Log data processing method, device, equipment, storage medium and program product

Abstract

The present disclosure provides a log data processing method and device, equipment, storage medium and program product, and relates to the technical field of vehicles. The method is applied to a vehicle machine, and includes: determining target log data based on initial log data associated with a preset vehicle running scene; reconstructing a three-dimensional graph corresponding to the preset vehicle running scene based on the target log data; and in response to the target log data including abnormal log data, performing abnormal marking on the three-dimensional graph corresponding to the abnormal log data. The method can intuitively and prominently display abnormal conditions occurring in the vehicle running process in the reconstructed three-dimensional scene, and can improve the positioning efficiency of abnormal vehicle running conditions.

Description

Technical Field

[0001] This disclosure relates to the field of vehicle technology, and in particular to a log data processing method, apparatus, device, storage medium, and program product. Background Technology

[0002] Currently, with the development of vehicle electronics and intelligence, it is possible to develop 3D models of real-world scenarios related to vehicle operation. This allows for the reconstruction of these scenarios, displaying 3D images of pedestrians, lane lines, parking lines, buildings, obstacles, and other vehicles around the vehicle, thereby improving the user experience. However, during the use of this function, discrepancies may arise between the reconstructed real-world scenario and the actual scenario. In such cases, it is necessary to analyze the relevant vehicle operation log data to find the cause of the discrepancy. However, log data is usually displayed in plain text format, which cannot intuitively reflect the reasons for the above anomalies. Summary of the Invention

[0003] This disclosure presents a log data processing method, log data processing device, electronic device, computer-readable storage medium, and computer program product. By intuitively and prominently displaying abnormal situations that occur during vehicle operation in a reconstructed three-dimensional scene, the efficiency of locating abnormal vehicle operation situations can be improved.

[0004] In a first aspect, embodiments of this disclosure propose a log data processing method applied to a vehicle infotainment system, comprising: determining target log data based on initial log data associated with a preset vehicle operating scenario; reconstructing a three-dimensional graph corresponding to the preset vehicle operating scenario based on the target log data; and, in response to the target log data including abnormal log data, marking the three-dimensional graph corresponding to the abnormal log data as abnormal.

[0005] Secondly, this disclosure provides a log data processing device applied to an in-vehicle infotainment system, comprising: a filtering module, a construction module, and a processing module. The filtering module is configured to determine target log data based on initial log data associated with a preset vehicle operating scenario; the construction module is configured to reconstruct a 3D graph corresponding to the preset vehicle operating scenario based on the target log data; and the processing module is configured to mark anomalies in the 3D graph corresponding to the abnormal log data in response to the inclusion of abnormal log data in the target log data.

[0006] Thirdly, embodiments of this disclosure provide an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to implement the log data processing method as described in any implementation of the first aspect.

[0007] Fourthly, embodiments of this disclosure provide a non-transitory computer-readable storage medium storing computer instructions that enable a computer to implement the log data processing method described in any implementation of the first aspect.

[0008] Fifthly, embodiments of this disclosure provide a computer program product including a computer program that, when executed by a processor, can implement the log data processing method as described in any implementation of the first aspect.

[0009] The log data processing scheme provided in this disclosure can filter target log data from initial log data associated with a preset vehicle operation scenario to reconstruct a 3D map corresponding to that scenario. Furthermore, during the reconstruction of the 3D map based on the target log data, if abnormal log data is detected, the generated 3D map based on the abnormal log data needs to be marked as abnormal for prominent display. In this way, by intuitively and prominently displaying abnormal situations occurring during vehicle operation in the reconstructed 3D scene, the efficiency of locating abnormal vehicle operation situations can be improved.

[0010] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0011] Other features, objects, and advantages of this disclosure will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0012] Figure 1 This is an exemplary system architecture to which this disclosure can be applied;

[0013] Figure 2 A flowchart of a log data processing method provided in this embodiment of the disclosure;

[0014] Figure 3 A flowchart of another log data processing method provided in this disclosure embodiment;

[0015] Figure 4 This is a flowchart illustrating a log data processing method in an application scenario provided by an embodiment of the present disclosure.

[0016] Figure 5 A top-view schematic diagram of a reconstructed 3D scene containing anomaly markers, provided as an embodiment of this disclosure;

[0017] Figure 6 A structural block diagram of a log data processing device provided in an embodiment of this disclosure;

[0018] Figure 7 This is a schematic diagram of the structure of an electronic device suitable for performing a log data processing method, provided as an embodiment of the present disclosure. Detailed Implementation

[0019] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding; these should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description. It should be noted that, unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0020] It should be noted that the collection, acquisition, storage, processing, transmission, provision, disclosure, and application of user personal information (such as identity verification information) involved in the technical solution disclosed herein are all carried out with the user's knowledge and explicit authorization, comply with the provisions of relevant laws and regulations, and do not violate public order and good morals.

[0021] With the development of vehicle electrification and intelligence, it is possible to develop 3D models of real-world scenarios related to vehicle operation, enabling the reconstruction of corresponding real-world scenes. For example, Advanced Driving Assistance Systems (ADAS) and Automatic Parking Assist (APA) can use 3D to display a realistic reconstruction of the surrounding environment, including pedestrians, lane lines, parking lines, buildings, obstacles, and other vehicles, thereby improving the user experience. However, during the use of this function, discrepancies may arise between the reconstructed real-world scene and the actual scene. For instance, a parking space might not be displayed, a lane line might be shown, or a warning might not be displayed. In such cases, it is necessary to analyze the relevant vehicle log data to find the cause of the discrepancy. However, log data is usually displayed in plain text, which does not intuitively reflect the reasons for these anomalies. For example, for a parking space, the log data typically only records four coordinate points, making it difficult to intuitively see whether these four coordinate points form a complete parking space. Furthermore, as log data accumulates, the presence of abnormal data within a large amount of log data further increases the difficulty of finding abnormal log data.

