Geographic information collecting and processing device

Abstract

The invention discloses a geographic information acquisition and processing device, which comprises a multi-mode driving module, a multi-mode sensing module, a data processing module and a data processing module, wherein the multi-mode driving module enables the device to adapt to flexible movement of different complex terrains on the basis of a multi-mode driving structure; the multi-mode sensing module is used for synchronously acquiring positioning and attitude determination data, three-dimensional terrain data and environment parameter data and performing time-space calibration of multi-source data; the adaptive control module dynamically adjusts acquisition parameters of each sensor based on environment parameters acquired in real time, and the edge calculation module performs real-time compression, target identification and quality evaluation on original data based on a parallel calculation low-power-consumption embedded GPU and an ARM processor. The cloud collaborative interaction module is used for executing collaborative calculation of edge preprocessing and cloud precision processing; through synchronous triggering and space-time calibration, millimeter-level space-time consistency of positioning, terrain and environment parameters is achieved, the data fusion precision is improved, sensor parameters can be dynamically adjusted through the self-adaptive control module, and the problems of signal shielding, illumination change and the like are solved.

Description

Technical Field

[0001] This invention relates to the field of geographic information acquisition and processing technology, and in particular to a geographic information acquisition and processing device. Background Technology

[0002] Geographic information acquisition and processing devices are core equipment in surveying, geological exploration, smart cities, and other fields. Currently, mainstream equipment mainly uses fixed wheeled or tracked chassis, which is difficult to cope with complex terrains such as mountains, ravines, shrubs, and steps. They have weak obstacle-crossing capabilities and are easily blocked by vegetation, resulting in data loss. They lack dynamic environmental perception and adaptive adjustment mechanisms, and data loss or distortion often occurs due to fixed parameters. At the same time, existing information acquisition devices mostly rely on single or limited types of sensors, making it difficult to simultaneously acquire multi-dimensional information such as terrain, vegetation, and surface temperature. Furthermore, the spatiotemporal calibration errors of different sensors are large, which limits the accuracy of data fusion. In addition, current devices acquire and transmit massive amounts of raw data with high latency, which cannot meet the "ready-to-use" requirements in emergency mapping. Relying on local high-performance computing results in large device size and high power consumption. Therefore, this invention proposes a geographic information acquisition and processing device to solve the problems existing in the prior art. Summary of the Invention

[0003] To address the aforementioned problems, the present invention aims to propose a geographic information acquisition and processing device. This device achieves high-precision, high-timeliness, and low-energy geographic information acquisition and processing in complex environments through multimodal sensor integration, environmental adaptive perception, edge-cloud collaborative computing, and low-power design, thus meeting the application needs of multiple scenarios.

[0004] To achieve the objectives of this invention, the invention is implemented through the following technical solution: a geographic information acquisition and processing device, comprising a multi-mode driving module, a multi-modal perception module, an adaptive control module, an edge computing module, and a cloud collaborative interaction module. The multi-mode driving module, based on a multi-morphological driving structure, enables the device to adapt to flexible movement in various complex terrains. The multi-modal perception module is used to simultaneously acquire positioning and orientation data, 3D terrain data, and environmental parameter data, and to perform spatiotemporal calibration of multi-source data. The adaptive control module dynamically adjusts the acquisition parameters of each sensor based on the real-time acquired environmental parameters. The edge computing module, based on parallel computing, low-power embedded GPUs and ARM processors, performs real-time compression, target recognition, and quality assessment of raw data. The cloud collaborative interaction module is used to perform collaborative computing of edge preprocessing and cloud fine processing.

[0005] Further improvements are made in the following aspects: The multi-mode drive module includes a portable case, folding slots, a rotating electric rod, a telescopic sleeve, a drive wheel, a folding arm, a flight rotor, a mounting frame, and a multi-functional rotating probe. The portable case has symmetrically arranged folding slots on its lower and side sides. The folding slots on the lower side of the portable case have six symmetrically arranged sets and are equipped with rotating electric rods. The telescopic end of the rotating electric rod is equipped with a drive wheel via a telescopic sleeve. The folding slots on the side of the portable case have folding arms, and the front end of the folding arms is equipped with flight rotors. The multi-functional rotating probe is mounted on the upper part of the portable case via a mounting frame.

[0006] Further improvements include: the portable case contains a supercapacitor bank and a main controller; solar panels are symmetrically arranged on the top of the portable case; the solar panels are electrically connected to the supercapacitor bank through the main controller; the main controller is used for device control and data transmission; the supercapacitor bank has a built-in low-power management unit, which includes monitoring battery power through a power management integrated circuit and switching working modes according to task status.

