A method, apparatus, device and medium for processing vehicle-mounted data

Abstract

This invention provides a method, apparatus, device, and medium for processing vehicle-mounted data, including: acquiring multiple vehicle-mounted data and vehicle condition information; querying a preset filtering information table based on the vehicle condition information to obtain a data filtering module corresponding to the vehicle condition information, wherein the filtering information table includes multiple vehicle condition information and corresponding multiple data filtering modules; classifying the multiple vehicle-mounted data to generate multiple categories of vehicle-mounted datasets; and filtering the multiple categories of vehicle-mounted datasets based on multiple preset sub-filtering modules in the data filtering modules to obtain multiple valid datasets. This invention can improve the processing efficiency of vehicle-mounted data, enhance the timeliness of data processing, and reduce the cost of data processing.

Description

Technical Field

[0001] This application relates to the field of intelligent driving technology, specifically to a method, apparatus, device, and medium for processing vehicle data. Background Technology

[0002] With the development of intelligent driving technology, various vehicle sensors are gradually being applied to vehicles, recording various types of vehicle data. After collecting and storing various types of vehicle data, the vehicle's central control unit can provide data support for the intelligent driving system.

[0003] Currently, due to the massive amount of vehicle data generated by various vehicle sensors, vehicle data processing systems are experiencing problems such as reduced processing efficiency, poor processing timeliness, and increased processing costs. Summary of the Invention

[0004] In view of the shortcomings of the prior art described above, the present invention provides a method, apparatus, device and medium for processing vehicle data, so as to solve the technical problems of reduced processing efficiency, poor processing timeliness and increased processing cost of vehicle data.

[0005] The present invention provides a method for processing vehicle data, comprising:

[0006] Acquire multiple vehicle data and vehicle condition information;

[0007] Based on the vehicle condition information, a preset filtering information table is queried to obtain the data filtering module corresponding to the vehicle condition information. The filtering information table includes multiple vehicle condition information and multiple corresponding data filtering modules.

[0008] The multiple vehicle data are classified to generate vehicle datasets of multiple categories;

[0009] Based on the multiple sub-filtering modules preset in the data filtering module, the vehicle datasets of multiple categories are filtered to generate multiple valid datasets.

[0010] Multiple valid datasets are aggregated to generate a total dataset.

[0011] In one embodiment of the present invention, the step of acquiring vehicle condition information and multiple vehicle data includes:

[0012] Acquire vehicle data from multiple onboard sensors;

[0013] Based on the vehicle data, vehicle condition information is obtained.

[0014] In one embodiment of the present invention, the step of classifying the plurality of vehicle data to generate a vehicle dataset of multiple categories includes:

[0015] The multiple vehicle data are classified to generate category labels for the multiple vehicle data.

[0016] The vehicle data corresponding to each category label is summarized to generate vehicle datasets for multiple categories.

[0017] In one embodiment of the present invention, the step of filtering the vehicle-mounted datasets of multiple categories to generate multiple valid datasets includes:

[0018] Obtain vehicle environmental information;

[0019] Calculate the matching degree between the vehicle data and the environmental information in each category of the vehicle data set;

[0020] Based on the matching degree, the vehicle data in each type of vehicle dataset is filtered to generate multiple valid datasets.

[0021] In one embodiment of the present invention, the step of obtaining the vehicle's environmental information includes:

[0022] Environmental information is obtained based on onboard data from the vehicle's onboard sensors.

[0023] In one embodiment of the present invention, each type of vehicle-mounted dataset is matched with a corresponding sub-filtering module.

[0024] In one embodiment of the present invention, the step of filtering the vehicle data in each type of vehicle dataset based on the matching degree to generate multiple valid datasets includes:

[0025] In each category of vehicle data sets, vehicle data with a matching degree greater than a preset matching degree threshold are obtained;

[0026] Summarize the vehicle data in each type of vehicle dataset that have a matching degree greater than the matching degree threshold to generate multiple valid datasets.

[0027] The present invention also provides an in-vehicle data processing device, comprising:

[0028] The acquisition module is used to acquire multiple vehicle-mounted data and vehicle condition information.

