A detailed planning process review method, system, medium, and product

Abstract

This application provides a detailed planning process review method, system, medium, and product, relating to the field of data processing technology applicable to administration. It involves parallel extraction of procedural evidence data (unstructured text) and spatial feature data (vector graphics and discrete numerical values). Spatial planning is separated into vector geometry and attached attribute parameters, which are then uniformly imported into a rule engine and spatial coordinate system for cross-calculation. This method, which extracts workflow characteristics, spatial topological features, and graphic semantic features to the underlying logic for unified feature sequence construction and mapping calculation, overcomes the previous technical deficiencies of lacking cross-modal collaboration between text, numerical values, and spatial drawings. It eliminates the need for frequent switching between multiple systems, improving the data collaboration efficiency of the entire review process and reducing false alarm rates.

Description

Technical Field

[0001] This application relates to the field of data processing technology applicable to administration, and in particular to a method, system, medium, and product for detailed planning and full-process review. Background Technology

[0002] Detailed planning involves making implementation arrangements for the land use nature, development intensity, spatial environment, and configuration of public service facilities for specific plots of land. Each confirmation in detailed planning not only involves the overlapping and updating of a large number of spatial drawing elements and diverse attribute indicators, but also involves the simultaneous advancement of complex legal procedures, multi-entity approval and circulation, and the issuance of licenses and permits.

[0003] To meet the digital management needs of various detailed plans, the relevant technologies adopt a collaborative operating architecture of a parallel e-government workflow platform and a Geographic Information System (GIS) graphical client. Specifically, for business workflow review, a comprehensive approval workflow transfer ledger is built using the workflow management system, recording project submission processes, workflow timestamps, and logs accessed by responsible personnel in a database. For the spatial planning content itself, technical personnel rely on a separate GIS client to overlay and render the submitted planning maps and baseline map features on layers. The system also includes a built-in attribute form query module, allowing reviewers to manually click on corresponding map features to verify and confirm digital indicators.

[0004] However, in practical use, the relevant technologies are essentially just loosely coupled integrations of heterogeneous and independent systems. This lack of cross-modal collaboration and the scarcity of graph-based error handling mechanisms not only force reviewers to frequently switch between multiple systems, but also lead to an excessively high false alarm rate. Summary of the Invention

[0005] This application provides a detailed planning process review method, system, medium, and product for reducing false alarm rates.

[0006] Firstly, this application provides a detailed planning method for the entire review process, including:

[0007] Obtain planning data for the target planning project, and extract procedural proof data and spatial feature data from the planning data; among which, procedural proof data is a dataset recording approval and transfer events, and spatial feature data is vector geometric parameters and attached attribute parameters;

[0008] Extract process node status information from procedural proof data; compare the process node status information with preset procedural templates using logical rules, and output the process compliance judgment result.

[0009] The attached attribute parameters are matched with the corresponding constraint fields in the target control benchmark dataset to obtain the attribute compliance judgment identifier.

[0010] The vector geometric parameters are mapped to the corresponding constraint baseline primitives in the target control benchmark dataset to the same coordinate space, and spatial geometric relationship calculations are performed to obtain the spatial safety detection identifier;

[0011] Integrate attribute compliance assessment labels and spatial safety detection labels to generate content compliance assessment results;

[0012] The review opinion data is determined based on the results of the process compliance assessment and the content compliance assessment.

[0013] By employing the aforementioned technical solution, program verification data (unstructured text) and spatial feature data (vector graphics and discrete numerical values) are extracted in parallel. Spatial planning is separated into vector geometry and attached attribute parameters, which are then uniformly imported into the rule engine and spatial coordinate system for cross-calculation. This method, which extracts workflow characteristics, spatial topological features, and graphic semantic features to the underlying logic for unified feature sequence construction and mapping calculation, overcomes the previous technical deficiencies of lacking cross-modal collaboration between text, numerical values, and spatial drawings. It eliminates the need for frequent switching between multiple systems, improving the data collaboration efficiency of the entire review process and reducing false alarm rates.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, the step of mapping the vector geometric parameters and the corresponding constraint baseline primitives in the target control benchmark dataset to the same coordinate space, and performing spatial geometric relationship calculations to obtain spatial safety detection identifiers, specifically includes:

[0015] Extract the target boundary polygon sequence from the vector geometric parameters, and obtain the target bottom line polygon sequence of the corresponding constraint bottom line primitive elements;

[0016] Map the target boundary polygon sequence and the target bottom line polygon sequence to the same coordinate system dimension, and perform a topological intersection superposition operation to obtain the target intersection connected domain set;

[0017] Calculate the geometric area scalar of the target intersection connected domain set. If the geometric area scalar is greater than the fault tolerance calculation truncation threshold, generate a spatial safety detection label containing the bottom line conflict constraint label and the violation spatial coordinates.

[0018] By adopting the above technical solution, when performing spatial boundary constraint verification, the target boundary polygon sequence and the externally fortified target boundary polygon sequence are mapped to the same coordinate system dimension, and a topological intersection and overlay operation is performed. This process locates the physical overlap area between layers, i.e., generates a set of target intersection connected components. Furthermore, instead of simply judging conflicts based on spatial geometric intersection, a fault-tolerant calculation truncation threshold mechanism is introduced. By calculating the geometric area scalar of the calculated target intersection connected component set, a spatial security detection identifier containing a boundary conflict constraint label is triggered only when this scalar exceeds the aforementioned threshold. This judgment logic with tolerance limits filters out pixel-level low-level morphological distortions caused by coordinate system transformation, manual drawing on paper, or raster resampling, reduces false positives and false alarms caused by traditional GIS computing power, and improves the accuracy of spatial geometric boundary detection.

[0019] In conjunction with some embodiments of the first aspect, in some embodiments, after the step of calculating the geometric area scalar of the target intersection connected region set, the method further includes:

[0020] Under the condition that the geometric area scalar is not greater than the fault-tolerant calculation truncation threshold:

[0021] Extract the geometric perimeter scalar of the outer boundary of the target intersection connected domain set, and obtain the sequence of overlapping vertices inside;

[0022] Spatial morphology index operations are performed based on geometric perimeter scalars and geometric area scalars to obtain spatial compactness parameters of the target intersection connected domain set;

[0023] If the spatial compactness parameter exceeds the preset narrow shape distribution range, perform a topological central axis transformation on the target intersection connected domain set to extract the skeleton network, and calculate the first and last extreme value span scalar of the generated skeleton network.

[0024] Traverse each vertex in the overlapping vertex sequence and calculate the vertical mapping distance of the normal projection to the original boundary of the target bottom polygon sequence to extract the maximum intrusion depth scalar.

[0025] If the extreme span scalar is determined to be greater than the pre-configured linear crossing cutoff threshold, or if the maximum intrusion depth scalar is determined to be greater than the preset forced limit piercing range, an additional spatial safety detection label containing a bottom line conflict constraint label, intrusion vector features, and violation spatial coordinates is generated.

[0026] By adopting the above technical solution, when the intersection area is small and the area tolerance condition is met, geometric perimeter is introduced and spatial compactness parameters are further calculated. Normal map coordinate translation errors typically manifest as small, compact blocks, while actual pipeline or road encroachments are often small in area but extremely long and narrow strips. Therefore, spatial compactness features can quickly filter out elongated regions with abnormal shapes at a low computational cost, eliminating harmless normal drawing errors. For the elongated primitives initially screened out, a topological centerline transformation is performed to calculate the span of the first and last ends, and the maximum intrusion depth projected onto the reference boundary normal is measured by traversing the vertices. The original single-area size judgment is transformed into an analysis of primitive span. Through the above judgment steps combining initial screening based on shape compactness and verification of depth features, it is possible to identify and intercept spatial violations with small coverage areas but severe transverse damage, thereby reducing the false negative rate caused by relying solely on area thresholds.

[0027] In conjunction with some embodiments of the first aspect, in some embodiments, the step of performing a matching calculation between the attached attribute parameter and the corresponding constraint field in the target control benchmark dataset to obtain the attribute compliance determination identifier specifically includes:

[0028] Extract the target function identifier string from the attachment attribute parameters;

[0029] Obtain the pre-defined functional compatibility knowledge graph stored in the target control benchmark dataset. The functional compatibility knowledge graph uses standard land use classification as nodes and mutually exclusive, permitted, or conditionally compatible settings as connecting edges.

[0030] Map the target function identifier string to a preset function compatibility knowledge graph to perform path connectivity retrieval, and output the classification status with attribute exclusion or attribute compatibility;

[0031] Generate attribute compliance determination identifiers based on logical truth values ​​of classification status.

[0032] By adopting the above technical solution, after parsing and extracting the target function identifier string from the attached attribute parameters, a functional compatibility knowledge graph is used to replace the simple character text matching mechanism. This functional compatibility knowledge graph abstracts the hashed legal land use attribute regulations into a network connectivity structure with standard land use classifications as nodes and mutually exclusive, permissive, or conditionally compatible settings as connecting edges. During comparison and judgment, the extracted target function identifier string is mapped to this graph to initiate a connectivity retrieval of a specific path, filling the qualitative judgment blind spot where character matching cannot identify implicit substitutions of land use concepts, and enhancing the review and identification capabilities of qualitative assessment of land parcel functions.

[0033] In conjunction with some embodiments of the first aspect, in some embodiments, the step of mapping the vector geometric parameters and the corresponding constraint baseline primitives in the target control benchmark dataset to the same coordinate space, and performing spatial geometric relationship calculations to obtain spatial safety detection identifiers, specifically includes:

[0034] Extract the boundary polygons of the structure entity from the vector geometric parameters, and obtain a dataset of all target vertex sequences of the boundary polygons of the structure entity;

[0035] Obtain the target adjacent baseline frame from the corresponding constraint baseline primitive element, and extract the corresponding statutory minimum spatial setback distance threshold from the configuration parameters;

[0036] Traverse each target vertex in the target vertex sequence dataset and calculate the theoretical vertical projection distance sequence set from each target vertex to the target adjacent baseline frame;

[0037] Extract the minimum value from the theoretical vertical projection distance sequence set as the actual topological extreme value scalar of the boundary polygon of the structure entity;

[0038] The actual topological extreme value scalar is compared with the statutory minimum space setback distance threshold. If the actual topological extreme value scalar is determined to be less than the statutory minimum space setback distance threshold, a setback violation label is written into the space safety detection identifier.

[0039] By employing the aforementioned technical solution, and in accordance with building space setback control specifications, a dataset of all target vertex sequences of the boundary polygons of the structure entity is extracted from vector geometric parameters. Based on this, an iterative walking pointer traverses each spatial vertex, solving for the theoretical vertical projection distance sequence from each target vertex to the target's adjacent baseline frame. Then, by extracting the minimum value from this floating-point sequence, a real topological extreme value scalar reflecting the building's external contour approaching the baseline is constructed. This measured scalar is compared with the statutory minimum spatial setback distance threshold using difference algebra to determine whether the writing action of the spatial safety detection mark is triggered. This approach, which deconstructs the topological constraints between irregular geometric entities and complex curves into underlying vector differentiation and matrix optimization data processing logic, improves the accuracy of setback calibration detection for complex contour buildings.

