Coordinated multi-plan fusion village and town territorial space planning system

Abstract

The invention relates to the technical field of territorial space planning, and discloses a coordinated multi-plan fusion village and town territorial space planning system. Comprising a multi-source data standardization processing module, a multi-rule collaborative analysis module, a dynamic boundary management and control module, a digital twin simulation module, an intelligent decision support module, a block chain evidence storage module, a public participation interaction module, a planning implementation monitoring module, a system management module and a data security protection module, transmitting data in a JSON format to form a whole-process closed-loop management and control system; coordinate unification, field mapping and conflict automatic solution of multi-specification data are realized through the standardized processing module, the data fusion efficiency is greatly improved, manual processing errors are effectively reduced, the data sleeving precision is guaranteed, the interface response time is shortened, a solid and reliable data base is laid for multi-specification cooperative work, and the data fusion efficiency is improved. And the integrity and consistency of multi-source data integration are remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of land and space planning technology, specifically a coordinated multi-plan integration township land and space planning system. Background Technology

[0002] Territorial spatial planning serves as a guide for national spatial development, and "multi-plan integration" is the core requirement of current territorial spatial governance. It aims to integrate various spatial plans, such as functional zoning plans, land use plans, and urban and rural plans, to achieve comprehensive and all-encompassing management and control. Townships, as the smallest unit for implementing territorial spatial planning, have planning quality that directly impacts the effectiveness of the rural revitalization strategy and the sustainable development of the ecological environment.

[0003] The existing township-level territorial spatial planning system has many technical defects: First, there are significant data barriers. Different planning data use different coordinate systems, coding systems, and storage formats, making it difficult to accurately match urban and rural planning data with land use planning data. Manual processing is inefficient and prone to errors. Secondly, the coordination among multiple plans is insufficient, and there is a lack of systematic evaluation models to support it, making it difficult to effectively resolve spatial conflicts between ecological protection, agricultural production, and construction development. Third, the control mechanism is rigid, mostly adopting static boundary control, which cannot adapt to the dynamic needs brought about by population flow and industrial upgrading, making it difficult to balance rigid protection and flexible development. Fourth, decision-making support and public participation are weak. Planning relies heavily on experience and judgment, lacks digital simulation and quantitative analysis tools, and has limited channels for villager participation, with feedback on opinions not being adopted in a timely manner. Fifth, the planning implementation monitoring and traceability capabilities are insufficient, the adjustment process lacks full-process recording, and the data security and credibility are difficult to guarantee.

[0004] To address the aforementioned issues, there is an urgent need to construct a coordinated multi-plan integration system that combines data fusion, collaborative analysis, dynamic management and control, and public participation capabilities. This system will overcome existing technological bottlenecks and enhance the scientific rigor and operability of township-level territorial spatial planning. Summary of the Invention

[0005] In view of the above situation and to overcome the shortcomings of the prior art, the present invention provides a coordinated multi-plan integration township land spatial planning system, which effectively solves the problems raised in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a coordinated multi-plan integration township territorial spatial planning system, comprising a multi-source data standardization processing module, a multi-plan collaborative analysis module, a dynamic boundary control module, a digital twin simulation module, an intelligent decision support module, a blockchain evidence storage module, a public participation and interaction module, a planning implementation monitoring module, a system management module, and a data security protection module. Each module communicates bidirectionally through a RESTful interface and transmits data in JSON format, forming a closed-loop control system for the entire process.

[0007] Preferably, the multi-source data standardization processing module is used to integrate various types of township land space planning data, and performs the following sub-steps: S2.1 Data Acquisition: Synchronously acquire land survey data, urban and rural planning data, ecological protection data, agricultural production data, infrastructure data, and socio-economic statistics. The acquisition accuracy reaches sub-meter level, and the data formats cover vector, raster, and text types. The government data platform is accessed through HTTP / HTTPS interfaces, and offline import formats support SHP, GDB, Excel, and PDF. S2.2 Coordinate unification: Forcefully transform all spatial data to the CGCS2000 National Geodetic Coordinate System, store it in the GDB geographic database format, and use the Gauss-Kruger projection (3° zone) to eliminate spatial benchmark differences between different planning data; S2.3 Intelligent Cleaning: Automatic field mapping is achieved through a preset land category code comparison table. The topology processor is used to repair topology errors with surface overlap error ≤0.1㎡ and line breakage distance <0.5m. The conflict coordination rule engine is embedded to retain rigid attribute data according to the priority of "ecological protection red line > permanent basic farmland > cultivated land > construction land > other land". The conflict resolution threshold is set to 0.3㎡.

[0008] Preferably, the multi-plan collaborative analysis module constructs a three-dimensional evaluation model of "ecology-agriculture-construction", specifically including: S3.1 Ecological Sensitivity Assessment: Eight indicators were selected: slope, vegetation cover (NDVI index), water system buffer zone, soil texture, geological disaster risk level, biodiversity distribution, air quality, and water resource carrying capacity. The weights were determined using the Analytic Hierarchy Process (AHP), with the weights allocated as follows: slope 0.3, vegetation cover 0.25, water system buffer zone 0.2, and the remaining five indicators 0.05 each. The indicators were normalized from 0 to 1 and weighted overlay was performed using the ArcGIS raster calculator to generate an ecological sensitivity classification map. S3.2 Agricultural suitability assessment: Focusing on core factors such as arable land quality grade, irrigation guarantee rate, and soil organic matter content, with weights of 0.4, 0.35, and 0.25 respectively, objective weights are calculated using the entropy weight method, with an entropy threshold of 0.8. If the value is lower than the threshold, the factor system is recalibrated. S3.3 Construction Carrying Capacity Analysis: Integrating geological hazard risk, high / medium / low levels correspond to scores of 3 / 2 / 1; road 500m coverage rate; power supply radius, ≤1km is qualified, score 1; >1km is unqualified, score 0.5; accessibility of public service facilities and water resource supply capacity parameters are comprehensively scored using the TOPSIS superior-inferior solution distance method to generate a suitability classification map with a resolution of 20m×20m. A score ≥0.6 is a high suitability area, 0.3-0.6 is a medium suitability area, and <0.3 is a low suitability area.