[0022] Therefore, a solution is needed that can intuitively display abnormal log data in order to efficiently locate the problems causing the abnormalities.

[0023] Figure 1 An exemplary architecture of a vehicle 100 to which embodiments of the log data processing methods, log data processing apparatus, electronic devices, computer-readable storage media, and computer program products of this disclosure can be applied is illustrated. The vehicle 100 may include a first controller 110, a second controller 120, a screen 130, and peripherals 140. The first controller 110 and the second controller 120 are communicatively connected to enable data interaction.

[0024] In some optional implementations of the embodiments of this disclosure, the first controller 110 can be implemented as a microcontroller (MCU), mainly responsible for controlling and managing various devices and sensors of the vehicle. The first controller 110 can be used to acquire and process sensor data, control and schedule actuators, etc. It should be noted that, without departing from the teachings of this disclosure, the first controller 110 can also be implemented as other devices with data processing capabilities, and no specific limitations are made herein.

[0025] In some optional implementations of the embodiments of this disclosure, the second controller 120 can be implemented as a system-on-a-chip (SoC), which integrates a processor, memory, peripherals, and other functions. Due to its powerful computing and processing capabilities, the second controller 120 can be integrated into an in-vehicle infotainment system (referred to as an in-vehicle infotainment system) and used to process and analyze sensor data in real time, and make decisions. It should be noted that, without departing from the teachings of this disclosure, the second controller 120 can also be implemented as other devices with computing and processing capabilities, and no specific limitations are made herein.

[0026] In some optional implementations of the embodiments of this disclosure, the first controller 110 and the second controller 120 may be specifically set in the domain controller (such as the cockpit domain controller) of the vehicle 100.

[0027] It should be noted that in some optional implementations of the embodiments of this disclosure, other controllers may also be provided in the vehicle 100 to better realize the functions of the vehicle system, etc., which are not specifically limited here.

[0028] In some optional implementations of the embodiments of this disclosure, the screen 130 may include, but is not limited to, at least one of an instrument cluster display, a central control display, and a rear-seat display. In some optional implementations, after the vehicle system mounted on the second controller 120 is started, it can output the instrument cluster interface, etc., to the instrument cluster display for display, and output the central control interface, etc., to the central control display for display.

[0029] In some optional implementations of the embodiments of this disclosure, the peripheral device 140 of the vehicle 100 may refer to the device of the vehicle system mounted on the second controller 120, which may include, but is not limited to, devices such as microphones and vehicle cameras, etc., which will not be listed here.

[0030] Please refer to Figure 2 , Figure 2 A flowchart of a log data processing method provided in this disclosure embodiment, which can be applied to in-vehicle systems, wherein process 200 includes the following steps:

[0031] Step 201: Determine the target log data based on the initial log data associated with the preset vehicle operation scenario.

[0032] This step is intended for the entity executing the log data processing method (e.g., Figure 1The vehicle-mounted system (V2X) on the second controller 120 (shown in the diagram) filters the required target log data from the initial log data associated with a preset vehicle operation scenario. This preset vehicle operation scenario refers to an operation scenario that requires reconstructing a 3D map of the actual environment (also known as the real environment or physical environment). The preset vehicle operation scenario can include various environmental objects related to vehicle operation, including but not limited to parking spaces, lanes, the current vehicle itself, objects surrounding the current vehicle (such as other vehicles, pedestrians, non-motorized vehicles, obstacles, etc.), and road surfaces. The initial log data and target log data can exist in the form of text data, JSON (JavaScript Object Notation) format, Extensible Markup Language (XML) format, etc., and the target log data can include part or all of the initial log data.

[0033] Step 202: Reconstruct a 3D map corresponding to the preset vehicle operation scenario based on the target log data.

[0034] Building upon step 201 above, this step aims to enable the executing entity to reconstruct the 3D model corresponding to the preset vehicle operation scenario based on the selected target log data, thereby visually displaying the environmental objects included in the preset vehicle operation scenario in the form of a 3D model. It should be noted that the 3D model corresponding to the preset vehicle operation scenario can be reconstructed using 3D development tools. The specific 3D development tools used can be selected according to specific needs and are not specifically limited here.

[0035] Step 203: In response to the inclusion of abnormal log data in the target log data, anomaly markers are applied to the 3D graph corresponding to the abnormal log data.

[0036] Based on step 202 above, this step aims to mark the 3D map generated based on the abnormal log data in the 3D map corresponding to the preset vehicle operation scenario when abnormal log data is detected in the target log data, so as to more efficiently locate the corresponding abnormality.

[0037] The log data processing method provided in this disclosure can filter target log data from initial log data associated with a preset vehicle operation scenario to reconstruct a 3D map corresponding to that scenario. Furthermore, during the reconstruction of the 3D map based on the target log data, if abnormal log data is detected, the 3D map generated based on the abnormal log data needs to be marked as abnormal for prominent display. In this way, by intuitively and prominently displaying abnormal situations occurring during vehicle operation in the reconstructed 3D scene, the efficiency of locating abnormal vehicle operation situations can be improved.

[0038] Please refer to Figure 3 , Figure 3 A flowchart of another log data processing method provided in this disclosure embodiment, which is applied to an in-vehicle system, wherein process 300 includes the following steps:

[0039] Step 301: Determine the target log data based on the initial log data associated with the preset vehicle operation scenario.

