A beidou surveying and mapping positioning system and method

Abstract

The application discloses a Beidou surveying and mapping positioning system and method, relates to the technical field of satellite surveying and mapping, and constructs an observation data pool, generates a unified space-time reference, outputs real-time coordinates, corrects system errors online, generates three-dimensional surveying and mapping results, and closes loop to update a model. The application improves positioning accuracy and stability through multi-modal fusion technology. In particular, in the environment with weak GNSS signals, the pseudo-satellite and the inertial navigation system effectively supplement each other, improve system reliability, fuse multi-source data, ensure that other modules continue to provide positioning information when any sensor fails, enhance redundancy and fault tolerance capability, the error compensation module adopts multipath learning and temperature drift compensation, ensures the stability of the accuracy under the change of the environment, the digital surveying and mapping module generates three-dimensional grid data, the feedback iteration module continuously optimizes the pseudo-satellite scheduling and the data acquisition frequency, ensures the accuracy and reduces the energy consumption, and improves the efficiency and the endurance.

Description

TECHNICAL FIELD

[0001] The application relates to the field of satellite surveying and mapping technology, and in particular to a Beidou surveying and mapping positioning system and method. BACKGROUND

[0002] With the wide application of the global positioning system, the traditional Beidou satellite positioning technology is challenged in reliability and accuracy in harsh environments. In these environments, the traditional single positioning technology is difficult to meet the high-precision and continuous positioning requirements. Therefore, it is particularly important to develop a high-precision positioning system based on the fusion of multiple technologies.

[0003] At present, the patent with the application number CN202410808246.5 discloses a photothermal power plant pipeline displacement monitoring system and method based on a Beidou navigation system, which comprises a ground calibration box arranged in a photothermal power plant pipeline, a microelectronic sensor arranged on the photothermal power plant pipeline, an internal detector placed in the photothermal power plant pipeline and a monitoring platform. The internal detector measures the center line Beidou coordinates of the photothermal power plant pipeline based on the Beidou coordinate reference point provided by the ground calibration box, and draws a center line trend chart of the photothermal power plant pipeline based on the center line Beidou coordinates. The internal detector collects three-way gyroscopes, three-way accelerometers and odometer data of the inertial surveying unit at a preset frequency to obtain inertial measurement data. The microelectronic sensor collects the acceleration value of the photothermal power plant pipeline at a preset sampling frequency. The monitoring platform predicts the displacement monitoring result of the bending strain of the photothermal power plant pipeline according to the center line trend chart, the inertial measurement data and the acceleration value.

[0004] The above-mentioned technology is difficult to realize high-precision and high-stability positioning and surveying in complex and dynamic environments, especially in the case of serious GNSS signal attenuation, interference or multipath effect, and cannot effectively fuse multi-source data and perform real-time error compensation and adaptive optimization. SUMMARY

[0005] The technical problem solved by the application is that the prior art is difficult to realize high-precision and high-stability positioning and surveying in complex and dynamic environments, especially in the case of serious GNSS signal attenuation, interference or multipath effect, and cannot effectively fuse multi-source data and perform real-time error compensation and adaptive optimization.

[0006] To solve the above technical problems, the application provides the following technical solutions:

[0007] A Beidou surveying and mapping positioning system, comprising a satellite-ground data acquisition module, a reference enhancement module, a multi-modal fusion positioning module, an error compensation module, a digital surveying and mapping output module and a feedback iteration module;

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

[0009] The reference enhancement module is configured to generate a unified space-time reference;

[0010] The multi-modal fusion positioning module is configured to output real-time coordinates;

[0011] The error compensation module is configured to correct system errors online;

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

[0013] The feedback iteration module is configured to update the model in a closed loop;

[0014] The space-ground data acquisition module includes a Beidou observation unit, a pseudolite observation unit, an inertial observation unit, and an environmental observation unit:

[0015] The Beidou observation unit is configured to receive Beidou-3 satellite-based carrier phase data and pseudorange data and write them into an observation data pool;

[0016] The pseudolite observation unit is configured to receive ultra-low frequency pseudolite signals and UWB pseudolite pseudorange data, convert them into auxiliary observation sets, and write them into the observation data pool;

[0017] The inertial observation unit is configured to collect three-axis angular velocity data and three-axis acceleration data, generate inertial observation sets, and write them into the observation data pool;

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

[0019] The reference enhancement module includes a space-time synchronization unit, a pseudorange correction unit, and a pseudolite scheduling unit:

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

[0021] The pseudorange correction unit is configured to call differential correction parameters to correct the satellite-based pseudorange data and auxiliary observation sets in the unified space-time reference frame, and generate corrected observation sets;

[0022] Preferably, the multi-path learning unit dynamically updates the error digital twin model by continuously comparing the corrected observation sets and real-time coordinate residuals, automatically adjusts the multi-path 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.