[0009] Preferably, the dynamic boundary control module sets up a three-level partitioning system: "rigid control zone - flexible development zone - strategic reserve zone," and executes the following control rules: S4.1 The rigid control zone shall occupy no less than 25% of the township's land area, including ecological protection red lines and permanent basic farmland, and shall be subject to negative list management, prohibiting any development and construction activities; S4.2 The flexible development zone adopts a combination of positive and negative lists, allowing for the linkage of construction land increases and decreases based on population flow and industrial needs, with the scale of the linked turnover quota not exceeding 5% of the total cultivated land area of ​​the township; S4.3. 5%-10% of the land space in the strategic reserve area shall be reserved as reserve land. A dynamic adjustment mechanism with a cycle of 5 years shall be established. The adjustment shall meet the requirements of ecological impact assessment, with an impact value of ≤0.2, and the dual requirements of farmland occupation and compensation balance assessment (the quality of the compensated farmland shall not be lower than that of the occupied farmland). The adjustment process shall implement a four-level linkage mechanism of "township application - county-level preliminary review (5 working days) - city-level review (3 working days) - provincial-level filing (10 working days)".

[0010] Preferably, the digital twin simulation module integrates BIM and GIS technologies, and uses a drone equipped with a five-lens camera to conduct oblique photography to acquire a real-scene 3D model with a resolution of ≤5cm. It integrates DEM digital elevation data with an accuracy of ±0.1m; BIM building information and underground pipeline network data to construct a full-element digital twin of "surface-above-underground". Based on the Unity3D engine (version 2021.3.0f1), it realizes immersive simulation comparison of planning schemes with a simulation frame rate of not less than 30FPS.

[0011] Preferably, the intelligent decision support module embeds a GM(1,1) grey prediction model and a Markov chain, using a weighted integration method. The GM(1,1) weight is 0.6, and the Markov chain weight is 0.4. It inputs population and homestead data from the past 5 years to predict land demand for the next 5 years, with a prediction accuracy R0.2 >0.95; Combining the "indicator pool-project library" linkage mechanism, the allocation plan is automatically generated by using SQL statements to fuzzy match the increase / decrease linkage surplus indicators with the planning project requirements, and the matching degree is ≥80%.

[0012] Preferably, the blockchain evidence storage module adopts a consortium blockchain architecture, with nodes including four types: township governments, county-level natural resources departments, municipal-level natural resources departments, and third-party auditing institutions. It adopts the PBFT consensus mechanism with a fault tolerance rate of 33%. The entire process of planning, boundary adjustment records, and indicator allocation information is stored on the blockchain. Smart contracts are used to execute the distribution of land value-added revenue, clearly defining the allocation ratio of 60% retained by the collective economic organization and 40% coordinated by the government. The block generation interval is 10 minutes to ensure that the data is tamper-proof and traceable.

[0013] Preferably, the public participation interaction module is developed into a mobile APP based on Android 10.0 and iOS 14.0 or above. It supports villagers to provide feedback on the planning by swiping to rate it from 1 to 5 stars. The system automatically collects the opinions and generates a heat analysis chart. The adoption rate and reasons for the opinions must be made public within 3 working days. The public disclosure channels include the APP homepage, the township government affairs bulletin board and the village committee bulletin board.

[0014] Preferably, the planning implementation monitoring module uses a combination of IoT sensors (deployment density of 2 per square kilometer), satellite remote sensing (resolution 2m, once a month), and drone patrols (once a quarter) to collect real-time data on land use, changes in ecological indicators, and infrastructure operation. It generates an implementation evaluation report every quarter and automatically triggers an early warning when the indicator deviation exceeds 10%, pushing the warning information to the mobile terminal of the person in charge of the corresponding responsible unit.

[0015] Preferably, the data security protection module adopts a hierarchical encryption mechanism, using AES-256 encryption for core spatial data and DES encryption for ordinary query data. The transmission process uses SSL / TLS 1.3 protocol encryption to prevent data leakage. It adopts a role-based access control (RBAC) mechanism, refining operation permissions to the data field level, and establishes a real-time data backup mechanism with a backup frequency of no less than once a day, and adopts off-site disaster recovery backup (the distance between the primary and backup nodes is ≥50km).