[0007] Further improvements include: the working modes include a data acquisition mode with a total power consumption of ≤8W, a processing mode with a total power consumption of ≤12W, and a standby mode with a total power consumption of ≤0.5W. When the supercapacitor bank's power is below 20%, a low power consumption alarm is triggered, and key data such as the positioning trajectory and quality report are uploaded first.

[0008] Further improvements are made in that: the multimodal perception module includes a positioning and attitude determination unit, a terrain perception unit, and an environmental parameter perception unit. The positioning and attitude determination unit integrates a multi-system GNSS receiver, an inertial measurement unit, and a wheeled odometer, and outputs centimeter-level positioning trajectory and attitude angle through a tightly coupled Kalman filter algorithm. The terrain perception unit includes 16-line and 32-line lidar and an 8-band panoramic camera. The environmental parameter perception unit includes a multispectral camera, an infrared thermal imager, and temperature, humidity, and air pressure sensors.

[0009] Further improvements are made in that: the adaptive control module includes an environmental parameter acquisition unit and a fuzzy control unit. The environmental parameter acquisition unit is used to acquire light intensity, GNSS satellite visibility, temperature and humidity, and lens fogging risk values ​​in real time. The fuzzy control unit dynamically adjusts sensor parameters based on a preset fuzzy rule base.

[0010] Further improvements include: the fuzzy rule base is generated based on historical environmental data and sensor performance curves. The rules include: when the light intensity is <1000 lux, extending the exposure time of the multispectral camera from 1 / 1000s to 1 / 100s and triggering the infrared thermal imager to start the completion mode; when the number of visible GNSS satellites is <4, increasing the sampling frequency of the inertial measurement unit from 100Hz to 500Hz and enabling the dead reckoning algorithm to compensate for positioning errors; and when the difference between the lens temperature and the dew point temperature is <2℃, activating the PTC heating element for defogging.

[0011] Further improvements are made in that: the edge computing module includes a data processing unit, an intelligent analysis unit, and a quality assessment unit. The data processing unit is used to perform voxel downsampling on the lidar point cloud and principal component analysis on the multispectral image. The intelligent analysis unit identifies the categories of buildings, trees, and roads in the point cloud in real time based on a lightweight CNN model and outputs semantic segmentation results. The quality assessment unit generates a data quality report by statistically analyzing the point cloud density, image signal-to-noise ratio, and zero-bias drift of the inertial measurement unit, triggering retesting logic.

[0012] Further improvements include: the training method for the lightweight CNN model includes constructing a training dataset based on real geographic scene point cloud data; replacing standard convolution with depthwise separable convolution, removing redundant branches through channel pruning, reducing computational load through quantization training; and optimizing inference speed through the TensorRT acceleration engine.

[0013] Further improvements are made in that: the cloud collaborative interaction module includes a communication unit and a collaborative processing unit. The communication unit transmits data based on the MQTT protocol. The collaborative processing unit is used to upload the compressed point cloud, image and quality report from the edge end, and combine the full-precision point cloud stitching algorithm and multispectral inversion model to generate digital surface model, digital orthophoto and thematic analysis report in the cloud, and feed the results generated in the cloud back to the device for storage.

[0014] The beneficial effects of this invention are as follows: This invention achieves millimeter-level spatiotemporal consistency of positioning, terrain, and environmental parameters through synchronous triggering and spatiotemporal calibration, thereby improving data fusion accuracy. The adaptive control module can dynamically adjust sensor parameters to solve problems such as signal obstruction and illumination changes. Through collaborative computing processing at the edge and cloud, the collected information balances timeliness and accuracy, meeting the immediate use requirements of emergency mapping. Furthermore, the multi-mode drive module enables the device to adapt to any terrain, effectively improving the device's adaptability and meeting the needs of multiple application scenarios. Attached Figure Description

[0015] Figure 1 This is a diagram of the module architecture of the present invention.

[0016] Figure 2This is a front view of the device structure of the present invention.

[0017] Figure 3 This is a side sectional view of the device structure of the present invention.

[0018] Figure 4 This is a side-view sectional view of the device of the present invention with the rotating electric rod unfolded.

[0019] The components include: 1. Portable case; 2. Folding slot; 3. Rotating electric rod; 4. Telescopic sleeve; 5. Drive wheel; 6. Folding arm; 7. Flight rotor; 8. Fixing frame; 9. Multifunctional rotating probe; 10. Supercapacitor group; 11. Main controller; 12. Solar panel. Detailed Implementation

[0020] To enhance understanding of the present invention, the present invention will be further described in detail below with reference to embodiments. These embodiments are only used to explain the present invention and do not constitute a limitation on the scope of protection of the present invention.

