Land surveying and mapping field acquisition system based on mobile terminal

Abstract

The invention relates to the technical field of land surveying and mapping, in particular to a land surveying and mapping field acquisition system based on a mobile terminal, which comprises a hardware resource scheduling layer, a high-precision positioning processing engine, a field operation interaction engine, a real-time data quality verification module, an off-line and on-line asynchronous collaborative system and a multi-source heterogeneous data encapsulation center. According to the system, differential correction data and inertial navigation data are fused by using a positioning engine to realize centimeter-level positioning; executing geometric topology check and spatial precision evaluation through a verification module; and realizing incremental synchronization of end cloud data by adopting an asynchronous collaborative system. The invention aims to solve the problems that surveying and mapping depend on special hardware and the working continuity is poor in a weak network environment. By adopting the technical scheme, high-precision and high-reliability field acquisition is realized, the deployment cost is remarkably reduced, and the operation efficiency and the data quality are improved.

Description

Technical Field

[0001] This invention relates to the field of land surveying technology, and in particular to a land surveying field data acquisition system based on a mobile terminal. Background Technology

[0002] With the increasing demands for land resource management and territorial spatial planning, land surveying, as a fundamental task, places higher demands on the real-time nature, accuracy, and efficiency of data acquisition and field operations. In recent years, the development of mobile terminal technology has provided new possibilities for field surveying; however, existing technologies still have significant shortcomings in terms of system integration, data interaction methods, and mobile terminal adaptability, making it difficult to meet the needs of efficient, intelligent, and integrated land surveying field operations. A search revealed a method for real-time data acquisition and processing in land surveying operations, published under publication number CN114493333A. This method proposes a data processing flow based on a combination of image acquisition and zoning surveying, achieving land functional zoning through image information construction, field surveying, and potential analysis. However, this solution mainly relies on image processing and backend analysis, failing to explicitly adopt general-purpose mobile terminals (such as smartphones or tablets) as the core acquisition device. It lacks deep integration of mobile terminal hardware capabilities (such as GNSS, cameras, and sensors) and does not provide a lightweight, interactive data entry and real-time verification mechanism for field personnel, resulting in limitations in field adaptability, ease of operation, and immediate data feedback.

[0003] On the other hand, a method for collecting and processing information for land surveying and real-time monitoring using field equipment was disclosed (publication number CN101159068B). This method employs a portable handheld device as a mobile GIS platform, combining a total station with satellite positioning technology to achieve on-site mapping and data uploading. Although this solution incorporates mobile communication networks and Bluetooth / serial communication, providing some on-site data collection capabilities, its reliance on a dedicated handheld device rather than widely available commercial mobile terminals (such as Android / iOS devices) results in high hardware costs, complex maintenance, and a closed application ecosystem. Furthermore, the system does not fully utilize the multi-mode positioning (such as BeiDou + GPS + GLONASS), high-precision RTK support, offline map caching, and cloud synchronization capabilities of modern mobile terminals, leading to poor adaptability in weak or no-network environments and difficulty in supporting large-scale, high-efficiency land surveying operations.

[0004] The aforementioned problems indicate that existing technologies either focus on backend data processing while neglecting mobile terminal on-site interaction capabilities, or rely on dedicated hardware without adapting and optimizing for general mobile terminals, resulting in insufficient system flexibility, high deployment costs, and poor user experience. Therefore, this invention provides a land surveying on-site data acquisition system based on mobile terminals, aiming to fully utilize the hardware and software capabilities of general mobile devices such as smartphones to achieve integrated on-site operations encompassing high-precision positioning, structured data entry, multimedia acquisition, online / offline collaboration, and real-time quality control, thereby improving the intelligence level and field efficiency of land surveying. Summary of the Invention

[0005] To achieve the above-mentioned objectives, this invention provides a land surveying and mapping field data acquisition system based on a mobile terminal. The system includes: a hardware resource scheduling layer, a high-precision positioning processing engine, a field operation interaction engine, a real-time data quality verification module, an online / offline asynchronous collaborative system, and a multi-source heterogeneous data encapsulation center.

[0006] The hardware resource scheduling layer is integrated into the system kernel of the general-purpose mobile terminal. It calls the GNSS chip, MEMS inertial sensor, CMOS optical imaging sensor, and mobile communication module built into the mobile terminal through a hardware abstraction interface. The hardware resource scheduling layer establishes a sensor data synchronization clock to uniformly map sensor data with different sampling frequencies to a nanosecond-level time coordinate system, eliminating the timing deviation of asynchronous sensor data when projected into space.

