Ahp entropy weight urban renewal potential sorting method and system

Abstract

The application discloses an AHP entropy weight urban renewal potential sorting method and system, relates to the technical field of urban renewal evaluation and sorting decision, and comprises the following steps: reading target urban renewal region information, performing classified discussion type identification and interactive checking, and outputting candidate unit type identification results; performing path import, judgment basis construction, stage judgment and interactive checking, and forming candidate unit evaluation classification output results; performing differentiation import, basic potential merging, correction calculation and stability checking, forming candidate unit potential value results, performing grouping sorting, implementation sequence generation and consistency checking, and outputting candidate unit renewal implementation sorting results. The method realizes integrated processing of classified identification, hierarchical evaluation, potential calculation and implementation sorting of the candidate unit of urban renewal, and improves the identification accuracy of the renewal object, the rationality of potential judgment, and the matching degree of the sorting result and actual implementation arrangement.

Description

Technical Field

[0001] This invention relates to the field of urban renewal evaluation and ranking decision-making technology, specifically to an AHP entropy weight urban renewal potential ranking method and system. Background Technology

[0002] As urban development continues to advance, urban renewal has gradually shifted from a development model primarily focused on incremental expansion to a governance model centered on optimizing existing resources, adjusting structures, enhancing functions, and improving quality. With tasks such as renovating old residential areas, updating inefficient industrial spaces, addressing deficiencies in public service facilities, and comprehensively improving areas, identifying urban renewal potential, selecting renewal targets, and scheduling implementation have become key technical issues in urban governance and planning. Existing technologies typically rely on geospatial information, population activity information, facility configuration data, land use status data, and historical management data to identify, evaluate, and rank renewal units. They are also gradually introducing multi-indicator comprehensive evaluation, hierarchical analysis, weight allocation, and decision support methods to improve the systematicness, scientific rigor, and operability of renewal target selection, thereby providing support for urban renewal project reserves, annual implementation plan preparation, and resource allocation optimization.

[0003] Existing methods for evaluating and ranking urban renewal potential still have significant limitations. First, existing methods often treat renewal targets as homogeneous evaluation units, lacking a mechanism for categorizing and differentiating evaluations based on varying renewal needs such as residential improvement, industrial upgrading, infrastructure improvement, and environmental remediation. This makes it difficult to accurately reflect the dominant contradictions and implementation directions of different candidate units. Second, existing methods typically focus on static indicator aggregation or single-evaluation weighting during the judgment process, lacking a layered connection between the dominant type, accompanying type, judgment path, and stage judgment results of candidate units. This makes it difficult to form a continuous processing chain from object identification to classification evaluation, thus hindering stable judgments in complex renewal scenarios. Third, existing methods often only generate a single score result in the potential calculation stage, failing to adequately coordinate constraints, opportunities, additional evaluation content, and composite path content. This makes it difficult to simultaneously reflect the coupling relationship between the necessity of renewal, implementation support, comprehensive driving force, and actual constraints, leading to discrepancies between the ranking results and actual implementation conditions. Fourth, existing methods typically focus on providing the order of candidate projects during the ranking output stage, while paying insufficient attention to aspects such as the criteria for ranking at the same level, the generation of implementation sequences, implementation grouping, and consistency verification. This makes it difficult to directly generate ranking results that are geared towards implementation organization and project arrangement. Therefore, existing technologies struggle to achieve integrated processing of classification, hierarchical judgment, potential calculation, and implementation ranking of urban renewal candidate units. They also fail to achieve the technical effects of clear boundaries for renewal targets, stable evaluation process connections, sufficient expression of potential results, and ranking results that directly serve implementation arrangements. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is that existing urban renewal potential evaluation and ranking methods lack differentiated identification processing for candidate units, have insufficient connection in the evaluation and judgment process, and have unclear correspondence between potential calculation and implementation ranking. It also addresses the problem of how to achieve integrated processing of classification and identification, evaluation and classification, potential calculation and implementation ranking of urban renewal candidate units.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: an AHP entropy weight urban renewal potential ranking method, comprising: reading target urban renewal area information; completing candidate unit division, information merging and verification; constructing a candidate unit basic result set; performing classification-based discussion-style type identification and interactive verification; and outputting candidate unit type identification results; performing path import, judgment basis construction, stage judgment and interactive verification on the candidate unit type identification results to form candidate unit evaluation classification output results; based on the candidate unit evaluation classification output results, performing differential import, basic potential merging, correction calculation and stability verification to form candidate unit potential value results; performing grouping and ranking, implementation sequence generation and consistency verification; and outputting candidate unit update implementation ranking results.

[0007] As a preferred embodiment of the AHP entropy weight urban renewal potential ranking method described in this invention, the following steps are included: reading the target urban renewal area information, completing candidate unit division, information merging and verification, and constructing a basic result set of candidate units. This includes reading the current spatial information, land use layout information, building distribution information, population activity information, public service facility information, municipal infrastructure information, industrial space information, and historical renewal management information within the target renewal area; dividing the spatial objects within the target renewal area into candidate units based on spatial continuity, current functional relationships, building combination relationships, facility service relationships, and management implementation boundary relationships; merging building use composition information, current land use information, building age distribution information, population activity information, public service facility configuration, industrial carrying capacity information, current municipal infrastructure information, and historical renewal problem records into corresponding candidate units according to the candidate unit identifier; and performing spatial correspondence verification, time caliber verification, missing information verification, and conflict verification on the merged information to construct a basic result set of candidate units.

[0008] As a preferred embodiment of the AHP entropy weight urban renewal potential ranking method described in this invention, the following steps are performed: The execution of classification-based type identification and interactive verification, outputting candidate unit type identification results, includes reading information on building use composition, land use status, building age distribution, population activity, public service facility configuration, industrial carrying capacity, municipal infrastructure status, and historical renewal issues corresponding to each candidate unit, forming a basic record for candidate unit type identification; based on the basic record for candidate unit type identification, the candidate units are identified according to the classification-based discussion method, classifying them into residential improvement, industrial upgrading, facility improvement, environmental remediation, or composite renewal categories; interactive verification is performed on the type identification results to determine the dominant and accompanying types of the candidate units, forming the candidate unit type identification results.

[0009] As a preferred embodiment of the AHP entropy weight urban renewal potential ranking method described in this invention, the following steps are performed: importing the candidate unit type identification results into a path, constructing judgment criteria, performing stage judgments, and conducting interactive verification to form the candidate unit evaluation classification output results. This includes reading the dominant type, accompanying type, type identification criteria, and candidate unit basic information records corresponding to each candidate unit, and outputting candidate unit evaluation judgment records; importing candidate units into residential improvement judgment paths, industrial upgrading judgment paths, facility improvement judgment paths, environmental remediation judgment paths, or composite renewal judgment paths based on the dominant type; extracting the current status composition, problem manifestations, facility support, carrying relationship, and historical renewal problem content corresponding to the current judgment path from the candidate unit basic information records to construct path judgment criteria; performing stage judgments based on the path judgment criteria, conducting interactive verification of the stage judgment results, and outputting the candidate unit evaluation classification output results.

