An ecological green core construction project access intelligent auditing method and system

Abstract

The application relates to an ecological green core construction project access intelligent auditing method and system, relates to the field of text examination and approval, and comprises the following steps: extracting project red line vector data and construction content from a declaration data packet; superimposing and analyzing the red line data and a preset control factor layer set to obtain a space compliance detection conclusion and construct a space compliance characteristic parameter; selecting a rule subset from a hierarchical rule library according to a partition category and a superimposed area, assigning values to a judgment threshold according to a first difference value between a scale proportion of urban and rural construction land and a control upper limit and a second difference value between a scale proportion of residential land and the control upper limit, and obtaining a parameterized rule subset; comparing the construction content according to the parameterized rule subset, and outputting a list compliance conclusion; merging the space compliance detection conclusion and the list compliance conclusion to generate an access level conclusion, and triggering a corresponding examination and approval path and a compulsory examination and approval node. By implementing the method, the deviation between a list compliance auditing conclusion and the actual situation of land index control in a region where a project is located can be reduced.

Description

Technical Field

[0001] This application relates to the field of document approval, and in particular to an intelligent review method and system for the access of ecological green heart construction projects. Background Technology

[0002] Existing project access review systems typically require assessments of spatial compliance and list compliance. Regarding spatial compliance, reviewers need to overlay and compare the project site with various control element layers, including but not limited to core protected areas, integrated development zones, ecological protection red lines, permanent basic farmland, urban development boundaries, and planning zones at all levels, to determine whether the project meets the control requirements of these elements. Regarding list compliance, reviewers need to compare the project's construction content with the positive and negative lists item by item to determine whether the project's construction content falls within the permitted or prohibited scope.

[0003] As construction activities in the ecological green heart area continue to accumulate, different locations within the region exhibit varying ecological carrying capacity. Because the construction content review module applies the same list of terms and judgment criteria to all projects, similar projects in different locations will receive the same judgment conclusion during the construction content review process. When the cumulative scale of approved construction projects in a certain location has already placed sustained pressure on the ecological environment, the construction content of newly submitted projects is still compared and evaluated using the same standards as other locations, leading to a discrepancy between the construction content review conclusion and the actual current ecological carrying capacity of that location. Summary of the Invention

[0004] This application provides an intelligent review method and system for the access of ecological green heart construction projects, which can reduce the discrepancy between the review conclusion of the list compliance and the actual situation of the current land use index control in the project area.

[0005] Firstly, this application provides an intelligent review method for the access of ecological green heart construction projects, applied to an intelligent access review system. The method includes: receiving a project application data packet; extracting project redline vector data and construction content from the project application data packet; performing overlay analysis on the project redline vector data and a preset control element layer set; calculating the overlay area of ​​the project redline vector data and each control element layer to obtain a spatial compliance detection conclusion; and constructing spatial compliance feature parameters, which include the project's zoning category, the overlay area of ​​the project scope and each control element layer, the proportion of urban and rural construction land and the proportion of residential land in the project's area; and, based on the zoning category and overlay area, analyzing the project's redline vector data against the preset control element layer set to obtain a spatial compliance detection conclusion. A subset of rules is selected from the rule base, and the judgment thresholds of each clause in the rule subset are assigned based on the first difference between the proportion of urban and rural construction land and the preset upper limit of urban and rural construction land control, and the second difference between the proportion of residential land and the preset upper limit of residential land control, to obtain a parameterized rule subset. The judgment threshold is negatively correlated with the first and second differences. The construction content is compared with the parameterized rule subset, and the list compliance conclusion is output. The list compliance conclusion includes positive list access category, negative list prohibition category, or pending assessment category. The spatial compliance detection conclusion and the list compliance conclusion are merged to generate an access level conclusion, and the corresponding approval path and mandatory approval node are triggered based on the access level conclusion.

[0006] In the above embodiments, after receiving the project application data packet, the system synchronously obtains spatial compliance detection conclusions and constructs spatial compliance feature parameters through spatial overlay analysis. It locates rule subsets using zoning categories and overlay areas, and parameterizes the judgment thresholds for each clause using the differences between the proportions of urban and rural construction land and residential land and their corresponding control limits. This dynamically matches the judgment standards used for comparing construction content with the land use indicator control status of the project's location. The spatial compliance detection conclusions and the list compliance conclusions are merged to generate an access level conclusion, triggering differentiated approval paths and mandatory approval nodes, reducing the deviation between the list compliance review conclusions and the actual land use indicator control status of the project's location.

[0007] In conjunction with some embodiments of the first aspect, in some embodiments, the step of selecting a rule subset from the hierarchical rule base according to the zoning category and the superimposed area, and assigning judgment thresholds to each clause in the rule subset based on the first difference between the proportion of urban and rural construction land and the preset upper limit of urban and rural construction land control, and the second difference between the proportion of residential land and the preset upper limit of residential land control, to obtain a parameterized rule subset, specifically includes: mapping the superimposed area to a control level identifier according to a preset area grading standard; constructing a two-dimensional query key based on the zoning category and the control level identifier; retrieving a rule subset matching the two-dimensional query key in the hierarchical rule base based on the two-dimensional query key, wherein each clause in the rule subset contains a corresponding benchmark judgment threshold; calculating the first difference between the proportion of urban and rural construction land and the preset upper limit of urban and rural construction land control, calculating the second difference between the proportion of residential land and the preset upper limit of residential land control; assigning and correcting the benchmark judgment thresholds of each clause in the rule subset based on the first difference and the second difference to obtain a corrected judgment threshold; and assigning the corrected judgment thresholds to the corresponding clauses in the rule subset to obtain a parameterized rule subset.

[0008] In the above embodiments, the system constructs a two-dimensional query key using the control level identifier obtained by overlaying area mapping and the zoning category, and locates the rule subset containing the benchmark judgment threshold in the hierarchical rule base. It calculates the first difference between the proportion of urban and rural construction land and the preset upper limit of urban and rural construction land control, and the second difference between the proportion of residential land and the preset upper limit of residential land control. Based on the first and second differences, it assigns and corrects the benchmark judgment threshold of each clause in the rule subset. The judgment threshold tightens as the difference between the land use index and the corresponding upper limit of control decreases, ensuring that the judgment standards of each clause in the parameterized rule subset match the land use index control margin of the project area.

[0009] In conjunction with some embodiments of the first aspect, in some embodiments, the step of comparing the construction content according to a subset of parameterized rules and outputting a list compliance conclusion specifically includes: extracting keywords and semantically segmenting the construction content to obtain several construction behavior description fragments, and encoding the several construction behavior description fragments to obtain a set of construction behavior feature codes; encoding the text content of each clause in the subset of parameterized rules to obtain a set of clause feature codes; calculating the matching degree between each construction behavior feature code in the set of construction behavior feature codes and the clause feature codes of the negative list clauses in the set of clause feature codes, and outputting a negative list prohibition conclusion if any matching degree is not lower than the corresponding judgment threshold; if no negative list prohibition conclusion is output, calculating the similarity between each construction behavior feature code in the set of construction behavior feature codes and the clause feature codes of the positive list clauses in the set of clause feature codes, and outputting a pending judgment conclusion if the matching degree between a construction behavior feature code and the clause feature codes of all positive list clauses is lower than the corresponding judgment threshold; and outputting a positive list admission conclusion if the matching degree between each construction behavior feature code and the clause feature code of at least one positive list clause is not lower than the corresponding judgment threshold.

[0010] In the above embodiments, the system performs keyword extraction and semantic segmentation on the construction content to obtain several construction behavior description fragments. Each fragment is then feature-encoded to obtain a set of construction behavior feature codes, and the textual features of each clause in the parameterized rule subset are encoded into a set of clause feature codes. Matching degree calculation is performed in the order of priority given to the negative list, using a parameterized judgment threshold as the basis for judgment: when the matching degree between any construction behavior feature code and a clause in the negative list reaches the corresponding judgment threshold, a prohibition-type conclusion is output; when there are construction behaviors that do not match the positive list, a pending-assessment-type conclusion is output; and when all construction behaviors match the positive list, an admission-type conclusion is output, ensuring that the list compliance judgment results match the parameterized threshold standards.

[0011] In conjunction with some embodiments of the first aspect, in some embodiments, the step of merging the spatial compliance detection conclusion and the list compliance conclusion to generate an access level conclusion, and triggering the corresponding approval path and mandatory approval node based on the access level conclusion, specifically includes: dividing the spatial compliance detection conclusion into three levels: spatial compliance, spatial warning, and spatial violation; using the combination of the level to which the spatial compliance detection conclusion belongs and the list compliance conclusion as the query condition; retrieving the access level conclusion corresponding to the query condition from the preset compliance decision matrix; retrieving the corresponding approval path from the preset approval path library based on the access level conclusion; and injecting the mandatory approval node corresponding to the access level conclusion into the preset insertion position of the approval path; and outputting the approval path containing the mandatory approval node.

[0012] In the above embodiments, the system classifies spatial compliance detection conclusions into three levels: spatial compliance, spatial warning, and spatial violation. Spatial compliance means that the spatial relationships corresponding to the superimposed area meet the construction requirements of each control element. Spatial warning means that the proportion of urban and rural construction land or residential land is close to the corresponding control limit. Spatial violation means that the spatial relationships corresponding to the superimposed area do not meet the construction requirements of each control element. The system uses the combination of this level and the list compliance conclusion as the query condition to retrieve the corresponding access level conclusion from the preset compliance decision matrix. Based on the access level conclusion, the system retrieves the approval path, injects the corresponding mandatory approval node at a preset insertion position, and outputs the approval path. This allows the approval paths and mandatory approval nodes corresponding to different combinations of spatial compliance levels and list compliance conclusions to be configured differently based on the comprehensive level conclusion.

