Fusion positioning analysis method, device, electronic device and storage medium

Abstract

The present invention discloses a fusion positioning analysis method, apparatus, electronic device, and storage medium. The present invention relates to the technical field of intelligent driving. The method includes: obtaining fusion positioning logs at a set frame rate; based on a user analysis request, using a fusion positioning analysis algorithm to determine an analysis result according to the fusion positioning logs, where the analysis result is used to determine the fusion positioning accuracy, the positioning accuracy of measurement sources, and positioning problem information; wherein, the fusion positioning analysis algorithm generates an analysis result that matches the analysis target based on the target data corresponding to the analysis target in the fusion positioning logs. By analyzing the content of the fusion positioning logs through the fusion positioning analysis algorithm in the embodiments of the present invention, the fusion positioning accuracy, the positioning accuracy of measurement sources, and positioning problem information are obtained, solving the problems existing in the accuracy of positioning accuracy statistics and the reliability of positioning problem troubleshooting in the related art, improving the accuracy of positioning accuracy statistics, and enhancing the reliability of positioning problem troubleshooting.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent driving, and in particular, to a fusion positioning analysis method, apparatus, electronic device, and storage medium. Background Art

[0002] Autopilot is the product of the deep integration of the automotive industry with new-generation information technologies such as artificial intelligence, the Internet of Things, and high-performance computing, and is the main direction of the current intelligent and connected development in the global automotive and transportation fields.

[0003] A key prerequisite for the continuous safe and reliable operation of autopilot is that the vehicle's positioning system must continuously and stably output position and position-related information with sufficient high precision. In related technologies, the GPS reference signal is usually used to analyze the accuracy of the positioning result and the positioning error. There is a defect that when the GPS signal is weak, the longitude and latitude coordinates output by the GPS reference device cannot be used as the true value to evaluate the positioning result of the positioning system, nor can it be used to troubleshoot positioning problems. The inventor found during the implementation of the present invention that there are defects in the accuracy of the positioning accuracy statistics of the autopilot positioning module and the reliability of troubleshooting positioning problems in related technologies. Summary of the Invention

[0004] The present invention provides a fusion positioning analysis method, apparatus, electronic device, and storage medium, which solve the problems existing in the accuracy of positioning accuracy statistics and the reliability of troubleshooting positioning problems in related technologies.

[0005] According to one aspect of the present invention, a fusion positioning analysis method is provided, including:

[0006] Obtaining a fusion positioning log at a set frame rate;

[0007] Based on a user analysis request, determining an analysis result according to the fusion positioning log by using a fusion positioning analysis algorithm, where the analysis result is used to determine the fusion positioning accuracy, the positioning accuracy of the measurement source, and the positioning problem information;

[0008] Wherein, the fusion positioning analysis algorithm generates an analysis result matching the analysis target based on the target data corresponding to the analysis target in the fusion positioning log.

[0009] According to another aspect of the present invention, a fusion positioning analysis method is provided, where the fusion positioning analysis algorithm includes a first deviation distribution analysis algorithm. When the target data is the measurement source input information and the fusion positioning output information in the effective state of the measurement source time confidence, the first deviation distribution analysis algorithm includes:

[0010] Determine a first deviation based on the measurement source input information and the fused positioning output information in the effective state of the measurement source time confidence, where the first deviation includes the eastward position deviation between each measurement source and the fused positioning, the northward position deviation between each measurement source and the fused positioning, and the heading angle deviation between each measurement source and the fused positioning;

[0011] Determine a fifth frame number of the fused positioning log according to the first deviation, where the fifth frame number is the number of frames of the fused positioning log in which the first deviation is within each first set deviation interval;

[0012] Determine a fifth proportional relationship between the fifth frame number and the number of frames of the effective measurement source time confidence, and generate a first deviation distribution analysis table according to the fifth proportional relationship.

[0013] According to another aspect of the present invention, a fused positioning analysis method is provided. The fused positioning analysis algorithm includes a second deviation analysis algorithm. When the target data is the measurement source input information of the measurement source in the first set positioning state and the measurement source input information of a specific measurement source participating in the fusion, the second deviation analysis algorithm includes:

[0014] For each positioning state in the first set positioning state, determine a second average deviation, a second maximum deviation, and a second minimum deviation according to the measurement source input information and the measurement source input information of the specific measurement source participating in the fusion;

[0015] Generate a second deviation analysis table according to the second average deviation, the second maximum deviation, and the second minimum deviation;

[0016] Wherein, the first set positioning state includes all measurement source states, the effective state of the measurement source time confidence, and the measurement source participation in the fusion state;

[0017] The second average deviation includes the eastward position average deviation between each measurement source and the specific measurement source participating in the fusion, the northward position average deviation between each measurement source and the specific measurement source participating in the fusion, and the heading angle average deviation between each measurement source and the specific measurement source participating in the fusion;

[0018] The second maximum deviation includes the eastward position maximum deviation between each measurement source and the specific measurement source participating in the fusion, the northward position maximum deviation between each measurement source and the specific measurement source participating in the fusion, and the heading angle maximum deviation between each measurement source and the specific measurement source participating in the fusion;

[0019] The second minimum deviation includes the eastward position minimum deviation between each measurement source and the specific measurement source participating in the fusion, the northward position minimum deviation between each measurement source and the specific measurement source participating in the fusion, and the heading angle minimum deviation between each measurement source and the specific measurement source participating in the fusion.

[0020] According to another aspect of the present invention, a fusion positioning analysis method is provided. The fusion positioning analysis algorithm includes a second deviation distribution analysis algorithm. When the target data is the measurement source input information in the effective state of the measurement source time confidence and the measurement source input information of a specific measurement source participating in the fusion, the second deviation distribution analysis algorithm includes:

[0021] Determine a second deviation according to the measurement source input information in the effective state of the measurement source time confidence and the measurement source input information of a specific measurement source participating in the fusion. Wherein, the second deviation includes the eastward position deviation of each measurement source from the specific measurement source participating in the fusion, the northward position deviation of each measurement source from the specific measurement source participating in the fusion, and the heading angle deviation of each measurement source from the specific measurement source participating in the fusion;

[0022] Determine the sixth frame number of the fusion positioning log according to the second deviation. Wherein, the sixth frame number is the number of frames of the fusion positioning log in which the second deviation is within each second set deviation interval;

[0023] Determine the sixth proportional relationship between the sixth frame number and the number of frames with effective measurement source time confidence, and generate a second deviation distribution analysis table according to the sixth proportional relationship.

[0024] According to another aspect of the present invention, a fusion positioning analysis method is provided. The fusion positioning analysis algorithm includes a quantity distribution analysis algorithm. When the target data is the number of measurement sources participating in the fusion, the fusion positioning eastward position, and the fusion positioning northward position, the quantity distribution analysis algorithm includes:

[0025] Obtain the number of measurement sources participating in the fusion in each frame of the fusion positioning log;

[0026] Generate a quantity distribution heat map according to the fusion positioning eastward position and the fusion positioning northward position in each frame of the fusion positioning log;

[0027] Determine the display attribute information of each position point in the quantity distribution heat map according to the number of measurement sources participating in the fusion.

[0028] According to another aspect of the present invention, a fusion positioning analysis method is provided. The fusion positioning analysis algorithm includes a state distribution analysis algorithm. When the target data is the measurement source state, the measurement source eastward position, and the measurement source northward position, the state distribution analysis algorithm includes:

[0029] For each measurement source, generate a state distribution analysis heat map according to the measurement source eastward position and the measurement source northward position of the current measurement source in each frame of the fusion positioning log;

[0030] For the heat map of the state distribution analysis of each measurement source, determine the display attribute information of each position point in the state distribution analysis heat map according to the measurement source state of the current measurement source.

[0031] According to another aspect of the present invention, a fusion positioning analysis method is provided. The fusion positioning analysis algorithm includes a second confidence distribution analysis algorithm. When the target data is the measurement source confidence, the eastward position of the measurement source, and the northward position of the measurement source, the second confidence distribution analysis algorithm includes:

[0032] For each measurement source, generate a confidence distribution analysis map according to the eastward position of the measurement source and the northward position of the measurement source in each frame of the fusion positioning log of the current measurement source;

[0033] For the confidence distribution analysis map of each measurement source, match the second set confidence interval according to the measurement source confidence of the current measurement source, and determine the display attribute information of each position point in the confidence distribution analysis map. Among them, the display attribute information of the position point in the invalid state of the measurement source time confidence is the default attribute.

[0034] According to another aspect of the present invention, a fusion positioning analysis method is provided. The fusion positioning analysis algorithm includes a third deviation analysis algorithm. When the target data is the measurement source input information and the fusion positioning output information of the measurement source, the third deviation analysis algorithm includes:

[0035] For each measurement source, generate a deviation analysis map according to the eastward position of the measurement source and the northward position of the measurement source in each frame of the fusion positioning log of the current measurement source;

[0036] Obtain the third deviation between the measurement source input information and the fusion positioning output information of the current measurement source. Among them, the third deviation includes the eastward position deviation between the current measurement source and the fusion positioning, the northward position deviation between the current measurement source and the fusion positioning, and the heading angle deviation between the current measurement source and the fusion positioning;

[0037] For the deviation analysis map of each measurement source, match the third set deviation interval according to the third deviation, and determine the display attribute information of each position point in the deviation analysis map according to the matching result to obtain the deviation analysis map of the measurement source and the fusion positioning. Among them, the display attribute information of the position point in the invalid state of the measurement source time confidence is the default attribute.

