Beidou surveying and mapping positioning system and method

Abstract

The invention discloses a Beidou surveying and mapping positioning system and a Beidou surveying and mapping positioning method, which relate to the technical field of satellite surveying and mapping and are characterized in that an observation data pool is constructed, a unified space-time reference is generated, real-time coordinates are output, system errors are corrected online, three-dimensional surveying and mapping results are generated and a model is updated in a closed-loop manner. Especially in an environment with weak GNSS signals, the pseudo satellite and the inertial navigation system effectively supplement, improve the system reliability, fuse multi-source data, ensure that other modules continue to provide positioning information and enhance redundancy and fault-tolerant capability when any sensor fails, and the error compensation module adopts multipath learning and temperature drift compensation to ensure that the precision is stable under the environment change, so that the accuracy of the system is improved. The digital surveying and mapping module generates three-dimensional grid data, the feedback iteration module continuously optimizes pseudo satellite scheduling and data acquisition frequency, precision is ensured, energy consumption is reduced, and efficiency and endurance are improved.

Description

Technical Field

[0001] This invention relates to the field of satellite mapping technology, and in particular to a positioning system and method for BeiDou mapping. Background Technology

[0002] With the widespread application of the Global Positioning System (GPS), traditional BeiDou satellite positioning technology faces challenges in reliability and accuracy in harsh environments. In these environments, traditional single positioning technologies struggle to meet the demands for high-precision, continuous positioning. Therefore, developing a high-precision positioning system based on the fusion of multiple technologies is particularly important.

[0003] Currently, Chinese patent application number CN202410808246.5 discloses a displacement monitoring system and method for a solar thermal power plant pipeline based on the BeiDou navigation system. The system includes a ground calibration box installed inside the solar thermal power plant pipeline, a microelectronic sensor installed on the pipeline, an internal detector placed inside the pipeline, and a monitoring platform. The internal detector, based on the BeiDou coordinate reference points provided by the ground calibration box, maps the BeiDou coordinates of the pipeline's centerline and draws a centerline alignment diagram of the pipeline. The internal detector collects data from three gyroscopes, three accelerometers, and an odometer from an inertial measurement unit at a preset frequency to obtain inertial measurement data. The microelectronic sensor collects the acceleration values ​​of the pipeline at a preset sampling frequency. The monitoring platform predicts the displacement monitoring results of the pipeline's bending strain based on the centerline alignment diagram, inertial measurement data, and acceleration values.

[0004] The aforementioned technologies are difficult to achieve high-precision and high-stability positioning and mapping in complex and dynamic environments, especially when GNSS signal attenuation, interference, or multipath effects are severe. They cannot effectively fuse multi-source data and perform real-time error compensation and adaptive optimization. Summary of the Invention

[0005] The technical problem solved by this invention is that existing technologies are difficult to achieve high-precision and high-stability positioning and mapping in complex and dynamic environments, especially when GNSS signal attenuation, interference or multipath effects are severe, and it is impossible to effectively fuse multi-source data and perform real-time error compensation and adaptive optimization.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] A BeiDou mapping positioning system includes a satellite-to-ground data acquisition module, a reference enhancement module, a multimodal fusion positioning module, an error compensation module, a digital mapping output module, and a feedback iteration module.

[0008] The satellite-to-ground data acquisition module is used to construct an observation data pool;

[0009] The benchmark enhancement module is used to generate a unified spatiotemporal benchmark;

[0010] The multimodal fusion positioning module is used to output real-time coordinates;

[0011] The error compensation module is used to correct system errors online.

[0012] The digital mapping output module is used to generate three-dimensional mapping results;

[0013] The feedback iteration module is used for closed-loop model updates.

[0014] Preferably, the satellite-to-ground data acquisition module includes a BeiDou observation unit, a pseudo-satellite observation unit, an inertial observation unit, and an environmental observation unit.

[0015] The BeiDou observation unit is used to receive BeiDou-3 satellite-based carrier phase data and pseudorange data and write them into the observation data pool.

[0016] The pseudo-satellite observation unit is used to receive ultra-low frequency pseudo-satellite signals and UWB pseudo-range data, convert them into auxiliary observation sets and write them into the observation data pool.

[0017] The inertial observation unit is used to collect triaxial angular velocity data and triaxial acceleration data, generate an inertial observation set, and write it into the observation data pool;

[0018] The environmental observation unit is used to collect temperature data, magnetic field data, and vibration state data, and write them into the environmental state set.

[0019] Preferably, the reference enhancement module includes a spatiotemporal synchronization unit, a pseudorange correction unit, and a pseudosatellite scheduling unit:

[0020] The spatiotemporal synchronization unit is used to time-calibrate the satellite-based carrier phase data, pseudorange data, and inertial observation set in the observation data pool based on the atomic clock time tag, and generate a unified spatiotemporal reference frame;

[0021] The pseudorange correction unit is used to call differential correction parameters to correct the satellite-based pseudorange data and auxiliary observation set in the unified spatiotemporal reference frame, and generate a corrected observation set.

