Natural resource supervision system and method based on Beidou satellite navigation and positioning

Abstract

The invention belongs to the technical field of natural resource monitoring, and discloses a natural resource supervision system and method based on Beidou satellite navigation positioning, and the method comprises the steps: a data collection module collects address data; the path planning module plans a path; natural resources are supervised and inspected through Beidou probe equipment and a mobile phone; the precise positioning module is used for precise positioning; the positioning navigation module provides navigation service; the intelligent supervision module carries out intelligent supervision and trajectory tracking on the business process; the space analysis comprises land category analysis, land category proportion analysis, overlay analysis and the like; and the security and authentication module ensures data security and user permission compliance. According to the method, full-process digital evidence obtaining is achieved, centimeter-level positioning is obtained in real time through a Beidou probe, surrounding pattern spot information is automatically associated, coordinates and timestamps are automatically bound during photographing evidence obtaining, and it is ensured that the site evidence obtaining position is accurately matched with the pattern spot information; and the spatial analysis module identifies a high-risk area in advance through buffer area analysis and violation trend prediction.

Description

Technical Field

[0001] This application relates to the field of natural resource monitoring technology, and more specifically, to a natural resource supervision system and method based on BeiDou satellite navigation and positioning. Background Technology

[0002] Natural resource supervision is a crucial means of safeguarding resource security and ensuring sustainable economic and social development. Its importance lies in its profound value across multiple dimensions, including resource protection, ecological security, economic development, and social equity. The following analyzes its significance from six core aspects: curbing illegal occupation; ensuring the rational development and utilization of resources; supervising the order of mineral resource development; and protecting ecological red lines and biodiversity.

[0003] Existing technology publication CN114491187A provides an integrated intelligent monitoring system for natural resources, including a platform login module, an import / upload module, and a map patch generation module. The data output terminal of the platform login module is connected to the data input terminal of the import / upload module, and the data output terminal of the import / upload module is connected to the data input terminal of the map patch generation module. The platform login module is used to log in to the intelligent management platform for natural resources; the import / upload module is used to upload imported collected data to the intelligent management platform for natural resources; the map patch generation module is used by the intelligent management platform for natural resources to process the uploaded data and obtain a map patch. It can securely upload collected data to the intelligent management platform for natural resources and display each map patch on the map with a different color.

[0004] While the existing technical solutions described above can achieve the relevant beneficial effects through their structure, they still have the following drawbacks: 1. Real-time fusion of multi-source spatial data is difficult in natural resource field supervision scenarios, and ordinary navigation apps cannot overlay professional supervision layers; 2. Centimeter-level positioning is disconnected from business operations, and mobile geographic information systems can load Web map tile services but cannot link with high-precision positioning devices; 3. Insufficient accuracy in field evidence collection, traditional supervision relies on manual interpretation of drawings + handheld GPS (meter-level error), which cannot be linked to real-time images and other multi-source data, resulting in large deviations in evidence collection locations and high difficulty in spatial analysis.

[0005] In view of this, we propose a natural resource supervision system and method based on BeiDou satellite navigation and positioning. Summary of the Invention

[0006] 1. The technical problems to be solved.

[0007] The purpose of this application is to provide a natural resource supervision system and method based on BeiDou satellite navigation and positioning, which solves the technical problems mentioned in the background, realizes full-process digital evidence collection, enables BeiDou probe to obtain centimeter-level positioning in real time, automatically associates with surrounding map information, and automatically binds coordinates and timestamps when taking photos for evidence collection, ensuring accurate matching between the on-site evidence collection location and map information; the spatial analysis module identifies high-risk areas in advance through buffer analysis and violation trend prediction; the map data module achieves second-level retrieval of massive data through dynamic layer overlay and spatiotemporal indexing technology; it supports real-time conversion of multiple coordinate systems, is compatible with Baidu / Gaode Maps standardized interfaces, and solves the technical effect of cross-platform coordinate deviation problem of traditional geographic information systems.

[0008] 2. Technical solution.

[0009] This application provides a natural resource supervision system based on BeiDou satellite navigation and positioning, including...

[0010] Data collection module: Collects address data, vegetation data, and building data of natural resources, and annotates the data.

[0011] Data acquisition module: Acquires real-time data from continuously operating reference stations, precise ephemeris data from the International Organization for Global Navigation Satellite Systems (IOGS), and global navigation satellite system data from smartphones.

[0012] Path planning module: Based on the address data of natural resources and the inspection requirements, the inspection route is planned. Multi-objective optimization is achieved based on the core computing engine algorithm or genetic algorithm to plan the optimal travel route.

[0013] BeiDou probe equipment: In terms of signal reception, it supports multi-frequency signal reception from the BeiDou / Global Navigation Satellite System, enabling simultaneous reception of satellite signals from multiple frequency bands, thus enhancing signal stability and anti-interference capabilities. For communication, it adopts Bluetooth 5.0 low-power communication technology, featuring fast transmission speed, stable connection, and low power consumption.

[0014] Hybrid navigation system: Uses Baidu Maps' standardized interface for route planning and supports overlaying of special point of interest layers for inspection purposes.

[0015] Smart terminals: including smartphones and tablets, integrating accelerometers and gyroscopes.

[0016] Precise Positioning Module: This module decodes and fuses real-time data from continuously operating reference stations, precise ephemeris data from the International Organization for Global Navigation Satellite Systems (IOGS) and observation data from smartphones using the IOGS, corrects error models, calculates positioning results, and performs precise positioning using the BeiDou probe to achieve accurate positioning.

[0017] Positioning and navigation module: includes a centimeter-level positioning engine, a coordinate transformation module, and a navigation service module.

[0018] Centimeter-level positioning engine: The centimeter-level positioning engine is the core component for the system to achieve high-precision positioning.

[0019] Coordinate transformation module: Supports transformations between multiple coordinate systems to meet the needs of different application scenarios. During real-time transformation, an efficient coordinate transformation algorithm is employed to ensure the accuracy and real-time performance of the transformation results.

[0020] Navigation service module: This module integrates with standardized interfaces from Baidu Maps and Gaode Maps, employing offline map caching strategies; it also provides trajectory playback and correction. The navigation service module achieves deep integration with standardized interfaces from mainstream map providers such as Baidu Maps and Gaode Maps. Users can flexibly switch between different map services based on their needs and usage habits to obtain detailed map data and navigation functions.

[0021] Map data module: includes dynamic layer management, spatial database and data synchronization engine.

[0022] Intelligent supervision module: includes evidence collection workflow engine, trajectory management module and business approval module.

[0023] Spatial Analysis Module: Performs buffer analysis, real-time querying, and statistical analysis.

[0024] Security and authentication module: including Bluetooth security protocol, user rights management and data security module.

[0025] Software Development Kit Integration Module: Integrated into electronic devices such as mobile phones or tablets; includes core software development kit interfaces, third-party system integration modules, and cross-platform support modules.

[0026] System management module: includes device management center, system configuration module and log monitoring module.

[0027] This invention provides a natural resource supervision method based on BeiDou satellite navigation and positioning, comprising the following steps.

[0028] S1. The data collection module collects address data of natural resources and labels the data.

[0029] S2, the route planning module, and the hybrid navigation system plan the optimal route based on the address data of natural resources and the needs of supervision; supervisors conduct supervision of natural resources through Beidou probe equipment and smartphones.

[0030] S3, the data acquisition module acquires real-time data from continuously operating reference stations, precise ephemeris data from the International Global Navigation Satellite System Service (IGNSS), and global navigation satellite system data from smartphones.

[0031] S4, the precise positioning module decodes and fuses real-time data from continuously operating reference stations, precise ephemeris data from the International Organization for Global Navigation Satellite Systems (IOGS) and observation data from smartphones' global navigation satellite systems, corrects error models, calculates positioning results, and performs precise positioning using the BeiDou probe to achieve precise positioning.

[0032] S5, the positioning and navigation module provides navigation services for inspectors.

[0033] S6, the map data module integrates three major components: dynamic layer management, spatial database, and data synchronization engine, to achieve efficient management, storage, display, and updating of geographic data.

[0034] S7, the intelligent supervision module integrates the evidence collection workflow engine, trajectory management module and business approval module to achieve intelligent supervision, trajectory tracking and approval management of business processes.

[0035] S8, the spatial analysis module performs buffer analysis, real-time query, and statistical analysis.

[0036] S9, the security and authentication module ensures data security, user permission compliance, and communication security. The spatial analysis engine provides land use compliance analysis functions.

[0037] S10, the system management module, is responsible for the unified management and monitoring of basic resources such as system devices, configurations, and logs to ensure the stable, efficient, and secure operation of the system.

[0038] 3. Beneficial effects.

[0039] One or more technical solutions provided in this application have at least the following technical effects or advantages.

[0040] 1. This invention enables full-process digital evidence collection, realizing an integrated workflow of "location-query-photographing".

[0041] 2. The BeiDou probe acquires centimeter-level positioning in real time, automatically associates it with surrounding map information, and automatically binds coordinates and timestamps when taking photos for evidence, improving the integrity of the evidence chain and increasing on-site work efficiency. The precise positioning module integrates real-time data from continuously operating reference stations with precise ephemeris data from the International Network for Global Navigation Satellite Systems (INFGS), achieving centimeter-level positioning through error correction. This significantly improves positioning accuracy compared to traditional global navigation satellite systems, ensuring accurate matching between on-site evidence collection locations and map information.

[0042] 3. The business approval module supports mobile case reporting and multi-level review, and the electronic process greatly shortens the approval cycle.

[0043] 4. Spatial analysis assists decision-making and enables precise targeted supervision: The spatial analysis module identifies high-risk areas in advance through buffer zone analysis and violation trend prediction, making the allocation of patrol resources more scientific.

[0044] 5. The data collection module integrates multi-source data such as satellite remote sensing and UAV imagery, and realizes natural resource classification management through attribute tags (such as color and priority).

[0045] 6. The map data module achieves second-level retrieval of massive amounts of data through dynamic layer overlay (such as farmland red lines + illegal patches) and spatiotemporal indexing technology. It supports real-time conversion between multiple coordinate systems, is compatible with Baidu / Gaode Maps standardized interfaces, and solves the problem of cross-platform coordinate deviation in traditional geographic information systems.

[0046] 7. The trajectory management module records the movement trajectory, dwell time, and map patch association information of the inspectors. Abnormal trajectory detection (such as prolonged stillness) automatically issues warnings to ensure the standardization of patrol work.