[0038] According to another aspect of the present invention, a fusion positioning analysis method is provided. The fusion positioning analysis algorithm includes a fourth deviation analysis algorithm. When the target data is the measurement source input information and the measurement source input information of a specific measurement source participating in the fusion, the fourth deviation analysis algorithm includes:

[0039] For each measurement source, a deviation analysis diagram is generated according to the eastward position and northward position of the measurement source in the current measurement source in each frame of the fusion positioning log;

[0040] Obtain the fourth deviation between the measurement source input information of the current measurement source and the measurement source input information of a specific measurement source participating in fusion, where the fourth deviation includes the eastward position deviation of the measurement source between the current measurement source and the specific measurement source participating in fusion, the northward position deviation of the current measurement source and the specific measurement source participating in fusion, and the heading angle deviation of the current measurement source and the specific measurement source participating in fusion;

[0041] For the deviation analysis diagram of each measurement source, a set fourth deviation interval is matched according to the fourth deviation, and the display attribute information of each position point in the deviation analysis diagram is determined according to the matching result, so as to obtain the deviation analysis diagram between measurement sources, where the display attribute information of the position points in the invalid state of the measurement source time confidence is the default attribute.

[0042] According to another aspect of the present invention, a fusion positioning analysis method is provided. The fusion positioning analysis algorithm includes a measurement source and a fusion positioning analysis algorithm. When the target data is the measurement source input information and the fusion positioning output information of the measurement source in the second set positioning state, the measurement source and the fusion positioning analysis algorithm include:

[0043] According to the measurement source input information of each measurement source in each frame of the fusion positioning log, and the fusion positioning output information in each frame of the fusion positioning log, a measurement source and a fusion positioning analysis diagram are generated, where the measurement source input information includes the eastward position of the measurement source, the northward position of the measurement source, the elevation of the measurement source, the pitch angle of the measurement source, the roll angle of the measurement source, the heading angle of the measurement source, and the measurement source confidence, and the fusion positioning output information includes the eastward position of the fusion positioning, the northward position of the fusion positioning, the elevation of the fusion positioning, the pitch angle of the fusion positioning, the roll angle of the fusion positioning, the heading angle of the fusion positioning, and the fusion positioning confidence;

[0044] According to the second set positioning state, the display attribute information of each position point of each measurement source in the measurement source and the fusion positioning analysis diagram is determined, where the second set positioning state includes the measurement source participation in fusion state, the measurement source time confidence is normal but not participating in fusion state, the measurement source time confidence abnormal state, and the measurement source abnormal state. The display attribute information of the fusion positioning trajectory in the measurement source and the fusion positioning analysis diagram is the default attribute.

[0045] According to another aspect of the present invention, a fusion positioning analysis method is provided. The fusion positioning algorithm includes an odometer analysis algorithm. When the target data is odometer input information, the odometer analysis algorithm includes:

[0046] Generate an odometer analysis graph based on the odometer input information in each frame of the fused positioning log, where the odometer input information includes odometer speed or odometer front wheel angle.

[0047] According to another aspect of the present invention, a fused positioning analysis method is provided. The fused positioning algorithm includes an inertial measurement unit analysis algorithm. When the target data is the inertial measurement unit input information, the inertial measurement unit analysis algorithm includes:

[0048] Generate an inertial measurement unit analysis graph based on the inertial measurement unit input information in each frame of the fused positioning log, where the inertial measurement unit input information includes linear acceleration information and attitude angle information.

[0049] According to another aspect of the present invention, a fused positioning analysis method is provided. The fused positioning algorithm includes a time analysis algorithm. When the target data is the measurement source timestamp and the fused positioning timestamp, the measurement source time analysis algorithm includes:

[0050] Generate a time analysis graph based on the delay time between the measurement source timestamp and the fused positioning timestamp of each measurement source in each frame of the fused positioning log.

[0051] According to another aspect of the present invention, a fused positioning analysis method is provided. The fused positioning algorithm includes a first calibration analysis algorithm. When the target data is the odometer speed, odometer calibration parameters, eastward position of the reference measurement source, and northward position of the reference measurement source, the first calibration analysis algorithm includes:

[0052] Determine the vehicle speed according to the eastward position of the reference measurement source and the northward position of the reference measurement source;

[0053] Determine the calibrated odometer speed according to the odometer speed and odometer calibration parameters;

[0054] Generate an odometer speed analysis graph according to the vehicle speed and the calibrated odometer speed.

[0055] According to another aspect of the present invention, a fused positioning analysis method is provided. The fused positioning algorithm includes a second calibration analysis algorithm. When the target data is the odometer input information, odometer calibration parameters, eastward position of the reference measurement source, northward position of the reference measurement source, and heading angle of the reference measurement source, the second calibration analysis algorithm includes:

[0056] Generate the calibrated odometer heading angle, calibrated odometer eastward position, and calibrated odometer northward position according to the odometer input information, vehicle wheelbase, odometer calibration parameters, eastward position of the reference measurement source, northward position of the reference measurement source, and heading angle of the reference measurement source;

[0057] Generate an odometer heading angle analysis chart based on the heading angle of the reference measurement source and the calibrated odometer heading angle;

[0058] Generate an odometer position analysis chart based on the eastward position of the reference measurement source, the northward position of the reference measurement source, the eastward position of the odometer, and the northward position of the odometer.

[0059] According to another aspect of the present invention, there is provided a fusion positioning analysis method. The fusion positioning algorithm includes a heading angle analysis algorithm. When the target data is the eastward position of the reference measurement source, the northward position of the reference measurement source, and the heading angle of the measured measurement source, the heading angle analysis algorithm includes:

[0060] Generate a vehicle heading angle based on the eastward position of the reference measurement source and the northward position of the reference measurement source;

[0061] Generate a measurement source heading angle analysis chart based on the vehicle heading angle and the heading angle of the measured measurement source.

[0062] According to one aspect of the present invention, there is provided a fusion positioning analysis device, including:

[0063] A log acquisition module for acquiring fusion positioning logs at a set frame rate;

[0064] A result determination module for determining an analysis result based on a user analysis request using a fusion positioning analysis algorithm according to the fusion positioning logs. The analysis result is used to determine the fusion positioning accuracy, the measurement source positioning accuracy, and the positioning problem information;

[0065] Wherein, the fusion positioning analysis algorithm generates an analysis result matching the analysis target based on the target data corresponding to the analysis target in the fusion positioning logs.

[0066] According to another aspect of the present invention, there is provided an electronic device, the electronic device includes:

[0067] At least one processor; and

[0068] A memory communicatively connected to the at least one processor; wherein,

[0069] The memory stores a computer program executable by the at least one processor. The computer program is executed by the at least one processor so that the at least one processor can execute the fusion positioning analysis method according to any embodiment of the present invention.

[0070] According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the fusion positioning analysis method according to any embodiment of the present invention when executed.

[0071] In the technical solution of the embodiment of the present invention, fusion positioning logs are obtained at a set frame rate; based on a user analysis request, an analysis result is determined according to the fusion positioning logs by using a fusion positioning analysis algorithm, and the analysis result is used to determine the fusion positioning accuracy, the positioning accuracy of the measurement source, and the positioning problem information; wherein, the fusion positioning analysis algorithm generates an analysis result matching the analysis target based on the target data corresponding to the analysis target in the fusion positioning logs. In the embodiment of the present invention, the content of the fusion positioning logs is analyzed by the fusion positioning analysis algorithm to obtain the fusion positioning accuracy, the positioning accuracy of the measurement source, and the positioning problem information, which solves the problems existing in the accuracy of positioning accuracy statistics and the reliability of positioning problem troubleshooting in the related art, improves the accuracy of positioning accuracy statistics, and enhances the reliability of positioning problem troubleshooting.

[0072] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0074] Figure 1 It is a flowchart of a fusion positioning analysis method provided by an embodiment of the present invention;

[0075] Figure 2 It is a flowchart of a measurement source quantity analysis algorithm provided by an embodiment of the present invention;

[0076] Figure 3 It is a flowchart of a confidence analysis algorithm provided by an embodiment of the present invention;

[0077] Figure 4 It is a flowchart of a first confidence distribution analysis algorithm provided by an embodiment of the present invention;

[0078] Figure 5 It is a flowchart of a first deviation analysis algorithm provided by an embodiment of the present invention;

[0079] Figure 6 Flow chart of a first deviation distribution analysis algorithm provided by an embodiment of the present invention;

[0080] Figure 7 Flow chart of a second deviation analysis algorithm provided by an embodiment of the present invention;

[0081] Figure 8 Flow chart of a second deviation distribution analysis algorithm provided by an embodiment of the present invention;

[0082] Figure 9 Flow chart of a quantity distribution analysis algorithm provided by an embodiment of the present invention;

[0083] Figure 10 Flow chart of a state distribution analysis algorithm provided by an embodiment of the present invention;

[0084] Figure 11 Flow chart of a second confidence level distribution analysis algorithm provided by an embodiment of the present invention;

[0085] Figure 12 Flow chart of a third deviation analysis algorithm provided by an embodiment of the present invention;

[0086] Figure 13 Flow chart of a fourth deviation analysis algorithm provided by an embodiment of the present invention;

[0087] Figure 14 Flow chart of a measurement source and fusion positioning analysis algorithm provided by an embodiment of the present invention;

[0088] Figure 15 Flow chart of a first calibration analysis algorithm provided by an embodiment of the present invention;

[0089] Figure 16 Flow chart of a second calibration analysis algorithm provided by an embodiment of the present invention;

[0090] Figure 17 Flow chart of a course angle analysis algorithm provided by an embodiment of the present invention;

[0091] Figure 18 Schematic structural diagram of a fusion positioning analysis device provided by an embodiment of the present invention;

[0092] Figure 19 Schematic structural diagram of an electronic device provided by an embodiment of the present invention. Detailed implementation manners

[0093] To enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0094] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily need to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0095] Figure 1 A flowchart of a fusion positioning analysis method is provided for an embodiment of the present invention. This embodiment is applicable to the situation of performing fusion positioning analysis on an autonomous driving positioning module based on fusion positioning logs. This method can be executed by a fusion positioning analysis device, which can be implemented in the form of hardware and / or software, and the fusion positioning analysis device can be configured in an electronic device. For example, the electronic device can be a server or a server cluster, etc.

