Intelligent vehicle data processing method and device, equipment and storage medium

Abstract

The invention relates to an intelligent vehicle data processing method and device, equipment and a storage medium, and the method comprises the steps: determining a first parameter corresponding to vehicle information according to the vehicle information of a vehicle end, and transmitting the first parameter to the vehicle end; receiving first data screened by the vehicle end according to the first parameter, and storing the first data in a data warehouse based on a preset form; determining a second parameter corresponding to the service information according to the vehicle service information, calling first data stored based on a preset form in the data warehouse, identifying a field corresponding to the second parameter in the first data stored based on the preset form, and analyzing the field to obtain second data; and determining a processing result of the vehicle service information based on the second data. According to the invention, computing resources and storage resources can be saved.

Description

Technical Field

[0001] The present application relates to the field of automobile control technology, and in particular to an intelligent vehicle data processing method, device, equipment and storage medium. Background Art

[0002] With the rapid development of automobile intelligence and networking, the scale of data messages reported by vehicles has expanded dramatically. This trend poses a severe challenge to automobile manufacturers, especially large automobile manufacturers. The massive data generated by vehicles during operation includes but is not limited to the usage data of various intelligent driving functions such as smart cruise, NOA (Navigate on Autopilot), CNO (City Navigation Guided Pilot), memory parking, AEB (Autonomous Emergency Braking), automatic parking, etc. However, the surge in the amount of data not only increases the burden on storage and computing resources, but also makes it particularly complex and difficult to extract valuable information from massive data and conduct efficient analysis.

[0003] Although existing solutions can process the data reported by vehicles to a certain extent, they generally have problems such as low resource utilization and insufficient data analysis depth. Traditional methods often fail to dynamically configure vehicle information according to vehicle model differences, resulting in unnecessary redundant data storage and invalid calculations, increasing the system burden and causing waste of storage and computing resources. Therefore, a method is urgently needed to solve the above problems. Summary of the invention

[0004] Based on this, the present application provides an intelligent vehicle data processing method, device, equipment and storage medium to solve the problems existing in the prior art.

[0005] In a first aspect, a method for processing intelligent vehicle data is provided, the method comprising:

[0006] Determine a first parameter corresponding to the vehicle information according to the vehicle information of the vehicle end, and send the first parameter to the vehicle end;

[0007] Receiving first data obtained by the vehicle terminal through screening according to the first parameter, and storing the first data in a data warehouse based on a preset format;

[0008] Determine a second parameter corresponding to the business information according to the vehicle business information, call the first data stored in a preset format in the data warehouse, identify a field corresponding to the second parameter in the first data stored in the preset format, and parse the field to obtain the second data;

[0009] A processing result of the vehicle service information is determined based on the second data.

[0010] According to an achievable method in an embodiment of the present application, the receiving of the first data obtained by the vehicle terminal according to the first parameter screening and storing the first data in a data warehouse based on a preset format includes:

[0011] Receiving first data obtained by the vehicle terminal according to the first parameter screening through a message queue transmission protocol, and storing the first data in a message middleware;

[0012] The message middleware sends the first data to the data warehouse, and the data warehouse stores the first data based on a preset format.

[0013] According to an achievable manner in an embodiment of the present application, the message middleware sends the first data to the data warehouse, including:

[0014] The message middleware sends the first data to the data processing platform in batches based on a preset time interval;

[0015] The data processing platform receives the first data and removes abnormal data from the first data;

[0016] The data processing platform acquires vehicle information corresponding to the first data, and binds the first data to the corresponding vehicle information to obtain first data bound to the vehicle information;

[0017] The first data bound with the vehicle information is sent to the data warehouse.

[0018] According to an achievable method in an embodiment of the present application, storing the first data in a data warehouse based on a preset format includes:

[0019] The first data is stored in the first layer of the data warehouse in JSON format.

[0020] According to an achievable method in an embodiment of the present application, determining a second parameter corresponding to the business information according to vehicle business information, calling first data stored in a preset form in the data warehouse, identifying a field corresponding to the second parameter in the first data stored in a preset form, and parsing the field to obtain the second data includes:

[0021] Acquiring vehicle service information and determining a second parameter corresponding to the service information;

[0022] The first data stored in the data warehouse in the JSON format is called according to a pre-written SQL statement, a field corresponding to the second parameter is identified in the first data, the field is parsed to obtain the second data, and the second data is stored in the second layer of the data warehouse.

