Satellite navigation outfield test method, device and medium of high-precision space-time reference

Abstract

The application discloses a satellite navigation field test method and device of high-precision space-time reference, and a medium. The method comprises the following steps: acquiring vehicle axle operation data in real time by using an encoder, and forming original dynamic data; obtaining a preliminary position of the current vehicle by combining original dynamic data and accurate orbit information of each part of the orbit measured by orbit determination through an upper computer; monitoring real-time dynamic attitude change state of the vehicle in the driving process in real time through time sequence data collected by an optical-electric sampling device, and correcting the preliminary position by using correction data in a state feature model matched from a prior reference model library according to the real-time dynamic attitude change state of the vehicle to obtain an accurate position of the vehicle. The application solves the problem that reference orbit and speed true value are not easy to obtain in the existing field test by using high-precision surveying and mapping positioning as the positioning basis, and using high-precision space-time reference sensing equipment to acquire and correct accurate position information.

Description

Technical Field

[0001] This application relates to the field of satellite navigation testing technology, and in particular to satellite navigation field testing methods, equipment and media with high-precision spatiotemporal references. Background Technology

[0002] To address issues such as the difficulty in replicating the real-world usage scenarios of satellite navigation indoor testing and the inaccuracy of satellite navigation indoor test signals, outdoor testing is increasingly becoming an indispensable part of the identification and verification process for navigation terminals.

[0003] When building dynamic testing platforms for satellite navigation, domestic testing and metrology centers often adopt the traditional "manned testing vehicle + integrated navigation" approach. This approach suffers from several drawbacks: difficulty in reproducing the trajectory due to human operation; poor anti-interference capability, requiring additional anti-interference antennas or testing at different interference frequencies, making full-band anti-interference reception testing impossible; and potential impact on positioning results in multipath scenarios such as ultra-long tunnels and urban buildings. Furthermore, significant differences in positioning algorithms between various terminal types mean that selecting a particular terminal as a benchmark inevitably introduces bias, potentially affecting the impartiality of the results. Typical examples include the Chongqing Institute of Metrology, the 2301 Testing Center, and Chongqing StarNet Application Company.

[0004] When conducting tests on in-vehicle satellite navigation terminals, domestic automakers often adopt a full-vehicle test run approach. New-generation automakers are using a "track laying + unmanned vehicle operation" approach, which yields highly reliable results, but is extremely costly. For example, a 2km track and its supporting facilities cost over 300 million yuan (including land costs); furthermore, it cannot solve the problem of interference. Typical examples include BYD's test track in Shenzhen, Dingyuan Test Track in Chuzhou, Anhui, and Dongfeng Motors' test track in Xiangyang, Hubei.

[0005] In addition to the problems mentioned above, existing field tests also have the following shortcomings: (1) The common problem of difficulty in obtaining the true value of the baseline trajectory and velocity needs to be solved; (2) Positioning benchmark testing is prone to errors and deviations, affecting the test and evaluation results. Even high-precision RTK positioning benchmarks used in actual road tests have significant errors, especially in environments with severe multipath effects (such as between tall buildings) or satellite signal obstruction. If the benchmark itself has errors, the performance evaluation results of the product may be overestimated or underestimated. Furthermore, high-precision anti-interference and anti-spoofing RTK receivers and inertial navigation systems are required for sports car testing to perform anti-interference and anti-spoofing, and the accuracy difference is an order of magnitude. (3) Poor test repeatability and difficulty in locating test faults. Test results for the same road segment at different times (such as changes in traffic flow or differences in lighting conditions) may be inconsistent, making it difficult to accurately reproduce the problem. The sporadic nature of positioning errors (such as instantaneous loss of satellite signals) makes fault tracing difficult. Summary of the Invention

[0006] This application provides a high-precision spatiotemporal reference satellite navigation field test system, which solves the technical problems of existing satellite navigation field test technology, such as difficulty in obtaining the true value of the reference trajectory and velocity, easy occurrence of errors and deviations in positioning reference testing, poor test repeatability, and difficulty in locating test faults.

[0007] This application is achieved through the following solution: A high-precision spatiotemporal reference satellite navigation field test method is applied to a satellite navigation field test system. The satellite navigation field test system includes a positioning device and a track for which orbit determination measurements have been pre-completed. The positioning device includes a vehicle that moves along the track and a host computer. Encoders are installed on the wheels of the vehicle. The method includes the following steps: The encoder uses the vehicle wheel axle running data in real time to form raw dynamic data, which includes torque, rotational speed and rotational acceleration. By combining the original dynamic data and the precise track determination information obtained from various points on the track by the host computer, the preliminary position of the current vehicle is obtained. The preliminary position includes longitude, latitude and elevation data. The real-time dynamic attitude change status of the vehicle during driving is monitored in real time by collecting time-series data through photoelectric sampling device. After matching the corresponding state feature model from the prior reference model library according to the real-time dynamic attitude change status of the vehicle, the correction data in the state feature model is extracted and the preliminary position is corrected to obtain the precise position of the vehicle. The real-time dynamic attitude change status includes braking status, slipping status, drifting status, over-starting status, and sharp turning status.

[0008] Furthermore, the orbit determination measurement process includes the following steps: Complete land exploration and install precision control points; Several high-precision benchmark points were determined through surveying; Through joint testing, several typical data collection points were identified, and the density of these typical data collection points was directly proportional to the complexity of the track. Based on high-precision benchmarks and typical data collection points, the orbit determination work of the set track is completed, and the preliminary positions of various points on the track are obtained. The preliminary positions include longitude, latitude and elevation data. By using three-dimensional laser scanning calibration, the data verification of the track is completed to obtain precise track information at various points on the track.