[0023] Preferably, the pseudolite scheduling unit is configured to periodically read the electromagnetic noise power density value in the set of environmental states and a real-time path loss estimation value output by a path loss model, the path loss model being constructed using the Okumura-Hata model based on electromagnetic wave propagation theory and environmental characteristics, the path loss model calculating path loss based on real-time collected environmental data including electromagnetic noise power density value and signal propagation path characteristics, the calculation logic of the real-time path loss estimation value being:

[0024] According to the electromagnetic noise power density value, the path loss based on distance and frequency is calculated, the attenuation effect of the signal is considered according to the electromagnetic wave propagation theory, the actual loss is calculated, the path loss estimation value is corrected in combination with real-time environmental data, the path loss estimation value is dynamically adjusted according to the real-time change of the environmental state, and the real-time path loss estimation value is output;

[0025] According to a preset channel availability decision model, the ultra-low frequency pseudolite channel availability score and the ultra-wideband pseudolite channel availability score are calculated respectively;

[0026] When the ultra-low frequency pseudolite channel availability score is higher than the preset threshold of the ultra-wideband pseudolite channel availability score, a first scheduling instruction is generated to set the ultra-low frequency pseudolite as the current working channel, and the minimum transmission power is calculated according to the target signal-to-noise ratio lower limit and then a power margin is added to write the power field;

[0027] When the ultra-low frequency pseudolite channel availability score is less than or equal to the preset threshold of the ultra-wideband pseudolite channel availability score, a second scheduling instruction is generated to set the ultra-wideband pseudolite as the current working channel, and the transmission power is calculated according to the same inverse calculation algorithm;

[0028] According to the vibration root mean square value in the set of environmental states, the pseudolite pulse repetition frequency and duty cycle are dynamically adjusted to maintain the pseudorange sampling density in the high-speed motion state.

[0029] The first scheduling instruction or the second scheduling instruction is encapsulated into a TLV structure and attached with a validity period label and a generation timestamp before being written into a scheduling instruction pool.

[0030] Preferably, the multi-modal fusion positioning module includes an attitude solving unit, an observation optimization unit, and a constraint fusion unit:

[0031] The attitude solving unit is configured to call the triaxial angular velocity data, the triaxial acceleration data and the temperature mark in the inertial observation set at the same time and space reference label constraint, first implement zero offset correction and random drift suppression on the triaxial angular velocity data, then convert the triaxial acceleration data to the inertial coordinate system and eliminate the gravity component, and then calculate the carrier instantaneous attitude quaternion by using the quaternion recursive integral algorithm, and write the quaternion and the attitude reliability score calculated according to the measurement noise covariance into the attitude cache area.

[0032] The observation optimization unit is configured to read the pseudo-range observation value, the carrier phase observation value and the signal-to-noise ratio indicators in the corrected observation set and the auxiliary observation set at the same time and space reference epoch, calculate the weight score of each observation value according to a preset confidence evaluation model, and the confidence evaluation model comprehensively considers the signal-to-noise ratio, the epoch continuity, the cycle slip flag and the multipath residual error.

[0033] When the weight score of an observation value is higher than a preset threshold, the observation value is marked as a high-quality observation and written into a high-quality observation buffer area, otherwise, the observation value is discarded or processed with a reduced weight, and a high-quality observation set is generated.

[0034] The constraint fusion unit is configured to call the carrier attitude quaternion in the attitude cache area and the pseudo-range observation value and the carrier phase observation value in the high-quality observation buffer area in the unified time and space reference frame, construct a joint state vector and perform coupled constraint solving by using an extended Kalman filtering algorithm, output real-time coordinate solutions and corresponding observation residuals, write the real-time coordinate solutions into a digital mapping output module, and write the observation residuals into an error compensation module.

[0035] Preferably, the error compensation module comprises a multipath learning unit, a temperature drift compensation unit and a magnetic interference suppression unit.

[0036] The multipath learning unit is configured to construct an error digital twin model based on the corrected observation set and the environmental state set, and extract multipath features.

[0037] The temperature drift compensation unit is configured to correct the temperature drift error of the inertial observation set and the electronic components by using the temperature data.