[0040] In conjunction with some embodiments of the first aspect, in some embodiments, the step of extracting process node status information from procedural proof data, comparing the process node status information with preset prescribed procedural templates according to logical rules, and outputting a process compliance determination result specifically includes:

[0041] For program proof data, character parsing and text feature segmentation calculations are performed to extract the corresponding entity feature string sequence and operation timestamp sequence, which are used to construct process node status information;

[0042] Extract the pre-stored target pre-constraint set from the preset prescribed procedure template. The target pre-constraint set includes the benchmark subject library, the quantity lower limit threshold, and the circulation duration time limit threshold.

[0043] The entity feature string sequence is traversed sequentially and substituted into the baseline subject target library to perform text comparison, and the strings that match the rules are merged to filter out the corresponding subject subset;

[0044] The total number of elements in the main subset is counted as a scalar. If the total number of elements is greater than the lower limit threshold, the corresponding integrity state quantity is determined to be generated.

[0045] Extract the initial and final time boundaries of the operation timestamp sequence, calculate the process time difference scalar of the span between the two, and generate the corresponding time limit status quantity when it is determined that the process time difference scalar is greater than the flow duration limit threshold.

[0046] Perform logical conjunction calculation on integrity state variables and time-limited state variables, and generate process compliance judgment results based on the output global Boolean truth value.

[0047] By employing the aforementioned technical solution, character parsing and text feature segmentation calculations are used to selectively extract entity feature string sequences and timestamp sequences. Subsequently, the extracted feature strings are mapped to a baseline subject database for traversal comparison. Strings matching the rules are merged to filter out the subject subset, and a scalar count of the total number of elements is initiated. Simultaneously, timestamp features are extracted to calculate and generate a process time difference scalar. These two separated scalar features are then fed into the quantity lower limit threshold and the flow duration limit threshold for judgment, respectively, outputting the corresponding integrity status quantity and time limit status quantity. Finally, the process compliance judgment result is derived from the logical conjunction operation of these two values. This mechanism transforms the business process into a closed Boolean combinational logic operation flow, ensuring the continuity and traceability of the pre-administrative review status tracking.

[0048] In conjunction with some embodiments of the first aspect, in some embodiments, the step of performing a matching calculation between the attached attribute parameter and the corresponding constraint field in the target control benchmark dataset to obtain the attribute compliance determination identifier specifically includes:

[0049] Extract the target planning feature value set from the attached attribute parameters. The target planning feature value set is obtained based on the regular expression truncation of the graph text or the extraction of the spatial attribute table.

[0050] Obtain the legally defined numerical control interval segment corresponding to the current spatial element from the target control benchmark dataset;

[0051] Substitute the target planning feature value set into the legally defined numerical control interval and execute the two-way boundary comparison algorithm to output the numerical verification status.

[0052] Based on the numerical verification status that deviates from the legally defined numerical control range, an attribute compliance judgment identifier containing the extreme value deviation range is generated.

[0053] By employing the aforementioned technical solution, and through text regularization based on diagrams or spatial attribute table extraction, the target planning feature value set is obtained from the attached attribute parameters, transforming the static construction intensity indicators in conventional forms into floating-point data features that support calculation. Furthermore, by combining the legally defined numerical control intervals extracted from the target control benchmark dataset, the target planning feature value set is substituted into these intervals to execute a bidirectional boundary comparison algorithm, achieving threshold screening for upper limit overflow and lower limit deficiency of indicators. This generates attribute compliance judgment identifiers containing extreme value deviation ranges, reducing visual errors during inspection and providing a standardized objective basis for the measurement of quantitative indicators in the review process.

[0054] Secondly, this application provides a detailed planning full-process review system, which includes: one or more processors and a memory; the memory is coupled to one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and one or more processors call the computer instructions to cause the detailed planning full-process review system to perform the method as described in the first aspect and any possible implementation thereof.

[0055] Thirdly, this application provides a computer program product containing instructions that, when run on a detailed planning full-process review system, cause the detailed planning full-process review system to perform the method described in the first aspect and any possible implementation thereof.

[0056] Fourthly, this application provides a computer-readable storage medium including instructions that, when executed on a detailed planning full-process review system, cause the detailed planning full-process review system to perform the method described in the first aspect and any possible implementation thereof.

[0057] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0058] Parallel extraction of procedural proof data (unstructured text) and spatial feature data (vector graphics and discrete numerical values) is performed. Spatial planning is separated into vector geometry and attached attribute parameters, which are then uniformly imported into the rule engine and spatial coordinate system for cross-calculation. This approach, which extracts workflow characteristics, spatial topological features, and textual / graphical semantic features to the underlying logic for unified feature sequence construction and mapping calculation, overcomes the previous technical shortcomings of lacking cross-modal collaboration between text, numerical values, and spatial drawings. It eliminates the need for frequent switching between multiple systems, improving the data collaboration efficiency of the entire review process and reducing false alarm rates. Attached Figure Description

[0059] Figure 1 This is a flowchart illustrating a detailed full-process review method as described in this application embodiment;

[0060] Figure 2 This is an exemplary hardware structure diagram of a detailed planning full-process review system in this application embodiment. Detailed Implementation

[0061] 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 and appended claims of this application, the singular expressions “a,” “an,” “the,” “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 and includes any or all possible combinations of one or more of the listed items.

[0062] 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.

[0063] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a detailed full-process review method as described in this application embodiment;

[0064] A detailed planning process review method includes:

[0065] S101. Obtain the planning data of the target planning project, and extract procedural proof data and spatial feature data from the planning data; among which, the procedural proof data is a dataset recording approval and transfer events, and the spatial feature data is vector geometric parameters and attached attribute parameters;

[0066] Among them, planning data refers to approval procedure documents, graphic descriptions, and data packages containing geospatial information for the target planning projects.

[0067] In some embodiments, step S101 specifically includes:

[0068] S1011. According to the network security isolation level, the data acquisition channel configuration of the planned data is divided into the government extranet channel and the classified internal network channel.

[0069] Among them, the government external network channel refers to the network communication link used to carry graphic and textual data that do not involve classified information or are not publicly available; the classified internal network channel refers to the dedicated data transfer line built using physical isolation barriers or logical isolation gateways for transmitting classified spatial data such as geographic coordinates.

[0070] This step establishes an isolation architecture for the subsequent classification and secure transmission and reception of data by building data access communication links with different security strength levels at the underlying layer, from the network environment configuration level.

[0071] S1012. Receive text atlas data that represents the status of the review process and does not contain geographic coordinates via the government extranet channel, and parse it into procedural proof data.

[0072] The system receives review meeting minutes and accompanying specification text attachments without spatial geographic coordinates through an interface server connected to the external office network, and extracts the inner encapsulated object to generate program proof class data for subsequent rule comparison module to call and analyze.

[0073] S1013. Receive a classified spatial planning element package containing a controlled geographic coordinate database via a classified intranet channel and parse it into spatial element data.

[0074] By relying on isolated terminals or encrypted communication transmission protocols in a classified and secure environment, network data retrieval operations are carried out to obtain planning geographic database file packages with confidential latitude and longitude positioning characteristics, and entity file stream deconstruction processing is performed to extract and generate corresponding spatial feature data.

[0075] In some embodiments, the method further includes the following steps prior to step S101:

[0076] S201. Extract the file structure encapsulation format tags of the planning data, call the built-in planning result specification standard configuration library to perform file format rule matching, and block the process when the matching fails and output a format forced correction prompt message for the data source format type.

[0077] Among them, the planning results specification standard configuration library refers to the set of digital rule dictionaries built into the internal underlying layer, which are used to record text format and geospatial atlas standard encapsulation parameters.

[0078] Specifically, before the planning data is actually read into the buffer pool, a pre-format verification component is invoked. This component reads the metadata feature sequence embedded in the outer encapsulation message of the received planning data and extracts the file structure encapsulation format marker corresponding to that file. Subsequently, this marker is used as a query parameter and input into the association rule list of the planning results specification standard configuration library for comparison and retrieval, automatically verifying whether the format type of the uploaded planning data matches the preset allowed whitelist sequence. If the comparison function captures a type entity that does not pass the format definition or a mismatched object, the data read / write thread of the currently associated upload channel is immediately truncated to block subsequent parsing processes from processing invalid data. A format mandatory correction prompt message containing the standard encapsulation specification requirements is generated and pushed back to the user submission end along the reverse communication link of the signaling reception.

[0079] In some embodiments, a method for hierarchical unpacking detection and filtering verification is implemented, in which the acquired planning data transmission packets are imported into the isolated sandbox computing node for preprocessing to extract their hierarchical metadata architecture, and the detected encapsulation format is compared with the topology template defined in the planning result specification standard configuration library to output the final judgment result instruction, which is not limited here.

[0080] S202. After successful matching, optical character recognition and image / text object segmentation and extraction are performed on the planning data of unstructured images to form a preprocessed text set, and the source coordinate system information of spatial feature data is converted and generated into a unified internal standard data model by calling the mapping protocol.

[0081] The unified internal standard data model refers to a data structure object that is bound to the constraints of the standard map projection coordinate grid specification within the platform.

[0082] Specifically, planning data that has passed format verification and been approved by the system enters the subsequent data processing flow. For planning data segments presented as unstructured categories such as scanned images, layout analysis is introduced to determine and isolate meaningless image object bounding box interference areas such as background textures, extract and restore text field array combinations with paragraph structure, and aggregate them to generate a structured preprocessed text set. In the parallel branch at the same time, for spatial feature data represented by multi-dimensional vector coordinate sets, the reference information in the header of its data file is analyzed to determine the coordinate parameters of the basic plane control network used by the current spatial entity, extract the target mapping transformation scaling matrix that matches it from the external pre-set library to start the geometric affine calculation model, and perform translation and projection transformation operations on the high-precision discrete spatial primitive calibration point matrix data to the unified internal standard data model constraints of the fixed basic reference surface, so as to avoid geometric projection calculation errors caused when initiating graphic topology calculations across coordinate references.

[0083] S102. Extract the process node status information of the procedural proof data; compare the process node status information with the preset prescribed procedural template according to logical rules, and output the process compliance judgment result.

[0084] Among them, process node status information refers to the actual evolution characteristics parameters that characterize each statutory review pre-procedure in terms of the timing of the process, the participating entities, and the completeness of the materials; the prescribed procedure template refers to the set of settings in the control library that is transformed from management regulations into process nodes containing the baseline architecture.

[0085] Specifically, for procedural proof data, optical character recognition and natural language feature analysis services are initiated to separate key organizational strings, the distribution of participating member characteristics, and the sequence of operation time nodes from the data. Then, the transformed factual information fragments are imported and, together with the main dictionary labels, feedback identification rate, and flow duration boundary parameters in the prescribed procedural template, multi-logic branch calculations and status code matching are performed to determine the completeness and timeliness compliance of the data in the corresponding approval process.