[0009] Furthermore, when recording the axle running data of the carrier in real time through the encoder and forming the original dynamic data, the process also includes the step of: setting the data recorded in the encoder to zero after the carrier has traveled a set distance, thereby achieving cumulative error suppression.

[0010] Furthermore, after the vehicle has traveled a set distance, the data recorded in the encoder is reset to zero, thereby suppressing accumulated errors. This process includes the following steps: During the vehicle's operation, when the guide device is detected by the photoelectric sampling device, the difference between the number of times the wheel rolls between the two guide devices and the theoretical number of rolls is recorded from the original dynamic data sampled by the encoder. The guide devices are known to be evenly spaced along the track length extension direction at a set fixed distance. When the difference data is greater than the set threshold, the data recorded in the encoder is set to zero, and a flag bit is placed in the time series data collected by the photoelectric sampling device; Repeat the above steps until the test is complete.

[0011] Furthermore, the time-series data collected by the photoelectric sampling device monitors the real-time dynamic attitude change state of the vehicle during its driving process. Based on the real-time dynamic attitude change state of the vehicle, a corresponding state feature model is matched from the prior reference model library. Then, the corrected data from the state feature model is extracted, and the preliminary position is corrected to obtain the precise position of the vehicle. Specifically, this includes the following steps: Ranging data acquisition is achieved by measuring the reflective optical tracks fixed on both sides along the track length extension direction using laser transceivers set on the left and right sides of the vehicle. The ranging data is purified by sequentially performing Kalman filtering and low-frequency filtering to obtain characteristic perturbations. The labeled segments and ordinary segments in the purified ranging data are compressed and fused using a neural network, and then spliced ​​together in time sequence to restore the feature sequence. The labeled segments are the interval data with marked bits in the ranging data, and the remaining interval data are ordinary segments. Model matching involves matching the time-domain, frequency-domain, and time-frequency-domain processed time-domain sequences of the basic segments in the feature sequence with the prior reference model library using Mahalanobis distance and cosine similarity. After a successful match, the initial position of the current vehicle is corrected using the correction data contained in the successfully matched state feature model in the prior reference model library to obtain the precise position of the vehicle.

[0012] Furthermore, the labeled and ordinary segments in the purified ranging data are compressed and fused using a neural network, and then concatenated in time sequence to restore the feature sequence. The specific steps include: The labeled segments expanded according to the set scaling factor are regarded as a 2-channel 2D dataset. They are copied three times and passed through 1×1 convolution kernel, 3×3 convolution kernel and 3×3 pooling respectively, and then weighted and merged. The ordinary segments expanded according to a set scaling factor are regarded as a 2-channel 2D dataset. After being directly pooled by 3×3, the data is compressed by a 1×1 convolution kernel and then fused. The processed labeled segments and ordinary segments are concatenated in time sequence to restore the feature sequence. .

[0013] Furthermore, the model matching involves matching the time-domain, frequency-domain, and time-frequency-domain processed time-domain sequences, frequency-domain sequences, and time-frequency-domain sequences of the basic segments in the feature sequence with the prior reference model library using Mahalanobis distance and cosine similarity. Specifically, this includes the following steps: Obtain a prior reference model library based on prior testing; feature sequence After splitting the sequence into basic segments, each segment is processed in the time domain, frequency domain, and time-frequency domain. The frequency domain processing involves performing an FFT transform on the sequence to expand it into a frequency domain sequence. Time-frequency domain processing involves performing wavelet transform on the feature sequence to obtain the time-frequency domain sequence. ; When performing model matching, the Mahalanobis distance is first calculated between the data of the basic segments in the feature sequence and the prior reference model library as a coarse match. When the similarity is higher than a certain threshold, the cosine similarity of the data in the time domain, frequency domain, and time-frequency domain is further calculated with the three-dimensional data of each model in the prior reference model library. If a certain threshold is met, the model matching is considered successful.

[0014] Furthermore, after a successful match, the preliminary position of the current vehicle is corrected using the correction data contained in the successfully matched state feature model in the prior reference model library to obtain the precise position of the vehicle. This specifically includes the following steps: Establishing a coordinate system: The track data from the survey is directly established under the geodetic coordinate system xyz. All points on the track are marked with their specific latitude, longitude, and altitude. The encoder can determine the latitude, longitude, and altitude based on the number of wheels. The track coordinate set is as follows: ,in As the initial point, To determine the total number of discrete points on the track; the wheel radius is... Encoder counts wheel rolling Next, the circumference of the wheel Cumulative distance from the test point to the initial point ; Encoder measurement parameter calculation: two adjacent discrete points in the trajectory coordinate set and The straight-line distance between ,but arrive cumulative arc length Then there is an initial point. , Given the total length of the entire trajectory, the correspondence between discrete points on the trajectory and the cumulative arc length is... ; Encoder measurement data mapping: cumulative distance measured by the encoder It involves finding the corresponding position of the test point in the discrete coordinate set by matching the arc length along the rolling length of the trajectory. At this time, the test point exists as a point that has already been calibrated in the coordinate system, or between two adjacent points. When it happens to match a discrete point on the calibrated track, it is directly mapped to the coordinates of the corresponding point. When the value falls within two adjacent points, a linear interpolation is performed between the two points. Data fusion: All segments of the matching test sequence are mapped to calibration data, and the encoder data corresponding to the geodetic coordinates are calibrated. That is, the corrected parameters are summed with each coordinate point to obtain the precise position of the vehicle.

[0015] This application also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the satellite navigation field test method for the high-precision spatiotemporal reference.

[0016] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the satellite navigation field test method for the high-precision spatiotemporal reference.

[0017] This application also provides a computer program product, including a computer program or computer-executable instructions, which, when executed by a processor, implement the satellite navigation field test method for the high-precision spatiotemporal reference.