[0007] The high-precision positioning processing engine establishes an NTRIP link with the base station network through the mobile communication module to acquire differential correction data in RTCM format in real time. The positioning processing engine includes a carrier phase observation processing unit, a multipath effect suppression unit, and a combined navigation fusion unit. The carrier phase observation processing unit uses a dual-frequency, three-band carrier phase differential algorithm to solve the raw observations output by the mobile terminal's GNSS chip, obtaining centimeter-level positioning coordinates. The multipath effect suppression unit constructs a weighted least squares estimation algorithm based on the signal-to-noise ratio and elevation angle of the satellite signal to dynamically eliminate non-line-of-sight signals caused by obstruction from buildings and vegetation. The combined navigation fusion unit uses the Extended Kalman Filter (EKF) algorithm to deeply couple the GNSS positioning results with the angular velocity and acceleration data output by the MEMS inertial sensor. Even when the GNSS signal is lost, it maintains the continuity of positioning accuracy through a dead-forward algorithm. Its discretized prediction equation is described as: ,in, express The state vector at any given time includes three-dimensional coordinates, three-dimensional velocity, and attitude angle. This indicates the input quantity of the MEMS inertial sensor. This indicates system process noise.

[0008] The on-site operation interaction engine is built on the application layer of the mobile terminal and includes a dynamic base map rendering module, a vector feature editing module, and a structured attribute input module. The dynamic base map rendering module supports tiled loading of vector maps and high-resolution remote sensing imagery, utilizing a GPU for accelerated rendering. The vector feature editing module supports on-site point marking, line drawing, and polygon closure operations based on coordinate capture, and incorporates a touch sensitivity compensation algorithm to accurately convert screen coordinates into geospatial coordinates. The structured attribute input module dynamically generates hierarchical input forms based on preset land survey metadata standards, achieving standardized collection of land parcel categories, ownership units, ownership nature, and current land use status.

[0009] The real-time data quality verification module runs in the edge computing environment of the mobile terminal, intercepting workflow data in real time to perform logical constraint checks. The verification module includes a geometric topology check submodule, a spatial accuracy assessment submodule, and an attribute logic verification submodule. The geometric topology check submodule performs self-intersection detection, overlap detection, and gap detection in real time during polygon acquisition. If the currently acquired feature has a topological conflict with an existing vector base map, the system forcibly triggers an early warning and locks the submission interface. The spatial accuracy assessment submodule calculates the standard deviation of the current observation point in real time based on the GNSS positioning accuracy factor PDOP value and difference decomposition status. When the standard deviation exceeds a set threshold (e.g., 0.05m), the system automatically switches to a multiple sampling average mode. The attribute logic verification submodule establishes an attribute association rule base and uses regular expressions and conditional constraint logic to perform real-time legality checks on the entered text and numerical values, ensuring the uniqueness and accuracy of the data at the acquisition source.

[0010] The online / offline asynchronous collaborative system employs a dual-layer storage architecture of a local cache database and a cloud database. In the absence of network coverage, all collected data is temporarily stored in a local SQLite extended database and assigned a unique globally unique identifier (UUID). When the mobile communication module detects network recovery, the collaborative system initiates an incremental synchronization algorithm, constructing an incremental packet including geographic coordinate increments, attribute modification records, and multimedia file hash values. This incremental synchronization algorithm uses a version vector-based conflict resolution strategy, automatically identifying and resolving concurrent conflicts during multi-user collaborative work by comparing local timestamps with cloud version numbers. The synchronization process utilizes a breakpoint resume protocol to ensure data integrity during the transmission of large files (such as panoramic videos and high-fidelity photos).

[0011] The multi-source heterogeneous data encapsulation center is responsible for atomizing and encapsulating various types of hash data collected on-site. It encapsulates high-precision spatial coordinates, structured attribute records, on-site photographs with azimuth and tilt information, and GNSS trajectory logs recording the acquisition process into a unified land surveying result package using binary serialization technology. The result package contains a spatiotemporal correlation index, enabling the binding of multimedia data with the geographical location of spatial geometric elements. The encapsulation center also includes a digital signature unit, which encrypts and signs the result package using the hardware security module of the mobile terminal, ensuring the immutability of the data during transmission and post-processing.

[0012] Furthermore, as a preferred embodiment of the present invention, the high-precision positioning processing engine also includes a signal-to-noise ratio compensation mechanism for insufficient antenna gain of the mobile terminal. This mechanism establishes a spherical harmonic function model of the mobile terminal antenna gain, and corrects the received carrier phase by adjusting the phase center deviation (PCO) and phase center variation (PCV) based on the current azimuth and elevation angle of the satellite in the celestial coordinate system, thereby virtually normalizing the antenna center of the general mobile terminal to the geometric centroid.

[0013] Furthermore, as a preferred embodiment of the present invention, the real-time data quality verification module also integrates an indoor and outdoor integrated positioning verification function based on visual inertial odometry (VIO). When entering GNSS blind zones (such as culverts or forest-covered areas), the system activates the optical imaging sensor to extract environmental feature points and performs relative displacement calculations in conjunction with inertial navigation data. The accuracy of the calculations is corrected in real time through a closed-loop detection algorithm to ensure the accuracy of plot boundary delineation in complex environments.