[0086] In some embodiments, a structured matching mechanism based on regular expressions and trie retrieval is adopted to load the underlying program proof data to perform term segmentation, extract the corresponding entity feature string sequences and assemble them to generate a wide table of factual state information, and then drive the matching verification engine to perform node prefix traversal verification under the rule-based judgment network to capture abnormal links in the pre-constraint state, which is not limited here.

[0087] In some specific embodiments, step S102 specifically includes:

[0088] S1021. Perform character parsing and text feature segmentation calculations on procedural proof data to extract the corresponding entity feature string sequence and operation timestamp sequence, which are used to construct process node status information. Procedural proof data refers to a collection of unstructured documents such as opinion solicitation letters, public participation materials, and review materials. The entity feature string sequence is a data sequence composed of core information such as the names of participating entities. The operation timestamp sequence represents the time data for the completion of each business approval action.

[0089] Specifically, as the basic entry point for automated process compliance detection, the microservices of character recognition and natural language processing are used to read the uploaded program proof data, perform data parsing and word segmentation calculation on the embedded text content, extract the entity feature string sequence representing the identity of the participating objects from the unstructured text, and the operation timestamp sequence used to estimate the timeliness of the process, thereby constructing structured process node status information for process status judgment.

[0090] In some embodiments, the optical character recognition model is scheduled to parse the program proof data to obtain basic text blocks, and a pre-trained entity recognition algorithm is used to extract the entity feature string sequence of administrative divisions and organizations from the text blocks. At the same time, regular expressions are called to capture date and time values ​​to form an operation timestamp sequence, which is not limited here.

[0091] In some embodiments, step S1201 specifically includes:

[0092] S12011, Execute matching trigger calculation.

[0093] This step utilizes the existing factual data to initiate the calculation and execution phase of subsequent verification logic.

[0094] S12012, Deserialize the acquired process node status information to construct a target fact information data group for network mapping.

[0095] Among them, the target factual information data group refers to the set of computable feature objects that conform to the logic of direct access to computer memory after format restoration and structure extraction.

[0096] This step aims to internally parse and restore the received external transport protocol formatted status information into a structured data entity attribute group that can be read and compared by the logical network environment.

[0097] S12013. A forward matching computational topology algorithm is adopted to generate a reasoning judgment tree network based on the target benchmark library and introduce it into the working memory unit.

[0098] Among them, the forward matching computation topology algorithm refers to the graph structure-driven evaluation computation method that deduces backward layer by layer based on known fact constraints until the target evaluation conclusion is triggered; the reasoning judgment tree network container is used to represent the business rule topology model composed of multi-dimensional hierarchical judgment nodes and associated state transition edges and is encapsulated as a whole.

[0099] Specifically, based on the discrete logical criteria mapping set in the target benchmark database regarding the completeness of various application processes and the control of entity content, the logical criteria mapping set is configured into a directed tree structure with nodes as the basis for judgment and directed edges as the transmission path through a forward matching computational topology algorithm. Subsequently, the tree topology structure carrying the rule judgment system is solidified and packaged to generate a reasoning judgment tree network assembly and mounted and mapped into the working memory unit, thereby defining a business analysis block that supports logical state deduction to isolate the addressing overhead of physical storage.

[0100] In some embodiments, the decision logic tree is pre-converted into a serialized binary execution template diagram file in an offline computing environment. When a matching trigger signal event is received, the memory mapping read interface of the file is activated, and the entire process decision object is relocated and loaded into the storage stack memory. This is not limited here.

[0101] S12014. Input the target fact information data group, perform pattern node cross-traversal activation operation for the response mechanism of the leaf nodes of the reasoning judgment tree network, calculate and analyze the output reasoning behavior instructions, and synthesize the corresponding compliance judgment event objects.

[0102] Specifically, when the target fact information data group is injected into the working memory unit, a probe operation is initiated on the inference judgment tree network according to the field labels contained in the target fact information data group. The probe traverses the rule state flow conditions step by step from the root of the node until it hits the leaf node with the final judgment characteristic and satisfies the response mechanism conditions configured inside the leaf node. At this time, the rule joint operation comparison between different verification nodes is triggered. After completing the response cycle of condition map recognition, the inference behavior instruction that allows the process to enter the next node or warns of missing data is generated at the calculation output end. Then, the judgment time record and event result encoding are combined to encapsulate it and construct a standardized judgment event object.

[0103] In some embodiments, a time-window-based multiplexing traversal mechanism is adopted to extract the data tag parameters of the target factual information data group, and successively activate the verification logic with multi-branch node rule judgment function for comparison Boolean evaluation. When the operation feedback flag of blocking or full pass is obtained, an instance structure with review flow number and identification code is encapsulated as the judgment event object, which is not limited here.

[0104] S1022. Extract the pre-stored target pre-constraint set from the preset prescribed procedure template. The target pre-constraint set includes a benchmark subject database, a minimum quantity threshold, and a circulation time limit threshold. Specifically, the target pre-constraint set refers to the evaluation rule parameters set based on the statutory procedures for planning approval. The benchmark subject database contains a list of registered names of administrative departments and experts requiring consultation. The minimum quantity threshold specifies the required participation scale, and the circulation time limit threshold represents the statutory approval time standard.

[0105] This step involves retrieving the program verification criteria appropriate for the current detailed planning type from the backend, providing basic parameters for subsequent calculation of the number of participating departments, experts, and public announcement time requirements.

[0106] S1201. The benchmark subject library contains structural identifier classification scalars for monitoring process completeness. The mandatory matching field conditions set in the structural identifier classification scalars include: the dictionary of character identifiers for departmental opinion collection entities extracted for relevant management departments; the entity verification elements for expert demonstration for technical experts; the entity verification elements for public disclosure for public participation; the entity verification elements for hearing-related procedures for responding to the characteristics of concentrated public demands; and the entity verification elements for substantive review operations of the local government for the final legal confirmation stage.

[0107] Among them, the structural identifier classification scalar is used to represent the metric data structure that measures whether a process information fragment covers all preset review node categories.

[0108] This step aims to define the basic detection constraints for procedural-level comparison of detailed planning verification, so that each statutory approval process has corresponding field matching and verification parameters.

[0109] S1023. The entity feature string sequence is sequentially traversed and substituted into the baseline subject target library to perform text matching, and the strings that match the rules are merged to filter out the corresponding subject subset. Here, the subject subset refers to the set of text data of the associated objects confirmed to have participated in the review process after comparison.

[0110] Specifically, after loading the benchmark subject target library, the verification operator is started. The entity feature string sequence extracted from the program proof class data is traversed one by one. The matching engine is called to perform logical verification operations on the object text in the sequence in the benchmark subject target library. When the text is confirmed to meet the preset rules, it is determined to be a valid subject. Then, the strings that meet the consistency conditions are extracted and merged to form a subset of subjects that represent the actual performance of duties.

[0111] In some embodiments, an inverted index technique is used to traverse the entity feature string sequence and compare it with the corresponding record in the baseline subject database. Boolean logic is used to determine the mapping relationship between the two, and the successfully matched objects are merged and output as a specific subject subset. This is not limited here.

[0112] S1024. Calculate the total number of elements in the subset of subjects. If the total number of elements exceeds the lower limit threshold, determine the generation of the corresponding integrity status quantity. Here, the total number of elements refers to the total number of subject units that have been verified to meet the requirements, and the integrity status quantity is used to indicate that a specific step has met the set status record markers in terms of the number of participating subjects.

[0113] Specifically, after obtaining the main subset through computation, a counting operation is performed on the main unit within it to extract the total number of elements scalar. Then, a comparison operation mechanism is executed within the lower limit threshold. When the value of the total number of elements scalar is verified to meet the boundary requirements set by the lower limit threshold, it is determined that the scale of personnel and institutions involved in the planning review process meets the standard, and then an integrity status quantity containing verification passed parameters is generated.

[0114] In some embodiments, the hash table generated by the main subset is traversed and the total number of elements is output through a built-in function. The comparison operator is used to determine the relationship between the total number of elements and the lower limit threshold. The integrity status parameter of the compliance dimension is attached according to the returned condition, which is not limited here.

[0115] S1025. Extract the initial and final time boundaries of the operation timestamp sequence, calculate the process time difference scalar spanning these two boundaries, and generate the corresponding time limit status quantity if the process time difference scalar is greater than the workflow duration threshold. The process time difference scalar describes the time length of the specified review procedure interval, while the time limit status quantity is a conditional judgment attribute characterizing whether the duration of a specific workflow process meets business requirements.

[0116] Specifically, the operation timestamp sequence of unstructured states is extracted, arranged into an array according to the time sequence, and the beginning and end of the sequence are truncated. The initial time boundary of the action start trigger and the termination time boundary of the node end are located. The time difference function is applied to calculate the span interval of the two boundary intervals and extract it as a process time difference scalar. Then, the process time difference scalar is compared with the internally set flow duration threshold. When it is determined that the time difference value meets the legally prescribed approval span duration, a time limit status parameter with compliance characteristics is issued according to the instruction.

[0117] In some embodiments, the extracted operation timestamp sequence is mapped to the time management module to calculate the initial time boundary and the termination time boundary containing the reference format. The time span is calculated and the natural day difference result is output as the process time difference scalar. After determining that the natural day difference is greater than the flow duration time limit threshold, the corresponding time limit state quantity is projected. This is not limited here.

[0118] S1026. Perform logical conjunction calculation on the integrity status variable and the time limit status variable, and generate a process compliance judgment result based on the output global Boolean truth value. The global Boolean truth value refers to the judgment object obtained by performing compound logical operations on all concurrent condition variables after they have been output in their respective forms. The process compliance judgment result is used to indicate the judgment information issued regarding whether all relevant application steps have been fulfilled.

[0119] Specifically, after the parallel verification process is completed, the integrity status quantity of the program integrity dimension is acquired and loaded, and the time limit status quantity of the approval timeliness dimension is loaded. The calculation matrix is ​​started to perform logical conjunction operation. When it is determined that each input status quantity indicator meets the set conditions and is true under the verification algorithm, the global Boolean truth value of the verification information is output. Then, the structure generator is called to combine and encapsulate the global Boolean truth value according to the preset report template, and the output is the process compliance judgment result file.

[0120] In some embodiments, the received integrity status quantity and time limit status quantity are subjected to a bitwise AND operation using the computational function provided by the status record tree to obtain a global Boolean truth value as the bus. The configuration file is then read to translate and wrap the global Boolean truth value into a process compliance judgment result data packet with confirmation text markers; this is not limited here.

[0121] S103. Perform matching calculations between the attached attribute parameters and the corresponding constraint fields in the target control benchmark dataset to obtain the attribute compliance determination identifier;

[0122] Among them, the target control benchmark dataset refers to the set of static comparison bases formed by the digital mapping and translation of the planning and mandatory management regulations of the superior entity; the attribute compliance judgment identifier refers to the status code group that records the degree of deviation of the business function compatibility setting and control indicators.

