Positioning problem analysis method and device, electronic equipment and medium

Abstract

The invention provides a positioning problem analysis method and device, electronic equipment, a computer readable storage medium and a computer program product, and relates to the field of artificial intelligence, in particular to the technical field of automatic driving and map navigation. According to the implementation scheme, in response to a positioning problem, description information corresponding to the positioning problem and a problem category to which the positioning problem belongs in a plurality of preset problem categories are obtained; acquiring corresponding positioning data when the positioning problem occurs, wherein the positioning data comprises vehicle sensor data and positioning algorithm data; and based on the description information, the problem category and the positioning data, obtaining analysis data which is generated by the large model and aims at the positioning problem through the large model. The analysis data includes data related to a positioning problem in at least one of the sensor data and the positioning algorithm data.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence, in particular to the field of autonomous driving and map navigation technology, and specifically to a positioning problem analysis method, device, electronic device, computer-readable storage medium, and computer program product. Background Art

[0002] Artificial intelligence is a discipline that studies how to use computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.). It includes both hardware-level and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing. Artificial intelligence software technologies mainly include computer vision technology, speech recognition technology, natural language processing technology, machine learning / deep learning, big data processing technology, knowledge graph technology, and other major directions.

[0003] Vehicle positioning is a technology that allows autonomous vehicles to know their exact location, and it plays a very important role in the autonomous driving system. Vehicle positioning involves a variety of sensor types and related technologies, which can be mainly divided into satellite positioning, inertial navigation positioning, map matching positioning and fusion positioning. Fusion positioning technology mainly includes sensor data fusion and positioning algorithm fusion to achieve real-time autonomous driving positioning. Summary of the invention

[0004] The present disclosure provides a positioning problem analysis method, device, electronic device, computer-readable storage medium, and computer program product.

[0005] According to one aspect of the present disclosure, a positioning problem analysis method is provided, comprising: in response to the occurrence of a positioning problem, obtaining description information corresponding to the positioning problem and a problem category to which the positioning problem belongs among a plurality of preset problem categories; obtaining positioning data corresponding to when the positioning problem occurs, wherein the positioning data comprises vehicle sensor data and positioning algorithm data; and based on the description information, the problem category and the positioning data, obtaining analysis data for the positioning problem generated by the big model through a big model, wherein the analysis data comprises data related to the positioning problem in at least one of the sensor data and the positioning algorithm data.

[0006] According to another aspect of the present disclosure, a positioning problem analysis device is provided, comprising: a response unit, configured to, in response to the occurrence of a positioning problem, obtain descriptive information corresponding to the positioning problem and a problem category to which the positioning problem belongs among a plurality of preset problem categories; an acquisition unit, configured to obtain positioning data corresponding to the occurrence of the positioning problem, wherein the positioning data comprises vehicle sensor data and positioning algorithm data; and a production unit, configured to obtain, through a large model, analysis data for the positioning problem generated by the large model based on the descriptive information, the problem category and the positioning data, wherein the analysis data comprises data related to the positioning problem in at least one of the sensor data and the positioning algorithm data.

[0007] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described in the present disclosure.

[0008] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided. The computer instructions are used to cause a computer to execute the method described in the present disclosure.

[0009] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, which implements the method described in the present disclosure when executed by a processor.

[0010] According to one or more embodiments of the present disclosure, with the help of a large model, the causes of positioning problems can be accurately and comprehensively identified, thereby improving the analysis efficiency of positioning problems and enhancing user experience.

[0011] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings exemplarily illustrate the embodiments and constitute a part of the specification, and together with the text description of the specification, are used to explain the exemplary implementation of the embodiments. The embodiments shown are for illustrative purposes only and do not limit the scope of the claims. In all drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0013] Figure 1 A schematic diagram showing an exemplary system in which the various methods described herein may be implemented according to an embodiment of the present disclosure;

[0014] Figure 2 A flowchart of a positioning problem analysis method according to an embodiment of the present disclosure is shown;

[0015] Figure 3 A structural block diagram of a positioning problem analysis device according to an embodiment of the present disclosure is shown; and

[0016] Figure 4 A structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0017] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0018] In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, timing relationship, or importance relationship of these elements, and such terms are only used to distinguish one element from another element. In some examples, the first element and the second element may refer to the same instance of the element, and in some cases, based on the description of the context, they may also refer to different instances.