[0047] 8. The software development kit (SDK) integration module supports interface with third-party systems such as the Land Survey Cloud, enabling seamless data sharing and avoiding "information silos"; the cross-platform SSD reduces system development costs and is compatible with multiple terminals including iOS, Android, and Web. Attached Figure Description

[0048] Figure 1 This is a flowchart illustrating a preferred embodiment of the natural resource supervision method based on BeiDou satellite navigation and positioning disclosed in this application.

[0049] Figure 2 This is a schematic diagram illustrating the process of achieving high-precision positioning through multi-source data fusion and error correction in a natural resource supervision system based on BeiDou satellite navigation and positioning, as disclosed in a preferred embodiment of this application. Detailed Implementation

[0050] The present application will be further described in detail below with reference to the accompanying drawings.

[0051] Reference Figure 1 and Figure 2 This application provides a natural resource supervision system based on BeiDou satellite navigation and positioning, including...

[0052] Data Collection Module: This module collects and labels address data, building data, and vegetation data for natural resources; it also acquires and organizes relevant natural resource data. When collecting address data for natural resources, this module utilizes multi-source data acquisition technology. It can access satellite remote sensing imagery to obtain large-scale, high-resolution surface information and integrate drone aerial photography data to conduct detailed imaging of key areas, supplementing the lack of detail in satellite imagery. For different types of natural resource data, such as cultivated land, construction land, and ecological red lines, corresponding color-coded labels and priority levels are assigned.

[0053] Data acquisition module: Acquires real-time data from continuously operating reference stations, precise ephemeris data from the International Organization for Global Navigation Satellite Systems (IOGS), and global navigation satellite system data from smartphones.

[0054] Path planning module: Based on the address data of natural resources and the inspection requirements, the inspection route is planned. Multi-objective optimization is achieved based on the core computing engine algorithm or genetic algorithm to plan the optimal travel route.

[0055] The BeiDou probe device is a key hardware component for achieving high-precision positioning, integrating multiple advanced technologies. In signal reception, it supports multi-frequency signal reception from the BeiDou / Global Navigation Satellite System, enabling simultaneous reception of satellite signals from multiple frequency bands, enhancing signal stability and anti-interference capabilities. Even in urban environments with numerous high-rise buildings or mountainous terrain with complex topography, it can quickly and accurately capture satellite signals, ensuring continuous positioning. For communication, it adopts Bluetooth 5.0 low-power communication technology, featuring fast transmission speed, stable connection, and low power consumption. The device also incorporates an encryption chip to encrypt signals during data transmission, preventing data theft or tampering and ensuring the security of positioning data.

[0056] Smart terminals, including smartphones and tablets, integrate accelerometers and gyroscopes. The accelerometers can sense real-time changes in the device's acceleration in three-dimensional space, enabling the detection of personnel's movement. Combined with positioning data, they can accurately calculate the number of steps, distance, and speed, providing more detailed motion information for trajectory recording. The gyroscopes measure the device's rotational angular velocity, and by sensing changes in the device's rotation and tilt angles, they enable precise direction calibration in navigation functions.

[0057] Precise Positioning Module: This module decodes and fuses real-time data from continuously operating reference stations, precise ephemeris data from the International Organization for Global Navigation Satellite Systems (IOGS) and observation data from smartphones using the IOGS, corrects error models, calculates positioning results, and performs precise positioning using the BeiDou probe to achieve precise positioning.

[0058] Positioning and navigation module: includes a centimeter-level positioning engine, a coordinate transformation module, and a navigation service module.

[0059] Centimeter-level positioning engine: The centimeter-level positioning engine is the core component for the system to achieve high-precision positioning.

[0060] Coordinate conversion module: Supports conversion between multiple coordinate systems to meet the requirements of different application scenarios. During the real-time conversion from WGS84 to BD09, an efficient coordinate conversion algorithm is adopted to ensure the accuracy and real-time nature of the conversion results. When the supervisor clicks on any position on the map, the system can instantly convert the WGS84 coordinates of that point to BD09 coordinates for accurate display and navigation on domestic common map platforms such as Baidu Map. This module also supports the 2000 coordinate system and is compatible with more geographic information data. When performing cross-coordinate system route planning, the system will automatically convert the coordinates of the starting point and the ending point to the same coordinate system, then calculate the route, and finally convert the result back to the target coordinate system to ensure the accuracy and usability of the navigation route.

[0061] Navigation service module: Implements the integration of standardized interfaces of Baidu or Amap, offline map caching strategy; trajectory playback and correction. The navigation service module has achieved deep integration with the standardized interfaces of mainstream maps such as Baidu Map and Amap. According to the user's needs and usage habits, different map services can be flexibly switched to obtain detailed map data and navigation functions. In terms of the offline map caching strategy, the system will automatically cache the map data of the surrounding areas based on the user's historical usage records and current location. When the supervisor enters an area without network coverage, the map browsing and navigation functions can still be used normally. The trajectory playback and correction function provides a powerful means of retrospective and analysis for the supervision work. The system will record the movement trajectory of the supervisor in real time, including information such as location, time, and speed.

[0062] Hybrid navigation system: Uses the standardized interface of Baidu Map for route planning and supports overlaying special interest point layers for supervision.

[0063] Map data module: Includes dynamic layer management, spatial database, and data synchronization engine.

[0064] Dynamic layer management; The dynamic layer management module accesses Tianditu through the Web map tile service to obtain the basic geographic information layer and provides a clear and accurate map base for the system. On this basis, it supports overlaying various supervision thematic layers, such as the cultivated land red line layer, illegal map patch layer, etc. It includes the following steps.

[0065] 1. Basic layer access: The dynamic layer management module establishes a connection with Tianditu through the Web map tile service protocol, sends requests to the Tianditu server to obtain the basic geographic information layer, such as map base data of roads, water systems, residential areas, etc. After obtaining it, the basic layer is loaded into the system map interface to provide a clear and accurate geographic background for subsequent operations.

[0066] 2. Thematic Layer Overlay: Based on the needs of natural resource supervision, thematic layer data such as arable land red lines and illegal land parcels are imported into the system. The display style of the thematic layers is configured in the system backend, including attributes such as color, symbols, and transparency. After configuration, the thematic layers are overlaid on top of the basic geographic information layers to achieve layered display of map information.

[0067] 3. Dynamic Layer Updates: When natural resources management departments delineate new arable land red lines or discover new illegal land parcels during on-site inspections, the updated thematic layer data is uploaded to the system. Upon detecting a data update command, the system automatically refreshes the corresponding thematic layer, re-annotating and displaying the updated information on the map to ensure consistency between map content and actual conditions. Thematic layers can be dynamically updated and adjusted according to actual needs. For example, when new arable land red lines are delineated or illegal land parcels are discovered, the relevant layers can be promptly annotated and displayed on the map.

[0068] 4. Tiered Caching: A tiered caching strategy is employed, using a three-level caching mechanism: memory → disk → edge nodes. First, map tile data is preferentially read from the memory cache, as memory reads are fast and can quickly respond to user requests. If the required data is not in memory, it is searched from the disk cache. Only when the disk cache is also missing is data retrieved from edge nodes or remote servers. The retrieved data is sequentially stored in the disk and memory caches for faster retrieval next time. This strategy significantly improves map loading speed and ensures smooth map display even in weak network environments. When a user's operation on the system map interface triggers a map tile request, the system first checks if the required map tile data exists in the memory cache. If it exists, it is directly read from memory and loaded into the map interface, quickly responding to the user request; if the corresponding data is not in the memory cache, the process proceeds to the next step.

[0069] 5. Disk Cache Lookup: If the data is not found in the memory cache, the system searches for the required map tile data in the local disk cache. If found, the data is read and loaded into the map interface, and simultaneously stored in the memory cache for faster retrieval next time; if the data is not found in the disk cache either, proceed to the next step.

[0070] 6. Remote Data Acquisition: When the required data is missing from both memory and disk cache, the system requests map tile data from edge nodes or the Tianditu remote server via the network. After acquiring the data, it is stored sequentially in the disk and memory caches before being loaded onto the map interface for display. In environments with weak network signals, the cached data ensures the normal operation of basic map functions.

[0071] Spatial Database: The spatial database manages vector data and imagery data. For vector data, it employs efficient data structures for storage and organization, supporting rapid querying and editing of attribute information (such as type, area, ownership, etc.) and spatial geometric information (such as coordinates, shape). Simultaneously, it utilizes spatiotemporal indexing optimization techniques, such as R-trees and quadtree indexes, to accelerate the retrieval of massive amounts of vector data, achieving sub-second response times. Regarding imagery data storage, it supports the storage and management of various image formats, including satellite remote sensing imagery and UAV aerial imagery. By establishing an image pyramid model, it automatically loads imagery data of corresponding resolutions based on different display scales, improving image browsing efficiency.

[0072] Data Synchronization Engine: The data synchronization engine employs an incremental update mechanism. When the data source changes, only the changed data is synchronized and updated, rather than retransmitting all data, reducing data transmission volume and time costs. Regarding offline data caching, smart terminals pre-cache a certain range of map and business data when a network connection is available, enabling data queries, photo evidence collection, and other operations even offline. When data conflicts occur, the data synchronization engine has conflict resolution strategies. Based on the type and characteristics of the conflict, it automatically selects an appropriate resolution method, such as using the latest data or manual intervention, ensuring data consistency and accuracy.

[0073] Intelligent supervision module: includes evidence collection workflow engine, trajectory management module and business approval module.

[0074] Evidence Collection Workflow Engine: The evidence collection workflow engine implements a highly efficient workflow integrating location, query, and photography. When inspectors arrive at the scene, the smart terminal acquires centimeter-level positioning data via a BeiDou probe, then automatically queries the spatial database for information on map features within a 50cm radius. The retrieved map features are highlighted on the map for quick target identification. During the photo-based evidence collection phase, the system automatically binds the captured photos with the current location coordinates, time, and other information to form a complete chain of evidence. Watermarks are also added to the photos, along with relevant location information and case numbers, to prevent photo tampering. After evidence collection is complete, the evidence chain data is automatically uploaded to the server for storage and management, facilitating subsequent case processing and review.