[0018] Compared with the prior art, this application can produce the following beneficial effects: This invention provides a satellite navigation field test method, equipment, and medium with a high-precision spatiotemporal reference. The core innovation of the high-precision spatiotemporal reference satellite navigation field test method lies in obtaining the precise position of the vehicle by correcting the vehicle's position based on the feature sequence obtained after continuous ranging by the photoelectric sampling device and the state feature model matching in the prior reference model library. This overcomes the cumulative encoder positioning error that may exist in special dynamic scenarios such as vehicle braking, skidding, drifting, over-starting, and sharp turning. Thus, it achieves the goal of solving the problem of difficulty in obtaining the true value of the existing field test reference trajectory and velocity by using high-precision mapping and positioning as the positioning basis and using high-precision spatiotemporal reference sensing equipment to obtain and correct the precise position information.

[0019] In addition to the purposes, features, and advantages described above, this application has other purposes, features, and advantages. A further detailed description of this application will be provided below with reference to the figures. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating a preferred embodiment of a satellite navigation field testing method for a high-precision spatiotemporal reference. Figure 2 This is a schematic diagram of a survey of a preferred embodiment of this application; Figure 3 This is a schematic diagram of a preferred embodiment of this application; Figure 4 This is a schematic diagram of the joint test of a preferred embodiment of this application; Figure 5 This is a schematic diagram of the orbit determination of a preferred embodiment of this application; Figure 6 This is a schematic diagram of encoder positioning according to a preferred embodiment of this application; Figure 7 This is a schematic diagram of the encoder reset process according to a preferred embodiment of this application; Figure 8 This is a schematic diagram illustrating the principle of the scaling factor extended marker segment and the ordinary segment in this application; Figure 9 This is a schematic diagram of the marker segment processing procedure; Figure 10 This is a schematic diagram of the ordinary segment processing procedure; Figure 11 This is a schematic diagram illustrating the principle of model matching; Figure 12 This is a schematic diagram of the cross-section of the light track structure; Figure 13 This is a side view of the light track structure; Figure 14 This is a cross-sectional schematic diagram of the outer shell structure of the light track; Figure 15 This is a schematic block diagram of an electronic device according to a preferred embodiment of this application; Figure 16 This is a schematic diagram of the internal structure of a computer device according to a preferred embodiment of this application.

[0021] In the diagram: 1. Track; 2. Precision control point; 3. High-precision reference point; 4. Typical acquisition point; 5. Carrier vehicle; 6. Inner optical track; 7. Outer optical track; 8. Antenna of the object under test; 9. Guide channel; 10. Clip structure. Detailed Implementation

[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0023] like Figure 1 As shown, to address the aforementioned technical problems, a preferred embodiment of this application provides a high-precision spatiotemporal reference satellite navigation field testing method, applied to a satellite navigation field testing system. The satellite navigation field testing system includes a positioning device and a track 1 pre-measured for orbit determination. The positioning device includes a vehicle 5 moving along the track 1 and a host computer. Encoders are installed on the wheels of the vehicle 5. The method includes the following steps: S1. The encoder is used to collect the axle running data of the carrier wheel in real time and form raw dynamic data, which includes torque, rotational speed and rotational acceleration; S2. By combining the original dynamic data and the precise track determination information of each point on track 1 obtained by the host computer, the preliminary position of the current vehicle 5 is obtained. The preliminary position includes longitude, latitude and elevation data. S3. The real-time dynamic attitude change state of the vehicle 5 during driving is monitored in real time by the time-series data collected by the photoelectric sampling device. After matching the corresponding state feature model from the prior reference model library according to the real-time dynamic attitude change state of the vehicle 5, the correction data in the state feature model is extracted and the preliminary position is corrected to obtain the precise position of the vehicle 5. The real-time dynamic attitude change state includes braking state, slipping state, drifting state, over-starting state, and sharp turn state.

[0024] Preferably, the orbit determination measurement process of track 1 includes the following steps: S10. Complete land exploration and install control point 2 (see...) Figure 2 ); S11. Through surveying, several high-precision benchmark points 3 were determined, wherein the error of high-precision benchmark point 3 is 5mm (see...). Figure 3 ); S12. Through joint measurement, several typical sampling points 4 are determined. The density of these typical sampling points 4 is directly proportional to the complexity of track 1. That is, the density of the typical sampling points 4 depends on the complexity of the track. Theoretically, the more complex the track 1, the more measurement points there are; the simpler the track 1, the fewer measurement points there are. The accuracy of the relative positional relationship of the joint measurement is better than 2mm (see...). Figure 4 ); S13. Based on high-precision benchmarks and typical data collection points, complete the orbit determination work for the established track, obtaining the preliminary positions of various points on the track. These preliminary positions include longitude, latitude, and elevation data (see...). Figure 5 ); S14. Through three-dimensional laser scanning calibration, the data verification of the track is completed to obtain accurate track information at various points on the track.

[0025] The track itself is irregular, which is necessary to cover the testing needs of various special terrains. Figures 2-5The yellow track is for illustrative purposes only. In practice, this solution is applicable to tracks of any structure, including but not limited to spiral climbs, uphill sections, downhill sections, loops, straight sections, S-curves, etc.

[0026] Overall, the basic idea of ​​this application is as follows: 1) Obtain raw dynamic data The positioning device includes an encoder and a host computer control program. The encoder records the operating cycle of the five axles of the vehicle in real time and generates raw dynamic data such as torque, speed, and acceleration (see...). Figure 6 ); 2) The host computer calculates the current precise position. Based on the raw dynamic data (torque, rotational speed, and rotational acceleration) obtained by the encoder, combined with the precise track position information measured by the orbit determination, the precise position of the current multi-functional autonomous mobile test subsystem (including longitude, latitude, and elevation data) is obtained. 3) Suppress cumulative error The guiding device adopts a proximity switch structure. When the vehicle 5 travels a certain distance, the data recorded in the encoder is reset to zero, thereby suppressing the cumulative error. Theoretically, the closer the proximity switches are, the smaller the cumulative error. The error caused by the sliding and drifting of the vehicle 5 is spread to each section of the track, effectively controlling the track positioning accuracy error. 4) Combined positioning To improve positioning accuracy, laser-assisted combined positioning is employed. Data fitting is then performed using data fusion software.