[0096] As Figure 1 shown, the method includes:

[0097] S110. Obtain fusion positioning logs according to a set frame rate.

[0098] Specifically, the fusion positioning log is a log recording information related to the autonomous driving positioning module. Optionally, the fusion positioning log includes sensor input information (such as: odometer input information, IMU input information, measurement source input information), fusion positioning output information, and measurement source status output information.

[0099] Furthermore, each frame of the fusion positioning log can include: odometer input information, IMU input information, measurement source input information, fusion positioning output information, and measurement source status output information.

[0100] Among them, the odometer input information includes odometer speed, odometer front wheel angle, odometer timestamp, etc.; the IMU input information includes IMU attitude angle (for example: IMU pitch angle, IMU roll angle, IMU heading angle) information, IMU angular velocity information (for example: IMU pitch angle angular velocity, IMU roll angle angular velocity, IMU heading angle angular velocity), IMU linear acceleration information (IMU x-axis acceleration, IMU y-axis acceleration, IMU z-axis acceleration), IMU timestamp, etc.; the measurement source input information includes positioning information of different measurement sources (for example: measurement sources such as gps, vslam, lslam, visual semantic positioning, laser semantic positioning, etc.). Among them, the positioning information of the measurement source includes: measurement source eastward position, measurement source northward position, measurement source elevation, measurement source pitch angle, measurement source roll angle, measurement source heading angle, measurement source confidence, measurement source timestamp, etc.; the fused positioning output information includes fused positioning eastward position, fused positioning northward position, fused positioning elevation, fused positioning pitch angle, fused positioning roll angle, fused positioning heading angle, fused positioning confidence, fused positioning status, fused positioning timestamp, etc. Among them, the fused positioning status includes high-precision positioning, low-precision positioning, biased status, pure motion estimation status, invalid reset status, etc.; the measurement source status output information refers to the measurement source status output by the fused positioning module, including measurement source time confidence valid, measurement source time confidence invalid, measurement source participating in fusion, measurement source not participating in fusion, measurement source abnormal, and fused measurement source normal.

[0101] S120. Based on the user analysis request, use the fused positioning analysis algorithm according to the fused positioning log to determine the analysis result, and the analysis result is used to determine the fused positioning accuracy, the measurement source positioning accuracy, and the positioning problem information.

[0102] Among them, the fused positioning analysis algorithm can include measurement source quantity analysis algorithm, confidence analysis algorithm, first confidence distribution analysis algorithm, first deviation analysis algorithm, first deviation distribution analysis algorithm, second deviation analysis algorithm, second deviation distribution analysis algorithm, quantity distribution analysis algorithm, status distribution analysis algorithm, second confidence distribution analysis algorithm, third deviation analysis algorithm, fourth deviation analysis algorithm, measurement source and fused positioning analysis algorithm, odometer analysis algorithm, inertial measurement unit analysis algorithm, time analysis algorithm, first calibration analysis algorithm, second calibration analysis algorithm, heading angle analysis algorithm, etc.

[0103] Among them, the analysis result includes a visualization result.

[0104] Further, the visualization results may include a measurement source quantity analysis table, a confidence analysis table, a confidence distribution analysis table, a first deviation analysis table, a first deviation distribution analysis table, a second deviation analysis table, a second deviation distribution analysis table, a quantity distribution heat map, a state distribution analysis heat map, a confidence distribution analysis graph, a deviation analysis graph of the measurement source and the fusion positioning, a deviation analysis graph between measurement sources, a measurement source and fusion positioning analysis graph, an odometer analysis graph, an inertial measurement unit analysis graph, a time analysis graph, an odometer speed analysis graph, an odometer heading angle analysis graph, an odometer position analysis graph, and a measurement source heading angle analysis graph.

[0105] Among them, the fusion positioning analysis algorithm generates an analysis result matching the analysis target based on the target data corresponding to the analysis target in the fusion positioning log.

[0106] Specifically, the analysis target is determined based on the user's analysis request. For example, if the user's analysis request is to analyze the number of measurement sources participating in the fusion, the distribution of the number of measurement sources participating in the fusion is used as the analysis target. Or, if the user's analysis request is to analyze the confidence of the measurement source, the confidence distribution of the measurement source is used as the analysis target.

[0107] The target data refers to the contents of the fusion positioning log, which can be sensor input information, fusion positioning output information, and measurement source status output information.

[0108] Exemplarily, the target data corresponding to the analysis target in the fusion positioning log is obtained according to the user's analysis request, and the analysis result is determined by using the fusion positioning analysis algorithm based on the target data. The fusion positioning accuracy, the measurement source positioning accuracy, and the positioning problem information are determined based on the analysis result, so as to realize the positioning accuracy statistics and the positioning problem troubleshooting.

[0109] In this embodiment, the fusion positioning log is obtained at a set frame rate; based on the user's analysis request, the analysis result is determined by using the fusion positioning analysis algorithm according to the fusion positioning log, and the analysis result is used to determine the fusion positioning accuracy, the measurement source positioning accuracy, and the positioning problem information; among them, the fusion positioning analysis algorithm generates an analysis result matching the analysis target based on the target data corresponding to the analysis target in the fusion positioning log. In this embodiment, the content of the fusion positioning log is analyzed by the fusion positioning analysis algorithm to obtain the fusion positioning accuracy, the measurement source positioning accuracy, and the positioning problem information, which solves the problems existing in the accuracy of positioning accuracy statistics and the reliability of positioning problem troubleshooting in the related art, improves the accuracy of positioning accuracy statistics, and enhances the reliability of positioning problem troubleshooting.

[0110] In one embodiment, a process of a measurement source quantity analysis algorithm is provided. Figure 2The flowchart of an algorithm for analyzing the number of measurement sources provided by an embodiment of the present invention further refines the fusion positioning analysis algorithm based on the above embodiment. As Figure 2 shown, when the target data is the number of measurement sources participating in fusion, the algorithm for analyzing the number of measurement sources includes:

[0111] S210. Obtain the number of measurement sources participating in fusion in each frame of fusion positioning log.

[0112] Specifically, the number of measurement sources participating in fusion in each frame of fusion positioning log can be counted according to the output information of the measurement source status.

[0113] S220. Determine the first number of frames of the fusion positioning log according to the number of measurement sources participating in fusion, where the first number of frames is the number of frames of the fusion positioning log with the same number of measurement sources participating in fusion.

[0114] Specifically, count the number of measurement sources participating in fusion in each frame of fusion positioning log, group the fusion positioning logs according to the number of measurement sources participating in fusion, so that the fusion positioning logs with the same number of measurement sources participating in fusion belong to the same group, and determine the first number of frames according to the number of frames of the fusion positioning logs in each group. For example, the first number of frames may include the number of frames with 0 measurement sources participating in fusion, the number of frames with 1 measurement source participating in fusion, the number of frames with 2 measurement sources participating in fusion, the number of frames with 3 measurement sources participating in fusion, and the number of frames with 4 or more measurement sources participating in fusion.

[0115] S230. Determine the first proportional relationship between each of the first number of frames and the total number of frames of the fusion positioning log, and generate an analysis table of the number of measurement sources according to the first proportional relationship.

[0116] Exemplarily, count the proportion of the number of frames with 0 measurement sources participating in fusion, the proportion of the number of frames with 1 measurement source participating in fusion, the proportion of the number of frames with 2 measurement sources participating in fusion, the proportion of the number of frames with 3 measurement sources participating in fusion, and the proportion of the number of frames with 4 or more measurement sources participating in fusion in all fusion positioning logs. Generate an analysis table of measurement source data based on the above proportion of frames, and display the proportion of the number of different measurement sources participating in fusion through the analysis table of measurement source data.

[0117] In one embodiment, a process of a confidence analysis algorithm is provided. Figure 3 The flowchart of a confidence analysis algorithm provided by an embodiment of the present invention further refines the fusion positioning analysis algorithm based on the above embodiment. As Figure 3 shown, when the target data is the measurement source confidence of the measurement source in the first set positioning state, the confidence analysis algorithm includes:

[0118] S310. For each positioning state in the first set of preset positioning states, obtain the maximum confidence, minimum confidence, and average confidence in the measurement source confidence, where the first set of preset positioning states includes all measurement source states, measurement source time confidence valid states, and measurement source participation in fusion states.

[0119] Specifically, according to the output information of the measurement source state, count the maximum confidence, minimum confidence, and average confidence of each measurement source in all measurement source states.

[0120] According to the output information of the measurement source state, count the maximum confidence, minimum confidence, and average confidence of each measurement source in the measurement source time confidence valid state.

