Multi-factor intelligent site selection method and system

Abstract

The invention discloses a multi-factor intelligent site selection method and system, and relates to the field of ocean space planning, and the method comprises the steps: collecting administrative region data, shoreline data, ocean ecological red line data and ocean ownership data, and carrying out the standardization processing of the data; performing spatial intersection, buffering and area calculation according to site selection conditions to generate candidate regions; calculating the dynamic weight of the evaluation index through an analytic hierarchy process, and carrying out multi-dimensional value evaluation on the candidate region; and dynamically adjusting the evaluation index weight through periodic evaluation and a key event triggering mechanism. According to the technical scheme of the invention, intelligent and precise site selection can be realized, and efficient utilization and reasonable configuration of sea area resources are promoted.

Description

Technical Field

[0001] The present invention relates to the field of marine space planning, and in particular to a multi-factor intelligent site selection method and system. Background Art

[0002] In the field of marine resource development and sea area management, intelligent site selection technology is a key tool for optimizing spatial resource allocation and ensuring ecological compliance. Traditional site selection methods rely on manual experience, have low data utilization and strong subjectivity, and are difficult to meet the needs of modern marine resource management and development for scientific planning, efficient decision-making and comprehensive consideration of multiple factors. With the development of the marine economy, scientific and systematic planning methods and efficient decision support tools are needed to ensure the rational use and protection of marine resources.

[0003] The development of geographic information technology provides support for spatial data analysis, data integration and management, and visualization display for intelligent site selection, but it still has significant limitations in multi-factor collaborative analysis and dynamic decision support in complex marine environments, which are specifically manifested in the following aspects: traditional site selection systems mostly rely on a single data source or simple layer overlay, and it is difficult to efficiently integrate multi-dimensional heterogeneous data such as administrative divisions, coastline characteristics, marine functional zoning, ecological red lines, and historical reclamation data. Especially when processing large-scale vector data, there is a lack of automated preprocessing mechanism, which leads to a long time for data cleaning and standardization, and it is easy to cause spatial calculation deviations due to geometric errors; existing methods often use static indicator weight allocation, and cannot dynamically adjust the evaluation model according to the type of sea area use or ecological sensitivity. In addition, the site selection conditions are mostly independently screened, lacking the chain coupling of spatial logic, resulting in a split in the planning compliance and development feasibility of the candidate area. Therefore, based on this problem, this application proposes an intelligent site selection method and system with multi-factor collaboration, spatial logic chain optimization, and dynamic weight adjustable to improve the efficiency of marine resource allocation and the level of ecological security. Summary of the invention

[0004] Technical Purpose In order to solve the above problems, the purpose of the present invention is to provide a multi-factor intelligent site selection method and system, which aims to solve the problems existing in the existing marine intelligent site selection technology, such as low efficiency of multi-source heterogeneous data integration, insufficient multi-factor dynamic coupling capability, coarse granularity of ecological compliance analysis and single user decision support function. By constructing a multi-factor collaborative optimization model and spatial chain screening logic, high-precision, dynamic and eco-friendly marine resource allocation can be achieved, and the comprehensive decision-making level of the site selection plan in terms of planning compliance, development feasibility and ecological sustainability can be improved.

[0005] Technical Solution In order to achieve the above-mentioned purpose, the present invention provides a multi-factor intelligent site selection method and system. The method and system construct a geographic information database and integrate a spatial algorithm library to realize the automatic cleaning and standardized storage of multi-source heterogeneous data such as administrative divisions, shoreline data, and ecological red lines. The method and system optimize the screening of candidate areas through a progressive spatial screening process combined with elastic threshold setting and dynamic weight adjustment mechanism, and embed in-depth analysis of ecological compliance and historical reclamation and contiguous development optimization algorithms to ensure the ecological safety and development intensiveness of the site selection plan.

[0006] In a first aspect, the present invention provides a multi-factor intelligent site selection method, comprising: Collect administrative district data, coastline data, marine ecological red line data and marine ownership data, and standardize the data; Perform spatial intersection, buffer and area calculations based on site selection conditions to generate candidate areas; The dynamic weights of evaluation indicators are calculated through the hierarchical analysis method, and the candidate areas are evaluated in multiple dimensions; The weights of evaluation indicators are dynamically adjusted through periodic evaluation and key event triggering mechanism.