[0019] The terms used in the description of various examples in this disclosure are only for the purpose of describing specific examples and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element can be one or more. In addition, the term "and / or" used in this disclosure covers any one of the listed items and all possible combinations.

[0020] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0021] Figure 1 FIG. 1 is a schematic diagram of an exemplary system 100 in which various methods and apparatuses described herein may be implemented according to an embodiment of the present disclosure. Figure 1 , the system 100 includes one or more client devices 101, 102, 103, 104, 105, and 106, a server 120, and one or more communication networks 110 coupling the one or more client devices to the server 120. The client devices 101, 102, 103, 104, 105, and 106 may be configured to execute one or more applications.

[0022] In an embodiment of the present disclosure, the server 120 may run one or more services or software applications that enable the method of location problem analysis to be performed.

[0023] In some embodiments, server 120 may also provide other services or software applications, which may include non-virtualized environments and virtualized environments. In some embodiments, these services may be provided as web-based services or cloud services, such as provided to users of client devices 101, 102, 103, 104, 105, and / or 106 under a software as a service (SaaS) model.

[0024] exist Figure 1 In the configuration shown, the server 120 may include one or more components that implement the functions performed by the server 120. These components may include software components, hardware components, or a combination thereof that can be executed by one or more processors. Users operating client devices 101, 102, 103, 104, 105, and / or 106 may in turn utilize one or more client applications to interact with the server 120 to utilize the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from the system 100. Therefore, Figure 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

[0025] The user may use client devices 101, 102, 103, 104, 105 and / or 106 to input information related to the positioning problem. The client device may provide an interface that enables the user of the client device to interact with the client device. The client device may also output information to the user via the interface. Figure 1 Only six client devices are depicted, but one skilled in the art will appreciate that the present disclosure may support any number of client devices.

[0026] Client devices 101, 102, 103, 104, 105 and / or 106 may include various types of computer devices, such as portable handheld devices, general-purpose computers (such as personal computers and laptop computers), workstation computers, wearable devices, smart screen devices, self-service terminal devices, service robots, game systems, thin clients, various messaging devices, sensors or other sensing devices, etc. These computer devices may run various types and versions of software applications and operating systems, such as MICROSOFT Windows, APPLE iOS, UNIX-like operating systems, Linux or Linux-like operating systems (such as GOOGLE Chrome OS); or include various mobile operating systems, such as MICROSOFT Windows Mobile OS, iOS, Windows Phone, Android. Portable handheld devices may include cellular phones, smart phones, tablet computers, personal digital assistants (PDAs), etc. Wearable devices may include head-mounted displays (such as smart glasses) and other devices. Game systems may include various handheld game devices, Internet-enabled game devices, etc. Client devices are capable of executing various different applications, such as various Internet-related applications, communication applications (such as email applications), short message service (SMS) applications, and may use various communication protocols.

[0027] The network 110 may be any type of network known to those skilled in the art that may support data communications using any of a variety of available protocols, including but not limited to TCP / IP, SNA, IPX, etc. By way of example only, the one or more networks 110 may be a local area network (LAN), an Ethernet-based network, a token ring, a wide area network (WAN), the Internet, a virtual network, a virtual private network (VPN), an intranet, an extranet, a blockchain network, a public switched telephone network (PSTN), an infrared network, a wireless network (e.g., Bluetooth, WIFI), and / or any combination of these and / or other networks.

[0028] Server 120 may include one or more general purpose computers, dedicated server computers (e.g., PC (personal computer) servers, UNIX servers, mid-range servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and / or combination. Server 120 may include one or more virtual machines running virtual operating systems, or other computing architectures involving virtualization (e.g., one or more flexible pools of logical storage devices that may be virtualized to maintain a server's virtual storage device). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.

[0029] The computing units in the server 120 may run one or more operating systems including any of the above operating systems and any commercially available server operating systems. The server 120 may also run any of a variety of additional server applications and / or middle-tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, etc.

[0030] In some implementations, server 120 may include one or more applications to analyze and consolidate data feeds and / or event updates received from users of client devices 101, 102, 103, 104, 105, and 106. Server 120 may also include one or more applications to display data feeds and / or real-time events via one or more display devices of client devices 101, 102, 103, 104, 105, and 106.

