Positioning data protocol parsing and high-precision error comparison detection method

Abstract

The application relates to the technical field of satellite navigation and positioning, in particular to a positioning data protocol analysis and high-precision error comparison and detection method and system, which comprises the following steps: multi-channel positioning data logs are subjected to multi-protocol compatible analysis to obtain standardized positioning records, separation and unification of a calculation reference and a display reference are realized through coordinate format conversion and multi-coordinate system bidirectional transformation; multi-source trajectory time synchronization is completed through a double-mode alignment mechanism of UTC accurate matching and time sequence proportion nearest neighbor matching; multi-dimensional errors are calculated through a great circle distance model, and root mean square errors, probabilistic circle errors and positioning quality proportion indexes are counted; finally, trajectory comparison results, error statistical curves and standardized precision detection reports are output. Through cascaded processing of multi-protocol analysis, double-mode time matching and multi-dimensional error evaluation, the application realizes full-process automatic precision detection of multi-source heterogeneous positioning data, and improves protocol compatibility, cross-map display consistency and error evaluation integrity.

Description

Technical Field

[0001] This invention relates to the field of satellite navigation positioning and integrated navigation accuracy detection technology, specifically to a positioning data protocol parsing and high-precision error comparison detection method and system. Background Technology

[0002] With the rapid popularization of centimeter-level RTK positioning, multi-frequency GNSS receivers, fiber optic inertial navigation, and integrated navigation technologies, the demand for accuracy testing of positioning equipment in fields such as vehicle and ship navigation, unmanned systems, and intelligent transportation continues to increase. In actual field tests, situations often arise where multiple devices collect data in parallel, multiple protocols output data, multiple coordinate systems are used interchangeably, and multiple sampling frequencies coexist, posing numerous challenges to the quantitative testing of positioning accuracy. Most existing positioning testing solutions only support the parsing of GGA and RMC statements of the basic NMEA-0183 protocol, and are incompatible with high-precision extended protocols such as AGRICA. This makes it difficult to extract extended fields such as dual-set coordinates, three-axis velocity, and heading angle, resulting in a significant waste of high-precision data. Furthermore, different map platforms use different coordinate benchmarks such as WGS84, GCJ02, and BD09. Traditional solutions do not separate calculated coordinates from displayed coordinates, leading to offsets and inconsistent comparison results for the same data displayed under different map kernels. In addition, the sampling frequency, timestamp accuracy, and log recording rhythm of multiple positioning devices often differ, making traditional line-by-line comparison methods prone to trajectory mismatches and causing statistical distortion of errors. In terms of evaluation dimensions, existing solutions mostly use mean plane error as a single indicator, which cannot comprehensively reflect key information such as error distribution, three-dimensional deviation, heading error, and positioning quality ratio, making it difficult to meet the needs of high-precision equipment acceptance and R&D testing. Therefore, there is an urgent need for an automated testing solution that can cover the entire process of multi-protocol parsing, multi-coordinate benchmark unification, high-precision time matching, and multi-dimensional error evaluation, in order to adapt to the accuracy testing requirements of multi-source heterogeneous positioning data and improve testing efficiency and result reliability. Summary of the Invention

[0003] To address the aforementioned technical shortcomings, this invention provides a method and system for parsing positioning data protocols and comparing and detecting high-precision errors. It aims to solve the technical problems in the prior art, such as poor compatibility of extended protocols, inconsistent coordinate references, low accuracy of multi-channel data matching, and a single dimension for error evaluation.

[0004] This invention is achieved through the following technical solution: A method for parsing positioning data protocols and comparing and detecting high-precision errors is provided, the method comprising the following steps: Step S10: Collect at least two positioning data logs, one of which is used as the positioning data to be measured, and the other is used as high-precision reference data; Step S20: Identify the positioning statement type of each positioning data log line by line, and perform field parsing for GGA, ZDA, RMC and AGRICA statements respectively to generate standardized positioning records containing latitude and longitude, elevation, positioning quality, number of satellites, UTC time, heading angle and speed information; Step S30: Perform unified processing of coordinate and time references on the standardized positioning record, complete the conversion from degree division format to decimal degree and bidirectional transformation of multiple coordinate systems, and at the same time supplement and normalize the UTC timestamp, and output a standardized positioning sequence with unified coordinate and time references. Step S40: Based on the unified UTC timestamp of the standardized positioning sequence, a dual-mode mechanism combining UTC precise matching and time-series proportional nearest neighbor matching is adopted to complete the time synchronization and alignment of the measured data and the reference reference data, and obtain an effective matching data pair with consistent coordinate reference. Step S50: Calculate the multidimensional positioning error based on the matching data, statistically analyze multiple accuracy evaluation indicators, generate accuracy rating results, and output trajectory comparison visualization results, error statistics results, and standardized detection reports.

