Real-time data processing method and system for BeiDou satellite positioning error correction

Abstract

This invention provides a real-time data processing method and system for BeiDou satellite positioning error correction, belonging to the field of data processing technology. The method includes: Step 1, real-time acquisition of raw BeiDou satellite observation data of the current position of the mining truck, and simultaneous acquisition of dynamic environmental information of the working area; Step 2, based on the signal reception quality and dynamic environmental information of the raw BeiDou satellite observation data, real-time sensing and quantification of the dynamic obstruction status of the BeiDou signal at the current position, obtaining a signal obstruction level assessment result; Step 3, based on the signal obstruction level assessment result, adaptively adjusting the error correction strategy to obtain the adjusted error correction strategy. This invention achieves targeted and precise error compensation, improving the accuracy and stability of positioning data.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a real-time data processing method and system for correcting BeiDou satellite positioning errors. Background Technology

[0002] BeiDou satellite positioning technology has been widely used in scenarios such as mine truck operation management to ensure production safety. However, the complex terrain and dense large-scale machinery in mining areas can easily cause dynamic blockage of satellite signals, which in turn affects positioning accuracy.

[0003] For example, when mining trucks travel in mining areas where large machinery operates frequently, satellite signals often become unstable due to mechanical obstruction or terrain barriers. Existing BeiDou positioning error correction systems mostly adopt fixed correction strategies, without dynamically adjusting correction parameters based on real-time environmental information and signal reception status, nor do they provide targeted compensation for the spatial distribution characteristics of observation points. This results in large fluctuations in error correction effects, making it difficult to meet the control requirements for precise scheduling and safe collision avoidance of mining trucks. This practical application problem highlights the core defects of existing technologies, such as a lack of adaptive adaptability to dynamic obstruction scenarios and insufficient matching degree between error correction logic and complex operating environments. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a real-time data processing method and system for BeiDou satellite positioning error correction, so as to achieve targeted and accurate error compensation and improve the accuracy and stability of positioning data.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] Firstly, a real-time data processing method for BeiDou satellite positioning error correction, the method comprising:

[0007] Step 1: Collect raw BeiDou satellite observation data of the current location of the mining truck in real time, and simultaneously obtain dynamic environmental information of the work area;

[0008] Step 2: Based on the signal reception quality and dynamic environment information of the original BeiDou satellite observation data, the dynamic obstruction status of the BeiDou signal at the current location is perceived and quantified in real time to obtain the signal obstruction level assessment result.

[0009] Step 3: Based on the signal obstruction level assessment results, adaptively adjust the error correction strategy to obtain the adjusted error correction strategy;

[0010] Step 4: Process the raw BeiDou satellite observation data using the adjusted error correction strategy to construct a discrete set of observation points; construct an auxiliary geometric reference surface based on the discrete set of observation points.

[0011] Step 5: Set a basic coordinate reference frame on the auxiliary geometric reference surface; based on the distribution characteristics of the discrete observation point set within the basic coordinate reference frame, adaptively divide the frame into multiple independent analysis sub-regions; map each observation point in the discrete observation point set to the corresponding independent analysis sub-region.

[0012] Step 6: For each independent analysis sub-region, calculate the topological connectivity distance between each observation point in the sub-region and the preset benchmark observation point, and combine the signal characteristic deviation analysis to determine the correlation between distance and error, and solve the corresponding positioning error compensation parameters; use the positioning error compensation parameters to compensate and correct the original BeiDou satellite observation data to obtain high-precision positioning data.

[0013] Furthermore, it collects real-time raw BeiDou satellite observation data of the current location of the mining truck and simultaneously acquires dynamic environmental information of the work area, including:

[0014] The Beidou positioning receiver deployed on the mining truck acquires raw observation data in real time, including pseudorange, carrier phase and satellite ephemeris; at the same time, through the environmental perception sensors on the mining truck and the monitoring deployed in the work area, dynamic environmental information reflecting the current terrain undulations and the position and outline of large surrounding machinery is collected synchronously.

[0015] Furthermore, based on the signal reception quality and dynamic environmental information from the original BeiDou satellite observation data, the dynamic obstruction status of the BeiDou signal at the current location is sensed and quantified in real time, resulting in a signal obstruction level assessment, including:

[0016] By extracting signal quality features from the received raw observation data, the carrier-to-noise ratio, multipath effect intensity, and signal continuity index of each satellite signal are obtained, resulting in a multidimensional feature vector of signal quality.

[0017] Based on the multidimensional feature vector of signal quality and dynamic environmental information, a digital elevation occlusion surface is constructed by combining terrain undulation data, and dynamic obstacle occlusion body is formed by integrating the position and contour data of large machinery. The theoretical visible space angle of each satellite direction is calculated to obtain three-dimensional spatial occlusion relationship data.

[0018] By using three-dimensional spatial occlusion relationship data, the multi-dimensional feature vector of signal quality is spatiotemporally aligned with the three-dimensional spatial occlusion relationship data. By comparing the deviation between the theoretical visible space angle and the actual signal reception quality, the signal occlusion influence coefficient is calculated.

[0019] Based on the signal obstruction impact coefficient, a fuzzy clustering algorithm is used to classify the obstruction status into four levels: no obstruction, slight obstruction, moderate obstruction, and severe obstruction. The resulting signal obstruction level assessment results include obstruction level identifiers, affected satellite numbers, and predicted obstruction duration.

[0020] Furthermore, based on the signal obstruction level assessment results, the error correction strategy is adaptively adjusted to obtain the adjusted error correction strategy, including:

[0021] Based on the signal obstruction level assessment results, the obstruction level assessment results include obstruction level identifier, affected satellite number, and predicted obstruction duration information;

[0022] By matching the occlusion level identifier with the preset error correction strategy template, the initial error correction strategy type is determined.

[0023] Based on the predicted information of the satellite number and the duration of obstruction, the weight allocation parameters, filter window length parameters, and outlier removal threshold parameters in the determined initial error correction strategy type are dynamically adjusted to obtain the adjusted parameters.

[0024] The adjusted parameters are integrated into the initial error correction strategy type to obtain an adjusted error correction strategy optimized for the current signal obstruction state.

[0025] Furthermore, the raw BeiDou satellite observation data is processed using the adjusted error correction strategy to construct a discrete set of observation points. Based on this discrete set of observation points, an auxiliary geometric reference surface is constructed, including:

[0026] By using the adjusted error correction strategy, the original BeiDou satellite observation data is filtered and outlier removed in real time to obtain a preliminarily corrected satellite observation data sequence.

[0027] The satellite observation data sequence, after initial correction, is discretized according to timestamps and spatial coordinates. The location coordinate information of the effective observation time is extracted to construct a set of discrete observation points with spatiotemporal distribution characteristics.

[0028] Based on the set of discrete observation points, the spatial distribution trend of the point set is calculated using the least squares fitting algorithm to obtain the auxiliary geometric reference surface of the terrain features of the current working area.

[0029] Furthermore, a basic coordinate reference frame is established on the auxiliary geometric reference surface; based on the distribution characteristics of the discrete observation point set within the basic coordinate reference frame, the frame is adaptively divided into multiple independent analysis sub-regions; each observation point in the discrete observation point set is mapped to its corresponding independent analysis sub-region, including:

[0030] Based on the auxiliary geometric reference surface, a three-dimensional rectangular coordinate system is established on the auxiliary geometric reference surface as the basic coordinate reference frame, and the coordinate origin, coordinate axis direction and coordinate unit are determined;

[0031] The discrete set of observation points is projected onto the basic coordinate reference frame, and the coordinate distribution density and spatial clustering characteristics of each observation point within the frame are calculated to obtain the density gradient change information of the point distribution.

[0032] Based on the density gradient change information of the point distribution, an adaptive grid partitioning algorithm is adopted to divide the basic coordinate reference frame into regions according to the threshold characteristics of the density gradient change, resulting in multiple independent analysis sub-regions with clear boundaries and uniform internal point distribution.

[0033] Based on the spatial boundary range of multiple independent analysis sub-regions, each observation point in the discrete observation point set is matched and mapped to the corresponding independent analysis sub-region according to its spatial coordinate position.

[0034] Furthermore, for each independent analysis sub-region, the topological connectivity distance between each observation point within the sub-region and the preset benchmark observation point is calculated. Combined with signal characteristic deviation analysis of the correlation between distance and error, the corresponding positioning error compensation parameters are calculated. These parameters are then used to compensate and correct the original BeiDou satellite observation data, resulting in high-precision positioning data, including:

[0035] Based on the subset of local observation points corresponding to each independent analysis sub-region, a preset benchmark observation point located at the geometric center of the sub-region is set for each independent analysis sub-region.