[0021] according to Figures 1-4 As shown in the figure, this embodiment provides a geographic information acquisition and processing device, including a multi-mode driving module, a multi-modal perception module, an adaptive control module, an edge computing module, and a cloud collaborative interaction module.

[0022] The multi-mode drive module, based on a multi-form drive structure, enables the device to move flexibly to adapt to different complex terrains. The multi-mode drive module includes a portable case 1, a folding slot 2, a rotating electric rod 3, a telescopic sleeve 4, a drive wheel 5, a folding arm 6, a flight rotor 7, a fixed frame 8, and a multi-functional rotating probe 9. The portable case 1 has symmetrically arranged folding slots 2 on its lower and side sides. The folding slots 2 on the lower side of the portable case 1 have six sets of rotating electric rods 3. Specifically, the three sets of rotating electric rods on each side are folded and unfolded synchronously with the motor through a common rotating shaft. The telescopic end of the rotating electric rod 3 is connected to the drive wheel 5 through the telescopic sleeve 4. The folding arm 6 is arranged in the folding slot 2 on the side of the portable case 1. The folding arm rotates and folds and is locked by rotating the shaft. The front end of the folding arm 6 is equipped with a flight rotor 7. The multi-functional rotating probe 9 is arranged on the upper part of the portable case 1 through the fixed frame 8. Multiple sensors from the multi-modal perception module, adaptive control module, edge computing module, and cloud collaborative interaction module are integrated into the multi-functional rotating probe.

[0023] It can switch between ground walking and aerial surveying, and has multiple independently driveable rotating electric poles for telescopic adjustment, providing adaptive support drive to adapt to different terrains.

[0024] The portable case 1 houses a supercapacitor bank 10 and a main controller 11. Solar panels 12 are symmetrically arranged on the top of the portable case 1. The solar panels 12 are electrically connected to the supercapacitor bank 10 through the main controller 11. The main controller 11 is used for device control and data transmission. The supercapacitor bank 10 has a built-in low-power management unit to power each module. Combined with energy harvesting and dynamic power consumption distribution mechanisms, the device's battery life is extended, including monitoring battery power through a power management integrated circuit and switching working modes according to the task status.

[0025] The operating modes include a data acquisition mode with a total power consumption of ≤8W, a processing mode with a total power consumption of ≤12W, and a standby mode with a total power consumption of ≤0.5W. It only maintains basic communication and environmental monitoring. When the power of the supercapacitor bank 10 is lower than 20%, a low power alarm is triggered and key data such as the positioning trajectory and quality report are uploaded first.

[0026] The multimodal perception module is used to simultaneously acquire positioning and attitude data, 3D terrain data, and environmental parameter data, and to perform spatiotemporal calibration of multi-source data. The multimodal perception module includes a positioning and attitude unit, a terrain perception unit, and an environmental parameter perception unit.

[0027] The positioning and attitude determination unit integrates a multi-system GNSS receiver, an inertial measurement unit, and a wheeled odometer. It outputs centimeter-level positioning trajectory and attitude angles, including pitch, roll, and heading angles, through a tightly coupled Kalman filter algorithm with an accuracy of ≤0.1°. The terrain perception unit includes 16-line and 32-line lidar and an 8-band panoramic camera. The two achieve nanosecond-level time synchronization through a synchronous trigger signal. The environmental parameter perception unit includes a multispectral camera, an infrared thermal imager, and temperature, humidity, and air pressure sensors.

[0028] The GNSS receiver supports BeiDou / GPS / GLONASS; the inertial measurement unit includes a three-axis accelerometer, gyroscope, and magnetometer; the 16-line and 32-line lidar have a vertical field of view of ±15° and a ranging accuracy of ±2cm; the 8-band panoramic camera has a resolution of 4000×3000 and a frame rate of 30fps; the multispectral camera covers the 400-900nm band with 16 spectral channels; the infrared thermal imager has a resolution of 640×512 and a temperature measurement accuracy of ±1℃; the temperature, humidity, and barometric pressure sensors have an accuracy of ±0.1℃ / ±1%RH / ±0.1hPa.

[0029] Tightly coupled Kalman filtering algorithms include:

[0030] Observation equations: fused GNSS pseudorange / phase observations, IMU acceleration / angular velocity observations, and wheeled odometer odometer observations; State equations: included position, velocity, attitude, IMU zero bias, and gyro drift state variables; Filter update cycle: 10ms, outputting stable positioning and attitude determination results.