[0029] The matching module queries a preset filtering information table based on the vehicle condition information to obtain the data filtering module corresponding to the vehicle condition information. The filtering information table includes multiple vehicle condition information and multiple corresponding data filtering modules.

[0030] The generation module classifies the multiple vehicle data sets to generate vehicle datasets of multiple categories.

[0031] The processing module, based on the multiple sub-filtering modules preset in the data filtering module, filters the vehicle datasets of multiple categories respectively to generate multiple valid datasets.

[0032] The aggregation module aggregates multiple valid datasets to generate a total dataset.

[0033] The present invention also provides an electronic device, the electronic device comprising:

[0034] One or more processors;

[0035] A storage device for storing one or more programs, which, when executed by one or more processors, enable the electronic device to perform the above-described method for processing vehicle data.

[0036] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a computer processor, causes the computer to perform the above-described method for processing vehicle data.

[0037] The beneficial effects of the present invention are as follows: The processing of vehicle data in the present invention can improve the processing efficiency of vehicle data, improve the timeliness of data processing, and reduce the cost of data processing.

[0038] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0039] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0040] Figure 1 This is a schematic diagram illustrating the implementation environment of an exemplary embodiment of the vehicle data processing method of this application;

[0041] Figure 2 This is a flowchart illustrating a method for processing vehicle data in an exemplary embodiment of this application;

[0042] Figure 3 yes Figure 2 The flowchart of step S210 in the illustrated embodiment is shown in an exemplary embodiment;

[0043] Figure 4 yes Figure 2The flowchart of step S230 in the illustrated embodiment is shown in an exemplary embodiment;

[0044] Figure 5 yes Figure 2 The flowchart of step S240 in the illustrated embodiment is shown in an exemplary embodiment;

[0045] Figure 6 yes Figure 5 The flowchart of step S520 in the illustrated embodiment is shown in an exemplary embodiment;

[0046] Figure 7 yes Figure 6 The flowchart of step S610 in the illustrated embodiment is shown in an exemplary embodiment;

[0047] Figure 8 This is a block diagram illustrating an in-vehicle data processing apparatus according to an exemplary embodiment of this application;

[0048] Figure 9 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation

[0049] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0050] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0051] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0052] Firstly, with the development of intelligent driving technology, various vehicles are beginning to be equipped with intelligent driving systems. Intelligent driving systems are based on various onboard sensors, using data from these sensors to provide data support to the system's controller. For example, onboard cameras, millimeter-wave radar, and lidar sensors can collect and process information about the vehicle's surrounding environment. The collected data can then be sent to the vehicle controller to assist in achieving intelligent driving. It is important to note that when processing large amounts of data collected by various onboard sensors, low processing efficiency can occur.

[0053] Based on the above background, this application proposes a method for processing vehicle data. Figure 1 This is a schematic diagram illustrating an implementation environment of an exemplary embodiment of the vehicle data processing method of this application. Figure 1 As shown, the server 110 can communicate with the vehicle terminal 120 via a network. The vehicle terminal 120 may include various vehicle-mounted cameras, millimeter-wave radar, lidar, and other sensor devices. The server 110 can obtain vehicle condition information and multiple vehicle data from the vehicle terminal 120. When processing multiple vehicle data, the server can simplify the vehicle data and block the reception of useless data to improve data processing efficiency. For example, in the case of slow parking, the demand for environmental obstacle recognition is high, while the demand for other data is not high. Unnecessary data can be filtered out, and the reception of useless data can be blocked to obtain effective data. This reduces the amount of vehicle data to be processed and increases the efficiency of vehicle data processing. Therefore, under different vehicle conditions, the server 110 can perform different filtering processes on the vehicle data. The server 110 can pre-establish data filtering modules corresponding to different vehicle condition information to facilitate subsequent data filtering processing.