[0023] According to an achievable method in an embodiment of the present application, determining a processing result of the vehicle service information based on the second data includes:

[0024] Calculating an intermediate processing result of the vehicle business information based on the second data stored in the second layer of the data warehouse, and storing the intermediate processing result in the third layer of the data warehouse;

[0025] Calculating the final processing result of the vehicle business information based on the intermediate processing results stored in the third layer of the data warehouse, and storing the final processing result in the fourth layer of the data warehouse;

[0026] The final processing result is sent to the vehicle end based on the fourth layer of the data warehouse.

[0027] According to an achievable method in an embodiment of the present application, determining a first parameter corresponding to the vehicle information according to the vehicle information of the vehicle end, and sending the first parameter to the vehicle end, includes:

[0028] A first parameter corresponding to the vehicle end is determined according to the vehicle model, a first message is configured based on the first parameter, and the first message is sent to the vehicle end.

[0029] In a second aspect, a smart vehicle data processing method is provided, which is applied to a vehicle side, and the method includes:

[0030] Receiving a first parameter sent by the cloud, wherein the first parameter is determined by the cloud according to vehicle information of the vehicle end;

[0031] Based on the first parameter, first data corresponding to the first parameter is filtered and the first data is sent to the cloud. The cloud is used to store the first data in a preset format in a data warehouse, determine a second parameter corresponding to the business information according to the vehicle business information, call the first data stored in the preset format in the data warehouse, identify the field corresponding to the second parameter in the first data stored in the preset format, parse the field to obtain the second data, and determine the processing result of the vehicle business information based on the second data.

[0032] In a third aspect, a computer device is provided, comprising:

[0033] at least one processor; and

[0034] a memory communicatively connected to the at least one processor; wherein,

[0035] The memory stores computer instructions that can be executed by the at least one processor, and the computer instructions are executed by the at least one processor to enable the at least one processor to perform the method involved in the first aspect or the second aspect mentioned above.

[0036] In a fourth aspect, a computer-readable storage medium is provided, on which computer instructions are stored, characterized in that the computer instructions are used to enable a computer to execute the method involved in the first aspect or the second aspect above.

[0037] According to the technical content provided in the embodiments of the present application, the present application reduces the redundancy of data transmission and improves the efficiency of data processing by filtering the first data through the first parameter corresponding to the vehicle-side information and storing it in the data warehouse based on a preset form. The data warehouse adopts an efficient data storage and retrieval mechanism, which can quickly respond to query requests and provide strong support for the processing of vehicle business information. Determining the second parameter based on the vehicle business information and calling the field corresponding to the second parameter in the data warehouse can accurately obtain the required data and provide accurate data support for the processing of vehicle business information. This data utilization method improves the data processing speed, reduces the reporting of redundant messages, and saves computing resources and storage resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A schematic diagram of a cloud process of an intelligent vehicle data processing method in one embodiment;

[0039] Figure 2 A flowchart of a cloud-based intelligent vehicle data processing method in one embodiment;

[0040] Figure 3 A schematic diagram of a process flow of a vehicle-side intelligent vehicle data processing method in an embodiment;

[0041] Figure 4 A flow chart of a vehicle-side intelligent vehicle data processing method in one embodiment;

[0042] Figure 5 FIG. 4 is a schematic structural diagram of a computer device in one embodiment. DETAILED DESCRIPTION

[0043] The present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0044] Figure 1A flowchart of a smart vehicle data processing method provided in an embodiment of the present application, the method is applied to the cloud, such as Figure 1 As shown, the method may include the following steps:

[0045] Step 101: Determine a first parameter corresponding to the vehicle information according to the vehicle information of the vehicle end, and send the first parameter to the vehicle end.