[0040] Step 302: Reconstruct a 3D map corresponding to the preset vehicle operation scenario based on the target log data.

[0041] Step 303: In response to the inclusion of abnormal log data in the target log data, anomaly marking is performed on the 3D graph corresponding to the abnormal log data.

[0042] The above steps 301-303 and as follows Figure 2 Steps 201-203 shown are basically the same. For identical or similar parts, please refer to the corresponding parts of the previous embodiment; they will not be repeated here. Further, in response to the existence of multiple 3D graphs corresponding to the aforementioned preset vehicle operation scenario, step 302 can be specifically executed as: reconstructing multiple 3D graphs corresponding to the preset vehicle operation scenario based on the target log data; further, the log data processing method provided in this disclosed embodiment may also include the following steps:

[0043] Step 304: Obtain the generation time information of the target log data.

[0044] This step is intended for the entity executing the log data processing method (e.g., Figure 1 The vehicle-mounted system (V2X) on the second controller 120 shown acquires the generation time information corresponding to the target log data filtered from the initial log data. This generation time information can be in the form of a timestamp. Furthermore, in this embodiment, recording the corresponding generation time information during the generation of initial log data associated with a preset vehicle operating scenario facilitates fault diagnosis and system monitoring.

[0045] Step 305: Based on the generation time information of the target log data, determine the playback order of multiple 3D graphs reconstructed based on the target log data.

[0046] Step 306: Perform playback-related operations on multiple 3D images in the playback order.

[0047] Based on step 304 above, this step aims to enable the execution entity to determine the time information corresponding to each of the multiple 3D maps in the preset vehicle operation scenario based on the generation time information of the target log data. Then, based on the time sequence indicated by the time information corresponding to the multiple 3D maps, a playback order that can be used to control the dynamic display of the multiple 3D maps can be determined. Then, playback-related operations can be performed on the multiple 3D maps corresponding to the preset vehicle operation scenario according to the playback order.

[0048] The log data processing method provided in this embodiment can efficiently and accurately determine the playback order of multiple 3D images corresponding to the preset vehicle operation scenario for orderly dynamic display based on the generation time information of the target log data. It can also perform playback-related operations on these multiple 3D images accordingly, thereby improving the efficiency of locating abnormal vehicle operation conditions and enhancing the playback experience based on the 3D images. In this embodiment, the playback-related operations that can be performed on the multiple 3D images may include, but are not limited to, at least one of the following, to provide a diverse user experience: adjusting playback speed, pausing playback, forward playback (e.g., advancing a certain duration at once or advancing a corresponding duration by dragging the progress control), backward playback (e.g., rewinding a certain duration at once or rewinding a corresponding duration by dragging the progress control), and skipping to the next playback location.

[0049] Furthermore, in some optional implementations of the embodiments of this disclosure, in Figure 3 Based on the corresponding log data processing method embodiment, the playback-related operations performed on the multiple 3D images in step 306 above can be specifically executed as follows:

[0050] In response to the detection of a selection operation for the generation time information of the 3D graph corresponding to the abnormal log data, the playback progress of multiple 3D graphs is switched from the currently playing screen to the screen that includes the 3D graph corresponding to the abnormal log data.

[0051] In this embodiment, during the orderly playback of multiple 3D maps corresponding to the preset vehicle operation scenario according to the above playback order, in response to the detection of a playback jump command, that is, when the selection operation for the generation time information of any 3D map corresponding to the abnormal log data is detected, the playback progress can be switched from the current screen to the screen including the 3D map corresponding to the abnormal log data, thereby further improving the efficiency of locating abnormal vehicle operation conditions.

[0052] Furthermore, in some optional implementations of the embodiments of this disclosure, the generation time information of the target log data (including abnormal log data) can be marked in the form of time nodes in the progress bar of the interface displaying the playback of the multiple three-dimensional images, so that the selection operation can be received based on the progress bar.

[0053] Furthermore, in some optional implementations of the embodiments of this disclosure, the generation time information of the target log data (including abnormal log data) can be displayed in a list in a designated area of ​​the interface for playing the multiple 3D images, so that the selection operation can be received based on the display list; wherein, the generation time information of the target log data (including abnormal log data) can be displayed in the display list in the playback order, and further, when there are many items of generation time information, the information currently displayed in the display list can be refreshed to display more information. The specific refresh method is not specifically limited here and can be set according to actual needs.

[0054] Furthermore, in addition to displaying the content corresponding to the above-mentioned generation time information, the progress bar or display list can also display key descriptive information of the abnormal situation corresponding to the above-mentioned abnormal log data, so as to quickly obtain the key content corresponding to the abnormal situation, thereby further improving the efficiency of locating abnormal vehicle operation.

[0055] In some optional implementations of the embodiments of this disclosure, in Figure 2 or Figure 3 Based on the corresponding log data processing method embodiments, step 201 or step 301 above can be specifically executed as follows:

[0056] Determine the target data type corresponding to the preset vehicle operation scenario; wherein, the target data type includes at least one of the following: lane data, parking space data, vehicle data, vehicle surrounding object data, and road surface data; the log data of the target data type selected from the initial log data is determined as the target log data.

[0057] In this embodiment, considering the differences in log data data types across different vehicle operation scenarios, to improve the efficiency of filtering target log data for reconstructing the corresponding 3D map of the vehicle operation scenario, efficient and accurate data filtering can be achieved based on the data types of log data corresponding to different vehicle operation scenarios. The data types of the log data can correspond to the environmental objects related to vehicle operation included in the aforementioned vehicle operation scenario. Therefore, the target log data can include, but is not limited to, one or more of lane data, parking space data, vehicle data, vehicle surrounding object data, and road surface data.