[0036] By using preset benchmark observation points, the topological connectivity distance between each observation point in each independent analysis sub-region and the preset benchmark observation point is calculated, thus obtaining a dataset of point relationships with topological distance information.

[0037] By combining the point relationship dataset and the multidimensional feature vector of signal quality, the statistical correlation between topological connectivity distance and signal characteristic deviation is analyzed, and the distance-error mapping relationship is established.

[0038] Based on the distance and error mapping relationship, the corresponding positioning error compensation parameters are calculated for each independent analysis sub-region, resulting in the error compensation parameter set for each sub-region's compensation coefficient.

[0039] The error compensation parameter set is applied to the original BeiDou satellite observation data. The corresponding positioning error compensation parameters are matched according to the independent analysis sub-region to which each observation point belongs. The original observation data is then compensated and corrected point by point, and finally, high-precision positioning data after error correction is output.

[0040] Secondly, the real-time data processing system for BeiDou satellite positioning error correction includes:

[0041] The acquisition module is used to collect raw BeiDou satellite observation data of the current location of the mining truck in real time, and simultaneously acquire dynamic environmental information of the work area;

[0042] The evaluation module is used to sense and quantify the dynamic obstruction status of the BeiDou signal at the current location in real time based on the signal reception quality and dynamic environment information of the original BeiDou satellite observation data, and obtain the signal obstruction level evaluation result.

[0043] The adjustment module is used to adaptively adjust the error correction strategy based on the signal obstruction level assessment results to obtain the adjusted error correction strategy.

[0044] The construction module is used to process the raw BeiDou satellite observation data through the adjusted error correction strategy, construct the raw BeiDou satellite observation data into a discrete set of observation points, and construct an auxiliary geometric reference surface based on the discrete set of observation points.

[0045] The partitioning module is used to set a basic coordinate reference frame on the auxiliary geometric reference surface; based on the distribution characteristics of the discrete observation point set within the basic coordinate reference frame, the frame is adaptively partitioned into multiple independent analysis sub-regions; and each observation point in the discrete observation point set is mapped to the corresponding independent analysis sub-region.

[0046] The correction module is used to calculate the topological connectivity distance between each observation point in each independent analysis sub-region and the preset benchmark observation point, and to analyze the correlation between distance and error by combining signal characteristic deviation analysis, and to solve the corresponding positioning error compensation parameters; the original BeiDou satellite observation data is compensated and corrected by the positioning error compensation parameters to obtain high-precision positioning data.

[0047] Thirdly, a computing device, comprising:

[0048] One or more processors;

[0049] A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method.

[0050] Fourthly, a computer-readable storage medium storing a program that, when executed by a processor, implements the method.

[0051] The above-described solution of the present invention has at least the following beneficial effects:

[0052] By employing a technical approach that simultaneously collects raw BeiDou satellite observation data and dynamic environmental information of the work area, quantifies the dynamic obstruction level of BeiDou signals by combining signal quality and environmental information, adaptively adjusts error correction strategy parameters based on the obstruction level, constructs an auxiliary geometric reference surface and performs adaptive region division based on the distribution characteristics of observation points, and then calculates specific compensation parameters for each independent sub-region by combining topological connectivity distance and signal characteristic deviation and corrects them point by point, this approach overcomes the technical problems of existing technologies that cannot accurately perceive the dynamic obstruction status of BeiDou signals in complex work scenarios, have fixed correction strategies that are difficult to adapt to different obstruction conditions, and have insufficient compensation accuracy due to the lack of consideration of the spatial distribution characteristics of observation points. This approach achieves the technical effects of accurately identifying the impact of obstruction, improving the adaptability of error correction strategies to real-time obstruction status, realizing targeted and precise regional compensation, and ultimately improving the accuracy and stability of BeiDou positioning data for mining trucks, ensuring precise scheduling and operational safety of mining trucks. Attached Figure Description

[0053] Figure 1 This is a flowchart illustrating the real-time data processing method for BeiDou satellite positioning error correction provided in an embodiment of the present invention.

[0054] Figure 2 This is a schematic diagram of a real-time data processing system for BeiDou satellite positioning error correction provided in an embodiment of the present invention. Detailed Implementation

[0055] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0056] like Figure 1 As shown, embodiments of the present invention propose a real-time data processing method for BeiDou satellite positioning error correction, the method comprising the following steps:

[0057] Step 1: Collect raw BeiDou satellite observation data of the current location of the mining truck in real time, and simultaneously obtain dynamic environmental information of the work area;

[0058] Step 2: Based on the signal reception quality and dynamic environment information of the original BeiDou satellite observation data, the dynamic obstruction status of the BeiDou signal at the current location is perceived and quantified in real time to obtain the signal obstruction level assessment result.

[0059] Step 3: Based on the signal obstruction level assessment results, adaptively adjust the error correction strategy to obtain the adjusted error correction strategy;

[0060] Step 4: Process the raw BeiDou satellite observation data using the adjusted error correction strategy to construct a discrete set of observation points; construct an auxiliary geometric reference surface based on the discrete set of observation points.

[0061] Step 5: Set a basic coordinate reference frame on the auxiliary geometric reference surface; based on the distribution characteristics of the discrete observation point set within the basic coordinate reference frame, adaptively divide the frame into multiple independent analysis sub-regions; map each observation point in the discrete observation point set to the corresponding independent analysis sub-region.

[0062] Step 6: For each independent analysis sub-region, calculate the topological connectivity distance between each observation point in the sub-region and the preset benchmark observation point, and combine the signal characteristic deviation analysis to determine the correlation between distance and error, and solve the corresponding positioning error compensation parameters; use the positioning error compensation parameters to compensate and correct the original BeiDou satellite observation data to obtain high-precision positioning data.

[0063] In this embodiment of the invention, by employing the following technical means—simultaneously acquiring raw BeiDou satellite observation data of mining trucks and dynamic environmental information of the operating area, quantifying the dynamic obstruction level of BeiDou signals by combining signal reception quality and dynamic environmental information, adaptively adjusting the error correction strategy based on the obstruction level, constructing a discrete set of observation points and an auxiliary geometric reference surface based on the adjusted strategy, establishing a coordinate frame on the reference surface and adaptively dividing independent analysis sub-regions, and calculating compensation parameters for each sub-region based on the topological connectivity distance between the observation points and the reference points and the signal characteristic deviation, and completing point-by-point correction—the technical problems of existing BeiDou positioning error correction schemes for mining trucks being difficult to accurately perceive dynamic obstruction states, having fixed correction strategies that cannot adapt to complex working conditions, and having insufficient compensation accuracy due to the lack of consideration for the spatial distribution characteristics of observation points, are overcome. This achieves the technical effect of improving the adaptability of the error correction strategy to the real-time operating environment, realizing targeted and precise regional compensation, and ultimately improving the accuracy and stability of mining truck positioning data, providing reliable positioning support for safe scheduling and efficient operation of mining trucks.

[0064] In a preferred embodiment of the present invention, step 1 above may include:

[0065] Step 1.1: The Beidou positioning receiver deployed on the mining truck acquires raw observation data in real time, including pseudorange, carrier phase, and satellite ephemeris. Simultaneously, through the environmental perception sensors on the mining truck and the monitoring deployed in the work area, dynamic environmental information reflecting the current terrain undulations and the position and outline of surrounding large machinery is collected synchronously. Specifically, the Beidou satellite raw observation data acquisition process involves a Beidou positioning receiver fixedly installed at the center of the top of the mining truck's cab, connected via a dedicated wired line to ensure data transmission stability. When the mining truck is in motion or operation, the Beidou positioning receiver continuously captures navigation signals from all Beidou satellites in the sky that can receive signals, with a fixed acquisition cycle of 100 milliseconds. For each Beidou satellite whose signal is successfully acquired, the pseudorange data between the satellite and the receiver on the mining truck, the carrier phase data of the satellite signal, and the satellite ephemeris data used to determine the satellite's real-time position in space are acquired in real time. All raw observation data from all satellites acquired each time are directly transmitted to a dedicated temporary storage area, classified and stored according to the satellite's number, and marked with the corresponding acquisition timestamp during storage.