[0027] Since the vehicle 5 will generate positioning errors when it is in motion, the source of error in this solution is analyzed as follows: 1) Wheel slippage and drifting When vehicle 5 starts or accelerates, the driving force is greater than the friction force, causing slippage. The wheels rotate, but the vehicle body does not travel the theoretical distance. When vehicle 5 brakes or decelerates, the inertial force is greater than the friction force, causing drifting. The vehicle body continues to move forward, but the wheels have decelerated or stopped rotating. In addition to the dynamic operation of the vehicle, oil stains, dust, and slight vibrations on the vehicle track can also cause this phenomenon. In these scenarios, the encoder will record the wheels "spinning freely," resulting in an uncorrectable deviation between the displacement measurement value and the actual value, and this error will continue to accumulate. 2) Factors related to mechanical transmission Long-term operation of the wheels causes changes in diameter, resulting in a shorter actual distance corresponding to the number of encoder pulses; different loads on vehicle 5 cause different tire deformations, resulting in differences in the effective rolling radius under different loads; backlash in transmission gears, couplings, etc., causes the encoder reading to be out of sync with the actual displacement when vehicle 5 starts, stops, or reverses. 3) Installation and measurement errors Track flatness and straightness can also indirectly cause system errors. Although the track itself is highly accurate, slight undulations or bends can cause the wheels to be slightly suspended or slide sideways, affecting the accuracy of encoder counting. During high-speed movement, the PLC may lose pulses due to scanning cycles and other reasons.

[0028] (2) Analysis of the positioning accuracy of the whole system The positioning accuracy of this system mainly consists of four parts: track mapping accuracy, encoder loss accuracy, cumulative error elimination, and combined positioning loss elimination accuracy, as detailed in Table 1 below.

[0029] Table 1: System Positioning Accuracy Analysis Table

[0030] Essentially, this embodiment is based on a primary-secondary positioning system using laser ranging and an encoder. This type of system is technologically mature and widely used in industrial and commercial applications, such as large gantry cranes, bridge cranes, automated storage and retrieval systems (AS / RS), and large material handling systems. The innovation of this solution does not lie in the form or technical standard of the positioning system, but rather in the cumulative error solution, data processing and fusion methods, and its application in satellite navigation testing scenarios (high-precision reference position acquisition).

[0031] Therefore, preferably, when recording the axle running data of the carrier wheel in real time through the encoder and forming the original dynamic data, the method further includes the following steps: After the vehicle 5 has traveled a set distance, the data recorded in the encoder is reset to zero, thereby suppressing the cumulative error.

[0032] Specifically, after the vehicle 5 has traveled a set distance, the data recorded in the encoder is reset to zero, thereby suppressing cumulative errors. The specific steps include: During the operation of the vehicle 5, when the guide device is identified by the photoelectric sampling device, the difference between the number of times the wheel rolls between the two guide devices and the theoretical number of rolls is recorded from the original dynamic data sampled by the encoder. The guide devices are known to be evenly spaced along the track length extension direction at a set fixed distance. When the difference data is greater than the set threshold, the data recorded in the encoder is set to zero, and a flag bit is placed in the time series data collected by the photoelectric sampling device; Repeat the above steps until the test is complete.

[0033] In this embodiment, the guiding device scheme for resolving the cumulative error of the encoder is as follows: The guiding device adopts the form of a proximity switch, which is a mature and low-cost structure and will not be described in detail here. Combined with the subsequent laser ranging method, different reflective materials can be set in a fixed distance section of the optical track to replace the proximity switch. When a change in laser reflectivity is detected, the encoder is reset. The optical track includes an inner optical track 6 and an outer optical track 7 arranged in parallel.

[0034] The installation location of the guiding device is known, and the intervals are fixed according to the track length. (When the wheel radius is ≤100mm, the reference value is 15 times the wheel circumference) Installation is performed accordingly. To address the cumulative error of long-cycle pulse data caused by mechanical transmission factors and installation and measurement errors, the single sampling test distance is set to... Wheel radius is Encoder counts wheel rolling Next, the theoretical number of rolls can be obtained. : ;

[0035] Assuming the threshold is set as Design early warning procedures such as Figure 7 As shown, the above steps are briefly described below: S1: Reset the laser ranging device on vehicle 5; S2: Place interval markers at the corresponding positions of the ranging data to segment the laser sampling data; S3: Record the encoder's sampling data; S4: Travel to the fixed position of the light track; S5: The guiding device is activated, for example, by triggering a proximity switch or detecting a change in laser reflectivity; S6: During the period from S2 to S5, if If so, a warning flag will be set; S7: Reset encoder counter; S8: Determine if the test status is stopped. If yes, close the decision process; otherwise, jump to S2 to continue the loop counting.

[0036] During subsequent data processing, the interval data with the marked bits are identified as marked segments. .