[0075] The trajectory management module enables the storage, querying, visualization, and export of trajectory information to SHP files. It records the movement trajectories, dwell times, and associated information of inspection personnel, and automatically issues warnings for abnormal trajectories (such as prolonged periods of stillness), ensuring standardized inspection work. The trajectory management module comprehensively records and manages the movement trajectories of inspection personnel. Recorded trajectories include precision indicators, detailing the location, time, speed, and precision of each trajectory point. It features trajectory-map spatiotemporal correlation, matching and analyzing trajectories with map information of the areas they pass through, allowing for a clear view of which map areas inspection personnel were active in, as well as the time and duration of their activities. It also features abnormal trajectory detection, performing real-time analysis of trajectory data through reasonable rules and algorithms. When abnormal trajectories are detected, such as sudden jumps or prolonged stillness in non-working areas, the system automatically issues warnings, alerting management personnel to pay attention and handle the situation, ensuring the standardization and effectiveness of inspection work.

[0076] The business approval module includes mobile case reporting, a multi-level review mechanism, and a closed-loop rectification tracking system. After on-site evidence collection, inspectors can directly report case information (including location data, photos, and map attributes) to the system via a smart terminal app. Reported cases automatically flow to the appropriate reviewers according to a pre-set process. Reviewers can view detailed case information and perform review operations in the management backend, such as approving, rejecting, or requesting supplementary evidence. For rejected cases, the system automatically provides feedback to the inspectors for supplementation and modification. The closed-loop rectification tracking function tracks the rectification progress of approved cases. Inspectors periodically upload photos and data after rectification until the case is fully rectified, forming a complete business processing closed loop.

[0077] Spatial Analysis Module: Performs buffer analysis, real-time querying, and statistical analysis.

[0078] Spatial Analysis Engine: Provides land use compliance analysis functions, which are achieved by calling the standardized interface of the National Land Survey Cloud.

[0079] Security and authentication module: including Bluetooth security protocol, user rights management and data security module.

[0080] Bluetooth Security Protocol: Two-way device authentication; dynamic key exchange; communication encryption (AES-256); The Bluetooth security protocol ensures the security of communication between the BeiDou probe device and the smart terminal. During the device pairing phase, a two-way authentication mechanism is used, where the BeiDou probe device and the smart terminal mutually verify each other's identity information. Communication data is encrypted and transmitted by generating a temporary key and a session key. The temporary key is generated based on the device ID and timestamp, possessing randomness and time-sensitivity; the session key is obtained by encrypting the temporary key and a random number, and is used for subsequent data communication encryption. During data transmission, the AES-256 encryption algorithm is used to encrypt the data, ensuring that the data is not stolen or tampered with during transmission. Simultaneously, the protocol also has a heartbeat detection mechanism, periodically sending heartbeat packets to verify the connection status. If any abnormality is detected (such as signal interruption, unauthorized access, etc.), the connection is automatically disconnected immediately to prevent data leakage risks.

[0081] User access control includes role-based hierarchical control, data access permissions, and operation log auditing. This module implements role-based hierarchical control and data access permission management for system users. Users are categorized into different roles based on their responsibilities and work requirements, such as system administrators, supervisors, auditors, and decision-makers. Each role corresponds to different operation permissions and data access scopes. The system records detailed user operation logs for easy auditing and traceability, ensuring the standardization and security of system operations.

[0082] Data Security Module: Sensitive data anonymization; encrypted transmission (TLS 1.3); local encrypted data storage; The data security module ensures data security through multiple stages, including data transmission, storage, and processing. For data transmission, the TLS 1.3 encryption protocol is used to encrypt data transmission over the network, preventing eavesdropping and theft. Both data interaction between smart terminals and servers, and data transmission within the server itself, undergo rigorous encryption. For data storage, sensitive data is anonymized to hide critical information such as personal identification information and confidential business data. Local encrypted data storage technology is used to encrypt data stored on smart terminals and server hard drives, ensuring data integrity even if devices or hard drives are lost. The system regularly backs up data and uses off-site storage to prevent data loss due to natural disasters, hardware failures, or other reasons.

[0083] Software Development Kit Integration Module: Integrated into electronic devices such as mobile phones or tablets; includes core software development kit interfaces, third-party system integration modules, and cross-platform support modules.

[0084] Core Software Development Kit (SDK) Interfaces: The core SSD interfaces provide developers with convenient ways to call functions, including standardized interfaces for positioning services, spatial analysis, and data acquisition. The standardized positioning service interface allows developers to quickly integrate BeiDou high-precision positioning functionality into custom applications, acquiring centimeter-level positioning data, and setting parameters such as positioning accuracy thresholds and update frequency as needed. The standardized spatial analysis interface provides rich spatial analysis function interfaces, such as buffer analysis, patch query, and path planning. Developers only need to pass in the corresponding parameters to call the system's spatial analysis engine for calculation and obtain analysis results. The standardized data acquisition interface facilitates developers in implementing data acquisition functions in applications, including photo evidence collection and trajectory recording. The collected data can be automatically bound to positioning information to ensure data accuracy and integrity.

[0085] Third-party system integration module: This module enables interface with departmental systems such as the Land Survey Cloud and data exchange with other government platforms. By establishing unified data interface standards and protocols, it achieves seamless data sharing and interaction. For example, it synchronizes the system's inspection data to the Land Survey Cloud, providing real-time on-site data for land surveys; simultaneously, it retrieves the latest land use status data from the Land Survey Cloud to update the system's thematic layers. This module also supports a custom APP plugin mechanism, allowing developers to create plugins based on actual needs to expand system functionality. Plugins can be deeply integrated with the system, calling upon the system's core functions, and can also provide feedback on plugin functionality to the system, enabling flexible expansion and personalized customization.

[0086] Cross-platform support module: Ensures the system can run on different operating systems and development frameworks. For mobile devices, native Android and iOS software development kits are provided. Developers can choose the appropriate software development kit according to project needs to fully utilize the characteristics of each platform and achieve the best user experience. Simultaneously, to meet the needs of cross-platform development, React Native and Flutter wrapper versions of the software development kit are also provided. Developers can use a single codebase to run on multiple platforms, reducing development costs and time. On the web side, a JavaScript software development kit is provided, making it easy for developers to integrate system functions into web applications to implement browser-based natural resource supervision and management functions. It integrates with the natural resource supervision and management platform to automatically generate tracks and redirect to Amap for route planning. AR functionality is provided, overlaying underground pipelines and land ownership information in real time, and supporting voice command control for spatial analysis.

[0087] System management module: includes device management center, system configuration module and log monitoring module.

[0088] Equipment Management Center: Provides full lifecycle management for BeiDou probe equipment. This includes equipment registration, recording basic information, unique identifiers, and assigned personnel; equipment monitoring allows real-time viewing of operational status, such as battery level, signal strength, and connection status, and promptly issues alerts when abnormal conditions occur (e.g., low battery or signal loss). Remote firmware upgrades allow for firmware updates via the network, eliminating the need for manual operation and improving management efficiency. Furthermore, the Equipment Management Center also manages equipment repair and disposal, providing detailed records and statistical analysis of equipment usage to inform procurement and upgrade decisions.

[0089] The system configuration module allows administrators to configure and adjust various system parameters. Regarding continuous reference station account management, it allows adding, deleting, and modifying continuous reference station account information, assigning account usage permissions, and monitoring account usage. The layer style configuration function supports custom settings for the colors, symbols, transparency, and other styles of various thematic layers on the map, making the map display more in line with user needs. Algorithm parameter optimization allows for adjustments to algorithm parameters for core functions such as the positioning and navigation module and the spatial analysis module. For example, depending on different application scenarios, the filtering parameters of the multipath suppression algorithm and the construction parameters of the R-tree index can be adjusted to optimize system performance and improve calculation accuracy and efficiency.

[0090] Log monitoring module: The log monitoring module comprehensively monitors and records the system's operation. Performance indicator monitoring collects various system performance data in real time, such as server CPU utilization, memory usage, database query response time, map loading speed, etc., and displays them in the form of charts and reports, allowing administrators to understand the system's operating status in a timely manner.

[0091] Furthermore, the centimeter-level positioning engine includes RTK differential algorithm implementation, continuous running reference station account dynamic authentication, multi-path suppression module (wavelet transform + adaptive filtering), and ambiguity fixing module (optimized LAMBDA + PARC strategy).

[0092] In the implementation of the RTK differential algorithm, differential data from a continuously operating reference station system is received to correct the raw observation data acquired by the BeiDou probe equipment, eliminating errors in the satellite signal propagation process, thereby improving the positioning accuracy from the meter level to the centimeter level. This includes the following steps.

[0093] 1. Raw data acquisition: The Beidou probe device receives multi-frequency signals from the Beidou / Global Navigation Satellite System, acquires raw satellite observation data (pseudorange, carrier phase, etc.), and transmits it to a smart terminal via Bluetooth 5.0.

[0094] 2. Base station data acquisition: The intelligent terminal sends a request to the continuously operating reference station system via the network to obtain observation data and differential correction information (including phase and pseudorange corrections) from nearby base stations.

[0095] 3. Data matching and synchronization: The raw data collected by the probe equipment is matched with the base station data of the continuously operating reference station system in terms of time and satellite number to ensure that the observation data of both correspond to the same time and the same satellite.

[0096] 4. Error correction calculation: Based on the differential correction values ​​sent by the base station, the original observation data of the probe equipment is corrected for errors.

[0097] Pseudorange correction: The pseudorange correction calculated by the base station is applied to the pseudorange observations of the probe equipment to eliminate common errors such as satellite clock error and ionospheric delay.

[0098] Carrier phase correction: Differential calculations are performed using the carrier phase observations from the base station and the probe equipment to further eliminate spatial correlation errors.

[0099] 5. Positioning Calculation: Using the corrected observation data, positioning calculation is performed. The user's precise location coordinates are calculated using algorithms such as least squares method or Kalman filtering, improving the positioning accuracy from meters to centimeters.

[0100] 6. Output Results: The final centimeter-level positioning results are output to other components of the positioning and navigation module for subsequent coordinate transformation and navigation path planning.

[0101] Furthermore, the dynamic authentication mechanism for continuously operating reference station accounts ensures the security and legitimacy of the location service. The system dynamically allocates and verifies continuously operating reference station accounts based on user permissions and device information to prevent unauthorized access. Simultaneously, account usage is monitored in real time, and restrictive measures are immediately taken upon detection of abnormal behavior. This includes the following steps.

[0102] 1. User authentication: When a user logs into the system through a smart terminal APP, they enter their account and password for authentication. The system verifies the user's role (such as supervisor or administrator) and permission level.