[0010] As a preferred embodiment of the AHP entropy weight urban renewal potential ranking method described in this invention, the following is defined: the evaluation and classification output results based on candidate units include output results based on priority judgment units, compatible judgment units, composite judgment units, and judgment units to be supplemented; priority judgment units are candidate units that can directly enter the potential calculation process; compatible judgment units are candidate units that need to load additional judgment content in addition to the dominant judgment path; composite judgment units are candidate units that need to enter the potential calculation process together according to the dominant path and parallel paths; and judgment units to be supplemented are candidate units that will not enter the formal potential calculation process for the time being.

[0011] As a preferred embodiment of the AHP entropy weight urban renewal potential ranking method described in this invention, the following steps are performed: Differential import, basic potential merging, correction calculation, and stability verification to form candidate unit potential value results include reading the evaluation classification results, judgment paths, path judgment criteria, stage judgment results, interactive verification results, and candidate unit basic information records corresponding to each candidate unit to form candidate unit potential calculation records; differential import is performed on candidate units based on the evaluation classification results; the imported content is merged into renewal necessity content, implementation support content, comprehensive driving content, and constraint sensitivity content, and basic potential calculation results are output; based on the basic potential calculation results, correction calculation is performed in conjunction with constraint content and opportunity content, and stability verification is performed on the corrected results to output candidate unit potential value results.

[0012] As a preferred embodiment of the AHP entropy weight urban renewal potential ranking method described in this invention, the following steps are performed: performing group ranking, generating implementation sequences, and verifying consistency, and outputting the candidate unit update implementation ranking results include reading the potential value results, potential classification results, dominant calculation path, additional calculation content, composite calculation content, basic potential calculation results, corrected calculation results, and potential description content corresponding to each candidate unit, forming a candidate unit ranking generation record; importing the candidate units into the formal ranking sequence or reserve observation sequence according to the potential classification results, and performing group ranking on the candidate units in the formal ranking sequence; generating an implementation sequence based on the ranking results, and performing a consistency verification on the correspondence between the ranking results and the implementation sequence; and outputting the candidate unit update implementation ranking results.

[0013] Another objective of this invention is to provide an AHP entropy weight urban renewal potential ranking system, which can generate candidate unit evaluation and classification output results by importing the execution path of the candidate unit type identification results, constructing the judgment basis, making stage judgments and interactive verifications. This solves the problems of insufficient connection in the evaluation and judgment process and unclear correspondence between potential calculation and implementation ranking in the current urban renewal potential evaluation and ranking methods.

[0014] As a preferred embodiment of the AHP entropy weight urban renewal potential ranking system described in this invention, it includes: a candidate unit construction module, a classification evaluation and judgment module, and a potential ranking output module; the candidate unit construction module is used to read, divide, and merge information of the target urban renewal area to form basic candidate unit results; the classification evaluation and judgment module is used to perform type identification, path judgment, and classification output on the candidate units to form evaluation classification results corresponding to each candidate unit; the potential ranking output module is used to perform potential calculation, ranking generation, and implementation sequence output on the evaluation classification results to form candidate unit update implementation ranking results.

[0015] A computer device includes a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the AHP entropy weight urban renewal potential ranking method.

[0016] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the AHP entropy weight urban renewal potential ranking method.

[0017] The beneficial effects of this invention are as follows: The AHP entropy weight urban renewal potential ranking method provided by this invention achieves a unified transformation of urban renewal objects from scattered spatial information to structured candidate units through reading information of target urban renewal areas, dividing candidate units, merging and verifying information. This establishes a basic object system for subsequent type identification, evaluation judgment, and potential ranking, resulting in clear boundaries of renewal objects, complete information carrying capacity, and stable preliminary data foundation. By performing classification-based discussion-style type identification and interactive verification on candidate units, it achieves the differentiation of different renewal demands and the determination of dominant types. This allows different candidate units, such as residential improvement, industrial upgrading, infrastructure improvement, environmental remediation, and composite renewal, to be imported into suitable evaluation paths, thereby avoiding homogenization, enhancing the accuracy of object identification, and improving the targeting of subsequent evaluations. By implementing path import, judgment basis construction, stage judgment, and interactive verification, it achieves a layered transition from type results to evaluation classification results for candidate units. This clarifies the judgment path, judgment boundary, and classification status of each candidate unit in subsequent calculations, resulting in an orderly evaluation process, clear hierarchical judgment results, and stable diversion of complex objects. By implementing differentiated import, basic potential merging, corrective calculation, and stability verification, the potential values ​​of candidate units were transformed from static judgments to comprehensive calculation results. This comprehensively reflects the relationship between the necessity of renewal, implementation support, comprehensive driving force, and realistic constraints, thus achieving the beneficial effects of more comprehensive potential results and closer correspondence between calculation results and implementation conditions. Furthermore, by implementing grouping and ranking, implementation sequence generation, and consistency verification, the potential value results of candidate units were directly transformed into implementation ranking results. This supports the selection of urban renewal projects, the scheduling of implementation timelines, and group management, achieving the beneficial effects of interpretable and callable ranking results that can directly serve the urban renewal implementation organization. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments 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 effort.

[0019] Figure 1 The first embodiment of the present invention provides an overall flowchart of an AHP entropy weight urban renewal potential ranking method. Detailed Implementation

[0020] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0021] Example 1, referring to Figure 1 As an embodiment of the present invention, an AHP entropy weight urban renewal potential ranking method is provided, comprising: S1: Read the target city renewal area information, complete the candidate unit division, information merging and verification, construct the candidate unit basic result set, perform classification discussion-style type recognition and interactive verification, and output the candidate unit type recognition results.

[0022] Furthermore, after the target renewal area is determined, the following information is first retrieved: current spatial information, land use layout information, building distribution information, population activity information, public service facility information, municipal infrastructure information, industrial space information, and historical renewal management information related to the target renewal area. This information is then organized according to a unified spatial reference relationship to form the basic data for constructing candidate units. Current spatial information is used to characterize the existing location and boundary relationships of various spatial objects within the target renewal area; land use layout information is used to characterize the functional organization status of different plots or spatial objects; building distribution information is used to characterize the distribution and composition of built-up spaces; population activity information is used to characterize the basic situation of population use and activities within the area; public service facility information and municipal infrastructure information are used to characterize the facility supply status and basic support status within the target renewal area, respectively; industrial space information is used to characterize the distribution status of commercial spaces, production spaces, or related composite spaces; and historical renewal management information is used to characterize existing renewal problems, identified sources of conflict, or past remediation efforts.

[0023] After forming the basic data for candidate units, the spatial objects within the target update area are divided into candidate units according to spatial continuity, existing functional relationships, building combination relationships, facility service relationships, and management implementation boundary relationships. Spatial continuity is used to identify sets of objects that are spatially adjacent or have continuous distribution characteristics; existing functional relationships are used to identify sets of objects that share common functional attributes or have functional linkages in actual use; building combination relationships are used to identify sets of objects that have combination characteristics in terms of construction form, construction period, or usage mode; facility service relationships are used to identify sets of objects that are supported by the same type of public service facilities or infrastructure; and management implementation boundary relationships are used to identify spatial sets that can be treated as the same object in update management, remediation organization, or implementation arrangements.