[0038] The magnetic interference suppression unit is configured to calibrate the magnetic field anomaly and compensate the attitude solving result.

[0039] Preferably, the digital mapping output module comprises a three-dimensional grid generation unit, a deformation monitoring unit and a data interface unit.

[0040] The three-dimensional grid generation unit is configured to project the real-time coordinates and the sensor point cloud to a three-dimensional grid database.

[0041] The deformation monitoring unit is configured to measure the displacement of a target profile and mark an abnormal area.

[0042] The data interface unit is used to provide a surveying and mapping data calling interface for an upper system.

[0043] Preferably, the feedback iteration module comprises a model evaluation unit, a parameter updating unit and an adaptive scheduling unit.

[0044] The model evaluation unit is used to compare the surveying and mapping results generated by the digital surveying and mapping output module with a benchmark model and output evaluation indexes.

[0045] The parameter updating unit is used to update the error digital twin model parameters and the attitude solution parameters according to the evaluation indexes.

[0046] The adaptive scheduling unit is used to adjust the pseudolite scheduling strategy and the satellite-ground data acquisition frequency according to the updating results.

[0047] Preferably, the pseudolite 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 pseudolite is enabled, when the electromagnetic noise is higher than the preset threshold, the UWB pseudolite is switched to, and the transmission power is adjusted according to a real-time path loss model.

[0048] A Beidou surveying and mapping positioning method, comprising the following steps:

[0049] Step S1, constructing an observation data pool;

[0050] Step S2, generating a unified space-time benchmark;

[0051] Step S3, outputting real-time coordinates;

[0052] Step S4, online correcting system errors;

[0053] Step S5, generating three-dimensional surveying and mapping results;

[0054] Step S6, updating the model in a closed loop.

[0055] The present application has the following advantages: the present application improves the positioning accuracy and stability through multi-modal fusion technology, especially in an environment with weak GNSS signals, the pseudolite and the inertial navigation system effectively supplement each other, the system reliability is improved, multi-source data is fused, it is ensured that other modules continue to provide positioning information when any sensor fails, the redundancy and fault tolerance capability are enhanced, the error compensation module adopts multipath learning and temperature drift compensation, the accuracy is ensured to be stable under environmental changes, the digital surveying and mapping module generates three-dimensional grid data, the feedback iteration module continuously optimizes the pseudolite scheduling and the data acquisition frequency, the accuracy is ensured and the energy consumption is reduced, the efficiency and the endurance are improved. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1A basic flow diagram of a Beidou surveying and mapping positioning system provided for an embodiment of the present application is shown in the figure.

[0057] Figure 2 A step flow diagram of a Beidou surveying and mapping positioning method provided for an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0058] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments.

[0059] Embodiment one, referring to Figure 1 , a Beidou surveying and mapping positioning system is provided, comprising a satellite-ground data acquisition module, a reference enhancement module, a multi-modal fusion positioning module, an error compensation module, a digital surveying and mapping output module and a feedback iteration module.

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

[0061] The reference enhancement module is used to generate a unified space-time reference.

[0062] The multi-modal 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 surveying and mapping output module is used to generate three-dimensional surveying and mapping results.

[0065] The feedback iteration module is used to update the model in a closed loop.

[0066] The present application improves positioning accuracy and stability through multi-modal fusion technology, especially in weak GNSS signal environments, pseudolites and inertial navigation systems effectively supplement, improve system reliability, fuse multi-source data, ensure that other modules continue to provide positioning information when any sensor fails, enhance redundancy and fault tolerance, the error compensation module adopts multipath learning and temperature drift compensation to ensure accuracy stability under environmental changes, the digital surveying and mapping module generates three-dimensional grid data, the feedback iteration module continuously optimizes pseudolite scheduling and data acquisition frequency to ensure accuracy and reduce energy consumption, improve efficiency and endurance.

[0067] The satellite-ground data acquisition module comprises a Beidou observation unit, a pseudolite observation unit, an inertial observation unit and an environment observation unit:

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

[0069] The Beidou observation unit receives Beidou-3 satellite carrier phase and pseudorange data to achieve high-precision positioning data collection, ensuring that the system can obtain reliable satellite positioning information in a variable environment. The data is written into the observation data pool and provides accurate input for subsequent reference enhancement and positioning calculation.

[0070] The pseudolite observation unit is used to receive ultra-low frequency pseudolite signals and UWB pseudolite pseudorange data, which are converted into auxiliary observation sets and written into the observation data pool.