[0037] Preferably, the time-series data collected by the photoelectric sampling device monitors the real-time dynamic attitude change state of the vehicle 5 during its driving process. Based on the real-time dynamic attitude change state of the vehicle 5, a corresponding state feature model is matched from the prior reference model library. Then, the corrected data from the state feature model is extracted, and the preliminary position is corrected to obtain the precise position of the vehicle 5. Specifically, this includes the following steps: S31. Ranging data acquisition: Ranging data is obtained by measuring the reflective optical tracks fixed on both sides along the track length extension direction using laser transceivers set on the left and right sides of the vehicle 5. The laser transceiver equipment located on the left and right sides of vehicle 5 is expanded as a positioning device. It uses the Time-of-Flight (ToF) phase method to obtain the phase difference of continuously modulated lasers, thereby realizing distance measurement. The principle and product are mature solutions and will not be elaborated here. The formula for the relationship between time of flight and phase is as follows: ;

[0038] in, For single measurement time, For laser phase difference, The modulation frequency; The distance can be calculated from the time of a single measurement, using the following formula: ;

[0039] in To measure the distance for laser transceiver equipment, For the speed of light constant, For flight time; Substituting the two equations, we get: ;

[0040] Since the optical path is parallel to the vehicle, the beam output from the side of the vehicle is always perpendicular to the reflecting surface, so there is no need to consider the Doppler frequency shift.

[0041] In this embodiment, the ranging data source is the laser transceiver equipment on the left and right sides of the vehicle 5. This is an effective method for acquiring the dynamic attitude changes of the vehicle itself. Due to the high accuracy of laser ranging, this process is highly sensitive to dynamic attitude changes. The following steps will further extract this variability to form characteristic parameters, which will serve as the basis for system calibration. Unlike existing laser ranging applications on the market (which all measure in the direction of travel or in the opposite direction), this "customized optical track + fixed distance measurement in the left and right directions" method can also effectively overcome the variability of the track system and can well simulate all possible road conditions. S32. The ranging data is purified, including sequentially performing Kalman filtering and low-frequency filtering to obtain characteristic perturbations; Let i be the sampling times, and the ranging data be... From the true distance Vehicle speed Introduced measurement error , Other measurement errors (noise, circuitry, etc.), and disturbances caused by road conditions and attitude (bumps, tilting). .

[0042] ;

[0043] Because the laser rangefinder is installed on the side of the vehicle, and the speed of light... Very large, Assuming the vehicle is moving Let the side length of the ray be Calculate the optical path at this time .

[0044] ;

[0045] and ,generally Taylor expands on this: ;

[0046] And because Therefore, the equivalent distance for a one-way trip can be determined. : ;

[0047] Therefore there is Assuming the vehicle speed ,at this time It can be ignored; It is generally zero-mean noise, which can be reduced using Kalman filtering; For the actual distance, in If the light path is small enough, since the light path is parallel to the slide rail (the distance and time are consistent), therefore It can be considered a constant.

[0048] In summary, firstly... Obtain by Kalman filtering ,at this time It is difficult to measure directly (requiring even higher precision than laser ranging), and Since the mean is not zero (it has characteristic perturbations), the finite difference method is used to solve it. Let the upper limit of vehicle speed be denoted as... Before the test, five vehicles with the same load were used to maintain [the load]. Traveling at a constant speed across the track, record the reference value for this trip. The test was conducted continuously for u times, and the average reference value was calculated. For system reference; Based on engineering experience, disturbances arising from the vehicle's five dynamic attitude changes (braking, drifting, etc.) are high-frequency components. Therefore, the final result is filtered for low frequencies, resulting in: ;

[0049] The above explanation of the equivalent load vehicle is as follows: In actual testing, the weight of the object to be tested is... Full load of the vehicle Maintain full load when obtaining standard reference values. ; Add during actual testing To bear the load and to balance the load distribution inside the vehicle as much as possible; S33. After compressing the labeled segments and ordinary segments in the purified ranging data using a neural network, the data are fused and then spliced ​​together in time sequence to restore the feature sequence. The labeled segments are the interval data with labeled bits in the ranging data, and the remaining interval data are ordinary segments. First, let's analyze the data volume. The vehicle has one laser ranging device on each of its left and right sides. The typical laser ranging sampling frequency range is 10kHz to 100kHz. Therefore, the sampling frequency is 1 second. Point. Vehicle speed The value ranges from 0.5 to 5 m / s, and the single sampling test distance mentioned above is... Related to the wheel radius, with a value ranging from 5 to 20 meters, the amount of data collected in a single sampling session is then determined. for: ; The large amount of data negatively impacts subsequent FFT and similarity calculations during matching. Referring to the network architectures of GoogLeNet and SqueezeNet, the data is compressed to balance efficiency and matching performance on the processing network, facilitating embedded deployment. The specific design is as follows: The distance between the installation positions of the guiding devices was referred to as the single sampling test distance in the previous text. If the total test distance is So, the test data on the left and right sides , Each contains Group (segment); ;

[0050] The previous text obtained the marked segment by the ratio of scroll counts. And calculate the number of marked segments. Set a scaling factor It determines the balance between data accuracy and efficiency; general engineering experience suggests taking... ,pass Extend the marked segment. The number of expanded marker segments, ;

[0051] Expand to the left and right of the marker segment One, the principle is as follows Figure 8 As shown.

[0052] For the expanded marker segment and ordinary section Categorization and processing: a. Marker segment processing method like Figure 9 As shown, , Treat it as a 2D dataset with 2 channels, make three copies, and process them separately. convolution kernel, convolution kernel and Pooling, then weighted merging; b. Ordinary Section processing method like Figure 10 As shown, , Treating it as a 2-channel 2D dataset, directly... After pooling, through After convolution kernel compression, the data is fused. Finally, the processed marked segments and ordinary section Concatenate the sequences in time to restore the feature sequence, denoted as . ; S34. Model matching: The time-domain, frequency-domain, and time-frequency-domain processed time-domain sequences, frequency-domain sequences, and time-frequency-domain sequences of the basic segments in the feature sequence are matched with the prior reference model library using Mahalanobis distance and cosine similarity. Model matching relies on establishing a prior reference model library, which is a calibration dataset formed by extracting feature values ​​from multiple prior tests. On the one hand, the reference model library contains typical feature value sequences, enabling efficient model matching; on the other hand, it provides correction values ​​for each feature value sequence.