[0054] During actual driving, the server 110 can query a preset filtering information table based on the currently acquired vehicle condition information. After querying, it can obtain the data filtering module corresponding to the current vehicle condition information. This data filtering module can be pre-set with sub-filtering modules for different categories of vehicle data. Therefore, multiple vehicle data can be classified and processed to generate multiple categories of vehicle datasets, corresponding to different sub-filtering modules. This allows for targeted filtering of different categories of vehicle data. For example, vehicle data about the environment view can be divided into one category, and vehicle data about obstacle distance can be divided into another category. Based on multiple sub-filtering modules, the vehicle datasets of multiple categories can be filtered separately, blocking the reception of useless data in each category of vehicle dataset to generate multiple valid datasets. The multiple valid datasets are summarized to generate a total dataset. The amount of vehicle data in this total dataset has been reduced, which can greatly reduce the amount of subsequent data processing and improve data processing efficiency. The server 110 can be implemented using a separate server or a server cluster composed of multiple servers. The invention will be described in detail below through specific embodiments.

[0055] Please see Figure 2 As shown, Figure 2 A flowchart illustrating a method for processing vehicle data according to an embodiment of the present invention includes the following steps:

[0056] Step S210: Obtain multiple vehicle data and vehicle condition information.

[0057] Step S220: Based on the vehicle condition information, query a preset filtering information table to obtain the data filtering module corresponding to the vehicle condition information. The filtering information table includes multiple vehicle condition information and multiple corresponding data filtering modules.

[0058] Step S230: Classify the multiple vehicle data to generate multiple categories of vehicle datasets.

[0059] Step S240: Based on the multiple sub-filtering modules preset in the data filtering module, filter the vehicle datasets of multiple categories respectively to obtain multiple valid datasets.

[0060] Step S250: Summarize the multiple valid datasets to generate a total dataset.

[0061] For step S210, in order to filter and process the vehicle's onboard data according to different vehicle conditions, the vehicle's condition information and onboard data can be obtained first. For example, driving conditions can be high-speed multi-lane driving with clear lane markings, low-speed rural road driving, automatic parking, automatic driving, or other vehicle conditions. Onboard data can be environmental view data, driving speed parameters, distance data between the vehicle and the vehicle in front, or other data.

[0062] It should be noted that vehicle condition information can be obtained based on onboard data. Specifically, this includes acquiring vehicle speed data through onboard sensors such as speed sensors, and acquiring distance data between the vehicle in front and the vehicle itself, as well as a 3D model of the current environment, through onboard sensors such as LiDAR. Different onboard data can correspond to different vehicle conditions. The server 110 can pre-set multiple onboard data points corresponding to various vehicle conditions, establishing a mapping table between vehicle conditions and onboard data. By querying the corresponding vehicle condition in the mapping table using the acquired onboard data, the current vehicle condition information can be obtained.

[0063] For step S220, to improve the processing efficiency of large amounts of vehicle data, the vehicle data can be simplified in advance when processing multiple vehicle data sets, and the reception of useless data can be blocked to improve data processing efficiency. For example, in the case of slow parking, the demand for environmental obstacle recognition is high, while the demand for static map data is not high. The process of receiving useless data can be stopped, and only valid data can be received. This can reduce the amount of vehicle data to be processed and increase the processing efficiency of vehicle data. Furthermore, under different vehicle conditions, the server 110 can perform different filtering processes on the vehicle data by pre-establishing data filtering modules corresponding to different vehicle condition information to facilitate subsequent data filtering processing.

[0064] It should be noted that the server can pre-create a filtering information table for each type of vehicle condition and its corresponding data filtering module. This table allows for quick retrieval of the data filtering module for a specific vehicle condition. When a vehicle condition does not exist in the server's database, this new vehicle condition information can be stored, and a corresponding data filtering module can be created. This new vehicle condition information and its corresponding data filtering module can then be updated in the filtering information table.

[0065] For step S230, in multiple vehicle data sets under a single vehicle condition, since the attributes of these multiple sets of data differ, different filtering rules can be used when screening vehicle data with multiple attributes to improve the accuracy of the screening. Therefore, multiple sub-filtering modules can be pre-set in the data filtering module for each vehicle condition to filter vehicle data with different attributes. Specifically, multiple vehicle data sets can be classified, and vehicle data with the same attribute can be set as a single vehicle dataset. Each vehicle dataset corresponds to a sub-filtering module. This method of classifying and summarizing vehicle data allows for targeted screening of each type of vehicle dataset, improving the screening accuracy.

