A high-precision geographic data system device and a multi-source data fusion processing method thereof

Abstract

This invention discloses a high-precision geographic data system device and its multi-source data fusion processing method, belonging to the field of geographic information acquisition technology. The data collector adopts a modular design, including a multi-frequency GNSS positioning module, a multi-sensor collaborative system, a data processing and fusion module, a power supply and communication module, and a rugged structure. The GNSS module, combined with an IMU unit, achieves centimeter-level RTK positioning and maintains continuous positioning through inertial navigation and visual SLAM even in signal-blocked environments. Environmental sensors provide weather correction to improve elevation accuracy; the image unit synchronizes with the GNSS second pulse to achieve image marking. The outer shell employs layered electromagnetic shielding and IP67 protection design, and the antenna bracket allows for stepless adjustment of the elevation angle to reduce multipath errors. The system supports 5G and LoRa adaptive communication, enabling real-time data upload and edge fusion processing. This invention has the advantages of high positioning accuracy, strong environmental adaptability, and high data acquisition efficiency, and is suitable for scenarios such as surveying, emergency rescue, and geographic monitoring.

Description

Technical Field

[0001] This invention belongs to the field of geographic information acquisition and processing technology, specifically relating to a high-precision geographic data system device and its multi-source data fusion processing method. Background Technology

[0002] Existing geographic information acquisition equipment primarily relies on GNSS receivers, RTK rover stations, or handheld GPS terminals for positioning and data acquisition. While these devices can provide meter-level or decimeter-level positioning in open environments, their positioning accuracy and continuity significantly decrease in scenarios where satellite signals are easily blocked, such as urban canyons, forest areas, and mining areas. Furthermore, most existing equipment lacks multi-sensor fusion capabilities; inertial measurement units, environmental sensors, and image units often need to be deployed separately, leading to inconsistent time bases, complex data post-processing, and difficulty in guaranteeing accuracy. In addition, traditional equipment generally lacks sufficient protection and electromagnetic interference resistance, making it prone to failure during long-term use in complex field environments, resulting in low reliability. On the other hand, existing systems still rely heavily on post-processing in data transmission and processing, failing to meet the demands for real-time, high-precision results.

[0003] Therefore, how to achieve integrated multi-source sensors, continuous centimeter-level positioning in complex environments, and real-time and efficient data processing and transmission has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0004] In order to solve the technical problems existing in the background art, the present invention aims to provide a high-precision geographic data system device and its multi-source data fusion processing method.

[0005] To solve the technical problem, the technical solution of the present invention is as follows:

[0006] A high-precision geographic data system device includes: a multi-band GNSS positioning module and a multi-sensor collaborative system; the multi-band GNSS positioning module includes: a GNSS signal receiving unit, an RTK calculation unit, an antenna unit, a communication unit, and a clock synchronization unit; the GNSS signal receiving unit receives multi-band signals from GPS, BeiDou, and GLONASS; the RTK calculation unit is connected to the receiving unit and is responsible for real-time calculation of positioning data; the antenna unit is connected to the receiving unit and optimizes signal acquisition and transmission; the communication unit is connected to the calculation unit and transmits positioning results and receives differential information; the clock synchronization unit is connected to the calculation unit and the multi-sensor collaborative system and outputs a second pulse signal to achieve time synchronization;

[0007] The multi-sensor collaborative system includes a six-axis IMU unit, an environmental monitoring unit, and an imaging unit. The IMU unit uses sensors to capture the motion status of the equipment in real time, the environmental monitoring unit continuously collects temperature, humidity, and air pressure data based on sensors, and the imaging unit uses a camera for geotagging. All of the above sensors synchronize data at high frequency via an SPI bus.

[0008] Furthermore, the system equipment also includes an anti-interference mechanical module; the anti-interference mechanical module includes an outer aluminum alloy shell, a middle ferrite absorbing material, and an inner copper foil, which together form a composite protection to suppress interference signals and enhance the overall stability of the system.