[0123] Specifically, the qualitative and quantitative linkage analysis process in the content compliance detection mechanism is activated. The target features representing the business function direction and planning intensity limit in the attached attribute parameters are extracted and analyzed. They are then substituted into the network node connectivity assessment path of the pre-configured functional compatibility knowledge graph to detect attribute restriction risks, and imported into the legally controlled numerical range object to perform double-boundary span verification. This allows for the detection and calculation of the compliance and compatibility classification of the actual dominant land use nature and the boundary overflow of quantitative characteristic parameters such as building density.

[0124] In some embodiments, a qualitative compatibility detection logic based on graph path linkage is adopted to parse and extract the target function string identifier in the attached attribute parameter and map it to the primitive group of a set multiple node classification relationship graph. The state identifier of the associated topological connected edge from the planned business target point to the compatible target node is located, and the classification truth value signal identifier representing the repulsion, specific condition setting or compatibility judgment is summarized and output. This is not limited here.

[0125] S1031. Extract the target function identifier string from the attachment attribute parameters;

[0126] Among them, the target function identifier string refers to the string text extracted from the list of associated attributes of the examined vector geometric object, which is used to characterize the dominant land use classification features.

[0127] This step utilizes a character extraction component to read and parse the functional attribute names distributed within the external graphic element database structure or the planning register appendix, converting them into string object forms suitable for subsequent model comparison.

[0128] S1032. Obtain the preset functional compatibility knowledge graph stored in the target control benchmark dataset. The functional compatibility knowledge graph uses standard land use classification as nodes and mutually exclusive, permitted or conditionally compatible settings as connecting edges.

[0129] Among them, the functional compatibility knowledge graph refers to the topological structure model data that graphically represents the mutual exclusion or compatibility relationships between different land use classification codes in accordance with planning and management standards.

[0130] This step reads a predefined topological network graph model from the target control benchmark dataset and loads it into the computing memory pool to build a basic data discrimination environment for subsequent qualitative feature relationship comparison operations.

[0131] In some specific implementation scenarios, the underlying "functional compatibility knowledge graph" of the target management benchmark dataset is transformed from the list library. When constructing the knowledge graph, each land use code is mapped to a graph node, and the business logic of "mutual non-exclusivity and implementation of positive and negative list management" is transformed into the weight and attribute determination rules of directed edges between nodes in the graph.

[0132] Specifically, the underlying mapping rule set of the knowledge graph is constructed based on the following mixed positive and negative lists:

[0133] Table 1 Urban residential land (0701), urban community service facility land (0702) 1. Cultural land (0803), educational land (0804), sports land (0805), medical and health land (0806, excluding medical facilities involving infectious diseases and epidemics), social welfare land (0807) 2. Commercial land (0901, excluding wholesale market land and land for public utility outlets such as gas stations and CNG stations) 3. Park green space (1401) 1. Wholesale market land (090102), public utility service outlet land (090105, specifically referring to public utility service outlets such as gas stations and CNG stations), entertainment land (0903) 2. Special land (15) 3. Logistics and warehousing land (1101) 4. Industrial and mining land (10) Research land (0802) 1. Cultural land (0803), educational land (0804), sports land (0805) 2. New industrial land (M0) 3. Class I logistics and warehousing land (110101) 4. Park green space (1401) 1. Wholesale market land (090102), public utility service outlet land (090105, specifically referring to public utility service outlets such as gas stations and CNG stations), entertainment land (0903) 2. Special land (15) 3. Category III logistics and warehousing land (110103) 4. Category II industrial land (100102), Category III industrial land (100103), mining land (1002), salt fields (1003) Cultural land (0803), sports land (0805) 1. Scientific research land (0802), cultural land (0803), educational land (0804), sports land (0805) 2. Commercial land (0901, excluding wholesale market land and land for public utility outlets such as gas stations and CNG stations) 3. Urban rail transit land (1206), public transportation station land (120802), public parking lot land (120803) 4. Park green space (1401), plaza land (1403) 1. Wholesale market land (090102), public utility service outlet land (090105, specifically referring to public utility service outlets such as gas stations and CNG stations) 2. Special land (15) 3. Logistics and warehousing land (1101) 4. Industrial and mining land (10) Educational land (0804) 1. Scientific research land (0802), cultural land (0803), sports land (0805) 2. Public utility service outlet land (090105, excluding public utility service outlets with safety impacts such as gas stations and CNG stations) 3. Urban rail transit land (1206), public transportation station land (120802), public parking lot land (120803) 4. Park green space (1401), plaza land (1403) 1. Commercial land (0901, excluding public utility business outlets without safety impact), business and financial land (0902), entertainment land (0903), other commercial service land (0904) 2. Special land (15) 3. Logistics and warehousing land (1101) 4. Industrial and mining land (10) Commercial land (0901, excluding wholesale market land and land for public utility outlets such as gas stations and CNG stations), and business and financial land (0902). 1. Cultural land (0803), sports land (0805), medical and health land (0806, excluding medical facilities involving infectious diseases and epidemics), social welfare land (0807) 2. Commercial land (0901, excluding wholesale market land and public utility outlets such as gas stations), business and financial land (0902), entertainment land (0903), other commercial service land (0904) 3. Urban rail transit land (1206), public transportation station land (120802), public parking lot land (120803) 4. Park green space (1401), square land (1403) 1. Wholesale market land (090102), public utility service outlet land (090105, specifically referring to public utility service outlets such as gas stations and CNG stations) 2. Special land (15) 3. Class II logistics warehousing land (110102), Class III logistics warehousing land (110103) 4. Industrial and mining land (10) Wholesale market land (090102) 1. Class I logistics warehousing land (110101) 2. Retail commercial land (090101), catering land (090103), hotel land (090104), public utility service outlet land (090105), business and financial land 3. Public transportation station land (120802), public parking lot land (120803) 4. Park green space (1401), plaza land (1403) 1. Urban residential land (0701) 2. Special land (15) 3. Class II logistics warehousing land (110102), Class III logistics warehousing land (110103) 4. Class II industrial land (100102), Class III industrial land (100103), mining land (1002), salt fields (1003) Entertainment land (0903) 1. Cultural land (0803), sports land (0805) 2. Commercial land (0901, excluding wholesale market land and public utility outlets such as gas stations and CNG stations), business and financial land (0902), other commercial service land (0904) 3. Urban rail transit land (1206), public transportation station land (120802), public parking lot land (120803) 4. Park green space (1401), plaza land (1403) 1. Urban residential land (0701) 2. Wholesale market land (090102), public utility service outlet land (090105, specifically referring to public utility service outlets such as gas stations and CNG stations) 3. Special land (15) 4. Class II logistics warehousing land (110102), Class III logistics warehousing land (110103) 5. Industrial and mining land (10) Class I logistics warehousing land (110101) and Class II logistics warehousing land (110102, excluding land use prohibited by the Green Heart Ordinance). 1. Scientific research land (0802) 2. Retail commercial land (090101), wholesale market land (090102), public utility service outlet land (090105, excluding gas station and other public utility service outlet land), business and financial land (0902), other commercial service land (0904) 3. Public transportation station land (120802), public parking lot land (120803) 4. Emerging industry land (M0) 5. Protective green space (1402) 1. Urban residential land (0701) 2. Public utility service outlet land (090105, specifically referring to public utility service outlets such as gas stations and CNG stations) 3. Cultural land (0803), Educational land (0804), Sports land (0805) 4. Special land (15) 5. Category III logistics and warehousing land (110103) 6. Category II industrial land (100102), Category III industrial land (100103), Mining land (1002), Salt fields (1003) Urban rail transit land (1206), public transportation station land (120802), and public parking lot land (120803) 1. Scientific research land (0802), cultural land (0803), sports land (0805) 2. Commercial land (0901, excluding wholesale market land and public utility outlets such as gas stations and CNG stations), business and financial land (0902) 3. Class I logistics warehousing land (110101) 4. Park green space (1401), plaza land (1403) 1. Wholesale market land (090102), public utility service outlet land (090105, specifically referring to public utility service outlets such as gas stations and CNG stations) 2. Special land (15) 3. Class II logistics warehousing land (110102), Class III logistics warehousing land (110103) 4. Industrial and mining land (10) Parks and green spaces (1401) 1. Cultural land (0803), sports land (0805) 2. Urban rail transit land (1206), public transportation station land (120802), public parking lot land (120803) 1. Urban residential land (0701) 2. Wholesale market land (090102), public utility service outlet land (090105, specifically referring to public utility service outlets such as gas stations and CNG stations) 3. Special land (15) 4. Logistics and warehousing land (1101) 5. Industrial and mining land (10) Plaza site (1403) 1. Urban rail transit land (1206), transportation station land (1208) 1. Urban residential land (0701) 2. Wholesale market land (090102), public utility service outlet land (090105, specifically referring to public utility service outlets such as gas stations and CNG stations) 3. Special land (15) 4. Logistics and warehousing land (1101) 5. Industrial and mining land (10)

[0134] The system parses the above table data to generate graph connection edges: for example, taking urban residential land (0701) as the source node, it automatically extracts the "land use categories that encourage mixed use" in the table to establish compatible connection edges pointing to target nodes such as cultural land (0803) and commercial land (0901, configured to exclude gas stations and other constraint filtering nodes); at the same time, it extracts the "types that prohibit mixed use" to establish mutually exclusive blocking connection edges pointing to target nodes such as logistics and warehousing land (1101) and industrial and mining land (10).

[0135] S1033. Map the target function identifier string to the preset function compatibility knowledge graph to perform path connectivity retrieval and output the classification status with attribute exclusion or attribute compatibility.

[0136] Among them, the classification status refers to the output data code set that records whether there is a rule conflict between the dominant functional parameters of the inspected plan and the benchmark business classification.

[0137] Specifically, the extracted target function identifier string is mapped and introduced into the virtual input layer node of the function compatibility knowledge graph. Then, the legally dominant land use code spatially associated with the current planned project is selected as the target node for investigation. In the loaded function compatibility knowledge graph network, path connectivity is explored from the input layer node towards the target node. By extracting the attribute rules carried by the associated edges along the directed path, the existence of rule flag parameters with allowed compatibility or conditional mutual exclusion in the connected sequence is analyzed. Based on this underlying logic judgment result, a parameter set reflecting the qualitative characteristics of exclusion or compatibility is generated and output as the classification state.

[0138] In some embodiments, a depth-first iterative network algorithm based on graph algorithms is established to convert the target function identifier string into a retrieval cursor node. A topological exploration process for the target baseline feature node is initiated in the node hierarchy tree of the function compatibility knowledge graph. During this process, the boundary constraint attribute data of the path-connected network are collected and substituted into Boolean logic for comparison to output the corresponding mutually exclusive or compatible code to represent the truth value. No limitation is made here.

[0139] Continuing with the above scenario, if the mixed function extracted from the attached attribute parameters is "urban residential land / logistics warehousing land", the dominant function node "0701" is extracted, and the associated edge of the mixed-use target node "1101" is explored. When the path network marked with mutually exclusive blocking edges is encountered in the knowledge graph retrieval, it is determined to violate the negative list rule. The business flow is then intercepted, and a classification status carrying a "attribute exclusion (negative list conflict)" truth code is output, thereby accurately intercepting the planning map of the illegal mixed-use land.