[0066] As an example, among multiple vehicle-mounted sensors, such as LiDAR, can be used for surrounding environment detection and modeling, while cameras provide real-time 2D images. By combining environmental modeling with 2D images, environmental information can be accurately displayed. Vehicle-mounted data from LiDAR and cameras can be set as one type of vehicle-mounted dataset. Similarly, speed sensors can detect the vehicle's speed, and accelerometers can detect the vehicle's real-time acceleration. This type of speed data can be set as one type of vehicle-mounted dataset. Temperature and humidity sensors and light sensors can detect the driving environment; this type of data can be set as another type of vehicle-mounted dataset. The specific vehicle-mounted data in each type of dataset can be set according to actual processing needs, ensuring it matches the filtering rules of each sub-filtering module.

[0067] For step S240, specifically, for example, the temperature and humidity sensor and the light sensor are set as a type of vehicle-mounted dataset. When filtering vehicle-mounted data in this type of dataset, filtering can be performed based on the corresponding sub-filtering module. This sub-filtering module can pre-set filtering rules for this type of vehicle-mounted dataset, such as filtering based on the matching degree between each piece of vehicle-mounted data and the vehicle condition. For vehicle-mounted data with a matching degree lower than a preset threshold, the reception of this vehicle-mounted data can be blocked.

[0068] Furthermore, this matching-based filtering rule requires pre-setting precise conditions for matching in the sub-filtering modules for the corresponding vehicle datasets. For example, the data filtering module for urban road vehicle conditions has various sub-filtering modules. In the sub-filtering module for vehicle datasets such as temperature and humidity sensors and light sensors, the matching degree between humidity data and rainy day vehicle conditions can be preset to 60%, the matching degree between humidity data and sunny day vehicle conditions to 20%, and the matching degree between humidity data and urban vehicle conditions to 40%. In a rainy environment, the vehicle condition is then defined as rainy urban road vehicle conditions. When calculating the matching degree of this humidity data, the matching degree can be set to P, which can be expressed as P = 60%a + 40%b, where a represents the weight of the matching degree between humidity data and rainy day vehicle conditions in the total matching degree, and b represents the weight of the matching degree between humidity data and urban road vehicle conditions in the total matching degree. In a sunny environment, the vehicle condition is then defined as sunny urban road vehicle conditions. When calculating the matching degree of this humidity data, the matching degree can be set as P, which can be expressed as P = 60%a + 40%b, where a represents the weight of the matching degree between humidity data under sunny urban road conditions and sunny road conditions in the total matching degree, and b represents the weight of the matching degree between humidity data under sunny urban road conditions and urban road conditions in the total matching degree. After calculating and obtaining the matching degree of each vehicle data in the vehicle data set of temperature and humidity sensors and light sensors, the matching degree of each vehicle data in this vehicle data set can be sorted, and the vehicle data within the preset range after sorting can be selected to form a valid dataset. After filtering the vehicle data sets of multiple categories, multiple valid datasets can be obtained.

[0069] For step S250, after obtaining the valid datasets for each category of vehicle-mounted datasets, multiple valid datasets can be summarized to facilitate the subsequent transmission of the total dataset to the central control unit.

[0070] It should be noted that this total dataset includes multiple valid datasets after filtering, which effectively reduces the amount of vehicle-mounted data and improves data processing efficiency.

[0071] In one exemplary embodiment, such as Figure 3 As shown, the process of acquiring multiple vehicle-mounted data and vehicle condition information includes,

[0072] Step S310: Acquire vehicle data from multiple vehicle sensors.

[0073] Step S320: Obtain vehicle condition information based on the vehicle data.

