Uploading data efficient processing system and method applied to urban and rural planning

Abstract

The application discloses an uploaded data efficient processing system and method applied to urban and rural planning, relates to the technical field of urban and rural planning data processing, and comprises a data uploading and node anchoring module, a progress visualization management and control module, an abnormality identification and positioning module, a targeted correction and continuation module, a collaborative management and output module, a data traceability and backtracking module.The application builds an urban and rural planning uploaded data whole-process closed-loop processing system composed of the data uploading and node anchoring module, the progress visualization management and control module, the abnormality identification and positioning module, the targeted correction and continuation module, the collaborative management and output module, and the data traceability and backtracking module, so that the problems of low data processing efficiency, uncontrollable compliance risk and great difficulty in achievement approval docking caused by the dispersed functions, poor process connection, and lack of whole-process compliance management and traceability of the urban and rural planning uploaded data processing system in the prior art can be solved.

Description

Technical Field

[0001] This invention relates to the field of urban and rural planning data processing technology, and in particular to an efficient data processing system and method for urban and rural planning. Background Technology

[0002] The efficient data processing system and methods applied to urban and rural planning can improve the processing efficiency and data quality of uploaded data, solve industry pain points such as heterogeneous multi-source data, inconsistent standards, and information silos, and ensure data compliance and usability; provide accurate and reliable data support for the entire process of urban and rural planning preparation, approval, implementation and supervision, and effectively improve the scientific nature of planning decisions and the overall efficiency of digital governance of urban and rural construction.

[0003] Currently, the systems and methods for efficiently processing uploaded data used in urban and rural planning still suffer from core shortcomings, including fragmented functions, poor process integration, and a lack of full-process compliance control and traceability. Common issues include isolated data processing stages, disconnect between data and business processes, and a lack of collaborative functional modules. Furthermore, there are technical defects such as the lack of spatial anchoring for anomaly identification in planning data, mismatch between correction scope and verification rules, the need for repeated verification throughout the entire process after correction, and the inability to accurately resume broken points in the process. There are also industry pain points such as weak binding between planning data and legally mandated projects, unclear responsibilities among multiple stakeholders, easy tampering of operation records, and high barriers to integration between planning results and legally mandated approval systems. These problems not only significantly reduce the processing efficiency of uploaded data in urban and rural planning, lengthen anomaly handling cycles, waste computing resources, and cause uncontrollable project progress, but also lead to uncontrollable data compliance risks, insufficient legal validity of planning results, repeated approval processes, and a lack of evidence for tracing responsibilities, seriously affecting the scientific nature of urban and rural planning decisions and the overall effectiveness of digital governance in urban and rural construction.

[0004] Therefore, a high-efficiency data processing system and method for urban and rural planning is proposed to solve the above problems. Summary of the Invention

[0005] The main objective of this invention is to provide an efficient data processing system and method for urban and rural planning, in order to solve the problems mentioned in the background above.

[0006] To achieve the above objectives, the technical solution adopted by this invention is: a high-efficiency data processing system and method for urban and rural planning, wherein the system includes a data uploading and node anchoring module, a progress visualization and control module, an anomaly identification and location module, a targeted correction and continuation module, a collaborative control and output module, and a data traceability and backtracking module, wherein: The data upload and node anchoring module is used to strongly bind the uploaded urban and rural planning data with the statutory planning projects, perform pre-disassembly and unique identifier generation of the entire process nodes, and complete the pre-matching of data and processing flow. The progress visualization and control module is used to build a project-level spatial visualization platform, bind and map processing nodes to spatial elements, and perform real-time updates of processing progress, anomaly warnings, and multi-dimensional filtering and display. The anomaly identification and localization module is used to identify multi-dimensional anomalies across all nodes of the process based on a preset legal rule base, and to perform spatial localization, hierarchical judgment and information push of anomalies. The targeted correction and continuation module is used to control correction permissions and push correction guidance according to the anomaly level, receive the submitted correction content for targeted re-examination, and complete the process breakpoint continuation after the re-examination is passed. The collaborative management and output module is used for hierarchical access control and online collaborative review of all participating entities, generating standardized output packages and connecting with the approval system, and reconnecting the processing flow after receiving feedback. The data traceability and backtracking module is used to receive full-process operation data, automatically generate traceability records and preserve them in an tamper-proof manner, and perform full-process data backtracking operations and export traceability records.