[0071] Pseudolite observation enhances signal reception capability by receiving ultra-low frequency pseudolite signals and UWB pseudolite pseudorange data, especially in environments where GNSS signals are blocked or interfered, providing auxiliary positioning information for the system. The data is converted into auxiliary observation sets and written into the observation data pool, improving the system's anti-interference capability and precision.

[0072] The inertial observation unit is used to collect three-axis angular velocity data and three-axis acceleration data, generating an inertial observation set and writing it into the observation data pool.

[0073] The inertial observation unit achieves real-time collection of inertial navigation data by collecting three-axis angular velocity and three-axis acceleration data, providing support for short-time positioning. When GNSS signals cannot be received, the inertial observation unit provides key data, generates an inertial observation set, and writes it into the data pool, ensuring 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 state. These data provide 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-ground data collection module integrates the Beidou observation unit, pseudolite observation unit, inertial observation unit, and environmental observation unit to provide multi-source observation data in various environments, building an accurate observation data pool and providing reliable data support for subsequent reference enhancement, positioning calculation, and error compensation. The core effect of this module is to ensure the spatiotemporal consistency of all collected data, enabling it to cope with signal attenuation, interference, and dynamic changes in complex environments, thereby providing multi-level, multi-dimensional data support for precise positioning.

[0077] The reference enhancement module includes a space-time synchronization unit, a pseudorange correction unit, and a pseudolite scheduling unit:

[0078] The space-time synchronization unit is used for time calibration of 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 generates a unified space-time reference frame.

[0079] The space-time synchronization unit ensures the synchronization of all data under the unified space-time reference frame by time calibration of the satellite-based carrier phase data, pseudorange data and inertial observation set based on the atomic clock time tag. 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 for calling differential correction parameters to correct the satellite-based pseudorange data and auxiliary observation set in the unified space-time reference frame, and generate a corrected observation set.

[0081] The pseudorange correction unit corrects the pseudorange data in the satellite-based pseudorange data and auxiliary observation set in real time by calling differential correction parameters. Through differential correction, the errors caused by signal propagation delay and other factors are eliminated, and the corrected observation set provides more accurate data support for subsequent high-precision positioning calculation.

[0082] The pseudolite scheduling unit is used for periodically reading the electromagnetic noise power density value in the environment state set and the real-time path loss estimation value output by the path loss model. The path loss model is constructed based on electromagnetic wave propagation theory and environmental characteristics using the Okumura-Hata model. The path loss model calculates the path loss based on real-time collected environmental data, including electromagnetic noise power density value and signal propagation path characteristics. The calculation logic of real-time path loss estimation value is as follows:

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

[0084] According to the preset channel availability decision model, the ultra-low frequency pseudolite channel availability score and the ultra-wideband pseudolite channel availability score are calculated respectively.

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

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

[0087] The pseudolite pulse repetition frequency and duty cycle are dynamically adjusted according to the vibration root mean square value in the environmental state set to maintain the pseudorange sampling density in high-speed motion state.

[0088] The first scheduling instruction or the second scheduling instruction is encapsulated into a TLV structure and attached with a validity period label and a generation timestamp before being written into the scheduling instruction pool.

[0089] The pseudolite scheduling unit adopts a dual-band complementary strategy: when the environmental state set shows that the electromagnetic noise is below the preset threshold, the ultra-low frequency pseudolite is enabled, and when the electromagnetic noise is above the preset threshold, the UWB pseudolite is switched to, and the transmission power is adjusted according to the real-time path loss model.

[0090] The pseudolite scheduling unit calculates and selects the best pseudolite channel according to the channel availability decision model by periodically reading the electromagnetic noise and path loss information in the environmental state set. This unit dynamically adjusts the working channel according to the availability scores of the ultra-low frequency and ultra-wideband pseudolite channels, and calculates the transmission power according to the target signal-to-noise ratio to ensure the stability of the pseudolite signal and the accuracy of the positioning data. At the same time, according to the vibration information in the environmental state, the pseudolite pulse frequency and duty cycle are adjusted to maintain the pseudorange sampling density in high-speed state, optimizing the response speed and data processing efficiency of the system.

[0091] The reference enhancement module ensures that all observation data is accurately aligned and processed in a unified space-time framework through space-time synchronization, pseudorange correction, and pseudolite scheduling technology. This module can effectively improve the positioning accuracy and reliability of the system in complex environments, especially in environments with attenuated or interfered GNSS signals, where pseudolite signals and differential correction functions provide important compensation and correction support. In addition, the dynamic scheduling and adjustment functions of the pseudolite scheduling unit ensure that the system can adaptively select the best channel and power according to environmental changes, further improving the system's anti-interference ability and stability.