[0074] It's important to note that when processing large amounts of vehicle data, receiving all of it at once can easily clog the data processing channels, leading to low processing efficiency. To simplify the data structure, vehicle data can be filtered based on different driving conditions, blocking the reception of invalid data, freeing up processing channels, and improving data processing speed. In actual driving, different driving conditions require different data. For example, in slow-speed parking situations, the need for environmental obstacle recognition is high, while the need for other data is not high. Unnecessary data can be filtered out, blocking the reception of useless data to obtain valid data.

[0075] It's worth mentioning that vehicle condition information can be obtained based on onboard data. For example, onboard sensors can be configured as a humidity sensor, LiDAR, and a camera. LiDAR can acquire a 3D model of the current environment, while the camera provides real-time images. By combining the 3D environmental model with the images, environmental information can be accurately displayed. The humidity sensor can obtain the current humidity level. Based on this environmental information and humidity, vehicle condition information can be obtained. For instance, if the environmental information shows an urban road environment with a humidity of 70%, it can be determined that the vehicle is in a rainy urban road condition.

[0076] In one exemplary embodiment, such as Figure 4 As shown, the process of classifying multiple types of vehicle data to generate multiple categories of vehicle datasets includes,

[0077] Step S410: Classify the multiple vehicle data to generate category labels for the multiple vehicle data.

[0078] Step S420: Summarize the vehicle data corresponding to each category label to generate vehicle datasets for multiple categories.

[0079] It should be noted that multiple vehicle data sets can be categorized, with vehicle data sets sharing the same attribute grouped together. Each vehicle data set corresponds to a sub-filtering module. This method of categorizing and summarizing vehicle data allows for targeted filtering of each type of vehicle data set, improving filtering accuracy.

[0080] Specifically, when classifying vehicle data, the first step is to determine the category label for each piece of data based on its keywords. Multiple category labels can be pre-defined in the server-side database, and multiple keywords can be set under each category label. When classifying a piece of vehicle data, the keywords contained in that data can be extracted, and the corresponding category label in the database can be queried based on these keywords. After obtaining the category labels for each piece of vehicle data through multiple queries, the vehicle data under each different category label can be aggregated to generate a vehicle dataset for each category label.

[0081] In one exemplary embodiment, such as Figure 5 As shown, the process of filtering the vehicle-mounted datasets of multiple categories to obtain multiple valid datasets includes:

[0082] Step S510: Calculate the matching degree between each vehicle data in each type of vehicle data set and the vehicle condition information.

[0083] Step S520: Based on the matching degree, filter the vehicle data in each type of vehicle dataset to obtain multiple valid datasets.

[0084] It should be noted that when filtering vehicle data, filtering can be based on the matching degree between the vehicle data and vehicle condition information. This matching-based filtering rule requires pre-setting precise conditions for matching within the corresponding vehicle dataset in the sub-filtering module. For example, if a vehicle's current condition is urban road condition, after identifying this urban road condition based on the vehicle data, the corresponding urban road condition data filtering module can be selected for filtering. This urban road condition data filtering module has multiple pre-set sub-filtering modules to filter various types of vehicle datasets. Each sub-filtering module can pre-set filtering rules for various types of vehicle datasets.

[0085] For a specific example, in the sub-filtering module for vehicle-mounted datasets such as those from temperature and humidity sensors and light sensors, the matching degree between humidity data and rainy driving conditions can be preset to 60%, between humidity data and cloudy driving conditions to 40%, between humidity data and sunny driving conditions to 20%, and between humidity data and urban driving conditions to 40%. When the current environment is further identified as rainy, the driving conditions will be determined as rainy urban road driving conditions. When calculating the matching degree of humidity data, the matching degree can be set as P, which can be expressed as P = 60%a + 40%b, where a represents the weight of the matching degree between humidity data and rainy driving conditions in urban road driving conditions out of the total matching degree, and b represents the weight of the matching degree between humidity data and urban road driving conditions in urban road driving conditions out of the total matching degree. If the current environment is sunny, the driving conditions will be determined as sunny urban road driving conditions. When calculating the matching degree of this humidity data, the matching degree can be set as P, which can be expressed as P = 60%a + 40%b, where a represents the weight of the matching degree between humidity data under sunny urban road conditions and sunny road conditions in the total matching degree, and b represents the weight of the matching degree between humidity data under sunny urban road conditions and urban road conditions in the total matching degree. After calculating and obtaining the matching degree of each vehicle data in the vehicle data set of temperature and humidity sensors and light sensors, the matching degree of each vehicle data in this vehicle data set can be sorted, and the vehicle data within the preset range after sorting can be selected to form a valid dataset. After filtering the vehicle data sets of multiple categories, multiple valid datasets can be obtained.