[0007] Preferably, the data upload and node anchoring module is specifically used for: The input legal project code is verified and matched with the National Territorial Spatial Planning Use Control and Supervision System to obtain the basic information and planning type of the corresponding project; Based on the matching planning type, a standardized processing flow is matched, and the nodes of the entire process are pre-decomposed to generate node links bound to the project. To process tasks, process anchoring identifiers are generated, and the uploaded urban and rural planning data is identified by type and matched with node mapping to complete the pre-verification of data upload.

[0008] Preferably, the progress visualization and control module is specifically used for: In accordance with the legal scope of the project, a project-level spatial visualization carrier is constructed, and processing nodes are bound one by one to spatial elements; Real-time capture and update of the processing status of each node and spatial element, and targeted push of progress update messages; Trigger and lock nodes for progress anomalies, and perform multi-dimensional filtering and display of processing progress.

[0009] Preferably, the anomaly identification and localization module is specifically used for: At each processing node in the node chain, synchronous identification of the corresponding type of anomaly is performed based on the legal rule base; The identified anomaly information is bound to the corresponding spatial elements and attribute fields to perform spatial location and visual annotation of the anomaly; Anomalies are classified and information is pushed out based on their impact on the effectiveness of project approval and legal compliance.

[0010] Preferably, the targeting correction and continuation module is specifically used for: Preset correction operation permissions corresponding to different anomaly levels, and push corresponding standardized correction guidelines according to the anomaly level and type; The system receives submitted corrections and performs targeted re-checks only on the corrected content and the corresponding exception type's verification rules. After the re-inspection is passed, the subsequent process will be connected from the interrupted processing node, without re-executing the node that has already completed the verification.

[0011] Preferably, the collaborative management and output module is specifically used for: The system establishes tiered collaborative authority settings for all stakeholders involved in urban and rural planning projects, and supports online collaborative review by multiple stakeholders. After all nodes in the process are completed, a standardized deliverable package is generated, and the deliverable package is then integrated with and pushed to the natural resources government approval system. Receive feedback from authorized entities, break down the corresponding processing tasks, and reconnect the processing flow.

[0012] Preferably, the data tracing and backtracking module is specifically used for: Receive operation data synchronized from other functional modules, generate full-process traceability records and bind unique traceability identifiers; A dual-track evidence storage model is adopted for tamper-proof evidence storage of traceability records; The system receives the input traceability identifier, performs full-process processing to retrieve records, restore data status, and perform visualization comparison; it also receives submitted export commands to export standardized traceability records.

[0013] Preferably, the anomaly identification and location module identifies anomaly types including spatial benchmark anomalies, topological relationship anomalies, legal compliance anomalies, multi-source data consistency anomalies, and business process compliance anomalies.

[0014] Preferably, the dual-track evidence storage mode is an evidence storage method that stores hash digests on-chain and encrypted original text off-chain, wherein the on-chain is a government consortium chain and the off-chain is a private encrypted database of authorized nodes.

[0015] A preferred method for efficient processing of uploaded data applied to urban and rural planning includes the following specific processing methods: S1. Strongly bind the uploaded urban and rural planning data with the statutory planning projects, pre-decompose and generate unique identifiers for all processing nodes, and complete the pre-matching of data and processing flow; S2. Build a project-level spatial visualization platform, bind and map processing nodes to spatial elements, and perform real-time updates of processing progress, anomaly warnings, and multi-dimensional filtering and display. S3. Based on a pre-set legal rule base, perform multi-dimensional anomaly identification at all nodes in the process, and perform spatial location, hierarchical judgment and information push of anomalies; S4. Based on the anomaly level, implement correction permission control and push correction guidance, receive the submitted correction content for targeted re-examination, and complete the process breakpoint reconnection after the re-examination is passed. S5. Implement hierarchical access control and online collaborative review for all participating entities, generate standardized deliverable packages and connect them with the approval system, and reconnect the processing flow after receiving feedback. S6. Receive full-process operation data, automatically generate traceability records and preserve them tamper-proofly, and perform full-process data backtracking operations and export traceability records.