[0005] Preferably, in step S20, the AGRICA statement field parsing reads at least the first set of latitude and longitude and elevation, the second set of latitude and longitude and elevation, eastward speed, northward speed, celestial speed, number of satellites and heading angle fields, and generates a positioning quality identifier based on the positioning type field.

[0006] Preferably, the step S30, which involves converting the degree system to decimal degrees, specifically includes: The NMEA protocol's DDMM.MMMMMM degree-division format coordinates are split into degree components and component components, converted to decimal coordinates, and sign correction is performed on the south latitude and west longitude coordinates.

[0007] Preferably, the multi-coordinate system bidirectional transformation is completed in step S30, specifically including: the multi-coordinate system bidirectional transformation supports mutual conversion between WGS84, GCJ02, and BD09 coordinate systems; error calculation uses WGS84 coordinates as a unified reference; map display switches the corresponding coordinate system according to the target map kernel to achieve mutual isolation between calculated coordinates and displayed coordinates; horizontal distance error adopts the Haversine great circle distance model; vertical error is the absolute value of elevation difference; and three-dimensional error is the square root of the sum of the squares of horizontal distance error and vertical error.

[0008] Preferably, step S40 employs a dual-mode mechanism combining UTC exact matching and temporal proportional nearest neighbor matching, specifically including: A UTC time index for the reference baseline data is constructed. Precise matching of the same UTC date and time is performed first. When precise matching fails or the number of valid matching points is lower than a preset threshold, the nearest neighbor matching strategy of sequence length ratio mapping is enabled. The index of the tested sequence is mapped to the reference sequence index proportionally to form corresponding matching point pairs.

[0009] Preferably, in step S50, the multidimensional positioning error is calculated based on the matching data. The calculation of the multidimensional positioning error satisfies the following: the horizontal distance error is calculated using the Haversine great circle distance model, the vertical error is the absolute value of the elevation difference between the two points, and the three-dimensional error is the square root of the sum of the squares of the horizontal distance error and the vertical error.

[0010] Preferably, step S50 involves statistically analyzing multiple accuracy evaluation indicators, generating accuracy rating results, and outputting trajectory comparison visualization results, error statistics results, and a standardized detection report, specifically including: Accuracy evaluation indicators should include at least the average error, maximum error, minimum error, root mean square error (RMSE), 50% probability circle error (CEP50), 95% probability circle error (CEP95), heading angle error, and the ratio of fixed and floating RTK solutions. A standardized test report should include at least a multi-track overlay diagram, an error line graph, an error time variation curve, an error distribution histogram, a key indicator statistical bar chart, and a textual accuracy rating conclusion.

[0011] Furthermore, to achieve the above objectives, the present invention also proposes a positioning data protocol parsing and high-precision error comparison and detection system, wherein the positioning data protocol parsing and high-precision error comparison and detection system comprises: Data log acquisition module: used to collect at least two positioning data logs, one of which is used as the positioning data to be measured, and the other is used as high-precision reference data; Protocol parsing module: Used to identify the type of positioning statement in each positioning data log line by line, and to perform field parsing for GGA, ZDA, RMC and AGRICA statements respectively, generating standardized positioning records containing latitude and longitude, elevation, positioning quality, number of satellites, UTC time, heading angle and speed information; Coordinate transformation module: Used to perform unified processing of coordinate and time references on standardized positioning records, complete the conversion from degree division to decimal degree and bidirectional transformation of multiple coordinate systems, and at the same time supplement and normalize UTC timestamps, outputting a standardized positioning sequence with unified coordinate and time references; Dual-mode matching module: Based on the unified UTC timestamp of the standardized positioning sequence, it adopts a dual-mode mechanism combining UTC precise matching and time-series proportional nearest neighbor matching to complete the time synchronization and alignment of the measured data and the reference reference data, and obtain effective matching data pairs with consistent coordinate references. Error Calculation and Visualization Report Module: This module is used to calculate multidimensional positioning errors based on matching data, statistically analyze multiple accuracy evaluation indicators, generate accuracy rating results, and output trajectory comparison visualization results, error statistics results, and standardized test reports.