[0046] Specifically, through the TSP (Telematics Service Provider, Internet of Vehicles Service Provider) cloud configuration center, the first parameter corresponding to the vehicle end can be dynamically configured according to the vehicle information of the vehicle end, wherein the vehicle information can be vehicle model information or single vehicle information. According to the vehicle model information or single vehicle information of the vehicle, the first parameter corresponding to the vehicle model or single vehicle information is configured in the cloud, such as vehicle speed, average fuel consumption, mileage, power consumption, ICC (Intelligent Cruise Control, Intelligent Cruise Control), ACC (Adaptive Cruise Control, Adaptive Cruise Control), NOA (Navigate on Autopilot, Navigation Assisted Driving), CNOA (Conditional Navigate on Autopilot, Conditional Navigation Assisted Driving), HPA (Home-zone Parking Assist, Memory Parking Assist), RPA (Remote Parking Assist, Remote Parking Assist), APA (Automatic Parking Assist, Automatic Parking Assist), AEB (Autonomous Emergency Braking, Automatic Emergency Braking System) and other parameters, and the first parameter corresponding to the vehicle information of the vehicle end is sent to the vehicle end. Therefore, the vehicle side reports data according to the first parameter, which can reduce the reporting of unnecessary field information and save computing resources and storage resources.

[0047] Step 102: Receive the first data obtained by the vehicle terminal according to the first parameter screening, and store the first data in a data warehouse based on a preset format.

[0048] Specifically, the vehicle side selects the first data to be reported according to the first parameter, and reports the first data. The cloud receives the first data obtained by the vehicle side according to the first parameter, and stores the first data in a data warehouse based on a preset format. The preset format is a lightweight data exchange format that occupies less storage space, which makes data transmission and storage more efficient. For example, the preset format can be in json format, xml format, etc.

[0049] Step 103: Determine a second parameter corresponding to the business information based on the vehicle business information, call the first data stored in a preset format in the data warehouse, identify a field corresponding to the second parameter in the first data stored in the preset format, and parse the field to obtain the second data.

[0050] Specifically, vehicle business information refers to business information of intelligent vehicles, such as autonomous driving business. A second parameter corresponding to the business information is determined based on the vehicle business information, wherein the second parameter represents the parameter required to process the business information. For example, if the business information is an autonomous driving business, the second parameter required for the autonomous driving business includes ACC, NOA, AEB and other parameters. Furthermore, a field corresponding to the second parameter is identified in the first data stored based on a preset format, wherein the original data generated according to the TSP configuration based on the preset format in the data warehouse identifies the field corresponding to the second parameter, that is, only the fields required by the business are parsed according to business needs, thereby obtaining the second data, saving storage and computing resources.

[0051] Step 104: Determine a processing result of the vehicle service information based on the second data.

[0052] Specifically, based on the above steps, the processing result of the business information is calculated based on the second data in this step. For example, based on parameters such as ACC, NOA, and AEB, the intelligent driving usage of ACC, NOA, and AEB is calculated, thereby obtaining the processing result of the vehicle-side autonomous driving business. Then, the mileage, duration, and number of APA, ACC, and NOA of each vehicle can be calculated as needed for each vehicle series, which can indirectly reflect the safe driving of the vehicle. Users can free their hands better and take over the vehicle in dangerous times to ensure driving safety, making driving easier and safer.

[0053] It can be seen that the embodiment of the present application determines the first parameter corresponding to the vehicle information through the vehicle information of the vehicle end, and sends the first parameter to the vehicle end; receives the first data obtained by filtering the first parameter at the vehicle end, and stores the first data in a preset form in the data warehouse; determines the second parameter corresponding to the business information according to the vehicle business information, calls the first data stored in the preset form in the data warehouse, identifies the field corresponding to the second parameter in the first data stored in the preset form, and parses the field to obtain the second data; determines the processing result of the vehicle business information based on the second data. The first data is filtered by the first parameter corresponding to the vehicle end information and stored in the data warehouse based on the preset form, which reduces the redundancy of data transmission and improves the efficiency of data processing. The data warehouse adopts an efficient data storage and retrieval mechanism, which can quickly respond to query requests and provide strong support for the processing of vehicle business information. The second parameter is determined according to the vehicle business information, and the field corresponding to the second parameter is called in the data warehouse, which can accurately obtain the required data and provide accurate data support for the processing of vehicle business information. This data utilization method improves the data processing speed, reduces the reporting of redundant messages, and saves computing resources and storage resources.

[0054] In one embodiment of the present application, a first parameter corresponding to the vehicle information is determined based on the vehicle information on the vehicle side, and the first parameter is sent to the vehicle side, including: determining the first parameter corresponding to the vehicle side according to the vehicle model of the vehicle side, configuring a first message based on the first parameter, and sending the first message to the vehicle side.