[0016] The present invention has the following beneficial effects: 1. This invention constructs a closed-loop processing system for the entire process of urban and rural planning data upload, organically linking a data upload and node anchoring module, a progress visualization and control module, an anomaly identification and location module, a targeted correction and continuation module, a collaborative control and output module, and a data traceability and backtracking module. This system achieves strong binding between uploaded data and legally mandated planning projects, pre-disassembly and unique identification control of all nodes in the processing flow, closed-loop linkage of the entire anomaly handling chain, standardized hierarchical control of multi-entity collaboration, and integrated execution of tamper-proof traceability throughout the entire process. Compared with existing technologies, this system overcomes the industry pain points of fragmented processing of urban and rural planning data, disconnect between data and business processes, and lack of collaborative linkage between functional modules. It improves the automation, control accuracy, and operational efficiency of the entire process of planning data upload. Therefore, it solves the problems of fragmented functions, poor process integration, and lack of full-process compliance control and traceability capabilities in existing urban and rural planning data upload processing systems, which lead to low data processing efficiency, uncontrollable compliance risks, and difficulties in results approval and integration.

[0017] 2. This invention utilizes an anomaly identification and location module based on a legal rule base for multi-dimensional anomaly identification across all nodes, precise location of anomalies bound to spatial elements, and a compliance impact grading mechanism. Combined with a targeted correction and continuation module featuring anomaly level matching for access control, standardized correction guidelines, targeted re-inspection, and process breakpoint continuation design, and further supplemented by a progress visualization and control module with processing node-spatial element binding mapping and node locking mechanisms, this invention achieves precise identification, location, grading, and seamless process continuation of planning data anomalies. Compared to existing technologies, it significantly reduces the cost of repeated verification after anomaly correction, improves the accuracy, compliance, and project process efficiency of anomaly handling. Therefore, it solves the problems of existing technologies, such as lack of spatial anchoring in planning data anomaly identification, mismatch between correction scope and verification rules, the need for repeated verification across the entire process after correction, and the inability to accurately re-continuate process breakpoints, leading to long anomaly handling cycles, wasted computing resources, and uncontrollable project progress.

[0018] 3. This invention utilizes a data upload and node anchoring module for legally mandated planning project coding verification, standardized process matching, and a mechanism for generating unique identifiers for all process nodes. Combined with a collaborative management and output module featuring tiered access control for all participating entities, seamless integration with the online collaborative review and natural resources government approval system, and further enhanced by a data traceability and backtracking module with dual-track immutable evidence storage, full-process data status backtracking, and standardized traceability record export, this invention achieves legal compliance control throughout the entire lifecycle of planning data, standardized collaboration among multiple entities, and traceable evidence for the entire process. Compared to existing technologies, this invention improves the matching degree between planning data and legally mandated projects, clarifies the responsibilities and rights of multi-entity collaboration, enhances the efficiency of results approval, and strengthens the judicial evidentiary value of the entire process operation records. Therefore, it addresses the problems in existing technologies, such as weak binding between planning data and legally mandated projects, unclear responsibilities and rights in multi-entity collaboration, easily tampered operation records, and high barriers to integration between planning results and the legal approval system, leading to insufficient legal validity of planning results, repetitive approval processes, and a lack of evidence for tracing responsibilities. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the architecture of the efficient data processing system for urban and rural planning based on the present invention. Figure 2 This is a flowchart illustrating the anomaly identification and location module of the present invention; Figure 3 This is a flowchart illustrating the efficient data processing method for urban and rural planning based on the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments belong to some, but not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] This system is deployed using a B / S architecture, which stands for Browser / Server architecture. This refers to a software deployment architecture where users can access system functions through a general web browser without installing a dedicated client program on their local terminal. The front end is compatible with mainstream general browsers such as Chrome, IE11 and above, and government-specific browsers, eliminating the need for customized development.

[0022] The system connects to external systems through standardized data interfaces. The interfaces adopt the RESTful specification, and the data exchange formats are XML and SHP vector formats, enabling data transmission and command interaction between systems.

[0023] The system is built on general commercial service components to support core functions, including GIS spatial analysis services, workflow engine services, rule engine services, encryption authentication services, and message push services. GIS refers to Geographic Information System, which is used for the calculation, analysis, rendering and visualization of geospatial data, and uses general GIS engines such as ArcGIS Server and SuperMap iServer. Workflow engines are used for defining business process nodes, controlling status, transferring instructions, and automatically scheduling, and they adopt general workflow engines such as Activiti and Flowable. The rule engine is used for storing, matching, validating, and executing preset technical rules, and general rule engines such as Drools are selected. The encryption authentication service uses cryptographic technology to authenticate data integrity and authenticity, and employs national cryptographic algorithms to verify identity and prevent data tampering. The message push service is used for targeted distribution of messages within the system, supporting push notifications from multiple channels such as in-system messages and SMS.