[0007] Furthermore, the site selection conditions include flexible screening conditions that support setting of offshore distance and water depth value interval thresholds.

[0008] Furthermore, the analytic hierarchy process combines the subjective weights of experts to dynamically adjust the weights through error threshold determination, and fuses the updated weights with the initial weights to generate the final weights.

[0009] By combining the initial weight and the subjective weight, the method can more accurately reflect the importance of each evaluation index, thereby improving the accuracy and reliability of the evaluation; the application of the hierarchical analysis method enables the present invention to more comprehensively consider the influence of various factors when evaluating the site selection plan, avoiding the limitation of the single weight method; through the comparison of relative errors and the updating of weights, the weight of each index can be determined more accurately, thereby improving the accuracy and reliability of the evaluation results.

[0010] Furthermore, when selecting a site in the sea area on the sea side of the coastline, a buffer zone is generated based on the distance from the shore and spatially intersected with the functional area. The area within the water depth threshold is screened in combination with the isobath data, and the candidate blocks are output by using the gold grade division and area matching of the sea area.

[0011] Furthermore, when selecting a site for the sea area on the land side of the coastline, the unowned areas are screened in advance, and the historical reclamation report plans are matched to recommend priority blocks according to industry type and connectivity.

[0012] Furthermore, the ST_Buffer function of PostGIS is used to generate a buffer area for the coastline to expand outward, and the ST_Union and ST_Difference functions are combined to eliminate overlapping areas and retain the valid geographic range.

[0013] Furthermore, in the periodic assessment process, the ecological sensitivity and economic cost are predicted by the following formula:

[0014]

[0015] In the formula, For the The predicted value of ecological sensitivity in the year; For the Annual development cost forecast; is the prediction function; is the prediction function; is the error term; is the error term; n is the number of years of historical data referenced in the ecological sensitivity prediction; m is the number of years of historical data referenced in the development cost prediction.

[0016] The configuration of the weight optimization module not only ensures the stability of marine development projects, but also significantly improves ecological compliance and economic efficiency. It is particularly suitable for the full life cycle management of long-cycle projects such as fish farms and offshore wind power.

[0017] Furthermore, the method dynamically adjusts the scope and shape of the offshore buffer zone based on the ecological sensitivity quantification model, and the ecological sensitivity index function formula is:

[0018] In the formula, For location Ecological sensitivity index; is the total number of categories of ecological elements; For the The weight of ecological elements; For location To The closest distance to the ecological element; For the The maximum impact distance of this type of ecological element.

[0019] The buffer zone adjustment module avoids the coarse-grained defects of traditional fixed buffer zones by quantifying the spatial heterogeneity of sensitive factors, and significantly improves the ecological compliance and resource utilization efficiency of the site selection scheme.

[0020] Furthermore, the method includes a user interaction interface implementation step, which supports cascade selection of cities and counties and fuzzy matching of industry types, takes sea area functions, investment costs and natural coastline length as core indicators, dynamically adjusts indicator weights according to user-set priorities, so as to display planning compliance, traffic convenience and ecological impact score radar charts, and has a two-way saving function for query conditions and plans, and supports rapid calling of historical conditions and comparative analysis of plans.

[0021] Furthermore, the method also includes a marine ecological red line compliance analysis step, which identifies overlapping areas by superimposing the ecological red line layer and the candidate area. If there is an overlap, the red line area name and control measures are extracted, and the ratio of the length of the occupied natural coastline to the total coastline length is calculated. A compliance report is generated through the ST_Intersection function to mark the prohibited development area.

[0022] Furthermore, the method also includes a historical reclamation disposal analysis step, which involves associating a list of issues left over from reclamation with candidate areas to identify overlapping patches, extracting catalog numbers and disposal classifications, and correcting erroneous plots using spatial self-intersection repair technology. Disposal recommendations prioritize areas that are concentrated and contiguous and have matching industrial types.