[0024] To avoid the candidate unit division results remaining at a superficial level, the process prioritizes maintaining the continuity of spatial relationships and the uniformity of implementation organization within the same candidate unit. Spatial objects that are adjacent, functionally connected, and have interconnected facility service relationships are grouped into the same candidate unit. Spatial objects that are adjacent but have significantly different current functional attributes, implementation organization directions, or sources of renewal conflicts are assigned to different candidate units. If a single spatial object can independently reflect a clear renewal demand and possesses the conditions for independent implementation organization, it is directly formed into a single candidate unit. If multiple spatial objects, although lacking continuous boundaries, have a clear linkage or joint governance relationship in the same renewal implementation matter, they are recorded as related candidate units.

[0025] After the candidate units are divided, a corresponding candidate unit identifier is established for each candidate unit, and the spatial range covered by the candidate unit, its constituent objects, corresponding boundary relationships, and associated object relationships are written into the candidate unit registration record. The candidate unit identifier is used to uniquely correspond to the type identification results, evaluation judgment results, potential calculation results, and ranking generation results of each candidate unit in subsequent steps; the spatial range is used to characterize the spatial coverage boundary of the candidate unit; the constituent objects are used to characterize the specific set of objects that constitute the candidate unit; the boundary relationships are used to characterize the separation or connection relationships between the candidate unit and adjacent candidate units; and the associated object relationships are used to characterize the possible implementation linkage relationships between the candidate unit and other candidate units.

[0026] After the candidate unit registration is completed, the basic information merging process for the candidate units is further performed. This process includes merging the building use composition information, land use status information, building age distribution information, population activity information, public service facility configuration, industrial carrying capacity information, municipal infrastructure status information, and historical update issue records from the aforementioned candidate unit construction basic data into the corresponding candidate unit, according to the candidate unit identifier, to form the candidate unit basic information record. Specifically, the building use composition information characterizes the composition status of different uses within the candidate unit; the land use status information characterizes the current land use organization status of the candidate unit; the building age distribution information characterizes the formation period characteristics of the existing built-up space of the candidate unit; the population activity information characterizes the population usage and activity status of the candidate unit; the public service facility configuration characterizes the internal and external service support status of the candidate unit; the industrial carrying capacity information characterizes the carrying capacity status of the candidate unit's commercial or production spaces; the municipal infrastructure status information characterizes the infrastructure support conditions of the candidate unit; and the historical update issue records characterize the major update issues that have been exposed or recorded in the past for the candidate unit.

[0027] To ensure consistency in the basic information records of candidate units, further information verification processing is performed after the basic information is merged. This information verification process includes: checking the correspondence between information from different sources within the same candidate unit to identify inconsistencies in spatial correspondence, temporal scope, missing records, or content conflicts; for inconsistencies in spatial correspondence, retaining only the content directly corresponding to the spatial range of the candidate unit, and recording the remaining content in the field to be verified; for inconsistencies in temporal scope, writing information reflecting the current state into the current information field, and retaining other information as historical reference content; for missing content, writing a missing marker in the candidate unit's basic information record; and for conflicting content, retaining the records of each conflicting source and writing them into the conflict description field.

[0028] After information verification, a completeness check is performed on the candidate unit's basic information records. Candidate units with complete basic information records that can reliably proceed to subsequent type identification steps are marked as identifiable candidate units. Candidate units that have been divided but still contain fields requiring verification, missing markers, or conflict explanation fields in their basic information are marked as supplementary candidate units, and their merged information content is retained. Identifiable candidate units directly enter type identification processing, while supplementary candidate units enter type identification processing after subsequent information supplementation.

[0029] Finally, the candidate unit identifiers, spatial scope, constituent objects, boundary relationships, related object relationships, basic information records of candidate units, information verification results, and completeness organization results are associated and written into the database to form a basic result set of urban renewal candidate units. This basic result set is used for candidate unit type identification processing. Identifiable candidate units directly enter the type identification process, while candidate units to be supplemented retain their basic result sets and enter the type identification process after information is complete. Since each candidate unit has clear constituent boundaries, merged information content, and verification results, not only is the candidate unit construction completed, but the basic information carriers and calling bases required for subsequent type identification are also simultaneously formed.

[0030] It should be noted that after completing the basic information collection for candidate units, the following information is retrieved for each candidate unit: building use composition, land use status, building age distribution, population activity, public service facility configuration, industrial carrying capacity, municipal infrastructure status, and historical renewal issue records. This information is then grouped according to the candidate unit identifier to form a basic record for candidate unit type identification. This basic record for candidate unit type identification is used to characterize at least the dominant functional composition, current spatial state, facility configuration status, and main sources of renewal demands for the candidate unit. Specifically, building use composition information characterizes the main functional attributes of the candidate unit; land use status information characterizes the current land use organization status; building age distribution information characterizes the formation period characteristics of the existing built space; population activity information characterizes the population usage status of the candidate unit; public service facility configuration and municipal infrastructure status information respectively characterize the facility supply status and basic support status of the candidate unit; and historical renewal issue records characterize the main renewal contradictions and sources of remediation needs previously exposed by the candidate unit.

[0031] Based on the candidate unit type identification records, a classification and discussion-based identification process is performed on each candidate unit. Candidate units whose building use is primarily residential, whose existing buildings were formed relatively concentratedly during their formation period, and whose internal population usage, spatial carrying capacity, and the supply of living service facilities require adjustment, are included in the residential improvement identification path. Candidate units whose current land use includes industrial, warehousing, commercial, or other operational spaces, and whose current spatial organization, functional configuration, or land use methods require adjustment to the future development requirements of the area, are included in the industrial upgrading identification path. Candidate units whose configuration of education, medical, elderly care, cultural and sports, or community service facilities requires adjustment to the population distribution and activity status are included in the facility improvement identification path. Candidate units whose roads, pipelines, public spaces, streetscape, or environmental order require continuous improvement, and whose implementation focuses on improvement, facility renewal, or environmental restoration, are included in the environmental remediation identification path.

[0032] To avoid the type identification remaining at the level of general description, in the above classification discussion, the identification path for residential improvement mainly corresponds to candidate units whose main function is residential use and whose main renewal direction is to improve living space conditions and enhance living facilities; the identification path for industrial upgrading mainly corresponds to candidate units whose main function is commercial or production space and whose main renewal direction is to reorganize space, reconfigure functions, or adjust the carrying capacity; the identification path for facility improvement mainly corresponds to candidate units whose main renewal direction is to adjust the supply relationship of public services; and the identification path for environmental remediation mainly corresponds to candidate units whose main renewal direction is to restore the spatial environment, improve infrastructure, or enhance the quality of neighborhoods.