[0024] The system has a built-in planning technology rule library, project metadata database, spatial base database, process control database, and traceability and evidence storage database; The spatial base database is used to provide unified geospatial base map data, including administrative divisions, topographic images, elevation data, spatial grid data, etc. The planning technical rule library provides the system with computer-executable technical rules for spatial data processing, verification, and judgment. The project metadata database is used to store information such as project identifier, data type, and processing status; The process control database is used to store the execution order, execution status, and flow relationships of nodes; The traceability and evidence storage database uses encrypted storage to store the entire process operation record.

[0025] The system consists of six functional modules: data uploading and node anchoring, progress visualization and control, anomaly identification and location, targeted correction and continuation, collaborative control and output, and data traceability and backtracking. These modules interact through an internal data bus to form a fully automated closed-loop processing system.

[0026] The data upload and node anchoring module is used to bind the uploaded urban and rural planning data with the project's unique identifier, perform pre-disassembly of the entire process nodes and generate unique identifiers, and complete the pre-matching of data and processing flow. The input of this module is the project code and the uploaded planning data, and the output is the project information, node link, process anchoring identifier, and verified data, which is then synchronized to subsequent modules.

[0027] The data upload and node anchoring module is specifically used for: The input project's unique code is matched with the external monitoring system to verify the data's legality, thereby obtaining the corresponding project's basic information, spatial scope, and data type. Based on the matched project type, a standardized processing flow template is automatically matched, and the entire process processing nodes are pre-decomposed in a structured manner to generate a sequential execution node link bound to the project; To process tasks, a globally unique process anchor identifier is generated, and the uploaded data is subjected to format recognition, integrity checks, and node mapping to complete the pre-upload technical verification.

[0028] Specifically, the project's unique code is transmitted to an external monitoring system via an interface for code validity verification; if the verification passes, basic information such as the project's spatial range, data type, and project attributes is obtained; if the verification fails, the task initiation is terminated.

[0029] Furthermore, standardized processing procedures are matched according to project type, with different processing nodes and verification rules for different procedures; the overall processing procedure is broken down into multiple sequentially executed processing nodes to form a node chain, including technical processing nodes such as data initial screening, spatial benchmark unification, topology verification, attribute verification, data fusion, and result packaging. Spatial benchmarks are used to unify geographic coordinates, elevation, and projection parameters, and are the basis for spatial data alignment. Topological relationships refer to the geometric relationships between spatial elements, such as adjacency, inclusion, overlap, and connection. They are the core technical indicators of the rationality of spatial data.

[0030] Furthermore, a globally unique process anchor identifier is generated for each task, consisting of a project code, node number, and timestamp; at the same time, a sub-identifier is assigned to each data file and each spatial object, which is associated and bound to the main identifier; the uploaded data is subjected to format recognition and integrity verification, and if the verification fails, a prompt is displayed and the process is refused to proceed to the next step, while if the verification passes, the process proceeds to the next module.

[0031] The progress visualization and control module is used to build a project-level spatial visualization platform, bind and map processing nodes to spatial elements, and perform real-time updates of processing progress, anomaly warnings, and multi-dimensional filtering and display. The inputs are project information, node links, planning data, and status information of each module, and the outputs are a visualization interface, progress status, and warning information.

[0032] The progress visualization and control module is specifically used for: Based on the project's spatial scope, a project-level spatial visualization platform is built, and processing nodes are bound to spatial elements in a one-to-one correspondence. Real-time capture of the processing status of each node and spatial element, and status updates and message push; It can trigger and lock nodes for progress anomalies such as timeouts and multiple failures, and supports multi-dimensional filtering and display.

[0033] Specifically, basic geographic data is obtained from the spatial base database based on the spatial scope of the project, and a two-dimensional / three-dimensional visualization interface is constructed based on the GIS engine; each processing node is associated with spatial elements, so that each spatial element corresponds to an independent processing state; Different colors are used to distinguish the status in the visualization interface: green for completed, blue for in progress, yellow for abnormal, red for locked, and gray for unprocessed.