[0103] 2. Device Information Binding: The system reads the unique identifier of the Beidou probe device (such as MAC address and device serial number) and binds it with the logged-in user to confirm the device's legitimacy.

[0104] 3. Dynamic Account Allocation: Based on user permissions and device binding information, the system dynamically allocates available accounts from the continuously running reference station account pool: high-priority users (such as those with urgent tasks) are given priority for high-precision service accounts. Ordinary users are assigned standard service accounts, and the number of concurrent users is limited.

[0105] 4. Real-time authentication and verification: During the location process, the system continuously verifies the validity of the continuously running reference station account: every 10 minutes, it sends an authentication request to the continuously running reference station server to verify whether the account has expired or been disabled. It also checks whether the account usage frequency and traffic exceed preset thresholds to prevent abuse.

[0106] 5. Anomaly Handling Mechanism: If an account anomaly is detected (such as unauthorized device access or frequent authentication failures), the system will immediately take the following measures: terminate the current location service, disconnect from the continuously running reference station server, send an alarm notification to the administrator, and record the anomaly log, including the time, device information, and account status.

[0107] Furthermore, the multipath suppression module employs a combined wavelet transform and adaptive filtering method for denoising. Wavelet transform can decompose the signal at multiple scales, extracting noise features generated by multipath effects; adaptive filtering automatically adjusts filtering parameters according to signal changes, effectively removing noise interference and improving the purity of the positioning signal. This includes the following steps.

[0108] 1. Raw signal acquisition: Acquire the raw carrier phase and pseudorange observation signals output by the Beidou probe equipment as input data for multipath suppression.

[0109] 2. Wavelet Transform Decomposition: Wavelet transform (such as the db4 wavelet basis) is used to decompose the original signal into multi-scale components, breaking it down into sub-signals of different frequencies. The low-frequency component retains the main characteristics of the signal (true positioning signal). The high-frequency component contains noise and interference signals generated by multipath effects.

[0110] 3. Noise Feature Extraction: Analyze the statistical characteristics of high-frequency sub-signals and identify multipath noise features by calculating indicators such as root mean square error (RMSE) and peak value of the signal.

[0111] 4. Adaptive Filtering: Based on noise characteristics, filter parameters are dynamically adjusted, and an adaptive filtering algorithm (such as the Least Mean Square (LMS) algorithm) is used to process high-frequency sub-signals: noise intensity is estimated in real time, and the filter weight coefficients are automatically adjusted. High-frequency noise is filtered out, and useful signal components are retained.

[0112] 5. Signal Reconstruction: The filtered high-frequency sub-signal is reconstructed from the original low-frequency sub-signal to generate a denoised, clean observation signal for subsequent RTK calculations. Signal reconstruction is performed according to the following formula.

[0113] X ^ (t)=A J (t)+Σ[a j (t)D ^ j (t)];aj (t)=1 / {1+exp[γ(RMSE Dj (t)-τ)]};where, X ^ (t) is the denoised, clean observation signal, used for subsequent RTK positioning calculations. A J (t) represents the low-frequency component retained after wavelet transform of the original signal (the result of the J-th level decomposition), characterizing the main features of the signal (such as the true positioning signal). D ^ j (t) represents the high-frequency component after filtering following the decomposition at the j-th level, containing noise and multipath interference signals (j=1,2,......,J)). a j (t) is the high-frequency component D ^ j The weighting coefficients of (t) are dynamically adjusted based on the real-time noise intensity. RMSE Dj (t) represents the root mean square error of the high-frequency components in the j-th layer, serving as a quantitative indicator of the multipath interference intensity. γ is an adjustment coefficient that controls the weighting factor a. j (t) Sensitivity to changes in noise intensity (positive). τ is the noise threshold, used to determine whether high-frequency components need to be suppressed. When RMSE Dj When (t)>τ, the noise is considered strong). exp is an exponential operation with the natural constant e (approximately 2.71828) as the base.

[0114] Furthermore, the ambiguity fixing module employs an optimized LAMBDA algorithm combined with a single-frequency PARC strategy. Targeting the characteristics of single-frequency mobile phones, the LAMBDA algorithm is improved to reduce computational load and increase the speed and success rate of ambiguity fixing, ensuring fast and accurate centimeter-level positioning in various environments. This includes the following steps.

[0115] 1. Initial floating-point solution: Initial positioning solution is performed using the corrected carrier phase observations to obtain the floating-point solution (coordinates and floating-point values ​​of ambiguity without fixed ambiguity).

[0116] 2. LAMBDA Algorithm Optimization: The LAMBDA algorithm is improved to address the characteristics of single-frequency mobile phone observation data. Dimensionality reduction techniques are employed to decrease the search space and reduce computational complexity. This includes the following steps.

[0117] 2.1 Calculate the covariance matrix: Based on the coordinates and ambiguity floating-point values ​​obtained from the initial floating-point solution, calculate the covariance matrix, which reflects the error correlation between the parameters.

[0118] 2.2 Eigenvalue decomposition: The covariance matrix is ​​decomposed into eigenvectors and eigenvalues ​​to analyze the main direction and magnitude of the error of each parameter.

[0119] 2.3 Determining Key Feature Vectors: Sort the feature vectors according to their feature values. The larger the feature value, the more significant the error change in that direction. Select the top few feature vectors with larger feature values, as these vectors contain most of the error information.

[0120] 2.4 Constructing the transformation matrix: Construct a transformation matrix using the selected key feature vectors. This matrix is ​​used to perform dimensional transformation on the original fuzziness parameters.

[0121] 2.5 Perform dimensionality reduction operation: By transforming the matrix, the original high-dimensional fuzzy parameters are projected to the low-dimensional space to obtain the fuzzy parameters after dimensionality reduction, thereby greatly reducing the amount of computation and complexity of the LAMBDA algorithm search.

[0122] Introducing prior information (such as satellite geometric distribution and historical positioning data) narrows the scope of the ambiguity search. This includes the following steps.

[0123] 2.6. Collect satellite geometric distribution data: Obtain the geometric distribution information of visible satellites at the current moment, such as position, elevation angle, and azimuth angle. This data can be extracted from satellite ephemeris and used to analyze the geometric relationship between the satellite and the receiver.

[0124] 2.7 Organize historical positioning data: Summarize the positioning results of the device over a recent period, including coordinates and ambiguity floating-point values, and analyze the variation patterns and stability of positioning parameters.

[0125] 2.8 Constructing Geometric Constraints: Based on satellite geometric distribution data, establish geometric constraints for ambiguity parameters. For example, if a satellite's signal quality is poor due to obstruction by a building, its corresponding ambiguity can be subject to specific restrictions during the search; another example is using the relative positional relationships between satellites to eliminate ambiguity combinations that do not conform to geometric logic.

[0126] 2.9 Establishing Time Smoothing Constraints: Based on historical positioning data, analyze the trend of ambiguity changes over time and set reasonable change thresholds. If the currently calculated ambiguity deviates too much from the historical results and exceeds the threshold range, then that ambiguity combination can be excluded.

[0127] 2.10. Constraint Fusion: Prior information such as geometric constraints and time smoothing constraints are integrated into a unified set of constraints. During the fuzzy search process of the LAMBDA algorithm, only fuzzy combinations that satisfy these constraints are verified and calculated, thereby effectively narrowing the search range and improving the speed and success rate of fuzzy fixation.

[0128] 3. PARC Strategy Application: Implement the Partial Ambiguity Fixation (PARC) strategy: Select ambiguity parameters with higher confidence levels for fixing, while retaining parameters with greater uncertainty as floating-point values. Recalculate the fixed coordinate solution using the least squares method to improve positioning accuracy.

[0129] 4. Ambiguity verification: Verify the fixed ambiguity.

[0130] Calculate the residual between the fixed solution and the floating-point solution, and determine whether the residual is less than a preset threshold (e.g., 3cm).

[0131] Cross-validation using redundant observation data ensures the reliability of fixed ambiguity.

[0132] 5. Output Results: If the ambiguity verification passes, the fixed solution is output as the final centimeter-level positioning result; if the verification fails, the floating-point solution is returned or the ambiguity fixing process is re-executed.

[0133] Furthermore, the precise positioning module: performs data decoding and fusion, error model correction, positioning calculation, and precise positioning of the BeiDou probe based on real-time data from continuously operating reference stations, precise ephemeris data from the International Organization for Global Navigation Satellite Systems (IOGS) and observation data from smartphones' global navigation satellite systems. This includes the following steps.

[0134] 1. Data decoding and fusion.

[0135] 1.1 Continuously Operating Reference Station Data Reception and Decoding: This involves receiving real-time Global Navigation Satellite System (GNSS) observation data from continuously operating reference stations via a network connection. The data format is typically RTCM or NTRIP. The received data is then decoded to extract satellite observation values ​​(such as carrier phase and pseudorange), satellite ephemeris information, and reference station coordinates.

[0136] 1.2 Acquisition of Precise Ephemeris Data from the International Global Navigation Satellite System (IGGS) Service: Precise ephemeris data is obtained from the official website of the IGGS Service or other data providers. Precise ephemeris data contains accurate position and velocity information of satellites. The acquired precise ephemeris data undergoes format conversion and preprocessing to make it compatible with observation data from continuously operating reference stations and smartphones.

[0137] 1.3 Smartphone Global Navigation Satellite System Data Acquisition: Raw observation data is acquired through the smartphone's built-in global navigation satellite system chip, including satellite signal pseudorange, carrier phase, Doppler shift, and other information. Simultaneously, auxiliary positioning information from the phone, such as base station locations and Wi-Fi hotspot information, is obtained to enhance positioning reliability.

[0138] 1.4 Multi-source data time synchronization: Due to potential differences in the acquisition times of data from continuously operating reference stations, precise ephemeris data from the International Organization for Global Navigation Satellite Systems (IOGS), and smartphone observation data, time synchronization of these data is necessary. Using a unified time benchmark, timestamp matching and interpolation are performed on data from each data source to ensure data consistency in time.

[0139] 1.5 Data Quality Control: Perform quality checks on each decoded data source, removing data containing gross errors or noise. For example, identify and remove outlier observations by analyzing indicators such as residuals and signal-to-noise ratios. Repair or interpolate poor-quality data to improve its reliability and usability.