[0058] Specifically, the aforementioned lane data may include, but is not limited to, data related to lane lines, such as the starting point, ending point, and curvature parameters of lane lines; the aforementioned parking space data may include, but is not limited to, the position coordinates of each vertex corresponding to the parking space, and the parking space type; the aforementioned vehicle data may include, but is not limited to, the vehicle operation warning data, real-time location data, vehicle model data, and size parameters (such as length and width) of the current vehicle associated with the target log data. Among these, the vehicle operation warning data may include, but is not limited to, warning types (such as warnings that do not meet the conditions for changing lanes or turning due to the detection of pedestrians crossing the road or the current vehicle being too close to surrounding vehicles), and warning status data; the aforementioned data on objects surrounding the vehicle may include, but is not limited to, the position data and size parameters of objects surrounding the current vehicle, which may vary depending on the type of objects surrounding the current vehicle and are not specifically limited here; and the aforementioned road surface data may include, but is not limited to, road surface position coordinates and road surface width data.

[0059] In some optional implementations of the embodiments of this disclosure, the log data processing method may further include: setting a data type identifier for initial log data associated with a preset vehicle operation scenario based on the target data type. Furthermore, the efficiency and accuracy of filtering log data of the target data type from the initial log data can be improved based on this data type identifier.

[0060] Furthermore, in some optional implementations of any of the above-disclosed embodiments, the preset vehicle operation scenarios include, but are not limited to, at least one of vehicle driving assistance scenarios and vehicle parking assistance scenarios. The vehicle driving assistance scenario may refer to a vehicle operation scenario implemented based on the vehicle's ADAS function, and the vehicle parking assistance scenario may refer to a vehicle operation scenario implemented based on the vehicle's APA function.

[0061] Furthermore, in Figure 2 or Figure 3Based on the corresponding log data processing method implementation, abnormal log data can be accurately filtered from the target log data through different implementation methods to ensure the stability of marking abnormal situations occurring during vehicle operation in the reconstructed 3D scene. Specifically, these implementation methods may include, but are not limited to, the following:

[0062] In one implementation of this disclosure, the log data processing method may further include the following:

[0063] In response to the detection of data in the target log data whose values ​​do not match the preset standard values, it is determined that the target log data includes abnormal log data, and the data whose values ​​do not match the standard values ​​are identified as abnormal log data; wherein, the preset standard values ​​include the normal values ​​of parameters of environmental objects corresponding to the preset vehicle operation scenario in the actual environment.

[0064] In this implementation, abnormal log data included in the target log data can be efficiently and accurately filtered out by evaluating whether the values ​​of each data in the target log data are within the corresponding legal range.

[0065] Specifically, the pre-set standard value in this implementation method can refer to the normal parameter value of the environmental object corresponding to the preset vehicle operation scenario in the actual environment (or real environment) of vehicle operation, and the normal parameter value can refer to the actual measured value or actual design value corresponding to the parameter of the environmental object. Furthermore, the pre-set standard value can correspond to the above-mentioned target data type, that is, different standard values ​​can be set according to different data types to be applicable to the value legality assessment in different scenarios.

[0066] Furthermore, in response to the preset vehicle operation scenario being the aforementioned vehicle driving assistance scenario, the target data type corresponding to the vehicle driving assistance scenario may include (but is not limited to) lane data, vehicle data, vehicle surrounding object data, and road surface data. It can be further understood that the environmental objects corresponding to the vehicle driving assistance scenario may include (but are not limited to) lanes, the current vehicle itself, objects surrounding the current vehicle, and road surface, etc. Also, in response to the preset vehicle operation scenario being the aforementioned vehicle parking assistance scenario, the target data type corresponding to the vehicle parking assistance scenario may include (but is not limited to) parking space data, vehicle data, and vehicle surrounding object data. It can be further understood that the environmental objects corresponding to the vehicle driving assistance scenario may include (but are not limited to) parking spaces, the current vehicle itself, and objects surrounding the current vehicle, etc.

[0067] In a specific implementation, the environment object corresponding to the aforementioned preset vehicle operation scenario can be a parking space. The parking space data in the corresponding log data can include an indicator value for the parking space type. The normal values ​​of the parameters of the environment object can include a first value indicating the straight parking space type, a second value indicating the horizontal parking space type, and a third value indicating the diagonal parking space type. If the indicator value for the parking space type read in the target log data does not match any of the first to third values, then the data can be considered as abnormal log data.

[0068] Furthermore, the aforementioned parking space data may also include parking space size data, wherein the normal values ​​of the parameters related to the environment object may include the standard size value of the parking space, and may further include the standard width value and the minimum standard length value of the parking space; then, if the size value of the parking space read in the target log data does not match at least one of the standard width value and the minimum standard length value, the data can be considered as abnormal log data.

[0069] Furthermore, the aforementioned parking space data may also include the coordinate values ​​of the parking spaces. The normal values ​​of the parameters of the environment object may include the standard coordinate value range of the parking spaces. Therefore, if the coordinate values ​​of the parking spaces read in the target log data do not match the standard coordinate value range, the data can be considered as abnormal log data.

[0070] In another specific implementation, the environmental object corresponding to the aforementioned preset vehicle operation scenario can be the current vehicle itself or vehicles surrounding the current vehicle. Furthermore, the vehicle data or surrounding object data in the corresponding log data can include vehicle size data. The normal values ​​of the parameters of the environmental object can include the standard size values ​​of the vehicle, and can further include the standard length range and standard width range of the vehicle. Therefore, if the vehicle size data read in the target log data does not match at least one of the standard length range and standard width range of the vehicle, then the data can be considered as abnormal log data.