[0034] Furthermore, the workflow engine obtains the execution status of nodes in real time, automatically refreshes the interface and pushes messages when the status changes; timeout thresholds are set according to processing time limits, and warnings are triggered and subsequent unrelated nodes are locked when processing times out or consecutive verifications fail, to avoid process chaos; and filtering and viewing are supported by spatial range, processing status and data type.

[0035] The anomaly identification and localization module is used to identify multi-dimensional anomalies at all nodes in the entire process based on a preset technical rule base, and to perform spatial localization, hierarchical judgment and information push of anomalies. The inputs are node data, spatial elements and technical rule base, and the outputs are anomaly information, localization data and hierarchical results.

[0036] The anomaly identification and localization module is specifically used for: At each processing node in the node link, anomalies are synchronously identified based on a computer-executable rule base. The abnormal information is bound to spatial elements and attribute fields, and spatial positioning and highlighting are performed on the visualization interface; The system automatically classifies and pushes alerts based on the degree to which anomalies affect data validity and process stability.

[0037] Specifically, the system has a built-in spatial data processing technology rule library, including coordinate reference rules, topology rules, data format rules, multi-source data consistency rules, and process logic rules; and automatically calls the corresponding rules at each node to perform anomaly detection.

[0038] The anomaly types include: spatial reference anomalies, topological relationship anomalies, data consistency anomalies, format structure anomalies, and process logic anomalies, all of which are technical anomalies that are automatically identified by the computer.

[0039] Furthermore, the anomalies are associated with corresponding spatial elements and attribute fields, and their location is displayed in the visualization interface. The anomaly level is automatically classified into four levels: minor, moderate, moderate, and severe, based on the scope of impact, degree of obstruction, and cost of repair. The anomaly information is then pushed to the corresponding user terminals.

[0040] The targeted correction and continuation module is used to control operation permissions and push correction guidance according to the anomaly level, receive correction content for targeted re-examination, and complete the breakpoint continuation; the input is anomaly information, level, and spatial data, and the output is correction data, re-examination results, and continuation instructions.

[0041] The targeted correction and continuation module is specifically used for: The system pre-defines the correspondence between anomaly levels and operation permissions, and pushes standardized technical correction guidelines based on the anomaly type. Upon receiving the corrections, only targeted re-examinations are performed on fields and rules related to the anomalies; no full-scale, end-to-end verification is conducted. Once the re-inspection is passed, the process resumes from the breakpoint node, and completed and irrelevant nodes are not executed again.

[0042] Specifically, different operation permissions are set according to the anomaly level, and access control is implemented through user roles and tokens; executable technical correction steps are provided for each type of anomaly; and automatic correction functions are provided for minor anomalies, including format standardization, coordinate system conversion, and field completion.

[0043] After a user submits corrected data, the system only verifies the content related to the current anomaly, and does not repeatedly verify data and nodes that have already passed and are unrelated. After the re-verification is passed, the workflow engine continues execution from the interrupted position, achieving precise continuation of process breakpoints, reducing redundant calculations, and improving processing efficiency.

[0044] The collaborative management and output module is used to isolate access permissions for multiple users and conduct online collaborative operations, generate standardized output packages and interface with external systems, incrementally decompose tasks based on feedback and re-integrate them into the process; the inputs are processed data, project information, and feedback, and the outputs are output packages, interface data, and decomposed tasks.

[0045] The collaborative management and results output module is specifically used for: Set hierarchical access and operation permissions for different users, and support multiple users to view, annotate and collaborate online at the same time; Once the entire process is completed, a standardized deliverable package is automatically generated and pushed to an external approval system via an interface. Feedback is received and broken down into processing tasks for corresponding nodes. Only the relevant nodes are re-executed, without repeating the entire process.

[0046] Specifically, viewing, editing, reviewing, and management permissions are assigned according to user roles to achieve data security and operational isolation; multiple users can access the visual interface simultaneously to perform spatial annotation, upload opinions, and conduct online consultations; the system supports automatic distribution and circulation of opinions.

[0047] After all nodes have been processed, the system automatically encapsulates a standardized deliverable package containing spatial data, processing records, and status information, and pushes it to an external approval system via an interface. When feedback is received, the system locates the corresponding node and spatial element based on the feedback content, generates an incremental processing task, and re-integrates it into the process without the need for full retransmission and reprocessing.

