The proportion of pollutant emissions from marine activities that meet the standards (D) to the total emissions (T) The higher the compliance rate, the better Detection of emissions and calculation of compliance 0-20%: 1 point, 20-40%: 2 points, 40-60%: 3 points, 60-80%: 4 points, 80-100%: 5 points Resource Sustainable Utilization Index Measuring the impact of marine activities on the sustainable use of marine resources (I) The higher the sustainable use index, the better Comprehensively consider factors such as resource regeneration and utilization intensity 0-20 points: 1 point, 20-40 points: 2 points, 40-60 points: 3 points, 60-80 points: 4 points, 80-100 points: 5 points Social Indicators Employment driving effect Number of direct and indirect jobs created by marine activities in the local area (J) The higher the driving effect, the better Statistics on relevant employment 0-20%: 1 point, 20-40%: 2 points, 40-60%: 3 points, 60-80%: 4 points, 80-100%: 5 points Community satisfaction Satisfaction of residents in surrounding communities with sea-use activities (S) The higher the satisfaction, the better Questionnaire survey and interview collection 1 point: very low satisfaction, 2 points: low satisfaction, 3 points: average satisfaction, 4 points: high satisfaction, 5 points: very high satisfaction The grading evaluation module is used to determine the final score of the sea use project based on the assignment results and the weights of each dimension and indicator, and to make a graded evaluation based on the final score. The evaluation levels of the sea use project include excellent, good, average, poor and extremely poor. Among them, the sea use project with an excellent evaluation level is classified as an efficient sea use project.

[0051] The grading evaluation module determines the scores of each dimension of the sea use project according to the assignment results of the sea use benefit data and the weights of each indicator, and determines the final score of the sea use project according to the scores of each dimension of the sea use project and the weights corresponding to the dimensions.

[0052] The grading evaluation module calculates the score by a multi-objective linear weighted function method.

[0053] The comprehensive evaluation of efficient sea use is shown in Table 4.

[0054] Table 4. Comprehensive evaluation table of efficient sea use Total project score Rating illustrate 0≤Total score<1 Very bad The overall situation of sea use is poor, with many problems that need to be addressed with great attention 1≤Total score<2 Poor The overall situation of sea use is poor, and there are outstanding problems that need to be addressed 2≤Total score<3 generally The overall situation of sea use is average, but there are certain problems that need to be addressed. 3≤Total score<4 good The overall situation of sea use is good, but there are a few problems that need attention 4≤Total score excellent The overall situation of sea use is good, and attention is paid to individual issues The system optimization module is used to optimize the efficient sea use evaluation index system according to environmental changes, technological progress, rule updates and application feedback. The optimization methods of the system optimization module include regular review, response to rule changes, adaptation to technological progress, feedback on sea use practices, adjustment triggered by special events and consideration of regional differences. The adjustment and update process of the index system is shown in Table 5.

[0055] Table 5. Index system optimization standard table

[0056] Assume that the data of a certain indicator is The frequencies of occurrence are , the total number of samples is 100, and the steps to calculate information entropy are as follows:

[0057]

[0058]

[0059]

[0060]

[0061]

[0062] Embodiment 2: On the basis of the above-mentioned embodiment, the system adds a weight optimization module for searching the weight space through a particle swarm optimization algorithm and determining the optimal weight combination.

[0063] The number of evaluation indicators is determined according to the high-efficiency sea utilization evaluation index system, and each particle represents the weight of a group of evaluation indicators.

[0064] The position of each particle is randomly initialized, and the position of the particle corresponds to the weight of the evaluation index.

[0065] The speed of each particle is randomly initialized, and the speed of the particle determines the movement range of the particle in the search space.

[0066] The fitness value of the particle is calculated according to the comprehensive score calculation formula of the sea use project, and the comprehensive score calculation formula of the sea use project is as follows:

[0067] In the formula, It is the comprehensive score of the sea use project; for Evaluation scores of sea use projects in different dimensions; for The weight value of the dimension; is the number of dimensions.

[0068] For each particle, if the fitness value of the current position is better than the previously recorded individual optimal fitness value, the individual optimal position is updated.

[0069] Among all particles, the particle with the best fitness value is searched and its position is taken as the global optimal solution.