[0066] The first part of the dynamic environmental information collection process for the work area involves the environmental perception sensors mounted on the mining truck. A set of environmental perception sensors is deployed at the front of the truck, the middle of the left and right sides, and the rear of the truck. The terrain undulation detection sensors at the front and rear collect data on ground elevation changes within a 100-meter radius behind the truck, with a fixed collection period of 200 milliseconds, reflecting the current terrain undulation status. The surrounding object detection sensors deployed on the sides of the truck collect real-time object information within a 50-meter radius on each side, identifying and acquiring the real-time position and outline of large machinery such as excavators and loaders within this radius. The information consists of two parts: the first part is the environmental data collected by all sensors, marked with the same collection timestamp as the original BeiDou observation data; the second part is the monitoring and collection process deployed in the work area: eight sets of fixed monitoring equipment are deployed at various high points in the mining operation area. Each set of monitoring equipment includes a high-definition visual camera and a millimeter-wave ranging device. With a fixed collection cycle of 300 milliseconds, they collect detailed data on the terrain undulations within their respective monitoring coverage area, as well as the real-time location and outline information of large operating machinery in the area. The data collected by these monitoring devices are also marked with the corresponding collection timestamp through the mining area's dedicated industrial wireless transmission network, and are synchronized with the original BeiDou satellite observation data collected at the same time.

[0067] In this embodiment of the invention, because the original observation data, including pseudorange, carrier phase, and satellite ephemeris, is acquired in real time from the Beidou positioning receiver on the mining truck, and dynamic environmental information such as terrain undulations, the position and outline of surrounding large machinery are collected synchronously through the environmental perception sensor on the mining truck and the monitoring of the work area, the technical problem of the prior art, which only collects satellite observation data and lacks key information on the dynamic environment of the work area, is that it is difficult to accurately correlate environmental factors with satellite signal quality and to accurately analyze the causes of signal blockage, is thus achieved. This provides comprehensive and synchronous basic data support for signal blockage perception and error correction strategy adjustment, ensuring the accuracy and pertinence of the data processing process.

[0068] In a preferred embodiment of the present invention, step 2 above may include:

[0069] Step 2.1 involves extracting signal quality features from the received raw observation data to obtain the carrier-to-noise ratio (CNR), multipath effect strength, and signal continuity index of each satellite signal, resulting in a multidimensional signal quality feature vector. Specifically, this includes: retrieving the raw observation data of the acquired BeiDou satellites; for each BeiDou satellite that successfully acquired a valid signal, sequentially extracting signal quality features; obtaining the CNR directly from the real-time output data of the BeiDou positioning receiver, using 100 milliseconds as an extraction unit, and taking the average of three consecutive extractions as the current CNR feature value of the satellite; and comparing the change amplitude of the carrier phase data obtained from five consecutive acquisitions of the satellite during multipath effect strength extraction. For the signal continuity index, if the change exceeds 5, the multipath effect intensity is determined to be high and recorded as 5; if the change is between 2 and 5, the multipath effect intensity is determined to be medium and recorded as 3; if the change is less than 2, the multipath effect intensity is determined to be low and recorded as 1, thus obtaining the multipath effect intensity characteristic value of the satellite. For the signal continuity index, the number of signal interruptions of the satellite within a 1-second acquisition period is extracted and counted. If the number of interruptions is 0, it is recorded as 10; if the number of interruptions is 1 to 2, it is recorded as 5; if the number of interruptions is more than 2, it is recorded as 1, thus obtaining the signal continuity index characteristic value of the satellite. Finally, the carrier-to-noise ratio characteristic value, multipath effect intensity characteristic value, and signal continuity index characteristic value of each satellite are integrated in order to form the corresponding multidimensional feature vector of signal quality.

[0070] Step 2.2: Based on the multi-dimensional feature vector of signal quality and dynamic environmental information, a digital elevation occlusion surface is constructed by combining terrain undulation data. This is then integrated with the position and contour data of large machinery to form a dynamic obstacle occlusion body. The theoretical visible spatial angles for each satellite direction are calculated to obtain three-dimensional spatial occlusion relationship data. Specifically, this includes: retrieving the collected dynamic environmental information and the generated multi-dimensional feature vector of signal quality; constructing the digital elevation occlusion surface by using terrain detection sensors on the mining truck and monitoring of the work area to collect terrain undulation data, centered on the current position of the mining truck, covering a 200-meter radius, converting discrete terrain elevation data into a continuous three-dimensional digital elevation occlusion surface. The occlusion surface updates its data every 200 milliseconds as the mining truck moves; and forming the dynamic obstacle occlusion body by converting the collected position and contour data of surrounding large machinery into three-dimensional dynamic obstacles. The height of the dynamic obstacles is set according to the actual operating height of the large machinery. The height of the excavator model is set to 8 meters, the height of the loader model is set to 5 meters, and the height of the dump truck is set to 6 meters. As the large machinery moves, the position and outline are updated every 300 milliseconds. The theoretical visible space angle of each satellite direction is calculated. The installation position of the mining truck Beidou positioning receiver is taken as the observation vertex. A ray is emitted towards the real-time position direction of each Beidou satellite. It is detected whether the ray intersects with the digital elevation occlusion surface and the dynamic obstacle occlusion body. If there is no intersection, the theoretical visible space angle of the satellite direction is 90 degrees. If there is an intersection, the remaining visible space angle is calculated according to the distance between the intersection and the observation vertex. For example, when the intersection is 50 meters away from the observation vertex, the theoretical visible space angle is 45 degrees. The theoretical visible space angles of all satellites are summarized to obtain the three-dimensional spatial occlusion relationship data.

[0071] Step 2.3: Using the 3D spatial occlusion relationship data, the multi-dimensional feature vector of signal quality is spatiotemporally aligned with the 3D spatial occlusion relationship data. By comparing the deviation between the theoretical visible spatial angle and the actual signal reception quality, the signal occlusion impact coefficient is calculated. Specifically, this includes: matching the generated multi-dimensional feature vector of signal quality with the generated 3D spatial occlusion relationship data one-to-one according to the acquisition timestamp to ensure that the satellite signal quality data and spatial occlusion data acquired at the same time completely correspond; then, for each satellite, a carrier-to-noise ratio threshold of 30dB-Hz is set based on the deviation between the theoretical visible spatial angle and the actual signal reception quality, and multipath effect... With a strength threshold of 3 and a signal continuity index threshold of 5, if the theoretical visible space angle is 90 degrees (i.e., no theoretical obstruction), but the actual carrier-to-noise ratio is lower than 30 dB-Hz, or the multipath effect strength is higher than 3, or the signal continuity index is lower than 5, then the deviation value of the satellite is calculated. The deviation value is the ratio of the difference between the actual characteristic value and the corresponding threshold to the threshold. The average of the deviation values ​​of the three characteristics is taken to obtain the signal obstruction impact coefficient of the satellite. If the theoretical visible space angle is lower than 90 degrees (i.e., there is theoretical obstruction), then the deviation value is multiplied by a weight of 1.2, combining the actual signal quality characteristics and the degree of theoretical obstruction, to obtain the signal obstruction impact coefficient of the satellite.

[0072] Step 2.4: Based on the signal obstruction impact coefficient, a fuzzy clustering algorithm is used to classify the obstruction status into four levels: no obstruction, slight obstruction, moderate obstruction, and severe obstruction. A signal obstruction level assessment result is generated, including an obstruction level identifier, the affected satellite number, and a predicted obstruction duration. Specifically, the signal obstruction impact coefficients of all satellites are classified into levels according to the following fixed thresholds: a signal obstruction impact coefficient between 0 and 0.2 is classified as no obstruction and marked as level 0; a signal obstruction impact coefficient between 0.2 and 0.4 is classified as slight obstruction and marked as level 1; a signal obstruction impact coefficient between 0.4 and 0.7... The signal obstruction level is determined to be moderate and marked as Level 2; the signal obstruction impact coefficient is between 0.7 and 1, which is determined to be severe and marked as Level 3. Subsequently, the signal obstruction level assessment results are generated, including the obstruction level identifier for each satellite, the specific number of the satellite affected by the obstruction, and the predicted obstruction duration: if it is terrain obstruction, the predicted duration is the time it takes for the mine truck to reach the edge of the terrain area, based on the current speed of the mine truck; if it is obstruction by large machinery, the predicted duration is the time it takes for the large machinery to move until it no longer obstructs the satellite signal, based on the current direction and speed of movement of the large machinery, with the predicted time accurate to the second.