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention achieves coordinate unification, field mapping, and automated conflict resolution of multi-pattern data through a standardized processing module, which greatly improves data fusion efficiency, effectively reduces human processing errors, ensures data overlay accuracy, shortens interface response time, lays a solid and reliable data foundation for multi-pattern collaborative work, and significantly improves the integrity and consistency of multi-source data integration. 2. This invention constructs a three-dimensional evaluation model of "ecology-agriculture-construction", integrates multiple quantitative analysis algorithms, realizes the scientific and accurate delineation of spatial functional zoning, efficiently resolves spatial conflicts between ecological protection, agricultural production and construction development, takes into account the needs of multiple parties, improves the overall coordination and rationality of spatial planning, and effectively enhances collaborative capabilities. 3. This invention innovates a three-level zoning and dynamic adjustment mechanism. Under the premise of adhering to the rigid bottom line of ecological protection red line and permanent basic farmland, it flexibly adapts to the dynamic development needs brought about by rural population flow and industrial upgrading. Through standardized adjustment process and clear time limit requirements, it enhances the flexibility and adaptability of planning, ensures the compliance and standardization of boundary adjustment, and achieves dynamic control. 4. This invention relies on digital twin simulation and intelligent prediction models to achieve immersive comparison and quantitative analysis of planning schemes, reduce the bias caused by experience-based decision-making, improve the efficiency and scientific nature of planning, provide strong data support and technical guarantee for the optimization of planning schemes, and strengthen decision support. 5. This invention achieves full traceability and transparency in the planning process through blockchain notarization and public participation modules, ensuring data immutability, broadening public participation channels, enhancing public participation enthusiasm and planning acceptance, facilitating the smooth implementation of planning schemes, and safeguarding credibility. 6. This invention features a closed-loop management system covering the entire process, forming a closed-loop system of "data fusion - collaborative analysis - simulation optimization - implementation monitoring - dynamic updates". It realizes integrated management of planning, implementation and supervision, is suitable for various township land and space planning scenarios, and has broad application prospects and promotion value. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0018] In the attached diagram: Figure 1 This is a block diagram of the overall system architecture of the present invention; Figure 2 This is a flowchart of the multi-source data standardization processing of the present invention; Figure 3 This is a schematic diagram of the dynamic boundary control process of the present invention; Figure 4 This is a schematic diagram illustrating the linkage between digital twin simulation and intelligent decision-making in this invention. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0020] Depend on Figures 1-4 This invention relates to a coordinated, multi-plan integrated township territorial spatial planning system, comprising: I. Multi-source data standardization processing module This module is the core of the system's data foundation. Developed based on the FME 2023 platform, it is responsible for integrating various types of planning data across the entire township, eliminating data discrepancies and conflicts, and specifically executing the following sub-steps: 1. Data Acquisition: The system calls the county-level government data sharing platform via HTTP / HTTPS interface, supporting offline import formats such as SHP, GDB, Excel, and PDF. It simultaneously acquires land parcels, ownership boundaries, urban and rural planning schemes, ecological protection red line vector data, permanent basic farmland distribution maps, agricultural output statistics, infrastructure vector data such as roads and pipelines, population thermal distribution, and socio-economic data such as GDP statistics from the third national land survey. The system ensures that the acquisition accuracy reaches sub-meter level, covers all data formats including vector, raster, and text, and has an interface response time of ≤3 seconds. 2. Coordinate unification: The FME data conversion tool is used to force all spatial data to be converted to the CGCS2000 national geodetic coordinate system. The projection method is Gauss-Kruger projection (3° zone). The data is stored in the GDB geographic database format. The spatial index adopts a quadtree index, which completely solves the overlay deviation problem caused by the inconsistency of spatial benchmarks of different planning data in the past. The coordinate transformation error is ≤0.01m. 3. Intelligent Cleaning: Built-in FME-based intelligent cleaning toolchain, pre-constructed cross-planning land use code comparison table of "urban-rural-land-ecology" (covering 12 major categories and 56 subcategories of land use), to achieve automatic mapping and matching of different planning fields; the topology processor performs topology checks on the data, automatically repairing topology errors with surface overlap error ≤0.1㎡ and line breakage distance <0.5m, with a repair success rate ≥98%; embedded conflict coordination rule engine, retaining rigid attribute data according to the priority of "ecological protection red line > permanent basic farmland > cultivated land > construction land > other land use", with the conflict resolution threshold set at 0.3㎡, triggering manual review if the threshold is exceeded.

[0021] II. Multi-Rule Collaborative Analysis Module Based on a standardized data foundation, a three-dimensional collaborative evaluation model of "ecology-agriculture-construction" is constructed and integrated into the ArcGIS Pro 3.0 platform to provide quantitative support for spatial functional zoning. 1. Ecological sensitivity assessment: Select eight indicators including slope, vegetation coverage (NDVI index), water system buffer zone, soil texture, geological disaster risk level, biodiversity distribution, atmospheric environmental quality, and water resource carrying capacity. Use the AHP (Analytic Hierarchy Process) method to determine the weights, with the weight distribution as follows: slope 0.3, vegetation coverage 0.25, water system buffer zone 0.2, and the remaining five indicators each with 0.05. Construct a judgment matrix through the expert scoring method, and a consistency ratio CR < 0.1 is considered qualified. Implement 0-1 normalization processing and weighted overlay of the indicators through the ArcGIS raster calculator to generate an ecological sensitivity classification map, which is divided into four categories: extremely high sensitivity area, high sensitivity area, medium sensitivity area, and low sensitivity area. 2. Agricultural suitability assessment: Focus on three core factors including cultivated land quality grade (national unified classification and grading standard, grades 1-15), irrigation guarantee rate (≥85% is excellent, 70%-85% is good, <70% is poor), and soil organic matter content (≥20 g / kg is high, 10-20 g / kg is medium, <10 g / kg is low). The weights are 0.4, 0.35, and 0.25 respectively. Use the entropy weight method to calculate the objective weights, and set the entropy threshold to 0.8. If it is lower than the threshold, recalibrate the factor system to ensure the rationality of weight distribution. 3. Construction carrying capacity analysis: Integrate parameters such as geological disaster hazard (corresponding scores of 3 / 2 / 1 for high / medium / low levels), road coverage within 500m (coverage rate of 100% is 1 point, and the score decreases by 0.1 for every 10% reduction), power supply radius (≤1km is qualified, score 1; >1km is unqualified, score 0.5), accessibility of public service facilities (reachable within 15 minutes on foot is 1 point, and the score decreases by 0.2 for every additional 5 minutes), and water resource supply capacity (per capita water resource volume ≥1000m 3 is 1 point, <1000m 3 is 0.6 points). Through the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method for comprehensive scoring, generate a suitability classification map of the "production-living-ecological space" with a resolution of 20m × 20m. A score ≥ 0.6 is a high suitability area, 0.3-0.6 is a medium suitability area, and <0.3 is a low suitability area, and clarify the functional positioning of each region.