[0033] For cases where the same candidate unit corresponds to more than two identification paths, further interactive verification processing is performed. This interactive verification process includes: comparing the update request sources, spatial problem composition, and implementation content directions corresponding to each identification path; determining the identification path corresponding to the main source of contradiction for the candidate unit as the dominant identification path, and identifying its corresponding update type as the dominant type of the candidate unit; for the remaining identification paths that are still relevant, identifying their corresponding update types as the accompanying types of the candidate unit. The main source of contradiction is used to characterize the problem source that plays a dominant role in the necessity of updating, the organization of update content, or the implementation method of updating in the candidate unit. By setting a distinction between dominant and accompanying types, the system avoids forcibly classifying the same candidate unit into a single type when multiple update requests coexist, thus preventing distortion of subsequent evaluation path configurations.

[0034] For candidate units that do not stably correspond to any existing identification path but whose local update needs are already reflected in the basic records, they are marked as units to be verified. Simultaneously, their candidate unit type identification basic records, triggered local discrimination criteria, and the reasons for incomplete classification are retained. Units to be verified do not directly enter the final type determination result but are retained as objects for subsequent supplementary verification or rule improvement.

[0035] After completing the above classification discussion and interactive verification, the dominant type, accompanying type, corresponding identification path, and identification basis of each candidate unit are written into the candidate unit type record, forming the candidate unit type identification result. The candidate unit type identification result is used for subsequent configuration of differentiated evaluation content, allocation of candidate unit evaluation paths, and ranking of urban renewal potential. Since the type identification result of each candidate unit corresponds to its basic record, identification path, and identification basis, the subsequent ranking process can be based on clear object types, traceable identification basis, and corresponding evaluation paths, thereby improving the completeness and feasibility of the overall technical solution.

[0036] S2: The candidate unit type identification results are processed by importing the path, constructing the judgment criteria, making stage judgments and interactive verifications, and generating candidate unit evaluation and classification output results.

[0037] Furthermore, after completing the candidate unit type identification, the dominant type, accompanying type, type identification basis, and basic information records of each candidate unit are read, and the aforementioned contents are merged according to the candidate unit identifier to form a candidate unit evaluation and judgment record. The candidate unit evaluation and judgment record includes at least the dominant type field, accompanying type field, path judgment basis field, stage judgment result field, interactive verification result field, and evaluation classification result field. Among them, the dominant type field is used to determine which evaluation and judgment path the candidate unit enters; the accompanying type field is used to record the additional problem types that still need to be considered for the candidate unit outside the dominant judgment path; the path judgment basis field is used to carry the current status basic content, problem source content, and implementation direction content corresponding to the current judgment path; the stage judgment result field is used to record the preliminary judgment result formed by the candidate unit under the current judgment path; the interactive verification result field is used to record the verification processing result when multiple paths coexist or additional problems coexist; and the evaluation classification result field is used to record the classification status corresponding to the candidate unit when it finally enters the subsequent potential ranking process.

[0038] Based on the evaluation records of candidate units, path import processing is performed on each candidate unit. For candidate units whose dominant type is residential improvement, they are imported into the residential improvement judgment path; for those whose dominant type is industrial upgrading, they are imported into the industrial upgrading judgment path; for those whose dominant type is facility improvement, they are imported into the facility improvement judgment path; for those whose dominant type is environmental remediation, they are imported into the environmental remediation judgment path; and for those whose dominant type is composite renewal, they are imported into the composite renewal judgment path. Path import processing is not only used to distinguish the category of a candidate unit, but also to limit the scope and direction of subsequent judgment content extraction. That is, after different candidate units enter different judgment paths, only the judgment content corresponding to that path is extracted as the primary judgment basis, while the content corresponding to the accompanying type is retained as supplementary judgment basis. This avoids irrelevant content being mixed into a single path judgment, which would affect the stability of subsequent classification results.

[0039] After a candidate unit enters the corresponding judgment path, the path judgment basis construction process is performed on the content corresponding to the current judgment path. This process includes extracting the current status quo, problem manifestations, facility support, carrying relationships, and historical update issues from the candidate unit's basic information records, and organizing them according to three levels: current status quo, problem source, and implementation direction. The current status quo characterizes the candidate unit's current space usage, functional composition, or facility configuration; the problem source characterizes the main sources of update conflicts presented by the candidate unit under the current conditions; and the implementation direction characterizes the corresponding update implementation direction of the candidate unit under the current problem conditions.

[0040] For candidate units in the residential improvement assessment path, the assessment criteria are based on the state of residential function composition, existing building characteristics, population usage, connection status of living service facilities, and the direction of residential space improvement. It is also determined whether the candidate unit prioritizes improving living conditions and enhancing living amenities as its main renovation direction. For candidate units in the industrial upgrading assessment path, the assessment criteria are based on the state of commercial or production space composition, current spatial organization, functional configuration relationships, industrial carrying capacity, and the direction of spatial reorganization. It is also determined whether the candidate unit prioritizes functional adjustment, spatial restructuring, or optimization of carrying capacity as its main renovation direction. For candidate units in the facility improvement assessment path, the assessment criteria are based on public services... The criteria for determining the path for updating a candidate unit are based on the status of service facilities configuration, service coverage, service connection, and the direction of facility supplementation. It is also determined whether the candidate unit prioritizes supplementing public service supply and optimizing facility layout as its main update implementation direction. For candidate units in the environmental remediation judgment path, the criteria for determining the path are based on the status of infrastructure, public space, street environment, sources of remediation needs, and the direction of remediation implementation. It is also determined whether the candidate unit prioritizes environmental restoration, facility improvement, or street quality enhancement as its main update implementation direction. For candidate units in the composite update judgment path, the content corresponding to the dominant type is used as the main path judgment criterion, and the content corresponding to the accompanying type is extracted simultaneously as additional judgment criterion for subsequent composite judgment processing.

[0041] After establishing the criteria for path determination, a categorized discussion-based phased judgment process is performed on each candidate unit. Candidate units whose current situation, problem sources, and implementation directions consistently correspond to the same update direction, and whose extracted content does not conflict with each other, are designated as explicitly included units. Candidate units whose main content corresponds to the same update direction, but whose accompanying additional content still needs to be considered in subsequent evaluations, are designated as compatible units. Candidate units whose different content points to different implementation directions, and whose implementation directions require further comparison of their primary and secondary relationships, are designated as review units. Candidate units whose path determination criteria are insufficient to support the stable formation of a specific implementation direction, or whose current situation, problem sources, and implementation directions have not yet established a stable correspondence, are designated as units for deferred judgment. The explicit entry unit is used to characterize candidate units whose update direction has been stably determined and can directly enter the subsequent potential ranking process; the compatible entry unit is used to characterize candidate units whose main path has been determined, but whose additional problem content still needs to be loaded into the subsequent ranking process; the verification entry unit is used to characterize candidate units whose update direction still needs to be further verified between the main path and the additional path; the deferred judgment unit is used to characterize candidate units whose current information is not yet sufficient to support a stable judgment and whose records need to be retained for subsequent supplementary processing.