[0014] Furthermore, as a preferred embodiment of the present invention, the on-site operation interaction engine also has an intelligent automatic layer switching function. Based on the current GNSS coordinates and the current task type, the system automatically pre-fetches and renders the surrounding ownership red line layer, basic farmland protection zone layer, and the previous year's land use status layer from the cloud index library, enabling a virtual-real comparison analysis of the data collection site and assisting operators in quickly identifying land use changes.

[0015] Furthermore, as a preferred embodiment of the present invention, the online / offline asynchronous collaborative system also includes a topology data compression algorithm based on differential coding. This algorithm, targeting large-scale vector features, extracts the relative offsets of adjacent coordinate points for bitmap encapsulation, effectively reducing the bandwidth occupancy of synchronized spatial data in weak network environments and improving the response efficiency of edge-cloud interaction.

[0016] Furthermore, as a preferred embodiment of the present invention, the multi-source heterogeneous data encapsulation center supports dynamically adjusting the encapsulation structure of data packets according to different surveying task templates. For example, in a land consolidation project on-site data collection task, the encapsulation center automatically associates earthwork volume calculation parameters with on-site survey photos; in a real estate surveying task, it automatically associates the property floor plan and the signature information of the rights holder.

[0017] Furthermore, as a preferred embodiment of the present invention, the hardware resource scheduling layer establishes a refined power consumption management strategy. This strategy dynamically adjusts the GNSS sampling frequency and GPU rendering refresh rate based on the data contribution of the sensors and the current remaining battery power. When the positioning state is stable and static, the system automatically reduces the IMU sampling frequency to 20Hz and enters a low-power standby mode, responding to location movement through an external interrupt wake-up mechanism, thereby extending the battery life of the mobile terminal in field operation environments.

[0018] Furthermore, as a preferred embodiment of the present invention, the spatial accuracy assessment submodule in the real-time data quality verification module employs a robust estimation algorithm based on residual analysis. During multi-reference station differential calculations, the system calculates the posterior residuals of each observation to identify and suppress anomalous satellite messages subject to strong electromagnetic interference, ensuring the output coordinate sequence has smoothness and high confidence.

[0019] Furthermore, as a preferred embodiment of the present invention, the on-site operation interaction engine integrates an AR (Augmented Reality) auxiliary module. This module utilizes the mobile terminal's camera and posture sensor to overlay and render the ownership boundaries and planning red lines from the base map onto the real-world image in real time. Operators can visually observe the degree of conformity between the boundary points and the actual terrain features through the display unit, and can graphically correct any deviations in the boundary lines via screen interaction.

[0020] Furthermore, as a preferred embodiment of the present invention, the system also includes a sample training module based on federated learning. This module performs preliminary target detection and land use identification on the collected land use photos locally on the mobile terminal, extracting feature vectors through a lightweight neural network model. The identification results are fed back to the user interface as pseudo-labels. Operators only need to confirm or correct the classification. The corrected feature increments are fed back to the cloud without uploading the original photos, used to optimize the global land use classification model and improve the system's accuracy in automatically classifying complex land features.

[0021] Furthermore, as a preferred embodiment of the present invention, the multi-source heterogeneous data encapsulation center performs spatiotemporal topology consistency alignment during the encapsulation process. By comparing the GNSS trajectory timestamp with the image acquisition timestamp, it precisely interpolates the instantaneous coordinates at the time of image capture onto the centimeter-level trajectory curve, eliminating image position deviations caused by shutter delay and data bus transmission delay, ensuring that each on-site evidence photograph possesses spatial attributes that meet the surveying accuracy requirements.

[0022] Furthermore, as a preferred embodiment of the present invention, the system further includes a security audit module. This security audit module monitors the underlying environment of the mobile terminal in real time to prevent malicious programs from intercepting the positioning interface or forging GPS coordinates. This module verifies the integrity of positioning data from the driver layer to the application layer by establishing a hardware-software collaborative trust chain. If simulated positioning is detected or abnormal system permissions are detected, the data collection task will be immediately locked and a security log will be reported.

[0023] Furthermore, as a preferred embodiment of the present invention, the high-precision positioning processing engine supports multimodal differential access. The system is simultaneously compatible with BeiDou-3 B1I / B3I band signals and GPS L1 / L2 band signals, increasing the number of visible satellites through multi-system joint calculation. In urban canyons or deep mountain environments with obstructed signal coverage, the system automatically increases signal weight allocation to high-elevation-angle BeiDou satellites, effectively improving the time to first positioning (TTFF) and positioning availability.