[0055] Specifically, Figure 1 As shown, combined with Figure 2 The TSP cloud can determine the first parameter corresponding to the vehicle side according to the vehicle model, configure the required first message based on the first parameter, and send the command of configuring the first message to the vehicle side DHU (Desktop Head Unit, intelligent vehicle system) through the interface. The vehicle side receives the sent command, uploads the corresponding fields according to the specified, obtains the CAN bus on the DHU side, sends the frame number, APA, ACC, NOA, reporting time and other data to the FlexRay bus, and sends the data to the cloud through the BGM gateway.

[0056] It can be seen that the TSP cloud can determine the first parameter corresponding to the vehicle end according to the vehicle model, and data transmission is no longer stereotyped, but can be customized according to the needs of different vehicle models. This adaptability enhances the pertinence and effectiveness of data transmission and can achieve customized transmission.

[0057] In one embodiment of the present application, first data obtained by filtering the first parameter on the vehicle side is received, and the first data is stored in a data warehouse based on a preset format, including: receiving the first data obtained by filtering the first parameter on the vehicle side through a message queue transmission protocol, and storing the first data in a message middleware; the message middleware sends the first data to the data warehouse, and the data warehouse stores the first data based on a preset format.

[0058] Specifically, Figure 1 As shown, combined with Figure 2 The vehicle computer sends the first data obtained based on the first parameter screening to the message queue transmission protocol MQTT (Message Queuing Telemetry Transport) through the BGM gateway. The cloud receives the first data through the message queue transmission protocol and seamlessly connects the data in MQTT to the message middleware TSP KAFKA through Kafka-connect-mqtt. The message middleware TSP KAFKA sends the first data to the data warehouse, and the data warehouse stores the first data based on a preset format.

[0059] In one embodiment of the present application, the message middleware sends the first data to the data warehouse, including: the message middleware sends the first data to the data processing platform in batches based on a preset time interval; the data processing platform receives the first data and eliminates abnormal data in the first data; the data processing platform obtains vehicle information corresponding to the first data, binds the first data to the corresponding vehicle information to obtain the first data bound to the vehicle information; and sends the first data bound to the vehicle information to the data warehouse.

[0060] Specifically, Figure 1 As shown, combined with Figure 2 The message middleware TSP KAFKA collects logs as a high-performance message system and provides them to consumers through subscription. TSP KAFKA is consumed in batches through the data processing platform spark, for example, once every 15 minutes. The data processing platform spark parses the reported signal messages in batches through offline operations, and binds the vehicle series pushed by MES at the same time, so that each message is also bound to the vehicle series, and the messages bound to the vehicle series are stored in the data warehouse. The data processing platform spark also cleans the abnormal data and removes the abnormal data in the first data, such as removing abnormal longitude and latitude information, and filtering and removing the data that exceeds the normal longitude and latitude range according to the national longitude and latitude range.

[0061] It can be seen that the embodiment of the present application is responsible for consuming the data reported by the vehicle from the Kafka message system through the data processing platform, cleaning the abnormal data, binding the vehicle information, and storing the processed data in the data warehouse. The consumption of the data processing platform is executed once every preset time interval to ensure the timeliness and accuracy of the data. In this way, the data processing platform plays a key role in data processing and analysis, and supports subsequent processes such as data warehouse hierarchical management, intelligent driving usage analysis, and data visualization.

[0062] In one embodiment of the present application, storing the first data in a data warehouse based on a preset format includes: storing the first data in a first layer of the data warehouse based on a json format.

[0063] Specifically, in order to save storage resources, the data warehouse stores the first data in JSON format. JSON is a lightweight data exchange format that occupies less storage space than other formats, making data transmission and storage more efficient. The first data is stored in the first layer of the data warehouse in JSON format, where the first layer is the ODS layer. The ODS layer in the data warehouse only stores the original data generated according to the TSP configuration in JSON format.

[0064] In one embodiment of the present application, a second parameter corresponding to the business information is determined based on the vehicle business information, the first data stored based on a preset format is called in the data warehouse, the field corresponding to the second parameter is identified in the first data stored based on the preset format, and the second data is obtained by parsing the field, including: obtaining the vehicle business information and determining the second parameter corresponding to the business information; calling the first data stored based on json format in the data warehouse according to a pre-written SQL statement, identifying the field corresponding to the second parameter in the first data, parsing the field to obtain the second data, and storing the second data in the second layer of the data warehouse.