[0048] The data traceability and backtracking module is used to receive full-process operation data to generate traceability records. It uses dual-track encryption to ensure immutability and supports historical status backtracking and export. The input is the operation logs of each module, and the output is traceability records, hash evidence, backtracking data, and exported files.

[0049] The data tracing and backtracking module is specifically used for: Collect data from the entire process, generate structured traceability records, and bind unique traceability identifiers; A dual-track notarization model, employing both on-chain hash notarization and off-chain encrypted storage, ensures that records are tamper-proof. Historical data status can be restored based on the source identifier, and multi-version comparison and record export are supported.

[0050] Specifically, the system collects all operational behaviors in real time, including data upload, node execution, anomaly identification, correction operations, and result output, and generates traceability records with timestamps, user identifiers, and operation content, which are then associated with process anchoring identifiers.

[0051] The technical implementation of dual-track evidence storage is as follows: Off-chain, the original source text will be stored in an authorized database using national cryptographic algorithms; On-chain, a cryptographic hash digest of the encrypted traceability record is calculated and written to the consortium blockchain storage. Each completed node operation generates a hash and uploads it to the blockchain, leveraging the immutability of the blockchain to ensure the authenticity of the digest. During verification, the original text can be verified by comparing the hash value, thus achieving full traceability and verifiability.

[0052] Furthermore, users can retrieve the entire process record by entering the traceability identifier, restore the data state at any point in time, and realize the comparison of multiple versions of spatial data; it also supports the decryption, formatted export, and archiving of traceability records.

[0053] The specific steps of the efficient data processing method for urban and rural planning applications corresponding to the above system are as follows: S1. Strongly bind the uploaded urban and rural planning data with the statutory planning projects, pre-decompose and generate unique identifiers for all processing nodes, and complete the pre-matching of data and processing flow; S2. Build a project-level spatial visualization platform, bind and map processing nodes to spatial elements, and perform real-time updates of processing progress, anomaly warnings, and multi-dimensional filtering and display. S3. Based on a pre-set legal rule base, perform multi-dimensional anomaly identification at all nodes in the process, and perform spatial location, hierarchical judgment and information push of anomalies; S4. Based on the anomaly level, implement correction permission control and push correction guidance, receive the submitted correction content for targeted re-examination, and complete the process breakpoint reconnection after the re-examination is passed. S5. Implement hierarchical access control and online collaborative review for all participating entities, generate standardized deliverable packages and connect them with the approval system, and reconnect the processing flow after receiving feedback. S6. Receive full-process operation data, generate traceability records and preserve them in an immutable manner, and perform full-process data backtracking operations and export traceability records.

[0054] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A high-efficiency data processing system for urban and rural planning, characterized in that: The system includes a data upload and node anchoring module, a progress visualization and control module, an anomaly identification and location module, a targeted correction and continuation module, a collaborative control and results output module, and a data traceability and backtracking module, wherein: The data upload and node anchoring module is used to strongly bind the uploaded urban and rural planning data with the statutory planning projects, perform pre-disassembly and unique identifier generation of the entire process nodes, and complete the pre-matching of data and processing flow. The progress visualization and control module is used to build a project-level spatial visualization platform, bind and map processing nodes to spatial elements, and perform real-time updates of processing progress, anomaly warnings, and multi-dimensional filtering and display. The anomaly identification and localization module is used to identify multi-dimensional anomalies across all nodes of the process based on a preset legal rule base, and to perform spatial localization, hierarchical judgment and information push of anomalies. The targeted correction and continuation module is used to control correction permissions and push correction guidance according to the anomaly level, receive the submitted correction content for targeted re-examination, and complete the process breakpoint continuation after the re-examination is passed. The collaborative management and output module is used for hierarchical access control and online collaborative review of all participating entities, generating standardized output packages and connecting with the approval system, and reconnecting the processing flow after receiving feedback. The data traceability and backtracking module is used to receive full-process operation data, automatically generate traceability records and preserve them in an tamper-proof manner, and perform full-process data backtracking operations and export traceability records.