[0022] The pseudo-satellite scheduling unit is used to periodically read the electromagnetic noise power density value and the real-time path loss estimate output by the path loss model from the environmental state set. The path loss model is constructed using the Okumura-Hata model based on electromagnetic wave propagation theory and environmental characteristics. The path loss model calculates the path loss based on real-time acquired environmental data, including electromagnetic noise power density values ​​and signal propagation path characteristics. The calculation logic for the real-time path loss estimate is as follows:

[0023] Based on the electromagnetic noise power density value, the path loss based on distance and frequency is calculated. Considering the effect of electromagnetic wave propagation theory on signal attenuation, the actual loss is calculated. Combined with real-time environmental data, the path loss estimate is corrected. The path loss estimate will be dynamically adjusted according to the real-time changes in environmental conditions, and the real-time path loss estimate is output.

[0024] Based on the preset channel availability decision model, calculate the availability score of the ultra-low frequency pseudo-satellite channel and the availability score of the ultra-wideband pseudo-satellite channel respectively;

[0025] When the availability score of the ultra-low frequency pseudo-satellite channel is higher than the set threshold of the availability score of the ultra-wideband pseudo-satellite channel, a first scheduling instruction is generated to set the ultra-low frequency pseudo-satellite as the current working channel, and the minimum transmission power is calculated in reverse according to the target signal-to-noise ratio lower limit and then the power margin is added and written into the power field.

[0026] When the availability score of the ultra-low frequency pseudo-satellite channel is less than or equal to the set threshold of the availability score of the ultra-wideband pseudo-satellite channel, a second scheduling instruction is generated to set the ultra-wideband pseudo-satellite as the current working channel, and the transmission power is calculated according to the same inverse algorithm.

[0027] The pseudo-satellite pulse repetition frequency and duty cycle are dynamically adjusted based on the root mean square value of vibration in the environmental state set in order to maintain the pseudorange sampling density under high-speed motion.

[0028] The first scheduling instruction or the second scheduling instruction is encapsulated into a TLV structure and written into the scheduling instruction pool after being attached with an expiration tag and a generation timestamp.

[0029] Preferably, the multimodal fusion localization module includes an attitude calculation unit, an observation optimization unit, and a constraint fusion unit:

[0030] The attitude calculation unit is used to call up three-axis angular velocity data, three-axis acceleration data and temperature markers from the same epoch of the inertial observation set under the constraint of a unified spatiotemporal reference label. First, the three-axis angular velocity data is subjected to zero bias correction and random drift suppression. Then, the three-axis acceleration data is converted to the inertial coordinate system and the gravity component is removed. Subsequently, the instantaneous attitude quaternion of the carrier is calculated by the quaternion recursive integration algorithm. The quaternion and the attitude reliability score calculated according to the measurement noise covariance are written into the attitude buffer.

[0031] The observation selection unit is used to read pseudorange observations, carrier phase observations and their signal-to-noise ratio (SNR) in the corrected observation set and the auxiliary observation set for the same unified spatiotemporal reference epoch. It calculates the weight score of each observation value according to the preset confidence evaluation model. The confidence evaluation model comprehensively considers the SNR, epoch continuity, cycle slip flag and multipath residual.

[0032] When the weight score of an observation is higher than a preset threshold, it is marked as a high-quality observation and written into the high-quality observation buffer; otherwise, it is discarded or downweighted to generate a high-quality observation set.

[0033] The constraint fusion unit is used to call the carrier attitude quaternion in the attitude buffer and the pseudorange observation and carrier phase observation in the high-quality observation buffer within the unified spatiotemporal reference frame, construct the joint state vector using the extended Kalman filter algorithm and perform coupled constraint solution, output the real-time coordinate solution and the corresponding observation residual, write the real-time coordinate solution into the digital mapping output module, and write the observation residual into the error compensation module.

[0034] Preferably, the error compensation module includes a multipath learning unit, a temperature drift compensation unit, and a magnetic interference suppression unit:

[0035] The multipath learning unit is used to construct an error digital twin model based on the corrected observation set and the environmental state set, and to extract multipath features;

[0036] The temperature drift compensation unit is used to correct the temperature drift error between the inertial observation set and the electronic components using temperature data.

[0037] The magnetic interference suppression unit is used to calibrate magnetic field anomalies and compensate for attitude calculation results.

[0038] Preferably, the digital mapping output module includes a 3D grid generation unit, a deformation monitoring unit, and a data interface unit:

[0039] The 3D grid generation unit is used to project real-time coordinates and sensor point clouds onto the 3D grid database;

[0040] The deformation monitoring unit is used to measure the displacement of the target profile and mark abnormal areas;

[0041] The data interface unit is used to provide a mapping data call interface to the upper-level system.

[0042] Preferably, the feedback iteration module includes a model evaluation unit, a parameter update unit, and an adaptive scheduling unit:

[0043] The model evaluation unit is used to compare the surveying results generated by the digital surveying output module with the benchmark model and output evaluation indicators.

[0044] The parameter update unit is used to update the error digital twin model parameters and attitude calculation parameters according to the evaluation indicators;

[0045] The adaptive scheduling unit is used to adjust the pseudo-satellite scheduling strategy and the satellite-to-ground data acquisition frequency based on the update results.