[0140] S1034. Generate compliance judgment identifiers for attributes based on logical truth values ​​of classification status;

[0141] This step encodes and packages the Boolean results generated by the graph connectivity calculation module, transforming them into attribute compliance determination identifiers for standard metastructure data that are easy to integrate and package during execution.

[0142] Another embodiment of step S103 is: S1035, extract the target planning feature value set from the attached attribute parameters, the target planning feature value set is obtained based on the regular expression truncation of the graph text or the extraction of the spatial attribute table;

[0143] Among them, the target planning feature value set refers to the set of quantitative data parameters of various control indicators (such as plot ratio, building density, green space ratio and building height) of a plot or project.

[0144] This step utilizes regular expression text reading or layer attribute table parsing mechanisms to transform discrete quantitative index data mixed in attached attribute parameters into a target planning feature value set that can be directly processed by the algorithm platform, thus constructing a data source for subsequent quantitative detection.

[0145] S1036. Obtain the statutory numerical control interval segment corresponding to the current spatial element from the target control benchmark dataset;

[0146] Among them, the statutory numerical control interval refers to the upper and lower threshold range of the construction scale for a specific project space as defined by laws, regulations or higher-level plans.

[0147] This step uses the database's built-in relational query command to extract the control constraint specification range corresponding to the current spatial element from the target control benchmark dataset, in order to construct the comparison benchmark data for subsequent quantitative comparisons.

[0148] S1037. Substitute the target planning feature value set into the legally defined numerical control interval segment and execute the two-way boundary comparison algorithm to output the numerical verification status.

[0149] Among them, the numerical verification status refers to the attribute status output set that reflects whether the specific control indicator values ​​declared by the planning project are greater than the statutory control upper limit boundary or less than the statutory protection lower limit boundary.

[0150] Specifically, the process iterates through the quantitative feature sequences of each attribute within the target planning feature value set. For each measured feature value, the legally defined numerical control interval (including upper and lower limit calibration parameters) corresponding to the attribute name is called. The conditional comparison generator inside the processor triggers a bidirectional comparison function instruction stream, performing floating-point difference calculation between the measured feature value and the extracted two boundaries. When the measured evaluation value is determined to be greater than the upper limit boundary or less than the lower limit boundary, the comparison conclusion is recorded as a numerical verification status with a specific error direction and bias value and sent back.

[0151] In some embodiments, a decision tree network containing an index mapping dictionary is constructed. Specific feature values ​​are substituted into the topology network as input parameters and differentially processed with the threshold control vector cached by the corresponding level node. If the output represents the result output unit of extreme value deviation, deviation processing information is sent out and recorded and assembled into a numerical verification state. This is not limited here.

[0152] S1038. Based on the numerical verification status that deviates from the legally defined numerical control range, generate an attribute compliance judgment identifier that includes the extreme value deviation range.

[0153] Among them, the attribute compliance determination identifier refers to the data label structure that records whether the construction intensity of a single land parcel unit is compliant and the corresponding boundary difference.

[0154] This step performs a data encapsulation operation on the quantized difference output from the bidirectional boundary comparison and the alarm coding features, combining them to generate an independent evaluation identification unit, which can be used as a data unit for subsequent compliance inspection steps.

[0155] In some embodiments, another implementation of step S103 is as follows:

[0156] S1039. Collect the spatial area values ​​of each plot unit within the target planning feature value set and the development intensity configuration values ​​within each plot.

[0157] Among them, the unit segmentation space area value refers to the set of geometric area parameters of each polygonal object within the planning project; the development intensity configuration value refers to the proportional constant allocated to each grid area primitive to characterize the allocation limit of the construction volume parameter.

[0158] This step retrieves the calibration parameters from the planning attribute table through feature polling, and aggregates the spatial area values ​​of the discretely distributed units in the planning grid and the development intensity configuration values.

[0159] S10310. Using the unit segmentation spatial area value as the calibration weight parameter, perform global aggregation and cumulative calculation on the development intensity configuration value of the entire area to generate a total capacity summary scalar of the overall planning scope.

[0160] Among them, the total capacity summary scalar refers to the macroscopic measurement parameter that characterizes the sum of the overall building construction capacity accounting base within the geographical boundary of the target project being verified.

[0161] Specifically, a capacity merging and productization procedure is configured to extract the carrying capacity attribute status of separately distributed land parcel units. The development intensity configuration value of each parcel is multiplied and mapped to its corresponding unit segmentation spatial area value to obtain the transformed capacity element value. After this numerical calculation stage is completed, the aggregation and accumulation calculation controller is triggered to perform the merging, accumulation, and summation calculation of the capacity element values ​​within the defined planning boundary, thereby obtaining the total scalar of the geographical capacity summary.

[0162] S10311. The deviation extreme point control rule between the total approved capacity summary scalar and the overall capacity threshold set and delegated by the superior control is adopted using the verification ratio algorithm. When the deviation deviates from the overall capacity threshold, the corresponding total control violation traceability label is merged and output into the attribute compliance judgment identifier.

[0163] Among them, the overall capacity threshold refers to the parameter of the maximum total building development coverage set by the statutory management agency for the overall allocation of specific planning boundaries; the overall control violation tracing label refers to the data component code that characterizes the overall accounting overstepping status and the associated coordinate sequence source of the overstepping plots.

[0164] Specifically, when the total capacity scalar is transmitted to the overall comparison and monitoring port, the preset overall capacity threshold features are extracted and loaded into the work area for verification and comparison. The total capacity scalar is then compared with the overall capacity threshold to extract the feature ratio and obtain the absolute deviation value. If the comparison analysis reveals that the offset value does not meet the limit, triggering a warning, the offset difference parameter of the configured capacity and the unit data sequence that caused the out-of-bounds location are encoded and combined into a total control violation tracing tag with specific error indication features. This total control violation tracing tag is then merged and output into the attribute compliance judgment identifier structure for storage.

[0165] S104. Map the vector geometric parameters and the corresponding constraint baseline primitives in the target control benchmark dataset to the same coordinate space, and perform spatial geometric relationship calculation to obtain the spatial safety detection identifier;

[0166] Among them, the constrained baseline primitive element refers to the basic geographic graphic data used to construct mandatory control baselines or specify setback buffer calculation reference surfaces.

[0167] Specifically, a boundary, location, and calibration quantitative geometric review mechanism is implemented. Vector geometric parameters carrying different reference projection characteristics are normalized and integrated into the basic coordinate network system where the target control benchmark dataset is located. For the multi-level spatial control elements after re-projection, interlacing and superposition calculation based on the constraint bottom line area and encroachment and overlap area assessment are performed. When calculating the surrounding spacing parameters and facility radiation service area values, a mechanism for endpoint proximity detection and center range expansion calculation is added to verify the physical encroachment or configuration indicator deviance defects.

[0168] In some embodiments, a model deconstruction algorithm based on the bidirectional detection of centripetal beacon emission expansion and vertical projection is implemented. A buffer coverage surface with a regular radius parameter is released with the facility anchor point having personnel service allocation attributes as the center to calculate the compliance of the superimposed coverage ratio of the residential area frame. At the same time, the edge inflection point matrix array of the measured solid structure is extracted and the theoretical shortest vertical distance is calculated by projecting the limited benchmark control lead line to compare with the corresponding set safety setback width lower limit. This is not limited here.

[0169] In some embodiments, step S1041 specifically includes:

[0170] S1041. Extract the target boundary polygon sequence from the vector geometric parameters, and obtain the target bottom line polygon sequence of the corresponding constraint bottom line primitive elements.

[0171] Among them, the target boundary polygon sequence is used to represent the set of spatial closed contours of the planned land parcels to be detected, and the target bottom line polygon sequence refers to the set of spatial areal distributions of the legally protected area.

[0172] This step aims to separate the planning scope from the corresponding spatial control bottom line boundary in the spatial database, providing a basic data basis for subsequent spatial overlay conflict analysis.

[0173] S1042. Map the target boundary polygon sequence and the target bottom line polygon sequence to the same coordinate system dimension, and perform topological intersection superposition operation to obtain the target intersection connected domain set.

[0174] Among them, the target intersection connected domain set refers to the set of closed geometric regions generated by different spatial polygons in a unified coordinate system due to their overlapping spatial positions.

[0175] Specifically, the underlying coordinate metadata of the target boundary polygon sequence and the target baseline polygon sequence is first extracted. If the spatial reference systems of the two are found to be inconsistent, the predefined projection transformation function is called to unify them to the same geodetic plane coordinates. Subsequently, a node network of the two sets of polygons is constructed based on vector operations. The edge segments of the polygons are extracted to execute the planar scan line algorithm, locate all overlapping spatial intersections, and then sequentially connect the intersections and internal nodes of overlapping intervals to extract the target intersection connected domain set where spatial overlap occurs, completing the flow from two-dimensional independent polygons to the coordinate association of the exact conflict area.

[0176] In some embodiments, the minimum bounding rectangle index of the target boundary polygon sequence can be constructed, candidate constraint regions that intersect with the target bottom line polygon sequence can be screened, the node array of the candidate constraint regions can be extracted, and a region-by-region judgment operation can be performed in the mesh model based on the node array. The mesh cells that satisfy the internal feature point test rules can be merged into the target intersection connected domain set, which is not limited here.

[0177] S1043. Calculate the geometric area scalar of the target intersection connected domain set. If the geometric area scalar is greater than the fault tolerance calculation truncation threshold, generate a spatial safety detection label containing the bottom line conflict constraint label and the violation spatial coordinates.

[0178] Among them, the geometric area scalar represents the calculated value of the Gaussian two-dimensional projected area corresponding to the spatial overlapping region, and the fault tolerance calculation cutoff threshold refers to the maximum allowable coverage area tolerance parameter set for filtering the inherent reasonable error of the spatial data source.

[0179] Specifically, for each independent region within the generated target intersection connected domain set, the spatial polygon integral calculation rule is applied to perform serialized multiplication of vertex coordinates and summation of edge vector integrals to obtain its geometric area scalar. Then, a fault-tolerant calculation truncation threshold parameter is extracted from the judgment configuration information and compared with the calculated geometric area scalar using floating-point logic. When the geometric area scalar of the independent region exceeds this reference value, the judgment logic confirms that the region overlaps. At this point, the bottom-line violation anomaly response handling mechanism is triggered. The bottom-line conflict constraint label in the preset dictionary and the arithmetic geometric center point of the target intersection connected domain set are used as key-value pairs for binding and encapsulation. Finally, these are uniformly written into the data element structure representing the spatial safety detection identifier of the planning state for storage.

[0180] In some embodiments, the component can be invoked to extract the coordinate combination of all boundary vertexes of the target intersection connected domain set to construct the envelope polygon attribute set. The topological centroid coordinates of the envelope polygon attribute set are extracted as the violation space coordinates based on the area weighting ratio technique. The bottom line conflict constraint labels in the bottom line control dictionary are spliced ​​together, and finally the spatial safety detection identifier is generated by serialization and merging through table structure mapping. No limitation is made here.