[0073] In this embodiment of the invention, by employing technical means such as extracting satellite signal carrier-to-noise ratio, multipath effect intensity, and signal continuity indicators to construct a multi-dimensional feature vector of signal quality, combining dynamic environmental information to construct a digital elevation obstruction surface and dynamic obstacle obstruction body, calculating the theoretical visible space angle of satellite direction, calculating the obstruction impact coefficient through spatiotemporal alignment comparison theory and actual signal quality deviation, and then using a fuzzy clustering algorithm to classify four obstruction levels and generate evaluation results containing obstruction level identifiers, affected satellite numbers, and obstruction duration predictions, this invention overcomes the technical problems of existing technologies that cannot accurately correlate signal quality with dynamic environmental factors, are difficult to quantify the dynamic obstruction state of signals, have coarse obstruction level classifications and lack key impact information, resulting in a lack of reliable basis for subsequent error correction strategy adjustments. This achieves accurate perception and quantification of the dynamic obstruction state of BeiDou signals, and clarifies the core influencing factors and development trends of obstruction.

[0074] In a preferred embodiment of the present invention, step 3 above may include:

[0075] Step 3.1: Based on the signal obstruction level assessment results, analyze the obstruction level identifier, affected satellite number, and obstruction duration prediction information contained in the obstruction level assessment results. Specifically, this includes: retrieving the generated signal obstruction level assessment results, performing information parsing sequentially according to the data storage order; parsing the obstruction level identifier, extracting the obstruction level identifier corresponding to each BeiDou satellite (i.e., the numerical identifier from level 0 to level 3), sorting all satellite obstruction level identifiers by satellite number, and storing them separately as an obstruction level list; parsing the affected satellite number, filtering out satellites with obstruction level identifiers of 1 to 3 from the obstruction level list, i.e., satellites with signal obstruction, and organizing the official numbers of these satellites into an affected satellite number list; parsing the obstruction duration prediction information, extracting the predicted obstruction duration value corresponding to each affected satellite, for example, the duration corresponding to terrain obstruction is 12 seconds, and the duration corresponding to large machinery movement obstruction is 8 seconds, binding these time values ​​with the corresponding satellite number, and organizing them into a duration list; all parsed information will be stored in the dedicated parsing data area of ​​the vehicle data, updated every 100 milliseconds to ensure synchronization with the real-time obstruction status.

[0076] Step 3.2: By matching the occlusion level identifier with the preset error correction strategy template, the initial error correction strategy type is determined. Specifically, this includes: retrieving the locally stored error correction strategy template library. The library contains pre-set templates corresponding to the four occlusion levels. Level 0 corresponds to the basic error correction strategy template, which performs only routine signal filtering for stable signals without occlusion, with a low processing priority. Level 1 corresponds to the mild occlusion error correction strategy template, which adds a multipath effect suppression processing step for mild occlusion situations with small signal fluctuations, with a medium-low processing priority. Level 2 corresponds to the moderate occlusion error correction strategy template. The correction strategy template, for moderate signal interference and moderate obstruction, adds a step for identifying and removing abnormal observation data, with a medium processing priority; Level 3 corresponds to the severe obstruction error correction strategy template, for severe signal interference and severe obstruction, adding a step for fusing backup satellite observation data, with a high processing priority; then, from the list of obstruction levels obtained from the parsing, the highest obstruction level affecting the satellite is extracted, and the highest level is used as the matching basis. For example, if the highest obstruction level affecting the satellite is 2, the moderate obstruction error correction strategy template is matched, and the moderate obstruction error correction strategy template is determined as the initial error correction strategy type.

[0077] Step 3.3: Based on the predicted information of the affected satellite number and the duration of obstruction, dynamically adjust the weight allocation parameter, filter window length parameter, and outlier removal threshold parameter in the determined initial error correction strategy type to obtain the adjusted parameters. Specifically, this includes retrieving the parsed list of affected satellite numbers and the list of obstruction durations, and simultaneously retrieving the real-time elevation angle data of each affected satellite. Then, dynamically adjust the three parameters in the determined initial error correction strategy type sequentially. The default value of the weight allocation parameter in the initial error correction strategy type is 5. If the real-time elevation angle of the affected satellite is greater than 60 degrees, adjust the weight allocation parameter corresponding to that satellite to 8; if the elevation angle is between 30 and 60 degrees, adjust it to 6; if the elevation angle is less than 60 degrees, adjust it to 8. For values ​​below 30 degrees, adjust to 3; take the average of all weight allocation parameters affecting satellites as the final weight allocation parameter; for the filter window length parameter adjustment, the default value of the filter window length parameter in the initial error correction strategy type is 5. If the duration of the blockage corresponding to the affected satellite is greater than 10 seconds, adjust the filter window length parameter to 10; if the duration is between 5 and 10 seconds, adjust to 7; if the duration is less than 5 seconds, adjust to 3; for the abnormal data removal threshold parameter adjustment, the default value of the abnormal data removal threshold parameter in the initial error correction strategy type is 3. If the total number of affected satellites exceeds 3, adjust the abnormal data removal threshold parameter to 5; if the number of affected satellites is between 1 and 3, adjust to 4; if the number of affected satellites is 1, adjust to 3.

[0078] Step 3.4: Integrate the adjusted parameters into the initial error correction strategy type to obtain the adjusted error correction strategy optimized for the current signal obstruction state. Specifically, this includes replacing the original default parameters in the determined initial error correction strategy type one by one with the adjusted weight allocation parameters, filter window length parameters, and outlier data removal threshold parameters. Then, the execution flow of the strategy is adapted and optimized. For example, if the initial strategy type is a severe obstruction error correction strategy, the execution order of the backup satellite data fusion stage is adjusted to before the filtering process; if the initial strategy type is a mild obstruction error correction strategy, the execution order of the multipath effect suppression stage is adjusted to after the filtering process. After completing the parameter replacement and process optimization, the adjusted error correction strategy for the current signal obstruction state is generated.

[0079] In this embodiment of the invention, by employing the technical means of analyzing the obstruction level identifier, affected satellite number, and obstruction duration prediction information in the signal obstruction level assessment results, matching the obstruction level identifier with a preset basic template to determine the initial error correction strategy type, and then dynamically adjusting the weight allocation, filter window length, and abnormal data removal threshold parameters in the initial strategy based on the affected satellite number and obstruction duration prediction information, and integrating them to obtain an optimized correction strategy, the technical problems of fixed error correction strategies, inability to adapt to different signal obstruction conditions, and lack of targeted parameter adjustments in the prior art, resulting in low matching degree between the correction strategy and the real-time obstruction state, are overcome. This achieves the goal of making the error correction strategy accurately adapt to the current signal obstruction scenario, improving the strategy's targeting and rationality.

[0080] In a preferred embodiment of the present invention, step 4 above may include:

[0081] Step 4.1 involves using the adjusted error correction strategy to perform real-time filtering and outlier removal on the raw BeiDou satellite observation data, resulting in a preliminarily corrected satellite observation data sequence. Specifically, this includes: first, retrieving the generated adjusted error correction strategy from the strategy execution area; then, extracting the collected raw BeiDou satellite observation data from the temporary storage area; and performing real-time filtering and outlier removal sequentially according to the adjusted strategy. The real-time filtering process uses an adaptive sliding window filtering method based on the adjusted filter window length parameter in the strategy. Using the adjusted window length as a unit, it smooths the pseudorange and carrier phase data in the raw observation data. For example, if the adjusted filter window length is 10, the average value of observation data from 10 consecutive acquisition cycles is calculated and used to replace the intermediate time data within that window, thereby reducing signal fluctuations. To mitigate interference, filtering is performed synchronously every 100 milliseconds to ensure real-time data transmission. Outlier removal is based on the adjusted outlier removal threshold in the strategy. Each filtered observation is verified, with the criterion being that if the deviation between the observed data at a given moment and the average of the filtered data at five adjacent moments exceeds the removal threshold, the data is considered an outlier. For example, with a threshold of 5, observations with a deviation greater than 5 are marked as outliers. These outliers are then automatically removed, and the missing data at that position is filled using linear interpolation of the normal data from the two preceding and following moments. Finally, the data sequence is organized into a continuous satellite observation data sequence according to the acquisition timestamps, with each data point bound to its corresponding satellite number and acquisition time information.