[0140] 1.6 Data Fusion Processing: Data from continuously operating reference stations, which have undergone time synchronization and quality control, along with precise ephemeris data from the International Organization for Global Navigation Satellite Systems (IOGS) and smartphone observation data, are fused. Appropriate weights are assigned to each data source based on their characteristics and accuracy, constructing a unified observation dataset to provide richer and more accurate observation information for subsequent positioning calculations.

[0141] 2. Error model correction.

[0142] 2.1 Ionospheric Error Correction: This section analyzes the impact of the ionosphere on the propagation of signals in the Global Navigation Satellite System (GNSS), and corrects ionospheric errors using dual-frequency observations or ionospheric models (such as the Klobuchar model or the IRI model). For single-frequency receivers (such as most smartphones), error compensation is performed using an ionospheric grid model or ionospheric delay information from continuously operating reference stations.

[0143] 2.2 Tropospheric Error Correction: Considering the delay effect of the troposphere on the global navigation satellite system signal, the tropospheric delay is calculated using tropospheric models (such as the Hopfield model and the Saastamoinen model) based on meteorological data (such as air pressure, temperature, and humidity) and receiver altitude. The calculated tropospheric delay is then corrected using a mapping function to convert the delay in the zenith direction into the delay in the signal propagation direction.

[0144] 2.3 Satellite Orbit Error Correction: Satellite orbit errors are corrected using precise ephemeris data from the International Organization for Global Navigation Satellite Systems (IOGS). The satellite position and velocity information in the precise ephemeris is compared with the broadcast ephemeris to calculate the orbit correction amount, which is then applied to the observation data to improve the accuracy of satellite position.

[0145] 2.4 Satellite Clock Error Correction: Satellite clock error information can be obtained from continuously operating reference station data or from the International Organization for Global Navigation Satellite Systems (IOGS) precise clock error products. Based on the satellite clock error data, the satellite clock error in the observations is corrected to eliminate the impact of the satellite clock being out of sync with the receiver clock on the positioning results.

[0146] 2.5 Multipath Effect Suppression: Analyze the impact of multipath effects on global navigation satellite system signals and employ multipath suppression techniques to reduce errors. For example, by using antenna design (such as choke coil antennas), signal processing algorithms (such as narrow correlator technology), or multipath detection and elimination methods based on geometric features, the impact of multipath effects on positioning accuracy can be reduced.

[0147] 2.6 Receiver Noise Processing: Internal receiver noise is estimated and processed, and its impact on observations is reduced through methods such as smoothing filtering and Kalman filtering. The characteristics of noise from different types of receivers are analyzed, and targeted noise processing strategies are adopted to improve the quality of observations.

[0148] 3. Locating and solving.

[0149] 3.1 Preliminary Point Positioning Calculation: Based on corrected Global Navigation Satellite System (GNSS) observation data, a preliminary position of the receiver is calculated using a point positioning algorithm. Point positioning calculates the receiver's three-dimensional coordinates by measuring the pseudorange from the satellite to the receiver and combining this with satellite position information. The preliminary positioning result can be used as the initial value for subsequent precise positioning.

[0150] 3.2 Relative Positioning Baseline Calculation: The baseline vector between receivers is calculated using a relative positioning method. Differential processing is used to eliminate or reduce common error sources such as satellite orbit errors, ionospheric errors, and tropospheric errors, thereby improving the accuracy of the baseline calculation.

[0151] 3.3 Integer Ambiguity Fixation: In relative positioning, the integer ambiguity of the carrier phase observations is fixed. Algorithms such as LAMBDA (Least Squares Ambiguity Downcorrelation Adjustment) are used to determine the integer solutions for the integer ambiguity through search and verification. Successful integer ambiguity fixation significantly improves positioning accuracy, achieving centimeter-level positioning.

[0152] 3.4 Kalman Filter State Estimation: The Kalman filter algorithm is used to recursively estimate the receiver's state (position, velocity, clock deviation, etc.). By combining global navigation satellite system observations with the system's dynamic model, the receiver's state estimation results are continuously optimized through prediction and updating steps. Kalman filtering can effectively handle noise and uncertainty in dynamic positioning scenarios, improving the real-time performance and stability of positioning.

[0153] 3.5 Positioning Result Quality Assessment: The quality of the calculated positioning results is assessed by calculating positioning accuracy indicators such as position accuracy factors (PDOP, HDOP, VDOP) and root mean square error (RMSE). The reliability and stability of the positioning results are analyzed, and methods such as residual analysis and confidence interval tests are used to determine whether the quality of the positioning results meets the requirements.

[0154] 3.6 Iterative Optimization Solution: Based on the quality assessment results, the positioning solution process is iteratively optimized. If the positioning accuracy does not meet the requirements, the error model parameters are adjusted, observation data is added, or the solution algorithm is improved, and the positioning solution is re-performed until a satisfactory positioning result is obtained.

[0155] 4. The BeiDou probe provides precise positioning.

[0156] 4.1 BeiDou signal processing and fusion.

[0157] The BeiDou Navigation Satellite System provides multiple frequency signals, including B1I, B1C, B2a, and B3I. The BeiDou probe uses multi-frequency fusion technology to fuse the BeiDou signals.

[0158] Three-frequency ionospheric combination: Using the B1I / B2a / B3I three-frequency signals to construct ionospheric combination observations, eliminating the first-order ionospheric delay and significantly improving the positioning accuracy of a single station.

[0159] Inter-frequency bias (IFB) estimation: Through long-term series observations, the hardware delay bias between various BeiDou frequency points is estimated and corrected.

[0160] Multi-constellation joint processing: Simultaneously track satellite signals from BeiDou, Global Positioning System, GLONASS, Galileo, etc., to improve observation redundancy and positioning reliability.

[0161] 4.2 Construction of BeiDou Enhanced Positioning Model. A BeiDou enhanced positioning model is constructed by integrating multi-frequency observation data. Based on this data, error correction algorithms, integer ambiguity resolution, and filtering fusion strategies are integrated to build a comprehensive mathematical framework that includes observation equations, error models, and state estimation. Through systematic modeling of factors such as satellite signals, atmospheric delay, and equipment errors, high-precision positioning results are achieved.

[0162] 4.3 BeiDou Integer Ambiguity Fixation: This includes rapid fixation of wide-lane ambiguity, resolution of narrow-lane ambiguity, and ambiguity quality control.

[0163] Fast fixation of wide-lane ambiguity: Wide-lane combination (wavelength approximately 86cm) is constructed using B1C / B2a frequency points, and the LAMBDA algorithm is used for fast fixation.

[0164] Narrow alley ambiguity resolution: Based on the fixed wide alley ambiguity, the B1I / B3I narrow alley ambiguity (wavelength approximately 10.7cm) is further fixed.

[0165] Ambiguity quality control: Multiple testing strategies, such as the Ratio test and Bootstrap test, are adopted to ensure that the success rate of ambiguity fixation is >99.9%.

[0166] 4.4. Beidou probe enhances positioning.

[0167] 4.4.1 BeiDou Satellite Signal Acquisition and Tracking: Parallel code phase search and carrier loop tracking technology is adopted to achieve simultaneous tracking of more than 28 BeiDou satellites; the tracking loop bandwidth is dynamically adjusted to adapt to highly dynamic scenarios (such as UAV applications).

[0168] 4.4.2 Preprocessing of observation data: Cycle slip detection and repair are performed, and the observation data are preprocessed using the ionospheric residual method and the phase reduction pseudorange method; based on robust estimation theory, a 3σ threshold is set to remove observation gross errors.

[0169] 4.4.3 Parameter Estimation and Solution: The Kalman filter algorithm is used to realize real-time recursive estimation of position, velocity and clock deviation; dynamic model constraints are introduced to improve dynamic positioning accuracy.

[0170] 4.4.4 Positioning Result Quality Control: Calculate the GDOP value (Geometric Precision Factor) to evaluate the positioning geometric strength in real time; detect and remove abnormal observations based on residual analysis and chi-square test; and use RAIM (Receiver Autonomous Integrity Monitoring) technology to provide positioning reliability indicators.

[0171] 4.5. Integration with the BeiDou ground-based augmentation system to achieve higher precision positioning.

[0172] 4.5.1 Differential Correction Data Acquisition: Receives RTCM3.x format differential correction data broadcast by the BeiDou Ground-Based Augmentation System via the NTRIP protocol; supports multiple modes such as Single Base Station Differential (SBAS), Regional Differential (GBAS), and Network Differential (VRS).

[0173] 4.5.2 Differential Positioning Implementation: The received differential corrections are applied to the observation equations to eliminate or reduce satellite-related errors, thereby achieving centimeter-level real-time dynamic positioning (RTK).

[0174] 4.5.3 Multi-source data fusion: It integrates multi-source data such as Beidou satellite signals, ground-based augmentation system differential information, and inertial navigation data (IMU), and adopts a federated Kalman filter architecture to achieve optimal fusion of various data sources.

[0175] 4.6 Positioning Result Output: Based on the state vector estimate after federated Kalman filtering, parameters related to the receiver position (such as longitude, latitude, and altitude components) are extracted as the final BeiDou probe precise positioning result.

[0176] 4.7 Result Verification and Optimization: Compare the output positioning result with a known high-precision reference position (such as the coordinates of a benchmark point obtained through long-term static observation) and calculate the positioning error. If the error exceeds the set threshold, the parameters of the federated Kalman filter can be readjusted, or iterative calculations can be performed using fused data from more epochs until the positioning result meets the accuracy requirements.

[0177] Furthermore, the spatial analysis module performs buffer analysis, real-time query, statistical analysis, category analysis, land category proportion analysis, and overlay analysis, including the following steps.

[0178] 1. Buffer analysis.

[0179] 1.1 Target Patch Selection: Users can specify the target patch (such as illegal building patch) to be analyzed by clicking, selecting by box, or filtering by attributes on the map interface. The system will automatically obtain the spatial geometric information (such as coordinates and shape) of the patch.

[0180] 1.2 Buffer Parameter Settings: Users can set the distance range of the buffer (e.g., 50 meters, 100 meters) according to business needs. The system supports generating buffers at fixed distances or custom multi-level distances.

[0181] 1.3 Buffer Generation: The system generates a buffer polygon centered on the target patch at a set distance. This polygon covers a specified area around the target patch.

[0182] 1.4 Spatial Intersection Analysis: The generated buffer zone is spatially overlaid with thematic layers such as ecological red lines and arable land red lines to identify the intersection areas between the buffer zone and each thematic layer.