[0181] In practical use, relying on geometric area scalars as a tolerance mechanism has blind spots in shape recognition. Although it can filter out common drawing errors caused by coordinate drift, when intrusive primitives are elongated, their total intersection area is often lower than the tolerance truncation threshold, but their spatial span is large and their penetration depth into the bottom-line constraint area is extremely deep. The inability to identify this type of linear spatial encroachment leads to the system mistakenly allowing elongated drawings with a very high risk of topological disruption, thus creating a significant compliance review loophole.

[0182] Therefore, in some other embodiments, step S1043 further includes:

[0183] S10441. When the geometric area scalar is determined to be no greater than the fault-tolerant calculation truncation threshold:

[0184] The purpose of this step is to construct a preliminary area tolerance mechanism for the judgment and interception branch. By judging the lower limit of the geometric area scalar, it filters out tiny primitives that have the risk of local morphological distortion.

[0185] S10442. Extract the geometric perimeter scalar of the outer boundary of the target intersection connected domain set, and obtain the sequence of overlapping vertices inside.

[0186] Among them, the geometric perimeter scalar is used to represent the total length of the topological projection of the outer contour lines of the target intersection connected domain set; the overlapping vertex sequence refers to the discrete coordinate node data array that constitutes the boundary and internal path of the target intersection connected domain set.

[0187] This step aims to separate the one-dimensional geometric perimeter feature for evaluating the outline features and the zero-dimensional coordinate node data matrix for depth measurement verification from the two-dimensional projected polygon, thereby providing a structured data source for subsequent dimensionality reduction measurement of spatial morphology.

[0188] S10443. Perform spatial morphology index operation based on geometric perimeter scalar and geometric area scalar to obtain the spatial compactness metric of the target intersection connected domain set;

[0189] Among them, the spatial morphology index operation refers to a data processing model that combines the boundary length characteristics of a planar polygon with its coverage area characteristics to evaluate the degree of graphic regularity; the spatial compactness metric parameter refers to a quantitative evaluation parameter that reflects the degree to which the target intersection connected domain set deviates from the baseline compactness boundary distribution.

[0190] Specifically, the geometric perimeter scalar and the geometric area scalar of the map sheet, extracted in the preprocessing stage, are loaded into the computation memory queue. A preset shape comparison function model is then invoked to perform dimensionality reduction and ratio feature mapping operations. By analyzing the linkage and correlation features between the outward expansion of the boundary and the area change domain, a spatial compactness parameter representing whether the currently detected primitives tend to have a narrow and elongated trend distribution is calculated and output.

[0191] In some embodiments, a morphological feature extraction engine with topological analysis properties is invoked to construct a minimum bounding rectangle that encloses the set of connected domains of the target intersection in the coordinate system based on the sequence of overlapping vertices. The absolute difference between the major and minor axis spans of the minimum bounding rectangle is extracted and further compared with the geometric area scalar in the joint weighted evaluation matrix. The spatial compactness metric parameter used to determine the boundary elongation feature is output, which is not limited here.

[0192] S10444. When the spatial compactness parameter exceeds the preset narrow shape distribution range, perform a topological central axis transformation on the target intersection connected domain set to extract the skeleton network, and calculate the first and last extreme value span scalar of the generated skeleton network.

[0193] Among them, the narrow and elongated distribution range refers to the set of feature warning scalars that are pre-configured to represent the linear cutting and extending trend of the primitives; the topological central axis transformation is used to represent the feature dimensionality reduction algorithm that compresses and extracts the coordinate array of the trajectory of the center of the largest inscribed circle associated with the closed planar polygon; the skeleton network refers to the branch connected graph set that is composed of interconnected segments of the topological central axis to describe the internal structure trend of the polygon; the extreme span scalar is used to represent the length of the path along the network flow direction of the starting feature endpoint and the ending feature endpoint with the farthest addressing distance in the traversed skeleton network.

[0194] Specifically, the planar geometric package of the target intersection connected domain set is passed to the topological feature dimensionality reduction center, guiding the underlying computational model to perform a topological central axis transformation on the target intersection connected domain set, continuously approximating and shrinking it inward. This compresses and separates the two-dimensional planar category into a continuous linear skeleton network with one-dimensional extension properties. Subsequently, the network directional traversal retrieval mechanism is invoked to enter the addressing and comparison cycle. The coordinates of the first and last related feature nodes located at the far ends of the branches of this skeleton network structure are extracted. The surveying path span of the set first and last two points along the connected skeleton in the current skeleton network is calculated, and this distance measurement record is output and backed up as an extreme span scalar representing the absolute depth length of the illegal intrusion.

[0195] In some embodiments, a Thiessen polygon set is constructed based on the sequence of outer vertices of the target intersection connected domain set; boundary segments completely contained within the target intersection connected domain set are selected from the Thiessen polygon set, and the boundary segments are topologically spliced ​​together to generate a skeleton network; vertices with a topological connectivity of one in the skeleton network are obtained as a candidate endpoint set; any two candidate endpoints in the candidate endpoint set are traversed, and the path length connecting them along the skeleton network is calculated to generate a path length sequence; the path length with the largest value is extracted from the path length sequence and set as the first and last extreme span scalar of the skeleton network, which is not limited here.

[0196] S10445. Traverse each vertex in the overlapping vertex sequence and calculate the vertical mapping distance of the normal projection to the original boundary of the target bottom line polygon sequence in order to extract the maximum intrusion depth scalar.

[0197] The maximum intrusion depth scalar refers to the longest vertical interval value projected from any evaluation point in the overlapping vertex sequence onto the original boundary segment of the target bottom line polygon sequence.

[0198] Specifically, the coordinate elements contained in the overlapping vertex sequence and the corresponding target baseline polygon sequence's outer boundary baseline control line structure are sequentially read from the buffer sequence. For each read vertex coordinate, an orthogonal projection vector to the baseline control line is constructed, and the corresponding vertical mapping distance value is calculated and generated. All values ​​are then uniformly pushed into the set array. After the traversal operation is completed, a comparison function is executed on the distance values ​​in the set array to extract the largest value and assign it to generate a maximum intrusion depth scalar output reflecting the maximum depth to which the primitive penetrates into the protected area boundary.

[0199] In some embodiments, the original boundary line extending outward from the target bottom polygon sequence is parsed into a discrete line segment sequence; each target vertex in the overlapping vertex sequence is traversed, and the orthogonal projection point of the target vertex on the discrete line segment sequence is determined sequentially by using the spatial vector dot product operation to determine whether it falls within the interval of the line segment entity endpoints; the Euclidean line value between the orthogonal projection point of the hit line segment entity interval and the corresponding target vertex is calculated and written into the distance mapping table; at the end of the traversal, the maximum distance value in the distance mapping table is extracted to generate the maximum intrusion depth scalar, which is not limited here.

[0200] S10446. If the extreme span scalar is determined to be greater than the pre-configured linear crossing cutoff threshold, or if the maximum intrusion depth scalar is determined to be greater than the preset forced limit piercing range, an additional spatial safety detection mark containing a bottom line conflict constraint label, intrusion vector features, and violation spatial coordinates is generated.

[0201] Among them, the linear crossing cutoff threshold refers to the upper limit benchmark parameter used to limit the lateral cross-boundary size of the elongated spatial primitives; the forced limit penetration range refers to the control parameter used to limit the depth of primitives to cut into the bottom surface of the protection red line without considering area occupation; the intrusion vector feature refers to the digital packet that combines the extreme span and detection depth of the over-limit mapping parameters.

[0202] In some embodiments, step S104 further includes:

[0203] S1044. Extract the boundary polygons of the structure entity from the vector geometric parameters, and obtain the dataset of all target vertex sequences of the boundary polygons of the structure entity.

[0204] The target vertex sequence dataset is used to represent the set of two-dimensional coordinate node information of the closed contour edge of the planned structure's external footprint.

[0205] This step aims to filter redundant attached text and topological line attribute information from the spatial design graphic data to obtain the basic node coordinate feature array that determines the trend of the building's geographical outline structure, providing target vector data support for the quantitative calculation of building setback distance.

[0206] S1045. Obtain the target adjacent baseline frame in the corresponding constraint baseline primitive element, and extract the corresponding statutory minimum spatial setback distance threshold from the configuration parameters.

[0207] Among them, the target adjacency baseline frame refers to the main road red line or green space edge boundary that is spatially related to the plot under review and constitutes a setback restriction. The statutory minimum spatial setback distance threshold is used to represent the buffer zone length benchmark required by the review criteria for relevant construction entities.

[0208] This step aims to identify the control boundaries of objective public facilities with spatial limitations and simultaneously analyze and obtain the distance benchmark standards within the review rule system, thereby establishing a reference and comparison basis for subsequent assessment of the compliance of setback spaces.

[0209] S1046. Traverse each target vertex in the target vertex sequence dataset and calculate the theoretical vertical projection distance sequence set from each target vertex to the target adjacent baseline frame.

[0210] Among them, the theoretical vertical projection distance sequence set refers to the ordered set of measurement data calculated after mapping the coordinates of each node on the closed graphic of the building plot to the set target reference line segment for projection operation.

[0211] Specifically, each geometric coordinate point in the target vertex sequence dataset is read in the order of topological connectivity paths. For the target adjacency baseline, it is decomposed into multiple independent detection line segments connected by two endpoints. For each target vertex, the spatial positional relationship between the target vertex and each detection line segment is calculated, and the normal mapping path length scalar is solved by vector dot product and cross product operations. If the calculation determines that the perpendicular foot of the target vertex does not actually fall within the corresponding line segment, the straight-line distance from the target vertex to the two endpoints of the line segment is used as the distance metric. Local minimum value units are saved through iterative comparisons. After traversing all target vertices, the obtained spatial near point distance array items are summarized, sorted, and mapped and combined into a theoretical vertical projection distance sequence set with boundary mapping association characteristics.

[0212] In some embodiments, a spatial quadtree index matrix system with the target adjacency baseline frame as the core structure can be constructed. The target vertex array is substituted into the index matrix to perform the nearest neighbor search algorithm operation. After filtering the spatial oblique divergence points by algebraic vector inner product, the vertical distance calculation results of each target measurement node are obtained and collected to form a theoretical vertical projection distance sequence set. This is not limited here.

[0213] S1047. Extract the minimum value from the theoretical vertical projection distance sequence set as the actual topological extreme value scalar of the boundary polygon of the structure entity.

[0214] Among them, the actual topological extreme value scalar refers to the buffer net distance index data corresponding to the closest numerical part of the reference boundary surface among the convex measurement points of the building's external outline area.

[0215] This step obtains the minimum value within the combination of measurement points by calling the array traversal mechanism, extracts the data scalar value of the corresponding shortest relative outer edge in the measurement points of the structure, and thus focuses the core of the comparison and judgment on the spatial reference node where there is a risk of exceeding the standard red line.

[0216] S1048. Compare the actual topological extreme value scalar with the statutory minimum space setback distance threshold. If the actual topological extreme value scalar is determined to be less than the statutory minimum space setback distance threshold, write the setback violation label into the space safety detection mark.