[0042] After the initial judgment results are formed, further interactive verification processing is performed. For clearly defined entry units, the initial judgment result corresponding to their current path is directly retained, and this initial judgment result is written into the interactive verification result field. For compatible entry units, while keeping the current dominant judgment path unchanged, the additional judgment content corresponding to the accompanying type is written into the supplementary judgment content field, and the additional evaluation content that the candidate unit needs to load in the subsequent potential ranking process is recorded in the interactive verification result field. For review entry units, the relationship between the implementation direction corresponding to the dominant path and the implementation direction corresponding to the accompanying path is further compared. If the two have a sequential relationship, a supporting relationship, or a parallel implementation relationship in updating the implementation content, the current dominant path is retained, and the accompanying path is recorded as a parallel evaluation path. If the two do not have a sequential relationship, a supporting relationship, or a parallel implementation relationship in updating the implementation content, the candidate unit is transferred to composite judgment processing, and it is recorded in the interactive verification result field that it needs to enter the subsequent steps according to the composite evaluation path. For units with deferred judgment, the basis for their current path judgment, the reason for not forming a stable judgment, and the supplementary processing mark are retained, and the aforementioned content is written into the interactive verification result field. Interactive verification processing is used to address situations where primary and secondary issues coexist, additional problems coexist, or directions overlap after the stage judgment. This allows each candidate unit to undergo stability processing before entering the final classification, thereby improving the consistency of the final classification results.

[0043] After completing the interactive verification, evaluation and classification results are output for the candidate units. These results include at least priority judgment units, compatible judgment units, composite judgment units, and judgment units requiring supplementation. Priority judgment units correspond to candidate units that have established a stable implementation direction and can directly enter the subsequent potential ranking process; compatible judgment units correspond to candidate units that have established a dominant implementation direction but still require additional evaluation content to be loaded during the subsequent potential ranking process; composite judgment units correspond to candidate units that need to load evaluation content along both the dominant and parallel paths before entering the potential ranking process; and judgment units requiring supplementation correspond to candidate units whose current judgment criteria are insufficient to support a stable classification and need to be retained and further verified before entering the ranking process. Furthermore, the judgment path, path judgment criteria, stage judgment results, interactive verification results, and evaluation and classification results of the candidate units are written into the candidate unit evaluation judgment record, forming the candidate unit evaluation and classification output results.

[0044] The evaluation and classification results of candidate units are used for loading evaluation content, allocating potential ranking paths, and forming the ranking order in subsequent steps. For priority judgment units, the evaluation content corresponding to their dominant path is directly called into the potential ranking process. For compatible judgment units, additional evaluation content is loaded while calling the evaluation content of the dominant path into the potential ranking process. For composite judgment units, the corresponding evaluation content is loaded according to the composite evaluation path into the potential ranking process. For units to be supplemented in the judgment process, they are not entered into the formal potential ranking process for the time being, but their evaluation judgment records are retained for subsequent supplementary processing.

[0045] S3: Based on the evaluation and classification output results of candidate units, perform differentiated import, basic potential merging, correction calculation and stability verification to form the potential value results of candidate units, perform grouping and sorting, implementation sequence generation and consistency verification, and output the updated implementation sorting results of candidate units.

[0046] Furthermore, after completing the evaluation and classification output of candidate units, the evaluation and classification results, judgment paths, path judgment basis, stage judgment results, interactive verification results, and basic information records of each candidate unit are read and merged according to the candidate unit identifier to form a candidate unit potential calculation record. The candidate unit potential calculation record includes at least the following fields: dominant calculation path field, additional calculation content field, composite calculation content field, basic potential calculation field, corrected calculation field, potential value result field, and potential grading field. Among them, the dominant calculation path field is used to record the main path content based on when the candidate unit enters the potential calculation; the additional calculation content field is used to record the supplementary content that the compatible judgment unit needs to include in the calculation in addition to the main path; the composite calculation content field is used to record the multi-path content that the composite judgment unit needs to include in the calculation in parallel; the basic potential calculation field is used to carry the basic potential calculation result formed by the candidate unit before correction; the corrected calculation field is used to carry the adjustment result after combining the constraint content and opportunity content; the potential value result field is used to record the final potential value result formed by the candidate unit; and the potential grading field is used to record the potential grading level corresponding to the candidate unit. By setting up a potential calculation record for candidate units, the input sources, calculation process, and output results in the potential calculation process all have clearly defined locations, thereby ensuring clear boundaries for technical processing.

[0047] Based on the candidate unit potential calculation records, differentiated calculation import processing is performed on candidate units with different evaluation classification results. For priority judgment units, the evaluation content corresponding to their dominant judgment path is directly imported into the basic potential calculation process as the dominant calculation path content. For compatible judgment units, while importing the evaluation content corresponding to the dominant judgment path, the additional judgment content recorded in the interactive verification results is imported into the additional calculation content field, so that the candidate unit reflects both the main path content and the additional content in the basic potential calculation process. For composite judgment units, the dominant path content and the parallel path content are jointly imported into the composite calculation content field, and composite calculation processing is performed in the potential calculation process with the dominant path taking priority and the parallel path supplementing. For judgment units to be supplemented, they are not imported into the formal potential calculation process for the time being, but only their candidate unit potential calculation records and supplementation marks are retained, and they will enter the potential calculation process after the subsequent supplementary information verification is completed.

[0048] After a candidate unit enters the potential calculation process, the first step is to perform a basic potential calculation content merging process. This process includes extracting evaluation content related to the candidate unit's update potential from the dominant calculation path field, supplementary calculation content field, or composite calculation content field. The extracted results are then categorized into four types: update necessity content, implementation support content, comprehensive driving force content, and constraint sensitivity content. Specifically, update necessity content characterizes the candidate unit's current need to enter the update process; implementation support content characterizes the degree of support the candidate unit receives for update implementation under existing conditions; comprehensive driving force content characterizes the potential synergistic effects of the candidate unit's update on surrounding areas, facility configuration, functional improvements, or overall quality enhancement; and constraint sensitivity content characterizes the candidate unit's sensitivity to changes in ownership, timeline, implementation coordination, or other practical conditions during the update process.

[0049] After merging the basic potential calculation content, sequential calculation processing is performed on each merged category. For content related to the necessity of updating, the focus is on comparing the connection requirements between the current status of the candidate unit and its corresponding update implementation direction to identify the necessity of the candidate unit entering the update process. For content related to implementation support, the focus is on comparing the connection between the candidate unit's existing basic conditions and the conditions required for implementation to identify the support level for the candidate unit's implementation. For content related to comprehensive driving force, the focus is on comparing the potential regional linkages, service improvements, spatial optimization, or functional restructuring effects that the candidate unit may generate after updating to identify its driving force. For content related to constraint sensitivity, the focus is on identifying the sensitivity of the candidate unit to external and internal organizational conditions during the implementation process. Subsequently, the identification results of the above four categories of content are comprehensively processed in the order of prioritizing necessity confirmation, simultaneously verifying support, further identifying driving force, and finally balancing constraints to form the basic potential calculation results of the candidate units. The basic potential calculation results are not simply corresponding to a single content judgment, but are used to characterize the overall update potential status of the candidate unit before considering subsequent opportunity and constraint corrections.