[0022] III. Dynamic boundary control module Construct a three-level zoning control framework, and based on GIS spatial analysis technology, achieve zoning delineation and dynamic adjustment, realizing the organic unity of rigid protection and flexible development: 1. Zoning: Rigid control zones include ecological protection red lines and permanent basic farmland, accounting for no less than 25% of the township's land area. Negative list management is implemented, explicitly prohibiting all activities that conflict with protection functions, such as industrial development and real estate construction. Flexible development zones focus on village construction land, township industrial parks, etc., adopting a combination of positive and negative lists. Construction land increase / decrease linkage is permitted under the premise of complying with planning, with the linkage turnover index not exceeding 5% of the township's total cultivated land area and a turnover period not exceeding 3 years. Strategic reserve zones reserve 5%-10% of land space, with no specific purpose yet, as reserves for future major projects and emergency land use. The reserve period is consistent with the planning cycle. 2. Dynamic Adjustment Mechanism: A boundary adjustment assessment mechanism with a 5-year cycle will be established. The conditions that trigger adjustment include: population size exceeding the village planning limit (registered population growth exceeding 20%), industrial upgrading (traditional agriculture shifting to cultural tourism integration, rural industry, etc., with industrial land demand changing by more than 30%), and enhanced ecological function importance (discovery of new species habitats, ecological corridor optimization needs, as certified by forestry departments at the county level or above). The adjustment process follows a four-level linkage mechanism of "township application - county-level preliminary review (5 working days) - city-level review (3 working days) - provincial-level filing (10 working days)". Before adjustment, an ecological impact assessment (using the ecosystem service value assessment method, with an impact value ≤ 0.2 as qualified) and a farmland occupation and compensation balance assessment (the quality of the compensated farmland is not lower than that of the occupied farmland, and the area is not less than the occupied area) must be completed to ensure that the rigid bottom line is not breached. 3. Indicator Control: Construct a three-tiered "indicator pool" at the county, township, and village levels. Employ real-time database synchronization technology to collect surplus indicators from land consolidation, industrial and mining wasteland reclamation, and farmland replenishment in real time. Simultaneously establish a planning project database, inputting information such as the location, scale, purpose, and indicator requirements of the proposed projects. Use SQL statements to fuzzily match indicators with projects. If the matching degree is ≥80%, an allocation plan is automatically generated; if the matching degree is <80%, manual intervention is prompted.

[0023] IV. Digital Twin Simulation Module Integrating BIM and GIS technologies, and developed based on the Unity3D engine (version 2021.3.0f1), a full-element digital twin is constructed to achieve immersive simulation and optimization of planning schemes. 1. Digital Twin Construction: Using a drone equipped with a five-lens camera, oblique photography of the entire township is conducted at a flight altitude of 150m to acquire a real-world 3D model with a resolution of ≤5cm, accurately reproducing the texture of building facades, roof structures, and details of terrain undulations; integrating DEM digital elevation data (accuracy ±0.1m), BIM building information model (including structural layers, functions, and building material parameters, in IFC4.0 format), underground pipe network (water supply / drainage / electricity / gas, coordinate accuracy ±0.05m), road network (hierarchical coding, national highways / provincial highways / county roads / township roads / village roads corresponding to codes G / S / X / Y / C), public service facilities (coordinates and scale of schools, health clinics, cultural stations), and other thematic data, a full-element digital twin covering "surface topography - above-ground buildings - underground pipelines" is constructed, supporting hierarchical visualization from the township as a whole to individual buildings, with a hierarchical zoom response time of ≤1 second; 2. Simulation Analysis: Develop a dynamic simulation platform that supports importing CAD (DWG format) and BIM (IFC format) planning schemes. The system automatically parses drawings to generate 3D scenes and simulates factors such as population density (red for high density / blue for low density, with a density threshold of 50 people / hectare), traffic flow (green for smooth traffic / yellow for slow traffic / red for congestion, corresponding to vehicle speeds ≥60km / h, 30-60km / h, and <30km / h), and ecological environment changes (dynamic evolution of vegetation cover, with an annual change rate ≤5%) through a particle system. This enables immersive comparative analysis of multiple schemes. Simultaneously, it can simulate the impact of extreme weather (heavy rain, typhoons) and geological disasters (landslides, debris flows) on planning schemes. Based on finite element analysis algorithms, it optimizes building layout, road alignment, and disaster prevention facility locations. The simulation frame rate is no less than 30FPS to ensure smooth operation.