[0013] In conjunction with some embodiments of the first aspect, in some embodiments, after merging the spatial compliance detection conclusion and the list compliance conclusion to generate an access level conclusion, and triggering the corresponding approval path and mandatory approval node based on the access level conclusion, the method further includes: overlaying and analyzing the project redline vector data and the preset two-zone data layer with the administrative boundary layer to obtain the coverage of the project redline vector data in the core protection zone and the integrated development zone, and the spatial positional relationship between the project redline vector data and the municipal administrative boundary; generating a provincial approval suggestion identifier when the project redline vector data simultaneously covers the core protection zone and the integrated development zone, or when the project redline vector data crosses the municipal administrative boundary; generating a corresponding municipal approval suggestion identifier when the project redline vector data is entirely located in the integrated development zone and does not cross the municipal administrative boundary; and injecting the provincial approval suggestion identifier or the corresponding municipal approval suggestion identifier into the approval path to route the approval path to the corresponding approval authority level.

[0014] In the above embodiments, the system performs overlay analysis on the project's red line vector data with preset two-zone data layers and administrative boundary layers to obtain the coverage of the project scope in the core protection zone and the integrated development zone, as well as its spatial relationship with the municipal administrative boundary. A provincial approval suggestion identifier is generated when the project scope simultaneously covers the core protection zone and the integrated development zone, or crosses the municipal administrative boundary. A corresponding municipal approval suggestion identifier is generated when the project scope is entirely located in the integrated development zone and does not cross the municipal administrative boundary. Injecting these identifiers into the approval path allows the approval path to be routed to the corresponding approval authority level based on the project's spatial attributes, reducing rework in the approval process caused by configuration deviations in the approval level.

[0015] In conjunction with some embodiments of the first aspect, in some embodiments, after injecting the provincial approval suggestion identifier or the corresponding municipal approval suggestion identifier into the approval path to route the approval path to the corresponding approval authority level, the method further includes: taking the spatial compliance detection conclusion, the list compliance conclusion, and the access level conclusion as input, generating a draft review opinion according to a preset review opinion template, wherein each conclusion field in the draft review opinion is automatically filled by the spatial compliance detection conclusion, the list compliance conclusion, and the access level conclusion; pushing the draft review opinion to the mandatory approval node, receiving the approval personnel's revision operation on the draft review opinion, and obtaining the final review opinion; comparing the final review opinion with the draft review opinion field by field, generating review deviation records for the draft values ​​and final values ​​corresponding to the fields with differences, and storing the review deviation records in a deviation record library in association with the parameterized rule subset and the spatial compliance feature parameters.

[0016] In the above embodiments, the system takes the spatial compliance detection conclusion, list compliance conclusion, and access level conclusion as input, generates a draft review opinion with each conclusion field filled in according to a preset review opinion template, and pushes it to the mandatory approval node. After the approver completes the revision, the final review opinion is obtained. The system compares the final review opinion with the draft field by field, generates a review deviation record for the draft values ​​and final values ​​of the differing fields, and stores this record in the deviation record library along with the parameterized rule subset and spatial compliance feature parameters. This accumulates a data foundation for subsequent correction of the hierarchical rule base based on deviation data.

[0017] In conjunction with some embodiments of the first aspect, in some embodiments, after storing the review deviation records in a deviation record library in association with a subset of parameterized rules and spatial compliance feature parameters, the method further includes: extracting all review deviation records corresponding to the partition category from the deviation record library when the number of review deviation records in the deviation record library that are the same as the partition category in the spatial compliance feature parameters is not less than a preset deviation accumulation threshold; using the deviation direction and deviation frequency between the draft value and the final value of each deviation field in all review deviation records as input, correcting the judgment threshold of the clause corresponding to the partition category in the hierarchical rule library to obtain the corrected judgment threshold; and updating the corrected judgment threshold to the hierarchical rule library.

[0018] In the above embodiments, the system detects the number of review deviation records in the deviation record library that are the same as the current project partition category. When the number is not lower than the preset deviation accumulation threshold, all review deviation records corresponding to the partition category are extracted. The deviation direction and deviation frequency between the draft value and the final value of each deviation field are used as input to correct the judgment threshold of the corresponding clause in the hierarchical rule library for that partition category. The corrected judgment threshold is then updated to the hierarchical rule library, so that the judgment threshold is continuously corrected as approval practice data accumulates, thereby improving the accuracy of the rule library in judging the access of construction projects in different partition categories.

[0019] Secondly, embodiments of this application provide an intelligent access review system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the intelligent access review system to perform the method described in the first aspect and any possible implementation thereof.

[0020] Thirdly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on an access intelligent review system, cause the access intelligent review system to perform the method described in the first aspect and any possible implementation thereof.

[0021] Fourthly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on an access intelligent review system, cause the access intelligent review system to perform the method described in the first aspect and any possible implementation thereof.

[0022] Understandably, the access intelligent review system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the methods provided in the embodiments of this application. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0023] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0024] 1. Due to the adoption of a technical solution that overlays and analyzes project redline vector data with a preset control element layer set to construct spatial compliance characteristic parameters including zoning categories, project scope, the overlay area of ​​each control element layer, the proportion of urban and rural construction land and residential land in the project area, a two-dimensional query key is constructed based on zoning categories and control level identifiers to select rule subsets from the hierarchical rule base, and the first difference between the proportion of urban and rural construction land and the preset upper limit of urban and rural construction land control, and the second difference between the proportion of residential land and the preset upper limit of residential land control, the benchmark judgment thresholds of each clause are assigned and corrected to obtain parameterized rule subsets, the construction content is compared according to the parameterized rule subsets to output list compliance conclusions, and the spatial compliance detection conclusions are merged with the list compliance conclusions to generate access level conclusions and trigger corresponding approval paths and mandatory approval nodes, the spatial compliance characteristic parameters will cover the project area The system provides a structured representation of the zoning categories, project scope, and the overlapping area of ​​each control element layer, as well as the proportion of urban and rural construction land and residential land. The overlapping area, after mapping, participates in the positioning of rule subsets. The first and second differences are used to correct the assignment of the benchmark judgment threshold. This allows the judgment threshold applicable to similar projects in different regions to be configured differently based on the zoning category and the current land use indicator control status of that region. This effectively solves the technical problem in existing technologies where the same judgment standard is applied uniformly to all projects in the list compliance audit, leading to a discrepancy between the list compliance audit conclusion and the actual current land use indicator control status of the project's location. Furthermore, it achieves an effective match between the list compliance audit conclusion and the land use indicator control status of the project's location, and structurally merges the spatial compliance detection conclusion and the list compliance conclusion into an access level conclusion, ensuring that the approval path and mandatory approval nodes are triggered differently based on the comprehensive access level.

[0025] 2. By employing a technical solution that overlays the project's redline vector data with preset two-zone data layers and administrative boundary layers, generates provincial or municipal approval suggestion identifiers based on the project's coverage in the core protection zone and integrated development zone, as well as its spatial relationship with the municipal administrative boundary, and injects these identifiers into the approval path to route the approval path to the corresponding approval authority level, the spatial coverage of the core protection zone and integrated development zone, and the crossing of administrative boundaries, are structurally incorporated into the approval level judgment logic. When the project scope simultaneously covers the core protection zone and integrated development zone or crosses the municipal administrative boundary, a provincial approval suggestion identifier is triggered. When the project scope is entirely located in the integrated development zone and does not cross the municipal administrative boundary, a corresponding municipal approval suggestion identifier is triggered. After injecting these two types of identifiers into the approval path, the authority level routing of the approval path is determined based on the differences in the project's spatial coverage attributes. This effectively solves the technical problem in existing technologies where there is a lack of structured association mechanism between the approval level and the project's spatial attributes, and there is a risk of approval level configuration deviation. Thus, the approval path is routed to the corresponding approval authority level based on the project's spatial coverage attributes, reducing rework in the approval process caused by approval level configuration deviation.

[0026] 3. The technical solution employs a pre-defined review opinion template to generate a draft review opinion based on spatial compliance detection conclusions, list compliance conclusions, and access level conclusions. This draft is then pushed to the mandatory approval node, where it receives revisions from approvers to obtain final review opinions. The final review opinions are then compared field-by-field with the draft to generate review deviation records. These records are then linked to a subset of parameterized rules and spatial compliance feature parameters and stored in a deviation record database. Therefore, field differences arising from approvers' revisions to the draft are captured field-by-field. The draft and final review values ​​are structurally stored as deviation records. The linked storage of deviation records with the subset of parameterized rules and spatial compliance feature parameters ensures that each deviation record carries the rule parameter status and regional characteristic information corresponding to the deviation at the time of occurrence. This effectively solves the technical problem in existing technologies where the difference between approvers' professional judgments and rule base output conclusions lacks a systematic archiving mechanism and cannot support subsequent rule optimization. Furthermore, it achieves the linked storage of review deviation data with rule parameter status and regional characteristic information, providing a data foundation for subsequent adjustments to the tiered rule base judgment thresholds based on deviation data. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating the intelligent review method for access to the ecological green heart construction project in this application embodiment;

[0028] Figure 2 This is another flowchart illustrating the intelligent review method for access to the ecological green heart construction project in this application embodiment;

[0029] Figure 3This is a schematic diagram of the physical device structure of an access intelligent review system in the embodiments of this application. Detailed Implementation

[0030] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions “a,” “an,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0031] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0032] For ease of understanding, the method provided in this implementation is described in process below. Please refer to [link / reference]. Figure 1 This is a flowchart illustrating the intelligent review method for access to the ecological green heart construction project in this application embodiment.

[0033] S101. Receive the project application data packet and extract the project red line vector data and construction content from the project application data packet.

[0034] Among them, the project application data package refers to the data set containing various types of project information submitted by the project applicant when applying for a construction project. Its content covers the spatial location information of the project, the description of the construction content, and other relevant supporting materials; the project red line vector data refers to the boundary coordinate data of the project land area recorded in vector format, which is used to represent the spatial range and geographical location of the project construction land, and is usually stored in the form of polygon vector elements; the construction content refers to the information in the project application materials that describes the construction activities, construction scale, construction type, etc. of the project to be carried out, which refers to the expression of the specific plans and arrangements of the project at the construction level.