[0082] Step 4.2 involves discretizing the pre-corrected satellite observation data sequence according to timestamps and spatial coordinates, extracting the location coordinates of valid observation times, and constructing a set of discrete observation points with spatiotemporal distribution characteristics. Specifically, this includes: retrieving the pre-corrected satellite observation data sequence from the pre-processed data region, performing discretization processing according to timestamps and spatial coordinates, setting the time discretization interval to 200 milliseconds, and extracting the observation data with the timestamp closest to the midpoint of each interval as valid data. For example, in the first interval (0 to 200 milliseconds), observation data at 100 milliseconds is extracted. If there is no data at that time, linear interpolation is performed on the data before and after the closest points within the interval. The process involves several steps: First, ensuring that only one valid observation data point is retained within each time interval. Second, extracting and verifying spatial coordinates: From the selected valid observation data, extract the real-time spatial coordinate information corresponding to the mining truck. Simultaneously, retrieve the collected terrain undulation data of the work area, set a threshold for the effective spatial range, and if the extracted coordinates exceed the current work area boundary by 50 meters, the coordinates are deemed invalid and discarded, retaining only the coordinate data within the work area. Third, constructing a discrete observation point set: The valid data, after being filtered by timestamps and verified by spatial coordinates, are organized into discrete observation points according to the correspondence between timestamps and spatial coordinates. Each point contains time information and three-dimensional spatial coordinate information. Then, all points are sorted in timestamp order to construct a discrete observation point set with spatiotemporal distribution characteristics.

[0083] Step 4.3: Based on the set of discrete observation points, the least squares fitting algorithm is used to calculate the spatial distribution trend of the point set, obtaining an auxiliary geometric reference surface for the terrain features of the current working area. Specifically, this includes: retrieving the constructed set of discrete observation points from the spatial data processing area; calculating the spatial distribution trend using the least squares fitting algorithm; constructing the auxiliary geometric reference surface; point preprocessing first removes outlier points with large coordinate deviations from the discrete observation point set, setting a deviation threshold of 3. If the average coordinate deviation of a point from other points in the set exceeds 3, it is marked as an isolated point and removed to avoid affecting the fitting accuracy; the remaining valid points are retained as the fitting data source; the fitting range is determined by defining a 200m × 200m square fitting range centered on the current position of the mine car. If the number of points in the discrete observation point set is less than 50, the fitting range is expanded to 300m × 300m to ensure the accuracy of the fitted data. The source has sufficient spatial distribution density to ensure the accuracy of the reference surface. The spatial distribution trend calculation adopts the least squares fitting algorithm to fit the three-dimensional coordinate data of the preprocessed effective points, focusing on the relationship between the vertical elevation change and the horizontal position distribution of the points, and obtaining the parameters corresponding to the spatial fitting surface equation that reflects the terrain undulation characteristics of the current working area. The auxiliary geometric reference surface is generated based on the fitted parameters, and a three-dimensional auxiliary geometric reference surface that closely matches the terrain characteristics of the current working area is generated. This reference surface contains the elevation benchmark information corresponding to each horizontal coordinate. At the same time, the reference surface is compared and verified with the collected terrain undulation data. If the average deviation between the reference surface and the actual terrain data exceeds 2, the fitting process is re-executed until the deviation meets the requirements. The finally generated auxiliary geometric reference surface is stored in the spatial benchmark data area and is refitted and updated every 200 milliseconds as the mine car moves.

[0084] In this embodiment of the invention, because a preliminary corrected data is obtained by real-time filtering and outlier removal of the original BeiDou satellite observation data through an error correction strategy adapted to the current signal occlusion state, and the preliminary corrected data is discretized according to timestamps and spatial coordinates to construct a set of discrete observation points with spatiotemporal distribution characteristics, and then an auxiliary geometric reference surface matching the terrain features of the current working area is generated based on this set using a least squares fitting algorithm, the technical means of overcoming the technical problems in the prior art where the preprocessing of the original observation data is not adapted to the real-time occlusion state, the data is not effectively regularized in the spatiotemporal dimension, and there is a lack of spatial reference benchmarks that fit the terrain of the working area, resulting in a lack of reliable basic data and spatial reference for subsequent positioning error compensation, thereby achieving the filtering of invalid interference information in the original data, obtaining regularized and effective observation data with spatiotemporal attributes, and constructing a spatial reference benchmark adapted to the working terrain.

[0085] In a preferred embodiment of the present invention, step 5 above may include:

[0086] Step 5.1: Based on the auxiliary geometric reference surface, establish a three-dimensional rectangular coordinate system on the auxiliary geometric reference surface as the basic coordinate reference frame, and determine the coordinate origin, coordinate axis directions, and coordinate units. Specifically, this includes: First, retrieving the generated auxiliary geometric reference surface from the spatial reference data area. Using the auxiliary geometric reference surface as the spatial reference carrier, perform the establishment and parameter determination operation of the three-dimensional rectangular coordinate system. Determine the coordinate origin by selecting the installation center position of the Beidou positioning receiver on the top of the current mining truck as the coordinate origin. This origin is updated in real time as the mining truck moves, ensuring that the coordinate frame always fits the current working position of the mining truck, providing a precise reference starting point for subsequent point analysis. Determine the coordinate axis directions by defining the coordinate axis directions according to the actual needs of the mining truck's working scenario. The X-axis is set to the current position of the mining truck. The forward direction is calibrated using real-time attitude data collected by the mine car's onboard gyroscope; the Y-axis is set to a horizontal direction perpendicular to the X-axis, pointing to the left side of the mine car; the Z-axis is set to a vertical upward direction, perpendicular to the local horizontal plane, forming a right-handed coordinate system to ensure that the coordinate axis direction accurately matches the mine car's operating posture and terrain features; the coordinate unit is determined and the coordinate system is verified, with the unit uniformly adopted as meters, conforming to the conventional standards of engineering surveying; after the system is established, the terrain data of three known fixed landmark points are retrieved, and the landmark point coordinates are converted to the newly established coordinate system. If the converted coordinates deviate from the actual landmark position by more than 1, the coordinate axis direction and the origin position are recalibrated until the deviation meets the requirements; the final basic coordinate reference frame is stored in the spatial coordinate processing area.

[0087] Step 5.2 involves projecting the discrete observation point set onto the basic coordinate reference frame, calculating the coordinate distribution density and spatial clustering characteristics of each observation point within the frame, and obtaining the density gradient change information of the point distribution. Specifically, this includes: retrieving the discrete observation point set from the spatial data processing area, retrieving the basic coordinate reference frame from the spatial coordinate processing area, and sequentially performing point projection, feature calculation, and gradient extraction operations. Discrete observation point projection projects each observation point in the discrete observation point set onto the basic coordinate reference frame according to the 3D coordinate transformation rules, obtaining the 3D coordinate values ​​of each point within the frame. During the projection process, the original timestamp and satellite number of the point are recorded synchronously to ensure the integrity of the projected data. Coordinate distribution density calculation involves dividing the basic coordinate reference frame into 10m × 10m × 5m cubic statistical grids, traversing all projected observation points, counting the number of points contained in each cubic grid, and dividing the number of points in each grid by the grid number. The density distribution of a grid is calculated by analyzing its volume and the coordinate distribution density value. The density values ​​of all grids are then aggregated to form a density distribution matrix. Spatial clustering features are extracted based on this density distribution matrix by analyzing the point distribution of adjacent grids. If the density values ​​of two adjacent grids are both greater than 3 and the average distance between the points in the two grids is less than 5 meters, then the points in these two grids are considered to constitute a spatial clustering unit. All grids are traversed sequentially, and all independent clustering units are marked. The grid coverage area and the number of points contained in each clustering unit are recorded. Density gradient change information is obtained by calculating the density difference between each grid and its eight adjacent grids. The maximum density difference is taken as the density gradient value of that grid. The density gradient distribution of the entire region is obtained by traversing all grids. Grid locations with gradient values ​​greater than 2 are extracted to form the outlines of regions with significant density gradient changes. These outlines will serve as important bases for subsequent region division. All calculated distribution density, clustering features, and density gradient information are uniformly stored in the spatial feature data area.