[0046] Preferably, the pseudo-satellite scheduling unit adopts a dual-band complementary strategy: when the environmental state set shows that the electromagnetic noise is lower than a preset threshold, the ultra-low frequency pseudo-satellite is activated; when the electromagnetic noise is higher than the preset threshold, it switches to the UWB pseudo-satellite and adjusts the transmission power according to the real-time path loss model.

[0047] Preferably, the multipath learning unit continuously compares and corrects the observation set with the real-time coordinate residuals, dynamically updates the error digital twin model, and automatically adjusts the multipath feature weights when the residual change rate exceeds a preset threshold, and synchronizes the adjustment results to the constraint fusion unit and the three-dimensional grid generation unit.

[0048] A positioning method for BeiDou mapping includes the following steps:

[0049] Step S1: Construct the observation data pool;

[0050] Step S2: Generate a unified spatiotemporal reference.

[0051] Step S3: Output real-time coordinates;

[0052] Step S4: Correct system errors online;

[0053] Step S5: Generate 3D mapping results;

[0054] Step S6: Close the loop and update the model.

[0055] The beneficial effects of this invention are as follows: This invention improves positioning accuracy and stability through multimodal fusion technology. Especially in environments with weak GNSS signals, pseudo-satellites and inertial navigation systems effectively supplement each other, enhancing system reliability. By fusing multi-source data, it ensures that other modules continue to provide positioning information when any sensor fails, thus enhancing redundancy and fault tolerance. The error compensation module employs multipath learning and temperature drift compensation to ensure stable accuracy under environmental changes. The digital mapping module generates three-dimensional grid data, and the feedback iteration module continuously optimizes pseudo-satellite scheduling and data acquisition frequency, ensuring accuracy while reducing energy consumption and improving efficiency and endurance. Attached Figure Description

[0056] Figure 1 A basic flowchart of a BeiDou mapping positioning system is provided as an embodiment of the present invention;

[0057] Figure 2 The present invention provides a flowchart of a positioning method for BeiDou mapping according to an embodiment of the present invention. Detailed Implementation

[0058] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0059] Example 1, refer to Figure 1 This paper presents a positioning system for BeiDou mapping, including a satellite-to-ground data acquisition module, a reference enhancement module, a multi-modal fusion positioning module, an error compensation module, a digital mapping output module, and a feedback iteration module.

[0060] The satellite-to-ground data acquisition module is used to build an observation data pool.

[0061] The benchmark enhancement module is used to generate a unified spatiotemporal benchmark.

[0062] The multimodal fusion positioning module is used to output real-time coordinates.

[0063] The error compensation module is used to correct system errors online.

[0064] The digital mapping output module is used to generate 3D mapping results.

[0065] The feedback iteration module is used for closed-loop model updates.

[0066] This invention improves positioning accuracy and stability through multimodal fusion technology. Especially in environments with weak GNSS signals, pseudo-satellites and inertial navigation systems effectively complement each other, enhancing system reliability. By fusing multi-source data, it ensures that other modules continue to provide positioning information even if any sensor fails, thus enhancing redundancy and fault tolerance. The error compensation module employs multipath learning and temperature drift compensation to ensure stable accuracy under environmental changes. The digital mapping module generates three-dimensional grid data, and the feedback iteration module continuously optimizes pseudo-satellite scheduling and data acquisition frequency, ensuring accuracy while reducing energy consumption and improving efficiency and endurance.

[0067] The satellite-to-ground data acquisition module includes a BeiDou observation unit, a pseudosatellite observation unit, an inertial observation unit, and an environmental observation unit.

[0068] The BeiDou observation unit is used to receive BeiDou-3 satellite-based carrier phase data and pseudorange data and write them into the observation data pool.

[0069] The BeiDou observation unit acquires high-precision positioning data by receiving BeiDou-3 satellite-based carrier phase and pseudorange data, ensuring the system can obtain reliable satellite positioning information in variable environments. The data is written into the observation data pool, providing precise input for subsequent benchmark augmentation and positioning calculations.

[0070] The pseudo-satellite observation unit is used to receive ultra-low frequency pseudo-satellite signals and UWB pseudo-range data, convert them into auxiliary observation sets, and write them into the observation data pool.

[0071] Pseudo-satellite observations effectively enhance signal reception capabilities by receiving ultra-low frequency pseudo-satellite signals and UWB pseudo-range data. Especially in environments where GNSS signals are blocked or interfered with, it provides auxiliary positioning information for the system, converts it into an auxiliary observation set, and writes it into the observation data pool, thereby improving the system's anti-interference capability and accuracy.

[0072] The inertial observation unit is used to collect triaxial angular velocity data and triaxial acceleration data, generate inertial observation sets, and write them into the observation data pool.

[0073] The inertial observation unit (IUV) acquires triaxial angular velocity and triaxial acceleration data in real time, providing support for short-term positioning. When GNSS signals are unavailable, the IUV provides critical data, generates an inertial observation set, and writes it into the data pool to ensure the system's continuous positioning capability.

[0074] The environmental observation unit is used to collect temperature data, magnetic field data, and vibration state data, and write them into the environmental state set.

[0075] The environmental observation unit enhances the system's adaptability to environmental changes through real-time monitoring of temperature, magnetic field, and vibration. This data provides strong support for the error compensation module, helping the system correct inertial navigation and positioning errors in dynamic environments and ensuring the stability of positioning accuracy.