[0012] Furthermore, to achieve the above objectives, the present invention also proposes a positioning data protocol parsing and high-precision error comparison and detection device. The device includes: a memory, a processor, and a program for positioning data protocol parsing and high-precision error comparison and detection stored in the memory and executable on the processor. The program for positioning data protocol parsing and high-precision error comparison and detection is the step of implementing the positioning data protocol parsing and high-precision error comparison and detection method described above.

[0013] In addition, to achieve the above objectives, the present invention also provides a computer program product, which includes programs for positioning data protocol parsing and high-precision error comparison and detection, etc. When the positioning data protocol parsing and high-precision error comparison and detection programs are executed by a processor, they implement a positioning data protocol parsing and high-precision error comparison and detection method as described above.

[0014] The advantages and effects of this invention are: This invention proposes a positioning data protocol parsing and high-precision error comparison and detection method and system. Through a multi-protocol parsing mechanism compatible with both basic NMEA statements and AGRICA high-precision extended statements, it can completely extract extended fields such as dual-set coordinates, three-axis velocity, and heading angle, avoiding the loss of high-precision extended data and significantly improving adaptability to high-end RTK and integrated navigation equipment. By employing a multi-coordinate system bidirectional conversion mechanism that separates calculated and displayed coordinates, error calculation uniformly adopts the WGS84 benchmark to ensure result consistency. The display layer can flexibly adapt to the coordinate requirements of different map kernels, eliminating cross-platform trajectory display offsets and achieving a unified "one-time calculation, multi-terminal display" solution. Through a dual-mode time alignment mechanism combining UTC precise matching and time-series proportional nearest neighbor matching, it ensures matching accuracy in strictly synchronized time scenarios while solving effective matching problems in asynchronous sampling and timestamp-missing scenarios, significantly improving the number of available matching points and the reliability of error statistics for multi-path heterogeneous logs. By employing a multi-dimensional evaluation system that encompasses horizontal, vertical, and three-dimensional errors, RMSE, CEP50, and CEP95 statistics, as well as heading error and positioning quality percentage, the system comprehensively characterizes the error characteristics and working status of positioning equipment. This replaces the traditional evaluation method based solely on average error, forming a verifiable, comparable, and standardized accuracy testing and evaluation system. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of a positioning data protocol parsing and high-precision error comparison and detection method according to the present invention.

[0017] Figure 2 This is a comparison chart of key accuracy indicators for each test pair in one embodiment of the present invention.

[0018] Figure 3 This is a graph showing the change of error over time for multiple test pairs in one embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] like Figure 1 As shown, in one embodiment of the present invention, a method for parsing positioning data protocols and comparing and detecting high-precision errors includes the following steps: Step S10: Collect at least two positioning data logs, one of which is used as the positioning data to be measured, and the other is used as high-precision reference data.

[0021] This step acquires multi-channel positioning log data collected during field testing. The tested data comes from the GNSS, RTK, inertial navigation, or integrated navigation equipment under test, while the reference data comes from a higher-precision reference receiver, serving as the true benchmark for error calculation. The two data streams are acquired and stored independently, each retaining its original protocol format and timestamp information. This dual-channel comparison architecture makes this method applicable to various scenarios such as equipment acceptance, algorithm benchmarking, and performance testing. A unified post-processing workflow enables quantitative comparison of accuracy between different devices and algorithms.

[0022] Traditional testing methods often focus on quality statistics for single-device outputs, failing to quantify absolute accuracy. This step utilizes a dual-channel acquisition mode (tested device + reference) to provide a true benchmark for subsequent error comparison, achieving quantitative testing of positioning accuracy. For example, in field testing of integrated navigation equipment, the output logs of the tested INS / DVL integrated navigation equipment and the logs of a centimeter-level RTK reference benchmark are simultaneously acquired. The two data streams are stored independently, and subsequent automatic comparison is performed using this method to obtain the absolute accuracy index of the tested device.