[0121] According to the output information of the measurement source state, count the maximum confidence, minimum confidence, and average confidence of each measurement source in the measurement source participation in fusion state.

[0122] S320. For each positioning state in the first set of preset positioning states, obtain the second number of frames of the fused positioning log, where the second number of frames is the number of frames of the fused positioning log corresponding to each positioning state in the first set of preset positioning states.

[0123] Specifically, for all measurement source states, obtain the number of frames of all measurement source states in the fused positioning log as the second number of frames.

[0124] For the measurement source time confidence valid state, obtain the number of frames of the measurement source time confidence valid state in the fused positioning log as the second number of frames.

[0125] For the measurement source participation in fusion state, obtain the number of frames of the measurement source participation in fusion state in the fused positioning log as the second number of frames.

[0126] S330. Determine the second proportional relationship between the second number of frames and the total number of frames of the fused positioning log.

[0127] Specifically, respectively count the proportions of the number of frames of the fused positioning log corresponding to all measurement source states, measurement source time confidence valid states, and measurement source participation in fusion states relative to the total number of frames of the fused positioning log.

[0128] S340. Generate a confidence analysis table according to the maximum confidence, minimum confidence, average confidence, second number of frames, and second proportional relationship.

[0129] Among them, the confidence analysis table includes the confidence analysis table of the measurement source in all measurement source states, the confidence analysis table of the measurement source in the measurement source time confidence valid state, and the confidence analysis table of the measurement source in the measurement source participating in the fusion state.

[0130] Through the confidence analysis table of the measurement source in all measurement source states, the confidence statistics of different measurement sources in all measurement source states are shown.

[0131] Through the confidence analysis table of the measurement source in the measurement source time confidence valid state, the confidence statistics of different measurement sources in the measurement source time confidence valid state are shown.

[0132] Through the confidence analysis table of the measurement source in the measurement source participating in the fusion state, the confidence statistics of different measurement sources in the measurement source participating in the fusion state are shown.

[0133] In one embodiment, a process of a first confidence distribution analysis algorithm is provided. Figure 4 The flowchart of a first confidence distribution analysis algorithm provided by the embodiment of the present invention further refines the fusion positioning analysis algorithm on the basis of the above embodiment. As Figure 4 shown, when the target data is the measurement source confidence in the measurement source time confidence valid state and the third frame number of the fusion positioning log, and the third frame number is the frame number of the fusion positioning log in the measurement source time confidence invalid state, the first confidence distribution analysis algorithm includes:

[0134] S410. Determine the third proportional relationship between the third frame number and the total number of frames of the fusion positioning log.

[0135] Specifically, count the proportion of the number of frames of the fusion positioning log in the measurement source time confidence invalid state relative to the total number of frames.

[0136] S420. Obtain the fourth frame number of the fusion positioning log, where the fourth frame number is the frame number of the fusion positioning log of the measurement source confidence in each first set confidence interval in the measurement source time confidence valid state.

[0137] Specifically, according to the measurement source status output information, count the frame number of the fusion positioning log of the measurement source confidence in each first set confidence interval in the measurement source time confidence valid state. Among them, each first set confidence interval can be set based on experience. Exemplarily, it can be set as (0, 0.2], (0.2, 0.5], (0.5, 0.8], (0.8, 1.0].

[0138] S430. Determine the fourth proportional relationship between each of the fourth frame numbers and the total number of frames of the fusion positioning log.

[0139] Specifically, determine the proportion of each fourth frame number relative to the total number of frames in the fusion positioning log.

[0140] S440. Generate a confidence distribution analysis table according to the third proportional relationship and the fourth proportional relationship.

[0141] The confidence distribution analysis table is used to display the proportion of the third frame number relative to the total number of frames and the proportion of each fourth frame number relative to the total number of frames in the fusion positioning log.

[0142] In one embodiment, a process of a first deviation analysis algorithm is provided. Figure 5 The figure is a flowchart of a first deviation analysis algorithm provided by an embodiment of the present invention. This embodiment further refines the fusion positioning analysis algorithm on the basis of the above embodiment. As Figure 5 shown, when the target data is the measurement source input information and the fusion positioning output information in the first set positioning state, the first deviation analysis algorithm includes:

[0143] S510. For each positioning state in the first set positioning state, determine the first average deviation, the first maximum deviation, and the first minimum deviation according to the measurement source input information and the fusion positioning output information.

[0144] The first set positioning state includes all measurement source states, measurement source time confidence valid states, and measurement source participation in fusion states.

[0145] The first average deviation includes the average eastward position deviation of each measurement source from the fusion positioning, the average northward position deviation of each measurement source from the fusion positioning, and the average heading angle deviation of each measurement source from the fusion positioning.

[0146] The first maximum deviation includes the maximum eastward position deviation of each measurement source from the fusion positioning, the maximum northward position deviation of each measurement source from the fusion positioning, and the maximum heading angle deviation of each measurement source from the fusion positioning.

[0147] The first minimum deviation includes the minimum eastward position deviation of each measurement source from the fusion positioning, the minimum northward position deviation of each measurement source from the fusion positioning, and the minimum heading angle deviation of each measurement source from the fusion positioning.

[0148] For all measurement source states, calculate the eastward position deviation of each measurement source relative to the eastward position of the fusion positioning respectively, and calculate the average eastward position deviation according to the eastward position deviation. Calculate the northward position deviation of each measurement source relative to the northward position of the fusion positioning respectively, and calculate the average northward position deviation according to the northward position deviation. Calculate the heading angle deviation of each measurement source relative to the heading angle of the fusion positioning respectively, and calculate the average heading angle deviation according to the heading angle deviation.

[0149] For the effective state of the measurement source time confidence, calculate the eastward position deviation of each measurement source relative to the eastward position of the fused positioning respectively, and calculate the average eastward position deviation based on the eastward position deviation. Calculate the northward position deviation of each measurement source relative to the northward position of the fused positioning respectively, and calculate the average northward position deviation based on the northward position deviation. Calculate the heading angle deviation of each measurement source relative to the heading angle of the fused positioning respectively, and calculate the average heading angle deviation based on the heading angle deviation.

[0150] For the state where the measurement source participates in fusion, calculate the eastward position deviation of each measurement source relative to the eastward position of the fused positioning respectively, and calculate the average eastward position deviation based on the eastward position deviation. Calculate the northward position deviation of each measurement source relative to the northward position of the fused positioning respectively, and calculate the average northward position deviation based on the northward position deviation. Calculate the heading angle deviation of each measurement source relative to the heading angle of the fused positioning respectively, and calculate the average heading angle deviation based on the heading angle deviation.

[0151] S520. Generate a first deviation analysis table according to the first average deviation, the first maximum deviation, and the first minimum deviation.

[0152] Specifically, the first deviation analysis table is used to display the first average deviation, the first maximum deviation, and the first minimum deviation in each positioning state of the first set positioning state.

[0153] The first deviation analysis table is used to display the deviation statistics of different measurement sources from the fused positioning in different positioning states.

[0154] In an embodiment, a process of a first deviation distribution analysis algorithm is provided. Figure 6 The following is a flowchart of a first deviation distribution analysis algorithm provided by an embodiment of the present invention. Based on the above embodiment, the fused positioning analysis algorithm is further refined. As Figure 6 shown, when the target data is the measurement source input information and the fused positioning output information in the effective state of the measurement source time confidence, the first deviation distribution analysis algorithm includes:

[0155] S610. Determine a first deviation according to the measurement source input information and the fused positioning output information in the effective state of the measurement source time confidence, where the first deviation includes the eastward position deviation between each measurement source and the fused positioning, the northward position deviation between each measurement source and the fused positioning, and the heading angle deviation between each measurement source and the fused positioning.

[0156] Specifically, the first deviation includes the deviation of the eastward position of each measurement source relative to the eastward position of the fused positioning, the deviation of the northward position of each measurement source relative to the northward position of the fused positioning, and the deviation of the heading angle of each measurement source relative to the heading angle of the fused positioning.

[0157] Among them, the deviation of the eastward position of each measurement source relative to the eastward position of the fused positioning can be obtained by taking the absolute value of the subtraction of the coordinates of the eastward position of each measurement source and the eastward position of the fused positioning. The deviation of the northward position of each measurement source relative to the northward position of the fused positioning can be obtained by taking the absolute value of the subtraction of the coordinates of the northward position of each measurement source and the northward position of the fused positioning. The deviation of the heading angle of each measurement source relative to the heading angle of the fused positioning can be obtained by taking the absolute value of the difference between the heading angle of each measurement source and the heading angle of the fused positioning.

[0158] S620. Determine the fifth frame number of the fused positioning log according to the first deviation, where the fifth frame number is the number of frames of the fused positioning log within each first set deviation interval of the first deviation.

[0159] Specifically, each first set deviation interval can be set based on experience. Exemplarily, the first deviation intervals can be set as [0, 0.1), [0.1, 0.3), [0.3, 0.5), [0.5, 1.0), [1.0, ∞), and then determine the number of frames of the fused positioning log in the ranges of [0, 0.1), [0.1, 0.3), [0.3, 0.5), [0.5, 1.0), [1.0, ∞) for the deviation of the eastward position of each measurement source relative to the eastward position of the fused positioning, the deviation of the northward position of each measurement source relative to the northward position of the fused positioning, and the deviation of the heading angle of each measurement source relative to the heading angle of the fused positioning.