[0053] Performing prior testing involves using the system to iterate through a certain type of motion model multiple times, extracting the commonalities from the feature sequences. This set of feature sequences and model correction parameters, formed through repeated iterations, is essentially a method of obtaining high-precision correction values ​​through long-term, multiple tests.

[0054] Specifically, the complete model matching process is as follows: like Figure 11 As shown, for the feature sequence The matching results are output by matching the reference model library using Mahalanobis distance and cosine similarity. The original sequence consists of several marker segments. and its left and right sides expanded The structure consists of a large segment composed of several sub-segments, which will be referred to as the basic segment below.

[0055] The steps are briefly described below: (1) Obtain a prior reference model library based on prior testing; (2) Feature sequence After splitting the sequence into basic segments, each segment is processed in the time domain, frequency domain, and time-frequency domain. The frequency domain processing involves performing an FFT transform on the sequence to expand it into a frequency domain sequence. Time-frequency domain processing involves performing wavelet transform on the feature sequence to obtain the time-frequency domain sequence. ; (3) When performing model matching, the Mahalanobis distance is first calculated between the data of the basic segment in the feature sequence and the prior reference model library as a coarse match. When the similarity is higher than a certain threshold, the cosine similarity of the data in the time domain, frequency domain and time-frequency domain is further calculated with the three-dimensional data of each model in the prior reference model library. If a certain threshold is met, the model matching is considered successful.

[0056] S35. After successful matching, the initial position of vehicle 5 is corrected using the correction data contained in the successfully matched state feature model in the prior reference model library to obtain the precise position of vehicle 5. By performing low-frequency filtering on the high-precision data, several special motion states caused by large changes in dynamic posture are successfully identified through matching learning. These motion states are precisely what cause large offsets in the encoder data. Now, only calibration using the correction data contained in the prior model library is needed to ensure the output of high-precision reference data. The specific steps include: (1) Establishing a coordinate system: The track data from the survey is directly established in the geodetic coordinate system xyz. All points on the track are marked with specific latitude, longitude, and altitude. The encoder can determine the latitude, longitude, and altitude based on the number of wheels. The track coordinate set is as follows: ,in As the initial point, To determine the total number of discrete points on the track; the wheel radius is... Encoder counts wheel rolling Next, the circumference of the wheel Cumulative distance from the test point to the initial point ; (2) Encoder measurement parameter calculation: two adjacent discrete points in the trajectory coordinate set and The straight-line distance between ,but arrive cumulative arc length Then there is an initial point. , Given the total length of the entire trajectory, the correspondence between discrete points on the trajectory and the cumulative arc length is... ; (3) Encoder measurement data mapping: cumulative distance measured by the encoder It involves finding the corresponding position of the test point in the discrete coordinate set by matching the arc length along the rolling length of the trajectory. At this time, the test point exists as a point that has already been calibrated in the coordinate system, or between two adjacent points. When it happens to match a discrete point on the calibrated track, it is directly mapped to the coordinates of the corresponding point. When the value falls within two adjacent points, a linear interpolation is performed between the two points. (4) Data Fusion: All segments of the matched test sequence are mapped to calibration data, and the encoder data corresponding to the geodetic coordinates is calibrated. That is, the correction parameters are summed with each coordinate point to obtain the precise position of vehicle 5. The matching process and encoder measurement data mapping process have been described above. Now, all segments of the matched test sequence are mapped to calibration data, and the encoder data corresponding to the geodetic coordinates is calibrated. That is, the correction parameters are summed with each coordinate point. Specifically: (1) Segment the encoder mapping data, with the interval data containing the marker bits as the marker segment. Starting from the first marker bit and traversing backwards, only the marker bits need to be matched; (2) Determine the model matching. If successful, proceed to the data fusion compensation step. If unsuccessful, proceed to the next segment of the sequence. (3) Use the corrected data for compensation, and sum the data of the corresponding time point with the corrected data; (4) Move to the next marker segment; (5) Check whether the marked segment in the statistical sequence has been traversed. If not, check and compensate in a loop. If completed, exit the loop.

[0057] Specifically, such as Figure 12 As shown, the distance between the optical track and the vehicle 5 in this application needs to take into account the laser modulation method and calibration accuracy: 1) Starting from the laser modulation method, the phase method can achieve sub-millimeter accuracy within 10 meters. However, as the distance increases, Δt will exceed the modulation period, resulting in phase measurement skipping cycles and inaccurate data. 2) From the perspective of calibration accuracy, the greater the distance, the larger the theoretical range that can be calibrated, and the better the calibration effect.

[0058] From an engineering perspective, the reflector track is set up at a distance of 1m (the actual optical path is 1~2m, controlled by the incident angle) to ensure high precision while the calibration range meets the requirements.

[0059] like Figure 13 As shown, the optical track structure can also meet the test requirements of the antenna under test in the negative elevation angle receiving scenario. Typical negative elevation angle receiving scenarios include interference from road surfaces on both sides of the road and multipath signals reflected from the smooth surfaces of surrounding buildings.

[0060] Regarding system malfunctions caused by fallen leaves, rain, and structural changes in light trails, avoiding these at the algorithmic level would be too costly and complex. Therefore, a physical structure with an external transparent shell is used for protection. Materials such as flame-retardant transparent polycarbonate (FR-PC) can be selected. The design is as follows: Figure 14 As shown, the materials selected meet the requirements of high structural strength and high light transmittance (waveguide) rate. It has an internal guide channel 9. Each section of the pipe can be divided into four pieces and connected by a snap-fit ​​structure 10 to facilitate transportation and subsequent inspection and maintenance. The bottom guide channel has a ventilation and drainage hole at the end.