[0023] Step S20: Identify the positioning statement type of each positioning data log line by line, and perform field parsing for GGA, ZDA, RMC and AGRICA statements respectively to generate standardized positioning records containing latitude and longitude, elevation, positioning quality, number of satellites, UTC time, heading angle and speed information.

[0024] This step reads the log text line by line, identifies the statement type by the statement header, and calls the corresponding parsing branch to extract fields. For standard NMEA statements, core fields such as latitude and longitude, elevation, positioning quality, number of satellites, HDOP, and UTC time are parsed; for AGRICA high-precision extended statements, additional extended fields such as the second set of coordinates, three-axis velocity components, and heading angle are parsed; finally, the parsing results of all statements are uniformly mapped to standardized positioning records with the same structure. This unified standardized structure shields the format differences between different protocols, providing a unified data entry point for subsequent coordinate transformation, time matching, and error calculation, avoiding the redundancy of processing data from different protocols separately.

[0025] Traditional methods only support basic NMEA statement parsing, failing to extract and utilize many high-precision fields from extended protocols, resulting in low data utilization. This step's multi-protocol compatible parsing covers both basic and high-precision extended protocols, fully preserving all positioning-related fields and improving data utilization and detection dimensions. For example, a high-end RTK device outputs an AGRICA statement containing the coordinates of both primary and secondary antennas, as well as the three-axis velocity and heading angle (northeast, south, east). Traditional parsing tools can only recognize the basic fields, while this method can completely parse all fields, calculating not only position errors but also velocity and heading errors, enabling a more comprehensive performance evaluation.

[0026] Step S30: Perform unified processing of coordinates and time on the standardized positioning record, complete the conversion from degree division to decimal degree and the bidirectional transformation of multiple coordinate systems, and at the same time supplement and normalize the UTC timestamp, and output a standardized positioning sequence with unified coordinate and time references.

[0027] This step first performs coordinate format conversion: the NMEA protocol's unique DDMM.MMMMMM degree format is split into integer and decimal parts, and converted to decimal coordinates using the formula "degrees + minutes / 60". Simultaneously, negative signs are added to the south latitude and west longitude coordinates according to their latitude and longitude markings to complete sign correction. Based on this, multi-coordinate system conversion is performed: a built-in bidirectional conversion algorithm for WGS84, GCJ02, and BD09 coordinate systems allows for conversion between any two coordinate systems. The error calculation stage uniformly uses WGS84 geographic coordinates to ensure the uniqueness and accuracy of the calculation benchmark; the map display stage can flexibly convert to the corresponding coordinate system according to the requirements of the front-end map kernel, achieving decoupling of calculation and display. This separation of calculation and display design ensures the uniqueness and reproducibility of error calculation results while adapting to the coordinate requirements of different map platforms, avoiding the problem of the same batch of data being displayed off-center on different maps.

[0028] Traditional solutions typically use a single coordinate system for both calculation and display, which either results in calculations affected by map coordinate offsets or produces poor display quality. This step's dual-baseline design balances calculation accuracy and display compatibility, resolving inconsistencies across different map kernels. For example, the same test data is automatically converted to GCJ02 coordinates when displayed on Amap and to BD09 coordinates when displayed on Baidu Maps, but the underlying error calculation is always based on WGS84 coordinates, ensuring completely consistent detection results across different display scenarios and preventing deviations due to map switching.

[0029] Step S40: Based on the unified UTC timestamp of the standardized positioning sequence, a dual-mode mechanism combining UTC precise matching and time-series proportional nearest neighbor matching is adopted to complete the time synchronization and alignment of the measured data and the reference reference data, thereby obtaining an effective matching data pair with consistent coordinate reference.

[0030] This step first constructs a UTC time index for the reference baseline data, prioritizing exact matching: it iterates through each timestamp of the tested data, searching for identical UTC time points in the reference data to form exact matching pairs. When the number of valid exact matching points falls below a preset threshold, or when exact matching fails due to missing dates in the logs or insufficient time precision, it automatically switches to nearest neighbor matching mode: the tested sequence and the reference sequence are sorted by time, and the tested point index is mapped to the reference sequence index according to the sequence length ratio, with the nearest neighbor point being used as the matching pair. This dual-mode mechanism balances the strictness of high-precision synchronous scenarios with the compatibility of asynchronous sampling scenarios, ensuring the highest matching accuracy when time is strictly aligned, while also guaranteeing a sufficient number of valid matching points when timestamps are inconsistent, avoiding the poor adaptability of a single matching mode.