[0160] S630. Determine the fifth proportional relationship between the fifth frame number and the effective frame number of the measurement source time confidence, and generate a first deviation distribution analysis table according to the fifth proportional relationship.

[0161] Specifically, count the effective frame number of each measurement source time confidence, and then respectively determine the proportion of the number of frames of the deviation of the eastward position of each measurement source relative to the eastward position of the fused positioning within the first set deviation interval to the effective frame number of each measurement source time confidence, and determine the proportion of the number of frames of the deviation of the northward position of each measurement source relative to the northward position of the fused positioning within the first set deviation interval to the effective frame number of each measurement source time confidence, and determine the proportion of the number of frames of the deviation of the heading angle of each measurement source relative to the heading angle of the fused positioning within the first set deviation interval to the effective frame number of each measurement source time confidence, to generate the first deviation distribution analysis table. The first deviation distribution analysis table is used to show the deviation distribution of different measurement sources from the fused positioning under different positioning states.

[0162] In one embodiment, a process of a second deviation analysis algorithm is provided. Figure 7 The figure is a flowchart of a second deviation analysis algorithm provided by an embodiment of the present invention. On the basis of the above embodiment, the fusion positioning analysis algorithm is further refined. As Figure 7 shown, when the target data is the measurement source input information of the measurement source in the first set positioning state and the measurement source input information of the measurement source specifically participating in the fusion, the second deviation analysis algorithm includes:

[0163] S710. For each positioning state in the first set positioning state, determine a second average deviation, a second maximum deviation, and a second minimum deviation according to the measurement source input information and the measurement source input information of the measurement source specifically participating in the fusion.

[0164] It should be noted that the deviation is statistically calculated for different measurement sources in different positioning states from the measurement source specifically participating in the fusion. The deviation calculation is based on the measurement source specifically participating in the fusion as the reference measurement source.

[0165] Among them, the first set positioning state includes all measurement source states, measurement source time confidence valid states, and measurement source participation in fusion states. The second average deviation includes the eastward position average deviation of each measurement source from the measurement source specifically participating in the fusion, the northward position average deviation of each measurement source from the measurement source specifically participating in the fusion, and the course angle average deviation of each measurement source from the measurement source specifically participating in the fusion. The second maximum deviation includes the eastward position maximum deviation of each measurement source from the measurement source specifically participating in the fusion, the northward position maximum deviation of each measurement source from the measurement source specifically participating in the fusion, and the course angle maximum deviation of each measurement source from the measurement source specifically participating in the fusion. The second minimum deviation includes the eastward position minimum deviation of each measurement source from the measurement source specifically participating in the fusion, the northward position minimum deviation of each measurement source from the measurement source specifically participating in the fusion, and the course angle minimum deviation of each measurement source from the measurement source specifically participating in the fusion.

[0166] Specifically, for all measurement source states, determine the eastward position average deviation of each measurement source from the measurement source specifically participating in the fusion, the northward position average deviation of each measurement source from the measurement source specifically participating in the fusion, the course angle average deviation of each measurement source from the measurement source specifically participating in the fusion, the eastward position maximum deviation of each measurement source from the measurement source specifically participating in the fusion, the northward position maximum deviation of each measurement source from the measurement source specifically participating in the fusion, the course angle maximum deviation of each measurement source from the measurement source specifically participating in the fusion, the eastward position minimum deviation of each measurement source from the measurement source specifically participating in the fusion, the northward position minimum deviation of each measurement source from the measurement source specifically participating in the fusion, and the course angle minimum deviation of each measurement source from the measurement source specifically participating in the fusion according to the measurement source input information and the measurement source input information of the measurement source specifically participating in the fusion.

[0167] For the effective state of the measurement source time confidence, according to the measurement source input information and the measurement source input information of a specific measurement source participating in fusion, determine the average eastward position deviation of each measurement source from the specific measurement source participating in fusion, the average northward position deviation of each measurement source from the specific measurement source participating in fusion, the average heading angle deviation of each measurement source from the specific measurement source participating in fusion, the maximum eastward position deviation of each measurement source from the specific measurement source participating in fusion, the maximum northward position deviation of each measurement source from the specific measurement source participating in fusion, the maximum heading angle deviation of each measurement source from the specific measurement source participating in fusion, the minimum eastward position deviation of each measurement source from the specific measurement source participating in fusion, the minimum northward position deviation of each measurement source from the specific measurement source participating in fusion, and the minimum heading angle deviation of each measurement source from the specific measurement source participating in fusion.

[0168] For the state of the measurement source participating in fusion, according to the measurement source input information and the measurement source input information of a specific measurement source participating in fusion, determine the average eastward position deviation of each measurement source from the specific measurement source participating in fusion, the average northward position deviation of each measurement source from the specific measurement source participating in fusion, the average heading angle deviation of each measurement source from the specific measurement source participating in fusion, the maximum eastward position deviation of each measurement source from the specific measurement source participating in fusion, the maximum northward position deviation of each measurement source from the specific measurement source participating in fusion, the maximum heading angle deviation of each measurement source from the specific measurement source participating in fusion, the minimum eastward position deviation of each measurement source from the specific measurement source participating in fusion, the minimum northward position deviation of each measurement source from the specific measurement source participating in fusion, and the minimum heading angle deviation of each measurement source from the specific measurement source participating in fusion.

[0169] S720. Generate a second deviation analysis table according to the second average deviation, the second maximum deviation, and the second minimum deviation.

[0170] The second deviation analysis table is used to display the deviation statistics of different measurement sources from a specific measurement source participating in fusion in different positioning states.

[0171] In one embodiment, a process of a second deviation distribution analysis algorithm is provided. Figure 8 The following is a flowchart of a second deviation distribution analysis algorithm provided by an embodiment of the present invention. This embodiment further refines the fusion positioning analysis algorithm on the basis of the above embodiment. As Figure 8 shown, when the target data is the measurement source input information and the measurement source input information of a specific measurement source participating in fusion in the effective state of the measurement source time confidence, the second deviation distribution analysis algorithm includes:

[0172] S810. Determine a second deviation based on the measurement source input information in the effective state of the measurement source time confidence and the measurement source input information of a specific measurement source participating in fusion, where the second deviation includes the eastward position deviation of each measurement source from the specific measurement source participating in fusion, the northward position deviation of each measurement source from the specific measurement source participating in fusion, and the heading angle deviation of each measurement source from the specific measurement source participating in fusion.

[0173] Specifically, based on the measurement source input information in the effective state of the measurement source time confidence and the measurement source input information of a specific measurement source participating in fusion, determine the eastward position deviation of each measurement source from the specific measurement source participating in fusion, the northward position deviation of each measurement source from the specific measurement source participating in fusion, and the heading angle deviation of each measurement source from the specific measurement source participating in fusion.

[0174] Among them, the measurement source input information and the measurement source input information of the specific measurement source participating in fusion can be found in the fusion positioning log.

[0175] S820. Determine the sixth frame number of the fusion positioning log according to the second deviation, where the sixth frame number is the number of frames of the fusion positioning log in each second set deviation interval of the second deviation.

[0176] Specifically, each second set deviation interval can be set based on experience. Exemplarily, let the second deviation interval be [0, 0.1), [0.1, 0.3), [0.3, 0.5), [0.5, 1.0), [1.0, ∞), and then determine the number of frames of the fusion positioning log in which the eastward position deviation of each measurement source from the specific measurement source participating in fusion, the northward position deviation of each measurement source from the specific measurement source participating in fusion, and the heading angle deviation of each measurement source from the specific measurement source participating in fusion are within the ranges of [0, 0.1), [0.1, 0.3), [0.3, 0.5), [0.5, 1.0), [1.0, ∞).

[0177] S830. Determine the sixth proportional relationship between the sixth frame number and the number of effective frames of the measurement source time confidence, and generate a second deviation distribution analysis table according to the sixth proportional relationship.

[0178] Specifically, count the number of effective frames of each measurement source time confidence, determine the proportional relationship between the sixth frame number and the number of effective frames of the measurement source time confidence. Exemplarily, count the proportion of the deviation of the eastward position of each measurement source relative to the eastward position of the specific measurement source participating in fusion within the ranges of [0, 0.1), [0.1, 0.3), [0.3, 0.5), [0.5, 1.0), [1.0, ∞) relative to the number of effective frames of each measurement source time confidence, and generate a second deviation distribution analysis table.

[0179] The second deviation distribution analysis table is used to display the deviation distribution of different measurement sources from a specific measurement source participating in fusion under different positioning states.

[0180] In one embodiment, a process of a quantity distribution analysis algorithm is provided. Figure 9 The figure is a flowchart of a quantity distribution analysis algorithm provided by an embodiment of the present invention. This embodiment further refines the fusion positioning analysis algorithm on the basis of the above embodiment. As Figure 9 shown, when the target data is the number of measurement sources participating in fusion, the eastward position of the fusion positioning, and the northward position of the fusion positioning, the quantity distribution analysis algorithm includes:

[0181] S910. Obtain the number of measurement sources participating in fusion in each frame of the fusion positioning log.

[0182] Specifically, by querying the content of the fusion positioning log and according to the output information of the measurement source status, the number of measurement sources participating in fusion in each frame of the fusion positioning log can be counted.

[0183] S920. Generate a quantity distribution heat map according to the eastward position of the fusion positioning and the northward position of the fusion positioning in each frame of the fusion positioning log.

[0184] Specifically, a heat map coordinate system can be generated with the eastward position as the horizontal axis and the northward position as the vertical axis, and a heat map is generated according to the number of measurement sources participating in fusion and the heat map coordinate system.