[0070] When the maximum number of iterations is met or the preset fitness threshold is reached, the global optimal position is output, which corresponds to the optimal weight combination of the evaluation index system.

[0071] Among them, the particle velocity update formula is:

[0072] In the formula, For the Particles in time At the Speed ​​in dimension; For the Particles in time At the Speed ​​in dimension; Inertia weight, used to control the attenuation of particle velocity, usually ranging from 0.5 to 0.9; is the first acceleration coefficient, which is used to control the tendency of particles to move toward the individual optimal state, usually between 1.5 and 2.5; is the second acceleration coefficient, which is used to control the tendency of particles to move toward the global optimum, usually between 1.5 and 2.5; The third acceleration coefficient is used to control the tendency of particles to move toward the local optimum, usually between 0.5 and 1.5; , and is a random number, usually generated using a uniformly distributed random number generator; For the Particles in time At the The optimal position of an individual in dimension; is the global optimal position at time At the The value of the dimension; is the local optimal position at time At the The value of the dimension; For the Particles in time At the Current position on the dimension.

[0073] Generally, the number of particles ranges from 20 to 50, and the number of iterations is determined by the complexity of the problem. It is usually started from 100 to 500 times and adjusted according to the convergence speed of the algorithm and the optimization effect.

[0074] By finding the optimal weight combination of the evaluation index system, the rationality and accuracy of the sea use benefit evaluation of sea use projects can be improved. The algorithm is relatively simple and of low complexity. Verification shows that while obtaining an average error similar to that of the above-mentioned embodiment, only about 30% of the collected data volume is required to complete the task. This shows that adding a weight optimization module helps to reduce the workload of collecting sea use benefit data while improving the accuracy of benefit evaluation, effectively expand the applicable working conditions of the system, reduce the time cost of data collection, and improve the rationality of the system.

[0075] Embodiment 3: On the basis of the above-mentioned embodiment, the system adds a benefit prediction module for predicting the benefit rating of the sea use project in the future based on the historical benefit data of the sea use project and the multi-dimensional evaluation index system.

[0076] The historical benefit data of sea use projects are collected in advance, and the data are cleaned, missing values ​​are processed, outliers are processed and data standardization is performed.

[0077] The evaluation index with the highest weight is selected through the weight optimization module.

[0078] The loss function for the evaluation problem is the mean square error function.

[0079] Taking the decision tree as an example, a classification regression tree is used as a weak learner. The number of weak learners is set between 200 and 400, the maximum depth of the tree is set between 5 and 8, the learning rate is set between 0.01 and 0.5, and the minimum number of sample splits is set between 4 and 8. Each step iterates the weak learner on the residual of the previous step, wherein the evaluation model is adjusted by the following formula, and the contribution of each weak learner is calculated:

[0080] In the formula, For the The strong learner of the next step is the accumulation of all previous weak learners; For the Step-wise strong learner; is the learning rate, used to control the contribution of each step; For the The weak learner of the step is determined by model complexity, computational efficiency and data adaptability; To adjust the coefficients, used to balance the contributions of weak learners and geospatial models; It is a geospatial model used to capture the spatial correlation of the sea area, which is determined by the spatial correlation, data adaptability and computational efficiency of the model.

[0081] The model performance of different parameter combinations was evaluated through cross-validation, and the parameter combination with the best performance was selected.

[0082] For example, suppose there are three sea use projects: The sea use project is 1, the economic output score is 10, the ecological and environmental impact score is 2, the social environment score is 8, the spatial distance score is 0.5, and the sea use benefit rating is 1; There are 2 sea use projects, 5 economic output scores, 4 ecological and environmental impact scores, 6 social environment scores, 0.7 spatial distance scores, and 2 sea use benefit ratings; There are 3 sea use projects, economic output score of 3, ecological environmental impact score of 6, social environment score of 4, spatial distance score of 0.3, and sea use benefit rating of 3.

[0083] The target variable is coded as 1 for high benefit, 2 for medium benefit, and 3 for low benefit.

[0084] Based on the above features, spatial distance is introduced as a new feature.

[0085] Assume that one weak learner has been trained and a second weak learner needs to be trained. For each sea use project , with the following data: : characteristic vector, including economic output, ecological and environmental impact, social impact and spatial distance; : Actual value of sea use efficiency rating; : The predicted value of the first weak learner, where , , .