[0031] Traditional line-by-line matching or fixed-interval matching methods can produce severe matching deviations when the sampling frequencies and start times of the two data streams differ, leading to distorted error statistics. This step's dual-mode matching automatically selects the optimal strategy based on data quality, significantly improving matching reliability under different acquisition conditions. For example, when two devices use synchronous sampling with the same source pulse, the timestamps are completely identical, and the system automatically uses precise matching, resulting in the highest error calculation accuracy. If the two devices sample independently at frequencies of 1Hz and 5Hz respectively, the number of precise matching points is insufficient, and the system automatically switches to proportional nearest neighbor matching, ensuring that all measured points can find corresponding reference points, resulting in stable and reliable error statistics.

[0032] Step S50: Calculate the multidimensional positioning error based on the matching data, statistically analyze multiple accuracy evaluation indicators, generate accuracy rating results, and output trajectory comparison visualization results, error statistics results, and standardized detection reports.

[0033] This step calculates the horizontal, vertical, and three-dimensional errors point-by-point based on one-to-one matching point pairs. The horizontal error is calculated using Haversine's great circle distance formula, taking into account the Earth's curvature to ensure accuracy at long distances. The vertical error is the absolute value of the elevation difference between two points. The three-dimensional error is synthesized from the horizontal and vertical errors using the Pythagorean theorem. Based on the point-by-point errors, the average, maximum, minimum, and root mean square (RMSE) errors of the entire data segment are statistically analyzed. The CEP50 and CEP95 probability circle errors are calculated through error sorting and quantile calculations, representing the positioning error ranges at 50% and 95% probabilities, respectively. Simultaneously, the heading angle error and the proportions of fixed, floating, and invalid RTK solutions are statistically analyzed, forming a multi-dimensional accuracy evaluation index system. This multi-dimensional index system comprehensively characterizes equipment performance from multiple dimensions, including statistical features, probability distribution, and quality proportions, and more accurately reflects the equipment's error characteristics and operating status compared to a single average error.

[0034] Traditional methods only output the mean plane error, failing to reflect the error's dispersion, extreme values, and distribution characteristics, resulting in a single evaluation dimension. This step's multi-dimensional evaluation system encompasses multiple dimensions, including central tendency, dispersion, probability boundary, angle error, and mass percentage, providing a more comprehensive and objective evaluation. For example, in testing a certain inertial navigation device, it can not only output the average mean plane error but also provide the CEP95 error as the error boundary under 95% probability, while simultaneously statistically analyzing the error divergence rate in pure inertial navigation mode, providing multi-dimensional data support for device performance evaluation and algorithm optimization.

[0035] This step generates a complete set of visualized outputs and test reports based on the processing results. The visualizations include multi-track overlay plots, error line graphs, error-over-time curves, error distribution histograms, and key indicator comparison bar charts. The text report includes test condition descriptions, key indicator statistical tables, accuracy rating conclusions, and explanations of anomalies. All charts and indicators are generated according to standardized templates, ensuring consistent formatting and strong comparability across different batches of test reports. Standardized report output replaces manual statistics and graphing, significantly improving testing efficiency. The unified format also facilitates horizontal comparison and quality traceability of test results from different devices and batches.

[0036] Traditional testing often involves manual statistical tabulation, which is inefficient, lacks standardized formats, and is prone to human error. This automated report generation step automates the entire testing process, eliminating the need for manual intervention from data input to report output, thus improving testing efficiency and result consistency. For example, after importing two test logs, the system automatically performs parsing, matching, calculation, and graphing, generating a complete test report within minutes. This report includes all key indicators and visual charts, and can be directly used in equipment acceptance reports or R&D testing records.