[0024] Furthermore, as a preferred embodiment of the present invention, the incremental synchronization process in the online / offline asynchronous collaborative system supports priority scheduling based on topological semantics. The system prioritizes synchronizing vector data involving ownership disputes and boundary point changes, while placing large-volume unstructured data such as on-site videos and panoramic images into a background asynchronous queue. Transmission is automatically triggered in a Wi-Fi environment or when the network signal strength is higher than a threshold, ensuring the real-time storage of core surveying and mapping results.

[0025] Through the synergistic effect of the aforementioned system architecture and technical means, this invention achieves high-precision and high-reliability land surveying field data acquisition based on general-purpose mobile terminals. By deeply integrating the sensor capabilities of mobile terminals, this invention breaks away from the traditional surveying's reliance on expensive dedicated hardware, significantly reducing deployment costs. Through real-time logic verification and edge-side quality control, the data error correction process is moved forward to the moment of on-site operation, greatly reducing the rework rate in office work. Through offline / online asynchronous collaboration and multi-source data structured encapsulation, the invention solves the problems of poor operational continuity and severe data fragmentation in weak network environments, comprehensively improving the intelligence level, data acquisition accuracy, and on-site operational efficiency of land surveying fieldwork.

[0026] As a specific engineering implementation of this invention, the hardware resource scheduling layer communicates with the underlying driver of the mobile terminal through the JNI interface to obtain raw NMEA messages and IMU register data. The high-precision positioning processing engine uses a high-concurrency thread pool to process multiple signals in parallel, with the RTK differential data receiving frequency set to 5Hz and the IMU sampling frequency set to 100Hz. The on-site operation interaction engine is developed using a cross-platform rendering engine, supporting smooth scaling and roaming of tens of millions of vector elements. The real-time data quality verification module uses a spatial retrieval algorithm based on R-tree indexing to achieve millisecond-level topological conflict retrieval of plot elements within a 300-meter radius. The online / offline asynchronous collaborative system uses the AES-256 encryption protocol to ensure transmission security and uses the MD5 algorithm for data packet consistency verification.

[0027] In another embodiment of the invention, the system supports an external millimeter-wave ranging module, which transmits ranging data to a mobile terminal in real time via Bluetooth. The on-site operation interaction engine automatically calculates the coordinates of inaccessible points using a geometric projection algorithm based on the ranging results and the precise attitude angle of the current mobile terminal, further expanding the surveying capabilities under complex working conditions.

[0028] In summary, this invention constructs a complete land surveying and mapping field data acquisition technology system optimized for mobile terminals. It demonstrates significant technical advantages in terms of positioning accuracy, system integration, operational collaboration, and data quality control, meeting the stringent requirements of high-dynamic land surveying and mapping data for the new era of territorial spatial planning. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the overall architecture of the system of the present invention; Figure 2 This is a schematic diagram of the high-precision positioning processing engine of the present invention; Figure 3 This is a schematic diagram of the structure of the on-site operation interaction engine of the present invention; Figure 4 This is a schematic diagram of the real-time data quality verification module of the present invention; Figure 5 This is a schematic diagram of the architecture of the offline / online asynchronous collaborative system of the present invention. Detailed Implementation

[0030] To enable engineers in the field to more clearly understand and implement the technical solution of the present invention, the following description, in conjunction with the accompanying drawings and specific engineering implementation details, provides an in-depth and detailed explanation of a land surveying and mapping field data acquisition system based on a mobile terminal.

[0031] In the macro-architecture of the land surveying field data acquisition system based on a mobile terminal provided by this invention, the entire system is deconstructed into six mutually cooperating core logical layers: a hardware resource scheduling layer, a high-precision positioning processing engine, a field operation interaction engine, a real-time data quality verification module, an online / offline asynchronous collaborative system, and a multi-source heterogeneous data encapsulation center. For example... Figure 1 As shown, the system architecture is designed with six core layers that work together to ensure efficient operation. This layered architecture was designed to enable high-precision, high-reliability field data acquisition tasks—previously only achievable with professional GNSS mapping terminals—on general-purpose mobile terminals with limited hardware resources.

[0032] Specifically, the hardware resource scheduling layer, as the underlying support of the entire system, is deeply integrated between the mobile terminal's operating system kernel and the hardware abstraction layer. This layer, through a customized Hardware Abstraction Interface (HAL), enables exclusive or high-priority access to the mobile terminal's built-in GNSS chip, MEMS inertial sensor, CMOS optical imaging sensor, and mobile communication module. For example... Figure 2 As shown, the high-precision positioning processing engine calls hardware resources through the hardware interface of this layer. In actual engineering operation, the core task of the hardware resource scheduling layer is to establish a sensor data synchronization clock. Since the sampling frequency of GNSS chips is typically 1Hz to 10Hz, while the sampling frequency of MEMS inertial sensors' accelerometers and gyroscopes is often as high as 100Hz to 400Hz, significant clock drift exists between different sensors. The hardware resource scheduling layer obtains the system's monotonic timestamp and maps the raw observations of all sensors to a unified nanosecond-level time coordinate system. Through this high-precision time alignment, the system eliminates spatial projection deviations caused by asynchronous sensor acquisition, laying the physical time reference for subsequent integrated navigation fusion.