[0086] The residual is calculated as follows:

[0087]

[0088]

[0089]

[0090] In the process of training the second weak learner, the residual is used Training a decision tree , using spatial distance features to train geospatial models : Hypothesis Decision Tree predict: , , ; Assume a geospatial model predict: , , .

[0091] Update the evaluation model, assuming the learning rate , adjustment coefficient :

[0092]

[0093]

[0094] The new residual is calculated as follows:

[0095]

[0096]

[0097] Assume that there are data of 100 sea use projects, each with multiple evaluation indicators. The data set is randomly divided into a training set (60%), a validation set (20%), and a test set (20%). The parameter configuration of the gradient boosting model includes the number of weak learners: 300, the maximum depth of the tree: 5, the learning rate: 0.1, and the minimum number of sample splits: 5. The results show that the average absolute error of the model on the training set is 0.35, the average absolute error on the validation set is 0.40, and the average absolute error on the test set is 0.38; the performance of the model on the training set, validation set, and test set is relatively consistent, and the risk of overfitting is low; the model can explain 88% of the variable fluctuations on the training set, 85% of the variable fluctuations on the validation set, and 87% of the variable fluctuations on the test set.

[0098] The effect of applying the benefit prediction module is shown in Table 6.

[0099] Table 6. Summary of the effects of the benefit prediction module index Before applying the benefit prediction module After applying the benefit prediction module Improved performance Mean absolute error 0.8 0.5 -0.3 Overfitting risk high Low reduce Accuracy of sea use benefit prediction 15% 90% +75% Classification accuracy of sea-use projects 50% 85% +35% According to the experimental table, after the application of the benefit prediction module, the system has a lower mean absolute error, reduces the risk of overfitting, and has a higher accuracy in predicting the benefits of sea use. The results show that the application of the benefit prediction module can enable the system to more accurately identify and evaluate efficient sea use projects, and provide strong support for the optimal allocation of marine resources and sustainable development.

[0100] Embodiment 4: A hybrid energy supply monitoring device is deployed based on the aforementioned embodiment.

[0101] Equipment structure and power supply system: The equipment adopts a titanium alloy shell, has excellent corrosion resistance, and can operate stably for a long time in a marine environment.

[0102] It integrates solar photovoltaic panels and vertical axis micro wind turbines. The solar photovoltaic panels are made of high-efficiency monocrystalline silicon material with a conversion efficiency of up to 20.2% and a rated power of 300W. The vertical axis micro wind turbine has a rated power of 200W and can generate electricity efficiently under low wind speed conditions.

[0103] Equipped with a lithium iron phosphate battery pack with a capacity of 10kWh, the battery cycle life can reach 6000 times, and the operating temperature range is -20℃~45℃, ensuring stable power supply in harsh environments.

[0104] It adopts an advanced hybrid energy management system that can intelligently switch between solar energy, wind energy and lithium battery power supply, optimize energy utilization efficiency and ensure 24-hour uninterrupted operation of the equipment.

[0105] Sensor Integration: The device integrates a variety of high-precision sensors, including pH sensors, dissolved oxygen sensors and turbidity sensors for monitoring water quality. The accuracy of each sensor reaches laboratory level and can accurately monitor changes in ocean water quality in real time.

[0106] Equipped with a sonar depth sounder with an accuracy of ±0.1m, it can be used to accurately measure seabed topography and water depth.

[0107] Integrated GPS positioning module with an accuracy of ±1m and support for Beidou satellite navigation system to ensure accurate positioning of the device in the ocean.

[0108] It also includes wind speed and direction sensors and temperature and humidity sensors to monitor the marine meteorological environment and provide comprehensive data support for dynamic evaluation of sea use.

[0109] Communication and data transmission: The device uses the NB-IoT network to achieve low-power remote communication, which is suitable for wide-area coverage in marine environments and ensures stable data transmission.

[0110] In areas without network coverage, it automatically switches to Beidou short message transmission to ensure the continuity and reliability of data transmission.

[0111] It supports multiple communication protocols, including TCP, UDP, HTTP and MQTT, and can seamlessly connect with a variety of data processing platforms.