[0031] All sensors achieve time synchronization via the PTP protocol (synchronization error ≤ 100ns) and are unified to the same coordinate system through a pre-calibrated extrinsic parameter matrix (error ≤ 0.1° / 2mm).

[0032] The adaptive control module is connected to the multimodal sensing module and dynamically adjusts the acquisition parameters of each sensor based on the real-time acquired environmental parameters. The adaptive control module includes an environmental parameter acquisition unit and a fuzzy control unit.

[0033] The environmental parameter acquisition unit is used to acquire real-time light intensity, GNSS satellite visibility, temperature and humidity, and lens fogging risk values. The fuzzy control unit dynamically adjusts sensor parameters based on a preset fuzzy rule base.

[0034] The fuzzy rule base is generated based on historical environmental data and sensor performance curves. The rules include extending the exposure time of the multispectral camera from 1 / 1000s to 1 / 100s and triggering the infrared thermal imager to start the completion mode when the light intensity is <1000 lux; increasing the sampling frequency of the inertial measurement unit from 100Hz to 500Hz and enabling the dead reckoning algorithm to compensate for positioning errors when the difference between the lens temperature and the dew point temperature is <2℃; and activating the PTC heating element to defog when the lens temperature and dew point temperature difference is <2℃.

[0035] The edge computing module is connected to the multimodal perception module and the adaptive control module. Based on parallel computing, the low-power embedded GPU (such as NVIDIA Jetson Nano) and ARM processor perform real-time compression, target recognition and quality assessment of the raw data. The edge computing module includes a data processing unit, an intelligent analysis unit and a quality assessment unit.

[0036] The data processing unit is used to perform voxel downsampling on the lidar point cloud and principal component analysis on the multispectral image. The voxel downsampling voxel size is 0.1m×0.1m×0.1m with a compression rate of ≥80%. The principal component analysis of the spectral image retains more than 95% of the variance information. The intelligent analysis unit identifies the categories of buildings, trees, and roads in the point cloud in real time based on a lightweight CNN model and outputs semantic segmentation results. The quality assessment unit generates a data quality report by statistically analyzing the point cloud density, image signal-to-noise ratio, and zero-bias drift of the inertial measurement unit, and triggers the retest logic.

[0037] The training method for lightweight CNN models includes constructing a training dataset based on real geographic scene point cloud data, containing 10 types of land features and a sample size of ≥100,000; replacing standard convolution with depthwise separable convolution, removing redundant branches through channel pruning, and reducing computational load through quantization training; and optimizing inference speed through the TensorRT acceleration engine, with a single frame point cloud processing time of ≤100ms.

[0038] The cloud collaborative interaction module is connected to the edge computing module and supports multimodal communication. It is used to perform collaborative computing of edge preprocessing and cloud fine processing. The cloud collaborative interaction module includes a communication unit and a collaborative processing unit.

[0039] The communication unit transmits data based on the MQTT protocol and supports 4G / 5G / Wi-Fi multi-mode communication. The collaborative processing unit uploads the compressed point cloud, image, and quality report from the edge and combines it with a full-precision point cloud stitching algorithm (based on NDT registration, registration error ≤5cm) and a multispectral inversion model (such as random forest, vegetation cover inversion accuracy ≥90%) to generate digital surface models, digital orthophotos, and thematic analysis reports in the cloud. The results generated in the cloud are then fed back to the device for storage.

[0040] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A geographic information acquisition and processing device, characterized in that: The device includes a multi-mode driving module, a multi-modal perception module, an adaptive control module, an edge computing module, and a cloud collaborative interaction module. The multi-mode driving module, based on a multi-morphological driving structure, enables the device to adapt to flexible movement in various complex terrains. The multi-modal perception module is used to simultaneously collect positioning and attitude data, 3D terrain data, and environmental parameter data, and to perform spatiotemporal calibration of multi-source data. The adaptive control module dynamically adjusts the acquisition parameters of each sensor based on the real-time acquired environmental parameters. The edge computing module, based on parallel computing, low-power embedded GPUs and ARM processors, performs real-time compression, target recognition, and quality assessment of raw data. The cloud collaborative interaction module is used to perform collaborative computing of edge preprocessing and cloud fine processing.