[0092] The multi-modal fusion positioning module includes an attitude solving unit, an observation optimization unit, and a constraint fusion unit:

[0093] The attitude solving unit is configured to call the triaxial angular velocity data, triaxial acceleration data and temperature mark of the same epoch in the inertial observation set under the unified space-time reference label constraint, first implement zero offset correction and random drift suppression on the triaxial angular velocity data, then convert the triaxial acceleration data to the inertial coordinate system and eliminate the gravity component, and then calculate the carrier instantaneous attitude quaternion by using the quaternion recursive integral algorithm, and write the quaternion and the attitude reliability score calculated according to the measurement noise covariance into the attitude cache area.

[0094] The attitude solving unit eliminates the errors caused by zero offset, drift and gravity by accurately processing the angular velocity data and acceleration data in the inertial observation set. The quaternion recursive integral algorithm is used to calculate the carrier instantaneous attitude, so as to accurately provide the attitude information of the positioning system. The attitude quaternion and the reliability score generated by the unit ensure the high precision of the attitude information, and provide reliable input for subsequent constraint fusion and coordinate solution.

[0095] The observation optimization unit is configured to read the pseudo-range observation value, carrier phase observation value and signal-to-noise ratio index in the corrected observation set and auxiliary observation set for the same unified space-time reference epoch, calculate the weight score of each observation value according to the preset confidence evaluation model, and the confidence evaluation model comprehensively considers the signal-to-noise ratio, epoch continuity, cycle slip flag and multipath residual.

[0096] When the weight score of an observation value is higher than a preset threshold, the observation value is marked as a high-quality observation and written into a high-quality observation buffer area, otherwise, the observation value is discarded or processed with a low weight, and a high-quality observation set is generated.

[0097] The observation optimization unit dynamically calculates the weight score of each observation value by real-time evaluating the pseudo-range observation value and carrier phase observation value in the corrected observation set and auxiliary observation set according to multiple parameters such as signal-to-noise ratio, epoch continuity and cycle slip flag. The unit generates a high-quality observation set by screening high-quality observations and eliminating low-quality or invalid observations, thereby improving the overall observation quality and precision of the positioning system.

[0098] The constraint fusion unit is configured to call the carrier attitude quaternion in the attitude cache area and the pseudo-range observation value and carrier phase observation value in the high-quality observation buffer area in the unified space-time reference frame, construct a joint state vector by using an extended Kalman filtering algorithm, and perform coupled constraint solution, output real-time coordinate solution and corresponding observation residual, write the real-time coordinate solution into a digital mapping output module, and write the observation residual into an error compensation module.

[0099] The constraint fusion unit couples the carrier attitude quaternion in the attitude buffer with the pseudo-range observations and carrier phase observations in the high-quality observation buffer through an extended Kalman filter algorithm. This unit realizes accurate real-time coordinate calculation and effective observation residual output. Through joint state vector calculation, the constraint fusion unit effectively integrates various types of 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 multi-modal fusion positioning module ensures high-precision positioning and stability in various complex environments through the close cooperation of attitude calculation, observation selection, and constraint fusion technology. This module effectively integrates data from different observation units, including inertial data, satellite-based observation data, and auxiliary observation data, eliminating errors that may be caused by a single observation source and providing more reliable and accurate positioning results. Through constraint calculation using the extended Kalman filter algorithm, accurate real-time coordinate and attitude calculation is achieved, and the positioning accuracy of the system is further improved through dynamic selection of high-quality observation values.

[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 environment state set, and to extract multipath features.

[0103] The multipath learning unit dynamically updates the error digital twin model by continuously comparing the corrected observation set with real-time coordinate residuals, automatically adjusts the multipath feature weights when the residual change rate exceeds the preset threshold, and synchronizes the adjustment results to the constraint fusion unit and the three-dimensional grid generation unit (power 5).

[0104] The multipath learning unit constructs an error digital twin model by analyzing the corrected observation set and the environment 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 through dynamic updating of the error model, improving the positioning performance of the system in complex environments.

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

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

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

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

[0109] The error compensation module corrects various errors in the system in real time through technologies such as multipath learning, temperature drift compensation, and magnetic interference suppression, ensuring the stability of positioning accuracy and the high reliability of the system. This module can effectively eliminate the effects of multi-path effects, inertial errors caused by temperature changes, and magnetic field interference on attitude solution, thereby improving the adaptability and anti-interference ability of the system in complex environments. Through accurate error compensation, the positioning accuracy and long-term stability of the entire system are further enhanced.