[0217] Among them, the setback violation label is used to indicate the determination and confirmation record of the physical building design boundary crossing the bottom line of the review and management baseline buffer parameter.

[0218] Specifically, the difference parameter between two numerical scalars is evaluated to verify the clearance distance between the outer boundary of the planned structure and the safety control boundary. When the logical operation identifies that the difference parameter is negative and exceeds the basic tolerance limit, it indicates that the parameters of the detailed planning results have not met the basic standards for spatial setback.

[0219] In some embodiments, step S104 further includes:

[0220] S1049. Extract the target facility location coordinate parameters and the target residential land polygon parameters from the vector geometric parameters. The target facility location coordinate parameters are used to represent the discrete point location data of public service facilities or infrastructure in the spatial coordinate system within the planning area, and the target residential land polygon parameters are used to represent the spatial closed boundary data of the areal area used for residential or activity purposes in the plan.

[0221] The main purpose of this step is to separate the target facility location coordinates and target residential land polygon parameters from the vector geospatial dataset containing multi-source planning information, which are required for subsequent spatial coverage analysis.

[0222] S10410. Obtain the baseline radiation radius threshold set by the coordinate parameters of the corresponding target facility points in the target control baseline dataset. The baseline radiation radius threshold refers to the service coverage distance value set for various public facilities in planning standards or local regulations.

[0223] The purpose of this step is to retrieve numerical data based on the target control benchmark dataset to define the spatial service capacity of a specific type of facility, in order to support subsequent spatial extension analysis.

[0224] S10411. Using each target facility point in the target facility location coordinate parameters as the centripetal origin and the reference radiation radius threshold as the distance boundary, perform an extension operation to construct multiple corresponding facility coverage connected domains. Here, the extension operation represents the buffer generation operation based on the geographic information spatial analysis engine, and the facility coverage connected domain refers to a closed geometric space representing the spatial service coverage of a single facility.

[0225] Specifically, this step first traverses each centripetal origin in the coordinate parameters of the target facility location, then calls the spatial analysis microservice and loads the benchmark radiation radius threshold as the calculation parameter, and performs equidistant expansion projection along each centripetal origin to generate multiple corresponding facility coverage connected domains in the two-dimensional spatial plane to simulate the actual radiation reach of the currently planned public facilities.

[0226] In some embodiments, the coordinate parameters of the target facility point are read, an equidistant extended vector boundary is set for each target facility point based on the Euclidean space distance calculation logic, and a circular planar polygon corresponding to each target facility point is drawn in the memory object as a facility coverage connected domain, which is not limited here.

[0227] S10412. Perform topological union calculation on the generated multiple facility coverage connected domains to output global fusion surface features, and calculate the intersection area coverage ratio scalar between the global fusion surface features and the target residential land polygon parameters. The global fusion surface features refer to the planar spatial attributes formed after merging multiple spatially overlapping facility coverage connected domains and removing their inner boundaries. The intersection area coverage ratio scalar is used to characterize the proportion of residential land area within the service area to the total residential land area.

[0228] Specifically, the topology processing logic is applied to the multiple facility coverage connected domains output in the previous step. By eliminating the common boundary lines between adjacent or overlapping areas, a global fusion surface feature is synthesized. Then, the global fusion surface feature is spatially intersected with the target residential land polygon parameter. The intersecting area falling within the service surface is extracted. The intersecting area is divided by the total area of ​​the target residential land polygon parameter through a quotient calculation mechanism to obtain the intersection area coverage ratio scalar used for quantitative indicator evaluation.

[0229] In some embodiments, optionally, the analysis engine extracts vector boundary node information of multiple facility coverage connected domains and performs Boolean merging and reorganization to eliminate internal intersection points and generate global fusion surface features. It then uses clipping operations to cut the target residential land polygon parameters to extract intersecting geometric elements, and then calculates the ratio of the area of ​​the intersecting elements to the total area of ​​the target residential land polygon parameters as the intersection area coverage ratio scalar, which is not limited here.

[0230] S10413. When the coverage ratio scalar of the intersection area is determined to be less than the statutory lower boundary of the corresponding target facility location coordinate parameter, a coverage deficiency label shall be written into the spatial safety inspection mark. Here, the statutory lower boundary of coverage refers to the minimum coverage ratio requirement that facilities should meet as stipulated in higher-level plans or special regulations; the coverage deficiency label is used to characterize the current planning scheme's failure to meet specific supporting services; and the spatial safety inspection mark refers to the judgment record field attached to the corresponding compliance assessment and review report.

[0231] Specifically, upon receiving the intersection area coverage ratio scalar, the corresponding legal coverage lower boundary value is extracted, and numerical diagnostic calculation is performed. When it is determined that the intersection area coverage ratio scalar is lower than the legal coverage lower boundary, a status feedback mechanism is triggered to generate a corresponding coverage deficiency label. This coverage deficiency label is then associated with and written into the spatial safety detection identifier of the corresponding planning plot, serving as the basis for subsequent process evaluation.

[0232] In some embodiments, the numerical change status of the intersection area coverage ratio scalar is monitored, a relational database trigger is used to explore the legal coverage lower boundary rule, and when the value is detected to be lower than the threshold, a data change instruction package is constructed to write the coverage deficiency label into the spatial safety detection identifier field of the land parcel attribute table, which is not limited here.

[0233] S105. Integrate attribute compliance judgment mark and spatial safety detection mark to generate content compliance judgment result;

[0234] Among them, the content compliance judgment result refers to the integrated set of horizontally combined evaluation indicator documents, including the output of qualitative restrictions on planning projects, the output of numerical matching status verification, and the output of spatial layering relationship calculation.

[0235] Specifically, by performing object-level index combination splicing and formatting assembly actions on the compliance judgment identifiers of business attributes generated under the previous multi-branch structure and the spatial safety detection identifiers obtained through topology operations, a set of judgment benchmark label aggregation form packages covering various physical and data restriction settings of the target planning entity is constructed and encapsulated.

[0236] S106. Determine review opinion data based on process compliance assessment results and content compliance assessment results;

[0237] Among them, the review opinion data refers to the synthetic report data stream entity that integrates the source traceability anomaly identification instructions, legal provision support text constraint parameters, and conflict geographic tile anchor point rendering identification objects.

[0238] Specifically, after summarizing and extracting process compliance judgment results and content compliance judgment result fragments containing non-compliance status identifier logical fields, a matching search access is initiated to the legal norm control library pre-set on the internal network. The original text citation string of the processing legal provisions with unique mapping reference association with the captured abnormal violation event group sequence and the corresponding rectification progress guidance reference string data are extracted. The aforementioned text interpretation group is merged and filled with the tile map execution view component data of the encroachment coordinate frame positioning information returned by the geographic computing platform to merge and construct a synthetic display framework data with a multimodal comparative interpretation graphic structure and load it for export to be reviewed.

[0239] S1061. Aggregate and combine process compliance judgment result fragments and content compliance judgment result fragments that represent negative non-compliant logical attribute elements to generate a judgment violation event metagroup.

[0240] Among them, the judgment violation event metagroup refers to the set of attribute objects containing defect location and type code error obtained by merging and structured extraction within the judgment closed loop.

[0241] This step aims to extract program defect records or planning entity violation fragments from various independent detection tasks and integrate them into an abnormal alarm data package under a unified and standardized caliber.

[0242] S1062. Retrieve and match relevant legal provisions and corresponding rectification guidance reference strings from the knowledge graph intranet with the pre-set legal citation association control library and various judgment violation event metagroups.

[0243] Among them, the statutory citation association control library refers to a database instance that compiles and stores the mapping relationship between spatial planning policy and regulation provisions and their application graph scenario features; the knowledge graph refers to a data association grid that expresses the semantic network of compliance rule entities and relationship links.

[0244] Specifically, the non-compliance category identification tags and error entity elements covered within the metagroup of the violation event are retrieved and input into the knowledge graph engine for spatial addressing and traversal via the communication link. Based on the ontology attribute lookup algorithm, the system maps and locates the legal provisions node and the knowledge node with the corresponding constraint relationship. Using the mapping hit result as the target point, the system extracts the specific citation and interpretation text character data associated within the node through the data interception module. This process constructs the original legal text constraint basis and the correction instruction text paragraph to assist the reviewers in their review and reading, thereby forming the relevant legal provisions original text reference string and the corresponding rectification guidance opinion reference string.

[0245] In some embodiments, the feature primary key in the violation event metagroup is parsed and determined. Boolean relation search logic is applied to the feature primary key in the deployed distributed inverted index word segmentation library. The compliance basis document content fragments are extracted from the wide table entries obtained by mapping, and then concatenated to form the required legal text content and reference guidance string. This is not limited here.

[0246] S1063. Based on the retrieved reference strings of the original legal provisions and the reference strings of rectification guidance opinions, the conflict spatial geographic anchor point map image drawing object generated by the calculation of records triggered by spatial security detection marks is integrated and loaded into the information skeleton unit containing mixed text and image content of the front-end dynamic interactive review rendering component to construct the review opinion data with a visual diagram structure.

[0247] Among them, the conflict space geographic anchor point map image drawing object is used to represent the electronic spatial model that will be processed by grid slicing or vector identification of areas where there is superimposed interference, boundary crossing and other illegal geographic topological information; the information skeleton unit is used to represent the modular carrier view structure used in the front-end interface for typesetting and presenting review feature data.

[0248] Specifically, the system asynchronously captures and retrieves the structured reference strings of the corresponding legal provisions and the reference strings of rectification guidance opinions generated by the preceding business processes. Simultaneously, it receives conflict spatial geographic anchor point map image drawing objects output from the compliance inspection link, which carry coordinate frames and red line warning drawing layer element structural attributes. In the information assembly stage, according to the pre-processed mapping format rules, the text-type non-compliant explanation paragraphs are merged with image objects containing two-dimensional geographic topology and placed in the corresponding display slot component of the view carrier. This constructs a data information set model that both contains the basis for judgment data and carries positioning characteristics, and is specifically designed for client interface rendering and display, thereby deriving the review opinion data.

[0249] In some embodiments, the virtual format document typesetting processing engine is enabled to retrieve an array of empty tab forms with hypertext markup structures. According to the spatial location and description information alignment configuration criteria, the data file address links associated with the conflicting spatial geographic anchor map image drawing objects are filled and updated into the internal area of ​​the data node tree with text type data, thus completing and constructing a structured file output format that loads the relative displacement reference system. This is not limited here.

[0250] After step S106, the following is also included:

[0251] S3001. Transmit review opinion data containing multiple review interaction interfaces to the third-party technical review communication port that provides an independent proofreading and confirmation logic layer, and respond to the human intervention interaction confirmation status packet with supplementary review mark instruction or consent to take effect signature instruction.

[0252] Among them, the human intervention interaction confirmation status package refers to a data collection that records verification supplementary modification information or approval feedback judgment results from the verification personnel.

[0253] This step aims to push the generated review result data, which includes both graphical evidence and textual descriptions, to the manual verification control hub port, and obtain supplementary modification information or confirmation and approval feedback data status encapsulation object through the back-transmission communication probe.