[0086] In one exemplary embodiment, such as Figure 6 As shown, the process of filtering the vehicle data in each type of vehicle dataset based on the matching degree to obtain multiple valid datasets includes:

[0087] Step S610: In each type of vehicle data set, obtain vehicle data with a matching degree greater than a preset matching degree threshold.

[0088] Step S620: Summarize the vehicle data in each type of vehicle data set whose matching degree is greater than the matching degree threshold, and generate multiple valid data sets.

[0089] It should be noted that after calculating the matching degree between each piece of vehicle data and vehicle condition information in each type of vehicle dataset, vehicle data with a matching degree greater than a preset threshold can be selected as valid data. For example, the matching degree threshold can be set to 80%. When the matching degree between a piece of vehicle data and vehicle condition information is 88%, this piece of vehicle data can be considered valid data. When the matching degree between a piece of vehicle data and vehicle condition information is 60%, this piece of vehicle data can be considered invalid data. By blocking the reception of invalid data, the amount of vehicle data to be processed can be reduced, and data processing efficiency can be improved. Furthermore, vehicle data with a matching degree greater than the matching degree threshold in each type of vehicle dataset can be considered valid data, and the valid data in each type of vehicle dataset can be summarized to generate a valid dataset corresponding to each type of vehicle dataset.

[0090] In one exemplary embodiment, such as Figure 7 As shown, the process of obtaining vehicle data with a matching degree greater than a preset matching degree threshold in each type of vehicle dataset includes:

[0091] Step S710: In each type of vehicle data set, determine whether the matching degree between each vehicle data and the vehicle condition information is greater than the matching degree threshold.

[0092] Step S720: If the matching degree between a certain vehicle data and vehicle condition information is greater than the matching degree threshold, obtain the vehicle data.

[0093] It should be noted that in each type of vehicle-mounted dataset, vehicle-mounted data with a matching degree greater than a preset matching degree threshold can be acquired as valid data. The matching degree threshold can be set based on matching requirements. In each type of vehicle-mounted dataset, vehicle-mounted data with a matching degree less than or equal to the preset matching degree threshold can be acquired as invalid data. By blocking the reception of invalid data, the amount of vehicle-mounted data processed can be reduced, and data processing efficiency can be improved.

[0094] As another example, valid data can also be obtained by sorting based on matching degree. Specifically, after calculating the matching degree between the vehicle data and vehicle condition information in each type of vehicle dataset, the vehicle data in each type of vehicle dataset can be sorted based on the matching degree to generate a sorting table. The higher the matching degree, the higher the priority. Using this sorting table, multiple vehicle data with higher priority can be selected as valid data.

[0095] It is evident that the above solutions can improve the efficiency and timeliness of vehicle data processing while reducing the cost of data processing.

[0096] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0097] In one embodiment, a vehicle data processing device is provided, which corresponds one-to-one with the vehicle data processing methods described in the above embodiments. For example... Figure 8 As shown, the vehicle data processing device includes an acquisition module 801, a matching module 802, a generation module 803, a processing module 804, and a summarization module 805.

[0098] In one embodiment, the acquisition module 801 is specifically used for:

[0099] Acquire vehicle data from multiple onboard sensors;

[0100] Based on the vehicle data, vehicle condition information is obtained.

[0101] In one embodiment, the generation module 803 is specifically used for:

[0102] The multiple vehicle data are classified to generate category labels for the multiple vehicle data.

[0103] The vehicle data corresponding to each category label is summarized to generate vehicle datasets for multiple categories.