[0088] Step 5.3: Based on the density gradient change information of the point distribution, an adaptive mesh partitioning algorithm is used to segment the basic coordinate reference frame according to the threshold characteristics of density gradient change, resulting in multiple independent analysis sub-regions with clear boundaries and uniform internal point distribution. Specifically, this includes: retrieving the obtained density gradient change information of the point distribution, starting the adaptive mesh partitioning algorithm, performing region segmentation of the basic coordinate reference frame according to the density gradient threshold characteristics, setting the density gradient partitioning threshold based on the point distribution characteristics of the mining operation area, and pre-setting the density gradient partitioning threshold to 2. When the density gradient value at a certain location is greater than 2, that location is determined to be a candidate boundary location for different distribution density regions; simultaneously, a minimum sub-region size threshold is set, each independent analysis sub-region must contain at least 5 observation points and cover no less than 3 grids to avoid insufficient accuracy in subsequent compensation calculations due to excessively small sub-regions; the initial adaptive mesh partitioning is based on the candidate boundary locations with density gradient values ​​greater than 2. Based on this, multiple initial regions are initially divided. The size of each initial region is verified. If the number of points in an initial region is less than 5, it is merged into an adjacent larger initial region. If the number of grid cells in an initial region is too large (more than 20), it is further divided based on the internal subgradient change (gradient value greater than 1) to ensure the initial region size is appropriate. Region boundary optimization and sub-region determination involve smoothing the boundaries of the initially divided regions, removing small protruding grid cells with an area less than 2 square meters, making the region boundaries clear and regular. Then, the uniformity of point distribution within each region is verified again by calculating the standard deviation of the coordinates of all points within the region. If the standard deviation is greater than 3, the region boundaries are readjusted until the standard deviation of points within the region is less than 3, ensuring uniform point distribution within each sub-region. Finally, multiple independent analysis sub-regions with clear boundaries, appropriate size, and uniform internal points are obtained. Each sub-region is assigned a unique number, and the 3D boundary coordinate range of each sub-region is recorded.

[0089] Step 5.4: Based on the spatial boundary ranges of multiple independent analysis sub-regions, each observation point in the discrete observation point set is matched and mapped to its corresponding independent analysis sub-region according to its spatial coordinate position. Specifically, this includes: retrieving the boundary range data of all independent analysis sub-regions and the discrete observation point set after projection; performing the point-to-sub-region matching and mapping operation; organizing the three-dimensional boundary coordinate ranges of each independent analysis sub-region into a standardized boundary parameter table, specifying the minimum and maximum X-axis values, minimum and maximum Y-axis values, minimum and maximum Z-axis values ​​for each sub-region, and storing them in sub-region number order for easy subsequent quick query and comparison; and point-by-point matching and mapping: extracting the three-dimensional coordinate values ​​of each observation point from the discrete observation point set and comparing them sequentially with the boundary parameter table of each independent analysis sub-region to determine the position of the observation point. The system checks whether the X-axis coordinates, Y-axis coordinates, and Z-axis coordinates of an observation point fall within the X-axis range of the corresponding sub-region, whether they fall within the Y-axis range, and whether they fall within the Z-axis range. If all coordinates meet the requirements, the point is determined to belong to the independent analysis sub-region, and the point is bound to the sub-region number to complete the mapping. For boundary point processing and mapping verification, if the coordinates of an observation point fall exactly on the boundary of two sub-regions, that is, it simultaneously meets the boundary range requirements of two sub-regions, the distance between the point and the geometric center of the two sub-regions is calculated, and the point is mapped to the sub-region with the closer distance. After all points are mapped, the number of points contained in each sub-region is counted. If the number of points contained in a sub-region is less than 3, the sub-region is merged with the adjacent sub-region, and the mapping operation is re-executed. Finally, a complete mapping relationship table between the discrete observation point set and the independent analysis sub-region is formed and stored in the spatial mapping data area. The mapping relationship is updated synchronously with the sub-region updates.

[0090] In this embodiment of the invention, a three-dimensional rectangular coordinate system is established on an auxiliary geometric reference surface that conforms to the terrain features of the current work area as the basic coordinate reference frame. After projecting the set of discrete observation points onto this frame, the coordinate distribution density, spatial clustering features, and density gradient change information of the points are calculated. Then, based on the threshold features of density gradient change, an adaptive grid partitioning algorithm is used to segment multiple independent analysis sub-regions with clear boundaries and uniform internal point distribution. Finally, each observation point is mapped to the corresponding independent analysis sub-region according to its spatial coordinates. Therefore, this invention overcomes the technical problems of existing technologies that fail to construct a dedicated coordinate reference benchmark in conjunction with the terrain of the work area and use a fixed area partitioning method that does not match the actual distribution features of the observation points, resulting in subsequent positioning error compensation being unable to adapt to the differences in point distribution in different areas and insufficient local compensation targeting. This achieves the establishment of a spatial analysis benchmark that conforms to the work scenario and forms independent analysis units that adapt to the point distribution.

[0091] In a preferred embodiment of the present invention, step 6 above may include:

[0092] Step 6.1: Based on the subset of local observation points corresponding to each independent analysis sub-region, set a preset benchmark observation point located at the geometric center of the sub-region for each independent analysis sub-region. Specifically, this includes: first, retrieving the boundary range and corresponding subset of local observation points for each independent analysis sub-region from the spatial mapping data region; setting a preset benchmark observation point for each sub-region one by one; extracting local point data for each sub-region in the order of the independent analysis sub-region numbers; ensuring that all points within each subset have been projected under the basic coordinate reference frame and bound with complete coordinate information and timestamps; calculating the geometric center of the sub-region; and for each subset of local observation points, calculating the average value of the X-axis, Y-axis, and Z-axis coordinates of all points within the basic coordinate reference frame. The spatial location corresponding to the average of the three coordinates is taken as the initial geometric center of the sub-region. If the number of points in the sub-region is even, the average of the two middle values ​​after sorting the X-axis, Y-axis, and Z-axis coordinates is taken to ensure that the geometric center can represent the core location of the sub-region. The benchmark point is verified and determined to determine whether the initial geometric center falls within the boundary range of the sub-region. If the initial geometric center exceeds the boundary of the sub-region, the shortest distance between it and the boundary of the sub-region is calculated, and the initial geometric center is translated into the sub-region by this distance to obtain the corrected geometric center. The verification threshold is set to 1. If the deviation between the corrected geometric center and the average distance of all points in the sub-region exceeds 1, the geometric center is recalculated until the deviation meets the requirements. Finally, the corrected geometric center is determined as the preset benchmark observation point of the sub-region.

[0093] Step 6.2: Using preset benchmark observation points, calculate the topological connectivity distance between each observation point in each independent analysis sub-region and the preset benchmark observation point, obtaining a point relationship dataset of topological distance information. Specifically, this includes: retrieving the coordinates of the preset benchmark observation points for each sub-region from the benchmark point data region; retrieving the corresponding subset of local observation points from the spatial mapping data region; calculating the topological connectivity distance for each point and constructing the dataset; determining the topological connectivity path; and combining the generated auxiliary geometric reference surface. For each independent analysis sub-region's local observation points, starting from the preset benchmark observation point and ending at a single observation point within the sub-region, planning a topological connectivity path that conforms to the terrain of the current working area, avoiding crossing terrain protrusions or... Large mechanical obstacle areas; path planning is based on the elevation data of the auxiliary geometric reference surface to ensure that the path conforms to the spatial topology of the actual mine car travel; topological connectivity distance calculation follows the planned topological connectivity path, and the distance is calculated by segmented accumulation. The path is divided into 1-meter-long micro-segments, and the lengths of all micro-segments are accumulated to obtain the topological connectivity distance between the observation point and the reference point; if there is no obvious terrain obstruction between two points, the straight-line distance can be directly calculated as an approximate value of the topological connectivity distance, with the error controlled within 0.5; point relationship dataset is constructed by classifying and organizing the number, three-dimensional coordinates, and topological connectivity distance of all observation points in each sub-region according to the sub-region number, forming a point relationship dataset containing topological distance information.

[0094] Step 6.3 involves combining the point-to-point relationship dataset and the signal quality multidimensional feature vector to analyze the statistical correlation between topological connectivity distance and signal characteristic deviation, establishing a distance-error mapping relationship. Specifically, this includes: retrieving the point-to-point relationship dataset from the topological relationship data region and retrieving the generated signal quality multidimensional feature vector from the signal quality data region; conducting statistical correlation analysis and establishing a mapping relationship; matching the point-to-point relationship dataset and signal quality multidimensional feature vector of each independent analysis sub-region according to timestamps to ensure that the topological connectivity distance and signal quality characteristics, carrier-to-noise ratio, multipath effect strength, and signal continuity indicators corresponding to the same satellite at the same observation time are completely consistent, and eliminating invalid data pairs with mismatched timestamps; calculating the signal characteristic deviation based on the signal quality characteristic value corresponding to the preset benchmark observation point, calculating the signal quality characteristic deviation corresponding to each observation point within the sub-region; for example, if the carrier-to-noise ratio of an observation point is 28 dB / Hertz and the carrier-to-noise ratio of the benchmark point is 32 dB / Hertz, then the carrier-to-noise ratio deviation is 4 dB / Hertz; Instead of calculating carrier-to-noise ratio deviation, multipath effect intensity deviation, and signal continuity index deviation, the average of these three deviations is taken as the comprehensive signal characteristic deviation for that point. Statistical correlation analysis is performed on the topological connectivity distance and comprehensive signal characteristic deviation of all observation points within the same sub-region. A grouped statistical approach is used, grouping the topological connectivity distances at 1-meter intervals and calculating the average comprehensive signal characteristic deviation for each group. A correlation threshold of 0.7 is set; if the correlation coefficient between a group of distances and the average deviation is greater than 0.7, a statistical correlation is determined, and the distance range and deviation pattern are recorded. A distance-error mapping relationship is established. Based on the statistically obtained correlation patterns, a distance-error mapping relationship table is constructed for each independent analysis sub-region, clarifying the comprehensive signal characteristic deviation range corresponding to different topological connectivity distance intervals. For example, when the topological connectivity distance is in the range of 0 to 5 meters, the comprehensive deviation is 0 to 1.5; when the distance is in the range of 5 to 10 meters, the comprehensive deviation is 1.5 to 3, ensuring that the mapping relationship accurately reflects the correspondence between distance and error.