[0033] Based on this, the high-precision positioning processing engine undertakes the core function of improving the positioning accuracy of mobile terminals from consumer-grade to surveying-grade accuracy. Figure 2The detailed structure of the high-precision positioning processing engine is demonstrated, including the carrier phase observation processing unit. This engine establishes an NTRIP link with the Continuously Operating Reference Stations (CORS) network via a mobile communication module, receiving differential correction data in RTCM 3.2 or higher format in real time. The carrier phase observation processing unit within the high-precision positioning processing engine employs a dual-frequency, three-band (e.g., BDS B1I / B3I / B1C, GPS L1 / L2 / L5) carrier phase differential algorithm. During processing, this unit first eliminates ionospheric delay through wide-lane combination, and then uses the LAMBDA algorithm to search for and fix integer ambiguities. To address signal quality degradation in complex environments such as urban canyons or forest areas, the multipath effect suppression unit constructs a dynamic weighting matrix based on the signal-to-noise ratio (SNR) and elevation angle information of the satellite signals. In specific implementation, the system assigns lower weights to satellite signals with elevation angles below a certain degree or SNR values ​​below 35 dB-Hz, or even removes them directly during weight calculation, thereby effectively mitigating multipath errors caused by building reflections. The integrated navigation fusion unit uses the Extended Kalman Filter (EKF) algorithm to deeply couple the centimeter-level coordinates calculated from carrier phase difference decomposition with the six-axis motion vector output by the MEMS inertial sensor. In this deep coupling mode, the raw observations from the IMU are directly incorporated into the GNSS observation equations. Even during brief GNSS signal loss (such as when passing through a culvert) for 5 to 10 seconds, the system can maintain decimeter-level positioning accuracy using a dead-reckoning algorithm, ensuring the continuity of mapping operations. Its predicted state vector includes three-dimensional position, three-dimensional velocity, three-dimensional attitude, as well as the IMU's bias and scale factor.

[0034] Delving further into the implementation details of the on-site operation interaction engine, this engine is built on the application layer of mobile terminals and aims to provide operators with a smooth and precise graphical operating environment. For example... Figure 3As shown, the on-site operation interaction engine achieves a highly interactive user interface through a dynamic base map rendering module and a vector feature editing module. The dynamic base map rendering module adopts tile-based rendering technology accelerated by the graphics processing unit (GPU), capable of simultaneously loading tens of gigabytes of MBTiles format vector maps or GeoTIFF format high-resolution remote sensing images. To ensure a frame rate of 60fps even with low power consumption, the rendering module performs pre-clipping and memory release strategies for map data in non-visible areas. The vector feature editing module incorporates a touch sensitivity compensation algorithm. On mobile terminals, the touch area of ​​a user's fingertip is typically larger than a pixel-level point. This algorithm analyzes the center pressure distribution of the touch pressure sensitivity and combines it with the current map zoom level to convert the pixel coordinates on the screen into high-precision geospatial coordinates, thereby enabling centimeter-level precision point and line collection on the touch screen. The structured attribute entry module strictly follows the land survey metadata standards of the Ministry of Natural Resources, constructing entry forms through dynamically generated JSON Schema. This design allows the system to automatically switch attribute fields based on different survey objects (such as agricultural land, construction land, and unused land), achieving standardized constraints on core information such as land parcel categories and ownership units, and eliminating the generation of non-standard data from the source.

[0035] As the core of the quality control of this invention, the real-time data quality verification module runs in the edge computing environment of the mobile terminal. Its real-time and mandatory nature is the key to ensuring the quality of the surveying and mapping results. Figure 4 The structure of the real-time data quality verification module is demonstrated, including a geometric topology check submodule and a spatial accuracy assessment submodule. During each polygon delineation process, the geometric topology check submodule uses an R-tree spatial index to search for existing vector features in the surrounding area, performing self-intersection, overlap, and gap detection in real time. If a topological logic error is detected, the system will immediately provide a red warning through the interactive interface and prohibit saving the current plot. The spatial accuracy assessment submodule is a high-frequency monitoring unit that calculates the accuracy factor PDOP and three-dimensional standard deviation (RMS) of the current GNSS solution status in real time. In a preferred implementation, when the system detects that the horizontal accuracy standard deviation exceeds 0.05 meters, it automatically triggers a "multiple sampling averaging mode," requiring operators to stay at the current location and collect no fewer than 30 sets of observations, which are then smoothed using a Gauss-Markov model to ensure the reliability of the output coordinates. The attribute logic verification submodule uses a regular expression library to perform format verification on the entered ownership code, ID number and other information, and checks for contradictions between attributes according to preset logical relationships, such as the matching of "land category code" and "ownership attribute quality".