[0104] In one embodiment, the processing module 804 is specifically used for:

[0105] Calculate the matching degree between each vehicle data point in each category of the vehicle data set and the vehicle condition information.

[0106] Based on the matching degree, the vehicle data in each type of vehicle dataset is filtered to obtain multiple valid datasets.

[0107] In one embodiment, the processing module 804 is further configured to:

[0108] In each category of vehicle-mounted datasets, vehicle-mounted data with a matching degree greater than a preset matching degree threshold are obtained.

[0109] Summarize the vehicle data in each type of vehicle dataset that have a matching degree greater than the matching degree threshold to generate multiple valid datasets.

[0110] In one embodiment, the processing module 804 is further configured to:

[0111] In each type of vehicle data set, determine whether the matching degree between each vehicle data and the vehicle condition information is greater than the matching degree threshold.

[0112] If the matching degree between a certain vehicle data and vehicle condition information is greater than the matching degree threshold, the vehicle data is acquired.

[0113] It should be noted that the vehicle data processing device and the vehicle data processing method provided in the above embodiments belong to the same concept. The specific operation methods of each module and unit have been described in detail in the method embodiments and will not be repeated here. In practical applications, the traffic condition refresh device provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0114] Embodiments of this application also provide an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the vehicle data processing method provided in the above embodiments.

[0115] Figure 9 A schematic diagram of a computer system suitable for implementing the embodiments of this application is shown. It should be noted that... Figure 9 The computer system of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0116] like Figure 9 As shown, the computer system includes a Central Processing Unit (CPU) 901, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 902 or programs loaded from storage portion 908 into Random Access Memory (RAM) 903, such as performing the methods described in the above embodiments. The RAM 903 also stores various programs and data required for system operation. The CPU 901, ROM 902, and RAM 903 are interconnected via a bus 904. An Input / Output (I / O) interface 905 is also connected to the bus 904.

[0117] The following components are connected to I / O interface 905: an input section 906 including a keyboard, mouse, etc.; an output section 907 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 908 including a hard disk, etc.; and a communication section 909 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to I / O interface 905 as needed. Removable media 911, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 910 as needed so that computer programs read from them can be installed into storage section 908 as needed.

[0118] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 909, and / or installed from removable medium 911. When the computer program is executed by central processing unit (CPU) 901, it performs various functions defined in the system of this application.

[0119] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0120] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0121] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0122] Another aspect of this application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a computer's processor, causes the computer to perform the vehicle data processing method described above. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not assembled into the electronic device.

[0123] Another aspect of this application provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the vehicle data processing method provided in the various embodiments described above.

[0124] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method of processing in-vehicle data, characterized by, The method comprises the following steps: obtaining a plurality of vehicle-mounted data and vehicle condition information of a vehicle; based on the vehicle condition information, querying a preset screening information table to obtain a data screening module corresponding to the vehicle condition information, wherein the screening information table comprises a plurality of vehicle condition information and a plurality of corresponding data screening modules; classifying and processing the plurality of vehicle-mounted data to generate a plurality of categories of vehicle-mounted data sets; based on a plurality of sub-screening modules preset in the data screening module, screening the plurality of categories of vehicle-mounted data sets respectively to obtain a plurality of effective data sets; summarizing the plurality of effective data sets to generate a total data set; wherein the step of classifying and processing the plurality of vehicle-mounted data to generate a plurality of categories of vehicle-mounted data sets comprises: classifying and processing the plurality of vehicle-mounted data respectively through the keywords of each vehicle-mounted data to generate category labels of the plurality of vehicle-mounted data; summarizing the vehicle-mounted data corresponding to each category label respectively to generate a plurality of categories of vehicle-mounted data sets; wherein each category of vehicle-mounted data set is matched with a corresponding sub-screening module, and the sub-screening module is preset with screening rules for each category of vehicle-mounted data set; wherein the step of screening the plurality of categories of vehicle-mounted data sets respectively to obtain a plurality of effective data sets comprises: calculating the matching degree of each vehicle-mounted data in each category of vehicle-mounted data set with the vehicle condition information; based on the matching degree, screening the vehicle-mounted data in each category of vehicle-mounted data set to obtain a plurality of effective data sets; wherein the step of calculating the matching degree of each vehicle-mounted data in each category of vehicle-mounted data set with the vehicle condition information comprises: according to a plurality of sub-conditions contained in the vehicle condition information and a matching degree weight preset for each sub-condition and each category of vehicle-mounted data, the matching degree of each vehicle-mounted data with the vehicle condition information is calculated; wherein the step of screening the vehicle-mounted data in each category of vehicle-mounted data set based on the matching degree to obtain a plurality of effective data sets comprises: in each category of vehicle-mounted data set, obtaining vehicle-mounted data with a matching degree greater than a preset matching degree threshold, and blocking the reception of vehicle-mounted data with a matching degree less than or equal to the matching degree threshold; summarizing the vehicle-mounted data with a matching degree greater than the matching degree threshold in each category of vehicle-mounted data set to generate a plurality of effective data sets.