[0071] In another specific implementation, the environment object corresponding to the aforementioned preset vehicle operation scenario can be an urban road, and the road surface data in the corresponding log data can include the road width value. The normal value of the parameter of the environment object can include the standard width range of the road. Therefore, if the road width value of the urban road read in the target log data does not match the standard width range of the road, the data can be considered as abnormal log data.

[0072] In another implementation of the embodiments of this disclosure, the log data processing method described above may further include the following:

[0073] In response to the detection of data with missing fields in the target log data, it is determined that the target log data includes abnormal log data, and the data with missing fields is identified as abnormal log data; wherein, the fields include the complete description field of the environmental object corresponding to the preset vehicle operation scenario in the actual environment.

[0074] In this implementation, abnormal log data included in the target log data can be efficiently and accurately filtered out by evaluating whether the field composition of each data in the target log data is complete.

[0075] Specifically, the fields in this implementation can refer to the integrity description field of the environment object corresponding to the preset vehicle operation scenario in the actual environment (or real environment) of vehicle operation, and the integrity description field can refer to all the constituent fields corresponding to the environment object. Furthermore, the integrity description field of the environment object can correspond to the target data type mentioned above, that is, the integrity description field is different in response to different data types, so as to be suitable for integrity assessment of the field composition in different scenarios.

[0076] Furthermore, in response to the preset vehicle operation scenario being the aforementioned vehicle driving assistance scenario, the target data type corresponding to the vehicle driving assistance scenario may include (but is not limited to) lane data, vehicle data, vehicle surrounding object data, and road surface data. It can be further understood that the environmental objects corresponding to the vehicle driving assistance scenario may include (but are not limited to) lanes, the current vehicle itself, objects surrounding the current vehicle, and road surface, etc. Also, in response to the preset vehicle operation scenario being the aforementioned vehicle parking assistance scenario, the target data type corresponding to the vehicle parking assistance scenario may include (but is not limited to) parking space data, vehicle data, and vehicle surrounding object data. It can be further understood that the environmental objects corresponding to the vehicle driving assistance scenario may include (but are not limited to) parking spaces, the current vehicle itself, and objects surrounding the current vehicle, etc.

[0077] In a specific implementation, the environment object corresponding to the above-mentioned preset vehicle operation scenario can be a parking space, and the corresponding log data can include the constituent field data of the parking space. The complete description field of the environment object includes 4 coordinate fields (i.e., all constituent fields are 4 coordinate fields). If the number of coordinate fields of the parking space read in the target log data is not 4, then the data can be considered as abnormal log data.

[0078] In another specific implementation, the environmental object corresponding to the aforementioned preset vehicle operation scenario can be a lane. The corresponding log data can include lane line composition field data. The completeness description field of the environmental object includes a start point field, an end point field, and a preset number of curvature parameter fields (i.e., all composition fields are the start point field, end point field, and preset number of curvature parameter fields). The value of the preset number can be set according to specific circumstances and is not specifically limited here. In one example, the preset number can be 4. Therefore, if the lane coordinate field read in the target log data does not completely cover the start point field, end point field, and preset number of curvature parameter fields, the data can be considered as abnormal log data.

[0079] In another specific implementation, the environmental object corresponding to the aforementioned preset vehicle operation scenario can be the current vehicle itself or vehicles surrounding the current vehicle. The corresponding log data can include the constituent field data of the corresponding vehicle. The completeness description field of the environmental object can include the completeness description field of the vehicle operation warning. Furthermore, the completeness description field of the vehicle operation warning can include the warning type field and the warning status field (i.e., all constituent fields are the warning type field and the warning status field). Therefore, if the constituent fields of the vehicle operation warning read in the target log data do not completely cover the warning type field and the warning status field, the data can be considered as abnormal log data.

[0080] In another implementation of the embodiments of this disclosure, the log data processing method described above may further include the following:

[0081] In response to the detection of data in the target log data that does not match the reporting stage with the pre-set standard reporting stage, it is determined that the target log data includes abnormal log data, and the data whose reporting stage does not match the standard reporting stage is identified as abnormal daily data; wherein, the standard reporting stage includes the normal reporting stage of log data corresponding to the preset vehicle operation scenario in the actual environment.

[0082] In this implementation, abnormal log data included in the target log data can be efficiently and accurately filtered out by evaluating whether the reporting stage of the log data is logically reasonable.

[0083] Specifically, the pre-defined standard reporting stage in this implementation method can refer to the stage during which log data corresponding to a preset vehicle operation scenario in the actual (or real) environment of vehicle operation should be reported according to the vehicle configuration (i.e., the aforementioned normal reporting stage) during normal vehicle operation. Furthermore, this pre-defined standard reporting stage can correspond to the aforementioned target data type; that is, different reporting stages correspond to different data types, thus facilitating the logical rationality assessment of log data reporting in different scenarios.

[0084] Furthermore, in response to the preset vehicle operation scenario being the aforementioned vehicle driving assistance scenario, the target data type corresponding to the vehicle driving assistance scenario may include (but is not limited to) lane data, vehicle data, vehicle surrounding object data, and road surface data. It can be further understood that the environmental objects corresponding to the vehicle driving assistance scenario may include (but are not limited to) lanes, the current vehicle itself, objects surrounding the current vehicle, and road surface, etc. Also, in response to the preset vehicle operation scenario being the aforementioned vehicle parking assistance scenario, the target data type corresponding to the vehicle parking assistance scenario may include (but is not limited to) parking space data, vehicle data, and vehicle surrounding object data. It can be further understood that the environmental objects corresponding to the vehicle driving assistance scenario may include (but are not limited to) parking spaces, the current vehicle itself, and objects surrounding the current vehicle, etc.