[0065] Specifically, the first layer in the data warehouse, namely the ods layer, only stores the original data generated according to the TSP configuration, which is stored in the form of json. The second parameter corresponding to the business information is determined according to the business information, and only the field corresponding to the second parameter is parsed and placed in the second layer of the data warehouse, namely the dwd. In this embodiment, the data warehouse includes a first ods layer and a second dwd layer. According to the second parameter, only the required fields are parsed from the original data in the json form of the ods layer and stored in the dwd layer, thereby reducing the data of the parsed fields and further reducing the storage space. The DolphinScheduler offline scheduling task can call the original json data of the data warehouse according to the written SQL (Structured Query Language), parse the required fields, and only parse the required fields instead of parsing all the fields, thereby avoiding waste of resources and improving the efficiency of parsing.

[0066] In one embodiment of the present application, the processing result of the vehicle business information is determined based on the second data, including: based on the second data stored in the second layer of the data warehouse, calculating the intermediate processing result of the vehicle business information, and storing the intermediate processing result in the third layer of the data warehouse; based on the intermediate processing result stored in the third layer of the data warehouse, calculating the final processing result of the vehicle business information, and storing the final processing result in the fourth layer of the data warehouse; and sending the final processing result to the vehicle end based on the fourth layer of the data warehouse.

[0067] Specifically, based on the parsed second data stored in the second layer dwd of the data warehouse, the intermediate processing result of the vehicle business information is calculated, and the intermediate processing result is stored in the third layer, i.e., the dws layer, of the data warehouse; based on the intermediate processing result stored in the third layer, i.e., the dws layer, of the data warehouse, the final processing result of the vehicle business information is calculated, and the final processing result is stored in the fourth layer, i.e., the ads layer, of the data warehouse. In this embodiment, in addition to the first layer ods layer and the second layer dwd layer, the data warehouse also includes a third layer, i.e., the dws layer, and a fourth layer, i.e., the ads layer. The dws layer is mainly pre-aggregated according to the business, and as the intermediate layer of the business, it generates intermediate results, i.e., the preliminary calculation results of each segment. The ads layer mainly stores some final business indicators, and the data can be directly called to the business layer through the interface, and the final result is obtained by obtaining a collection through the intermediate results.

[0068] For example, for vehicle ACC services, the dws layer divides the vehicle ACC trips and calculates the duration of each trip based on the start and end time of each trip. The ads layer calculates the duration of the trip within a day based on the duration of each trip calculated by the dws layer, and derives the ACC driving time statistics of the smart vehicle in one day. For ICC, ACC, NOA, CNOA, HPA, RPA, APA and other smart driving services, the smart driving situation within the preset time period can also be obtained based on the same or similar methods.

[0069] Furthermore, the cloud interface obtains the calculated ads result set and displays it to the car computer or mobile phone through authentication authorization. The car computer displays data through the PKI (Public Key Infrastructure) two-way authorization interface; the mobile phone displays data through the PKI two-way authorization interface. As a result, the client can display the daily application of intelligent driving behavior statistics. By displaying historical intelligent driving data, customers will intuitively obtain intelligent driving assistance information and drive more safely.

[0070] It can be seen that the embodiments of the present application can calculate the relevant data of the intelligent driving business of each car series or each car, such as the mileage, duration, number of APA, ACC, and NOA. This data can reflect the situation of intelligent driving and the safe driving of the vehicle. Users can free their hands better, and the vehicle can take over the vehicle in dangerous times to ensure driving safety and make driving easier and safer.

[0071] Based on the above embodiments, Figure 2 In a specific embodiment of the present application, a smart vehicle data processing method includes the following steps:

[0072] [M001]TSP cloud can configure the required messages according to the vehicle model;

[0073] [M002] Send configuration instructions to the vehicle computer through the interface;

[0074] [M003] The DHU receives the command issued, and uploads the corresponding fields according to the specified fields. On the DHU side, the CAN bus is obtained to send the data (frame number, APA (automatic parking assist), ACC (adaptive cruise control system), NOA (high-level intelligent driving), reporting time, etc.) to the FlexRay bus, and the data is sent to MQTT through the BGM gateway. Through Kafka-connect-mqtt, the data in MQTT is seamlessly connected to TSP kafka;

[0075] [M004] The vehicle computer transmits the message reported by the vehicle to the TSP end through the MQTT channel;

[0076] [M005]TSP KAFKA is a high-performance messaging system that collects logs and makes them available to consumers through subscriptions.