[0031] In some embodiments, the server 120 may be a server of a distributed system, or a server combined with a blockchain. The server 120 may also be a cloud server, or an intelligent cloud computing server or intelligent cloud host with artificial intelligence technology. A cloud server is a host product in a cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and virtual private servers (VPS) services.

[0032] The system 100 may also include one or more databases 130. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 130 may be used to store information such as audio files and video files. The databases 130 may reside in various locations. For example, the database used by the server 120 may be local to the server 120, or may be remote from the server 120 and may communicate with the server 120 via a network-based or dedicated connection. The databases 130 may be of different types. In some embodiments, the databases used by the server 120 may be, for example, relational databases. One or more of these databases may store, update, and retrieve data to and from the databases in response to commands.

[0033] In some embodiments, one or more of the databases 130 may also be used by applications to store application data. The databases used by the applications may be different types of databases, such as a key-value store, an object store, or a conventional store backed by a file system.

[0034] Figure 1The system 100 may be configured and operated in various ways to enable the application of various methods and apparatuses described in the present disclosure.

[0035] With the continuous development of technology and the rapid development of intelligent transportation, intelligent driving systems have become the trend of future transportation. In intelligent driving systems, accurate positioning technology is the core to ensure vehicle driving safety and automated driving. Fusion positioning technology is a method that integrates multiple sensors and algorithms to provide more accurate and reliable positioning information.

[0036] The fusion positioning technology in the intelligent driving system mainly includes sensor data fusion and positioning algorithm fusion. Sensor data fusion refers to the integration of different types of sensor data to improve the accuracy and reliability of positioning. Common sensors include global positioning system (GPS), inertial measurement unit (IMU), visual sensor and radar sensor. Positioning algorithm fusion refers to the combination of multiple positioning algorithms to improve the accuracy and stability of positioning by complementing and correcting each other. Common positioning algorithms include extended Kalman filter (EKF), particle filter (PF) and Kalman filter (KF).

[0037] At present, the fusion positioning technology in intelligent driving systems has made some progress. First, the research on sensor data fusion can already fuse the data of multiple sensors such as GPS, IMU, visual sensors and radar sensors to improve the accuracy and reliability of positioning. Secondly, the research on positioning algorithm fusion can already combine multiple positioning algorithms such as EKF, PF and KF to improve the accuracy and stability of positioning.

[0038] High-precision positioning is a function that comprehensively outputs the absolute and relative positions of vehicles based on high-precision map data and sensor data. In high-precision positioning scenarios, the signals of various sensors (such as IMU, GNSS, cameras, etc.) are parsed and adapted before being passed to the fusion positioning algorithm module. The fusion positioning algorithm module combines high-precision map data to achieve high-precision fusion positioning. At the same time, based on high-precision position information, it constructs map element information (such as slope, curvature, main road tree MPP, NOA lane change information, etc.), and uses, for example, the ADASISv3 protocol to send the position information and map element information to the intelligent driving domain controller used to achieve autonomous driving, thereby providing vehicles with high-level assisted driving capability support.

[0039] The effect of high-precision positioning depends on the quality of the sensor (such as signal frequency, numerical accuracy, etc.) on the one hand, and on the design and implementation of the fusion positioning algorithm on the other hand. When a positioning problem occurs, the steps and logic of analyzing the problem are repetitive and consistent. The traditional high-precision positioning problem analysis process is highly consistent, relies on manual analysis, and is inefficient; limited by the accumulation of experience and knowledge, manual analysis may lead to incomplete problem analysis and inability to accurately identify the problem. Therefore, how to quickly and efficiently analyze and solve problems is a key issue that we must solve.

[0040] According to an embodiment of the present disclosure, a positioning problem analysis method is provided. Figure 2 A flowchart of a method for analyzing a positioning problem according to an embodiment of the present disclosure is shown. Figure 2 As shown, method 200 includes: in response to the occurrence of a positioning problem, obtaining description information corresponding to the positioning problem and the problem category to which the positioning problem belongs in a plurality of preset problem categories (step 210); obtaining positioning data corresponding to the occurrence of the positioning problem, wherein the positioning data includes vehicle sensor data and positioning algorithm data (step 220); and based on the description information, the problem category and the positioning data, obtaining analysis data for the positioning problem generated by the large model through a large model, wherein the analysis data includes data related to the positioning problem in at least one of the sensor data and the positioning algorithm data (step 230).