[0095] Step 6.4: Based on the distance-error mapping relationship, calculate the corresponding positioning error compensation parameters for each independent analysis sub-region to obtain the error compensation parameter set for each sub-region's compensation coefficient. Specifically, this includes: calculating the positioning error compensation parameters for each independent analysis sub-region one by one based on the established distance-error mapping relationship, forming an error compensation parameter set. The initial calculation of the compensation parameters is based on the distance-error mapping relationship table for each independent analysis sub-region, calculating the compensation coefficient one by one according to the distance interval; for example, in the 0 to 5 meter interval, if the positioning error increases by 0.8 for every 1 increase in the comprehensive signal characteristic deviation, the compensation coefficient for this interval is set to 0.8; in the 5 to 10 meter interval, if the positioning error increases by 1.2 for every 1 increase in the comprehensive deviation, the compensation coefficient is set to 1.2; the calculation of the compensation coefficient is to offset the current... The signal characteristic deviation of the preceding interval is the target, ensuring that the calculated deviation can be controlled within a preset range. The compensation parameter optimization and adjustment set the optimization threshold to 2, and the initial calculated compensation coefficient is applied to 3 randomly selected observation points within the sub-region to calculate the compensated positioning error. If the compensated positioning error exceeds 2, the compensation coefficient is adjusted proportionally to the error. For example, if the error exceeds 0.5, the compensation coefficient is increased by 0.1, and the compensation error is recalculated until the compensated positioning error is less than 2. The error compensation parameter set is integrated by organizing the optimized distance interval compensation coefficients, compensation applicable range, and sub-region number of each independent analysis sub-region into a positioning error compensation parameter set for that sub-region. All sub-region compensation parameter sets are sorted by sub-region number and integrated to form an error compensation parameter set covering the entire operation area.

[0096] Step 6.5: Apply the error compensation parameter set to the original BeiDou satellite observation data. Match the corresponding positioning error compensation parameters according to the independent analysis sub-region to which each observation point belongs, and perform point-by-point compensation correction on the original observation data. Finally, output high-precision positioning data after error correction. Specifically, this includes: retrieving the error compensation parameter set from the compensation parameter data area, retrieving the collected original BeiDou satellite observation data from the temporary storage area, performing point-by-point compensation correction and outputting high-precision positioning data. Point, sub-region, and parameter matching: extract each observation point from the original observation data in timestamp order; query the independent analysis sub-region number to which the point belongs from the spatial mapping data area; match the corresponding compensation parameter group from the error compensation parameter set according to the sub-region number; then determine the corresponding distance interval and compensation coefficient within the matching parameter group based on the topological connectivity distance between the point and the corresponding sub-region reference point; point-by-point compensation correction: apply the matched compensation coefficient to the original observation data of the observation point to compensate and correct the pseudorange and carrier phase data; for example, if the original pseudorange deviation of a certain point is 5 meters and the matched compensation coefficient is 0.8, then the compensated pseudorange deviation is corrected to: 5 minus 5 multiplied by 0.8 equals 1 meter; during the correction process, the compensation coefficient and the values ​​before and after correction are recorded simultaneously to ensure data traceability; after correction, the accuracy verification threshold is set to 1, and for each observation point that has completed the compensation correction, the coordinate deviation between the corrected positioning error and the known fixed landmark is calculated; if the positioning error exceeds 1, the compensation parameter group is rematched, the accuracy of the topological connectivity distance calculation is checked, and the compensation correction is re-executed until the positioning error is less than 1; extreme abnormal points that still cannot meet the accuracy requirements after multiple corrections are eliminated, and linear interpolation is performed using the corrected data of adjacent points; high-precision positioning data output: all observation data that has completed the compensation correction and passed the accuracy verification are organized into a high-precision positioning data sequence according to the timestamp order. Each data point includes the corrected three-dimensional coordinates, positioning accuracy level, corresponding satellite number, and compensation time; through the mining area's dedicated industrial communication network, the high-precision positioning data is output to the mining truck operation management platform in real time, and simultaneously stored in the vehicle's local storage device. The output frequency is consistent with the original data acquisition frequency to ensure continuous and reliable positioning support for precise scheduling and safe collision avoidance of mining trucks.

[0097] In this embodiment of the invention, because a preset benchmark observation point is set at the geometric center of each independent analysis sub-region, the topological connectivity distance between each observation point in the sub-region and the benchmark observation point is calculated to form a point relationship dataset, the statistical correlation between the topological connectivity distance and the signal characteristic deviation is analyzed by combining the multi-dimensional feature vector of signal quality and the distance-error mapping relationship is established, the positioning error compensation parameter set corresponding to each independent analysis sub-region is calculated, and the original Beidou satellite observation data is compensated and corrected point by point according to the corresponding compensation parameters of the independent analysis sub-region to which each observation point belongs, the technical means of overcoming the technical problems of the prior art that the positioning error compensation does not take into account the topological spatial relationship and signal characteristic differences of the points in the sub-region, and the use of a global unified compensation method cannot adapt to the local positioning error rules of different sub-regions, resulting in insufficient targeting of positioning error correction and difficulty in improving accuracy, the technical effect of achieving accurate positioning error compensation for the local features of each sub-region is achieved, effectively correcting the positioning deviation of observation points in different regions, and finally outputting Beidou satellite positioning data of mine trucks with higher accuracy and stronger stability, providing reliable data support for the safe scheduling and efficient operation of mine trucks.

[0098] like Figure 2 As shown, embodiments of the present invention also provide a real-time data processing system for BeiDou satellite positioning error correction, including:

[0099] The acquisition module is used to collect raw BeiDou satellite observation data of the current location of the mining truck in real time, and simultaneously acquire dynamic environmental information of the work area;

[0100] The evaluation module is used to sense and quantify the dynamic obstruction status of the BeiDou signal at the current location in real time based on the signal reception quality and dynamic environment information of the original BeiDou satellite observation data, and obtain the signal obstruction level evaluation result.

[0101] The adjustment module is used to adaptively adjust the error correction strategy based on the signal obstruction level assessment results to obtain the adjusted error correction strategy.

[0102] The construction module is used to process the raw BeiDou satellite observation data through the adjusted error correction strategy, construct the raw BeiDou satellite observation data into a discrete set of observation points, and construct an auxiliary geometric reference surface based on the discrete set of observation points.

[0103] The partitioning module is used to set a basic coordinate reference frame on the auxiliary geometric reference surface; based on the distribution characteristics of the discrete observation point set within the basic coordinate reference frame, the frame is adaptively partitioned into multiple independent analysis sub-regions; and each observation point in the discrete observation point set is mapped to the corresponding independent analysis sub-region.

[0104] The correction module is used to calculate the topological connectivity distance between each observation point in each independent analysis sub-region and the preset benchmark observation point, and to analyze the correlation between distance and error by combining signal characteristic deviation analysis, and to solve the corresponding positioning error compensation parameters; the original BeiDou satellite observation data is compensated and corrected by the positioning error compensation parameters to obtain high-precision positioning data.