[0076] The satellite-to-ground data acquisition module integrates BeiDou observation units, pseudosatellite observation units, inertial observation units, and environmental observation units. This enables it to provide multi-source observation data in various environments, constructing a precise observation data pool and providing reliable data support for subsequent benchmark enhancement, positioning calculations, and error compensation. The core effect of this module is ensuring the spatiotemporal consistency of all acquired data, enabling it to cope with signal attenuation, interference, and dynamic changes in complex environments, thereby providing multi-level and multi-dimensional data support for accurate positioning.

[0077] The benchmark enhancement module includes a spatiotemporal synchronization unit, a pseudorange correction unit, and a pseudosatellite scheduling unit:

[0078] The spatiotemporal synchronization unit is used to time-calibrate the satellite-based carrier phase data, pseudorange data, and inertial observation set in the observation data pool based on atomic clock time tags, and generate a unified spatiotemporal reference frame.

[0079] The spatiotemporal synchronization unit uses atomic clock-based time stamps to calibrate the time of satellite-based carrier phase data, pseudorange data, and inertial observation sets, ensuring that all data are synchronized under a unified spatiotemporal reference frame. This process guarantees the timeliness and consistency of the data, providing a reliable foundation for subsequent precise positioning and error correction.

[0080] The pseudorange correction unit is used to call differential correction parameters to correct the satellite-based pseudorange data and auxiliary observation set in the unified spatiotemporal reference frame, and generate a corrected observation set.

[0081] The pseudorange correction unit performs real-time corrections on satellite-based pseudorange data and pseudorange data in the auxiliary observation set by calling differential correction parameters. Differential correction eliminates errors caused by factors such as signal propagation delay, and the resulting corrected observation set provides more accurate data support for subsequent high-precision positioning calculations.

[0082] The pseudo-satellite scheduling unit is used to periodically read the electromagnetic noise power density value and the real-time path loss estimate output by the path loss model from the environmental state set. The path loss model is constructed using the Okumura-Hata model based on electromagnetic wave propagation theory and environmental characteristics. The path loss model calculates path loss based on real-time acquired environmental data, including electromagnetic noise power density values ​​and signal propagation path characteristics. The calculation logic for the real-time path loss estimate is as follows:

[0083] Based on the electromagnetic noise power density value, the path loss based on distance and frequency is calculated. Considering the effect of electromagnetic wave propagation theory on signal attenuation, the actual loss is calculated. Combined with real-time environmental data, the path loss estimate is corrected. The path loss estimate will be dynamically adjusted according to the real-time changes in environmental conditions, and the real-time path loss estimate is output.

[0084] Based on the preset channel availability decision model, the availability scores of the ultra-low frequency pseudo-satellite channel and the ultra-wideband pseudo-satellite channel are calculated respectively.

[0085] When the availability score of the ultra-low frequency pseudo-satellite channel is higher than the set threshold of the availability score of the ultra-wideband pseudo-satellite channel, a first scheduling instruction is generated to set the ultra-low frequency pseudo-satellite as the current working channel, and the minimum transmission power is calculated in reverse according to the target signal-to-noise ratio lower limit and then the power margin is added and written into the power field.

[0086] When the availability score of the ultra-low frequency pseudo-satellite channel is less than or equal to the set threshold of the availability score of the ultra-wideband pseudo-satellite channel, a second scheduling instruction is generated to set the ultra-wideband pseudo-satellite as the current working channel, and the transmission power is calculated according to the same inverse algorithm.

[0087] The pseudo-satellite pulse repetition frequency and duty cycle are dynamically adjusted based on the root mean square value of vibration in the environmental condition set in order to maintain the pseudorange sampling density under high-speed motion.

[0088] The first or second scheduling instruction is encapsulated into a TLV structure, and after being appended with an expiration tag and a generation timestamp, it is written into the scheduling instruction pool.

[0089] The pseudo-satellite scheduling unit adopts a dual-band complementary strategy: when the environmental state set shows that the electromagnetic noise is lower than the preset threshold, the ultra-low frequency pseudo-satellite is activated; when the electromagnetic noise is higher than the preset threshold, it switches to the UWB pseudo-satellite and adjusts the transmission power according to the real-time path loss model.

[0090] The pseudo-satellite scheduling unit periodically reads electromagnetic noise and path loss information from the environmental condition set, and calculates and selects the optimal pseudo-satellite channel based on the channel availability decision model. This unit dynamically adjusts the working channel based on the availability scores of ultra-low frequency and ultra-wideband pseudo-satellite channels, and calculates the transmission power based on the target signal-to-noise ratio to ensure the stability of the pseudo-satellite signal and the accuracy of the positioning data. Simultaneously, it adjusts the pseudo-satellite pulse frequency and duty cycle based on vibration information in the environmental condition to maintain pseudorange sampling density under high-speed conditions, optimizing the system's response speed and data processing efficiency.