[0041] According to the embodiments of the present disclosure, with the help of a large model, the causes of positioning problems can be accurately and comprehensively identified, thereby improving the analysis efficiency of positioning problems and enhancing user experience.

[0042] In some embodiments, the vehicle sensor data includes at least one of the following items: data obtained based on an inertial measurement unit IMU, data obtained based on a global satellite navigation system GNSS, and data obtained based on a camera.

[0043] An inertial measurement unit (IMU) is a device that contains multiple sensors, typically accelerometers, gyroscopes, and magnetometers. They are able to measure and report the acceleration, angular velocity, and direction of the vehicle (geomagnetic field information obtained through a magnetometer). These data are critical to understanding the dynamic behavior of the vehicle, such as the vehicle's tilt, roll, and steering. In autonomous driving and advanced driver assistance systems (ADAS), IMU data is used to calculate the vehicle's attitude, velocity, and position, and is fused with other sensor data to improve accuracy. Therefore, in some examples, the data obtained based on the inertial measurement unit (IMU) may include: timestamp, acceleration, angular velocity, direction, its data acquisition frequency, etc.

[0044] The Global Navigation Satellite System (GNSS) can receive signals from satellites and calculate the vehicle's geographic location (latitude and longitude), speed and time information. GNSS data plays a core role in navigation, path planning and location tracking. Therefore, in some examples, the data obtained based on the Global Navigation Satellite System (GNSS) may include timestamp, fixed solution status, longitude and latitude, heading, number of satellites, speed accuracy, horizontal accuracy, data collection frequency, etc.

[0045] Cameras are another important sensor on vehicles, which can capture images and video streams around the vehicle. Therefore, in some examples, the data obtained by the camera can include timestamps, road levels, number and type of lane lines, road conditions, and the frequency of data collection.

[0046] In some embodiments, the positioning algorithm data may include data obtained by monitoring the vehicle sensor status based on the algorithm, system or software status data generated in the process of generating the vehicle position, etc. For example, the preset fault type is represented by the corresponding data bit in the corresponding field, such as in the field for outputting GNSS fault, bit0 represents GNSS data loss, bit1 represents GNSS data format error, bit2 represents GNSS frequency abnormality, etc.

[0047] In some embodiments, the positioning algorithm data includes fusion positioning algorithm result information, for example, including: fusion algorithm state, roll angle, pitch angle, yaw angle, velocity component, fusion frame number, etc.

[0048] According to some embodiments, the preset multiple problem categories include at least two of the following items: an invalid positioning status problem, an incorrect map information problem, a positioning jitter problem, a long positioning initialization time problem, and a software stability problem.

[0049] In the present disclosure, the invalid positioning state problem refers to a state in which the software or system used for positioning cannot accurately output valid information about roads, lanes, and vehicle positioning based on vehicle sensors and high-precision map data. The causes of the invalid positioning state problem may include: signal interference. In complex environments, such as city centers and between high-rise buildings, electromagnetic signals may be interfered with, resulting in a decrease in the quality of the signal received by the positioning device; positioning device failure, hardware or software failure of the positioning device, such as antenna damage, power failure, firmware error, or algorithm defect. For example, the positioning state flag can be used to determine whether the positioning state is invalid, such as LOC_state: VALID.

[0050] Map information error problems usually refer to inaccurate, incomplete or outdated map data, resulting in invalid positioning status or valid positioning status, but the positioning result does not match the actual geographical location, and incorrect map feature information. The causes of map information error problems may include: untimely update of map data. Map data needs to be updated regularly to reflect road changes, new buildings and other information. If the update is not timely, errors may occur; errors in the map production process. During the map production process, there may be human errors or data processing errors, resulting in inaccurate map information.

[0051] In some embodiments, the map information error problem may specifically refer to a situation where, under the premise that the positioning status is valid, the positioning result does not match the actual geographical location and the map element information is incorrect.

[0052] Positioning jitter refers to the stability of the position in which the output absolute position of the vehicle jumps horizontally between adjacent frames during the positioning process. The causes of positioning jitter may include: positioning device problems. Insufficient performance or aging of positioning devices may also cause jitter in positioning results; positioning algorithm problems. Problems in the design or implementation of the positioning algorithm may also cause unstable positioning results.