[0023] Furthermore, the method also includes an uninhabited island site selection optimization step, which screens areas adjacent to uninhabited islands by constructing a 1km buffer zone analysis model, calculates the distance to the nearest island through the ST_Distance function based on the island type and utilization method, avoids ecologically sensitive areas and optimizes contiguous development plans by dynamically adjusting development priorities.

[0024] In a second aspect, the present invention further provides a multi-factor intelligent site selection system, the system is based on the method described in the first aspect, and comprises: Data collection module, used to collect administrative district data, coastline data, marine ecological red line data and marine ownership data; A data processing module is used to standardize the data, including converting administrative boundary lines into market data, classifying and merging shoreline data, and removing prohibited areas from ecological red line data; Database module, used to store multi-factor data and integrate PostGIS spatial processing functions. The database is built on PostgreSQL; The site selection generation module is used to call the PostGIS algorithm library to perform spatial intersection, buffering and area calculation according to the site selection conditions to generate candidate areas; Adaptability evaluation module, which is used to calculate the dynamic weight of evaluation indicators through the hierarchical analysis method and conduct multi-dimensional value evaluation of candidate areas; The weight optimization module is used to dynamically adjust the weights of evaluation indicators through periodic evaluation and key event triggering mechanism.

[0025] In a third aspect, the present invention also provides a computer device, comprising a management platform and a memory, wherein the management platform is connected to the memory, the memory is used to store computer programs, and the management platform is used to execute the computer programs stored in the memory, so that the computer device executes at least one step of the aforementioned multi-factor intelligent site selection method.

[0026] 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 management platform, at least one step of the aforementioned multi-factor intelligent site selection method is implemented.

[0027] The present invention collects data of various factors including administrative district data, sea areas on the sea side of the coastline, sea areas on the land side of the coastline, marine functional zoning, continental coastline, isobaths, marine ecological red lines, marine ownership data, marine classification data, etc., and performs data processing operations to form a site selection analysis database; the site selection schemes in the site selection candidate set are adaptively valued through the hierarchical analysis method, including determining evaluation indicators, calculating initial weights, obtaining subjective weights, comparing relative errors, updating weights, etc.; the weights of evaluation indicators are dynamically adjusted through periodic evaluation and key event triggering mechanisms; the scope and shape of the offshore buffer zone are dynamically adjusted based on the ecological sensitivity quantification model. The method and system have a wide range of application scenarios, can realize intelligent and precise site selection, and promote efficient utilization and reasonable allocation of marine resources.

[0028] Beneficial Effects By implementing the multi-factor intelligent site selection method and system provided by the present invention, the following technical effects are achieved: (1) The present invention collects data on various factors including administrative district data, sea areas on the sea side of the coastline, sea areas on the land side of the coastline, marine functional zoning, continental coastline, isobaths, marine ecological red lines, marine ownership data, marine classification data, etc., and performs data processing operations to form a site selection analysis database. This method overcomes the problem of low data utilization in traditional site selection methods, improves the comprehensiveness and accuracy of data by integrating multiple data sources, provides a solid data foundation for subsequent intelligent site selection, and can reduce site selection errors caused by incomplete or inaccurate data while improving site selection efficiency.

[0029] (2) Adaptive value evaluation of the site selection schemes in the site selection candidate set is performed by using the hierarchical analysis method, including determining evaluation indicators, calculating initial weights, obtaining subjective weights, comparing relative errors, updating weights, etc. This method can more accurately reflect the importance of each evaluation indicator by combining initial weights and subjective weights, thereby improving the accuracy and reliability of the evaluation; the application of the hierarchical analysis method enables the present invention to more comprehensively consider the influence of various factors when evaluating site selection schemes, avoiding the limitations of a single weight method; by comparing relative errors and updating weights, the weights of each indicator can be more accurately determined, thereby improving the accuracy and reliability of the evaluation results.

[0030] (3) Dynamically adjust the weights of evaluation indicators through periodic evaluation and key event triggering mechanisms. While ensuring the stability of marine development projects, it significantly improves ecological compliance and economic efficiency, and is particularly suitable for the full life cycle management of long-term projects such as fish farms and offshore wind power.