[0009] Furthermore, the GNSS signal receiving unit transmits satellite signals to the RTK calculation unit for positioning calculation. The positioning data output by the RTK calculation unit is uploaded through the communication unit and provides a unified time reference to the multi-sensor collaborative system through the clock synchronization unit, thereby achieving data timing consistency between the multi-band GNSS positioning module and the multi-sensor collaborative system.

[0010] A multi-source data fusion processing method, wherein the method is applied to any of the system devices described above, the method comprising:

[0011] Multi-source synchronous acquisition: After the system starts up, the GNSS module receives multi-band satellite signals at a fixed frequency and outputs raw positioning data; the IMU unit samples the motion status of the equipment at a high frequency; the environmental sensors synchronously acquire temperature, humidity and air pressure parameters; the image unit triggers image acquisition through the second pulse signal provided by GNSS, and various sensors perform microsecond-level data alignment through hardware-level timestamps to ensure that the acquired data is synchronous and consistent;

[0012] Dynamic compensation processing: In environments where satellite signals are unavailable or obstructed, the system automatically switches to compensation mode.

[0013] In the event of a short-term interruption, the system uses Kalman filtering to fuse IMU data and calculates the device trajectory through a motion model. In the event of a long-term signal loss, the system activates visual-assisted positioning and constructs a relative motion trajectory based on feature matching of image sequences and SLAM algorithm. At the same time, data collected by environmental sensors is input to the atmospheric correction module to calculate tropospheric delay and elevation correction.

[0014] Data fusion and encapsulation: The system performs spatial registration on GNSS, IMU, and image data, unifying them to the local coordinate system; it filters outliers, including low signal-to-noise ratio GNSS data and overrange IMU data, through a quality inspection module; the processed data is encapsulated according to geographic information structured standards to generate data packets containing location information, environmental parameters, image data, and time labels.

[0015] Communication transmission and feedback: The system dynamically selects the communication method according to the current network environment: In areas with cellular network coverage, data is uploaded to the cloud via TCP protocol; in areas without public network coverage, LoRa modules are used in combination with relay nodes to complete data compression and long-distance transmission; the cloud platform performs post-processing and modeling analysis on the received data, and sends updated parameters to the terminal devices through edge nodes to achieve closed-loop control.

[0016] Compared with the prior art, the advantages of the present invention are as follows:

[0017] 1. Improved accuracy: RTK+IMU fusion positioning error ≤1cm (actual measurement);

[0018] 2. Enhanced stability: Battery life increased by 300% in scenarios with signal obstruction;

[0019] 3. Efficiency optimization: Data acquisition time reduced by 50% (compared to split-type devices). Attached Figure Description

[0020] Figure 1 System framework diagram;

[0021] Figure 2 Method flowchart. Detailed Implementation

[0022] The specific implementation of the present invention is described below with reference to embodiments:

[0023] It should be noted that the structures, proportions, sizes, etc. shown in this specification are only used to complement the content disclosed in the specification for those skilled in the art to understand and read, and are not intended to limit the conditions under which the present invention can be implemented. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0024] Furthermore, the terms such as "upper," "lower," "left," "right," "middle," and "one" used in this specification are merely for clarity of description and are not intended to limit the scope of the invention. Any changes or adjustments to their relative relationships, without substantially altering the technical content, should also be considered within the scope of the invention.

[0025] Example 1:

[0026] like Figure 1As shown, the core hardware of this invention adopts a modular design, mainly comprising three parts: a multi-band GNSS positioning module, a multi-sensor collaborative system, and an anti-interference mechanical structure. The multi-band GNSS module uses the U-blox ZED-F9P dual-band chipset, supporting GPS L1 / L2, BeiDou B1 / B2, and GLONASS L1 / L2 bands, achieving a horizontal positioning accuracy of 8mm+1ppm through a built-in RTK calculation engine. This module integrates a quad-helical antenna and a Quectel RM500Q communication chip, enabling millimeter-level base station-free differential positioning under 4G / 5G networks. It also features a Semtech SX1262 LoRa module as a backup communication channel, ensuring reliable transmission within a 5km range in uninhabited areas.