[0085] In a specific implementation, the environmental object corresponding to the aforementioned preset vehicle operation scenario can be a parking space, and the corresponding log data can include indication information of the reporting stage of parking space data. The normal reporting stage of the log data corresponding to this environmental object can be the parking space search stage in the parking process (e.g., it can include the parking space search stage, the parking space determination stage, the parking start stage, and the parking end stage). If parking space data reported in a stage other than the parking space search stage is read in the target log data, then this data can be considered as abnormal log data.

[0086] In another specific implementation, the environmental object corresponding to the aforementioned preset vehicle operation scenario can be a lane, and the corresponding log data can include indication information of the reporting stage of lane data. The normal reporting stage of the log data corresponding to this environmental object can be reporting when in non-parking / parking / parking gear (i.e., P (Park) gear). If lane data reported in P gear is read in the target log data, then the data can be considered as abnormal log data.

[0087] In another specific implementation, the environmental object corresponding to the aforementioned preset vehicle operation scenario can be the current vehicle itself or vehicles around the current vehicle. The corresponding log data can include indication information of the reporting stage of vehicle operation warning data. The normal reporting stage of the log data corresponding to the environmental object can be reporting when the warning function is enabled. If vehicle operation warning data that was reported when the warning function was not enabled is read in the target log data, then the data can be considered as abnormal log data.

[0088] To enhance understanding, this disclosure also provides a specific implementation scheme based on a particular application scenario. Please refer to the example below. Figure 4 The process 400 shown can be applied to the logic layer of the vehicle's infotainment system. This process 400 is applicable to scenarios that require the reconstruction of 3D scenes, including but not limited to APA and ADAS scenarios, and includes the following:

[0089] Step 401: Obtain the raw log data (corresponding to the initial log data in the above embodiments). Each log message can be marked with an identifier (corresponding to the data type identifier in the above embodiments) to indicate the message type. For example, lane line information is identified by LaneInfo, parking space information is identified by Slot, etc., and the format of the log information can be JSON, XML, etc.

[0090] Step 402: Filter the log data required for scene reconstruction from the raw log data (corresponding to the target log data determined in the above embodiment); specifically, the linux grep command, etc., can be used to filter out the log data related to 3D scene reconstruction according to the identifier of the above message type.

[0091] Step 403: Parse the filtered log data to analyze its reasonableness (corresponding to identifying abnormal log data in the above embodiment); specifically, based on the above identifiers and information formats, parse information such as parking spaces, lane lines (e.g., solid lines, dashed lines, double yellow lines), vehicles, road surfaces, and warnings from the log data, and record the timestamp of each log entry. This includes performing reasonableness analysis on the log data and providing anomaly alerts to help locate problems, including but not limited to:

[0092] (1) Check if the value is within the legal range and if some data exceeds the limit. For example, if the value of the parking space type field is 1, it represents a horizontal parking space; 2 represents a straight parking space; and 3 represents a diagonal parking space. If the value of the parking space type field in the log is not one of 1, 2, or 3, it can be marked as an illegal range. If the parking space coordinate range is specified to be between [-15, 15], if the parking space coordinates in the log exceed this range, it can be marked as an illegal range. The standard size of a straight parking space is 2.5 meters wide and 5 meters long. If the width and / or length of the parking space in the log exceeds this range, it can be marked as an illegal range. Any deviation from standard dimensions (specific rules can be customized) can be marked as illegal. Urban road lane widths are generally 3.0-3.5 meters. If the road width in the log (e.g., the spacing between two lane lines) significantly deviates from the standard width (specific rules can be customized), it can be marked as illegal. The length of a car is typically between 3.8 and 4.3 meters, and the width is generally between 1.6 and 1.8 meters. If the length and / or width of a car in the log significantly deviates from the standard size (specific rules can be customized), it can be marked as illegal.

[0093] (2) Field integrity check, for example: a parking space information must have 4 coordinate points. If the number of coordinate points of the parking space information in the log is incorrect, it can be marked as an abnormal situation.

[0094] (3) Logical rationality check, for example: if the current state is not in the parking space search stage, but a message reporting parking space information appears in the log, it can be marked as an abnormal situation; the state of a vehicle when parking generally goes through 4 stages from searching for a parking space -> confirming a parking space -> starting parking -> parking ends. If the state goes directly from searching for a parking space to starting parking, skipping confirming a parking space, it can be marked as an abnormal situation.

[0095] Step 404: Using 3D development tools, such as Unity Editor and Unreal Editor, reconstruct the 3D world based on the parsed log data and output the reconstruction results. This will allow for a clear visualization of any anomalies in the 3D world reconstructed from the logs, thereby helping to resolve issues that arise during the development, testing, or use of the 3D scene reconstruction project.

[0096] Specifically, it supports playing logs in chronological order, and allows for adjusting playback speed, pausing, forward, rewinding, and jumping to a specific time point (such as jumping to the time point of an abnormal log, supporting one-click location of that time point to intuitively view the abnormal problem in the reconstructed 3D scene). The 3D scene reconstructed based on the logs can display information such as parking spaces, lane lines, vehicles, road surfaces, and warnings from the log data.

[0097] For example, in a 3D scene reconstructed from logs, parking space information exists in a certain agreed-upon format, such as a parking space entry in the log: Slot(slotId=1,type=Vertical,P0=(2.96,-1.70),P1=(2.97,0.64),P2=(8.36,-1.74),P3=(8.37,0.61)). The parking space information is reconstructed by parsing the logs and displayed in the 3D scene. During log parsing, four points are checked to determine the validity of the parking space, such as... Figure 5 The parking spaces marked with gray blocks (which can also be other colors depending on specific needs) are only 2.5 meters long, far shorter than the standard parking space length of 5 meters. Therefore, they are highlighted in an accent color in the 3D scene to remind developers to conduct further analysis. Thus, the solution in this embodiment helps to intuitively and quickly locate problems.