[0091] The reference enhancement module, through spatiotemporal synchronization, pseudorange correction, and pseudosatellite scheduling techniques, ensures that all observation data are accurately aligned and processed within a unified spatiotemporal framework. This module effectively improves the system's positioning accuracy and reliability in complex environments, especially in environments with GNSS signal attenuation or interference, where pseudosatellite signals and differential correction functions provide crucial compensation and correction support. Furthermore, the dynamic scheduling and adjustment functions of the pseudosatellite scheduling unit ensure that the system can adaptively select the optimal channel and power according to environmental changes, further enhancing the system's anti-interference capability and stability.

[0092] The multimodal fusion localization module includes an attitude calculation unit, an observation optimization unit, and a constraint fusion unit:

[0093] The attitude calculation unit is used to call up the three-axis angular velocity data, three-axis acceleration data and temperature markers of the same epoch in the inertial observation set under the constraint of unified spatiotemporal reference label. First, the three-axis angular velocity data is subjected to zero bias correction and random drift suppression. Then, the three-axis acceleration data is converted to the inertial coordinate system and the gravity component is removed. Then, the quaternion recursive integration algorithm is used to calculate the instantaneous attitude quaternion of the carrier. The quaternion and the attitude reliability score calculated according to the measurement noise covariance are written into the attitude buffer.

[0094] The attitude calculation unit eliminates errors caused by bias, drift, and gravity by accurately processing angular velocity and acceleration data from the inertial observation set. Employing a quaternion recursive integration algorithm, it calculates the instantaneous attitude of the carrier, providing accurate attitude information for the positioning system. The attitude quaternions generated by this unit and their reliability scores ensure high accuracy of the attitude information and provide reliable input for subsequent constraint fusion and coordinate calculation.

[0095] The observation selection unit is used to read pseudorange observations, carrier phase observations and their signal-to-noise ratio (SNR) in the corrected observation set and the auxiliary observation set for the same unified spatiotemporal reference epoch. It calculates the weight score of each observation based on the preset confidence evaluation model. The confidence evaluation model comprehensively considers the SNR, epoch continuity, cycle slip flag and multipath residual.

[0096] When the weight score of an observation is higher than a preset threshold, it is marked as a high-quality observation and written into the high-quality observation buffer; otherwise, it is discarded or downweighted to generate a high-quality observation set.

[0097] The observation optimization unit evaluates pseudorange and carrier phase observations in the corrected and auxiliary observation sets in real time. Based on multiple parameters such as signal-to-noise ratio, epoch continuity, and cycle slip flags, it dynamically calculates the weight score for each observation. This unit generates a high-quality observation set by selecting high-quality observations and eliminating low-quality or invalid observations, thereby improving the overall observation quality and accuracy of the positioning system.

[0098] The constraint fusion unit is used to call the carrier attitude quaternion in the attitude buffer and the pseudorange observation and carrier phase observation in the high-quality observation buffer within the unified spatiotemporal reference frame. It constructs a joint state vector using the extended Kalman filter algorithm and performs coupled constraint solution, outputs the real-time coordinate solution and the corresponding observation residual, and writes the real-time coordinate solution into the digital mapping output module, while writing the observation residual into the error compensation module.

[0099] The constraint fusion unit uses the extended Kalman filter algorithm to perform coupled constraint calculations on the carrier attitude quaternions in the attitude buffer and the pseudorange and carrier phase observations in the high-quality observation buffer. This unit achieves accurate calculation of real-time coordinates and effective output of observation residuals. Through the calculation of the joint state vector, the constraint fusion unit effectively integrates various observation data, providing high-precision real-time coordinate solutions for the digital mapping output module, while transmitting residual data to the error compensation module to provide a basis for subsequent error correction and optimization.

[0100] The multimodal fusion positioning module ensures high-precision positioning and stability of the system in various complex environments through the close coordination of attitude calculation, observation optimization, and constraint fusion techniques. This module effectively fuses data from different observation units, including inertial data, satellite-based observation data, and auxiliary observation data, eliminating errors that may arise from a single observation source and providing more reliable and accurate positioning results. Constraint calculation using the extended Kalman filter algorithm enables accurate calculation of real-time coordinates and attitude, and the dynamic selection of high-quality observations further improves the system's positioning accuracy.

[0101] The error compensation module includes a multipath learning unit, a temperature drift compensation unit, and a magnetic interference suppression unit.

[0102] The multipath learning unit is used to construct an error digital twin model based on the corrected observation set and the environmental state set, and to extract multipath features.

[0103] The multipath learning unit continuously compares and corrects the observation set with the real-time coordinate residuals, dynamically updates the error digital twin model, and automatically adjusts the multipath feature weights when the residual change rate exceeds a preset threshold, and synchronizes the adjustment results to the constraint fusion unit and the 3D grid generation unit.

[0104] The multipath learning unit constructs an error digital twin model by analyzing the corrected observation set and the environmental state set, and extracts multipath features from it. This unit can identify and learn errors caused by multipath effects, and effectively suppress the impact of multipath effects on positioning accuracy by dynamically updating the error model, thereby improving the system's positioning performance in complex environments.

[0105] The temperature drift compensation unit is used to correct the temperature drift error between the inertial observation set and electronic components using temperature data.

[0106] The temperature drift compensation unit corrects for temperature drift errors in inertial sensor data and electronic components in the inertial observation set by acquiring temperature data in real time. This unit effectively compensates for sensor drift caused by temperature changes, ensuring the positioning accuracy of the system under different ambient temperatures, and maintaining high accuracy of inertial data, especially under extreme temperature change conditions.