[0031] (4) Dynamically adjust the scope and shape of the offshore buffer zone based on the ecological sensitivity quantification model. By quantifying the spatial heterogeneity of sensitive elements, it avoids the coarse-grained defects of traditional fixed buffer zones and significantly improves the ecological compliance and resource utilization efficiency of the site selection plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to make the above-mentioned multi-factor intelligent site selection method and system of the present invention more obvious and easy to understand, the drawings required for use in the specific implementation 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 paying creative labor.

[0033] Figure 1 A schematic diagram showing the process flow of the multi-factor intelligent site selection method; Figure 2 A schematic diagram showing the logic of site selection for the seaward functional area; Figure 3 A logical diagram showing the site selection of land-side functional areas. DETAILED DESCRIPTION

[0034] Embodiment 1: A multi-factor intelligent site selection method and system are provided, as described below.

[0035] A multi-factor intelligent site selection method, the method is as follows Figure 1 As shown, including: Collect administrative district data, coastline data, marine ecological red line data and marine ownership data, and standardize the data; Perform spatial intersection, buffer and area calculations based on site selection conditions to generate candidate areas; The dynamic weights of evaluation indicators are calculated through the hierarchical analysis method, and the candidate areas are evaluated in multiple dimensions; The weights of evaluation indicators are dynamically adjusted through periodic evaluation and key event triggering mechanism.

[0036] The site selection conditions include flexible screening conditions that support setting of offshore distance and water depth value interval thresholds.

[0037] The steps of the site selection generation module for performing multi-factor spatial analysis through the PostGIS algorithm include: generating offshore buffer zones; screening water depth intervals; matching marine functional zoning and avoiding ecological red lines; eliminating ownership plots and filtering by area thresholds.

[0038] The analytic hierarchy process combines the subjective weights of experts to dynamically adjust the weights through error threshold determination, and fuses the updated weights with the initial weights to generate the final weights.

[0039] The logic of site selection for the seaward functional area is as follows: Figure 2 As shown, when selecting a site in the sea area on the sea side of the coastline, a buffer zone is generated based on the distance from the shore and spatially intersected with the functional area. The area within the water depth threshold is screened in combination with the bathymetric data. The candidate blocks are output through the gold level division and area matching of the sea area.

[0040] The logic of site selection for land-side functional areas is as follows: Figure 3 As shown, when selecting a site for the sea area on the land side of the coastline, the unowned areas are screened in advance, and the historical reclamation plan is matched, and priority blocks are recommended according to industry type and connectivity.

[0041] The ST_Buffer function of PostGIS is used to generate a buffer area that extends outward from the coastline. The ST_Union and ST_Difference functions are combined to eliminate overlapping areas and retain the valid geographic range.

[0042] The method includes a user interaction interface implementation step, which supports cascade selection of cities and counties and fuzzy matching of industry types, takes sea area functions, investment costs and natural coastline length as core indicators, dynamically adjusts indicator weights according to user-set priorities, so as to display planning compliance, traffic convenience and ecological impact scoring radar charts, and has a two-way saving function for query conditions and solutions, and supports rapid calling of historical conditions and comparative analysis of solutions.

[0043] When the user selects a map layer in the interactive interface, the system automatically locates the details of the associated plan (such as the unit price of sea area use gold, functional zoning name), and highlights the overlapping ecological red line areas, and displays the planning compliance, transportation convenience, spatial expansibility, supporting maturity, landscape pleasantness and overdue comprehensive benefit scores through radar charts.

[0044] The method also includes a marine ecological red line compliance analysis step, which identifies overlapping areas by superimposing the ecological red line layer and the candidate area. If there is an overlap, the red line area name and control measures are extracted, and the ratio of the length of the occupied natural coastline to the total coastline length is calculated. A compliance report is generated through the ST_Intersection function to mark the prohibited development area.

[0045] The method also includes a historical reclamation disposal analysis step, which identifies overlapping patches by associating a list of issues left over from reclamation with candidate areas, extracts catalog numbers and disposal classifications, and corrects erroneous plots using spatial self-intersection repair technology. Disposal recommendations prioritize concentrated and contiguous areas with matching industrial types.