[0053] The problem of long positioning initialization time refers to the situation that when the positioning software or system has valid sensor signals and accuracy and the high-precision engine is loaded, it takes a long time for the vehicle to enter the high-precision map area and output the positioning status. The reasons for the long positioning initialization time may include: large amount of data. During the positioning process, a large amount of data needs to be processed, such as satellite signals, base station signals, etc. The large amount of data will lead to longer calculation time; high algorithm complexity. The calculation complexity of the positioning algorithm is high, which requires more computing resources and time; equipment performance limitations. The performance of the positioning equipment is insufficient, such as slow processor speed and small memory, which may also lead to longer initialization time.

[0054] Software stability issues refer to crashes, unresponsiveness, error prompts, and other issues that occur during the operation of the positioning software, which will affect the normal use of the positioning function. Software stability issues may include: software defects. The positioning software may have defects in the design or implementation process, such as code errors, logical errors, etc.; system compatibility issues. The positioning software may be incompatible with certain operating system versions, resulting in problems during operation; excessive resource usage. The positioning software may occupy too many system resources during operation, such as CPU, memory, etc., resulting in other system functions being affected.

[0055] In some examples, description information corresponding to the positioning problem and the problem category of the positioning problem may be input into the large model.

[0056] According to some embodiments, the large model is obtained by training based on at least a preset scale of knowledge resources and sample data. In some examples, the large model can be a multimodal large model to be able to process various types of input data. Among them, a multimodal large model refers to a large model that can simultaneously process, fuse and understand data from different modalities (such as text, images, audio, video, etc.).

[0057] In some embodiments, the big model can be a knowledge-enhanced big language model for conversation (such as ERNIE bot, etc.), which is trained through massive knowledge resources and conversation data (for example, more than trillions of web page data, billions of search data, hundreds of millions of image data, billions of voice request data, more than 50 billion text request data, and more than 550 billion factual knowledge).

[0058] Therefore, by applying this type of model as a large model, in addition to directly processing chat conversation information, it can also directly generate reply information and analysis data for logical reasoning, common sense, and image generation information, further improving generation efficiency.

[0059] In some embodiments, the description information corresponding to the positioning problem may include description information corresponding to the problem category.

[0060] According to some embodiments, the description information includes at least one of time information and environment information when the positioning problem occurs.

[0061] Furthermore, in some examples, the description information corresponding to the positioning problem may include the time information when the positioning problem occurs. Therefore, the time information when the positioning problem occurs may be input into the large model to improve the accuracy, generalization ability and efficiency of the large model in problem analysis.

[0062] In some examples, when the problem category is a problem of long positioning initialization time, the time when the positioning problem occurs may be a time period when the positioning problem occurs, such as T1 to T2.

[0063] In some examples, the descriptive information corresponding to the positioning problem may include environmental information in which the positioning problem occurs, including but not limited to traffic environment information (such as lane information, on-ramp / tunnel scenes), weather information, etc.

[0064] According to some embodiments, the positioning data also includes video stream data or corresponding picture frame data in the video stream data, wherein the video stream data is a dynamic display of the driving scene of the vehicle on the corresponding road.

[0065] Video stream data plays a vital role in the fields of intelligent transportation, autonomous driving, vehicle monitoring, and driving recorders. It can provide real-time and dynamic display of vehicle driving scenes on the road. For example, by installing sensor equipment such as driving recorders on the vehicle, video stream data during vehicle driving can be recorded in real time. Alternatively, various changes in the vehicle driving process can be simulated in an immersive way through virtual reality (VR) and augmented reality (AR) technology, including changes in road conditions, vehicle acceleration, braking, etc. Video stream data is more intuitive and easy to understand, and can intuitively reflect various situations during vehicle driving. Therefore, by inputting the positioning data including video stream data into the large model, the analysis data for the positioning problem generated by the large model is more accurate.

[0066] In some embodiments, whether the positioning data specifically includes video stream data or picture frame data obtained based on the video stream data can be determined according to the problem category to which the positioning problem belongs. For example, when the problem category is a long positioning initialization time problem, the positioning data can include video stream data within the time period when the problem occurs.

[0067] According to some embodiments, when the problem category includes an invalid positioning status problem, the descriptive information includes at least one of the following items: an invalid positioning status occurs when the vehicle sensor data is normal; an invalid positioning status occurs when the vehicle sensor data is abnormal; an invalid positioning status occurs within a first time length; and an invalid positioning status occurs when the positioning algorithm data is abnormal.