[0107] The magnetic interference suppression unit is used to calibrate magnetic field anomalies and compensate for attitude calculation results.

[0108] The magnetic interference suppression unit is used to monitor and calibrate magnetic field anomalies in real time, identifying the effects caused by geomagnetic anomalies or other electromagnetic interference sources. By compensating for magnetic field interference in the attitude calculation results, the system can still provide high-precision attitude and positioning data in environments with strong magnetic field interference, avoiding the accumulation of errors caused by magnetic interference.

[0109] The error compensation module employs techniques such as multipath learning, temperature drift compensation, and magnetic interference suppression to correct various errors in the system in real time, ensuring the stability of positioning accuracy and the high reliability of the system. This module effectively eliminates the effects of multipath effects, inertial errors caused by temperature changes, and magnetic field interference on attitude calculation, thereby improving the system's adaptability and anti-interference capabilities in complex environments. Precise error compensation further enhances the overall positioning accuracy and long-term stability of the system.

[0110] The digital mapping output module includes a 3D grid generation unit, a deformation monitoring unit, and a data interface unit.

[0111] The 3D grid generation unit is used to project real-time coordinates and sensor point clouds onto a 3D grid database.

[0112] The 3D grid generation unit generates an accurate 3D geographic information model by projecting real-time coordinates and sensor point cloud data onto a 3D grid database. This unit maps the collected spatial data into a 3D mesh structure, ensuring the spatial accuracy and visualization effect of the surveying data. By comparing with the actual geographical location, the accuracy of the surveying results is guaranteed, making it particularly suitable for 3D modeling of large-scale terrain, buildings, or facilities.

[0113] The deformation monitoring unit is used to measure the displacement of the target profile and mark abnormal areas.

[0114] The deformation monitoring unit is responsible for measuring displacement of the target profile, monitoring and recording deformation within the area in real time. This unit can identify abnormal deformation caused by external factors and mark abnormal areas in the monitoring data. By continuously tracking changes in the target area, the deformation monitoring unit provides crucial data support for structural safety assessment and deformation early warning, helping to identify potential risks in engineering projects in advance.

[0115] The data interface unit is used to provide an interface for calling surveying and mapping data to the upper-level system.

[0116] The data interface unit provides a communication interface with the host system, enabling real-time transmission of surveying and mapping data. Through standardized data interface protocols, it ensures that surveying and mapping data can be efficiently and accurately transmitted to the host system for further analysis or decision-making. This unit supports integration with different platforms and systems, achieving data interoperability and sharing, and enhancing the system's scalability and applicability.

[0117] The digital mapping output module combines 3D grid generation, deformation monitoring, and data interface technologies to provide high-precision mapping data output and real-time deformation monitoring. This module can combine real-time coordinates with sensor point clouds to generate a 3D geographic information model and dynamically monitor the target area. Through an efficient data interface, this module can seamlessly interface with higher-level systems, providing real-time mapping data support. It is widely used in geographic information systems, building information modeling, and other fields, supporting precise spatial analysis and decision-making.

[0118] The feedback iteration module includes a model evaluation unit, a parameter update unit, and an adaptive scheduling unit.

[0119] The model evaluation unit is used to compare the surveying results generated by the digital surveying output module with the benchmark model and output evaluation indicators.

[0120] The model evaluation unit assesses the system's positioning accuracy and data accuracy in real time by comparing the mapping results generated by the digital mapping output module with the baseline model. This unit provides reliable evaluation metrics, identifies potential problems or deviations in system performance, and provides feedback to the parameter update unit. Through this process, the model evaluation unit ensures the system always operates at its optimal state and provides data for further adjustments.

[0121] The parameter update unit is used to update the error digital twin model parameters and attitude calculation parameters according to the evaluation indicators.

[0122] The parameter update unit automatically adjusts the parameters of the error digital twin model and the attitude calculation parameters based on the evaluation metrics output by the model evaluation unit. Through dynamic updates, the system can respond promptly to environmental changes, reducing error accumulation and improving positioning accuracy. This unit enhances the system's adaptability and long-term stability in complex environments through continuous parameter adjustments.

[0123] The adaptive scheduling unit is used to adjust the pseudo-satellite scheduling strategy and the satellite-to-ground data acquisition frequency based on the update results.

[0124] The adaptive scheduling unit adjusts the pseudosatellite scheduling strategy and satellite-to-ground data acquisition frequency based on evaluation and parameter update results. Through dynamic analysis of signal quality, system load, and data requirements, it optimizes the selection of pseudosatellites and their operating frequencies. This unit ensures that the system can automatically adjust its data acquisition strategy in environments with weak signals or strong interference, improving mapping efficiency and reducing power consumption, thereby enhancing the overall system performance.

[0125] The feedback iteration module effectively optimizes the system's operation through continuous model evaluation, parameter updates, and adaptive scheduling, ensuring continuous improvement in mapping accuracy and data acquisition efficiency. This module can dynamically adjust the error model and attitude calculation parameters based on real-time evaluation results, guaranteeing high-precision positioning and mapping performance under various environments. Furthermore, through adaptive scheduling, the system can flexibly adjust pseudosatellite scheduling strategies and data acquisition frequencies according to environmental changes, ensuring efficient operation under different environments and requirements.