[0024] V. Intelligent Decision Support Module Embedded quantitative prediction models and intelligent matching algorithms, developed based on Python 3.9 and integrated with the TensorFlow framework, provide data support for planning decisions: 1. Demand Forecasting: Integrating the GM(1,1) grey prediction model with a Markov chain, using a weighted integration method (GM(1,1) weight 0.6, Markov chain weight 0.4), the input includes data on registered population, resident population, homestead area, cultivated land area, and industrial land demand over the past 5 years. The GM(1,1) model is initialized with parameters α = -0.1 to 0, and the Markov chain state transition matrix has a dimension of 3×3 (corresponding to three states: growth / stable / decreasing). The forecast predicts the population change trend, total homestead demand, and industrial land scale for the next 5 years, with a prediction accuracy R0. 2>0.95; Combined with the per capita homestead standard (0.3 mu / person in mountainous areas and 0.25 mu / person in plains), the demand for homesteads is dynamically calculated. When the demand causes the reduction of cultivated land to exceed the annual replenishment plan, the indicator tightening mechanism is automatically triggered to ensure that the permanent basic farmland is not reduced. 2. Intelligent Matching: Through the linkage mechanism of "indicator pool - project library", the system automatically retrieves the indicator requirements of newly added construction projects in the project library, matches available indicators from the indicator pool, and generates an indicator allocation plan; at the same time, combined with the results of multi-plan collaborative analysis, the system automatically scores the suitability of project site selection. Projects with a score below 60 are automatically prompted to optimize the site selection. The scoring items include ecological suitability (30 points), agricultural impact (20 points), construction carrying capacity (30 points), and transportation convenience (20 points). 3. Scheme optimization: Based on simulation analysis results and demand forecast data, 2-3 optimized schemes are automatically generated. The analytic hierarchy process (AHP) is used to label the ecological benefits (40 points), economic benefits (30 points), and social benefits (30 points) of each scheme, providing a quantitative reference for decision-making. The scheme with the highest score is recommended.

[0025] VI. Blockchain Evidence Storage Module Employing a consortium blockchain architecture and developed based on Hyperledger Fabric 2.4, the entire planning process is ensured to be traceable and tamper-proof. 1. Data On-Chain: All elements of data during the planning process, including basic data, evaluation model parameters, zoning results, boundary adjustment records, indicator allocation information, public opinions and adoption status, are stored on the blockchain. Each block contains information such as timestamp, operating entity, and data digest (using SHA-256 hash algorithm). The block size is ≤100MB, enabling full-process traceability. On-chain data is classified and processed into "core data (real-time on-chain) and ordinary data (batch on-chain, one batch per hour)" to ensure a balance between efficiency and security. 2. Smart Contract: An embedded smart contract for the distribution of land value-added revenue is written in Solidity. When the land use conversion in the flexible development zone generates value-added revenue, it is automatically distributed according to the ratio of 60% retained by the collective economic organization and 40% by the government, reducing human intervention. At the same time, the smart contract manages the approval process. Adjustment applications that have not completed the pre-assessment are automatically rejected, and automatic reminders are given if the approval node is not processed within the time limit (the time limit is 24 hours). 3. Public credibility guarantee: The blockchain query interface (using HTTPS protocol) is made public, and the public can query the core planning data and adjustment records with their real names using their ID card numbers. The query scope is limited to public data (excluding classified data), which improves the transparency and credibility of the planning. The nodes include four types of nodes: township governments, county-level natural resources departments, municipal-level natural resources departments, and third-party auditing institutions. The PBFT consensus mechanism is adopted (fault tolerance rate of 33%), and the block generation interval is 10 minutes.

[0026] VII. Public Participation and Interaction Module Establish diversified channels for public participation, develop a mobile app based on Android 10.0 and iOS 14.0 and above, and link it with offline publicity channels to enhance the scientific nature and acceptance of the plan: 1. Mobile App: Develop a "Township Planning Crowdsourcing" mobile app that allows villagers to upload text opinions (≤500 words) and image suggestions (JPG / PNG format, ≤5MB in size). Villagers can evaluate planning schemes, locations of public facilities, and ecological protection measures through a sliding rating system (1-5 stars). The system automatically collects opinions and generates a heat map (heat value is graded according to the number of opinions: ≥50 opinions are high-heat areas, 20-50 opinions are medium-heat areas, and <20 opinions are low-heat areas), marking high-attention areas and core opinions. 2. Handling of Opinions: The planning team must review public opinions within 3 working days, clarify whether to adopt them and the reasons. Adopted opinions must be incorporated into the planning optimization scheme, and the reasons for not adopting opinions must be explained in detail and made public. The results of the opinion handling will be pushed to the submitter through the APP to ensure a closed feedback loop. 3. Offline Collaboration: Supports collaboration with township government service centers and village committee bulletin boards to simultaneously release draft plans, optimization schemes, and feedback adoption status. The public notice period is no less than 7 working days, making it convenient for elderly people and other villagers unfamiliar with smart devices to participate. Offline feedback is entered into the system by staff for unified processing.

[0027] VIII. Planning Implementation Monitoring Module Construct an implementation and supervision system that integrates "real-time monitoring, dynamic assessment, and early warning and response," combining IoT, satellite remote sensing, and drone patrol technologies to achieve full-process supervision. 1. Real-time monitoring: Through a combination of IoT sensors (deployment density of 2 per square kilometer, communication protocol LoRa), satellite remote sensing (resolution 2m, once a month, data source Sentinel-2 satellite), and drone patrols (once a quarter, flight altitude 200m, image resolution ≤10cm), data such as construction land use, farmland protection status, ecological indicators (vegetation coverage, water quality, air quality), and infrastructure operation status are collected in real time. Sensor data transmission frequency is once every 15 minutes, and abnormal data is reported in real time. 2. Dynamic evaluation: A planning implementation evaluation report is generated every quarter to compare the deviation between the actual situation and the planning objectives, including core indicators such as the scale of construction land, the amount of cultivated land, the compliance rate of ecological indicators, and the progress of project implementation. Indicators with a deviation of more than 10% are automatically marked as warning items. The evaluation report is generated in both Word and PDF formats and is uploaded to the system management module for archiving. 3. Early Warning and Handling: For each early warning item, handling suggestions will be generated and pushed to the mobile terminal of the person in charge of the corresponding responsible unit (dual reminder via SMS and APP push), specifying the rectification time limit (15 working days for general early warnings and 7 working days for severe early warnings) and requirements. After the rectification is completed, supporting materials (images, reports, etc.) must be uploaded to form a closed-loop management of "early warning-handling-closure". The closure of the case must be reviewed and confirmed by the county-level natural resources department.