[0037] Furthermore, in one embodiment of the present invention, a positioning data protocol parsing and high-precision error comparison and detection system is proposed, wherein the positioning data protocol parsing and high-precision error comparison and detection system includes: Data log acquisition module: used to collect at least two positioning data logs, one of which is used as the positioning data to be measured, and the other is used as high-precision reference data; Protocol parsing module: Used to identify the type of positioning statement in each positioning data log line by line, and to perform field parsing for GGA, ZDA, RMC and AGRICA statements respectively, generating standardized positioning records containing latitude and longitude, elevation, positioning quality, number of satellites, UTC time, heading angle and speed information; Coordinate transformation module: Used to perform unified processing of coordinate and time references on standardized positioning records, complete the conversion from degree division to decimal degree and bidirectional transformation of multiple coordinate systems, and at the same time supplement and normalize UTC timestamps, outputting a standardized positioning sequence with unified coordinate and time references; Dual-mode matching module: Based on the unified UTC timestamp of the standardized positioning sequence, it adopts a dual-mode mechanism combining UTC precise matching and time-series proportional nearest neighbor matching to complete the time synchronization and alignment of the measured data and the reference reference data, and obtain effective matching data pairs with consistent coordinate references. Error Calculation and Visualization Report Module: This module is used to calculate multidimensional positioning errors based on matching data, statistically analyze multiple accuracy evaluation indicators, generate accuracy rating results, and output trajectory comparison visualization results, error statistics results, and standardized test reports.

[0038] This application provides a positioning data protocol parsing and high-precision error comparison detection system, which employs a positioning data protocol parsing and high-precision error comparison detection method described in the above embodiments. This system addresses the technical problems of poor protocol compatibility, inconsistent coordinate references, low matching accuracy, and a single evaluation dimension in existing technologies. Compared with existing technologies, the beneficial effects of the positioning data protocol parsing and high-precision error comparison detection system provided in this application are the same as those of the positioning data protocol parsing and high-precision error comparison detection method described in the above embodiments. Furthermore, other technical features of the positioning data protocol parsing and high-precision error comparison detection system are the same as those disclosed in the methods of the above embodiments, and will not be elaborated upon here.

[0039] To verify the technical effectiveness of this invention, field tests were conducted on the integrated navigation system. The test objects included a fiber optic inertial navigation integrated navigation system and a centimeter-level RTK reference base. The test scenarios covered various operating conditions, including obstructed, pure inertial navigation, and unobstructed conditions. The test results are shown in the table below:

[0040] Experimental results show that this method can stably output various error statistics and clearly distinguish the accuracy differences under different test conditions, accurately reflecting the impact of factors such as occlusion, operating mode, and antenna height on positioning performance. The visualization results can intuitively display the trajectory overlap and error change trends, verifying the effectiveness and practicality of this method in real-world testing scenarios.

[0041] like Figure 2 The figure shows a comparison of key accuracy indicators for each test pair in one embodiment of the present invention, corresponding to the three core positioning accuracy indicators: Mean Horizontal Error (Mr. Horizontal Error), Root Mean Square Error (RMSE), and 95% Probability Circular Error (CEP95), all in meters. The test results show that the second group (Pair 2), in pure inertial navigation mode, has the lowest errors in all three categories, exhibiting the best positioning accuracy. The first group (Pair 1) has the highest error values ​​under occlusion conditions, resulting in the worst accuracy. The accuracy of the third and fourth groups (Pair 3 and Pair 4) is at an intermediate level. The overall trend of the three indicators is consistent, clearly demonstrating the differences in the impact of different test conditions on positioning accuracy.

[0042] like Figure 3As shown, this is a graph illustrating the variation of error over time for multiple test pairs in one embodiment of the present invention. The horizontal axis, Time (minutes), represents the test time (unit: minutes), and the vertical axis, Horizontal Error (m), represents the horizontal positioning error (unit: meters). "All Test Pairs - Horizontal Error Over Time" represents the curves showing the variation of horizontal error over time for all test pairs. The legend "NS2+RTK2" indicates the combination of an NS2 positioning device and an RTK2 reference device, "NS5+RTK5" indicates the combination of an NS5 positioning device and an RTK5 reference device, "Faser" indicates fiber optic inertial navigation scheme 1, and "Faser2" indicates fiber optic inertial navigation scheme 2. The four curves correspond to four test pairs under different working conditions. Test results show that test pair 2 (pure inertial navigation mode) had the lowest horizontal error throughout the test, remaining within 15 meters overall, with the error showing a gradual divergence trend over time. Test pair 1 initially had an error peak close to 60 meters, then quickly fell back and fluctuated between 25 and 40 meters. Test pair 3's error first rose to a peak of about 26 meters before gradually falling back and stabilizing at around 10 meters in the later stages. Test pair 4 had a relatively high overall error level, with a peak exceeding 40 meters, before falling back to around 18 meters and stabilizing. This curve can intuitively reflect the evolution and divergence characteristics of positioning error over time under different working conditions.