2. The geographic information acquisition and processing device according to claim 1, characterized in that: The multi-mode drive module includes a portable case (1), a folding slot (2), a rotating electric rod (3), a telescopic sleeve (4), a drive wheel (5), a folding arm (6), a flight rotor (7), a fixed frame (8), and a multi-functional rotating probe (9). The portable case (1) is symmetrically provided with folding slots (2) on its lower and side sides. The folding slots (2) on the lower side of the portable case (1) are symmetrically provided with six sets and a rotating electric rod (3). The telescopic end of the rotating electric rod (3) is provided with a drive wheel (5) through the telescopic sleeve (4). The folding slots (2) on the side of the portable case (1) are provided with a folding arm (6). The front end of the folding arm (6) is provided with a flight rotor (7). The multi-functional rotating probe (9) is provided on the upper side of the portable case (1) through the fixed frame (8).

3. The geographic information acquisition and processing device according to claim 2, characterized in that: The portable case (1) contains a supercapacitor group (10) and a main controller (11). A solar panel (12) is symmetrically arranged on the top of the portable case (1). The solar panel (12) is electrically connected to the supercapacitor group (10) through the main controller (11). The main controller (11) is used for device control and data transmission. The supercapacitor group (10) has a built-in low-power management unit, which monitors the battery power through a power management integrated circuit and switches the working mode according to the task status.

4. The geographic information acquisition and processing device according to claim 3, characterized in that: The working modes include a data acquisition mode with a total power consumption of ≤8W, a processing mode with a total power consumption of ≤12W, and a standby mode with a total power consumption of ≤0.5W. When the power of the supercapacitor group (10) is lower than 20%, a low power consumption alarm is triggered and key data such as the positioning trajectory and quality report are uploaded first.

5. The geographic information acquisition and processing device according to claim 1, characterized in that: The multimodal sensing module includes a positioning and attitude determination unit, a terrain sensing unit, and an environmental parameter sensing unit. The positioning and attitude determination unit integrates a multi-system GNSS receiver, an inertial measurement unit, and a wheeled odometer, and outputs centimeter-level positioning trajectory and attitude angles through a tightly coupled Kalman filter algorithm. The terrain sensing unit includes 16-line and 32-line lidar and an 8-band panoramic camera. The environmental parameter sensing unit includes a multispectral camera, an infrared thermal imager, and temperature, humidity, and air pressure sensors.

6. The geographic information acquisition and processing device according to claim 1, characterized in that: The adaptive control module includes an environmental parameter acquisition unit and a fuzzy control unit. The environmental parameter acquisition unit is used to acquire light intensity, GNSS satellite visibility, temperature and humidity, and lens fogging risk value in real time. The fuzzy control unit dynamically adjusts sensor parameters based on a preset fuzzy rule base.

7. A geographic information acquisition and processing device according to claim 6, characterized in that: The fuzzy rule base is generated based on historical environmental data and sensor performance curves. The rules include: when the light intensity is <1000 lux, extending the exposure time of the multispectral camera from 1 / 1000s to 1 / 100s and triggering the infrared thermal imager to start the completion mode; when the number of visible GNSS satellites is <4, increasing the sampling frequency of the inertial measurement unit from 100Hz to 500Hz and enabling the dead reckoning algorithm to compensate for positioning errors; and when the difference between the lens temperature and the dew point temperature is <2℃, activating the PTC heating element for defogging.

8. The geographic information acquisition and processing device according to claim 1, characterized in that: The edge computing module includes a data processing unit, an intelligent analysis unit, and a quality assessment unit. The data processing unit is used to perform voxel downsampling on the lidar point cloud and principal component analysis on the multispectral image. The intelligent analysis unit identifies the categories of buildings, trees, and roads in the point cloud in real time based on a lightweight CNN model and outputs semantic segmentation results. The quality assessment unit generates a data quality report by statistically analyzing the point cloud density, image signal-to-noise ratio, and inertial measurement unit zero-bias drift, triggering retesting logic.

9. A geographic information acquisition and processing device according to claim 8, characterized in that: The training method for the lightweight CNN model includes constructing a training dataset based on real geographic scene point cloud data; replacing standard convolution with depthwise separable convolution; removing redundant branches through channel pruning; and reducing computational load through quantization training. Optimize inference speed with the TensorRT acceleration engine.

10. A geographic information acquisition and processing device according to claim 1, characterized in that: The cloud collaborative interaction module includes a communication unit and a collaborative processing unit. The communication unit transmits data based on the MQTT protocol. The collaborative processing unit is used to upload the compressed point cloud, image and quality report from the edge end, and combine the full-precision point cloud stitching algorithm and multispectral inversion model to generate digital surface model, digital orthophoto and thematic analysis report in the cloud, and feed the results generated in the cloud back to the device for storage.

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