[0110] The digital mapping output module includes a three-dimensional grid generation unit, a deformation monitoring unit, and a data interface unit:

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

[0112] The three-dimensional grid generation unit generates an accurate three-dimensional geographic information model by projecting real-time coordinates and sensor point cloud data to a three-dimensional grid database. This unit maps the collected spatial data into a three-dimensional grid structure, ensuring the spatial accuracy and visualization of the mapping data. By comparing with the actual geographic location, it ensures the accuracy of the mapping results, especially suitable for three-dimensional 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 the displacement of the target profile, monitoring and recording the deformation in 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 key data support for structure safety evaluation and deformation warning, helping to identify potential risks in engineering projects in advance.

[0115] The data interface unit is used to provide a mapping data calling interface to the upper system.

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

[0117] The digital surveying and mapping output module provides high-precision surveying and mapping data output and real-time deformation monitoring through the combination of three-dimensional grid generation, deformation monitoring, and data interface technology. This module can combine real-time coordinates with sensor point clouds to generate a three-dimensional geographic information model and dynamically monitor target areas. Through efficient data interfaces, this module can seamlessly integrate with the upper system, providing real-time surveying and mapping data support, and is widely used in geographic information systems, building information modeling, and other fields, providing support for accurate 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 and mapping results generated by the digital surveying and mapping output module with the benchmark model and output evaluation indicators.

[0120] The model evaluation unit compares the surveying and mapping results generated by the digital surveying and mapping output module with the benchmark model to evaluate the positioning accuracy and data accuracy of the system in real time. This unit provides reliable evaluation indicators that can identify potential problems or deviations in system performance and provide feedback for the parameter update unit. Through this process, the model evaluation unit ensures that the system always operates in the best 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 based on the evaluation indicators.

[0122] The parameter update unit automatically adjusts the error digital twin model parameters and attitude calculation parameters based on the evaluation indicators output by the model evaluation unit. Through dynamic updates, it ensures that the system can respond to environmental changes in a timely manner, reducing error accumulation and improving positioning accuracy. This unit improves the system's adaptive ability and long-term stability in complex environments through continuous parameter adjustment.

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

[0124] The adaptive scheduling unit adjusts the pseudolite scheduling strategy and the satellite-ground data acquisition frequency according to the evaluation and parameter update results. Through dynamic analysis of signal quality, system load and data demand, the selection and working frequency of the pseudolite are optimized. This unit ensures that the system can automatically adjust the data acquisition strategy in weak signal or strong interference environment, improve the mapping efficiency and reduce the power consumption, thereby improving the overall performance of the system.

[0125] The feedback iteration module effectively optimizes the running state of the system through continuous model evaluation, parameter updating and adaptive scheduling, ensuring continuous improvement of mapping accuracy and data acquisition efficiency. This module can dynamically adjust the error model and attitude solution parameters according to real-time evaluation results, ensuring that the system always maintains high-precision positioning and mapping performance in various environments. In addition, through adaptive scheduling, the system can flexibly adjust the pseudolite scheduling strategy and data acquisition frequency according to environmental changes, ensuring efficient operation in different environments and demands.

[0126] Embodiment two, with reference to Figure 2 , provides a Beidou mapping positioning method, comprising the following steps:

[0127] Step S1, constructing an observation data pool.

[0128] Step S2, generating a unified space-time reference.

[0129] Step S3, outputting real-time coordinates.

[0130] Step S4, online correction of system errors.

[0131] Step S5, generating three-dimensional mapping results.

[0132] Step S6, closed-loop model updating.

[0133] The application can provide high-precision real-time coordinate solution through multi-modal fusion positioning, attitude solution, observation optimization and constraint fusion technology. Especially in the environment where traditional GNSS signals are difficult to receive, pseudolite auxiliary observation and inertial navigation system provide effective supplement, improve stability and reliability, fuse multi-source data, so that in the case of failure of any positioning sensor, other modules can still provide effective positioning information, enhance the redundancy and fault tolerance of the system. Especially the pseudolite scheduling unit dynamically switches the working channel according to the electromagnetic noise level and path loss, effectively avoids the problem of multipath interference and signal attenuation, adopts multipath learning, temperature drift compensation and magnetic interference suppression technology, effectively compensates the measurement error caused by environmental changes, so as to ensure the precision stability in various complex environments. Especially the multi-path feature weight can be dynamically adjusted, the model can be automatically optimized according to the residual change, the positioning precision is improved, the real-time coordinate solution is combined with the sensor point cloud data, the high-precision three-dimensional grid data is generated, the efficient data support is provided for geographic information system, building information model and other fields, the target profile can be accurately measured, and the abnormal area is marked, the data support is provided for the real-time monitoring of the engineering project, through the continuous model evaluation and parameter update, the pseudolite scheduling strategy and satellite data acquisition frequency can be automatically adjusted according to the change of external environment. In this way, not only the positioning precision can be ensured, but also the unnecessary energy consumption can be reduced, the work efficiency and battery endurance can be improved.