[0185] S930. Determine the display attribute information of each position point in the quantity distribution heat map according to the number of measurement sources participating in fusion.

[0186] The quantity distribution heat map is used to display the number of measurement sources participating in fusion at different fusion positioning positions.

[0187] Exemplarily, according to the number of measurement sources participating in fusion, the number of measurement sources can be divided into five levels: 0, 1, 2, 3, and greater than or equal to 4. The curves corresponding to each level are displayed in different colors in the figure to highlight different attributes.

[0188] In one embodiment, a process of a state distribution analysis algorithm is provided. Figure 10 The figure is a flowchart of a state distribution analysis algorithm provided by an embodiment of the present invention. This embodiment further refines the fusion positioning analysis algorithm on the basis of the above embodiment. As Figure 10 shown, when the target data is the measurement source status, the eastward position of the measurement source, and the northward position of the measurement source, the state distribution analysis algorithm includes:

[0189] S1010. For each measurement source, generate a heat map for state distribution analysis based on the eastward position and northward position of the measurement source in the current frame of the fused positioning log.

[0190] Specifically, query the fused positioning log to obtain the eastward position and northward position of the measurement source in the current frame of each measurement source. Generate a heat map coordinate system with the eastward position as the horizontal axis and the northward position as the vertical axis, and generate a heat map for state distribution analysis based on the measurement source status and the heat map coordinate system.

[0191] S1020. For the heat map for state distribution analysis of each measurement source, determine the display attribute information of each position point in the heat map according to the measurement source status of the current measurement source.

[0192] The heat map for state distribution analysis is used to display the measurement source status of each measurement source on each road section.

[0193] Specifically, query the fused positioning log to obtain the current measurement source status of each frame of the fused positioning log, and then obtain all the measurement source statuses. If the measurement source status is the participating in fusion status, the display status is participating in fusion (InFusion); if the measurement source status is time confidence valid, the display status is time confidence valid (hasData); if the measurement source status is abnormal, the display status is abnormal (Abnormal); if the display status is time confidence invalid (NoData). Different colors can be displayed in the heat map for state distribution analysis according to different display statuses to highlight different attributes.

[0194] In one embodiment, a process of a second confidence distribution analysis algorithm is provided. Figure 11 The figure is a flowchart of a second confidence distribution analysis algorithm provided by an embodiment of the present invention. This embodiment further refines the fused positioning analysis algorithm on the basis of the above embodiment. As Figure 11 shown, when the target data is the measurement source confidence, the eastward position of the measurement source, and the northward position of the measurement source, the second confidence distribution analysis algorithm includes:

[0195] S1110. For each measurement source, generate a confidence distribution analysis graph based on the eastward position and northward position of the measurement source in the current frame of the fused positioning log.

[0196] Specifically, query the fusion positioning log to obtain the measured source eastward position and the measured source northward position of the current measurement source in each frame of the fusion positioning log. Then, obtain the measured source eastward position and the measured source northward position of the current measurement source in each frame of the fusion positioning log of all measurement sources. Generate a confidence distribution analysis coordinate system with the eastward position as the horizontal axis and the northward position as the vertical axis, and generate a confidence distribution analysis graph based on the confidence of each measurement source and the confidence distribution analysis coordinate system. According to the confidence distribution analysis graph, the positioning conditions of different measurement sources in each section can be judged.

[0197] S1120. For the confidence distribution analysis graph of each measurement source, match the second set confidence interval according to the confidence of the current measurement source, and determine the display attribute information of each position point in the confidence distribution analysis graph. Among them, the display attribute information of the position points in the invalid state of the measurement source time confidence is the default attribute.

[0198] The confidence distribution analysis graph is a heat map showing the confidence of each measurement source in each section. According to the confidence distribution analysis graph, the positioning conditions of different measurement sources in each section can be determined.

[0199] Specifically, the second set confidence interval can be set according to the actual situation. Exemplarily, set the second set confidence interval to (0, 0.2], (0.2, 0.5], (0.5, 0.8], (0.8, 1.0]. Different colors can be drawn according to the situation of the confidence of each measurement source in different intervals to highlight different attributes.

[0200] In one embodiment, a process of a third deviation analysis algorithm is provided. Figure 12 This is a flowchart of a third deviation analysis algorithm provided by an embodiment of the present invention. In this embodiment, the fusion positioning analysis algorithm is further refined on the basis of the above embodiment. As Figure 12 shown, when the target data is the measured source input information and the fusion positioning output information of the measurement source, the third deviation analysis algorithm includes:

[0201] S1210. For each measurement source, generate a deviation analysis graph according to the measured source eastward position and the measured source northward position of the current measurement source in each frame of the fusion positioning log.

[0202] Specifically, three deviation analysis sub-graphs are generated for each measurement source, namely an eastward deviation heat map, a northward deviation heat map, and a course deviation heat map. The three deviation analysis sub-graphs together constitute a deviation analysis graph.

[0203] S1220. Obtain the third deviation between the measurement source input information of the current measurement source and the fusion positioning output information, where the third deviation includes the eastward position deviation between the current measurement source and the fusion positioning, the northward position deviation between the current measurement source and the fusion positioning, and the heading angle deviation between the current measurement source and the fusion positioning.

[0204] Specifically, the eastward position deviation between the previous measurement source and the fusion positioning, the northward position deviation between the current measurement source and the fusion positioning, and the heading angle deviation between the current measurement source and the fusion positioning can be obtained by querying the fusion positioning log.

[0205] S1230. For the deviation analysis diagram of each measurement source, match the third set deviation interval according to the third deviation, and determine the display attribute information of each position point in the deviation analysis diagram according to the matching result, so as to obtain the deviation analysis diagram between the measurement source and the fusion positioning, where the display attribute information of the position points in the invalid state of the measurement source time confidence is the default attribute.

[0206] The deviation analysis diagram between the measurement source and the fusion positioning is used to display the deviation between different measurement sources and the fusion positioning in each section. According to the deviation analysis diagram between the measurement source and the fusion positioning, the deviation situation between different measurement sources and the fusion positioning in each section can be judged.

[0207] Specifically, based on the empirical third set deviation interval, for example, the third set deviation interval is [0, 0.1), [0.1, 0.3), [0.3, 0.5), [0.5, 1.0), [1.0, ∞), the position points within the ranges of [0, 0.1), [0.1, 0.3), [0.3, 0.5), [0.5, 1.0), [1.0, ∞) according to the third deviation and the display attribute information of the position points in the invalid state of the measurement source time confidence can be used to draw a curve, and different attributes are represented by drawing curves of different colors.

[0208] In one embodiment, a process of a fourth deviation analysis algorithm is provided. Figure 13 It is a flowchart of a fourth deviation analysis algorithm provided by an embodiment of the present invention. In this embodiment, the fusion positioning analysis algorithm is further refined on the basis of the above embodiment. As Figure 13 shown, when the target data is the measurement source input information and the measurement source input information of a specific measurement source participating in the fusion, the fourth deviation analysis algorithm includes:

[0209] S1310. For each measurement source, generate a deviation analysis diagram according to the measurement source eastward position and the measurement source northward position of the current measurement source in each frame of the fusion positioning log.

[0210] Specifically, the eastward position and northward position of the current measurement source in each frame of the fusion positioning log can be obtained by querying the fusion positioning log.

[0211] S1320. Obtain a fourth deviation between the measurement source input information of the current measurement source and the measurement source input information of a specific measurement source participating in fusion, where the fourth deviation includes the eastward position deviation between the current measurement source and the specific measurement source participating in fusion, the northward position deviation between the current measurement source and the specific measurement source participating in fusion, and the heading angle deviation between the current measurement source and the specific measurement source participating in fusion.

[0212] Specifically, taking the specific measurement source participating in fusion measurement as the reference measurement source, calculate the deviation of the eastward position of each measurement source relative to the eastward position of the specific measurement source participating in fusion, the deviation of the northward position of each measurement source relative to the northward position of the specific measurement source participating in fusion, and the deviation of the heading angle of each measurement source relative to the heading angle of the specific measurement source participating in fusion.

[0213] S1330. For the deviation analysis diagram of each measurement source, match a fourth set deviation interval according to the fourth deviation, and determine the display attribute information of each position point in the deviation analysis diagram according to the matching result, to obtain the deviation analysis diagram between measurement sources, where the display attribute information of the position points in the invalid state of the measurement source time confidence is the default attribute.

[0214] The deviation analysis diagram between measurement sources is used to show the deviation conditions of each measurement source from the specific measurement source participating in fusion.

[0215] Specifically, the fourth set deviation interval can be set based on experience. Exemplarily, the fourth set deviation interval is [0, 0.1), [0.1, 0.3), [0.3, 0.5), [0.5, 1.0), [1.0, ∞). Curves can be drawn according to the position points of the fourth deviation in each range of [0, 0.1), [0.1, 0.3), [0.3, 0.5), [0.5, 1.0), [1.0, ∞) and the display attribute information of the position points in the invalid state of the measurement source time confidence, and different attributes can be represented by drawing curves of different colors.