[0183] 1.5 Distance Calculation and Visualization: Calculate the minimum distance between the target patch and intersecting thematic layers (such as ecological red line boundaries), mark the distance values ​​on the map with line segments, and highlight the buffer zone and intersecting areas with different colors.

[0184] 1.6 Resource Information Statistics: Classify and statistically analyze natural resource information within the buffer zone, such as cultivated land area, forest land area, and construction land area, generate statistical tables, and mark the statistical results on the map.

[0185] 1.7 Results Output: Users can export the analysis results (map screenshots, statistical reports, etc.) for reporting or formulating remediation plans.

[0186] 2. Real-time query.

[0187] 2.1 Building a data index: The system automatically builds an R-tree index for massive map patch data (vector data), organizing spatial objects (such as map patch polygons) hierarchically, with each node storing the spatial range and pointers to child nodes.

[0188] 2.2 Spatial Query Trigger: Users can initiate a query request for map features within a region by selecting a box, drawing a polygon, or entering a coordinate range on the map interface; or by setting query conditions such as type, area, ownership, and time through the attribute filter box.

[0189] 2.3 R-tree index retrieval: The system uses the R-tree index to quickly filter irrelevant data, locate the map nodes that intersect with the query area, and perform detailed attribute matching only on the map nodes corresponding to these nodes.

[0190] 2.4 Multi-condition combination filtering: The map patch data initially filtered by the R tree index is further filtered according to user-defined attribute conditions (such as "illegal map patch + area > 500 square meters + newly added in 2023") to accurately match the target data.

[0191] 2.5 Query Results Presentation: Highlight matching polygons on the map and display their attribute information (such as number, type, area, location description, etc.) in a list. Supports linked queries between the map and the list.

[0192] 2.6 Results Export and Analysis: Users can export query results as Excel spreadsheets, CSV files, or charts (such as bar charts and pie charts) for data archiving or further analysis.

[0193] 3. Statistical analysis.

[0194] 3.1 Analysis of violation trends.

[0195] 3.1.1 Historical data preprocessing: Collect and clean historical violation patch data, extract key features (such as occurrence time, geographical location, type, scale, etc.), and construct a dataset.

[0196] 3.1.2 Data Mining Modeling: Using statistical methods (such as regression analysis, time series analysis) or machine learning algorithms (such as random forest, LSTM), analyze the spatiotemporal distribution patterns of violations and establish a trend prediction model. The trend prediction model is: Y ^ =(1 / T)Σ T k=1 {f k (x)w k [dist(x,Ω)]}.

[0197] w k [dist(x,Ω)]=exp[-dist(x,Ω)2 / (2δ k 2 )];In the formula, Y ^ This is the predicted value of violation events after integrating spatial information (such as the future probability of violations for a certain plot of land, or the number of violation events in a region). The output can be directly used to generate a heatmap of violation risks, assisting in the allocation of regulatory resources. T is the total number of decision trees in the random forest. The optimal T value is usually determined through cross-validation to balance model accuracy and computational efficiency. k is the decision tree index, k=1,2,3,......,T; f k (x) is the prediction value of the k-th decision tree for sample x. For example, predicting whether a plot of land is an illegal building (0 / 1 classification) or the illegal area (regression value). x is the feature vector of the input sample, including: spatial features, such as latitude and longitude, distance from the ecological red line, road network density, etc.; and non-spatial features, such as land type, approval status, timestamp (e.g., quarter), etc. k `dist(x,Ω)` is the spatial weight function of the k-th tree, ranging from (0,1), used to adjust the importance of the tree's prediction results in the ensemble. `dist(x,Ω)` is the spatial distance from sample `x` to the center `Ω` of the high-incidence area of ​​violations, which can be determined through kernel density analysis of historical violation data, such as the center point of an industrial park. `Exp()` is the natural exponential function, used to convert the distance into exponentially decaying weight values. δ k It is the spatial radius of the k-th tree, a parameter that controls the rate of weight decay.

[0198] 3.1.3 Trend Prediction: Based on the trained model, predict the high-incidence areas and probabilities of violations in the future, generate trend analysis reports, and display them through visualization methods such as map heatmaps and line charts.

[0199] 3.1.4. Recommendations for preventive measures: Based on the forecast results, provide the management department with targeted preventive measures (such as increasing the frequency of patrols in a certain area).

[0200] 3.2 Regional Comparison Report.

[0201] 3.2.1 Regional Division and Data Extraction: Extract natural resource utilization data (such as cultivated land area, construction land area, number of illegal land parcels, etc.) within each administrative region (such as township, district / county) or a custom region.

[0202] 3.2.2 Setting of comparison indicators: Select comparison dimensions (such as farmland protection rate, violation rate, land use rate, etc.) and calculate the indicator values ​​for each region.

[0203] 3.2.3 Chart Generation and Analysis: Through bar charts, radar charts, tables, and other forms, the differences in indicators between different regions are displayed intuitively, and the rationality and existing problems of regional resource utilization are analyzed.

[0204] 3.2.4 Policy Recommendations Output: Based on the comparison results, provide decision-making basis for the rational allocation of resources and regional development planning.

[0205] 3.3 Change monitoring and early warning.

[0206] 3.3.1 Acquisition of multi-temporal data: Periodically acquire remote sensing images or vector data of the same area (such as quarterly or annual updates) as monitoring data sources.

[0207] 3.3.2 Application of change detection algorithm: By using techniques such as image difference and threshold segmentation, data from different periods are compared to identify changes in the area, type, and location of map patches (such as farmland being converted into construction land or forest land being encroached upon).

[0208] 3.3.3 Anomaly detection: Set change thresholds (such as area change exceeding 10% or type change to prohibited development use), automatically analyze detected changes, and mark abnormal change patches.

[0209] 3.3.4 Warning Information Release: Send warning information about abnormal changes to relevant personnel through system pop-ups, SMS, emails, etc., with details of the changed patches (such as location, before and after comparison images, magnitude of change, etc.).

[0210] 3.3.5 Verification and Feedback: Staff will conduct on-site verification based on the warning information. After confirming the violation or irregular behavior, the staff will report the processing results to the system and update the status of the map patch.

[0211] 4. Category analysis.

[0212] 4.1 Setting Analysis Objectives: Users select the categories and dimensions to be analyzed based on their natural resource supervision needs, such as by land use type (cultivated land, forest land, construction land, etc.), violation type (illegal land occupation, illegal construction, etc.), or vegetation type (trees, shrubs, herbs, etc.).

[0213] 4.2 Data Filtering and Extraction: The system filters corresponding data from the spatial database based on the set category standards. For example, if analyzing land use types, the system will extract the land use type attribute fields of all map features, separating different types of map feature data sets.

[0214] 4.3 Category Feature Statistics: Calculate basic statistics such as quantity, area, and distribution range for each type. For example, calculate the percentage of area covered by different vegetation types, or the number of cases for each type of violation. Analyze the spatial relationships between categories, and use spatial autocorrelation analysis (such as Moran's I index) to determine whether similar patches exhibit clustering.

[0215] 4.4 Visualization and Report Output: The results of the category analysis will be displayed in the form of thematic maps, pie charts, bar charts, etc. For example, a land use type distribution map can be generated, with different colors representing different types; a category analysis report can be output to summarize the characteristics and spatial distribution patterns of each category, helping managers to understand the resource distribution pattern.

[0216] 5. Analysis of the proportion of land types.

[0217] 5.1 Land use data integration: The system retrieves all land use map data from the spatial database, classifies and integrates them according to land use codes, and forms a complete land use data set.

[0218] 5.2 Area Calculation and Proportion Statistics: Using the geometric coordinate information of the map features, the area of ​​each map feature is calculated using a geographic information system area calculation algorithm (such as a polygon-based area calculation formula). The areas of all map features of the same land category are aggregated, and their proportion in the total area of ​​the study area is calculated.

[0219] 5.3 Hierarchical Comparison Analysis: Supports calculation and comparison of land use proportions by different administrative levels or custom regions. For example, comparing the proportion of cultivated land in different districts and counties, or analyzing the changing trend of the proportion of construction land in a certain region over time.

[0220] 5.4 Results Presentation and Application: The land use proportion results are presented in a visual manner, such as percentage values, stacked bar charts, and pie charts; providing data support for land planning and resource protection policy formulation, such as assessing the pressure on arable land protection based on the proportion of arable land and assisting in the delineation of permanent basic farmland protection zones.

[0221] 6. Overlay analysis.

[0222] 6.1 Layer Selection and Preprocessing: Users select the layers to participate in the overlay analysis on the system map interface, such as overlaying the cultivated land red line layer, the ecological protection red line layer, and the current land use layer; ensure that the coordinate systems of each layer are consistent, and if they are inconsistent, the coordinate transformation module will automatically unify them.

[0223] 6.2 Spatial overlay operation: Using overlay algorithms (such as intersection, merging, erasure, etc.) in the spatial analysis of geographic information systems, a new overlay result layer is generated.

[0224] 6.3 Attribute Fusion and Statistics: The attribute information of the overlay layers is fused to generate a new attribute table containing attribute fields from multiple layers. For example, after overlay analysis, the new patch attributes include information such as the protection level of the cultivated land red line, the functional zoning of the ecological protection red line, and the current land use type; the number and area of ​​patches with different attribute combinations in the overlay results are statistically analyzed.

[0225] 6.4 Conflict Detection and Analysis: Automatically detects conflict areas between overlay layers, calculates the area and location of conflict areas, generates conflict analysis reports, and provides a basis for natural resource planning adjustments; highlights conflict areas on the map to help inspectors quickly locate problem areas.

[0226] Furthermore, the hybrid navigation system uses Baidu Maps' standardized interface for route planning, including the following steps.

[0227] 1. System initialization configuration: Load Baidu Maps standardized interface software development kit (select Web JavaScript standardized interface or mobile Android / iOS software development kit according to terminal type); start coordinate system transformation engine to complete the coordinate adaptation between Beidou positioning data (WGS84) and Baidu Maps (BD09); initialize SQLite spatial database and create POI layer data cache table structure.

[0228] 2. Route planning parameter settings: Receive inspection task information, extract the starting point (current BeiDou positioning location), the destination (coordinates of the inspection target patch), and the task priority; select the navigation mode (walking / driving / cycling / off-road), configure the route calculation weight (such as the weight for improving traffic efficiency in emergency tasks); call the Baidu Maps route planning interface, pass in the basic parameters, and obtain the initial planned route.