[0028] IX. System Management Module Responsible for daily system operation and maintenance and access control, developed based on the Spring Boot 2.7 framework, supporting dual-system deployment on Windows and Linux: 1. User Management: Four roles are distinguished: administrators, planners, reviewers, and the general public. Different operation permissions are assigned to different roles. Administrators have full operation permissions, while the general public only have query and feedback permissions. User passwords are stored using BCrypt encryption and are required to be changed every 90 days. If the password is wrong 5 times in a row, the account will be locked (automatically unlocked after 24 hours). 2. System Operation and Maintenance: Supports batch data import and export (formats include Excel, CSV, and GDB), model parameter updates (visual adjustment through the backend management interface), and system version upgrades (supports online upgrades, and the upgrade process does not affect system operation). It automatically records operation and maintenance logs, including operation time, operation content, operation subject, and IP address. The log retention period is no less than 1 year. 3. Data Backup: Establish an off-site disaster recovery backup mechanism with a distance of ≥50km between primary and backup nodes. Automatically back up system data (incremental backup) every day from 2-4 AM, and perform a full backup every Sunday morning. The backup data storage period is no less than 6 months, and support rapid recovery of backup data (recovery time ≤2 hours).

[0029] 10. Data Security Protection Module A multi-layered security protection system has been constructed, conforming to the Level 3 standard of the Cybersecurity Classified Protection System, to ensure the security of core data. 1. Hierarchical encryption: Core spatial data, planning schemes, and privacy information are encrypted with AES-256, while ordinary query data is encrypted with DES. The transmission process uses SSL / TLS 1.3 encryption to prevent data leakage; classified data is stored in physical isolation and is not connected to the Internet. 2. Access Control: A role-based access control (RBAC) mechanism is adopted, which refines operation permissions to the data field level. At the same time, all access logs are recorded. Abnormal access (such as login from a different location, batch download, high-frequency query) automatically triggers secondary verification (SMS verification code) and alerts. Abnormal access logs are pushed to the administrator in real time. 3. Security Audit: Conduct a security audit once a month, using automated auditing tools (based on OpenVAS) to identify system vulnerabilities and abnormal data operations, generate an audit report and make rectifications. The audit report must be reviewed by a third-party security organization, and the rectification completion rate must reach 100%.

[0030] Example 1 This embodiment uses the land-use planning of a plain agricultural township as an application scenario. This township's core function is high-quality grain production, while also requiring rural industrial agglomeration and integrated urban-rural development. Its jurisdiction includes multiple administrative villages and a township-level industrial park, presenting needs such as coordinating farmland protection with industrial development space and dynamic monitoring of planning implementation. The system of this invention is used for planning preparation and full-process management, with the specific steps as follows: The first step is to standardize multi-source data. The system uses HTTP / HTTPS interfaces to access the county-level government data sharing platform, batch-acquiring land survey vector data, county-level urban and rural master plan maps, ecological protection red line vector data, and permanent basic farmland distribution maps. Simultaneously, it imports offline various planning-related data, including township agricultural production statistics, road and pipeline CAD drawings, and population and socio-economic statistics. The system automatically converts all spatial data to the CGCS2000 national geodetic coordinate system, using the Gauss-Kruger 3° zonal projection and GDB geographic database format. Through a built-in cross-planning land use code lookup table, it automatically maps and matches fields from different planning data. Simultaneously, an intelligent cleaning toolchain is activated to perform topological checks and error correction on the data, automatically resolving land parcel attribute conflicts. Priority is given to retaining rigid attribute data such as ecological protection and permanent basic farmland. Conflicts exceeding the system's automatic processing range trigger a manual review process, ultimately forming a standardized and undifferentiated data base. This significantly improves data integration efficiency, reduces manual processing errors, and lays a solid foundation for subsequent planning work.

[0031] The second step is multi-plan collaborative analysis. The system initiates a three-dimensional collaborative evaluation model integrating ecology, agriculture, and construction. Ecological sensitivity evaluation indicators such as slope, vegetation cover, and water system buffer zones are selected. The Analytic Hierarchy Process (AHP) is used to determine the weights of each indicator. After passing consistency checks, a weighted overlay is used to generate an ecological sensitivity grading map. Focusing on core agricultural indicators such as arable land quality grade, irrigation guarantee rate, and soil fertility, the entropy weight method is used to calculate objective weights, ensuring the rationality of weight allocation and completing the agricultural suitability assessment. Construction-related indicators such as geological hazard risk, infrastructure accessibility, and public service support capacity are integrated, and a spatial suitability grading map is generated through a comprehensive scoring method using the TOPSIS (Top-Solution Interaction) distance method. Through this three-dimensional evaluation model, the suitable spatial range for ecological protection, agricultural production, and construction development is clearly defined, effectively resolving potential conflicts among the three spatial types and providing scientific quantitative support for functional zoning.