[0216] In an embodiment, a process of a measurement source and a fusion positioning analysis algorithm is provided. Figure 14 This is a flowchart of a measurement source and a fusion positioning analysis algorithm provided by an embodiment of the present invention. In this embodiment, the fusion positioning analysis algorithm is further refined on the basis of the above embodiment. By using the generated measurement source and fusion positioning analysis diagram, the positioning conditions of the measurement sources in the problem scenario, the abnormal problems of the odometer signal, and the influence of the positioning source on the fusion positioning can be viewed. As Figure 14As shown, when the target data is the measurement source input information and the integrated positioning output information of the measurement source in the second set positioning state, the measurement source and the integrated positioning analysis algorithm include:

[0217] S1410. Generate a measurement source and integrated positioning analysis graph based on the measurement source input information of each measurement source in each frame of integrated positioning log and the integrated positioning output information in each frame of integrated positioning log, where the measurement source input information includes the measurement source eastward position, the measurement source northward position, the measurement source elevation, the measurement source pitch angle, the measurement source roll angle, the measurement source heading angle, and the measurement source confidence, and the integrated positioning output information includes the integrated positioning eastward position, the integrated positioning northward position, the integrated positioning elevation, the integrated positioning pitch angle, the integrated positioning roll angle, the integrated positioning heading angle, and the integrated positioning confidence.

[0218] Specifically, the measurement source and integrated positioning analysis graph includes an east-north curve graph, an east curve graph, a north curve graph, a heading angle curve graph, and a confidence curve graph. The east-north curve graph can be drawn based on the eastward position and northward position of each frame of integrated positioning and the eastward position and northward position of each measurement source. The east curve graph can be drawn based on the eastward position of each frame of integrated positioning and the eastward position of each measurement source. The north curve graph can be drawn based on the northward position of each frame of integrated positioning and the northward position of each measurement source. The heading angle curve graph can be drawn based on the heading angle of each frame of integrated positioning and the heading angle of each measurement source. The confidence curve graph can be drawn based on the confidence of each frame of integrated positioning and the confidence of each measurement source.

[0219] S1420. Determine the display attribute information of each position point of each measurement source in the measurement source and integrated positioning analysis graph according to the second set positioning state, where the second set positioning state includes the measurement source participating in the integration state, the measurement source having normal time confidence but not participating in the integration state, the measurement source time confidence abnormal state, and the measurement source abnormal state, and the display attribute information of the integrated positioning trajectory in the measurement source and integrated positioning analysis graph is the default attribute.

[0220] The measurement source and integrated positioning analysis graph is used to respectively display the deviation situations of the eastward position, northward position, heading angle, confidence of each frame of integrated positioning and the eastward position, northward position, heading angle, confidence of each measurement source.

[0221] Specifically, different symbols can be used to represent different states of each measurement source in the generated measurement source and integrated positioning analysis graph.

[0222] In one embodiment, an odometer analysis algorithm is provided. Based on the above embodiment, this embodiment further refines the fusion positioning analysis algorithm. By using the generated odometer component diagram, the odometer signal anomaly problem of the problem scenario can be viewed. When the target data is odometer input information, the odometer analysis algorithm includes:

[0223] Generate an odometer analysis diagram according to the odometer input information in each frame of the fusion positioning log, where the odometer input information includes odometer speed or odometer front wheel angle.

[0224] Specifically, the odometer analysis diagram can include two sub-diagrams: a speed curve diagram and a front wheel deflection angle curve diagram. The speed curve diagram can be drawn according to the speed value of each frame of the odometer, and the front wheel deflection angle curve diagram can be drawn according to the front wheel deflection angle value of each frame of the odometer.

[0225] In one embodiment, an inertial measurement unit analysis algorithm is provided. Based on the above embodiment, this embodiment further refines the fusion positioning analysis algorithm. When the target data is inertial measurement unit input information, the inertial measurement unit analysis algorithm includes:

[0226] Generate an inertial measurement unit analysis diagram according to the inertial measurement unit input information in each frame of the fusion positioning log, where the inertial measurement unit input information includes linear acceleration information and attitude angle information.

[0227] Specifically, the attitude angle information here refers to the course angular velocity. The inertial measurement unit analysis diagram includes two sub-diagrams: a linear acceleration curve diagram and a course angular velocity curve diagram. The linear acceleration curve diagram is drawn according to the linear acceleration value of each frame of the IMU. The course angular velocity curve diagram is drawn according to the attitude angular velocity value of each frame of the IMU. The linear acceleration curve diagram is used to display the linear acceleration value of each frame of the IMU. The course angular velocity curve diagram is used to display the attitude angular velocity value of each frame of the IMU.

[0228] In one embodiment, a time analysis algorithm is provided. Based on the above embodiment, this embodiment further refines the fusion positioning analysis algorithm. By using the generated time analysis diagram, the signal delay situation of the measurement source can be viewed. When the target data is the measurement source timestamp and the fusion positioning timestamp, the measurement source time analysis algorithm includes:

[0229] Generate a time analysis diagram according to the delay time between the measurement source timestamp and the fusion positioning timestamp of each measurement source in each frame of the fusion positioning log.

[0230] Specifically, different measurement sources can be distinguished by curves of different colors in the time analysis diagram.

[0231] In one embodiment, a process of a first calibration analysis algorithm is provided. Figure 15 The flowchart of a first calibration analysis algorithm provided by an embodiment of the present invention further refines the fusion positioning analysis algorithm on the basis of the above embodiment. As Figure 15 shown, when the target data is the odometer speed, the odometer calibration parameter, the eastward position of the reference measurement source, and the northward position of the reference measurement source, the first calibration analysis algorithm includes:

[0232] S1510. Determine the vehicle speed according to the eastward position and the northward position of the reference measurement source.

[0233] The vehicle speed calculation method is as follows:

[0234]

[0235] where v meas，i is the vehicle speed of the i-th frame; (x i , y i ), (x i-1 , y i-1 ) are the position coordinates of the reference measurement source using the previous and the next frames; Δt is the difference between the fusion positioning timestamps of the previous and the next frames.

[0236] S1520. Determine the calibrated odometer speed according to the odometer speed and the odometer calibration parameter.

[0237] The calibrated odometer speed calculation method is as follows:

[0238] v calib，i = k v * v 0，i

[0239] where v calib，i represents the calibrated odometer speed of the i-th frame; k v is the speed coefficient; v 0，i represents the odometer speed of the i-th frame.

[0240] S1530. Generate an odometer speed analysis graph according to the vehicle speed and the calibrated odometer speed.

[0241] Determine the odometer speed calibration situation at the problem site according to the odometer speed analysis graph. Specifically, plot the vehicle speed v meas calculated by the reference measurement source and the calibrated odometer speed v calib with curves, compare the differences between the two, and the accuracy of the odometer speed calibration parameter k v can be seen.

[0242] In one embodiment, a process of a second calibration analysis algorithm is provided. Figure 16 The flowchart of a second calibration analysis algorithm provided by an embodiment of the present invention further refines the fusion positioning analysis algorithm on the basis of the above embodiment. The calibration situation of the odometer at the problem site can be viewed by using the generated odometer position analysis diagram. As Figure 16 shown, when the target data is odometer input information, odometer calibration parameters, eastward position of the reference measurement source, northward position of the reference measurement source, and heading angle of the reference measurement source, the second calibration analysis algorithm includes:

[0243] S1610. Generate a calibrated odometer heading angle, a calibrated odometer eastward position, and a calibrated odometer northward position according to the odometer input information, vehicle wheelbase, odometer calibration parameters, eastward position of the reference measurement source, northward position of the reference measurement source, and heading angle of the reference measurement source.

[0244] Specifically, the vehicle pose calculation after odometer recursion is as follows:

[0245] s calib，i = k1 * s 0，i + k0

[0246] x odom，0 = x meas，0

[0247] y odom，0 = y meas，0

[0248] θ odom，0 = θ meas，0

[0249] Δt = t i - t i-1

[0250] D = v calib，i * Δt

[0251] R = L / tan s calib，i

[0252] C x = x odom，i-1 - R * sin θ odom，i-1

[0253] C y = y odom，i-1 + R * cos θ odom，i-1

[0254] θ odom，i = θ odom，i-1 + D / R

[0255] xodom，i = C x + D * cosθ odom，i

[0256] y odom，i = C y + D * sinθ odom，i

[0257] Wherein, x odom，i , y odom，i , θ odom，i are the odometer eastward position, northward position and heading angle after recursive use of odometer information, t i is the current frame fusion positioning timestamp, L is the vehicle wheelbase, k0 is the first-order coefficient of the front wheel steering angle, k1 is the zero-order coefficient of the front wheel steering angle, s0 is the front wheel steering angle, x meas , y meas , θ meas are respectively the eastward position, northward position and heading angle of the reference measurement source. v 0，i , s 0，i , x meas，i , y meas，i , θ meas，i represent the data of the i-th frame.

[0258] S1620. Generate an odometer heading angle analysis chart based on the heading angle of the reference measurement source and the calibrated odometer heading angle.

[0259] Verify the accuracy of the odometer calibration parameters k0 and k1 according to the odometer heading angle analysis chart. Specifically, the heading angle θ meas of the reference measurement source and the heading angle θ odom of the odometer after recursion can be plotted with curves, and the difference between the two can be compared to see the accuracy of the odometer heading angle calibration parameters k0 and k1.

[0260] S1630. Generate an odometer position analysis chart based on the eastward position of the reference measurement source, the northward position of the reference measurement source, the odometer eastward position and the odometer northward position.

[0261] Specifically, the eastward position x meas and the northward position y meas of the reference measurement source, as well as the eastward position x odom and the northward position y odom of the odometer after recursion can be plotted with curves, and the difference between the two can be compared to see the accuracy of the odometer speed calibration parameters k v , k0 and k1.