[0254] S3002. Decentralize and transfer the human intervention interaction confirmation status packet and review opinion data to the core licensing layer administrative review decision-making port to receive the review completion intention trigger event.

[0255] Among them, the "intention to conclude" event is used to indicate the flow approval data frame transmitted when a secure business terminal node with legal approval operation authority makes a confirmation operation on the flow information.

[0256] This step aims to send the verified status label data, along with its review report record, to the business node with approval and issuance confirmation authority to receive and process subsequent transaction orders.

[0257] S3003. When the monitoring captures the digital truth value feature code that allows the approval authority, the internal network document automatic electronic stamping sub-component is activated to generate a secure digital approval official receipt document; the generated data register is then transmitted via the data transmission bus to the platform to which it ultimately belongs.

[0258] Among them, the digital truth value feature code refers to the Boolean-form permission proof encryption group that indicates that the judgment process has been authorized and recognized and that its business flow is permitted.

[0259] Specifically, after the protocol service deployed on the transmission bus node detects and verifies the captured digital truth value feature code used to transmit data placement processing, the background starts the automatic electronic stamping sub-component embedded in the secure environment through the event scheduling service. It extracts the corresponding review indicators, control boundary limits, and target identification codes from the data recorded in the verification and confirmation opinion pool and maps them into the standard document template frame. Then, it uses the certificate interface to perform the public and private key encryption issuance and digital fingerprint attachment process to generate a secure digital approval official receipt document with anti-tampering features and legal circulation certificate effect. After that, the transmission program uses the cross-network conversion connection channel instruction to send the document package containing business base results and related spatial element information to the platform according to the storage transmission protocol. On the platform side, the corresponding land physical information and management logic information registration operation is completed to perform the formal data completion action.

[0260] In some embodiments, the extended style sheet service is used to perform dynamic data matching and replacement filling processing on the pre-embedded key information placeholders that store the format specification file. The underlying electronic authentication server is borrowed to make remote process control calls to configure the signature mechanism. The final format information package carrying the electronic anti-counterfeiting confirmation stamp and the vector feature library are distributed and pushed into the platform's receiving and processing pool through a dedicated loading link. This is not limited here.

[0261] S3004, or if a data link frame set event containing approval rejection and return intention status is captured, the recovery data information packet containing the characteristic attributes of the non-compliance reason table and repair pointer list for the object is sent through the anti-counterfeiting link according to the original information identification number, and the data mount point of the initiating transmission end is located along the inversion path to execute the rollback supervision and correction instruction.

[0262] The inversion path refers to the communication network path that locates the data source along the opposite direction of the information declaration and delivery flow chain and sends back the instruction data frame to the initial data originating terminal.

[0263] Specifically, at the receiving port, the protocol frame structure with approval rejection and return attributes is identified and extracted. First, the business origination identity traceability information code generated during the initial business establishment and attached to the data payload header is parsed and extracted, and configured as the primary key for subsequent communication alignment. Then, the program vulnerability forms and error exception lists exceeding the space control planning limits generated by the previous inspection modules are extracted, summarized, and an exception feedback set is established. With the anti-tampering link protection encrypted routing, the restored data information packet structure with error correction target indication business instructions is encapsulated and output. The route delivery instruction return operation is retrieved along the previous communication initiation feedback direction until the corresponding application start client access point is reached. The instruction return interaction return command issuance operation is executed to request the correction of the problem form.

[0264] The following describes an exemplary detailed planning full-process review system 200 provided in an embodiment of this application. Figure 2 This is an exemplary hardware structure diagram of the detailed planning full-process review system 200 provided in this application embodiment.

[0265] In some embodiments, the detailed planning full-process review system 200 is a computer device or includes a computer device in the detailed planning full-process review system 200. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores data. The network interface of the computer device is used to communicate with other external terminals or servers via a network connection. In some embodiments, the network interface can be a wired network interface; in some embodiments, the network interface can also be a wireless network interface. When the computer program is executed by the processor, it implements the methods in the embodiments of this application.

[0266] Those skilled in the art will understand that Figure 2 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0267] 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.

[0268] 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)".

[0269] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0270] 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 detailed planning process review method, characterized in that, include: Obtain planning data for the target planning project, and extract procedural proof data and spatial element data from the planning data; The procedural proof data is a dataset recording approval and approval events, and the spatial element data consists of vector geometric parameters and attached attribute parameters. Extract the process node status information of the program proof data; The process node status information is compared with the preset prescribed program template using logical rules, and the process compliance judgment result is output. The attached attribute parameters are matched with the corresponding constraint fields in the target control benchmark dataset to obtain the attribute compliance determination identifier. The vector geometric parameters are mapped to the corresponding constraint baseline primitives in the target control benchmark dataset to the same coordinate space, and spatial geometric relationship calculation is performed to obtain a spatial safety detection identifier; wherein: the geometric area scalar of the target intersection connected domain set is calculated, and if the geometric area scalar is determined to be greater than the fault tolerance calculation truncation threshold, the spatial safety detection identifier containing the baseline conflict constraint label and the violation spatial coordinates is generated; If it is determined that the geometric area scalar is not greater than the fault-tolerant calculation truncation threshold: Extract the geometric perimeter scalar of the outer boundary of the target intersection connected domain set, and obtain the sequence of overlapping vertices inside; Based on the geometric perimeter scalar and the geometric area scalar, perform spatial morphology index operation to obtain the spatial compactness metric of the target intersection connected domain set; If the spatial compactness parameter is determined to exceed the preset narrow shape distribution range, a topological central axis transformation is performed on the target intersection connected domain set to extract the skeleton network, and the first and last extreme value span scalar of the skeleton network is calculated and generated. Traverse each vertex in the overlapping vertex sequence and calculate the vertical mapping distance of the normal projection to the original boundary of the target bottom line polygon sequence to extract the maximum intrusion depth scalar; If the extreme span scalar is determined to be greater than the pre-configured linear crossing cutoff threshold, or if the maximum intrusion depth scalar is determined to be greater than the preset forced limit piercing range, an additional spatial safety detection identifier containing a bottom line conflict constraint label, intrusion vector features, and violation spatial coordinates is generated. Integrate the attribute compliance determination identifier with the spatial safety detection identifier to generate a content compliance determination result; The review opinion data is determined based on the process compliance determination results and the content compliance determination results.

2. The method according to claim 1, characterized in that, Before the step of generating the spatial safety detection identifier containing the bottom-line conflict constraint label and the violation spatial coordinates when determining that the geometric area scalar of the target intersection connected component set is greater than the fault tolerance calculation truncation threshold, the method further includes: Extract the target boundary polygon sequence from the vector geometric parameters, and obtain the target bottom line polygon sequence corresponding to the constrained bottom line primitive elements; The target boundary polygon sequence and the target bottom line polygon sequence are mapped to the same coordinate system dimension, and a topological intersection superposition operation is performed to obtain the target intersection connected domain set.

3. The method according to claim 1, characterized in that, The step of performing a matching calculation between the attached attribute parameters and the corresponding constraint fields in the target control benchmark dataset to obtain the attribute compliance determination identifier specifically includes: Extract the target function identifier string from the attachment attribute parameters; Obtain a pre-defined functional compatibility knowledge graph stored in the target control benchmark dataset. The functional compatibility knowledge graph uses standard land use classification as nodes and mutually exclusive, permitted, or conditionally compatible settings as connecting edges. The target function identifier string is mapped to the preset function compatibility knowledge graph for path connectivity retrieval, and the classification status with attribute exclusion or attribute compatibility is output. Based on the logical truth value of the classification status, an attribute compliance determination identifier is generated.

4. The method according to claim 1, characterized in that, The step of mapping the vector geometric parameters to the corresponding constraint baseline primitives in the target control benchmark dataset to the same coordinate space and performing spatial geometric relationship calculations to obtain the spatial safety detection identifier specifically includes: Extract the boundary polygons of the structure entity from the vector geometric parameters, and obtain a dataset of all target vertex sequences of the boundary polygons of the structure entity; Obtain the target adjacent baseline frame in the corresponding constraint baseline primitive element, and extract the corresponding statutory minimum spatial setback distance threshold from the configuration parameters; Traverse each target vertex in the target vertex sequence dataset and calculate the theoretical vertical projection distance sequence set from each target vertex to the target adjacent baseline frame; Extract the minimum value from the theoretical vertical projection distance sequence set as the actual topological extreme value scalar of the boundary polygon of the structure entity; The actual topological extreme value scalar is compared with the statutory minimum space setback distance threshold. If the actual topological extreme value scalar is determined to be less than the statutory minimum space setback distance threshold, a setback violation label is written into the space safety detection identifier.

5. The method according to claim 1, characterized in that, The steps of extracting the process node status information of the program proof data, comparing the process node status information with the preset program template according to logical rules, and outputting the process compliance judgment result specifically include: For the program proof data, perform character parsing and text feature segmentation calculation to extract the corresponding entity feature string sequence and operation timestamp sequence, which are used to construct the process node status information; Extract the pre-stored target pre-constraint condition set from the preset prescribed program template, wherein the target pre-constraint condition set includes the benchmark subject target library, the quantity lower limit threshold, and the circulation duration time limit threshold; The entity feature string sequence is sequentially traversed and substituted into the baseline subject target library to perform text comparison, and the strings that match the matching rules are merged to filter out the corresponding subject subset; The total number of elements in the main subset is counted as a scalar. If the total number of elements is greater than the lower limit threshold, the corresponding integrity state quantity is determined to be generated. Extract the initial time boundary and the termination time boundary of the operation timestamp sequence, calculate the process time difference scalar of the span between the two, and generate the corresponding time limit status quantity when it is determined that the process time difference scalar is greater than the flow duration threshold. The integrity state quantity and the time limit state quantity are logically conjuncted, and the process compliance judgment result is generated based on the output global Boolean truth value.

6. The method according to claim 1, characterized in that, The step of performing a matching calculation between the attached attribute parameters and the corresponding constraint fields in the target control benchmark dataset to obtain the attribute compliance determination identifier specifically includes: Extract the target planning feature value set from the attached attribute parameters. The target planning feature value set is obtained based on regular expression extraction of graph text or extraction from spatial attribute table. Obtain the legally defined numerical control interval segment corresponding to the current spatial element from the target control benchmark dataset; Substitute the target planning feature value set into the statutory numerical control interval segment and execute the two-way boundary comparison algorithm to output the numerical verification status. Based on the numerical verification status that deviates from the legally defined numerical control range, an attribute compliance determination identifier containing the extreme value deviation range is generated.

7. A detailed planning full-process review system, characterized in that, The detailed planning full-process 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 detailed planning full-process review system to perform the method as described in any one of claims 1-6.

8. A computer program product containing instructions, characterized in that, When the computer program product is run on the detailed planning full-process review system, the detailed planning full-process review system performs the method as described in any one of claims 1-6.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is run on the detailed planning full-process review system, the detailed planning full-process review system performs the method as described in any one of claims 1-6.

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