[0061] like Figure 15 As shown, a preferred embodiment of this embodiment also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the satellite navigation field test method for high-precision spatiotemporal reference in the above embodiment.

[0062] This embodiment also provides an electronic device that uses the high-precision spatiotemporal reference satellite navigation field testing method in the above embodiments to solve the technical problem that existing engineering machinery gearbox shifting methods are difficult to balance control accuracy, reliability and convenience in harsh operating environments with strong vibration, high dust and high and low temperatures. Compared with the prior art, the beneficial effects of the electronic device provided in this embodiment are the same as those of the high-precision spatiotemporal reference satellite navigation field testing method provided in the above embodiments, and other technical features of the electronic device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0063] like Figure 16 As shown, a preferred embodiment of this application also provides a computer device, which may be a terminal or a liveness detection server, and its internal structure diagram may be as follows. Figure 16 As shown. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with other external computer devices via a network connection. When the computer program is executed by the processor, it implements the steps of the aforementioned high-precision spatiotemporal reference satellite navigation field test method.

[0064] Those skilled in the art will understand that Figure 16The structure shown is merely a block diagram of a portion of the structure related to the solution of this embodiment, and does not constitute a limitation on the computer device to which the solution of this embodiment is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0065] The computer equipment provided in this embodiment adopts the satellite navigation field test method with high-precision spatiotemporal reference in the above embodiment, which solves the technical problem that the existing gearbox shifting method of engineering machinery is difficult to balance control accuracy, reliability and convenience in harsh working environments with strong vibration, high dust and high and low temperature. Compared with the prior art, the beneficial effects of the computer equipment provided in this embodiment are the same as the beneficial effects of the satellite navigation field test method with high-precision spatiotemporal reference provided in the above embodiment, and other technical features in the electronic equipment are the same as the features disclosed in the method of the above embodiment, which will not be repeated here.

[0066] A preferred embodiment of this application also provides a storage medium, the storage medium including a stored program, which, when the program is executed, controls the device where the storage medium is located to perform the steps of the satellite navigation field test method for the high-precision spatiotemporal reference described in the above embodiments.

[0067] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0068] If the functions described in this embodiment are implemented as software functional units and sold or used as independent products, they can be stored in one or more computing device-readable storage media. Based on this understanding, the parts of this embodiment that contribute to the prior art or the technical solution can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computing device (which may be a personal computer, server, mobile computing device, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this embodiment. The aforementioned storage media include: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0069] Those skilled in the art will understand that the embodiments of this example can be provided as methods, systems, or computer program products. Therefore, this example can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this example can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code. The solutions in this example can be implemented using various computer languages, such as the object-oriented programming language C++ and the embedded programming language C.

[0070] This embodiment is described with reference to flowchart illustrations and / or block diagrams of the method, apparatus (system), and computer program product according to this embodiment. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0071] 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 an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0072] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0073] This embodiment also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the satellite navigation field test method for a high-precision spatiotemporal reference as described above.

[0074] The computer program product provided in this embodiment solves the technical problem that existing gearbox shifting methods in engineering machinery struggle to balance control accuracy, reliability, and convenience in harsh operating environments characterized by strong vibration, high dust, and extreme temperatures. Compared to existing technologies, the beneficial effects of the computer program product provided in this embodiment are the same as those of the high-precision spatiotemporal reference satellite navigation field testing method provided in the above embodiments, and will not be elaborated upon here.

[0075] Obviously, those skilled in the art can make various modifications and variations to this embodiment without departing from the spirit and scope of this embodiment. Therefore, if these modifications and variations of this embodiment fall within the scope of the claims of this embodiment and their equivalents, this embodiment is also intended to include these modifications and variations.

Claims

1. A high-precision spatiotemporal reference satellite navigation field test method, applied to a satellite navigation field test system, the satellite navigation field test system including a positioning device and a track for which orbit determination measurements have been pre-completed, the positioning device including a vehicle moving along the track and a host computer, the wheels of the vehicle being equipped with encoders, characterized in that, Including the following steps: The encoder uses the vehicle's wheel axle running data in real time to form raw dynamic data, which includes torque, rotational speed and rotational acceleration. By combining the original dynamic data and the precise track determination information obtained from various points on the track by the host computer, the preliminary position of the current vehicle is obtained. The preliminary position includes longitude, latitude and elevation data. The real-time dynamic attitude change status of the vehicle during driving is monitored in real time by collecting time-series data through photoelectric sampling device. After matching the corresponding state feature model from the prior reference model library according to the real-time dynamic attitude change status of the vehicle, the correction data in the state feature model is extracted and the preliminary position is corrected to obtain the precise position of the vehicle. The real-time dynamic attitude change status includes braking status, slipping status, drifting status, over-starting status, and sharp turning status.

2. The satellite navigation field test method for high-precision spatiotemporal reference according to claim 1, characterized in that, The orbit determination measurement process includes the following steps: Complete land exploration and install precision control points; Several high-precision benchmark points were determined through surveying; Through joint testing, several typical data collection points were identified, and the density of these typical data collection points was directly proportional to the complexity of the track. Based on high-precision benchmarks and typical data collection points, the orbit determination work of the set track is completed, and the preliminary positions of various points on the track are obtained. The preliminary positions include longitude, latitude and elevation data. By using three-dimensional laser scanning calibration, the data verification of the track is completed to obtain precise track information at various points on the track.

3. The satellite navigation field test method for high-precision spatiotemporal reference according to claim 2, characterized in that, When recording the axle running data of the carrier wheel in real time through the encoder and forming raw dynamic data, the following steps are also included: After the vehicle has traveled a set distance, the data recorded in the encoder is reset to zero, thereby suppressing cumulative errors.