[0262] In one embodiment, a process of a heading angle analysis algorithm is provided. Figure 17The flowchart of a heading angle analysis algorithm provided by an embodiment of the present invention. Based on the above embodiment, this embodiment further refines the fusion positioning analysis algorithm, and the situation of the measured source heading angle can be analyzed by using the generated measured source heading angle analysis diagram. As Figure 17 shown, when the target data is the eastward position of the reference measurement source, the northward position of the reference measurement source, and the heading angle of the measured measurement source, the heading angle analysis algorithm includes:

[0263] S1710. Generate a vehicle heading angle according to the eastward position of the reference measurement source and the northward position of the reference measurement source.

[0264] Specifically, according to the position information of the reference measurement source, the heading information is calculated as follows:

[0265] θ calc，i = atan2(y i - y i-1 , x i - x i-1 )

[0266] S1720. Generate a measured source heading angle analysis diagram according to the vehicle heading angle and the heading angle of the measured measurement source.

[0267] Specifically, the calculated heading angle θ calc , and the heading angle θ meas of the measured measurement source can be plotted with curves to compare the differences between the two, and it can be seen whether the heading angle calibration information of the measured measurement source is accurate.

[0268] Figure 18 The structural schematic diagram of a fusion positioning analysis device provided by an embodiment of the present invention. As Figure 18 shown, the device includes:

[0269] A log acquisition module, configured to acquire fusion positioning logs at a set frame rate;

[0270] A result determination module, configured to determine an analysis result based on a user analysis request and using a fusion positioning analysis algorithm according to the fusion positioning logs, where the analysis result is used to determine fusion positioning accuracy, measurement source positioning accuracy, and positioning problem information;

[0271] Wherein, the fusion positioning analysis algorithm generates an analysis result matching the analysis target based on the target data corresponding to the analysis target in the fusion positioning logs.

[0272] The fusion positioning analysis device provided by the embodiment of the present invention can execute the fusion positioning analysis method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method.

[0273] Figure 19 The following is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as, for example, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as, for example, personal digital assistants, cellular telephones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0274] As Figure 19 shown, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. The memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0275] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0276] The processor 11 may be various general-purpose and / or special-purpose processing components having processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the method of fusion positioning analysis.

[0277] In some embodiments, the fusion positioning analysis method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the fusion positioning analysis method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the fusion positioning analysis method by any other suitable means (e.g., by means of firmware).

[0278] Various embodiments of the systems and techniques described above in this document may be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), systems on a chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs executable and / or interpretable on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0279] The computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, a special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer programs may be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine, or entirely on the remote machine or server.

[0280] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0281] To provide for interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide for interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic, speech, or tactile input).

[0282] The systems and techniques described herein can be implemented in a computing system that includes backend components (such as, for example, a data server), or a computing system that includes middleware components (such as, for example, an application server), or a computing system that includes frontend components (such as, for example, a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (such as, for example, a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0283] A computing system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The client-server relationship is created by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0284] It should be understood that various forms of the processes shown above can be used, steps can be reordered, added, or deleted. For example, the steps described in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and this is not limited herein.

[0285] The above specific embodiments do not constitute a limitation to the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A fusion positioning analysis method, characterized in that, Including: Obtaining the fused positioning log according to the set frame rate; The fused positioning log includes: sensor input information, fused positioning output information, and measurement source status output information; Based on the user analysis request, determining the analysis result by using the fused positioning analysis algorithm according to the fused positioning log, where the analysis result is used to determine the fused positioning accuracy, the measurement source positioning accuracy, and the positioning problem information; Wherein, the fused positioning analysis algorithm generates an analysis result matching the analysis target based on the target data corresponding to the analysis target in the fused positioning log; the analysis target is determined based on the user analysis request, and the target data includes at least any one type of information among the sensor input information, the fused positioning output information, or the measurement source status output information.

2. The method according to claim 1, wherein The fused positioning analysis algorithm includes a measurement source quantity analysis algorithm. When the target data is the number of measurement sources participating in the fusion, the measurement source quantity analysis algorithm includes: Obtaining the number of measurement sources participating in the fusion in each frame of the fused positioning log; Determining the first number of frames of the fused positioning log according to the number of measurement sources participating in the fusion, where the first number of frames is the number of frames of the fused positioning log with the same number of measurement sources participating in the fusion; Determining the first proportional relationship between each of the first number of frames and the total number of frames of the fused positioning log, and generating a measurement source quantity analysis table according to the first proportional relationship.

3. The method according to claim 1, wherein The fused positioning analysis algorithm includes a confidence analysis algorithm. When the target data is the measurement source confidence at the first set positioning state, the confidence analysis algorithm includes: For each positioning state in the first set positioning state, obtaining the maximum confidence, the minimum confidence, and the average confidence in the measurement source confidence, where the first set positioning state includes all measurement source states, the measurement source time confidence valid state, and the measurement source participation in fusion state; For each positioning state in the first set positioning state, obtaining the second number of frames of the fused positioning log, where the second number of frames is the number of frames of the fused positioning log corresponding to each positioning state in the first set positioning state; Determining the second proportional relationship between the second number of frames and the total number of frames of the fused positioning log; Generating a confidence analysis table according to the maximum confidence, the minimum confidence, the average confidence, the second number of frames, and the second proportional relationship.

4. The method according to claim 1, characterized in that, The fused positioning analysis algorithm includes a first confidence distribution analysis algorithm. When the target data is the measurement source confidence in the measurement source time confidence valid state and the third number of frames of the fused positioning log, where the third number of frames is the number of frames of the fused positioning log in the measurement source time confidence invalid state, the first confidence distribution analysis algorithm includes: Determining the third proportional relationship between the third number of frames and the total number of frames of the fused positioning log; Obtaining the fourth number of frames of the fused positioning log, where the fourth number of frames is the number of frames of the fused positioning log in which the measurement source confidence in the measurement source time confidence valid state is within each first set confidence interval; Determining the fourth proportional relationship between each of the fourth number of frames and the total number of frames of the fused positioning log; Generate a confidence distribution analysis table according to the third proportional relationship and the fourth proportional relationship.

5. The method according to claim 1, wherein The fusion positioning analysis algorithm includes a first deviation analysis algorithm. When the target data is the measurement source input information and the fusion positioning output information in the first set positioning state of the measurement source, the first deviation analysis algorithm includes: For each positioning state in the first set positioning state, determine the first average deviation, the first maximum deviation, and the first minimum deviation according to the measurement source input information and the fusion positioning output information; Generate a first deviation analysis table according to the first average deviation, the first maximum deviation, and the first minimum deviation; Among them, the first set positioning state includes all measurement source states, measurement source time confidence valid states, and measurement source participation in fusion states; The first average deviation includes the eastward position average deviation of each measurement source from the fusion positioning, the northward position average deviation of each measurement source from the fusion positioning, and the heading angle average deviation of each measurement source from the fusion positioning; The first maximum deviation includes the eastward position maximum deviation of each measurement source from the fusion positioning, the northward position maximum deviation of each measurement source from the fusion positioning, and the heading angle maximum deviation of each measurement source from the fusion positioning; The first minimum deviation includes the eastward position minimum deviation of each measurement source from the fusion positioning, the northward position minimum deviation of each measurement source from the fusion positioning, and the heading angle minimum deviation of each measurement source from the fusion positioning.

6. The method according to claim 1, characterized in that, The analysis result includes a visualization result; Among them, the visualization result includes a measurement source quantity analysis table, a confidence analysis table, a confidence distribution analysis table, a first deviation analysis table, a first deviation distribution analysis table, a second deviation analysis table, a second deviation distribution analysis table, a quantity distribution heat map, a state distribution analysis heat map, a confidence distribution analysis graph, a deviation analysis graph of the measurement source and the fusion positioning, a deviation analysis graph between measurement sources, a measurement source and fusion positioning analysis graph, an odometer analysis graph, an inertial measurement unit analysis graph, a time analysis graph, an odometer speed analysis graph, an odometer heading angle analysis graph, an odometer position analysis graph, and a measurement source heading angle analysis graph; Among them, the first deviation analysis table is used to display the deviation statistics of different measurement sources from the fusion positioning in different positioning states; the first deviation distribution analysis table is used to display the deviation distribution of different measurement sources from the fusion positioning in different positioning states; the second deviation analysis table is used to display the deviation statistics of different quantity sources from a specific measurement source participating in fusion in different positioning states; the second deviation distribution analysis table is used to display the deviation distribution of different measurement sources from a specific measurement source participating in fusion in different positioning states.

7. A fusion positioning analysis device, characterized in that, Include: A log acquisition module for acquiring fusion positioning logs at a set frame rate; The fusion positioning log includes: sensor input information, fusion positioning output information, and measurement source state output information; A result determination module for determining an analysis result based on a user analysis request according to the fusion positioning log using a fusion positioning analysis algorithm, where the analysis result is used to determine the fusion positioning accuracy, the measurement source positioning accuracy, and the positioning problem information. Among them, the fusion positioning analysis algorithm generates an analysis result matching the analysis target based on the target data corresponding to the analysis target in the fusion positioning log; the analysis target is determined based on a user analysis request, and the target data includes at least any one type of information among sensor input information, fusion positioning output information, or measurement source status output information.

8. An electronic device, characterized in that, The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, the at least one processor is enabled to execute the fusion positioning analysis method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for implementing the fusion positioning analysis method according to any one of claims 1-6 when executed by a processor.

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