[0229] 3. Loading of POI Layers for Inspection: Based on the current inspection area boundary, request POI data (including 6 major categories and 23 subcategories of attribute information) within that area from the server; perform coordinate transformation on the POI data (WGS84→BD09) to achieve spatial alignment with the Baidu Map base map. Load POI symbols by type (red solid polygons / yellow dashed boxes / annual color-coded dots), and set the initial transparency (default 50%).

[0230] 4. Navigation Execution and Dynamic Adjustment: Real-time navigation is initiated, and the location update (1Hz frequency) of the Beidou positioning module is received synchronously; when the deviation between the actual trajectory and the planned path is greater than 5 meters, the local path recalculation is triggered through the Baidu Maps standardized interface; meteorological warning information of the road ahead is monitored, and if there is a risk, alternative routes are automatically pushed and the risk level is marked.

[0231] 5. POI Layer Interaction and Linkage: When navigation enters the POI buffer zone (configurable from 50 to 500 meters), a pop-up window automatically displays the detailed attributes of the POI and associated tasks; supports users to initiate quick business operations (adding records / associating photos, etc.) through gesture operations (long press on the POI icon); detects POI density, and automatically starts aggregate display when there are more than 5 within 100 meters, and clicking the aggregate icon can expand the list of individual POIs.

[0232] 6. Real-time navigation status maintenance: Continuously monitors location updates and synchronously refreshes map display and POI relative positions; periodically checks network status and switches to offline mode when there is no network (using pre-downloaded map tiles and cached POI data); after the task is completed, it automatically saves the navigation trajectory to the system and generates field navigation logs by associating POI interaction records.

[0233] 7. Layer Management and Access Control: Provides a layer visibility switch and transparency adjustment slider (10%-100%), supports filtering display by POI type; filters sensitive POI data that exceeds the permission range based on the logged-in user's role (e.g., township-level users only display POIs within their jurisdiction); automatically clears temporary cache data after navigation ends, and retains frequently used POIs in the local database for quick loading next time.

[0234] Furthermore, the spatial analysis engine provides land use compliance analysis capabilities, including the following steps.

[0235] 1. Analysis Task Initialization: Receive analysis trigger command (AR voice command or manual selection of area), parse to obtain BeiDou coordinates (WGS84 format) of the analysis range; convert the coordinate format (WGS84→2000 coordinate system) to generate a polygon boundary with an accuracy of 0.1 square meters; start the quadtree coding tool to create a spatial index for the analysis range for quick location of related data.

[0236] 2. Land Survey Cloud Standardized Interface Authorization and Authentication: Read user role information, generate OAuth2.0 authorization request parameters; send a token application to the Land Survey Cloud authentication server to obtain an access token valid for 2 hours; verify the token's permission scope to ensure that the current user can access the survey data of the target area.

[0237] 3. Multi-type data retrieval and parsing: Call the "National Land Survey Results" interface, pass in the spatial index parameters, and obtain land parcel data (including 28 attributes) within the analysis range; call the "Approval Data" interface to request land use approval documents for the past 5 years, and establish an associated index according to "Approval Document Number + Parcel Code"; call the "Ecological Protection Red Line" interface to obtain red line boundary vector data, and perform coordinate alignment (deviation controlled within 0.5 meters); parse the returned data in both JSON and GeoJSO formats, and remove invalid fields (such as redundant log information).

[0238] 4. Compliance Verification Rule Loading: Load three levels of verification rules from the rule base: national basic rules (such as the provisions of the Land Administration Law), local supplementary rules (such as provincial farmland protection rules), and temporary special rules (such as quarterly inspection priorities); parse the rule expressions (such as "farmland converted to construction land → must include approval for conversion of agricultural land") and convert them into executable verification logic.

[0239] 5. Three-dimensional compliance verification execution.

[0240] 5.1 Land Category Consistency Verification: Compare the current land categories interpreted by remote sensing with the land categories filed in the Third National Land Survey, calculate the changed area and proportion, and mark the "unapproved change" areas.

[0241] 5.2 Verification of Approval Procedures: The correlation between approval documents and land parcels is verified through keyword matching algorithms to identify violations such as "construction without approval" and "occupying more land than approved".

[0242] 5.3 Ecological Red Line Verification: Activate the buffer zone analysis tool (default 100 meters) to calculate the percentage of overlap between the plot and the red line. If the overlap exceeds 5%, a Level 1 warning will be triggered.

[0243] 6. Result Quantification Rating and Report Generation: Compliance score is calculated using a weighted model (land category consistency 40% + approval completeness 35% + red line compliance 25%); grades are assigned based on the score; and an analysis report containing a spatial distribution map, violation details table, and rectification suggestions is automatically generated, supporting export in PDF / Excel format.

[0244] 7. Data Caching and Update Maintenance: Cache the accessed land survey data and approval documents locally (memory + disk two-level cache), setting a 24-hour validity period; monitor the Land Survey Cloud's "Data Update Notification" interface, triggering incremental updates upon receiving changed data. Regularly clean up expired cache.

[0245] This invention provides a natural resource supervision method based on BeiDou satellite navigation and positioning, comprising the following steps.

[0246] S1. The data collection module collects address data of natural resources and labels the data.

[0247] S2, the route planning module, and the hybrid navigation system plan the optimal route based on the address data of natural resources and the needs of supervision; supervisors conduct supervision of natural resources through Beidou probe equipment and smartphones.

[0248] S3, the data acquisition module acquires real-time data from continuously operating reference stations, precise ephemeris data from the International Global Navigation Satellite System Service (IGNSS), and global navigation satellite system data from smartphones.

[0249] S4, the precise positioning module decodes and fuses real-time data from continuously operating reference stations, precise ephemeris data from the International Organization for Global Navigation Satellite Systems (IOGS) and observation data from smartphones' global navigation satellite systems, corrects error models, calculates positioning results, and performs precise positioning using the BeiDou probe to achieve precise positioning.

[0250] S5, the positioning and navigation module provides navigation services for inspectors.

[0251] S6, the map data module integrates three major components: dynamic layer management, spatial database, and data synchronization engine, to achieve efficient management, storage, display, and updating of geographic data.

[0252] S7, the intelligent supervision module integrates the evidence collection workflow engine, trajectory management module and business approval module to achieve intelligent supervision, trajectory tracking and approval management of business processes.

[0253] S8, the spatial analysis module performs buffer analysis, real-time query, and statistical analysis.

[0254] S9, the security and authentication module ensures data security, user permission compliance, and communication security. The spatial analysis engine provides land use compliance analysis functions.

[0255] S10, the system management module, is responsible for the unified management and monitoring of basic resources such as system devices, configurations, and logs to ensure the stable, efficient, and secure operation of the system.

[0256] The working principle of this invention's natural resource supervision system based on BeiDou satellite navigation and positioning is as follows: A data collection module collects and labels the address data of natural resources; a route planning module plans the optimal route based on the address data and supervision requirements; supervisors conduct inspections of natural resources using BeiDou probe devices and smartphones; a data acquisition module acquires real-time data from continuously operating reference stations, precise ephemeris data from the International Organization for Satellite Navigation Services (IOSS), and global navigation satellite system data from smartphones; a precise positioning module decodes and fuses the real-time data from continuously operating reference stations, ISS precise ephemeris data, and smartphone global navigation satellite system observation data, corrects error models, calculates positioning, and performs precise positioning using the BeiDou probe, achieving accurate positioning; a positioning and navigation module provides navigation services for supervisors; and a map data module integrates dynamic layer management, a spatial database, and a data synchronization engine to achieve efficient management, storage, display, and updating of geographic data. An intelligent supervision module integrates an evidence collection workflow engine, a trajectory management module, and a business approval module to achieve intelligent supervision, trajectory tracking, and approval management of business processes. The spatial analysis module performs buffer analysis, real-time querying, and statistical analysis; the security and authentication module ensures data security, user permission compliance, and communication security; and the system management module is responsible for the unified management and monitoring of basic resources such as system devices, configurations, and logs to ensure the stable, efficient, and secure operation of the system.

[0257] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for natural resource supervision based on BeiDou satellite navigation and positioning, characterized in that, Includes the following steps: S1. The data collection module collects address data, vegetation data, and building data of natural resources and annotates the data; The S2 path planning module and hybrid navigation system plan the optimal route; inspectors use Beidou probe equipment and smartphones to inspect natural resources. S3, the data acquisition module acquires real-time data from continuously operating reference stations, precise ephemeris data from the International Global Navigation Satellite System Service (IGSS) and global navigation satellite system data from smartphones; S4, the precise positioning module performs data decoding and fusion, error model correction, and precise positioning of the BeiDou probe by real-time data from continuously operating reference stations, precise ephemeris data from the International Organization for Global Navigation Satellite Systems Service (IOGS) and global navigation satellite system observation data from smartphones. S5, the positioning and navigation module provides navigation services for inspectors; S6, the map data module, enables efficient management, storage, display, and updating of geographic data; S7, the intelligent supervision module provides intelligent supervision, trajectory tracking and approval management for business processes; S8, the spatial analysis module performs buffer analysis, real-time query, statistical analysis, land category analysis, land category proportion analysis, and overlay analysis; S9, the security and authentication module ensures data security, user permission compliance, and communication security; the spatial analysis engine provides land use compliance analysis functions; S10, the system management module, is responsible for the unified management and monitoring of system devices, configurations, and log resources to ensure the stable, efficient, and secure operation of the system.

2. The natural resource supervision method based on BeiDou satellite navigation and positioning according to claim 1, characterized in that: Step S4 includes the following steps: S41. Data Decoding and Fusion: Receives real-time global navigation satellite system observation data transmitted by continuously operating reference stations; decodes the received data from continuously operating reference stations; acquires precise ephemeris data from the International Global Navigation Satellite System Service (IGNSS) and global navigation satellite system data from smartphones; performs timestamp matching and interpolation on data from various data sources; and fuses the data from various data sources. S42, Error Model Correction; S43. Perform location calculation on the corrected data; S44, Beidou probe for precise positioning.