[0068] In some examples, whether the positioning state is invalid can be determined by a positioning state flag, such as LOC_state: INVALID.

[0069] According to some embodiments, when the problem category includes a map information error problem, the description information includes at least one of the following items: when the positioning state is valid, the lane information in the positioning result is incorrect; and when the positioning state is valid, the road attribute information in the positioning result is incorrect. For example, the description information may be: the positioning state is valid, but the lane number output by the positioning system is inconsistent with the lane number where the vehicle is actually traveling.

[0070] According to some embodiments, when the problem category includes a positioning jitter problem, the description information includes at least one of the following items: a positioning jitter problem occurs at a first location and within a first driving distance; and a positioning jitter problem occurs within a second driving distance in a preset scenario. For example, the description information may be: the vehicle is located at the location coordinates (114.144081, 22.572751) and the link number LinkID (2036070), the fusion positioning has lateral jitter, and the jitter distance value is 86.5 cm.

[0071] According to some embodiments, the preset scene includes at least one of the following items: an on-ramp or off-ramp scene, and a strong light condition scene.

[0072] According to some embodiments, when the problem category includes the problem of long positioning initialization time, the description information includes at least one of the following items: in a non-on-ramp scenario, the vehicle's current lane information cannot be output within a second time length; and in an on-ramp scenario or a curved road scenario, the vehicle's current lane information cannot be output within a third time length. For example, the description information may be: in a non-on-ramp scenario where the main and auxiliary roads are separated and the GPS is open and unobstructed, a valid road segment number LinkID cannot be output for a long time, and the road-level positioning initialization time is long, about 20 seconds; in an on-ramp driving scenario, it is impossible to accurately locate on the ramp for a long time, and the initialization time is long, about 30 seconds.

[0073] According to some embodiments, when the problem category includes a problem for software stability, the description information includes at least one of the following items: a software stability problem occurs at the second location within a third driving distance; and a CPU load exceeds a preset threshold.

[0074] It can be understood that the description information in the above embodiments is merely exemplary and is not intended to be limiting.

[0075] According to some embodiments, the positioning data also includes: log data generated by the software used for positioning. The log data generated by the software used for positioning is an important source of information for understanding and monitoring the positioning process, performance, and potential problems. These log data usually contain rich information, including but not limited to: recording various errors and exception information that occur during the positioning process, which helps to quickly locate the cause of the problem; recording various operating behaviors of users when using the positioning software, such as starting, shutting down, setting, etc.; recording various operating behaviors of users when using the positioning software, such as starting, shutting down, setting, etc.; and the configuration and status of network, battery and other equipment.

[0076] Therefore, according to some embodiments, the analysis data may also include data related to the positioning problem in the log data.

[0077] According to some embodiments, obtaining the analysis data for the positioning problem generated by the big model through a big model includes: inputting the descriptive information, the problem category, the positioning data and at least one preset instruction into the big model to obtain the analysis data generated by the big model, wherein the at least one preset instruction is used to guide the generation process of the analysis data.

[0078] In some embodiments, when applying the big model to generate analysis data, one or more preset instructions can be simultaneously input into the big model, so as to guide the big model to organize positioning data and generate analysis data for positioning problems through the preset instructions.

[0079] In some embodiments, the preset instructions may be, for example, instructions for guiding the analysis data generation process so that the analysis data is generated in a specific form (eg, in a list or table form) to improve the generation quality of the analysis data and enhance the user experience.

[0080] For example, the preset instructions may be "Please answer the questions in Chinese, and do not generate contradictory or semantically repetitive answers, and try to keep the answer content within 250 words", "Please try to use line breaks, "-" and other formats or symbols to organize the answers to make the answers easy to read and understand."

[0081] In some embodiments, by determining a positioning problem solution corresponding to the analysis data from a plurality of preset positioning problem solutions, the positioning problem is processed based on the positioning problem solution.

[0082] For example, based on a large model (such as a multimodal large model), the positioning data such as timestamp, sensor information, fused positioning results and fused positioning position information (such as lane number, heading angle, etc.) are extracted to obtain corresponding analysis data.