[0036] The design scheme of the offline asynchronous collaborative system fully considers the extreme working conditions of unstable network in the field surveying environment. Figure 5 This diagram illustrates the offline / online asynchronous collaborative system architecture, demonstrating the incremental synchronization and conflict resolution processes. The system employs a two-tiered storage architecture combining a local SQLite spatial extended database (SpatiaLite) and a remote cloud database. In offline conditions, each collected record is assigned a globally unique identifier (UUID) and stored locally. When the mobile communication module detects that network bandwidth has recovered to a preset threshold (e.g., 200kbps), the incremental synchronization algorithm is activated. This algorithm does not transmit the complete database file but extracts incremental data packets based on timestamps and version vectors. To resolve conflicts during multi-user collaborative work, the system employs either a "last writer wins" or "topology area percentage" conflict resolution strategy. During data transmission, for high-definition photos or panoramic videos captured on-site, the system uses a breakpoint resume protocol and MD5 integrity verification technology to ensure the reliability of large file transmission in weak network environments.

[0037] The multi-source heterogeneous data encapsulation center undertakes the final step in the output process. It encapsulates high-precision spatial coordinates, structured attribute records, field photos with Exif azimuth information, and the entire GNSS raw observation trajectory log into a unified land surveying result package using binary serialization technology (such as Protocol Buffers). This encapsulation method significantly compresses data volume while improving the reading speed of office software. The digital signature unit within the encapsulation center performs asymmetric encryption signing on the result package by calling the secure execution environment (TEE) or hardware security module (HSM) of the mobile terminal. Each result package carries a digital certificate based on a hardware fingerprint; any unauthorized tampering with the content of the result package will cause the signature verification to fail, thus technically ensuring the authenticity and legal validity of the original surveying data.

[0038] Furthermore, to optimize the performance of general-purpose mobile terminals in complex antenna environments, the high-precision positioning processing engine of this invention integrates a signal-to-noise ratio compensation mechanism. The antenna gain distribution of general-purpose smartphones is typically highly uneven and significantly affected by hand posture. This system establishes a spherical harmonic function model of the mobile terminal's antenna gain and dynamically corrects the carrier phase observation values ​​for phase center deviation (PCO) and phase center variation (PCV) based on the real-time azimuth and elevation angles of each satellite received in the celestial coordinate system. This correction can mathematically reduce the physical antenna center, originally located at the edge of the circuit board, to the geometric center of gravity of the mobile terminal, thereby reducing the ranging error caused by the antenna from the decimeter level to the centimeter level.

[0039] This invention demonstrates strong survivability when entering GNSS blind zones (such as dense forests, culverts, or areas obscured by large buildings). The real-time data quality verification module integrates indoor and outdoor integrated positioning functionality based on visual inertial odometry (VIO). At this point, the system automatically activates the CMOS optical imaging sensor, extracts feature points in the environment using ORB-SLAM3 or similar visual SLAM algorithms, and combines this with IMU integral data for relative displacement prediction. By performing closed-loop detection at the edge of the blind zone and the last valid GNSS point, the system can provide continuous relative position constraints during the delineation of land parcel boundaries, greatly expanding the applicable boundaries of the surveying system.

[0040] To further assist operators in improving efficiency, the on-site operation interaction engine features an intelligent automatic layer switching function. When the system detects that the current location has entered a preset sensitive area (such as a basic farmland protection area or ecological protection red line), it automatically retrieves the previous year's land use status layer and planning red line layer from the cloud index library. This geofencing-based pre-retrieval mechanism enables operators to quickly identify illegal occupation of agricultural land or unauthorized construction on-site through "virtual-real comparison," achieving seamless integration between field investigation and internal supervision.

[0041] In terms of data compression, the online / offline asynchronous collaborative system introduces a topology data compression algorithm based on differential coding. For complex polygonal plots containing tens of thousands of coordinate points, the algorithm does not store absolute coordinates, but records the relative displacement increments between adjacent points and uses variable-length coding technology to compress small displacements at a high ratio. Experimental data shows that the algorithm can achieve a compression ratio of 1:15 for vector map data, significantly reducing bandwidth consumption in weak network environments.

[0042] Furthermore, the refined power management strategy implemented at the hardware resource scheduling layer is crucial for ensuring long battery life. This strategy monitors battery level in real time; when the battery level drops below 30%, the system automatically reduces the GNSS sampling frequency from 5Hz to 1Hz and locks the GPU rendering frame rate at 30fps. When the built-in accelerometer detects that the device has been stationary for more than 30 seconds, the system automatically shuts down the IMU's high-frequency sampling and enters interrupt wake-up mode. This dynamic resource scheduling logic increases the battery life of ordinary smart terminals under high-intensity outdoor operations by approximately 40%.