[0035] Specifically, after receiving the project application data packet submitted by the project applicant, the intelligent access review system performs a structured parsing operation on the data packet. The data packet is typically encapsulated in a standardized format, and the system locates and reads the project redline vector data field and the construction content field according to preset data structure specifications. The extraction process of the project redline vector data includes format verification of the vector coordinate data, confirming that the data coordinate system is consistent with the coordinate system of the system's internal control layer. If the coordinate systems are inconsistent, a coordinate system transformation is performed to ensure that subsequent spatial overlay analysis can proceed normally. The extraction process of the construction content includes checking the integrity of the text fields, confirming that the text content is readable and that the encoding format meets the system's processing requirements to ensure that subsequent semantic matching operations can be executed normally. After extraction, the project redline vector data and the construction content are stored as two independent data objects and enter the subsequent processing flow.

[0036] In some embodiments, the extraction of project redline vector data and construction content from project application data packets can be achieved in several ways: Optionally, the system receives project application data packets packaged in compressed format, decompresses the compressed packets, locates the vector data file and text file according to a preset directory structure, reads coordinate information and attribute table data from the vector data file, and reads the construction content description field from the text file. After extraction, the two types of data are formatted and the processing results are stored in the system cache. Optionally, the system receives project application data packets submitted in the form of a structured form, reads the redline vector coordinate field from the form data according to the field name mapping rules, converts the coordinate array into the system's internal vector data format, and simultaneously reads the construction content field from the form data according to preset field identifiers. The text content is then formatted and standardized. After extraction, both types of data are recorded in the data processing queue, awaiting subsequent module calls. It is understood that other data interface forms can also be used to receive and extract project application data packets; this is not limited here.

[0037] S102. Overlay the project redline vector data with the preset control element layer set, calculate the overlay area of ​​the project redline vector data and each control element layer, obtain the spatial compliance detection conclusion, and construct spatial compliance feature parameters.

[0038] Among them, the preset control element layer set refers to a set of vector data layers pre-loaded into the system, containing various spatial control boundary information, used to represent the spatial extent of various control areas. The layer types include, but are not limited to, core protection zone layers, integrated development zone layers, city / county / district administrative boundary layers, urban development boundary layers, ecological protection red line layers, permanent basic farmland layers, ecological public welfare forest layers, natural forest layers, river and lake management scope layers, current land type layers, waterfront landscape control layers, mountainside landscape control layers, and building height control layers, used to represent the spatial extent of various control areas; overlay analysis refers to the analysis operation of performing geometric operations on two or more spatial data layers under the same coordinate reference system to obtain the spatial relationship results between the layers; overlay area refers to the area of ​​the overlapping region obtained after spatial overlay operations between the project red line vector data and each control element layer, used to represent the degree of spatial overlap between the project scope and each control element; spatial compliance detection conclusions. This refers to the compliance judgment result obtained by overlay analysis to determine the spatial relationship between the project's redline vector data and various control element layers. It is used to indicate the degree of compliance between the project's spatial location and various control requirements. Spatial compliance characteristic parameters refer to a multi-dimensional parameter set that expresses the control attribute information of the project's location in a structured form. It includes four components: the project's zoning category, the overlay area of ​​the project's scope and various control element layers, the proportion of urban and rural construction land in the project's location, and the proportion of residential land. Zoning category refers to the zoning category identifier corresponding to the project's location in the planning zoning layer, used to indicate the type of planning control zoning the area belongs to. The proportion of urban and rural construction land refers to the proportion of existing urban and rural construction land area in the project's location to the total area of ​​the area, used to indicate the degree of use of urban and rural construction land in the area. The proportion of residential land refers to the proportion of existing residential land area in the project's location to the total area of ​​the area, used to indicate the degree of use of residential land in the area.

[0039] Specifically, the system performs spatial overlay operations on the extracted project redline vector data and the preset control element layer set under a unified coordinate reference system. For spatial compliance detection, the system calculates the overlay area between the project redline vector data and each control element layer, including the core protection zone layer, integrated development zone layer, municipal / county administrative boundary layer, urban development boundary layer, ecological protection redline layer, permanent basic farmland layer, ecological public welfare forest layer, natural forest layer, river and lake management area layer, and current land use type layer. During the overlay analysis, spatial relationship parameters between the project area and the waterfront landscape control layer, mountain landscape control layer, and building height control layer are extracted. Based on the overlay area and its proportion to the total project area, the spatial compliance detection conclusion is determined according to preset compliance judgment rules. In constructing spatial compliance feature parameters, the system queries the planning zoning layer for zoning units that spatially overlap or contain the project's redline vector data, extracting the corresponding zoning category identifier as the zoning category component; it uses the overlapping area of ​​the project's redline vector data and each control element layer as the overlapping area component; by querying the land use status data of the project's area, it calculates the proportion of urban and rural construction land area to the total area of ​​the area, obtaining the urban and rural construction land scale proportion component; similarly, it calculates the proportion of residential land area to the total area of ​​the area, obtaining the residential land scale proportion component. These four components are combined in a fixed dimensional order to form complete spatial compliance feature parameters.

[0040] In some embodiments, the control element layers included in the preset control element layer set specifically include the following categories: core protection zone layer and integrated development zone layer, used to represent the spatial scope of different control zones in the green heart area; city and county administrative boundary layer, used to represent the spatial scope of the administrative divisions crossed by the project; urban development boundary layer, ecological protection red line layer, and permanent basic farmland layer, used to represent the spatial scope of the three control lines in the national land spatial planning; ecological public welfare forest layer, natural forest layer, and river and lake management scope layer, used to represent the spatial scope of various ecological resource protection areas; current land type layer, used to represent the current land use type and area corresponding to each map patch within the project scope; waterfront landscape control layer and mountain landscape control layer, used to represent the landscape control requirements within the project scope; building height control layer, used to represent the building height restriction requirements in the area where the project is located. The system calculates the superposition area of ​​the project red line vector data and the above layers respectively, and extracts the corresponding spatial relationship parameters as the basis for judging spatial compliance detection.

[0041] The process of constructing spatial compliance characteristic parameters also includes: performing spatial overlay and clipping operations on the project redline vector data and the existing land use map patch data layer to extract the various land use map patches falling within the project redline area; classifying and statistically analyzing the various land use map patches according to the primary and secondary land use code in the land use classification coding system of the land change survey; calculating the area and area ratio of each primary land use within the project area to obtain the current land use composition parameters of the project. The current land use composition parameters of the project include the proportion of cultivated land area, plantation land area, forest land area, grassland area, industrial and mining land area, residential land area, transportation land area, and water area; and further analyzing the cultivated land area from the current land use composition parameters of the project. The proportion of cultivated land area and the proportion of industrial and mining land area are included in the spatial compliance characteristic parameters. The proportion of cultivated land area is used to characterize the degree of cultivated land resources involved in the project scope, and the proportion of industrial and mining land area is used to characterize the scale of existing industrial and mining land in the project scope. When the benchmark judgment thresholds of each clause in the rule subset are subsequently assigned and corrected based on the first difference and the second difference, the proportion of cultivated land area and the proportion of industrial and mining land area are used as additional correction inputs. If the proportion of cultivated land area is not lower than the preset cultivated land sensitivity threshold, a tightening correction increment is applied to the benchmark judgment thresholds of clauses involving cultivated land protection. If the proportion of industrial and mining land area is not lower than the preset industrial and mining stock threshold, a relaxing correction increment is applied to the benchmark judgment thresholds of clauses involving ecological restoration.

[0042] In some embodiments, overlay analysis and the construction of spatial compliance feature parameters can be achieved in multiple ways: Optionally, the system adopts a raster-based overlay analysis method, rasterizing the project redline vector data and each control element layer into raster data of the same pixel size according to a preset spatial resolution, determining the control area category of each pixel within the project area through pixel-level Boolean operations, summarizing the number and area of ​​each category of pixels to calculate the overlay area, determining the spatial compliance detection conclusion based on the overlay area and the proportion threshold, and extracting the partition category, overlay area, urban and rural construction land scale proportion, and residential land scale proportion according to the query logic corresponding to each component to complete the assignment of spatial compliance feature parameters; Optionally, the system adopts a vector geometric operation-based overlay analysis method, performing intersection operations on the project redline vector polygon and the vector polygons of each control element layer one by one to calculate the overlay area, and performing topological relationship judgments such as inclusion and adjacency, determining the spatial compliance detection conclusion based on the combination results of each overlay area and topological relationship, and completing the construction of spatial compliance feature parameters by querying the land use status data of the project area to statistically analyze the urban and rural construction land scale proportion and the residential land scale proportion. It is understandable that other spatial analysis methods can also be used to achieve overlay analysis and the construction of spatial compliance characteristic parameters, which are not limited here.

[0043] S103. Select a subset of rules from the hierarchical rule base according to the zoning category and the superimposed area, and assign values ​​to the judgment thresholds of each clause in the subset of rules according to the first difference between the proportion of urban and rural construction land and the preset upper limit of urban and rural construction land control, and the second difference between the proportion of residential land and the preset upper limit of residential land control, to obtain a parameterized subset of rules.

[0044] The hierarchical rule base refers to a rule database that organizes and stores construction project access rules hierarchically according to zoning categories and control levels. It represents the set of access judgment rules for different zoning categories and control levels. The rule subset refers to a set of access judgment clauses in the hierarchical rule base corresponding to specific zoning category and control level identifiers. It represents the set of rule clauses applicable to the control status of the current project area. The judgment threshold is the numerical boundary of the matching degree used in matching degree calculation to determine whether the construction behavior description fragment matches the list clauses. It indicates that a match is determined when the matching degree reaches this value. The parameterized rule subset refers to the rule subset obtained by assigning values ​​to the judgment thresholds of each clause according to the difference between the land use index of the project area and the corresponding control upper limit. It represents personalized judgments configured for the land use index control status of the current project area. The threshold is a set of rule clauses; the two-dimensional query key is a composite retrieval identifier composed of partition category and control level identifier, used to locate the target rule subset in the hierarchical rule base; the control level identifier is the level category code obtained after dividing the superimposed area according to the preset area grading standard, used to indicate the discrete level of the superimposed relationship between the current project scope and the control element layer; the benchmark judgment threshold is the initial matching degree judgment boundary value of each clause in the rule subset before difference correction, used to indicate the standard judgment threshold when the land use index difference factor is not considered; the first difference is the difference between the proportion of urban and rural construction land scale and the preset urban and rural construction land control upper limit, used to indicate the margin of urban and rural construction land index from the control upper limit; the second difference is the difference between the proportion of residential land scale and the preset residential land control upper limit, used to indicate the margin of residential land index from the control upper limit.