[0043] This application provides a positioning data protocol parsing and high-precision error comparison and detection device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the positioning data protocol parsing and high-precision error comparison and detection method in the above embodiment 1.

[0044] In one embodiment of the present invention, a positioning data protocol parsing and high-precision error comparison detection device may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), etc., and fixed terminals such as digital TVs, desktop computers, etc. The aforementioned positioning data protocol parsing and high-precision error comparison detection device is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0045] The aforementioned positioning data protocol parsing and high-precision error comparison detection device may include a processor (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) or a program loaded from a storage device into a machine-readable storage medium (RAM). The RAM also stores various programs and data required for the operation of the positioning data protocol parsing and high-precision error comparison detection device. The processor, ROM, and machine-readable storage medium are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus. Typically, the following systems can be connected to the I / O interface: input devices including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices including, for example, magnetic tapes, hard disks, etc.; and a communication unit. The communication unit allows the positioning data protocol parsing and high-precision error comparison detection device to communicate wirelessly or wiredly with other devices to exchange data.

[0046] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication unit, or installed from a storage device, or installed from a read-only memory. When the computer program is executed by a processor, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0047] This application provides a positioning data protocol parsing and high-precision error comparison detection device, which employs a positioning data protocol parsing and high-precision error comparison detection method described in the above embodiments. This addresses the technical problems in the prior art, such as poor protocol compatibility, inconsistent coordinate references, low matching accuracy, and a single evaluation dimension. Compared with the prior art, the beneficial effects of the positioning data protocol parsing and high-precision error comparison detection device provided in this application are the same as those of the positioning data protocol parsing and high-precision error comparison detection method described in the above embodiments. Furthermore, other technical features of this positioning data protocol parsing and high-precision error comparison detection device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0048] The various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0049] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the positioning data protocol parsing and high-precision error comparison detection method described above.

[0050] The computer program product provided in this application can solve the technical problems of poor protocol compatibility, inconsistent coordinate references, low matching accuracy, and single evaluation dimensions in the prior art. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the positioning data protocol parsing and high-precision error comparison and detection method provided in the above embodiments, and will not be repeated here.

[0051] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for parsing positioning data protocols and comparing and detecting high-precision errors, characterized in that, The method includes the following steps: Step S10: Collect at least two positioning data logs, one of which is used as the positioning data to be measured, and the other is used as the reference baseline data; Step S20: Identify the positioning statement type of each positioning data log line by line, and perform field parsing for GGA, ZDA, RMC and AGRICA statements respectively to generate standardized positioning records containing latitude and longitude, elevation, positioning quality, number of satellites, UTC time, heading angle and speed information; Step S30: Perform unified processing of coordinate and time references on the standardized positioning record, complete the conversion from degree division format to decimal degree and bidirectional transformation of multiple coordinate systems, and at the same time supplement and normalize the UTC timestamp, and output a standardized positioning sequence with unified coordinate and time references. Step S40: Based on the unified UTC timestamp of the standardized positioning sequence, a dual-mode mechanism combining UTC precise matching and time-series proportional nearest neighbor matching is adopted to complete the time synchronization and alignment of the measured data and the reference reference data, and obtain an effective matching data pair with consistent coordinate reference. Step S50: Calculate the multidimensional positioning error based on the matching data, statistically analyze multiple accuracy evaluation indicators, generate accuracy rating results, and output trajectory comparison visualization results, error statistics results, and standardized detection reports; The step S40 employs a dual-mode mechanism combining UTC precise matching and time-proportional nearest neighbor matching, specifically including: A UTC time index for the reference baseline data is constructed. Precise matching of the same UTC date and time is performed first. When precise matching fails or the number of valid matching points is lower than a preset threshold, the nearest neighbor matching strategy of sequence length ratio mapping is enabled. The index of the tested sequence is mapped to the reference sequence index proportionally to form corresponding matching point pairs.