[0050] To avoid the basic potential calculation remaining merely a static state assessment, a further correction calculation process is performed after the basic potential calculation results are generated. This correction calculation process includes constraint correction and opportunity correction. Constraint correction identifies whether candidate units have any limitations that affect the update sequence, implementation organization, spatial adjustments, or overall progress efficiency during the current update process. For candidate units with persistent constraints that significantly impact the recent implementation organization, their basic potential calculation results are adjusted downwards. For candidate units with partial constraints that do not change the dominant update implementation direction, only the constraint impact status is recorded in the correction calculation field without altering the dominant direction of the basic potential. Opportunity correction identifies whether candidate units have any realistic opportunities conducive to advancing the update within the current update cycle. For candidate units with relatively clear opportunities that directly connect with the candidate unit's dominant update direction, their basic potential calculation results are adjusted upwards. For candidate units with opportunities that have not yet formed a stable connection, their opportunity identification results are retained and used as subsequent ranking explanations.

[0051] After completing the correction calculation, potential value results are output for candidate units. These potential value results are expressed using a sequential ranking rather than a direct mathematical score formula, and include at least high-priority potential results, medium-priority potential results, collaborative advancement potential results, and reserve observation potential results. Specifically, high-priority potential results characterize candidate units with clear necessity for updating, adequate implementation support conditions, a relatively clear driving effect, and suitability for priority inclusion in the update implementation plan after correction; medium-priority potential results characterize candidate units with established necessity for updating, generally adequate implementation support conditions, but still requiring coordinated advancement in conjunction with local constraints or additional content; collaborative advancement potential results characterize candidate units that, while possessing update value, are more suitable for joint advancement in conjunction with leading areas, parallel projects, or adjustments to related facilities; and reserve observation potential results characterize candidate units that, while currently having update needs or potential update opportunities, are not yet sufficient to directly enter the recent top ranking and require observation and subsequent updates. Furthermore, the potential value results are written into the potential value result field, and the content of its potential classification field is simultaneously determined based on the potential value results to form the candidate unit potential classification results.

[0052] After the potential classification results are generated, a further potential value stability verification process is performed. For candidate units whose basic potential calculation results and revised calculation results are consistent in direction, their current potential value results are directly maintained. For candidate units whose basic potential calculation results and revised calculation results have an adjustment relationship but do not change the dominant potential direction, their constraint correction content or opportunity correction content is recorded in the potential description field while maintaining the current potential level. For candidate units whose revised calculation results have changed the dominant direction of the basic potential, they are marked as reclassified units after correction, and the correspondence between their dominant calculation path, additional calculation content, or composite calculation content and the final potential result is rechecked to ensure that the potential value results are consistent with the calculation basis.

[0053] Finally, the dominant calculation path, additional calculation content, composite calculation content, basic potential calculation results, revised calculation results, potential value results, potential classification results, and potential descriptions of each candidate unit are written into the candidate unit potential calculation record, forming the candidate unit potential value output result. The candidate unit potential value output result is used in subsequent steps to generate the sorting order, prioritize similar candidate units, and output the urban renewal implementation list. Since the potential value result of each candidate unit can be traced back to its evaluation classification results, calculation content merging results, and revised calculation results, this step not only completes the potential calculation but also simultaneously generates a complete calculation record that supports subsequent sorting and result interpretation.

[0054] It should be noted that after outputting the potential values ​​of candidate units, the process reads the potential value results, potential classification results, dominant calculation path, additional calculation content, composite calculation content, basic potential calculation results, corrected calculation results, and potential description content corresponding to each candidate unit. These contents are then merged according to the candidate unit identifier to form a candidate unit ranking generation record. The candidate unit ranking generation record includes at least the following fields: Potential Level Field, Peer Ranking Basis Field, Ranking Position Field, Implementation Sequence Field, Implementation Group Field, and Ranking Description Field. Specifically, the Potential Level Field records the potential classification status of the candidate unit; the Peer Ranking Basis Field records the basis used to further differentiate candidate units within the same potential level; the Ranking Position Field records the sequential position of the candidate unit among all participating units; the Implementation Sequence Field records the implementation order of the candidate unit in subsequent urban renewal efforts; the Implementation Group Field records the implementation batch or category to which the candidate unit belongs; and the Ranking Description Field records the basis for the candidate unit ranking and a description of the ranking results.

[0055] Based on the candidate unit ranking generation record, a ranking import process is performed on the candidate units participating in the formal ranking. For candidate units that have generated high-priority potential results, medium-priority potential results, and synergistic advancement potential results, they are imported into the formal ranking sequence. For candidate units that have generated reserve observation potential results, they are not directly imported into the recent formal ranking sequence, but rather into the reserve observation sequence, and their candidate unit ranking generation record is retained for subsequent dynamic updates. The formal ranking sequence is used to determine the order of candidate units that can enter urban renewal arrangements in the near future or the current cycle, while the reserve observation sequence is used to retain candidate units that have renewal value but are not yet suitable for entry into the recent formal arrangements.

[0056] After candidate units enter the formal ranking sequence, the potential level grouping process is first performed. The potential level grouping process includes: classifying candidate units in the formal ranking sequence into corresponding level groups according to high-priority potential results, medium-priority potential results, and collaborative advancement potential results. The high-priority potential group is used to accommodate candidate units that are suitable for priority entry into the update implementation arrangement; the medium-priority potential group is used to accommodate candidate units that have the foundation for update but still need to be promoted in coordination with local constraints; and the collaborative advancement potential group is used to accommodate candidate units that are suitable for joint advancement in conjunction with regional linkage, project coordination, or synchronous adjustment of facilities.

[0057] After grouping by potential level, a peer-to-peer ranking criteria are constructed for candidate units within the same potential level group. This process includes: reading the basic potential calculation results, revised calculation results, dominant calculation path content, additional calculation content, composite calculation content, and potential description content for each candidate unit; and constructing peer-to-peer ranking criteria based on the distinguishing directions required for ranking. Specifically, if there are differences in the order of basic potential calculation results among candidate units, the order of basic potential formation is used as the priority ranking criterion; if the basic potential formation states of candidate units are similar, the actual advancement opportunities, implementation constraints, and post-revision stability reflected in the revised calculation results are further compared, and the comparison results are used as supplementary ranking criteria; if it is still difficult to stably distinguish the order of candidate units, the clarity of the dominant update direction corresponding to their dominant calculation path, the impact of additional calculation content on the current ranking, and the coordination requirements of composite calculation content are further compared, and the final peer-to-peer ranking criteria are formed accordingly.

[0058] After establishing the ranking criteria at the same level, candidate units within each potential level group undergo a categorized discussion-based ranking process. Candidate units that demonstrate a clear dominant update direction, stable correction results, and established implementation conditions within the ranking criteria are placed at the top of the current potential level group. Candidate units with a clear dominant update direction but requiring further comprehensive calculations or local implementation conditions are placed in the middle of the current potential level group. Candidate units that possess the overall conditions corresponding to the current potential level but still need to await coordination of parallel paths, regional collaboration, or further integration of relevant implementation conditions are placed at the bottom of the current potential level group. The top, middle, and bottom positions do not independently form new potential levels but are used to further establish an ordered ranking relationship within the same potential level.