[0032] The third step is dynamic boundary control and scheme development. Based on the results of multi-plan collaborative analysis, the system delineates three levels of control zones: Rigid control zones encompass ecological protection red lines and permanent basic farmland, implementing strict negative list management and prohibiting any development or construction activities that contradict the protection function; Flexible development zones focus on concentrated village construction areas and township industrial parks, adopting a combined positive and negative list management model, allowing for land use quota adjustments within planning requirements to meet the needs of industrial development and improved living conditions for villagers; Strategic reserve zones reserve a certain proportion of land space as reserves for future major industrial projects, public service facilities, and emergency response land. Combining a digital twin simulation module, a full-element digital twin of the township's "surface-above-ground-underground" elements is constructed using UAV oblique photography. Multiple planning schemes are imported, and the simulation platform simulates the population distribution, traffic flow, ecological impact, and industrial layout effects of different schemes, conducting immersive comparative analysis. Simultaneously, the impact of extreme weather and geological disasters on the planning schemes is simulated to optimize building layout, road alignment, and disaster prevention facility configuration, ultimately determining the optimal planning scheme that balances farmland protection, industrial development, and improvement of people's livelihoods.

[0033] The fourth step is intelligent decision-making and public participation. The system integrates the GM(1,1) grey prediction model and Markov chain with weighting. By inputting historical data such as population changes, homestead use, and industrial land demand in recent years, it predicts the population development trend, residential land demand, and industrial land scale in a future period. Combining with the regional per capita land use standard, it dynamically calculates the total land demand. When the demand may affect the rigid protection bottom line, it automatically triggers the index control mechanism. Through the "index pool - project library" linkage mechanism, the system automatically matches available planning indicators with the needs of proposed projects, generates an index allocation plan, and automatically scores the suitability of project locations. Projects with scores lower than the reasonable score are prompted to optimize the location. At the same time, it publishes the planning scheme through the mobile APP, builds an online public participation channel, supports villagers to upload opinions and suggestions, score the planning content, the system automatically collects public opinions and generates a heat analysis map, clarifying high - attention areas and core demands; the planning compilation team reviews and processes public opinions within the specified time limit, clarifies whether to adopt and the reasons, the adopted opinions are incorporated into the planning optimization scheme, and the processing results are publicly announced through multiple online and offline channels, broadening the path of public participation and enhancing the recognition and scientific nature of the planning scheme.

[0034] Step 5, Implement monitoring and dynamic update After the implementation of the plan, the system constructs a three - in - one implementation monitoring system of "Internet of Things monitoring + satellite remote sensing + drone inspection", real - time collecting data such as the use of construction land, the status of cultivated land protection, changes in ecological indicators, and the operation status of infrastructure, regularly generating an evaluation report on the implementation of the plan, comparing the deviation between the actual implementation situation and the planning goal. When the deviation of the core index exceeds the reasonable range, it automatically triggers the early warning mechanism, and through dual reminders of SMS and APP push, it sends the early warning information to the person in charge of the corresponding responsible unit, clarifying the rectification requirements and time limit. After the rectification is completed, the responsible unit uploads supporting materials, and after verification and confirmation, the early warning is cancelled, forming a closed - loop management of "early warning - disposal - cancellation". For key links such as planning boundary adjustment and index allocation, the whole process is recorded on the blockchain through the blockchain evidence - storing module. Relying on the consortium chain architecture and consensus mechanism, it ensures that the data cannot be tampered with and is traceable. At the same time, it executes the distribution of land value - added income through smart contracts, reducing human intervention and enhancing the credibility of planning management.

[0035] Through the system of the present invention in this embodiment, it effectively solves technical problems such as data barriers, spatial conflicts, and rigid control in the territorial space planning of plain agricultural towns, realizes the full - process closed - loop control of planning compilation, decision - making, implementation, monitoring, and update, takes into account multiple goals of food security, ecological protection, industrial upgrading, and people's livelihood improvement, verifies the practicability, innovation, and wide applicability of the system of the present invention, and can provide reliable technical support for the territorial space planning work of similar towns.

[0036] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0037] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A coordinated multi-plan integration township territorial spatial planning system, characterized in that, It includes modules for multi-source data standardization processing, multi-plan collaborative analysis, dynamic boundary control, digital twin simulation, intelligent decision support, blockchain evidence storage, public participation and interaction, planning implementation monitoring, system management, and data security protection. Each module communicates bidirectionally through a RESTful interface and transmits data in JSON format, forming a closed-loop control system for the entire process.

2. The coordinated multi-plan integration township territorial spatial planning system according to claim 1, characterized in that: The multi-source data standardization processing module is used to integrate various types of township land space planning data, and performs the following sub-steps: S2.1 Data Acquisition: Synchronously acquire land survey data, urban and rural planning data, ecological protection data, agricultural production data, infrastructure data, and socio-economic statistics. The acquisition accuracy reaches sub-meter level, and the data formats cover vector, raster, and text types. The government data platform is accessed through HTTP / HTTPS interfaces, and offline import formats support SHP, GDB, Excel, and PDF. S2.2 Coordinate unification: Forcefully transform all spatial data to the CGCS2000 National Geodetic Coordinate System, store it in the GDB geographic database format, and use the Gauss-Kruger projection to eliminate spatial benchmark differences between different planning data. S2.3 Intelligent Cleaning: Automatic field mapping is achieved through a preset land category code comparison table. Topology processor is used to repair topology errors with surface overlap error ≤0.1㎡ and line breakage distance <0.5m. A conflict coordination rule engine is embedded to retain rigid attribute data according to the priority of "ecological protection red line > permanent basic farmland > cultivated land > construction land > other land". The conflict resolution threshold is set to 0.3㎡.