[0027] The multi-sensor collaborative system consists of three parts: ① A six-axis IMU unit using a Bosch BMI088 sensor, which captures the device's motion status in real time through a ±250° / s gyroscope and a ±8g accelerometer, and controls the inertial navigation error to within 0.1° / h with an automatic temperature drift compensation algorithm; ② An environmental monitoring unit equipped with a BME680 four-in-one sensor, which continuously collects temperature (-40~85℃±0.5℃), humidity (0-100%RH±3%) and air pressure (300-1100hPa±0.6Pa) data at the ventilation holes at the bottom of the device; ③ An imaging unit using an 8.3-megapixel Sony IMX415 camera, which achieves millisecond-level geotagging through GNSS second pulse synchronization, and all sensor data is synchronized to the main controller via the SPI bus at a frequency of 100Hz.

[0028] The anti-interference structure adopts a three-layer composite design: the outer layer is a 2mm thick 6061-T6 aluminum alloy shell, anodized to achieve IP67 protection; the middle layer is filled with 1mm ferrite absorbing material, effectively absorbing electromagnetic interference in the 30MHz-1GHz frequency band; the inner layer is covered with 0.1mm rolled copper foil and grounded at multiple points, achieving an electromagnetic shielding effectiveness of >60dB. The key innovation lies in the adjustable antenna bracket, which achieves stepless adjustment of the elevation angle from 0° to 90° through a worm gear mechanism, combined with antenna phase center stabilization technology to reduce multipath effect errors by more than 30%.

[0029] Example 2:

[0030] This embodiment 2 is applied to embodiment 1, such as Figure 2As shown, the workflow of this invention adopts an intelligent multi-level processing architecture, achieving high-precision data acquisition and transmission throughout the entire process through hardware collaboration and algorithm optimization. After the system starts up, it first performs multi-source data synchronous acquisition: the GNSS module receives multi-band satellite signals at a frequency of 10Hz and calculates the raw positioning data in real time. At the same time, the six-axis IMU unit continuously monitors the device's motion status at a sampling rate of 100Hz. Environmental sensors synchronously collect parameters such as temperature, humidity, and air pressure. The 13-megapixel high-definition camera synchronously triggers the capture of geotagged images through GNSS second pulses. All sensor data achieves microsecond-level synchronization through hardware-level timestamps.

[0031] When the equipment enters an environment where satellite signals are obstructed, the system automatically activates a dynamic compensation mechanism. In the event of a short-term signal interruption (<30 seconds), the system uses a Kalman filter algorithm to fuse IMU inertial data and predicts the equipment trajectory through a motion model, controlling position drift to within 2 cm / s. In the event of a long-term signal loss, a visual-assisted positioning mode is activated, utilizing feature matching of consecutive camera frames and a SLAM algorithm to construct a relative motion trajectory. Environmental data is input into the atmospheric correction model in real time, with temperature and humidity parameters used to calculate the tropospheric delay of the signal, and barometric pressure data assisting in elevation positioning calibration. This significantly reduces the elevation error of traditional GNSS from the 10 cm level to below 3 cm.

[0032] After data acquisition, the system performs intelligent data fusion and encapsulation processing. First, multi-source data spatial registration is performed, unifying GNSS WGS84 coordinates, IMU relative displacement, and image spatial information to the local engineering coordinate system. Then, a quality inspection module filters out abnormal data, removing GNSS observations with insufficient signal-to-noise ratio and IMU data exceeding its measurement range. The processed data is encapsulated into structured data packets according to ISO geographic information standards, containing high-precision coordinates, environmental parameters, compressed imagery, and metadata, using a JSON+binary format to optimize storage efficiency.

[0033] The data transmission phase employs an adaptive communication strategy, dynamically selecting the optimal transmission channel based on network conditions. In 4G / 5G network coverage areas, complete data packets are directly uploaded to the cloud processing platform via TCP protocol. In remote areas, LoRa long-distance communication is used, with data compression and Mesh network relay transmission to ensure the reachability of critical data. After receiving the data, the cloud platform performs in-depth processing such as RTK post-processing, 3D modeling, and deformation analysis. Simultaneously, optimization parameters are fed back to the terminal device through edge computing nodes, forming a closed-loop optimization system. The entire workflow achieves a 99.7% data integrity rate in typical scenarios, with end-to-end latency controlled within 500ms, representing an efficiency improvement of over 40% compared to traditional acquisition solutions.