[0059] After establishing a ranking order within each potential level group, a cross-level ranking concatenation process is further performed. This process includes: placing candidate units already ranked within the high-priority potential group at the beginning of the formal ranking sequence; placing candidate units already ranked within the medium-priority potential group in the middle of the formal ranking sequence; and placing candidate units already ranked within the collaborative advancement potential group at the end of the formal ranking sequence, while maintaining the existing ranking order within each potential level group. For candidate units in the reserve observation sequence, a separate reserve observation ranking list is created and not directly concatenated with the formal ranking sequence.

[0060] After the formal ranking sequence and the reserve observation sequence are formed, the implementation sequence generation process is further performed. The implementation sequence generation process includes: based on the ranking position of candidate units in the formal ranking sequence, writing candidate units at the preceding position into the priority implementation sequence, candidate units at the middle position into the follow-up implementation sequence, and candidate units at the following position into the collaborative implementation sequence; for candidate units in the reserve observation sequence, they are written into the reserve observation sequence. The priority implementation sequence is used to accommodate candidate units suitable for inclusion in the near-term implementation plan, the follow-up implementation sequence is used to accommodate candidate units suitable for inclusion in subsequent follow-up plans, the collaborative implementation sequence is used to accommodate candidate units that need to be promoted simultaneously with related projects, area remediation, or facility upgrades, and the reserve observation sequence is used to accommodate candidate units that are not currently included in the near-term implementation plan but should be continuously observed and have their conditions for subsequent upgrades identified.

[0061] After the implementation sequence is generated, further implementation grouping is performed. This grouping includes dividing candidate units into independent implementation groups, collaborative implementation groups, composite coordination groups, and reserve tracking groups based on their implementation sequence, dominant update path, and collaborative advancement relationships. Specifically, the independent implementation group accommodates candidate units with clear dominant paths and relatively independent implementation conditions; the collaborative implementation group accommodates candidate units that require joint advancement with adjacent areas, related projects, or supporting facilities; the composite coordination group accommodates candidate units with composite update paths that require the coordinated organization of multiple implementation contents; and the reserve tracking group accommodates candidate units currently under observation, awaiting changes in conditions before entering the formal implementation sequence.

[0062] To ensure consistency between the sorting results and the basis for their formation, a further sorting consistency check is performed after the grouping is completed. For candidate units where the sorting position, implementation sequence, and implementation group maintain a consistent correspondence, their current sorting results are directly retained. For candidate units where the sorting position has been formed, but there is an inconsistency between the implementation sequence or implementation group and its potential description, the sorting basis at the same level is rechecked, the correspondence between the calculation results and the implementation sequence is corrected, and the implementation group is adjusted if necessary. For candidate units where the sorting position has been formed but the sorting description fields cannot fully support the current sorting results, they are marked as sorting supplementary description units, and their sorting basis source, position formation reason, and implementation grouping reason are supplemented.

[0063] Finally, the potential level, peer ranking criteria, ranking position, implementation sequence, implementation grouping, and ranking description of each candidate unit are written into the candidate unit ranking generation record, forming the updated implementation ranking results for candidate units. The updated implementation ranking results for candidate units include at least the formal ranking list, the reserve observation list, the priority implementation sequence, the follow-up implementation sequence, the collaborative implementation sequence, and the corresponding implementation grouping results. Further, the aforementioned ranking results are linked and archived with the candidate unit identifier, the dominant update path, the potential description content, and the ranking description content, forming an urban renewal implementation ranking result set. The urban renewal implementation ranking result set is used for subsequent steps such as outputting the implementation list, selecting and calling projects, adjusting annual arrangements, and dynamic update management. Since the ranking position and implementation grouping results of each candidate unit can be traced back to its potential calculation results, peer ranking criteria, and consistency verification results, not only is the ranking output completed, but a complete ranking record that can be verified, called upon, and updated subsequently is also simultaneously formed.

[0064] Example 2, one embodiment of the present invention, provides an AHP entropy weight urban renewal potential ranking method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0065] First, taking a proposed urban renewal area in a central urban area of ​​a certain city as the implementation scenario, seven candidate units were selected as experimental subjects: old residential area A, industrial renewal area B, facility improvement area C, environmental remediation area D, mixed-use renewal area E, station-city integration area F, and reserve observation area G. In the preparation phase, information such as building surveys, current land use, population activity patterns, public service facilities, industrial spaces, municipal pipelines, and existing renewal issue logs were collected, and timeframes and spatial coordinates were standardized to eliminate missing and conflicting records. Subsequently, candidate units were divided according to spatial continuity, functional relationships, facility service boundaries, and implementation management boundaries, and all information was merged under a unified unit identifier to form a basic result set for candidate units. A classification-based discussion-style type identification and interactive verification were performed on the seven candidate units, identifying them as residential improvement, industrial upgrading, facility improvement, environmental remediation, and mixed-use renewal, avoiding the current unified evaluation method from treating different renewal demands together. Based on the dominant type, corresponding evaluation paths are imported. Judgment criteria are constructed around the current situation, problem sources, and implementation direction, and subjective judgment intensity is formed by combining AHP (Analytic Hierarchy Process) to express the relative importance of update necessity, implementation support, and comprehensive driving force. Then, an entropy-weighted objective discrimination degree is formed based on the degree of difference in evaluation content among candidate units, and constraint and opportunity corrections are superimposed to output the potential level. The potential results are then grouped, sorted, and subjected to implementation sequence generation and consistency verification to obtain the final update implementation ranking result. To verify the effectiveness of this invention, a prior art method is set as a control group. The control group only uses a unified index for direct weighted sorting, without performing candidate unit classification, path judgment, interactive verification, or stability verification. The actual implementation priority is determined by the order of project entry and funding arrangement in the region over the next nine months, serving only as a verification benchmark and not participating in the ranking process.

[0066] Table 1 Experimental Data

[0067] As shown in Table 1, the type matching rate of this invention is significantly higher than that of the prior art across all seven candidate units. Specifically, the rate for composite renewal area E increased from 61% to 93%, and for station-city collaborative area F from 66% to 91%. This indicates that by dividing candidate units, merging information, and using a classification-based discussion approach, the probability of composite objects being misclassified as a single type can be significantly reduced. This is fundamental for subsequent urban renewal path selection. Secondly, the completeness of the evaluation path has increased from 2-3 items in the prior art to 4-5 items in this invention. This demonstrates that by importing the path, constructing judgment criteria, and making stage judgments, the current situation, the source of problems, and the implementation direction are linked into a complete evaluation chain. This makes the evaluation results no longer merely static scoring, but possesses stronger interpretability and verifiability. Furthermore, the parallel setting of AHP subjective judgment strength and entropy weight objective discrimination reflects that this invention simultaneously retains two types of information: governance orientation and sample differences. For example, the subjective judgment strength of composite update area E is 5, and the objective discrimination is 88, which corresponds to a high priority potential and is highly consistent with the actual priority of 1st place. On the other hand, the subjective judgment strength of environmental remediation area D is only 2, and the objective discrimination is 63. It was ultimately identified as a reserve observation area, which is more in line with the actual resource investment rhythm. It is particularly noteworthy that for station-city collaboration area F, existing technologies directly ranked it as 2nd place because they ignored constraint correction and implementation sequence. However, this invention adjusted it to 4th place after potential correction and stability verification and included it in the collaborative implementation sequence, making the ranking closer to the actual implementation conditions. Finally, from the overall results, the average ranking deviation of existing technologies is 0.86 places, while this invention reduces it to 0.29 places, a deviation reduction of 66.7%. This shows that this invention not only improves the identification accuracy of individual candidate units, but also significantly enhances the consistency between the overall ranking and the actual implementation sequence. Therefore, it can be seen that, compared with the existing unified index direct sorting method, the present invention demonstrates significant creativity and novelty in terms of object classification, judgment path construction, subjective and objective information fusion, constraint opportunity correction, and implementation sequence generation.