[0045] Specifically, the system first discretizes and maps the project scope and the overlapping area of ​​each control element layer in the spatial compliance feature parameters according to a preset area grading standard, converting the overlapping area values ​​into several control level identifiers, such as low, medium, or high control level identifiers depending on the interval the overlapping area falls into. A two-dimensional query key is constructed using the partition category in the spatial compliance feature parameters and the mapped control level identifiers. The system then retrieves a subset of rules matching this two-dimensional query key from the index structure of the grading rule base. Each clause in this rule subset includes a corresponding benchmark judgment threshold. After obtaining the rule subset, the system calculates the first difference between the proportion of urban and rural construction land and the preset upper limit of urban and rural construction land control, and the second difference between the proportion of residential land and the preset upper limit of residential land control. The system then adjusts the benchmark judgment thresholds of each clause in the rule subset based on the first and second differences. The adjusted judgment thresholds are calculated by the benchmark judgment thresholds and the first and second differences according to preset adjustment rules. Since the judgment thresholds are negatively correlated with the first and second differences, the adjusted judgment thresholds increase as the difference between the land use index and the corresponding control upper limit decreases. The revised judgment threshold is assigned to the corresponding clauses in the rule subset to complete the construction of the parameterized rule subset.

[0046] Based on the assignment correction performed according to the first and second differences, the system further reads the proportion of cultivated land area and the proportion of industrial and mining land area from the spatial compliance feature parameters. For clauses involving cultivated land protection in the rule subset, if the proportion of cultivated land area is not lower than the preset cultivated land sensitivity threshold, the difference between the cultivated land area proportion and the preset cultivated land sensitivity threshold is multiplied by the preset cultivated land correction coefficient to obtain the cultivated land tightening increment. This cultivated land tightening increment is then added to the judgment threshold of the corresponding cultivated land protection clause after the first and second differences are corrected, so that the judgment standard of clauses involving cultivated land resources varies with the project scope. The increased proportion of arable land within the designated area leads to further tightening of regulations. For clauses related to ecological restoration within the rule subset, provided the proportion of industrial and mining land area is not lower than a preset industrial and mining stock threshold, the relaxation increment for industrial and mining land use is obtained by multiplying the difference between the proportion of industrial and mining land area and the preset industrial and mining stock threshold by a preset industrial and mining correction coefficient. This relaxation increment is subtracted from the judgment threshold of the corresponding ecological restoration clause after correction by the first and second differences. This allows projects whose main construction content is ecological restoration, such as mine pit restoration and ecological governance of abandoned mines, to receive a moderately relaxed matching judgment standard in areas with a large stock of industrial and mining land. The judgment threshold after applying the tightening increment for arable land and the relaxation increment for industrial and mining land use is assigned to the corresponding clause in the rule subset, completing the construction of the parameterized rule subset.

[0047] In some embodiments, the calculation of the first difference and the second difference, as well as the assignment and correction of the judgment threshold, can be achieved in multiple ways: Optionally, the system adopts a linear correction method to calculate the first difference between the proportion of urban and rural construction land and the preset upper limit of urban and rural construction land control, and the second difference between the proportion of residential land and the preset upper limit of residential land control. The first difference and the second difference are weighted according to their respective preset correction coefficients, and the sum of the benchmark judgment threshold and the weighted calculation result is used as the corrected judgment threshold. After assigning values ​​to each rule, a parameterized rule subset is obtained. Optionally, the system adopts a segmented correction method, presets several difference intervals and the correction range corresponding to each interval. When the first difference falls into a certain interval, it corresponds to a correction increment. When the second difference falls into a certain interval, it corresponds to another correction increment. The two correction increments are merged into a comprehensive correction amount according to a preset fusion rule. The sum of the benchmark judgment threshold and the comprehensive correction amount is used as the corrected judgment threshold. The corrected judgment threshold is assigned to the corresponding clauses of the rule subset one by one to complete the construction of the parameterized rule subset. It is understandable that other calculation methods can be used to calculate the difference and correct the judgment threshold, which is not limited here.

[0048] In some embodiments, this step specifically includes:

[0049] The superimposed area is mapped to a control level identifier according to a preset area grading standard. A two-dimensional query key is constructed using the zoning category and the control level identifier. Based on the two-dimensional query key, a subset of rules matching the two-dimensional query key is retrieved from the hierarchical rule base. Each clause in this subset of rules contains a corresponding benchmark judgment threshold. The first difference between the proportion of urban and rural construction land and the preset upper limit of urban and rural construction land control is calculated, and the second difference between the proportion of residential land and the preset upper limit of residential land control is calculated. The benchmark judgment thresholds of each clause in the subset of rules are assigned and corrected according to the first and second differences to obtain the corrected judgment thresholds. The corrected judgment thresholds are assigned to the corresponding clauses in the subset of rules to obtain the parameterized rule subset.

[0050] The overlapping area is the area of ​​the overlapping region obtained after spatial overlay calculation of the project's red line vector data and the layers of various control elements. For example, the overlapping area of ​​the project scope and the ecological protection red line layer is 500 square meters. The preset area grading standard is a rule for dividing this value into discrete levels, such as 0 to 200 square meters for low control, 200 to 1000 square meters for medium control, and above 1000 square meters for high control. The control level identifier is the code of the mapping result, such as "MEDIUM". The two-dimensional query key is a composite search identifier composed of the partition category and the control level identifier, such as "Integrated Development Zone—MEDIUM". UM"; The benchmark judgment threshold is the initial matching degree boundary value of the clause before the difference correction. For example, the benchmark threshold of a negative list clause is 0.75; The first difference is the difference between the proportion of urban and rural construction land and the preset control limit of urban and rural construction land. For example, if the proportion of urban and rural construction land is 22% and the control limit is 25%, then the first difference is 3%; The second difference is the difference between the proportion of residential land and the preset control limit of residential land. For example, if the proportion of residential land is 8% and the control limit is 10%, then the second difference is 2%; The parameterized rule subset is the final rule set after the corrected judgment threshold is written into each clause.

[0051] The superimposed area of ​​500 square meters is determined to fall within the 200-1000 square meter range according to the preset area grading standard, and mapped to the control level identifier "MEDIUM". A two-dimensional query key "Integrated Development Zone—MEDIUM" is constructed by concatenating the zoning category "Integrated Development Zone" and "MEDIUM". In the grading rule base, an exact match search is performed using this key as an index to read the corresponding rule subset and the benchmark judgment threshold of each clause. The first difference between the urban and rural construction land scale ratio of 22% and the preset urban and rural construction land control upper limit of 25% is calculated as 3%, and the second difference between the residential land scale ratio of 8% and the preset residential land control upper limit of 10% is calculated as 2%. The benchmark judgment threshold T0 for each clause is adjusted using the formula: T'=T0+w1×(1 / Δ1)+w2×(1 / Δ2), where Δ1 is the first difference, Δ2 is the second difference, and w1 and w2 are preset weights for the application location indicator type. For example, if T0 for a certain clause is 0.75 and the calculation result of w1×(1 / Δ1)+w2×(1 / Δ2) is 0.08, then the adjusted T' is 0.83. The smaller the difference, the greater the adjustment amount and the higher the judgment threshold. T' is assigned to the corresponding clause field of the rule subset one by one to complete the construction of the parameterized rule subset.

[0052] In some embodiments, to further illustrate the impact of the deviation record-driven rule base self-correction mechanism on the accuracy of the review, the following is described through a quantitative verification example. Assume that under a certain integrated development zone category, the initial benchmark judgment threshold for a positive list clause in the hierarchical rule base is set to 0.75. After completing the access review of 50 projects under this zone category, 35 review deviation records have accumulated in the deviation record library, exceeding the preset deviation accumulation threshold of 30, triggering the rule correction process. The system extracts these 35 review deviation records and performs deviation direction statistics on the deviation field corresponding to the positive list clause: 28 records show that the approver changed the positive list access category conclusion in the draft to a pending assessment conclusion, which is a positive deviation, meaning that the original threshold was too low, causing projects that should not be approved to be judged as accessible by the system; another 4 records show that the approver changed the pending assessment conclusion in the draft to a positive list access category conclusion, which is a negative deviation; the remaining 3 records show that the draft value of this field is consistent with the final review value, and do not constitute deviation records. According to the formula for calculating the correction magnitude coefficient, the correction magnitude coefficient = (28-4) / 35 = 0.686. Using a preset step size parameter of 0.05, the threshold correction amount is calculated as 0.686 × 0.05 = 0.034. The corrected judgment threshold is 0.75 + 0.034 = 0.784. This 0.784 is then updated to the baseline judgment threshold field for this clause in the hierarchical rule base. After the update, when new projects under this partition category construct parameterized rule subsets, the baseline judgment threshold read will increase from 0.75 to 0.784. The final judgment threshold after the first and second difference assignments will also increase accordingly. This causes construction activities that were previously incorrectly judged as eligible due to a low threshold to become pending assessment under the corrected threshold standard, requiring manual assessment. A statistical comparison was made using the review results of 25 similar projects before and after the revision: Before the revision, in 14 of the 25 projects, approvers revised the compliance conclusion field of the checklist during the final review, resulting in a final review revision rate of 56%. After the revision, the number of projects where approvers revised the compliance conclusion field of the checklist decreased to 5, resulting in a final review revision rate of 20%, a decrease of 36 percentage points. This indicates that the deviation record-driven rule base self-correction mechanism effectively reduced the deviation between the system's output conclusions and the professional judgment of approvers in this category, and improved the matching degree between the judgment threshold settings in the hierarchical rule base and approval practice. The above quantitative verification shows that the associated storage and accumulation-triggered correction mechanism of deviation records form a closed-loop feedback, allowing the judgment threshold of the hierarchical rule base to gradually approach the actual judgment standards of approvers as approval practice data continues to accumulate, reducing the frequency and magnitude of revisions by approvers in subsequent project reviews.