[0083] In some examples, based on the OCR or video stream encoding and decoding capabilities of the large model, according to the problem category, the information in the positioning data is refined, and analysis data including the problem time, problem category, specific problem cause / conclusion, etc. is output. For example, if the problem category is invalid positioning information, the analysis data may include abnormal GNSS sensor frequency, excessive IMU angular velocity, etc.

[0084] Based on the analysis data, a positioning problem solution corresponding to the analysis data is determined from a plurality of preset positioning problem solutions, so that the positioning problem is processed by the positioning problem solution. For example, long-term learning memory information is called based on the analysis data to find out whether there is a similar problem solution in the long-term learning memory information. In some examples, if a positioning problem solution corresponding to the analysis data is not found, a new solution can be generated and the long-term learning memory information can be updated.

[0085] For example, if the problem category is an invalid positioning status problem, the corresponding positioning problem solution may include: calling corresponding tools to monitor the GNSS sensor frequency within a preset time period, analyzing the IMU angular velocity value, etc.

[0086] Through the embodiments of the present disclosure, a process plan can be formulated based on the recognition results of the large model and combined with the long-term memory results, and relevant plug-ins can be called autonomously to complete problem analysis and output results, thereby improving the analysis efficiency of locating problems.

[0087] According to the embodiments of the present disclosure, Figure 3 As shown, a positioning problem analysis device 300 is also provided, including: a response unit 310, configured to obtain, in response to the occurrence of a positioning problem, description information corresponding to the positioning problem and a problem category to which the positioning problem belongs in a plurality of preset problem categories; an acquisition unit 320, configured to obtain positioning data corresponding to the occurrence of the positioning problem, wherein the positioning data includes vehicle sensor data and positioning algorithm data; and an analysis unit 330, configured to obtain, through a large model, analysis data for the positioning problem generated by the large model based on the description information, the problem category and the positioning data, wherein the analysis data includes data related to the positioning problem in at least one of the sensor data and the positioning algorithm data.

[0088] Here, the operations of the above-mentioned units 310-330 of the positioning problem analysis device 300 are similar to the operations of the steps 210-230 described above, and will not be repeated here.

[0089] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0090] According to an embodiment of the present disclosure, an electronic device, a readable storage medium and a computer program product are also provided.

[0091] refer to Figure 4 , a block diagram of an electronic device 400 that can be used as a server or client of the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0092] like Figure 4As shown, the electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0093] Multiple components in the electronic device 400 are connected to the I / O interface 405, including: an input unit 406, an output unit 407, a storage unit 408, and a communication unit 409. The input unit 406 can be any type of device that can input information to the electronic device 400. The input unit 406 can receive input digital or character information and generate key signal input related to user settings and / or function control of the electronic device, and can include but is not limited to a mouse, a keyboard, a touch screen, a track pad, a track ball, a joystick, a microphone, and / or a remote controller. The output unit 407 can be any type of device that can present information, and can include but is not limited to a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 408 can include but is not limited to a disk, an optical disk. The communication unit 409 allows the electronic device 400 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include but is not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth device, an 802.11 device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0094] The computing unit 401 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 401 performs the various methods and processes described above, such as method 200. For example, in some embodiments, the method 200 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 400 via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by the computing unit 401, one or more steps of the method 200 described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the method 200 in any other appropriate manner (e.g., by means of firmware).

[0095] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0096] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0097] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0098] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer 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 pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the 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 input, voice input, or tactile input).

[0099] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations 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 (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0100] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0101] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0102] Although the embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the above-mentioned methods, systems and devices are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but only by the claims after authorization and their equivalent scope. Various elements in the embodiments or examples can be omitted or replaced by their equivalent elements. In addition, each step can be performed in an order different from that described in the present disclosure. Further, the various elements in the embodiments or examples can be combined in various ways. It is important that with the evolution of technology, many elements described herein can be replaced by equivalent elements that appear after the present disclosure.

Claims

1. A positioning problem analysis method, comprising: In response to a positioning problem occurring, obtaining description information corresponding to the positioning problem and a problem category to which the positioning problem belongs among a plurality of preset problem categories; Acquiring positioning data corresponding to when the positioning problem occurs, wherein the positioning data includes vehicle sensor data and positioning algorithm data; and Based on the description information, the problem category and the positioning data, analysis data for the positioning problem generated by the big model is obtained through the big model, wherein the analysis data includes data related to the positioning problem in at least one of the sensor data and the positioning algorithm data.