[0098] Further reference Figure 6 As an implementation of the methods shown in the above figures, this disclosure provides an embodiment of a log data processing apparatus, which is similar to... Figure 2 or Figure 3 Corresponding to the method embodiment shown, the device can be specifically applied to in-vehicle infotainment systems.

[0099] like Figure 6 As shown, the log data processing device 600 of this embodiment may include: a filtering module 601, a construction module 602, and a processing module 603.

[0100] The filtering module 601 is configured to determine target log data based on initial log data associated with a preset vehicle operation scenario; the construction module 602 is configured to reconstruct a 3D graph corresponding to the preset vehicle operation scenario based on the target log data; and the processing module 603 is configured to mark the 3D graph corresponding to the abnormal log data as abnormal in response to the inclusion of abnormal log data in the target log data.

[0101] In the log data processing device 600 of this embodiment, the specific processing of the filtering module 601, the construction module 602, and the processing module 603, and the resulting technical effects, can be found in the following references: Figure 2 The relevant descriptions of steps 201-203 in the corresponding embodiments will not be repeated here.

[0102] In some optional implementations of this embodiment, the filtering module 601 is further configured to: determine the target data type corresponding to the preset vehicle operation scenario; wherein the target data type includes at least one of the following: lane data, parking space data, vehicle data, vehicle surrounding object data, and road surface data; and determine the log data of the target data type filtered from the initial log data as the target log data.

[0103] In some optional implementations of this embodiment, the processing module 603 may further be configured to: in response to detecting that there is data in the target log data whose value does not match the preset standard value, determine that the target log data includes abnormal log data, and determine the data whose value does not match the standard value as abnormal log data; wherein, the preset standard value includes the normal value of the parameter of the environmental object corresponding to the preset vehicle operation scenario in the actual environment.

[0104] In some optional implementations of this embodiment, the processing module 603 may further be configured to: in response to detecting that there is data with missing fields in the target log data, determine that the target log data includes abnormal log data, and determine the data with missing fields as abnormal log data; wherein, the fields include a complete description field of the environmental object corresponding to the preset vehicle operation scenario in the actual environment.

[0105] In some optional implementations of this embodiment, the processing module 603 may further be configured to: in response to detecting data in the target log data that does not match the reporting stage with the preset standard reporting stage, determine that the target log data includes abnormal log data, and identify the data whose reporting stage does not match the standard reporting stage as abnormal daily data; wherein, the standard reporting stage includes the normal reporting stage of log data corresponding to the preset vehicle operation scenario in the actual environment.

[0106] In some optional implementations of this embodiment, the number of the three-dimensional graphs reconstructed based on the target log data is multiple; and the log data processing device 600 may further include: an acquisition module, a determination module, and a playback module (not shown in the figure).

[0107] The acquisition module is configured to acquire the generation time information of the target log data; the determination module is configured to determine the playback order of multiple 3D graphs reconstructed based on the generation time information of the target log data; and the playback module is configured to perform playback-related operations on the multiple 3D graphs according to the playback order.

[0108] In the log data processing device 600 of this embodiment, the specific processing of the acquisition module, the determination module, and the playback module, and the resulting technical effects, can be referred to respectively. Figure 3 The relevant descriptions of steps 304-306 in the corresponding embodiments will not be repeated here.

[0109] In some optional implementations of this embodiment, the playback module is further configured to: in response to detecting a selection operation for the generation time information of the 3D graph corresponding to the abnormal log data, switch the playback progress of the multiple 3D graphs from the currently playing screen to the screen including the 3D graph corresponding to the abnormal log data.

[0110] In some optional implementations of this embodiment, the preset vehicle operation scenario includes at least one of a vehicle driving assistance scenario and a vehicle parking assistance scenario.

[0111] This embodiment is a device embodiment corresponding to the method embodiment described above. The log data processing device provided in this embodiment can filter target log data from initial log data associated with a preset vehicle operation scenario to reconstruct a 3D map corresponding to the preset vehicle operation scenario. Furthermore, during the reconstruction of the corresponding 3D map based on the target log data, when abnormal log data is detected in the target log data, the 3D map generated based on the abnormal log data needs to be marked as abnormal for prominent display. Thus, by intuitively and prominently displaying abnormal situations occurring during vehicle operation in the reconstructed 3D scene, the efficiency of locating abnormal vehicle operation situations can be improved.

[0112] According to embodiments of this disclosure, this disclosure also provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to implement the log data processing method described in any of the above embodiments.

[0113] According to embodiments of this disclosure, this disclosure also provides a readable storage medium storing computer instructions that enable a computer to implement the log data processing method described in any of the above embodiments when executed.

[0114] According to embodiments of this disclosure, this disclosure also provides a computer program product that, when executed by a processor, can implement the log data processing method described in any of the above embodiments.

[0115] Figure 7 A schematic block diagram of an example electronic device 700 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as in-vehicle systems in vehicles. 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 claimed herein.