[0068] Example 3, an embodiment of the present invention, provides an AHP entropy weight urban renewal potential ranking system, including a candidate unit construction module, a classification evaluation and judgment module, and a potential ranking output module.

[0069] The candidate unit construction module is used to read, divide, and merge information on the target urban renewal area to form the basic results of candidate units; the classification and evaluation judgment module is used to perform type identification, path judgment, and classification output on the candidate units to form the evaluation and classification results corresponding to each candidate unit; the potential ranking output module is used to perform potential calculation, ranking generation, and implementation sequence output on the evaluation and classification results to form the candidate unit update implementation ranking results.

[0070] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0071] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0072] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0073] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0074] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An AHP entropy weight ranking method for urban renewal potential, characterized in that, include: Read the target city urban renewal area information, complete the candidate unit division, information merging and verification, construct the basic result set of candidate units, perform classification and discussion-based type recognition and interactive verification, and output the candidate unit type recognition results; The candidate unit type identification results are processed through path import, judgment basis construction, stage judgment and interactive verification to form candidate unit evaluation and classification output results. Based on the evaluation and classification output of candidate units, perform differentiated import, basic potential merging, correction calculation and stability verification to form the potential value results of candidate units. Then, perform grouping and sorting, implementation sequence generation and consistency verification to output the updated implementation sorting results of candidate units.

2. The AHP entropy weight urban renewal potential ranking method as described in claim 1, characterized in that: The process of reading information about the target urban renewal area, completing the division of candidate units, information merging and verification, and constructing a basic result set of candidate units includes reading the current spatial information, land use layout information, building distribution information, population activity information, public service facility information, municipal infrastructure information, industrial spatial information and historical renewal management information within the target renewal area; Based on spatial continuity, existing functional relationships, building combination relationships, facility service relationships, and management implementation boundary relationships, candidate unit division is performed on spatial objects within the target update area; Information on building use composition, current land use, building age distribution, population activity, configuration of public service facilities, industrial carrying capacity, current status of municipal infrastructure, and records of historical update issues are merged into the corresponding candidate units according to the candidate unit identifier. The merged information is checked for spatial correspondence, time caliber, missing information, and conflict, and a basic result set for candidate units is constructed.

3. The AHP entropy weight urban renewal potential ranking method as described in claim 2, characterized in that: The execution of classification and discussion-based type identification and interactive verification outputs candidate unit type identification results, including reading the building use composition information, land use status information, building age distribution information, population activity information, public service facility configuration, industrial carrying capacity information, municipal infrastructure status information, and historical update problem records corresponding to each candidate unit, forming a basic record for candidate unit type identification; Based on the candidate unit type identification records, the candidate units are identified by type according to the classification discussion method, and the candidate units are identified as residential improvement, industrial upgrading, infrastructure improvement, environmental remediation or composite renewal. Interactive verification is performed on the type recognition results to determine the dominant and accompanying types of candidate units, thus forming the candidate unit type recognition results.

4. The AHP entropy weight urban renewal potential ranking method as described in claim 3, characterized in that: The process of importing the candidate unit type identification results, constructing the judgment basis, making stage judgments and interactive verifications, and forming the candidate unit evaluation and classification output results includes reading the dominant type, accompanying type, type identification basis and candidate unit basic information records corresponding to each candidate unit, and outputting the candidate unit evaluation and judgment records. Based on the dominant type, candidate units will be respectively imported into the judgment path of residential improvement, industrial upgrading, infrastructure improvement, environmental remediation or composite renewal. Extract the current status composition, problem manifestations, facility support, carrying relationships, and historically updated problem information corresponding to the current judgment path from the basic information records of candidate units to construct the basis for path judgment; Based on the path judgment criteria, the system performs interactive verification on the stage judgment results and outputs the candidate unit evaluation and classification results.

5. The AHP entropy weight urban renewal potential ranking method as described in claim 4, characterized in that: The evaluation and classification output results based on candidate units include output results based on priority judgment units, compatibility judgment units, composite judgment units, and judgment units to be supplemented. The priority judgment unit is the candidate unit that can directly enter the potential calculation process; The compatibility judgment unit is a candidate unit that needs to load additional judgment content in addition to the main judgment path; The composite judgment unit is a candidate unit that needs to enter the potential calculation process along both the dominant path and the parallel path; The units to be supplemented for judgment are candidate units that will not be included in the formal potential calculation process for the time being.

6. The AHP entropy weight urban renewal potential ranking method as described in claim 5, characterized in that: The process of performing differentiated import, basic potential merging, correction calculation and stability verification to form the candidate unit potential value result includes reading the evaluation classification result, judgment path, path judgment basis, stage judgment result, interactive verification result and candidate unit basic information record corresponding to each candidate unit to form the candidate unit potential calculation record; Differentiated import is performed on candidate units based on the evaluation and classification results; The imported content is categorized into update necessity content, implementation support content, comprehensive driving content, and constraint sensitivity content, and the basic potential calculation results are output. Based on the basic potential calculation results, a correction calculation is performed in combination with the constraints and opportunities, and the stability of the correction results is checked to output the potential value of the candidate units.

7. The AHP entropy weight urban renewal potential ranking method as described in claim 6, characterized in that: The execution of group sorting, implementation sequence generation and consistency verification, and output of candidate unit update implementation sorting results include reading the potential value results, potential classification results, dominant calculation path, additional calculation content, composite calculation content, basic potential calculation results, corrected calculation results and potential description content corresponding to each candidate unit, forming a candidate unit sorting generation record; The candidate units are imported into the formal sorting sequence or reserve observation sequence according to their potential classification results, and the candidate units in the formal sorting sequence are grouped and sorted. An implementation sequence is generated based on the sorting results, and a consistency check is performed on the correspondence between the sorting results and the implementation sequence. Output the candidate cell update sorting results.

8. A system employing the AHP entropy weight urban renewal potential ranking method as described in any one of claims 1 to 7, characterized in that: It includes a candidate unit construction module, a classification and evaluation module, and a potential ranking output module; The candidate unit construction module is used to read, divide, and merge information on the target urban renewal area to form basic results of candidate units; The classification evaluation and judgment module is used to perform type identification, path judgment and classification output on the candidate units, and form the evaluation and classification results corresponding to each candidate unit. The potential ranking output module is used to perform potential calculation, ranking generation and implementation sequence output on the evaluation and classification results, and to form candidate units to update the ranking results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the AHP entropy weight urban renewal potential ranking method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the AHP entropy weight urban renewal potential ranking method as described in any one of claims 1 to 7.

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