3. The natural resource supervision method based on BeiDou satellite navigation and positioning according to claim 2, characterized in that: Step S43 includes the following steps: S43.1 Preliminary calculation of single-point positioning: Based on the corrected observation data of the global satellite navigation system, the preliminary position of the receiver is calculated using the single-point positioning algorithm; S43.2, Relative Positioning Baseline Calculation: The baseline vector between receivers is calculated using the relative positioning method; S43.3 Fixed Integer Ambiguity: Fixed solution for integer ambiguity of carrier phase observations; S43.4 Kalman Filter State Estimation: The state of the receiver is recursively estimated using the Kalman filter algorithm; S43.5 Positioning Result Quality Assessment: The quality of the calculated positioning results is assessed, and the positioning accuracy index is calculated; the reliability and stability of the positioning results are analyzed, and it is determined whether the quality of the positioning results meets the requirements. S43.6 Iterative optimization solution: Based on the quality assessment results, the positioning solution process is iteratively optimized.

4. The natural resource supervision method based on BeiDou satellite navigation and positioning according to claim 2, characterized in that: Step S44 includes the following steps: S44.1, BeiDou signal processing fusion; S44.2 Construction of BeiDou Enhanced Positioning Model: Integrating multi-frequency observation data to construct a BeiDou enhanced positioning model; S44.3, BeiDou Integer Ambiguity Fixation: including wide-lane ambiguity rapid fixation, narrow-lane ambiguity resolution, and ambiguity quality control; S44.4, BeiDou probe-enhanced positioning: The integration of S44.5 and BeiDou ground-based augmentation systems enables higher-precision positioning. S44.6 Positioning Result Output: Based on the state vector estimate after federated Kalman filtering, parameters related to the receiver position are extracted from it as the final BeiDou probe precise positioning result; S44.7 Result Verification and Optimization: Compare the output positioning results with the known high-precision reference position and calculate the positioning error.

5. The natural resource supervision method based on BeiDou satellite navigation and positioning according to claim 4, characterized in that: Step S44.4 includes the following steps: S44.4.1, BeiDou satellite signal acquisition and tracking: Parallel code phase search and carrier loop tracking technology is adopted to achieve simultaneous tracking of more than 28 BeiDou satellites; S44.4.2 Observation data preprocessing: Cycle slip detection and repair are performed, and the observation data are preprocessed using the ionospheric residual method and the phase reduction pseudorange method; S44.4.3 Parameter Estimation and Solution: The Kalman filter algorithm is used to realize real-time recursive estimation of position, velocity, and clock deviation; dynamic model constraints are introduced to improve dynamic positioning accuracy. S44.4.4 Positioning Result Quality Control: Real-time assessment of positioning geometric strength; detection and removal of outlier observations based on residual analysis and chi-square test; provision of positioning reliability indicators.

6. The natural resource supervision method based on BeiDou satellite navigation and positioning according to claim 4, characterized in that: Step S44.5 includes the following steps: S44.5.1 Differential Correction Data Acquisition: Receive differential correction data in RTCM3.x format broadcast by the BeiDou Ground-Based Augmentation System via the NTRIP protocol; S44.5.2 Differential Positioning Implementation: The received differential correction is applied to the observation equation to eliminate or reduce satellite-related errors and achieve centimeter-level real-time dynamic positioning; S44.5.3 Multi-source data fusion: It integrates multi-source data such as Beidou satellite signals, ground-based augmentation system differential information, and inertial navigation data, and adopts a federated Kalman filter architecture to achieve optimal fusion of various data sources.

7. The natural resource supervision method based on BeiDou satellite navigation and positioning according to claim 1, characterized in that: Step S8 includes the following steps: S81, Buffer Analysis; S82. Real-time query: Automatically builds R-tree index to organize massive map patch data hierarchically. After users initiate query requests through various methods, the system uses R-tree index to quickly locate relevant map patch nodes, combines multiple conditions to filter and accurately match target data, and finally highlights the results. S83. Statistical Analysis: Conduct violation trend analysis, generate regional comparison reports, and monitor and warn of changes; S84. Category Analysis: Classify and statistically analyze the map patch data according to the preset classification dimensions to obtain the quantity, area and spatial distribution characteristics of different categories of map patches; S85. Land Category Proportion Analysis: The area of ​​different land category patches within the study area is summarized and calculated to obtain the area proportion of each land category in the study area. S86. Overlay Analysis: Perform spatial overlay operation on at least two thematic layers to generate an overlay result layer, and perform statistical analysis on the attribute information and spatial conflict areas in the overlay result.

8. The natural resource supervision method based on BeiDou satellite navigation and positioning according to claim 7, characterized in that: Step S81 includes the following steps: S81.1 Target Patch Selection: Select the target patch to be analyzed and obtain its spatial geometric information; S81.2 Buffer Parameter Settings: Set the distance range of the buffer; S81.3 Buffer Generation: Generate a buffer polygon centered on the target patch at a set distance; S81.4 Spatial Intersection Analysis: The generated buffer zone is spatially overlaid with the ecological red line and arable land red line thematic layers to identify the intersection areas between the buffer zone and each thematic layer; S81.5 Distance Calculation and Visualization: Calculate the minimum distance between the target patch and the intersecting thematic layers, mark the distance values ​​on the map with line segments, and highlight the buffer zone and intersecting area with different colors; S81.6 Resource Information Statistics: Classify and statistically analyze natural resource information within the buffer zone; S81.7 Visualization of analysis results.

9. The natural resource supervision method based on BeiDou satellite navigation and positioning according to claim 2, characterized in that: Step S5 includes the following steps: The S51 centimeter-level positioning engine achieves high-precision positioning through multi-source data fusion and error correction. The S511 and RTK differential algorithm is implemented to correct the raw observation data obtained by the Beidou probe equipment by receiving differential data from the continuously operating reference station system, thereby eliminating errors in the satellite signal propagation process. S512. The security and legality of the location service are ensured by the continuous operation of the reference station account dynamic authentication mechanism. S513, the multipath suppression module uses a combination of wavelet transform and adaptive filtering to improve the purity of the positioning signal; S514, the ambiguity fixing module performs accurate positioning; S52, the coordinate transformation module enables rapid conversion between different coordinate systems, adapting to map services and business needs on multiple platforms; S53, the navigation service module, provides intelligent navigation guidance for inspectors based on high-precision positioning and map data.

10. The natural resource supervision method based on BeiDou satellite navigation and positioning according to claim 9, characterized in that: Step S511 includes the following steps: S511.1 Raw Data Acquisition: Acquire raw satellite observation data; S511.2, Base Station Data Acquisition: Acquire observation data and differential correction information from nearby base stations; S511.3 Data Matching and Synchronization: Match the raw data collected by the probe equipment with the base station data of the continuously operating reference station system in terms of time and satellite number; S511.4 Error Correction Calculation: Error correction is performed on the raw observation data of the probe equipment. S511.5 Positioning Calculation: Positioning calculation is performed using the corrected observation data, and the user's precise location coordinates are calculated using the least squares method; S511.6 Result Output: Output the final centimeter-level positioning result.

11. The natural resource supervision method based on BeiDou satellite navigation and positioning according to claim 9, characterized in that: Step S513 includes the following steps: S513.1 Raw Signal Acquisition: Acquire the raw carrier phase and pseudorange observation signals output by the Beidou probe equipment; S513.2 Wavelet Transform Decomposition: Wavelet transform is used to decompose the original signal into multiple scales; S513.3 Noise Feature Extraction: Analyze the statistical characteristics of high-frequency sub-signals and identify multipath noise features; S513.4 Adaptive Filtering Processing: Adaptive filtering algorithm is used to process high-frequency sub-signals; S513.5 Signal Reconstruction: The filtered high-frequency sub-signal is reconstructed with the original low-frequency sub-signal to generate a denoised and clean observation signal.

12. The natural resource supervision method based on BeiDou satellite navigation and positioning according to claim 9, characterized in that: Step S514 includes the following steps: S514.1 Initial floating-point solution: Initial positioning solution is obtained by using the corrected carrier phase observation values; S514.2, LAMBDA Algorithm Optimization: In view of the characteristics of single-frequency mobile phone observation data, the LAMBDA algorithm is improved by using dimensionality reduction technology to reduce the search space and reduce computational complexity; S514.3 PARC Strategy Application: Select ambiguity parameters with higher confidence levels for fixing, retain parameters with greater uncertainty as floating-point values, and recalculate the fixed coordinate solution using the least squares method to improve positioning accuracy; S514.4 Ambiguity Verification: Verify the fixed ambiguity; S514.5 Result Output: If the ambiguity verification passes, the fixed solution is output as the final centimeter-level positioning result; if the verification fails, the floating-point solution is returned or the ambiguity fixing process is re-executed.

13. A natural resources supervision system based on BeiDou satellite navigation and positioning, comprising: The system comprises a data collection module, a data acquisition module, a path planning module, a BeiDou probe device, a hybrid navigation system, a smart terminal, a precise positioning module, a positioning and navigation module, a map data module, an intelligent supervision module, a spatial analysis module, a security and authentication module, a software development kit integration module, and a system management module; its features are: Data collection module: Collects address data, vegetation data, and building data of natural resources, and annotates the data; Data acquisition module: Acquires real-time data from continuously operating reference stations, precise ephemeris data from the International Organization for Global Navigation Satellite Systems (IOGS) and global navigation satellite system data from smartphones; Route planning module: Based on the address data of natural resources and the inspection requirements, the module plans the inspection route and, based on the core computing engine algorithm, plans the optimal route. Beidou probe equipment: Supports multi-frequency signal reception of Beidou / Global Navigation Satellite System, and can simultaneously receive satellite signals from multiple frequency bands; adopts Bluetooth 5.0 low-power communication technology; Hybrid navigation system: Uses Baidu Maps' standardized interface for route planning and supports overlaying of special point-of-interest layers for inspection purposes; Smart terminals: including smartphones and tablets, integrating accelerometers and gyroscopes; Precise positioning module: Decodes and fuses real-time data from continuously operating reference stations, precise ephemeris data from the International Organization for Global Navigation Satellite Systems (IOGS) and observation data from smartphone global navigation satellite systems, corrects error models and calculates positioning results to achieve precise positioning; Positioning and navigation module: includes a centimeter-level positioning engine, a coordinate transformation module, and a navigation service module; Map data module: includes dynamic layer management, spatial database and data synchronization engine; The intelligent supervision module includes an evidence collection workflow engine, a trajectory management module, and a business approval module. Spatial analysis module: performs buffer analysis, real-time querying, and statistical analysis; Security and authentication module: including Bluetooth security protocol, user access control and data security modules; Software Development Kit Integration Module: Integrated onto the mobile phone; includes core software development kit interfaces, third-party system integration modules, and cross-platform support modules; System management module: includes device management center, system configuration module and log monitoring module.

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