[0116] like Figure 7 As shown, the electronic device 700 includes a processing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. The RAM 703 may also store various programs and data required for the operation of the electronic device 700. The processing unit 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0117] Multiple components in electronic device 700 are connected to I / O interface 705, including: input unit 706, such as a touch screen; output unit 707, such as various types of displays, speakers, etc.; storage unit 708, such as an embedded multi-media card (EMMC), universal flash storage (UFS), etc.; and communication unit 709, such as a network card, modem, wireless transceiver, etc. Communication unit 709 allows electronic device 700 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0118] Processing unit 701 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processing unit 701 performs the various methods and processes described above, such as log data processing methods. For example, in some embodiments, the log data processing method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 708. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 700 via ROM 702 and / or communication unit 709. When the computer program is loaded into RAM 703 and executed by processing unit 701, one or more steps of the log data processing method described above may be performed. Alternatively, in other embodiments, processing unit 701 may be configured to perform log data processing methods by any other suitable means (e.g., by means of firmware).

[0119] Various embodiments 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-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0120] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0121] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0122] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide 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 sound input, voice input, or tactile input).

[0123] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0124] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud microprocessor, a microprocessor product within the cloud computing service system, designed to address the shortcomings of traditional physical microprocessors and Virtual Private Server (VPS) services, such as high management difficulty and weak business scalability.

[0125] According to the log data processing scheme of this disclosure, target log data for reconstructing a 3D map corresponding to a preset vehicle operation scenario can be filtered from the initial log data associated with that scenario. Furthermore, during the reconstruction of the 3D map based on the target log data, if abnormal log data is detected, the 3D map generated based on the abnormal log data needs to be marked as abnormal for prominent display. Thus, by intuitively and prominently displaying abnormal situations occurring during vehicle operation in the reconstructed 3D scene, the efficiency of locating abnormal vehicle operation situations can be improved.

[0126] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not a limitation herein; and the terms "first," "second," "third" (if any) in this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence, nor do they constitute a specific limitation.

[0127] It should also be understood that expressions such as "comprising," "including," "having," "containing," and / or "comprising" are open-ended rather than closed-ended expressions in this disclosure, indicating the presence of the stated features, elements, and / or components, but not excluding the presence of one or more other features, elements, components, and / or combinations thereof. Furthermore, when expressions such as "at least one of..." appear after a list of listed features, they modify the entire list of features, not just individual elements in the list. Additionally, when describing embodiments of this disclosure, the word "may" is used to mean "one or more embodiments of this disclosure." And the term "exemplary" is intended to refer to an example or illustration.

[0128] Unless otherwise specified, all terms used herein (including engineering and technical terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. It should also be understood that, unless expressly stated in this disclosure, terms as defined in common dictionaries shall be interpreted as having the meaning consistent with their meaning in the context of the relevant art, and not as having an idealized or overly formalized meaning.

[0129] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. 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 this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A log data processing method, applied to an in-vehicle infotainment system, comprising: Target log data is determined based on initial log data associated with a preset vehicle operation scenario; Reconstruct a 3D graph corresponding to the preset vehicle operation scenario based on the target log data; In response to the inclusion of abnormal log data in the target log data, anomaly marking is performed on the 3D graph corresponding to the abnormal log data.

2. The method according to claim 1, wherein, The determination of target log data based on initial log data associated with a preset vehicle operation scenario includes: Determine the target data type corresponding to the preset vehicle operation scenario; wherein, the target data type includes at least one of the following: lane data, parking space data, vehicle data, vehicle surrounding object data, and road surface data; Log data of the target data type selected from the initial log data is determined as the target log data.

3. The method according to claim 1, further comprising: In response to the detection that there is data in the target log data whose value does not match the preset standard value, it is determined that the target log data includes the abnormal log data, and the data whose value does not match the standard value is identified as the abnormal log data; wherein, the preset standard value includes the normal parameter values ​​of environmental objects corresponding to the preset vehicle operation scenario in the actual environment.

4. The method according to claim 1, further comprising: In response to the detection of data with missing fields in the target log data, it is determined that the target log data includes the abnormal log data, and the data with missing fields is identified as the abnormal log data; wherein, the fields include a complete description field of the environmental object corresponding to the preset vehicle operation scenario in the actual environment.

5. The method according to claim 1, further comprising: In response to the detection of data in the target log data that does not match the reporting stage with the preset standard reporting stage, it is determined that the target log data includes the abnormal log data, and the data whose reporting stage does not match the standard reporting stage is identified as the abnormal daily data; wherein, the standard reporting stage includes the normal reporting stage of log data corresponding to the preset vehicle operation scenario in the actual environment.

6. The method according to claim 1, wherein, The number of 3D graphs reconstructed based on the target log data is multiple; as well as The method further includes: Obtain the generation time information of the target log data; Based on the generation time information of the target log data, the playback order of multiple 3D graphs reconstructed based on the target log data is determined; Playback-related operations are performed on the multiple 3D images according to the playback order.

7. The method according to claim 6, wherein, The operation of playing related functions on the plurality of 3D images includes: In response to detecting a selection operation for the generation time information of the 3D graph corresponding to the abnormal log data, the playback progress of the multiple 3D graphs is switched from the currently playing screen to the screen including the 3D graph corresponding to the abnormal log data.

8. The method according to any one of claims 1-7, wherein, The preset vehicle operation scenarios include at least one of vehicle driving assistance scenarios and vehicle parking assistance scenarios.

9. A log data processing device, applied to an in-vehicle infotainment system, comprising: The filtering module is configured to determine target log data based on initial log data associated with a preset vehicle operation scenario; The construction module is configured to reconstruct a 3D graph corresponding to the preset vehicle operation scenario based on the target log data; The processing module is configured to mark anomalies in a 3D graph corresponding to the abnormal log data in response to the inclusion of abnormal log data in the target log data.

10. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the log data processing method according to any one of claims 1-8.

11. A non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the log data processing method according to any one of claims 1-8.

12. A computer program product comprising a computer program that, when executed by a processor, implements the steps of the log data processing method according to any one of claims 1-8.

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