[0043] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A land surveying and mapping field data acquisition system based on a mobile terminal, characterized in that, The system includes: The hardware resource scheduling layer is integrated into the system kernel of a general-purpose mobile terminal. It calls the GNSS chip, MEMS inertial sensor, CMOS optical imaging sensor, and mobile communication module built into the mobile terminal through a hardware abstraction interface. The hardware resource scheduling layer establishes a sensor data synchronization clock to uniformly map sensor data with different sampling frequencies to a nanosecond-level time coordinate system, thereby eliminating the timing deviation of asynchronous sensor data when projected into space. The high-precision positioning processing engine establishes an NTRIP link with the base station network through the mobile communication module to obtain real-time differential correction data. It includes a carrier phase observation processing unit, a multipath effect suppression unit, and a combined navigation fusion unit. The on-site operation interaction engine is built on the application layer of the mobile terminal and includes a dynamic base map rendering module, a vector element editing module, and a structured attribute input module. The real-time data quality verification module runs in the edge computing environment of the mobile terminal. It performs logical constraint checks by intercepting job flow data in real time, including a geometric topology check submodule, a spatial accuracy evaluation submodule, and an attribute logic verification submodule. The offline-online asynchronous collaboration system adopts a two-layer storage architecture of local cache database and cloud database, and achieves asynchronous consistency of edge and cloud data through incremental synchronization algorithm; The multi-source heterogeneous data encapsulation center is used to encapsulate the spatial coordinates, structured attribute records, field photos, and GNSS trajectory logs collected on-site into land surveying result packages with digital signatures through binary serialization technology.

2. The land surveying and mapping field data acquisition system based on a mobile terminal according to claim 1, characterized in that, In the high-precision positioning processing engine: The carrier phase observation processing unit uses a dual-frequency three-band carrier phase differential algorithm to solve the raw observations output by the GNSS chip to obtain centimeter-level positioning coordinates; The multipath effect suppression unit constructs a weighted least squares estimation algorithm based on the signal-to-noise ratio and elevation angle of the satellite signal to dynamically eliminate non-line-of-sight signals caused by obstruction by buildings or vegetation. The integrated navigation fusion unit employs the Extended Kalman Filter (EKF) algorithm to deeply couple the GNSS positioning results with the angular velocity and acceleration data output by the MEMS inertial sensor. In the event of GNSS signal loss, it maintains positioning continuity through a dead-guessing algorithm. Its discretized prediction equation is described as follows: ； in, express The state vector at any given time includes three-dimensional coordinates, three-dimensional velocity, and attitude angle. This indicates the input quantity of the MEMS inertial sensor. Indicates system process noise; In addition, the high-precision positioning processing engine also includes a signal-to-noise ratio compensation mechanism. By establishing a spherical harmonic function model of the mobile terminal antenna gain, and based on the azimuth and elevation angle of the satellite in the celestial coordinate system, the phase center deviation (PCO) and phase center change (PCV) of the carrier phase are corrected, and the antenna center of the mobile terminal is virtually normalized to the geometric centroid.

3. The land surveying and mapping field data acquisition system based on a mobile terminal according to claim 2, characterized in that, The high-precision positioning processing engine supports multimodal differential access and is compatible with BeiDou-3 B1I / B3I band signals and GPS L1 / L2 band signals for multi-system joint calculation. In urban canyons or deep mountain environments, the system automatically increases signal weights allocated to high-elevation BeiDou satellites to improve the time to first positioning (TTFF) and positioning availability.

4. The land surveying and mapping field data acquisition system based on a mobile terminal according to claim 1, characterized in that, In the on-site operation interaction engine: The dynamic base map rendering module supports tiled vector maps and high-resolution remote sensing images, uses the graphics processing unit (GPU) for rendering acceleration, and performs pre-clipping and memory release strategies for map data in non-visible areas. The vector feature editing module supports on-site point marking, line segment drawing, and polygon closure operations based on coordinate capture. It has a built-in touch sensitivity compensation algorithm that analyzes the center pressure distribution of touch pressure and combines it with the current map zoom level to convert screen touch coordinates into geospatial coordinates. The structured attribute entry module is based on a preset land survey metadata standard. It constructs forms through dynamically generated JSON Schema to achieve standardized collection of land parcel categories, ownership units, ownership nature, and current utilization status.