[0105] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A real-time data processing method for BeiDou satellite positioning error correction, characterized in that, The method includes: Step 1: Collect raw BeiDou satellite observation data of the current location of the mining truck in real time, and simultaneously obtain dynamic environmental information of the work area; Step 2: Based on the signal reception quality and dynamic environment information of the original BeiDou satellite observation data, the dynamic obstruction status of the BeiDou signal at the current location is perceived and quantified in real time to obtain the signal obstruction level assessment result. Step 3: Based on the signal obstruction level assessment results, adaptively adjust the error correction strategy to obtain the adjusted error correction strategy; Step 4: Process the raw BeiDou satellite observation data using the adjusted error correction strategy to construct a discrete set of observation points; construct an auxiliary geometric reference surface based on the discrete set of observation points. Step 5: Set a basic coordinate reference frame on the auxiliary geometric reference surface; based on the distribution characteristics of the discrete observation point set within the basic coordinate reference frame, adaptively divide the frame into multiple independent analysis sub-regions; map each observation point in the discrete observation point set to the corresponding independent analysis sub-region. Step 6: For each independent analysis sub-region, calculate the topological connectivity distance between each observation point in the sub-region and the preset benchmark observation point, and combine the signal characteristic deviation analysis to determine the correlation between distance and error, and solve the corresponding positioning error compensation parameters; use the positioning error compensation parameters to compensate and correct the original BeiDou satellite observation data to obtain high-precision positioning data.

2. The real-time data processing method for BeiDou satellite positioning error correction according to claim 1, characterized in that, Real-time acquisition of raw BeiDou satellite observation data of the current location of the mining truck, and simultaneous acquisition of dynamic environmental information of the work area, including: The Beidou positioning receiver deployed on the mining truck acquires raw observation data in real time, including pseudorange, carrier phase and satellite ephemeris; at the same time, through the environmental perception sensors on the mining truck and the monitoring deployed in the work area, dynamic environmental information reflecting the current terrain undulations and the position and outline of large surrounding machinery is collected synchronously.

3. The real-time data processing method for BeiDou satellite positioning error correction according to claim 2, characterized in that, Based on the signal reception quality and dynamic environment information from the original BeiDou satellite observation data, the dynamic obstruction status of the BeiDou signal at the current location is sensed and quantified in real time, resulting in a signal obstruction level assessment, including: By extracting signal quality features from the received raw observation data, the carrier-to-noise ratio, multipath effect intensity, and signal continuity index of each satellite signal are obtained, resulting in a multidimensional feature vector of signal quality. Based on the multidimensional feature vector of signal quality and dynamic environmental information, a digital elevation occlusion surface is constructed by combining terrain undulation data, and dynamic obstacle occlusion body is formed by integrating the position and contour data of large machinery. The theoretical visible space angle of each satellite direction is calculated to obtain three-dimensional spatial occlusion relationship data. By using three-dimensional spatial occlusion relationship data, the multi-dimensional feature vector of signal quality is spatiotemporally aligned with the three-dimensional spatial occlusion relationship data. By comparing the deviation between the theoretical visible space angle and the actual signal reception quality, the signal occlusion influence coefficient is calculated. Based on the signal obstruction impact coefficient, a fuzzy clustering algorithm is used to classify the obstruction status into four levels: no obstruction, slight obstruction, moderate obstruction, and severe obstruction. The resulting signal obstruction level assessment results include obstruction level identifiers, affected satellite numbers, and predicted obstruction duration.

4. The real-time data processing method for BeiDou satellite positioning error correction according to claim 3, characterized in that, Based on the signal obstruction level assessment results, the error correction strategy is adaptively adjusted to obtain the adjusted error correction strategy, including: Based on the signal obstruction level assessment results, the obstruction level assessment results include obstruction level identifier, affected satellite number, and predicted obstruction duration information; By matching the occlusion level identifier with the preset error correction strategy template, the initial error correction strategy type is determined. Based on the predicted information of the satellite number and the duration of obstruction, the weight allocation parameters, filter window length parameters, and outlier removal threshold parameters in the determined initial error correction strategy type are dynamically adjusted to obtain the adjusted parameters. The adjusted parameters are integrated into the initial error correction strategy type to obtain an adjusted error correction strategy optimized for the current signal obstruction state.

5. The real-time data processing method for BeiDou satellite positioning error correction according to claim 4, characterized in that, The original BeiDou satellite observation data is processed by the adjusted error correction strategy to construct a discrete set of observation points. Based on a set of discrete observation points, an auxiliary geometric reference surface is constructed, including: By using the adjusted error correction strategy, the original BeiDou satellite observation data is filtered and outlier removed in real time to obtain a preliminarily corrected satellite observation data sequence. The satellite observation data sequence, after initial correction, is discretized according to timestamps and spatial coordinates. The location coordinate information of the effective observation time is extracted to construct a set of discrete observation points with spatiotemporal distribution characteristics. Based on the set of discrete observation points, the spatial distribution trend of the point set is calculated using the least squares fitting algorithm to obtain the auxiliary geometric reference surface of the terrain features of the current working area.

6. The real-time data processing method for BeiDou satellite positioning error correction according to claim 5, characterized in that, A basic coordinate reference frame is set on the auxiliary geometric reference surface; based on the distribution characteristics of the discrete observation point set within the basic coordinate reference frame, the frame is adaptively divided into regions to obtain multiple independent analysis sub-regions; Mapping each observation point in the discrete observation point set to its corresponding independent analysis sub-region, including: Based on the auxiliary geometric reference surface, a three-dimensional rectangular coordinate system is established on the auxiliary geometric reference surface as the basic coordinate reference frame, and the coordinate origin, coordinate axis direction and coordinate unit are determined; The discrete set of observation points is projected onto the basic coordinate reference frame, and the coordinate distribution density and spatial clustering characteristics of each observation point within the frame are calculated to obtain the density gradient change information of the point distribution. Based on the density gradient change information of the point distribution, an adaptive grid partitioning algorithm is adopted to divide the basic coordinate reference frame into regions according to the threshold characteristics of the density gradient change, resulting in multiple independent analysis sub-regions with clear boundaries and uniform internal point distribution. Based on the spatial boundary range of multiple independent analysis sub-regions, each observation point in the discrete observation point set is matched and mapped to the corresponding independent analysis sub-region according to its spatial coordinate position.

7. The real-time data processing method for BeiDou satellite positioning error correction according to claim 6, characterized in that, Step 6 includes: Based on the subset of local observation points corresponding to each independent analysis sub-region, a preset benchmark observation point located at the geometric center of the sub-region is set for each independent analysis sub-region. By using preset benchmark observation points, the topological connectivity distance between each observation point in each independent analysis sub-region and the preset benchmark observation point is calculated, thus obtaining a dataset of point relationships with topological distance information. By combining the point relationship dataset and the multidimensional feature vector of signal quality, the statistical correlation between topological connectivity distance and signal characteristic deviation is analyzed, and the distance-error mapping relationship is established. Based on the distance and error mapping relationship, the corresponding positioning error compensation parameters are calculated for each independent analysis sub-region, resulting in the error compensation parameter set for each sub-region's compensation coefficient. The error compensation parameter set is applied to the original BeiDou satellite observation data. The corresponding positioning error compensation parameters are matched according to the independent analysis sub-region to which each observation point belongs. The original observation data is then compensated and corrected point by point, and finally, high-precision positioning data after error correction is output.

8. A real-time data processing system for BeiDou satellite positioning error correction, wherein the system implements the method as described in any one of claims 1 to 7, characterized in that, include: The acquisition module is used to collect raw BeiDou satellite observation data of the current position of the mining truck in real time, and simultaneously acquire dynamic environmental information of the work area; The evaluation module is used to sense and quantify the dynamic obstruction status of the BeiDou signal at the current location in real time based on the signal reception quality and dynamic environment information of the original BeiDou satellite observation data, and obtain the signal obstruction level evaluation result. The adjustment module is used to adaptively adjust the error correction strategy based on the signal obstruction level assessment results to obtain the adjusted error correction strategy. The construction module is used to process the raw BeiDou satellite observation data through the adjusted error correction strategy, and construct the raw BeiDou satellite observation data into a discrete set of observation points; An auxiliary geometric reference surface is constructed based on a set of discrete observation points. The partitioning module is used to set the basic coordinate reference frame on the auxiliary geometric reference surface; Based on the distribution characteristics of the discrete observation point set within the basic coordinate reference frame, the frame is adaptively divided into multiple independent analysis sub-regions; each observation point in the discrete observation point set is mapped to the corresponding independent analysis sub-region. The correction module is used to calculate the topological connectivity distance between each observation point in each independent analysis sub-region and the preset benchmark observation point, and to analyze the correlation between distance and error by combining signal characteristic deviation analysis, and to solve the corresponding positioning error compensation parameters; the original BeiDou satellite observation data is compensated and corrected by the positioning error compensation parameters to obtain high-precision positioning data.

9. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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