2. The method of claim 1, wherein The step of obtaining a plurality of vehicle-mounted data and vehicle condition information of a vehicle comprises: obtaining vehicle-mounted data of a plurality of vehicle-mounted sensors of a vehicle; based on the vehicle-mounted data, obtaining vehicle condition information.

3. The method of claim 1, wherein Each category of vehicle-mounted data set is matched with a corresponding sub-screening module.

4. The method of claim 1, wherein The step of obtaining vehicle-mounted data with a matching degree greater than a preset matching degree threshold in each category of vehicle-mounted data set comprises: in each category of vehicle-mounted data set, judging whether the matching degree of each vehicle-mounted data with the vehicle condition information is greater than the matching degree threshold; if the matching degree of a certain vehicle-mounted data with the vehicle condition information is greater than the matching degree threshold, obtaining the vehicle-mounted data.

5. An apparatus for processing in-vehicle data, characterized by comprising: The method comprises the following steps: an obtaining module for obtaining a plurality of vehicle-mounted data and vehicle condition information of a vehicle; The matching module is configured to query a preset screening information table based on the vehicle condition information to obtain a data screening module corresponding to the vehicle condition information, wherein the screening information table includes a plurality of vehicle condition information and a plurality of corresponding data screening modules. The generating module is configured to perform classification processing on the plurality of vehicle data to generate a plurality of categories of vehicle data sets, specifically configured to perform classification processing on the plurality of vehicle data by keywords of the vehicle data respectively to generate category labels of the plurality of vehicle data, and respectively aggregate vehicle data corresponding to each category label to generate a plurality of categories of vehicle data sets, wherein each category of vehicle data set is matched with a corresponding sub-screening module, and the sub-screening module is preset with screening rules of each category of vehicle data set. The processing module is configured to perform screening processing on the plurality of categories of vehicle data sets based on a plurality of sub-screening modules preset in the data screening module to obtain a plurality of effective data sets, specifically configured to calculate a matching degree of each vehicle data in each category of vehicle data set with the vehicle condition information, and perform screening processing on the vehicle data in each category of vehicle data set based on the matching degree to obtain a plurality of effective data sets, and further specifically configured to calculate the matching degree of each vehicle data with the vehicle condition information according to a plurality of sub-conditions included in the vehicle condition information and a matching degree weight set in advance for each sub-condition and each category of vehicle data, and further specifically configured to obtain vehicle data with a matching degree greater than a preset matching degree threshold in each category of vehicle data set, block reception of vehicle data with a matching degree less than or equal to the matching degree threshold, aggregate vehicle data with a matching degree greater than the matching degree threshold in each category of vehicle data set, and generate a plurality of effective data sets. The aggregation module is configured to aggregate the plurality of effective data sets to generate a total data set.

6. An electronic device, comprising: The electronic device includes: One or more processors; A storage device configured to store one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the vehicle data processing method of any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, A computer program is stored thereon, which, when executed by a processor of a computer, causes the computer to perform the vehicle data processing method of any one of claims 1 to 4.

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