[0034] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

[0035] Many other changes and modifications can be made without departing from the concept and scope of this invention. It should be understood that this invention is not limited to the specific embodiments, and the scope of this invention is defined by the appended claims.

Claims

1. A high-precision geographic data system device, characterized in that, The system equipment includes: a multi-band GNSS positioning module and a multi-sensor collaborative system; the multi-band GNSS positioning module includes: a GNSS signal receiving unit, an RTK calculation unit, an antenna unit, a communication unit, and a clock synchronization unit; the GNSS signal receiving unit receives multi-band signals from GPS, BeiDou, and GLONASS; the RTK calculation unit is connected to the receiving unit and is responsible for real-time calculation of positioning data; the antenna unit is connected to the receiving unit and optimizes signal acquisition and transmission; the communication unit is connected to the calculation unit and transmits positioning results and receives differential information; the clock synchronization unit is connected to the calculation unit and the multi-sensor collaborative system and outputs a second pulse signal to achieve time synchronization; The multi-sensor collaborative system includes a six-axis IMU unit, an environmental monitoring unit, and an imaging unit. The IMU unit uses sensors to capture the motion status of the equipment in real time, the environmental monitoring unit continuously collects temperature, humidity, and air pressure data based on sensors, and the imaging unit uses a camera for geotagging. All of the above sensors synchronize data at high frequency via an SPI bus.

2. The high-precision geographic data system equipment according to claim 1, characterized in that, The system equipment also includes an anti-interference mechanical module; the anti-interference mechanical module includes an outer aluminum alloy shell, a middle ferrite absorbing material, and an inner copper foil, which together form a composite protection to suppress interference signals and enhance the overall stability of the system.

3. The high-precision geographic data system equipment according to claim 1, characterized in that, The GNSS signal receiving unit transmits satellite signals to the RTK calculation unit for positioning calculation. The positioning data output by the RTK calculation unit is uploaded through the communication unit and provides a unified time reference to the multi-sensor collaborative system through the clock synchronization unit, so as to achieve data timing consistency between the multi-band GNSS positioning module and the multi-sensor collaborative system.

4. A method for multi-source data fusion processing, characterized in that, The method is applied to the system device according to any one of claims 1-3, and the method includes: Multi-source synchronous acquisition: After the system starts up, the GNSS module receives multi-band satellite signals at a fixed frequency and outputs raw positioning data; the IMU unit samples the motion status of the equipment at a high frequency; the environmental sensors synchronously acquire temperature, humidity and air pressure parameters; the image unit triggers image acquisition through the second pulse signal provided by GNSS, and various sensors perform microsecond-level data alignment through hardware-level timestamps to ensure that the acquired data is synchronous and consistent; Dynamic compensation processing: In environments where satellite signals are unavailable or obstructed, the system automatically switches to compensation mode. In the event of a short-term interruption, the system uses Kalman filtering to fuse IMU data and calculates the device trajectory through a motion model. In the event of a long-term signal loss, the system activates visual-assisted positioning and constructs a relative motion trajectory based on feature matching of image sequences and SLAM algorithm. At the same time, data collected by environmental sensors is input to the atmospheric correction module to calculate tropospheric delay and elevation correction. Data fusion and encapsulation: The system performs spatial registration on GNSS, IMU, and image data, unifying them to the local coordinate system; it filters outliers, including low signal-to-noise ratio GNSS data and overrange IMU data, through a quality inspection module; the processed data is encapsulated according to geographic information structured standards to generate data packets containing location information, environmental parameters, image data, and time labels. Communication transmission and feedback: The system dynamically selects the communication method according to the current network environment: In areas with cellular network coverage, data is uploaded to the cloud via TCP protocol; in areas without public network coverage, LoRa modules are used in combination with relay nodes to complete data compression and long-distance transmission; the cloud platform performs post-processing and modeling analysis on the received data, and sends updated parameters to the terminal devices through edge nodes to achieve closed-loop control.

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