2. The efficient data processing system for urban and rural planning as described in claim 1, characterized in that, The data upload and node anchoring module is specifically used for: The input legal project code is verified and matched with the National Territorial Spatial Planning Use Control and Supervision System to obtain the basic information and planning type of the corresponding project; Based on the matching planning type, a standardized processing flow is matched, and the nodes of the entire process are pre-decomposed to generate node links bound to the project. To process tasks, process anchoring identifiers are generated, and the uploaded urban and rural planning data is identified by type and matched with node mappings to complete the pre-verification of data upload.

3. The efficient data processing system for urban and rural planning as described in claim 1, characterized in that, The progress visualization and control module is specifically used for: In accordance with the legal scope of the project, a project-level spatial visualization carrier is constructed, and processing nodes are bound one by one to spatial elements; Real-time capture and update of the processing status of each node and spatial element, and targeted push of progress update messages; Trigger and lock nodes for progress anomalies, and perform multi-dimensional filtering and display of processing progress.

4. The efficient data processing system for urban and rural planning as described in claim 1, characterized in that, The anomaly identification and localization module is specifically used for: At each processing node in the node chain, synchronous identification of the corresponding type of anomaly is performed based on the legal rule base; The identified anomaly information is bound to the corresponding spatial elements and attribute fields to perform spatial location and visual annotation of the anomaly; Anomalies are classified and information is pushed out based on their impact on the effectiveness of project approval and legal compliance.

5. The efficient data processing system for urban and rural planning as described in claim 1, characterized in that, The targeted correction and continuation module is specifically used for: Preset correction operation permissions corresponding to different anomaly levels, and push corresponding standardized correction guidelines according to the anomaly level and type; The system receives submitted corrections and performs targeted re-checks only on the corrected content and the corresponding exception type's verification rules. After the re-inspection is passed, the subsequent process will be connected from the interrupted processing node, without re-executing the node that has already completed the verification.

6. The efficient data processing system for urban and rural planning as described in claim 1, characterized in that, The collaborative management and output module is specifically used for: The system establishes tiered collaborative authority settings for all stakeholders involved in urban and rural planning projects, and supports online collaborative review by multiple stakeholders. After all nodes in the process are completed, a standardized deliverable package is generated, and the deliverable package is then integrated with and pushed to the natural resources government approval system. Receive feedback from authorized entities, break down the corresponding processing tasks, and reconnect the processing flow.

7. The efficient data processing system for urban and rural planning as described in claim 1, characterized in that, The data tracing and backtracking module is specifically used for: Receive operation data synchronized from other functional modules, generate full-process traceability records and bind unique traceability identifiers; A dual-track evidence storage model is adopted for tamper-proof evidence storage of traceability records; The system receives the input traceability identifier, performs full-process processing to retrieve records, restore data status, and perform visualization comparison; it also receives submitted export commands to export standardized traceability records.

8. The efficient data processing system for urban and rural planning according to claim 4, characterized in that, The anomaly identification and location module identifies anomaly types including spatial benchmark anomalies, topological relationship anomalies, legal compliance anomalies, multi-source data consistency anomalies, and business process compliance anomalies.

9. The efficient data processing system for urban and rural planning according to claim 7, characterized in that, The dual-track evidence storage mode is an evidence storage method that stores hash digests on-chain and encrypted original text off-chain. The on-chain is the government consortium chain, and the off-chain is the private encrypted database of authorized nodes.

10. A method for efficient processing of uploaded data applied to urban and rural planning, characterized in that, The efficient data processing system for urban and rural planning, applicable to any one of claims 1-9, specifically includes the following processing method: S1. Strongly bind the uploaded urban and rural planning data with the statutory planning projects, pre-decompose and generate unique identifiers for all processing nodes, and complete the pre-matching of data and processing flow; S2. Build a project-level spatial visualization platform, bind and map processing nodes to spatial elements, and perform real-time updates of processing progress, anomaly warnings, and multi-dimensional filtering and display. S3. Based on a pre-set legal rule base, perform multi-dimensional anomaly identification at all nodes in the process, and perform spatial location, hierarchical judgment and information push of anomalies; S4. Based on the anomaly level, implement correction permission control and push correction guidance, receive the submitted correction content for targeted re-examination, and complete the process breakpoint reconnection after the re-examination is passed. S5. Implement hierarchical access control and online collaborative review for all participating entities, generate standardized deliverable packages and connect them with the approval system, and reconnect the processing flow after receiving feedback. S6. Receive full-process operation data, automatically generate traceability records and preserve them tamper-proofly, and perform full-process data backtracking operations and export traceability records.

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