[0053] S104. Compare the construction content according to a subset of parameterized rules and output a compliance conclusion for the list. Here, comparison refers to the operation of determining whether the description of construction behavior matches the text of rule clauses through feature encoding and matching degree calculation, used to represent the measurement and comparison of the degree of matching between the construction content and the list clauses; the list compliance conclusion refers to the classification judgment result made on the construction content based on the matching degree calculation result, including three categories: positive list access category, negative list prohibition category, and pending judgment category; positive list access category refers to the judgment conclusion corresponding to the case where all construction behavior description fragments in the construction content meet the matching standard of at least one clause in the positive list; negative list prohibition category refers to the judgment conclusion corresponding to the case where at least one construction behavior description fragment in the construction content meets the matching standard of a clause in the negative list; pending judgment category refers to the construction content... The judgment conclusion is as follows: when at least one construction behavior description fragment fails to meet the matching criteria of any positive list clause and does not trigger the negative list prohibition conclusion; a construction behavior description fragment refers to a text unit that describes a single construction behavior after performing keyword extraction and semantic segmentation on the construction content, and refers to a text paragraph with independent semantic integrity obtained after being segmented at the semantic boundary; the construction behavior feature encoding set refers to the encoding set obtained after performing feature encoding on all construction behavior description fragments respectively, used to represent the numerical representation of each construction behavior description; the clause feature encoding set refers to the encoding set obtained after performing feature encoding on each clause text of the parameterized rule subset respectively, used to represent the numerical representation of each list clause.

[0054] Specifically, the system first performs keyword extraction and semantic segmentation on the construction content, identifying the semantic boundaries between various construction behaviors within the content, and dividing the content into several construction behavior description fragments, each fragment corresponding to a specific construction behavior description. Feature encoding is then performed on each construction behavior description fragment, mapping each fragment to a fixed-dimensional numerical code using a pre-defined encoding model, resulting in a set of construction behavior feature codes. Simultaneously, feature encoding is performed on the text content of each clause in the parameterized rule subset, resulting in a set of clause feature codes. The feature codes for positive list clauses and negative list clauses are stored separately according to list category. Following a negative list priority judgment order, the system calculates the matching degree between each construction behavior feature code in the construction behavior feature code set and all negative list clause feature codes in the clause feature code set. If the matching degree between any construction behavior feature code and any negative list clause feature code is not lower than the parameterized judgment threshold corresponding to that clause, a negative list prohibition conclusion is output, and subsequent positive list matching calculations are not performed. If the negative list detection does not trigger a prohibition conclusion, the matching degree between each construction behavior feature code and all positive list clause feature codes is calculated one by one. If the matching degree between a certain construction behavior feature code and all positive list clause feature codes is lower than the parameterized judgment threshold corresponding to each clause, a pending judgment conclusion is output. If the matching degree between all construction behavior feature codes and at least one positive list clause feature code is not lower than the corresponding parameterized judgment threshold, a positive list access conclusion is output.

[0055] In some embodiments, the comparison of construction content and the output of the list compliance conclusion can be achieved in multiple ways: Optionally, the system adopts a rule-based segmentation method, pre-defines a set of text boundary marker symbols for the construction behavior description, including semicolons, newlines, and specific conjunctions, performs keyword extraction and segmentation on the construction content according to the boundary marker symbols, obtains a set of construction behavior description fragments, performs feature encoding on each fragment through a preset encoding model, calculates the matching degree with the feature codes of negative list clauses and positive list clauses respectively, and outputs the list compliance conclusion level by level according to the parameterized judgment thresholds corresponding to each clause, prioritizing the negative list; optional The system employs a semantic model-based segmentation approach. The entire construction content is input into a pre-defined semantic segmentation model. The model automatically identifies the start and end positions of each construction behavior description and extracts keywords based on semantic coherence and semantic boundary features. Feature encoding is then performed sequentially on each segmented construction behavior description fragment, generating a set of construction behavior feature codes. Next, the text features of each clause in the parameterized rule subset are encoded to construct a set of clause feature codes. A batch matching degree matrix is ​​then calculated to obtain a matching degree matrix between all construction behavior feature codes and all clause feature codes. Following a negative list priority order, the system progressively judges compliance based on parameterized judgment thresholds and outputs a list compliance conclusion. It is understood that other text segmentation and feature encoding methods can also be used to achieve the comparison and list compliance conclusion output; this is not limited here.

[0056] Before classifying the spatial compliance inspection results into levels, the system performs a pre-collision detection judgment on the overlay analysis results of the project redline vector data with the three-zone three-line layer set and the rigid control element layer set. The three-zone three-line layer set includes the ecological protection redline layer, the permanent basic farmland layer, and the urban development boundary layer. The rigid control element layer set includes the ecological public welfare forest layer, the natural forest layer, the river and lake management boundary demarcation layer, and the cultural relic protection unit layer. The system reads the overlay area of ​​the project redline vector data with each of the above layers one by one, generates a collision detection icon for each layer, sets the collision detection icon of the corresponding layer to the triggered state and records the corresponding overlay area value when the overlay area is greater than zero, and sets the collision detection icon of the corresponding layer to the non-triggered state when the overlay area is equal to zero.

[0057] In some embodiments, this step specifically includes:

[0058] Keyword extraction and semantic segmentation are performed on the construction content to obtain several construction behavior description fragments. These fragments are then feature-encoded to obtain a set of construction behavior feature codes. The text content of each clause in the parameterized rule subset is also feature-encoded to obtain a set of clause feature codes. The matching degree of each construction behavior feature code in the set of construction behavior feature codes is calculated between it and the clause feature codes of the negative list clauses in the set of clause feature codes. If any matching degree is not lower than the corresponding judgment threshold, a negative list prohibition conclusion is output. If no negative list prohibition conclusion is output, the similarity of each construction behavior feature code in the set of construction behavior feature codes is calculated between it and the clause feature codes of the positive list clauses in the set of clause feature codes. If the matching degree between a construction behavior feature code and the clause feature codes of all positive list clauses is lower than the corresponding judgment threshold, a pending judgment conclusion is output. If the matching degree between each construction behavior feature code and the clause feature code of at least one positive list clause is not lower than the corresponding judgment threshold, a positive list admission conclusion is output.

[0059] Feature encoding is the operation of mapping text to fixed-dimensional numerical codes through a preset encoding model. For example, the clause text "prohibits the construction of new industrial and mining enterprises" is mapped to a fixed-dimensional numerical code through the encoding model. The clause feature code set is a set of codes that are grouped and stored according to positive and negative lists after feature encoding is performed on each clause text of the parameterized rule subset. Construction behavior feature encoding is the code obtained by performing feature encoding on a single construction behavior description fragment. For example, "constructing one new factory building" corresponds to a fixed-dimensional numerical code. The matching degree is a measure of the degree of matching between two feature codes. The value ranges from 0 to 1. The closer the value is to 1, the higher the degree of matching. The negative list prohibition conclusion, the pending judgment conclusion, and the positive list admission conclusion are the judgment conclusion categories output under the three matching results.

[0060] All clause texts in the parameterized rule subset are feature-encoded one by one using a preset encoding model. The feature codes for negative list clauses and positive list clauses are grouped and stored to form a clause feature code set. For each construction behavior feature code in the construction behavior feature code set, a matching calculation is performed in the order of priority based on the negative list: the matching degree between each feature code and all negative list clause feature codes is calculated. For example, the feature code corresponding to the construction behavior "constructing one new industrial or mining plant" and the feature code corresponding to the negative list clause "prohibiting the construction of new industrial or mining enterprises" have a matching degree of 0.86. The parameterized judgment threshold for this clause is 0.83. If 0.86 is not lower than 0.83, a negative list prohibition conclusion is triggered, and all subsequent calculations are terminated. If no prohibition conclusion is triggered after traversing all negative list clauses, then calculate the matching degree between each construction behavior feature code and all positive list clause feature codes. If there is a construction behavior feature code whose matching degree with all positive list clause feature codes is lower than the corresponding judgment threshold, for example, the highest matching degree of a construction behavior description feature code with 12 positive list clauses is only 0.51 while the corresponding thresholds are not lower than 0.70, then output a pending judgment conclusion; if all construction behavior feature codes can find at least one positive list clause with a matching degree not lower than the corresponding judgment threshold, then output a positive list access conclusion.

[0061] S105. Merge the space compliance test results with the list compliance results to generate an access level result, and trigger the corresponding approval path and mandatory approval node based on the access level result.

[0062] Among them, the access level conclusion refers to the comprehensive access judgment result obtained by combining the spatial compliance test conclusion and the list compliance conclusion, which is used to indicate the access level category of the project after comprehensively considering spatial compliance and list compliance; the compliance decision matrix refers to the pre-configured structured decision rule table with the combination of spatial compliance level and list compliance conclusion as the row and column dimensions, and the access level conclusion corresponding to each combination as the matrix element, which is used to indicate the access level corresponding to different combination scenarios; the approval path refers to the approval process plan configured according to the access level conclusion, which specifies the sequence of nodes and process order that the project approval needs to go through, which is used to indicate the process arrangement that the project approval should follow; the mandatory approval node refers to the approval link that is forcibly set in the approval path and cannot be bypassed in the approval process, which is used to indicate the key approval points that must be manually reviewed or specially reviewed according to the access level conclusion.