2. The method of claim 1, wherein: The preset multiple problem categories include at least two of the following items: invalid positioning status problem, map information error problem, positioning jitter problem, long positioning initialization time problem, and software stability problem.

3. The method according to claim 1 or 2, wherein: The positioning data also includes video stream data or corresponding picture frame data in the video stream data, wherein the video stream data is a dynamic display of the driving scene of the vehicle on the corresponding road.

4. The method according to claim 1 or 2, wherein: The description information includes at least one of time information and environment information of when the positioning problem occurs.

5. The method of claim 2, wherein: When the problem category includes an invalid positioning status problem, the description information includes at least one of the following items: The positioning status is invalid when the vehicle sensor data is normal; The positioning state is invalid when the vehicle sensor data is abnormal; The positioning status is invalid within the first time length; as well as The positioning state is invalid when the positioning algorithm data is abnormal.

6. The method of claim 2, wherein: When the problem category includes a map information error problem, the description information includes at least one of the following items: When the positioning status is valid, the lane information in the positioning result is wrong; as well as While the positioning status is valid, the road attribute information in the positioning result is incorrect.

7. The method of claim 2, wherein: When the problem category includes a positioning jitter problem, the description information includes at least one of the following items: A positioning jitter problem occurs at a first location and within a first travel distance; and In the preset scenario, positioning jitter problems occurred within the second driving distance.

8. The method of claim 6, wherein: The preset scene includes at least one of the following items: an on-ramp or off-ramp scene, and a strong light condition scene.

9. The method of claim 2, wherein: When the problem category includes a problem of long positioning initialization time, the description information includes at least one of the following items: In a non-on-ramp or off-ramp scenario, the lane information in which the vehicle is currently located cannot be output within the second time length; as well as In an on-ramp or off-ramp scenario or a curve scenario, the lane information in which the vehicle is currently located cannot be output within the third time length.

10. The method of claim 2, wherein: When the problem category includes a problem for software stability, the description information includes at least one of the following items: a software stability issue occurs at a second location and within a third driving distance; and The CPU load exceeds the preset threshold.

11. The method of claim 1, wherein: The positioning data further includes: log data generated by software used for positioning, and wherein the analysis data further includes data in the log data related to the positioning problem.

12. The method of claim 1, wherein: Obtaining analysis data for the positioning problem generated by the language model through the language model includes: The description information, the question category, the positioning data and at least one preset instruction are input into the language model to obtain the analysis data generated by the language model, wherein the at least one preset instruction is used to guide the generation process of the analysis data.

13. A positioning problem analysis device, comprising: A response unit, configured to, in response to a positioning problem, obtain description information corresponding to the positioning problem and a problem category to which the positioning problem belongs among a plurality of preset problem categories; An acquisition unit, configured to acquire positioning data corresponding to when the positioning problem occurs, wherein the positioning data includes vehicle sensor data and positioning algorithm data; as well as An analysis unit is configured to obtain, through a large model, analysis data for the positioning problem generated by the large model based on the description information, the problem category and the positioning data, wherein the analysis data includes data related to the positioning problem in at least one of the sensor data and the positioning algorithm data.

14. The device of claim 13, wherein: The preset multiple problem categories include at least two of the following items: invalid positioning status problem, map information error problem, positioning jitter problem, long positioning initialization time problem, and software stability problem.

15. The device according to claim 13 or 14, wherein: The positioning data also includes video stream data or corresponding picture frame data in the video stream data, wherein the video stream data is a dynamic display of the driving scene of the vehicle on the corresponding road.

16. The device according to claim 13 or 14, wherein: The description information includes at least one of time information and environment information of when the positioning problem occurs.

17. The device of claim 13, wherein: The positioning data further includes: log data generated by software used for positioning, and wherein the analysis data further includes data in the log data related to the positioning problem.

18. The device of claim 13, wherein: The analysis unit comprises: A unit for inputting the description information, the question category, the positioning data and at least one preset instruction into the language model to obtain the analysis data generated by the language model, wherein the at least one preset instruction is used to guide the generation process of the analysis data.

19. An electronic device comprising: at least one processor; as well as a memory communicatively coupled to the at least one processor; in The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 12.

20. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-12.

21. A computer program product comprising a computer program, wherein: When the computer program is executed by a processor, the method according to any one of claims 1 to 12 is implemented.

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