[0046] The method also includes an uninhabited island site selection optimization step, which screens areas adjacent to uninhabited islands by constructing a 1km buffer zone analysis model, calculates the distance to the nearest island through the ST_Distance function in combination with the island type and utilization method, avoids ecologically sensitive areas and optimizes contiguous development plans by dynamically adjusting development priorities.

[0047] A multi-factor intelligent site selection system, based on the aforementioned method, comprising: Data collection module, used to collect administrative district data, coastline data, marine ecological red line data and marine ownership data; A data processing module is used to standardize the data, including converting administrative boundary lines into market data, classifying and merging shoreline data, and removing prohibited areas from ecological red line data; Database module, used to store multi-factor data and integrate PostGIS spatial processing functions. The database is built on PostgreSQL, and the data table structure design covers spatial geometry fields (wkb_geometry) and attribute fields (such as code, control_measures, and use_type); The site generation module is used to call the PostGIS algorithm library to perform spatial intersection, buffering and area calculation according to the site selection conditions (city / county, sea use type, industry type, and area used) to generate candidate areas; The adaptability evaluation module is used to calculate the dynamic weights of evaluation indicators through the hierarchical analysis method and conduct multi-dimensional value evaluation on candidate areas.

[0048] Embodiment 2: Based on the above-mentioned embodiments, the system proposes a weight optimization algorithm based on historical data accumulation and trend prediction in view of the long-term characteristics of marine development projects. Through periodic evaluation and key event triggering mechanism, the weights of evaluation indicators are dynamically adjusted to balance long-term ecological stability and development needs.

[0049] A time series database is pre-built to store ecological parameters (water quality, tidal frequency, biodiversity index) and development cost data on an annual basis.

[0050] The ecological sensitivity and economic cost in the next 10 years are predicted by the autoregressive integrated moving average model. The formula is:

[0051]

[0052] In the formula, For the The predicted value of ecological sensitivity in the year; For the Annual development cost forecast; is the prediction function; is the prediction function; is the error term; is the error term; n is the number of years of historical data referenced in the ecological sensitivity prediction; m is the number of years of historical data referenced in the development cost prediction.

[0053] The indicator weights are recalculated every five years, and based on historical trends and the latest requirements, the weight adjustment formula is:

[0054] In the formula, are weights generated by historical trend forecasts; is the mandatory weight; Coefficients are reserved for historical weights, reflecting the degree of reliance on long-term data.

[0055] When changes in the ecological red line are detected, the weights are immediately recalculated to prioritize improving ecological indicators. For example, after red tides or typhoon disasters, the weight of ecological restoration costs is temporarily increased and development priorities are adjusted.

[0056] A weight change threshold is introduced to trigger adjustment only when the predicted weight change exceeds the threshold to avoid frequent fluctuations. The formula is:

[0057] Verification shows that while obtaining an average error similar to that of the above embodiment, within the 30-year simulation period, the ecological conflict rate decreased by an average of 2.5% per year, and high-cost areas were avoided in advance through trend prediction, saving 18%-25% of the total development cost in 30 years. The average annual adjustment of weights was ≤2 times, avoiding frequent changes that interfered with long-term planning. The algorithm significantly improved ecological compliance and economy while ensuring the stability of marine development projects by integrating long-term trend prediction with event-driven mechanisms. It is particularly suitable for the full life cycle management of long-term projects such as fish farms and offshore wind power.

[0058] Embodiment 3: On the basis of the aforementioned embodiments, the system dynamically adjusts the scope and shape of the offshore buffer zone based on a quantitative model of ecological sensitivity to achieve an optimal balance between development needs and ecological protection.

[0059] The ecological sensitivity index function is constructed, and the function formula is:

[0060] In the formula, For location Ecological sensitivity index; is the total number of categories of ecological elements; For the The weight of ecological elements; For location To The closest distance to the ecological element; For the The maximum impact distance of this type of ecological element.

[0061] The adaptive buffer boundary is generated by a nonlinear buffer function, the function formula is:

[0062] In the formula, is the adaptive buffer boundary distance; is the basic buffer distance; is the sensitivity adjustment coefficient, which is used to control the buffer expansion amplitude; The decay rate is used to determine how fast the sensitivity decays to the buffer range.