[0063] Specifically, the system first categorizes spatial compliance detection conclusions into three levels—spatial compliance, spatial warning, and spatial violation—based on preset grading standards. Spatial compliance corresponds to situations where the spatial relationship of the superimposed area meets the construction requirements of each control element; spatial warning corresponds to situations where the proportion of urban and rural construction land or residential land is close to the corresponding control limit; and spatial violation corresponds to situations where the spatial relationship of the superimposed area does not meet the construction requirements of each control element. Using the combination of the spatial compliance detection conclusion's level and the list compliance conclusion as a two-dimensional query condition, the system locates the corresponding matrix element in the preset compliance decision matrix and reads the access level conclusion recorded in that element. The compliance decision matrix consists of three rows (corresponding to the three levels of spatial compliance, spatial warning, and spatial violation, respectively) and three columns (corresponding to three conclusions: positive list access category, negative list prohibition category, and pending assessment category, respectively), forming nine possible combinations. Each combination corresponds to a predefined access level conclusion. Based on the retrieved access level conclusion, the system searches the preset approval path library for the approval path corresponding to that level. This approval path specifies the sequence of nodes in the project approval process and the processing requirements for each node. The system injects a mandatory approval node corresponding to the access level conclusion at a preset insertion position in the approval path. The type of the mandatory approval node and its insertion position in the approval path are pre-configured by the approval path library based on the access level conclusion. The type and insertion position of the mandatory approval node differ for different access level conclusions. The complete approval path after injecting the mandatory approval node is output for subsequent approval process scheduling.

[0064] In some embodiments, the determination of access level conclusions and the triggering of approval paths and mandatory approval nodes can be achieved in multiple ways: Optionally, the system determines the level of spatial compliance detection conclusions according to the spatial relationship corresponding to the superimposed area and the closeness of land use indicators to the corresponding control limits. The system uses the level code and the list compliance conclusion code as a composite key, performs an exact match query in the compliance decision matrix data table with the composite key as the primary key, reads the corresponding access level conclusion field value, and then queries the approval path database data table using the access level conclusion field value as the search condition to retrieve the approval path sequence. The system then determines the level based on each mandatory approval node in the path sequence. The preset insertion rules inject mandatory approval nodes one by one into the corresponding positions of the approval path, and output the completed approval path assembly. Optionally, the system adopts a decision tree structure to organize the compliance decision logic, using the spatial compliance detection conclusion level as the first-level branch node and the list compliance conclusion as the second-level branch node. The system determines the access level conclusion corresponding to the leaf node through tree path traversal, and then retrieves the basic approval path template from the approval path library using the access level conclusion as an index. The positions marked with mandatory approval node placeholders in the template are replaced with mandatory approval node instances corresponding to the access level conclusions, generating a complete approval path containing mandatory approval nodes before outputting it. It is understood that other rule engines or matrix query methods can also be used to determine the access level conclusions and trigger the approval path and mandatory approval nodes; this is not limited here.

[0065] The system determines the level of spatial compliance detection conclusions based on the trigger status of each collision detection marker and its corresponding superimposed area value, according to a preset rigid control priority judgment rule: If the collision detection marker for the ecological protection red line layer is triggered, regardless of the status of collision detection markers in other layers, the spatial compliance detection conclusion is directly judged as a spatial violation. If the collision detection marker for the ecological protection red line layer is not triggered, but the collision detection marker for the permanent basic farmland layer is triggered, the system reads the proportion of the superimposed area of ​​permanent basic farmland to the total project area. If this proportion is not lower than a preset basic farmland proportion threshold, the spatial compliance detection conclusion is judged as a spatial violation; otherwise, if the proportion is lower than the preset basic farmland proportion threshold, the spatial compliance detection conclusion is judged as a spatial violation. The compliance detection conclusion is determined to be a spatial warning level, and a permanent basic farmland occupation mark is added to the spatial warning level conclusion. If the collision detection markers of both the ecological protection red line layer and the permanent basic farmland layer are in a non-triggered state, the collision detection markers of each layer in the rigid control element layer set are further detected. If the collision detection marker of any rigid control element layer is in a triggered state, the spatial compliance detection conclusion is determined to be a spatial warning level, and a corresponding control element type mark is added. If the collision detection markers of all layers in the three-zone three-line layer set and the rigid control element layer set are in a non-triggered state, and the proportions of urban and rural construction land and residential land are not close to the corresponding control limits, the spatial compliance detection conclusion is determined to be a spatial compliance level. The added occupation mark or control element type mark is transmitted along with the spatial compliance detection conclusion to the subsequent compliance decision matrix query stage to support the configuration of differentiated access level conclusions for different control element triggering scenarios under the same spatial warning level in the compliance decision matrix.

[0066] In some embodiments, this step specifically includes: classifying the spatial compliance detection conclusion into three levels: spatial compliance, spatial warning, and spatial violation; using the combination of the spatial compliance detection conclusion's level and the list compliance conclusion as the query condition; retrieving the access level conclusion corresponding to the query condition from the preset compliance decision matrix; retrieving the corresponding approval path from the preset approval path library based on the access level conclusion; and injecting the mandatory approval node corresponding to the access level conclusion into the preset insertion position of the approval path; and outputting the approval path containing the mandatory approval node.

[0067] Spatial compliance, spatial early warning, and spatial violation are three levels of spatial compliance assessment conclusions categorized by severity. For example, if the overlapping area between the project scope and each control element layer is zero and the proportions of urban and rural construction land and residential land are far below the corresponding control limits, it is considered spatial compliance; if the proportions of urban and rural construction land or residential land are close to the corresponding control limits, it is considered spatial early warning; and if the project scope has a clear overlapping area with ecological protection red lines or drinking water source protection areas, it is considered spatial violation. The compliance decision matrix is ​​a three-row, three-column structured decision table consisting of the three spatial compliance levels as rows and the three types of list compliance conclusions as columns. For example, "Spatial Compliance - Positive List Compliance" is a table that defines spatial compliance. The system includes nine combinations of entry categories: "Element Records" (direct admission), "Element Records in the Spatial Warning - Positive List Admission Category" (conditional admission), and "Element Records in the Spatial Violation - Negative List Prohibition Category" (admission denied). Each combination corresponds to a unique admission level conclusion. The approval path database is a database that pre-stores the corresponding approval path sequences according to the admission level conclusion. Mandatory approval nodes are manual review nodes that cannot be bypassed in the approval path. For example, the mandatory approval node corresponding to "direct admission" is the formal review node of the municipal competent department, and the mandatory approval node corresponding to "conditional admission" is the provincial expert review node. The preset insertion position is a placeholder field in the approval path data structure that marks the injection point of the mandatory approval node.

[0068] The spatial compliance test results are categorized into three levels based on a preset grading standard. A two-dimensional query condition is created by concatenating the level code with the list compliance conclusion code. For example, "spatial warning" and "positive list access category" can be concatenated as the query condition. The intersecting element of the row and column in the preset compliance decision matrix is ​​located, and the pre-stored access level conclusion "conditional access" is retrieved. Each of the nine possible combinations is pre-assigned a unique access level conclusion in the matrix, ensuring that any input combination yields a definite result. Using "conditional access" as the search condition, the corresponding basic approval path is retrieved from the approval path database. This path data structure contains several mandatory approval node insertion positions marked with placeholder fields. The provincial expert review node instances corresponding to "conditional access" are sequentially replaced with these placeholder fields to generate and output an approval path containing a complete sequence of mandatory approval nodes. The types and insertion positions of mandatory approval nodes corresponding to different access level conclusions vary according to the preset configuration of the approval path database. The number of mandatory approval nodes in the approval path corresponding to the "no access" level is greater than that of the "direct access" level, achieving differentiated configuration between approval paths and access level conclusions.

[0069] The preset compliance decision matrix, based on the original two-dimensional combination of spatial compliance detection conclusion and list compliance conclusion as query conditions, further subdivides spatial early warning levels into two sub-levels according to the type of additional marker: permanent basic farmland occupation early warning and rigid control element occupation early warning. Using the combination of spatial early warning sub-level and list compliance conclusion as refined query conditions, the matrix retrieves the corresponding access level conclusion in the extended area. Specifically, the access level conclusion corresponding to the combination of permanent basic farmland occupation early warning and positive list access category is strict conditional access; the access level conclusion corresponding to the combination of permanent basic farmland occupation early warning and pending assessment category is deferred access; and the access level conclusion corresponding to the combination of rigid control element occupation early warning and positive list access category is conditional access. Different combinations of sub-levels and list compliance conclusions correspond to differentiated approval paths and mandatory approval node configurations, ensuring that projects involving permanent basic farmland occupation receive a more stringent approval path arrangement than projects involving general rigid control element occupation.

[0070] The following provides a more detailed description of the process of the method provided in this implementation. Please refer to [link / reference]. Figure 2 This is another flowchart illustrating the intelligent review method for access to the ecological green heart construction project in this application embodiment.

[0071] S201. Overlay the project redline vector data and the preset two-zone data layers with the administrative boundary layer to obtain the coverage of the project redline vector data in the core protection zone and the integrated development zone, as well as the spatial relationship between the project redline vector data and the municipal administrative boundary.

[0072] The preset two-zone data layer is a vector layer pre-loaded into the system that contains the spatial boundaries of the core protection zone and the integrated development zone, such as a set of polygons representing the boundaries of the two types of zones as defined by the ecological management plan; the administrative boundary layer is a vector layer that records the spatial boundaries of administrative divisions at all levels, specifically referring to the polygons representing the boundaries of municipal administrative divisions; the coverage status refers to the spatial overlap between the project's red-line polygons and the core protection zone and the integrated development zone, such as covering only the integrated development zone or covering both zones simultaneously; the spatial positional relationship refers to whether the project's red-line polygons cross the municipal administrative boundaries, such as the project scope spanning two municipal administrative districts.