[0126] Example 2, refer to Figure 2 This paper provides a positioning method for BeiDou mapping, which includes the following steps:

[0127] Step S1: Construct the observation data pool.

[0128] Step S2: Generate a unified spatiotemporal reference.

[0129] Step S3: Output real-time coordinates.

[0130] Step S4: Correct system errors online.

[0131] Step S5: Generate 3D mapping results.

[0132] Step S6: Close the loop and update the model.

[0133] This invention provides high-precision real-time coordinate solutions through multimodal fusion positioning attitude calculation, observation optimization, and constraint fusion techniques. Especially in environments where traditional GNSS signals are difficult to receive, pseudo-satellite-assisted observation and inertial navigation systems provide an effective supplement, improving stability and reliability. By fusing multi-source data, even if one positioning sensor fails, other modules can still provide effective positioning information, enhancing system redundancy and fault tolerance. In particular, the pseudo-satellite scheduling unit dynamically switches working channels based on electromagnetic noise levels and path loss, effectively avoiding multipath interference and signal attenuation problems. Employing multipath learning, temperature drift compensation, and magnetic interference suppression techniques, it effectively compensates for measurement errors caused by environmental changes, thereby ensuring stable accuracy in various complex environments. In particular, it can dynamically adjust multipath feature weights and automatically optimize the model in response to residual changes, improving positioning accuracy. By combining real-time coordinate solutions with sensor point cloud data, it generates high-precision 3D grid data, providing efficient data support for fields such as Geographic Information Systems (GIS) and Building Information Modeling (BIM). It can accurately measure the displacement of target profiles and mark abnormal areas, providing data support for real-time monitoring of engineering projects. Through continuous model evaluation and parameter updates, it can automatically adjust pseudosatellite scheduling strategies and satellite-to-ground data acquisition frequencies according to changes in the external environment. This not only ensures positioning accuracy but also reduces unnecessary energy consumption, improving work efficiency and battery life.

[0134] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0135] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A positioning system for BeiDou mapping, characterized in that, It includes a satellite-to-ground data acquisition module, a reference enhancement module, a multimodal fusion positioning module, an error compensation module, a digital mapping output module, and a feedback iteration module; The satellite-to-ground data acquisition module is used to construct an observation data pool; The benchmark enhancement module is used to generate a unified spatiotemporal benchmark; The multimodal fusion positioning module is used to output real-time coordinates; The error compensation module is used to correct system errors online. The digital mapping output module is used to generate three-dimensional mapping results; The feedback iteration module is used for closed-loop model updates.

2. The positioning system for BeiDou mapping as described in claim 1, characterized in that, The satellite-to-ground data acquisition module includes a BeiDou observation unit, a pseudo-satellite observation unit, an inertial observation unit, and an environmental observation unit. The BeiDou observation unit is used to receive BeiDou-3 satellite-based carrier phase data and pseudorange data and write them into the observation data pool. The pseudo-satellite observation unit is used to receive ultra-low frequency pseudo-satellite signals and UWB pseudo-range data, convert them into auxiliary observation sets and write them into the observation data pool. The inertial observation unit is used to collect triaxial angular velocity data and triaxial acceleration data, generate an inertial observation set, and write it into the observation data pool; The environmental observation unit is used to collect temperature data, magnetic field data, and vibration state data, and write them into the environmental state set.

3. A positioning system for BeiDou mapping as described in claim 2, characterized in that, The reference enhancement module includes a spatiotemporal synchronization unit, a pseudorange correction unit, and a pseudosatellite scheduling unit: The spatiotemporal synchronization unit is used to time-calibrate the satellite-based carrier phase data, pseudorange data, and inertial observation set in the observation data pool based on the atomic clock time tag, and generate a unified spatiotemporal reference frame; The pseudorange correction unit is used to call differential correction parameters to correct the satellite-based pseudorange data and auxiliary observation set in the unified spatiotemporal reference frame, and generate a corrected observation set. The pseudo-satellite scheduling unit is used to periodically read the electromagnetic noise power density value and the real-time path loss estimate output by the path loss model from the environmental state set. The path loss model is constructed using the Okumura-Hata model based on electromagnetic wave propagation theory and environmental characteristics. The path loss model calculates the path loss based on real-time acquired environmental data, including electromagnetic noise power density values ​​and signal propagation path characteristics. The calculation logic for the real-time path loss estimate is as follows: Based on the electromagnetic noise power density value, the path loss based on distance and frequency is calculated. Considering the effect of electromagnetic wave propagation theory on signal attenuation, the actual loss is calculated. Combined with real-time environmental data, the path loss estimate is corrected. The path loss estimate will be dynamically adjusted according to the real-time changes in environmental conditions, and the real-time path loss estimate is output. Based on the preset channel availability decision model, calculate the availability score of the ultra-low frequency pseudo-satellite channel and the availability score of the ultra-wideband pseudo-satellite channel respectively; When the availability score of the ultra-low frequency pseudo-satellite channel is higher than the set threshold of the availability score of the ultra-wideband pseudo-satellite channel, a first scheduling instruction is generated to set the ultra-low frequency pseudo-satellite as the current working channel, and the minimum transmission power is calculated in reverse according to the target signal-to-noise ratio lower limit and then the power margin is added and written into the power field. When the availability score of the ultra-low frequency pseudo-satellite channel is less than or equal to the set threshold of the availability score of the ultra-wideband pseudo-satellite channel, a second scheduling instruction is generated to set the ultra-wideband pseudo-satellite as the current working channel, and the transmission power is calculated according to the same inverse algorithm. The pseudo-satellite pulse repetition frequency and duty cycle are dynamically adjusted based on the root mean square value of vibration in the environmental state set in order to maintain the pseudorange sampling density under high-speed motion. The first scheduling instruction or the second scheduling instruction is encapsulated into a TLV structure and written into the scheduling instruction pool after being attached with an expiration tag and a generation timestamp.