3. The coordinated multi-plan integration township territorial spatial planning system according to claim 1, characterized in that: The multi-plan collaborative analysis module constructs a three-dimensional evaluation model of "ecology-agriculture-construction", specifically including: S3.1 Ecological Sensitivity Assessment: Eight indicators were selected, including slope, vegetation cover, water system buffer zone, soil texture, geological disaster risk level, biodiversity distribution, air quality, and water resource carrying capacity. The weights were determined using the Analytic Hierarchy Process (AHP), with the weights allocated as follows: slope 0.3, vegetation cover 0.25, water system buffer zone 0.2, and the remaining five indicators 0.05 each. The indicators were normalized from 0 to 1 and weighted overlay was performed using the ArcGIS raster calculator to generate an ecological sensitivity classification map. S3.2 Agricultural suitability assessment: Focusing on core factors such as arable land quality grade, irrigation guarantee rate, and soil organic matter content, with weights of 0.4, 0.35, and 0.25 respectively, objective weights are calculated using the entropy weight method, with an entropy threshold of 0.

8. If the value is lower than the threshold, the factor system is recalibrated. S3.3 Construction Carrying Capacity Analysis: Integrating geological hazard risk, high / medium / low levels correspond to scores of 3 / 2 / 1; road 500m coverage rate; power supply radius, ≤1km is qualified, score 1; >1km is unqualified, score 0.5; accessibility of public service facilities and water resource supply capacity parameters are comprehensively scored using the TOPSIS superior-inferior solution distance method to generate a suitability classification map with a resolution of 20m×20m. A score ≥0.6 is a high suitability area, 0.3-0.6 is a medium suitability area, and <0.3 is a low suitability area.

4. The coordinated multi-plan integration township territorial spatial planning system according to claim 1, characterized in that: The dynamic boundary control module is divided into three levels: "rigid control zone - flexible development zone - strategic reserve zone", and executes the following control rules: S4.1 The rigid control zone shall occupy no less than 25% of the township's land area, including ecological protection red lines and permanent basic farmland, and shall be subject to negative list management, prohibiting any development and construction activities; S4.2 The flexible development zone adopts a combination of positive and negative lists, allowing for the linkage of construction land increases and decreases based on population flow and industrial needs, with the scale of the linked turnover quota not exceeding 5% of the total cultivated land area of ​​the township; S4.

3. 5%-10% of the land space in the strategic reserve area shall be reserved as reserve land. A dynamic adjustment mechanism with a cycle of 5 years shall be established. The adjustment shall meet the requirements of ecological impact assessment, with an impact value of ≤0.2, and the dual requirements of farmland occupation and compensation balance assessment. The adjustment process shall implement a four-level linkage mechanism of "township application - county-level preliminary review - city-level review - provincial-level filing".

5. A coordinated multi-plan integration township territorial spatial planning system according to claim 1, characterized in that: The digital twin simulation module integrates BIM and GIS technologies. It uses a drone equipped with a five-lens camera to conduct oblique photography to acquire a real-world 3D model with a resolution of ≤5cm. It integrates DEM digital elevation data with an accuracy of ±0.1m. It also incorporates BIM building information and underground pipeline network data to construct a full-element digital twin of "surface-above-underground". Based on the Unity3D engine, it realizes immersive simulation comparison of planning schemes with a simulation frame rate of no less than 30FPS.

6. A coordinated multi-plan integration township territorial spatial planning system according to claim 1, characterized in that: The intelligent decision support module embeds a GM(1,1) grey prediction model and a Markov chain, using a weighted integration method. The GM(1,1) model has a weight of 0.6, and the Markov chain has a weight of 0.

4. It inputs population and homestead data from the past 5 years to predict land demand for the next 5 years, with a prediction accuracy R0. 2 >0.95; Combining the "indicator pool-project library" linkage mechanism, the allocation plan is automatically generated by using SQL statements to fuzzy match the increase / decrease linkage of surplus indicators with the planning project requirements, and the matching degree is ≥80%.

7. A coordinated multi-plan integration township territorial spatial planning system according to claim 1, characterized in that: The blockchain evidence storage module adopts a consortium blockchain architecture, with nodes including four types: township governments, county-level natural resources departments, municipal-level natural resources departments, and third-party auditing institutions. It adopts the PBFT consensus mechanism with a fault tolerance rate of 33%. The entire process of planning, boundary adjustment records, and indicator allocation information is recorded on the blockchain. Smart contracts are used to execute the distribution of land value-added revenue, clearly defining the allocation ratio of 60% retained by the collective economic organization and 40% coordinated by the government. The block generation interval is 10 minutes to ensure that the data is tamper-proof and traceable.

8. A coordinated multi-plan integration township territorial spatial planning system according to claim 1, characterized in that: The public participation interaction module is developed into a mobile APP based on Android 10.0 and iOS 14.0 and above. It allows villagers to provide feedback on the planning by swiping to rate it from 1 to 5 stars. The system automatically collects the opinions and generates a heat analysis chart. The adoption rate and reasons for the opinions must be made public within 3 working days. The public disclosure channels include the APP homepage, the township government affairs bulletin board, and the village committee bulletin board.

9. A coordinated multi-plan integration township territorial spatial planning system according to claim 1, characterized in that: The planning implementation monitoring module collects real-time data on land use, changes in ecological indicators, and infrastructure operation through a combination of IoT sensors, satellite remote sensing, and drone patrols. It generates an implementation evaluation report every quarter and automatically triggers an early warning when the indicator deviation exceeds 10%, pushing the warning information to the mobile terminal of the person in charge of the corresponding responsible unit.

10. A coordinated multi-plan integration township territorial spatial planning system according to claim 1, characterized in that: The data security protection module adopts a hierarchical encryption mechanism, using AES-256 encryption for core spatial data and DES encryption for ordinary query data. The transmission process uses SSL / TLS 1.3 encryption to prevent data leakage. It also adopts a role-based access control mechanism, refining operation permissions down to the data field level, and establishes a real-time data backup mechanism with a backup frequency of no less than once a day, employing off-site disaster recovery backup.

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