[0073] Spatial overlay operations are performed on the project redline vector data, the preset two-zone data layers, and the administrative boundary layer under a unified coordinate reference system. Regarding coverage, the intersection area of ​​the project redline polygon with the core protection zone polygon and the integrated development zone polygon is calculated. If the intersection area is greater than zero, the corresponding zone is determined to be covered, thus determining the specific coverage status of the project redline: covering only the integrated development zone, only the core protection zone, or both zones simultaneously. Regarding spatial location, the intersection area of ​​the project redline polygon with the polygons of each municipal administrative region is determined. If the intersection area of ​​the project redline polygon with two or more municipal administrative region polygons is greater than zero, the project redline is determined to cross a municipal administrative boundary; otherwise, the project redline is determined to be within a single municipal administrative region. Both analysis results serve as input for subsequent approval level determination.

[0074] S202. When the project's red line vector data simultaneously covers the core protected area and the integrated development area, or when the project's red line vector data crosses the municipal administrative boundary, a provincial approval suggestion identifier will be generated.

[0075] The provincial approval suggestion identifier refers to a field identifier generated based on the spatial attributes of the project, marking that the project must be submitted to the provincial competent department for approval. For example, the identifier value is set to the enumeration value "PROVINCE_LEVEL" and written into the permission level field of the approval path data structure, which is used to route the approval process to the provincial approval node queue.

[0076] Read the coverage and spatial relationship obtained from S201, and perform judgments based on logic or conditions: If the intersection area of ​​the project's red line polygon with the core protection zone polygon is greater than zero and the intersection area with the integrated development zone polygon is also greater than zero, it is determined that the project's red line covers both zones simultaneously; if the intersection area of ​​the project's red line polygon with two or more municipal-level administrative region polygons is greater than zero, it is determined that the project's red line crosses the municipal-level administrative boundary. When any of the above conditions are met, a provincial approval suggestion identifier is generated, and the identifier value is written into the approval level field of the project record, marking that the project must be submitted to the provincial competent department for approval and entering the subsequent provincial approval path.

[0077] S203. If the project's red line vector data is entirely located within the integrated development zone and does not cross the municipal administrative boundary, generate the corresponding municipal approval suggestion identifier.

[0078] The corresponding municipal-level approval suggestion identifier refers to a field identifier generated based on the municipal-level administrative division where the project is located, marking that the project must be approved by the relevant municipal-level competent department. For example, the identifier value includes the corresponding municipal-level administrative division code, with the format "CITY_LEVEL_430100", which is used to route the approval task to the queue of the municipal-level approval node corresponding to the code.

[0079] Read the coverage and spatial relationship obtained from S201, and perform judgment according to logic and conditions: the intersection area of ​​the project red line polygon and the core protection zone polygon is equal to zero, and the project red line polygon is completely contained within the polygon of the integrated development zone (i.e., the intersection area of ​​the two is equal to the area of ​​the project red line polygon), and the project red line polygon has a non-zero intersection area with only one municipal administrative region polygon. When all three conditions are met, extract the administrative division code of the municipal administrative region, concatenate the code into the municipal approval suggestion identifier value, write it into the approval level field of the project record, and generate a municipal approval suggestion identifier that marks the corresponding municipal approval authority level.

[0080] S204. Inject the provincial approval suggestion identifier or the corresponding municipal approval suggestion identifier into the approval path to route the approval path to the corresponding approval authority level.

[0081] Injection refers to the assignment operation of writing the identifier field value into the reserved permission level field position in the approval path data structure; the approval permission level is the administrative approval permission category to which the node in the approval process belongs, which is divided into two categories: provincial and municipal. For example, the provincial level corresponds to the approval queue of the provincial ecological authority, and the municipal level corresponds to the approval queue of the ecological authorities of each city; routing refers to the scheduling operation of the approval process engine to allocate the approval task to the corresponding processing queue based on the permission level field value.

[0082] Write the field value of the approval suggestion identifier generated by S202 or S203 into the permission hierarchy field of the approval path data structure. When scheduling the approval path, the approval process engine reads this field value: if the field value is a provincial approval suggestion identifier, the approval process engine allocates the approval path to the provincial approval node queue, and subsequent approval nodes are handled by personnel of the provincial competent department; if the field value is a municipal approval suggestion identifier containing the municipal administrative division code, the approval process engine parses the code, allocates the approval path to the municipal approval node queue corresponding to the code, and subsequent approval nodes are handled by personnel of the corresponding municipal competent department, thus completing the permission hierarchy routing configuration of the approval path.

[0083] S205. Using the spatial compliance test results, list compliance results, and access level results as inputs, generate a draft review opinion according to the preset review opinion template.

[0084] The preset review opinion template is a predefined review opinion document structure containing placeholders for each conclusion field. For example, the template includes placeholder fields such as "Spatial compliance: {spatial_result}", "Construction content compliance: {list_result}", and "Admission level: {admission_level}". The draft review opinion is an initial review opinion document generated by filling in the corresponding placeholders for the above three conclusions, which is then used by the approvers to revise it at the mandatory approval stage.

[0085] Read the field values ​​of the spatial compliance inspection conclusion, the list compliance conclusion, and the access level conclusion, and perform field replacement one by one according to the mapping relationship between each placeholder and the conclusion field in the preset review opinion template: fill the spatial compliance inspection conclusion into the spatial compliance status placeholder, fill the list compliance conclusion into the construction content compliance placeholder, and fill the access level conclusion into the access level placeholder; for basic project information fields in the template, such as project name, project number, and applicant unit, extract the corresponding field values ​​from the project application data package and fill them in; after all placeholders are replaced, output a draft review opinion document with all fields filled in. This document records the generation timestamp and is stored in association with the current project record, and is pushed to the mandatory approval node for revision by the personnel awaiting approval.

[0086] S206. Push the draft review opinion to the mandatory approval node, receive the approval personnel's revision operations on the draft review opinion, and obtain the final review opinion.

[0087] Mandatory approval nodes are manual review stages that cannot be bypassed in the approval process, such as provincial expert review nodes or municipal competent department review nodes identified by node codes; revision operations refer to the editing, replacement, or supplementary operations performed by approvers on the content of each field of the draft review opinion at mandatory approval nodes, such as changing the access level field from "access" to "restricted access"; final review opinion is the final review opinion document formed after the approvers complete the revision and submit it for confirmation.

[0088] The draft review comments generated by S205 are pushed to the mandatory approval node in the current project approval path via the approval process engine. The account of the person handling the mandatory approval node is determined based on the permission level field of the approval path. After logging into the corresponding node, the approver reviews each field of the draft, performs revision operations on fields that need adjustment, and the system records the changes made each time a field is edited. After the approver confirms the submission, the revised document version is stored as the final review comments, and the submission timestamp and approver account are recorded. The approval process engine marks the mandatory approval node as completed, and the final review comments enter the subsequent comparison and processing flow.

[0089] S207. Compare the final review opinion with the draft review opinion field by field, generate review deviation records for the draft values ​​and final review values ​​corresponding to the fields with differences, and store the review deviation records in the deviation record library in association with the parameterized rule subset and spatial compliance feature parameters.

[0090] Field-by-field comparison refers to the operation of sequentially reading the values ​​of the same fields in the final review opinion and the draft review opinion according to the field list defined in the review opinion template and performing an equality judgment; review deviation record refers to a structured data entry that records the difference information between the field values ​​in the draft and the field values ​​in the final review, such as including three attributes: field name, draft value, and final value; deviation record library is a database that stores review deviation records and their associated information, used to accumulate data on the revision behavior of approvers.

[0091] Following the field list of the preset review opinion template, the values ​​of each corresponding field in the final review opinion and the draft review opinion are read one by one, and string equality checks are performed. For fields that are not equal, the field name, draft value, and final review value are extracted to construct a review deviation record data entry. All review deviation records corresponding to unequal fields are combined into a deviation record set. Using the parameterized rule subset identifier and spatial compliance feature parameter of the current project as the association fields, this set is encapsulated together with the deviation record set into an associated record and written to the deviation record database. After associated storage, each deviation record can trace back the state of the rule parameters corresponding to the deviation occurrence through the parameterized rule subset identifier and trace back the project's location attribute information through the spatial compliance feature parameter.

[0092] S208. If the number of review deviation records in the deviation record library that are the same as the partition category in the spatial compliance feature parameters is not less than the preset deviation accumulation threshold, extract all review deviation records corresponding to the partition category from the deviation record library.

[0093] The preset deviation accumulation threshold is the minimum number of deviation records required to trigger the rule correction process. For example, the preset threshold is 30 records, which means that the threshold correction calculation will be started after accumulating 30 or more deviation records in the same partition category. The extraction operation refers to the database query operation that retrieves and reads all matching records in the deviation record database using the partition category as the query condition.

[0094] Using the partition category field value in the spatial compliance feature parameters of the current project as the query condition, the total number of review deviation records in the deviation record database whose partition category field value in the spatial compliance feature parameters is equal to the query condition is counted. The counted number is compared with a preset deviation accumulation threshold. If the number is not lower than the threshold, a full search is performed in the deviation record database using the partition category as the filter condition. All review deviation records that meet the condition, along with their associated parameterized rule subset identifiers and spatial compliance feature parameters, are read, and the search result set is passed into the subsequent correction calculation process. If the number is lower than the threshold, the subsequent correction calculation is not performed.

[0095] S209. Using the deviation direction and frequency between the draft value and the final value of each deviation field in all review deviation records as input, the judgment threshold of the corresponding clause of the partition category in the hierarchical rule base is corrected to obtain the corrected judgment threshold.

[0096] Deviation direction refers to the direction of threshold adjustment reflected by the difference between the draft value and the final value. It is divided into two categories: positive deviation and negative deviation. For example, if the draft outputs an admission conclusion but the final review changes it to a prohibition conclusion, it indicates that the original judgment threshold was too low, and the corresponding positive deviation means that the threshold needs to be raised. Deviation frequency refers to the number of records in all extracted review deviation records where a specific deviation field has the same deviation direction, which is used to quantify the intensity of the adjustment requirement in that direction.