4. The satellite navigation field test method for high-precision spatiotemporal reference according to claim 3, characterized in that, After the vehicle has traveled a set distance, the data recorded in the encoder is reset to zero, thereby suppressing accumulated errors. The specific steps include: During the vehicle's operation, when the guide device is detected by the photoelectric sampling device, the difference between the number of times the wheel rolls between the two guide devices and the theoretical number of rolls is recorded from the original dynamic data sampled by the encoder. The guide devices are known to be evenly spaced along the track length extension direction at a set fixed distance. When the difference data is greater than the set threshold, the data recorded in the encoder is set to zero, and a flag bit is placed in the time series data collected by the photoelectric sampling device; Repeat the above steps until the test is complete.

5. The satellite navigation field test method for high-precision spatiotemporal reference according to claim 4, characterized in that, The real-time dynamic attitude change of the vehicle during its driving process is monitored by time-series data collected through a photoelectric sampling device. Based on the real-time dynamic attitude change of the vehicle, a corresponding state feature model is matched from a priori reference model library. Then, the corrected data from the state feature model is extracted, and the initial position is corrected to obtain the precise position of the vehicle. Specifically, the steps include: Distance data acquisition is achieved by measuring the reflective optical tracks fixed on both sides along the track length extension direction using laser transceivers set on the left and right sides of the vehicle. The ranging data is purified by sequentially performing Kalman filtering and low-frequency filtering to obtain characteristic perturbations. The labeled segments and ordinary segments in the purified ranging data are compressed and fused using a neural network, and then spliced ​​together in time sequence to restore the feature sequence. The labeled segments are the interval data with marked bits in the ranging data, and the remaining interval data are ordinary segments. Model matching involves matching the time-domain, frequency-domain, and time-frequency-domain processed time-domain sequences of the basic segments in the feature sequence with the prior reference model library using Mahalanobis distance and cosine similarity. After a successful match, the initial position of the current vehicle is corrected using the correction data contained in the successfully matched state feature model in the prior reference model library to obtain the precise position of the vehicle.

6. The satellite navigation field test method for high-precision spatiotemporal reference according to claim 5, characterized in that, The purified ranging data is compressed and fused using a neural network, and then concatenated in time sequence to restore the feature sequence. The specific steps include: The labeled segments expanded according to the set scaling factor are regarded as a 2-channel 2D dataset. They are copied three times and passed through 1×1 convolution kernel, 3×3 convolution kernel and 3×3 pooling respectively, and then weighted and merged. The ordinary segments expanded according to a set scaling factor are regarded as a 2-channel 2D dataset. After being directly pooled by 3×3, the data is compressed by a 1×1 convolution kernel and then fused. The processed labeled segments and ordinary segments are concatenated in time sequence to restore the feature sequence. .

7. The satellite navigation field test method for high-precision spatiotemporal reference according to claim 6, characterized in that, The model matching involves matching the time-domain, frequency-domain, and time-frequency-domain processed time-domain sequences, frequency-domain sequences, and time-frequency-domain sequences of the basic segments in the feature sequence with a prior reference model library using Mahalanobis distance and cosine similarity. The specific steps include: Obtain a prior reference model library based on prior testing; feature sequence After splitting the sequence into basic segments, each segment is processed in the time domain, frequency domain, and time-frequency domain. The frequency domain processing involves performing an FFT transform on the sequence to expand it into a frequency domain sequence. Time-frequency domain processing involves performing wavelet transform on the feature sequence to obtain the time-frequency domain sequence. ; When performing model matching, the Mahalanobis distance is first calculated between the data of the basic segments in the feature sequence and the prior reference model library as a coarse match. When the similarity is higher than a certain threshold, the cosine similarity of the data in the time domain, frequency domain, and time-frequency domain is further calculated with the three-dimensional data of each model in the prior reference model library. If a certain threshold is met, the model matching is considered successful.

8. The satellite navigation field test method for high-precision spatiotemporal reference according to claim 7, characterized in that, After a successful match, the preliminary position of the current vehicle is corrected using the correction data contained in the successfully matched state feature model in the prior reference model library to obtain the precise position of the vehicle. This process includes the following steps: Establishing a coordinate system: The track data from the survey is directly established under the geodetic coordinate system xyz. All points on the track are marked with their specific latitude, longitude, and altitude. The encoder can determine the latitude, longitude, and altitude based on the number of wheels. The track coordinate set is as follows: ,in As the initial point, To determine the total number of discrete points on the track; the wheel radius is... Encoder counts wheel rolling Next, the circumference of the wheel Cumulative distance from the test point to the initial point ; Encoder measurement parameter calculation: two adjacent discrete points in the trajectory coordinate set and The straight-line distance between ,but arrive cumulative arc length Then there is an initial point. , Given the total length of the entire trajectory, the correspondence between discrete points on the trajectory and the cumulative arc length is... ; Encoder measurement data mapping: cumulative distance measured by the encoder It involves finding the corresponding position of the test point in the discrete coordinate set by matching the arc length along the rolling length of the trajectory. At this time, the test point exists as a point that has already been calibrated in the coordinate system, or between two adjacent points. When it happens to match a discrete point on the calibrated track, it is directly mapped to the coordinates of the corresponding point. When the value falls within two adjacent points, a linear interpolation is performed between the two points. Data fusion: All segments of the matching test sequence are mapped to calibration data, and the encoder data corresponding to the geodetic coordinates are calibrated. That is, the corrected parameters are summed with each coordinate point to obtain the precise position of the vehicle.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements a satellite navigation field test method for a high-precision spatiotemporal reference as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements a satellite navigation field test method for a high-precision spatiotemporal reference as described in any one of claims 1 to 8.

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