[0112] Intelligent management and self-checking system: The device has a built-in intelligent management system that can monitor the operating status of the device in real time and report operating data regularly.

[0113] When an abnormality occurs, the alarm mechanism is automatically triggered and the backup power supply is started to ensure the continuous operation of the equipment.

[0114] Supports remote configuration and FOTA remote upgrade to minimize on-site maintenance costs.

[0115] Environmental adaptability and protection: The protection level of the equipment casing is IP65, which can effectively prevent the intrusion of seawater and moisture.

[0116] It adopts military-grade shockproof design and can operate stably in harsh marine environments.

[0117] The operating temperature range of the equipment is -25℃~+65℃, adapting to various extreme climatic conditions.

[0118] 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.

[0119] 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.

[0120] 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 manufactured product including an instruction device that implements the functions of the system.

[0121] 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 dynamic evaluation system for efficient sea use based on three-dimensional right confirmation, including a data processing module for collecting and processing sea use benefit data, characterized in that: Also includes: Index assignment module, grading evaluation module, system optimization module, weight optimization module; The index assignment module is used to assign values ​​to sea use benefit data according to the high-efficiency sea use evaluation index system, and determine the weights of each dimension and index by combining the hierarchical analysis method and the Delphi method; The grading evaluation module is used to determine the final score of the sea use project according to the assignment results and the weights of each dimension and indicator, and to make a grading evaluation according to the final score; The system optimization module is used to optimize the efficient sea use evaluation index system according to environmental changes, technological progress, rule updates and application feedback. The optimization methods of the system optimization module include regular review, response to rule changes, adaptation to technological progress, feedback on sea use practices, special event triggering adjustments and consideration of regional differences; The weight optimization module is used to search the weight space through a particle swarm optimization algorithm and determine the optimal weight combination.

2. The system according to claim 1, characterized in that: The data processing module determines the overlapping areas and proportions of sea use projects in different functional areas and ecological red lines through a geographic information system.

3. The system according to claim 1, characterized in that: The dimensions of sea use benefit assessment include space utilization, time utilization, functional utilization, economic indicators, ecological environment and social indicators.

4. The system according to claim 1 or 3, characterized in that: The grading evaluation module determines the scores of each dimension of the sea use project according to the assignment results of the sea use benefit data and the weights of each indicator, and determines the final score of the sea use project according to the scores of each dimension of the sea use project and the weights corresponding to the dimensions.

5. The system according to claim 1, characterized in that: During the process of searching the weight space by the weight optimization module, the particle speed update formula is: In the formula, For the Particles in time At the Speed ​​in dimension; is the inertia weight; is the first acceleration coefficient; is the second acceleration factor; is the third acceleration factor; , and is a random number; For the Particles in time At the The optimal position of an individual in dimension; is the global optimal position at time At the The value of the dimension; is the local optimal position at time At the The value of the dimension; For the Particles in time At the Current position on the dimension.

6. The system according to claim 1, characterized in that: The system also includes a benefit prediction module, which is used to predict the benefit rating of the sea use project in the future based on the historical benefit data of the sea use project and the multi-dimensional evaluation index system.

7. The system according to claim 6, characterized in that: The benefit prediction module adjusts the evaluation model through the gradient boosting machine, and the update formula of the evaluation model is: In the formula, For the Step-wise strong learner; is the learning rate; For the Step weak learner; is the adjustment factor; A geospatial model.

8. A method for dynamic evaluation of efficient sea use based on three-dimensional land rights confirmation, characterized by: The method is implemented based on the system described in any one of claims 1 to 7: The method comprises: Collect and process data on sea use benefits; The sea use benefit data are assigned values ​​according to the high-efficiency sea use evaluation index system, and the weights of each dimension and index are determined by combining the analytic hierarchy process and the Delphi method; The final score of the sea use project is determined based on the assignment results and the weights of each dimension and indicator, and a graded evaluation is made based on the final score; Optimize the evaluation index system for efficient sea use based on environmental changes, technological progress, rule updates, and application feedback; The particle swarm optimization algorithm is used to find the optimal weight combination.

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 of the method according to claim 8.

10. A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, characterized in that: The computer program implements at least one step of the method of claim 8 when executed.

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