[0134] Those skilled in the art will appreciate that embodiments of the present application can be readily used as a method, a system or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (or computer- readable storage media) having computer-usable program code embodied in the medium. The medium can be any available medium or combination thereof that is accessible by a general purpose or special purpose computer. By way of example, such computer-usable storage media can include a volatile memory, such as a random access memory (RAM), a non-volatile memory, such as a read-only memory (ROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic disk, a flash memory, a compact disk (CD) or a digital versatile disk (DVD). The computer-usable program code can include any suitable set of instructions, statements or Figure 1 one or more functions specified in the flow or flows and / or blocks Figure 1 one or more functions specified in the flow or flows and / or blocks

[0135] It should be noted that the above-mentioned embodiments are only used to illustrate but not to limit the technical solutions of the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A Beidou surveying and mapping positioning system, characterized in that, The system comprises a satellite-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, which are sequentially connected. The satellite-ground data acquisition module is used to construct an observation data pool. The reference enhancement module is used to generate a unified space-time reference. The multi-modal 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 to update the model in a closed loop. The satellite-ground data acquisition module comprises a Beidou observation unit, a pseudolite observation unit, an inertial observation unit and an environment observation unit. The Beidou observation unit is used to receive Beidou-3 satellite carrier phase data and pseudorange data and write them into the observation data pool. The pseudolite observation unit is used to receive ultra-low frequency pseudolite signals and UWB pseudolite pseudorange data, convert them into auxiliary observation sets and write them into the observation data pool. The inertial observation unit is used to collect three-axis angular velocity data and three-axis acceleration data, generate inertial observation sets and write them into the observation data pool. The environment observation unit is used to collect temperature data, magnetic field data and vibration state data and write them into an environment state set. The reference enhancement module comprises a space-time synchronization unit, a pseudorange correction unit and a pseudolite scheduling unit. The space-time synchronization unit is used to time tag the satellite carrier phase data, pseudorange data and inertial observation sets in the observation data pool based on atomic clock time tags, and generate a unified space-time reference frame. The pseudorange correction unit is used to call differential correction parameters to correct the satellite pseudorange data and auxiliary observation sets in the unified space-time reference frame, and generate corrected observation sets. The error compensation module comprises 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 sets and the environment state set, and extract multipath features. The temperature drift compensation unit is used to correct the temperature drift error of the inertial observation sets and electronic components using temperature data. The magnetic interference suppression unit is used to calibrate magnetic field anomalies and compensate for the attitude solution results. The multipath learning unit dynamically updates the error digital twin model by continuously comparing the corrected observation sets and real-time coordinate residuals, automatically adjusts the multipath feature weights when the residual rate exceeds the preset threshold, and synchronizes the adjustment results to the constraint fusion unit and the three-dimensional grid generation unit.

2. The Beidou surveying and mapping positioning system according to claim 1, wherein, The pseudolite scheduling unit is used to periodically read the electromagnetic noise power density value in the environment state set and the real-time path loss estimation value output by the path loss model. The path loss model is constructed based on electromagnetic wave propagation theory and environmental characteristics using the Okumura-Hata model. The path loss model calculates the path loss based on real-time collected environmental data, including electromagnetic noise power density value and signal propagation path characteristics. The calculation logic of the real-time path loss estimation value is: According to the electromagnetic noise power density value, the path loss based on distance and frequency is calculated, the attenuation effect of electromagnetic wave propagation theory on the signal is considered to calculate the actual loss, the path loss estimation value is corrected combined with real-time environmental data, the path loss estimation value will be dynamically adjusted according to the real-time change of the environmental state, and the real-time path loss estimation value is output; According to the preset channel availability decision model, the ultra-low frequency pseudolite channel availability score and the ultra-wide band pseudolite channel availability score are calculated respectively; When the ultra-low frequency pseudolite channel availability score is higher than the ultra-wide band pseudolite channel availability score set threshold, a first scheduling instruction is generated to set the ultra-low frequency pseudolite as the current working channel, and after the minimum transmission power is inversely solved according to the target signal-to-noise ratio lower limit and the power margin is added, the power field is written; When the ultra-low frequency pseudolite channel availability score is less than or equal to the ultra-wide band pseudolite channel availability score set threshold, a second scheduling instruction is generated to set the ultra-wide band pseudolite as the current working channel, and the transmission power is calculated according to the same inverse algorithm; According to the vibration root mean square value in the environmental state set, the pseudolite pulse repetition frequency and the duty cycle are dynamically adjusted to maintain the pseudorange sampling density in the high-speed motion state. The first scheduling instruction or the second scheduling instruction is packaged into a TLV structure and attached with a validity period label and a generation timestamp, and then written into a scheduling instruction pool.