4. A positioning system for BeiDou mapping as described in claim 3, characterized in that, The multimodal fusion localization module includes an attitude calculation unit, an observation optimization unit, and a constraint fusion unit: The attitude calculation unit is used to call the three-axis angular velocity data, three-axis acceleration data and temperature markers of the same epoch in the inertial observation set under the constraint of unified spatiotemporal reference label. First, the three-axis angular velocity data is subjected to zero bias correction and random drift suppression. Then, the three-axis acceleration data is converted to the inertial coordinate system and the gravity component is removed. Then, the quaternion recursive integration algorithm is used to calculate the instantaneous attitude quaternion of the carrier. The quaternion and the attitude reliability score calculated according to the measurement noise covariance are written into the attitude buffer. The observation selection unit is used to read pseudorange observations, carrier phase observations and their signal-to-noise ratio (SNR) in the corrected observation set and the auxiliary observation set for the same unified spatiotemporal reference epoch. It calculates the weight score of each observation value according to the preset confidence evaluation model. The confidence evaluation model comprehensively considers the SNR, epoch continuity, cycle slip flag and multipath residual. When the weight score of an observation is higher than a preset threshold, it is marked as a high-quality observation and written into the high-quality observation buffer; otherwise, it is discarded or downweighted to generate a high-quality observation set. The constraint fusion unit is used to call the carrier attitude quaternion in the attitude buffer and the pseudorange observation and carrier phase observation in the high-quality observation buffer within the unified spatiotemporal reference frame, construct the joint state vector using the extended Kalman filter algorithm and perform coupled constraint solution, output the real-time coordinate solution and the corresponding observation residual, write the real-time coordinate solution into the digital mapping output module, and write the observation residual into the error compensation module.

5. A positioning system for BeiDou mapping as described in claim 4, characterized in that, The error compensation module includes a multipath learning unit, a temperature drift compensation unit, and a magnetic interference suppression unit. The multipath learning unit is used to construct an error digital twin model based on the corrected observation set and the environmental state set, and to extract multipath features; The temperature drift compensation unit is used to correct the temperature drift error between the inertial observation set and the electronic components using temperature data. The magnetic interference suppression unit is used to calibrate magnetic field anomalies and compensate for attitude calculation results.

6. A positioning system for BeiDou mapping as described in claim 5, characterized in that, The digital mapping output module includes a 3D grid generation unit, a deformation monitoring unit, and a data interface unit. The 3D grid generation unit is used to project real-time coordinates and sensor point clouds onto the 3D grid database; The deformation monitoring unit is used to measure the displacement of the target profile and mark abnormal areas; The data interface unit is used to provide a mapping data call interface to the upper-level system.

7. A positioning system for BeiDou mapping as described in claim 5, characterized in that, The feedback iteration module includes a model evaluation unit, a parameter update unit, and an adaptive scheduling unit: The model evaluation unit is used to compare the surveying results generated by the digital surveying output module with the benchmark model and output evaluation indicators. The parameter update unit is used to update the error digital twin model parameters and attitude calculation parameters according to the evaluation indicators; The adaptive scheduling unit is used to adjust the pseudo-satellite scheduling strategy and the satellite-to-ground data acquisition frequency based on the update results.

8. A positioning system for BeiDou mapping as described in claim 7, characterized in that, The pseudo-satellite scheduling unit adopts a dual-band complementary strategy: when the environmental state set shows that the electromagnetic noise is lower than the preset threshold, the ultra-low frequency pseudo-satellite is activated; when the electromagnetic noise is higher than the preset threshold, it switches to the UWB pseudo-satellite and adjusts the transmission power according to the real-time path loss model.

9. A positioning system for BeiDou mapping as described in claim 8, characterized in that, The multipath learning unit continuously compares and corrects the observation set with the real-time coordinate residuals, dynamically updates the error digital twin model, and automatically adjusts the multipath feature weights when the residual change rate exceeds a preset threshold, and synchronizes the adjustment results to the constraint fusion unit and the three-dimensional grid generation unit.

10. A positioning method for BeiDou mapping, applied to a BeiDou mapping positioning system as described in any one of claims 1-9, characterized in that, Includes the following steps: Step S1: Construct the observation data pool; Step S2: Generate a unified spatiotemporal reference. Step S3: Output real-time coordinates; Step S4: Correct system errors online; Step S5: Generate 3D mapping results; Step S6: Close the loop and update the model.

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