[0063] For example, suppose the ecological element, seagrass beds ( , , )、Coral Reefs( , , ); target position, Distance to seagrass beds , distance to coral reef ; Parameter setting, , , .

[0064] Calculate the ecological sensitivity index:

[0065] Calculate the adaptive buffer distance:

[0066] The effect of the buffer adjustment module is shown in Table 1.

[0067] Table 1. Summary of buffer adjustment module effects

[0068] According to the experimental table, in ecologically highly sensitive areas, the buffer zone is expanded to 1.7 times the basic distance, the ecological conflict rate is reduced by 45%, and the development cost is saved by 20%. The model quantifies the spatial heterogeneity of sensitive elements, and the buffer distance expands nonlinearly with the sensitivity index. The exponential term ensures that the buffer zone in highly sensitive areas expands rapidly and the low-sensitivity area shrinks moderately, avoiding the coarse-grained defects of traditional fixed buffer zones and significantly improving the ecological compliance and resource utilization efficiency of the site selection plan.

[0069] 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.

[0070] The present invention can provide computer program instructions to a management platform 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 management platform of the computer or other programmable data processing device produce a device for implementing the system.

[0071] 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.

[0072] 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 multi-factor intelligent site selection method, characterized in that: include: Collect administrative district data, coastline data, marine ecological red line data and marine ownership data, and standardize the data; Perform spatial intersection, buffer and area calculations based on site selection conditions to generate candidate areas; The dynamic weights of evaluation indicators are calculated through the hierarchical analysis method, and the candidate areas are evaluated in multiple dimensions; The weights of evaluation indicators are dynamically adjusted through periodic evaluation and key event triggering mechanism.

2. The method according to claim 1, characterized in that: The site selection conditions include flexible screening conditions that support setting of offshore distance and water depth value interval thresholds.

3. The method according to claim 1, characterized in that: The analytic hierarchy process combines the subjective weights of experts to dynamically adjust the weights through error threshold determination, and fuses the updated weights with the initial weights to generate the final weights.

4. The method according to claim 1, characterized in that: When selecting a site in the sea area on the sea side of the coastline, a buffer zone is generated based on the distance from the shore and spatially intersected with the functional area. The area within the water depth threshold is screened in combination with the bathymetric data, and the sea area is divided into gold grades and matched in area.

5. The method according to any one of claims 1 to 4, characterized in that: During the periodic assessment process, the ecological sensitivity and economic cost are predicted by the following formula: In the formula, For the The predicted value of ecological sensitivity in the year; For the Annual development cost forecast; is the prediction function; is the prediction function; is the error term; is the error term; n is the number of years of historical data referenced in the ecological sensitivity prediction; m is the number of years of historical data referenced in the development cost prediction.

6. The method according to any one of claims 1 to 4, characterized in that: The method dynamically adjusts the scope and shape of the offshore buffer zone based on an ecological sensitivity quantification model.

7. The method according to claim 1, characterized in that: The method also includes a user interaction interface implementation step, which supports cascade selection of cities and counties and fuzzy matching of industry types, takes sea area functions, investment costs and natural coastline length as core indicators, and dynamically adjusts indicator weights according to the priorities set by users.

8. A multi-factor intelligent site selection system, 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 administrative district data, coastline data, marine ecological red line data and marine ownership data; A data processing module is used to standardize the data, including converting administrative boundary lines into market data, classifying and merging shoreline data, and removing prohibited areas from ecological red line data; Database module, used to store multi-factor data and integrate spatial processing functions; The site selection generation module is used to perform spatial intersection, buffer and area calculation according to the site selection conditions to generate candidate areas; Adaptability evaluation module, which is used to calculate the dynamic weight of evaluation indicators through the hierarchical analysis method and conduct multi-dimensional value evaluation of candidate areas; The weight optimization module is used to dynamically adjust the weights of evaluation indicators through periodic evaluation and key event triggering mechanism.

9. A computer device, comprising a management platform and a memory, wherein the management platform is connected to the memory, and the memory is used to store a computer program, characterized in that: The management platform is used 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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