3. The Beidou surveying and positioning system according to claim 2, characterized in that, The multi-modal fusion positioning module includes a pose solving unit, an observation optimization unit, and a constraint fusion unit: The pose solving unit is used to call the triaxial angular velocity data, triaxial acceleration data, and temperature mark of the same epoch in the inertial observation set under the constraint of unified space-time reference label, first performs zero offset correction and random drift suppression on the triaxial angular velocity data, then converts the triaxial acceleration data to the inertial coordinate system and removes the gravity component, and then calculates the carrier instantaneous attitude quaternion by using the quaternion recursive integral algorithm, and writes the quaternion and the attitude reliability score calculated according to the measurement noise covariance into the attitude cache area; The observation optimization unit is used to read the pseudorange observation value, carrier phase observation value, and signal-to-noise ratio index in the correction observation set and auxiliary observation set for the same unified space-time reference epoch, calculate the weight score of each observation value according to the preset confidence evaluation model, and the confidence evaluation model considers the signal-to-noise ratio, epoch continuity, cycle slip flag, and multipath residual error; When the weight score of an observation value is higher than a preset threshold, it is marked as a high-quality observation and written into a high-quality observation buffer area, otherwise it is discarded or down-weighted, and a high-quality observation set is generated; The constraint fusion unit is used to call the carrier attitude quaternion in the attitude cache area and the pseudorange observation value and carrier phase observation value in the high-quality observation buffer area in the unified space-time reference frame, construct a joint state vector and perform coupled constraint solving by using an extended Kalman filtering algorithm, output real-time coordinate solution and corresponding observation residual, and write the real-time coordinate solution into a digital mapping output module, and write the observation residual into an error compensation module.

4. The Beidou surveying and mapping positioning system according to claim 3, wherein, The digital mapping output module includes a three-dimensional grid generation unit, a deformation monitoring unit, and a data interface unit: The three-dimensional grid generation unit is configured to project real-time coordinates and sensor point clouds to a three-dimensional grid database; The deformation monitoring unit is configured to measure displacement of the target profile and mark abnormal areas; The data interface unit is configured to provide a surveying and mapping data calling interface to an upper system.

5. The Beidou surveying and positioning system according to claim 4, characterized in that, The feedback iteration module includes a model evaluation unit, a parameter updating unit, and an adaptive scheduling unit: The model evaluation unit is configured to compare surveying and mapping results generated by the digital surveying and mapping output module with a benchmark model and output evaluation indexes; The parameter updating unit is configured to update error digital twin model parameters and attitude solution parameters according to the evaluation indexes; The adaptive scheduling unit is configured to adjust pseudo-satellite scheduling strategies and satellite-ground data acquisition frequencies according to the updating results.

6. The Beidou surveying and positioning system according to claim 5, wherein, The pseudo-satellite scheduling unit adopts a dual-frequency complementary strategy: when the environmental state set shows that electromagnetic noise is lower than a preset threshold, an ultra-low frequency pseudo-satellite is enabled; when the electromagnetic noise is higher than the preset threshold, a UWB pseudo-satellite is switched to, and transmission power is adjusted according to a real-time path loss model.

7. A method for positioning by Beidou surveying, applied to the positioning system by Beidou surveying as claimed in any one of claims 1-6, characterized in that, The method includes the following steps: Step S1, constructing an observation data pool; Step S2, generating a unified space-time benchmark; Step S3, outputting real-time coordinates; Step S4, correcting system errors online; Step S5, generating three-dimensional surveying and mapping results; Step S6, updating the model in a closed loop.

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