[0097] Iterate through all review deviation records. For each deviation field in each record, determine the deviation direction based on the semantic difference between the draft and final review values: a more stringent final review value is recorded as a positive deviation, and a more lenient final review value is recorded as a negative deviation. Count the occurrence frequency of each deviation field in all records for both positive and negative deviations, obtaining the corresponding positive and negative deviation frequencies. Use the ratio of the difference in deviation frequencies in both directions to the total number of deviation records as the correction magnitude coefficient. The calculation formula is: correction magnitude coefficient equals positive deviation frequency minus negative deviation frequency divided by the total number of deviation records. A positive result increases the threshold, and a negative result decreases the threshold. Multiply the correction magnitude coefficient by a preset step size parameter to obtain the threshold correction amount. Add the threshold correction amount to the current judgment threshold of the corresponding clause in the hierarchical rule base for that partition category to obtain the corrected judgment threshold.

[0098] S210. Update the revised judgment threshold to the hierarchical rule base.

[0099] The hierarchical rule base update operation refers to the data writing operation that overwrites the original judgment threshold of the corresponding clause in the hierarchical rule base with the corrected judgment threshold; the version record refers to the historical version entry that is synchronously written when the update operation is executed, which records the threshold values ​​before and after the update and the update time, and is used to support the traceability and verification of the correction process.

[0100] Using the revised judgment threshold calculated by S209 as the write content, and the partition category and corresponding clause identifier as the location conditions, the judgment threshold field of each target clause is found in the hierarchical rule base. The original judgment threshold is replaced with the revised judgment threshold, completing the update write to the hierarchical rule base. Before the update operation is executed, the current clause identifier, the original judgment threshold, the revised judgment threshold, the number of deviation records that triggered this revision, and the update timestamp are written to the version history table of the hierarchical rule base, forming the version record of this revision. After the thresholds of all target clauses are updated, the current valid threshold of the hierarchical rule base is the revised value. The baseline judgment threshold read by newly added projects when performing parameterized rule subset construction will be the value after this update.

[0101] The intelligent access control system in the embodiments of this invention is described below from the perspective of hardware processing. Please refer to [link / reference]. Figure 3 This is a schematic diagram of the physical device structure of an access intelligent review system in this application embodiment.

[0102] It should be noted that, Figure 3 The structure of the intelligent access review system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0103] like Figure 3 As shown, the access control intelligent verification system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 302 or programs loaded from storage section 308 into random access memory (RAM) 303, such as performing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0104] The following components are connected to I / O interface 305: input section 306 including audio input devices, push-button switches, etc.; output section 307 including liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 308 including hard disks, etc.; and communication section 309 including network interface cards such as LAN (Local Area Network) cards, modems, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0105] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the various functions defined in the present invention.

[0106] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0107] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0108] Specifically, the intelligent access review system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it implements the intelligent access review method for the ecological green heart construction project provided in the above embodiment.

[0109] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the access intelligent review system described in the above embodiments; or it may exist independently and not assembled into the access intelligent review system. The storage medium carries one or more computer programs, which, when executed by a processor of the access intelligent review system, enable the access intelligent review system to implement the ecological green heart construction project access intelligent review method provided in the above embodiments.

[0110] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0111] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0112] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for intelligent review of eligibility for ecological green heart construction projects, characterized in that, The method, applied to an intelligent access control system, includes: Receive project application data packets and extract project red line vector data and construction content from the project application data packets; The project redline vector data is overlaid and analyzed with a preset control element layer set. The overlay area of ​​the project redline vector data and each control element layer is calculated to obtain the spatial compliance detection conclusion. Spatial compliance feature parameters are constructed, including the project's zone category, the overlay area of ​​the project scope and each control element layer, the proportion of urban and rural construction land and the proportion of residential land in the project area. Based on the partition category and the superimposed area, a subset of rules is selected from the hierarchical rule base. The judgment threshold of each clause in the subset of rules is assigned according to the first difference between the proportion of urban and rural construction land and the preset upper limit of urban and rural construction land control, and the second difference between the proportion of residential land and the preset upper limit of residential land control, to obtain a parameterized subset of rules. The judgment threshold is negatively correlated with the first difference and the second difference. The construction content is compared according to the parameterized rule subset, and the list compliance conclusion is output. The list compliance conclusion includes positive list access category, negative list prohibition category, or category to be evaluated. The spatial compliance test results and the list compliance results are combined to generate an access level result, and the corresponding approval path and mandatory approval node are triggered based on the access level result.

2. The method according to claim 1, characterized in that, The step of selecting a rule subset from the hierarchical rule base based on the partition category and the superimposed area, and assigning judgment thresholds to each clause in the rule subset based on the first difference between the proportion of urban and rural construction land and the preset upper limit of urban and rural construction land control, and the second difference between the proportion of residential land and the preset upper limit of residential land control, to obtain a parameterized rule subset, specifically includes: The superimposed area is mapped to a control level identifier according to a preset area grading standard. A two-dimensional query key is constructed using the partition category and the control level identifier. Based on the two-dimensional query key, a subset of rules matching the two-dimensional query key is retrieved in the grading rule base. Each clause in the subset of rules contains a corresponding benchmark judgment threshold. Calculate the first difference between the proportion of urban and rural construction land and the preset upper limit of urban and rural construction land control, calculate the second difference between the proportion of residential land and the preset upper limit of residential land control, and assign and correct the benchmark judgment threshold of each clause in the rule subset according to the first difference and the second difference to obtain the corrected judgment threshold. The modified judgment threshold is assigned to the corresponding clause in the rule subset to obtain the parameterized rule subset.

3. The method according to claim 1, characterized in that, The step of comparing the construction content according to the parameterized rule subset and outputting a list compliance conclusion specifically includes: Keyword extraction and semantic segmentation are performed on the construction content to obtain several construction behavior description fragments, and feature encoding is performed on the several construction behavior description fragments to obtain a construction behavior feature encoding set; The text content of each clause in the parameterized rule subset is feature-encoded to obtain a clause feature code set; The matching degree of each construction behavior feature code in the construction behavior feature code set is calculated with the clause feature code of the negative list clause in the clause feature code set. If the matching degree is not lower than the corresponding judgment threshold, the negative list prohibition conclusion is output. Without outputting the negative list prohibition conclusion, the similarity between each construction behavior feature code in the construction behavior feature code set and the clause feature code of the positive list clause in the clause feature code set is calculated. If the matching degree between a construction behavior feature code and the clause feature codes of all positive list clauses is lower than the corresponding judgment threshold, the pending judgment conclusion is output. If the matching degree between each construction behavior feature code and the feature code of at least one positive list clause is not lower than the corresponding judgment threshold, the positive list admission conclusion is output.

4. The method according to claim 1, characterized in that, The steps of merging the space compliance test conclusion and the list compliance conclusion to generate an access level conclusion, and triggering the corresponding approval path and mandatory approval node based on the access level conclusion, specifically include: The spatial compliance detection results are divided into three levels: spatial compliance, spatial warning, and spatial violation. The combination of the spatial compliance detection result level and the list compliance result is used as the query condition. The access level result corresponding to the query condition is retrieved from the preset compliance decision matrix. Based on the admission level conclusion, the corresponding approval path is retrieved from the preset approval path library, and a forced approval node corresponding to the admission level conclusion is injected at the preset insertion position of the approval path. The approval path containing the mandatory approval node will be output.

5. The method according to claim 1, characterized in that, After the steps of merging the space compliance detection conclusion and the list compliance conclusion to generate an access level conclusion, and triggering the corresponding approval path and mandatory approval node based on the access level conclusion, the method further includes: The project redline vector data and the preset two-zone data layers are overlaid and analyzed with the administrative boundary layer to obtain the coverage of the project redline vector data in the core protection zone and the integrated development zone, as well as the spatial relationship between the project redline vector data and the municipal administrative boundary. When the project's red line vector data simultaneously covers both the core protection zone and the integrated development zone, or when the project's red line vector data crosses the municipal administrative boundary, a provincial approval suggestion identifier is generated. If the project's red line vector data is entirely located within the integrated development zone and does not cross the municipal administrative boundary, a corresponding municipal approval suggestion identifier will be generated. The provincial-level approval suggestion identifier or the corresponding municipal-level approval suggestion identifier is injected into the approval path to route the approval path to the corresponding approval authority level.

6. The method according to claim 5, characterized in that, After the step of injecting the provincial-level approval suggestion identifier or the corresponding municipal-level approval suggestion identifier into the approval path to route the approval path to the corresponding approval authority level, the method further includes: Using the space compliance test results, the list compliance results, and the access level results as input, a draft review opinion is generated according to a preset review opinion template. Each conclusion field in the draft review opinion is automatically filled in by the space compliance test results, the list compliance results, and the access level results. The draft review opinion is pushed to the mandatory approval node, where the approver makes revisions to the draft review opinion, and the final review opinion is obtained. The final review opinion is compared with the draft review opinion field by field. The draft value and the final review value corresponding to the field with difference are used to generate review deviation records. The review deviation records are associated with the parameterized rule subset and the spatial compliance feature parameters and stored in the deviation record library.

7. The method according to claim 6, characterized in that, After the step of associating and storing the review deviation records with the parameterized rule subset and the spatial compliance feature parameters in the deviation record library, the method further includes: If the number of review deviation records in the deviation record library that are the same as the partition category in the spatial compliance feature parameters is not less than a preset deviation accumulation threshold, all review deviation records corresponding to the partition category are extracted from the deviation record library. Using the deviation direction and frequency between the draft value and the final value of each deviation field in all the review deviation records as input, the judgment threshold of the clause corresponding to the partition category in the hierarchical rule base is corrected to obtain the corrected judgment threshold. The revised judgment threshold is updated to the hierarchical rule base.

8. An intelligent access control system, characterized in that, The access intelligent review system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the access intelligent review system to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the access intelligent review system, the access intelligent review system performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the access intelligent review system, the access intelligent review system performs the method as described in any one of claims 1-7.

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