5. The land surveying and mapping field data acquisition system based on a mobile terminal according to claim 4, characterized in that, The on-site operation interaction engine also has the following features: The intelligent automatic layer switching function allows the system to automatically pre-fetch and render the surrounding ownership red line layer, basic farmland protection zone layer, and previous year's land use status layer from the cloud index library based on the current GNSS coordinates and the type of data acquisition task. The AR augmented reality assistance module utilizes the CMOS optical imaging sensor and attitude sensor of the mobile terminal to overlay and render the ownership boundary lines and planning red lines in the base map onto the real-world image in real time, and supports graphical correction of deviated boundary lines through screen interaction.

6. The land surveying and mapping field data acquisition system based on a mobile terminal according to claim 1, characterized in that, In the real-time data quality verification module: The geometric topology inspection submodule uses a spatial retrieval algorithm based on R-tree indexing to perform self-intersection detection, overlap detection, and gap detection in real time during polygon acquisition. If a topology conflict is detected, the submission interface is locked. The spatial accuracy assessment submodule calculates the standard deviation of the current observation point in real time based on the accuracy factor PDOP value and difference decomposition status of GNSS positioning. When the standard deviation exceeds the set threshold, it automatically switches to the multiple sampling average mode and adopts an anti-gross error estimation algorithm based on residual analysis to identify and suppress abnormal satellite messages that are interfered with by calculating the posterior residual of the observation values. The attribute logic verification submodule has an attribute association rule library, which uses regular expressions and condition constraint logic to perform real-time legality verification on the entered data.

7. The land surveying and mapping field data acquisition system based on a mobile terminal according to claim 6, characterized in that, The real-time data quality verification module also integrates an indoor and outdoor integrated positioning verification function based on visual inertial odometry (VIO). When entering a GNSS blind zone, the system activates the CMOS optical imaging sensor to extract environmental feature points and combines them with the MEMS inertial sensor data to perform relative displacement calculation. Real-time correction is performed through a closed-loop detection algorithm to maintain the accuracy of plot boundary delineation in complex environments.

8. The land surveying and mapping field data acquisition system based on a mobile terminal according to claim 1, characterized in that, In the aforementioned offline asynchronous collaborative system: In the absence of network coverage, the collected data is temporarily stored in a local SQLite extended database and assigned a globally unique identifier (UUID). After the network is restored, the incremental synchronization algorithm is started to construct an incremental packet including geographic coordinate increments, attribute modification records, and multimedia file hash values; The incremental synchronization algorithm adopts a version vector-based conflict handling strategy, which automatically resolves concurrent conflicts by comparing the local timestamp with the cloud version number, and uses a breakpoint resume protocol to ensure the integrity of large file return. In addition, the online / offline asynchronous collaborative system also includes a topology data compression algorithm based on differential coding, which extracts the relative offset of adjacent coordinate points for bitmap encapsulation, and the incremental synchronization process supports priority scheduling based on topology semantics, prioritizing the synchronization of vector data involving ownership disputes or boundary point changes.

9. The land surveying and mapping field data acquisition system based on a mobile terminal according to claim 1, characterized in that, The multi-source heterogeneous data encapsulation center performs the following operations: The spatial coordinates output by the high-precision positioning processing engine, the structured attributes generated by the field operation interaction engine, the field photos with azimuth and tilt information, and the GNSS trajectory logs recording the acquisition process are encapsulated using binary serialization technology. During the encapsulation process, spatiotemporal topology consistency alignment is performed by comparing the GNSS trajectory timestamp with the image timestamp, and the instantaneous coordinates at the time of image capture are extrapolated onto the centimeter-level trajectory curve. The digital signature unit inside the packaging center encrypts and signs the result package through the hardware security module of the mobile terminal. The encapsulation center also supports dynamically adjusting the data packet encapsulation structure according to different surveying task templates, automatically associating earthwork volume calculation parameters in land consolidation tasks, and automatically associating property floor and unit maps with rights holder signature information in real estate surveying tasks.

10. The land surveying and mapping field data acquisition system based on a mobile terminal according to claim 1, characterized in that, The system also includes: The federated learning-based sample training module performs target detection and recognition on the collected land use photos on the mobile terminal. It extracts feature vectors through a lightweight neural network model, feeds the recognition results back to the user interface as pseudo-labels, and feeds back the feature increments corrected by the operators to the cloud to optimize the global classification model without uploading the original photos. The security audit module monitors the underlying environment of the mobile terminal in real time. It verifies the integrity of the location data from the driver layer to the application layer by establishing a trust chain of software and hardware collaboration. If it detects that simulated location is enabled or the system permissions are abnormal, it locks the collection task and reports the security log. The hardware resource scheduling layer establishes a refined power consumption management strategy, which dynamically adjusts the GNSS sampling frequency and GPU rendering refresh rate based on the contribution of sensor data and the remaining battery power. When the device is in a stable static state, the sampling frequency of the MEMS inertial sensor is reduced and a low-power standby mode is entered. The device responds to position movement through an external interrupt wake-up mechanism.

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