2. The positioning data protocol parsing and high-precision error comparison and detection method according to claim 1, characterized in that, In step S20, the AGRICA statement field parsing reads at least the first set of latitude and longitude and elevation, the second set of latitude and longitude and elevation, eastward velocity, northward velocity, celestial velocity, number of satellites and heading angle fields, and generates a positioning quality identifier based on the positioning type field.

3. The positioning data protocol parsing and high-precision error comparison and detection method according to claim 1, characterized in that, The step S30, which involves converting the degree system to a decimal system, specifically includes: The NMEA protocol's DDMM.MMMMMM degree-division format coordinates are split into degree components and component components, converted to decimal coordinates, and sign correction is performed on the south latitude and west longitude coordinates.

4. The positioning data protocol parsing and high-precision error comparison and detection method according to claim 1, characterized in that, In step S30, the multi-coordinate system bidirectional transformation is completed. Specifically, the multi-coordinate system bidirectional transformation supports mutual conversion between WGS84, GCJ02, and BD09 coordinate systems. Error calculation uses WGS84 coordinates as a unified reference. The map display switches the corresponding coordinate system according to the target map kernel to achieve mutual isolation between the calculated coordinates and the displayed coordinates.

5. The positioning data protocol parsing and high-precision error comparison and detection method according to claim 1, characterized in that, In step S50, the multidimensional positioning error is calculated based on the matching data. The calculation of the multidimensional positioning error satisfies the following: the horizontal distance error is calculated using the Haversine great circle distance model, the vertical error is the absolute value of the elevation difference between the two points, and the three-dimensional error is the square root of the sum of the squares of the horizontal distance error and the vertical error.

6. The positioning data protocol parsing and high-precision error comparison and detection method according to claim 1, characterized in that, In step S50, multiple accuracy evaluation indicators are statistically analyzed to generate accuracy rating results, and trajectory comparison visualization results, error statistics results, and standardized detection reports are output, specifically including: Accuracy evaluation indicators should include at least the average error, maximum error, minimum error, root mean square error (RMSE), 50% probability circle error (CEP50), 95% probability circle error (CEP95), heading angle error, and the ratio of fixed and floating RTK solutions. A standardized test report should include at least a multi-track overlay diagram, an error line graph, an error time variation curve, an error distribution histogram, a key indicator statistical bar chart, and a textual accuracy rating conclusion.

7. A positioning data protocol parsing and high-precision error comparison and detection system, characterized in that, The system executes the positioning data protocol parsing and high-precision error comparison and detection method as described in claim 1, including: Data log acquisition module: used to collect at least two positioning data logs, one of which is used as the positioning data to be measured, and the other is used as high-precision reference data; Protocol parsing module: Used to identify the type of positioning statement in each positioning data log line by line, and to perform field parsing for GGA, ZDA, RMC and AGRICA statements respectively, generating standardized positioning records containing latitude and longitude, elevation, positioning quality, number of satellites, UTC time, heading angle and speed information; Coordinate transformation module: Used to perform unified processing of coordinate and time references on standardized positioning records, complete the conversion from degree division to decimal degree and bidirectional transformation of multiple coordinate systems, and at the same time supplement and normalize UTC timestamps, outputting a standardized positioning sequence with unified coordinate and time references; Dual-mode matching module: Based on the unified UTC timestamp of the standardized positioning sequence, it adopts a dual-mode mechanism combining UTC precise matching and time-series proportional nearest neighbor matching to complete the time synchronization and alignment of the measured data and the reference reference data, and obtain effective matching data pairs with consistent coordinate references. Error Calculation and Visualization Report Module: This module is used to calculate multidimensional positioning errors based on matching data, statistically analyze multiple accuracy evaluation indicators, generate accuracy rating results, and output trajectory comparison visualization results, error statistics results, and standardized test reports.

8. A positioning data protocol parsing and high-precision error comparison and detection device, characterized in that, include: The system includes a memory, a processor, and a positioning data protocol parsing and high-precision error comparison detection program stored in the memory and executable on the processor. When the positioning data protocol parsing and high-precision error comparison detection program is executed by the processor, it implements a positioning data protocol parsing and high-precision error comparison detection method as described in any one of claims 1 to 6.

9. A computer program product, characterized in that, The computer program product includes a positioning data protocol parsing and high-precision error comparison and detection program, which, when executed by a processor, implements a positioning data protocol parsing and high-precision error comparison and detection method as described in any one of claims 1 to 6.

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