[0077] [M006] Spark parses the reported signal messages in batches through offline operations, and binds them to the vehicle series pushed by MES. In this way, each message is bound to the vehicle series at the same time, and the messages bound to the vehicle series are stored in the data warehouse;

[0078] [M007]DolphinScheduler offline scheduling tasks can call the original json data of the data warehouse according to the written SQL, and parse the required fields, only the required fields, instead of parsing all the fields. Data stratification is performed in the data warehouse, including ods layer, dwd layer, dws layer, ads layer;

[0079] [M008] The ods layer in the data warehouse only stores the original data generated according to the TSP configuration, which is stored in json format. According to business needs, only the required fields are parsed and put into the dwd data warehouse. The storage data warehouse stores data from the ods layer, dwd layer, dws layer, and ads layer. The ads layer contains aggregated result data, which can be directly provided to the interface call;

[0080] [M009] Divide the vehicle's ICC / ACC / NOA / CNOA / HPA / RPA / APA trips and calculate the driving time or mileage of each segment based on the start time, end time, duration or mileage of each segment;

[0081] [M010] Calculate the use of intelligent driving of ICC / ACC / NOA / CNOA / HPA / RPA / APA;

[0082] [M011] The cloud interface obtains the calculated ads result set and authorizes it to be displayed on the vehicle or mobile phone through authentication;

[0083] [M012] The vehicle terminal displays data through a PKI two-way authorized interface;

[0084] [M013] The mobile phone displays data through the PKI two-way authorized interface.

[0085] It can be seen that the embodiment of the present application can dynamically configure the reporting message according to the vehicle model through the TSP cloud configuration center, and can also be configured according to the single vehicle, reducing the reporting of unnecessary field information and saving computing resources and storage resources. In the big data warehouse, since the reporting message is configured according to the needs, storage resources are saved, and the storage format is json, only the required field data can be parsed as needed, saving storage and computing resources. The intelligent driving situation of each car series or each car can be calculated, which can indirectly reflect the safe driving situation of the vehicle, making driving easier and safer.

[0086] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in the application, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0087] Figure 3 A flowchart of an intelligent vehicle data processing method provided in an embodiment of the present application, the method is applied to a vehicle side, such as Figure 3 As shown, the method may include the following steps:

[0088] Step 301: Receive a first parameter sent from the cloud, wherein the first parameter is determined by the cloud based on vehicle information on the vehicle side.

[0089] Step 302: Filter the first data corresponding to the first parameter based on the first parameter, and send the first data to the cloud, the cloud is used to store the first data in a data warehouse based on a preset format, determine the second parameter corresponding to the business information according to the vehicle business information, call the first data stored in the preset format in the data warehouse, identify the field corresponding to the second parameter in the first data stored in the preset format, parse the field to obtain the second data, and determine the processing result of the vehicle business information based on the second data.

[0090] Specifically, Figure 3 As shown, combined with Figure 4After the vehicle computer is started, it receives the first parameter sent by the cloud, filters the required intelligent driving data based on the first parameter to obtain the first data and uploads the first data through the cloud interface, the cloud stores the first data in a preset format in a data warehouse, determines the second parameter corresponding to the business information according to the vehicle business information, calls the first data stored in the preset format in the data warehouse, identifies the field corresponding to the second parameter in the first data stored in the preset format, parses the field to obtain the second data, determines the processing result of the vehicle business information based on the second data, and authorizes it to the vehicle computer through authentication, and the vehicle computer obtains and displays the processing result data.

[0091] In one embodiment of the present application, first data obtained by filtering the first parameter on the vehicle side is received, and the first data is stored in a data warehouse based on a preset format, including: receiving the first data obtained by filtering the first parameter on the vehicle side through a message queue transmission protocol, and storing the first data in a message middleware; the message middleware sends the first data to the data warehouse, and the data warehouse stores the first data based on a preset format.

[0092] In one embodiment of the present application, the message middleware sends the first data to the data warehouse, including: the message middleware sends the first data to the data processing platform in batches based on a preset time interval; the data processing platform receives the first data and eliminates abnormal data in the first data; the data processing platform obtains vehicle information corresponding to the first data, binds the first data to the corresponding vehicle information to obtain the first data bound to the vehicle information; and sends the first data bound to the vehicle information to the data warehouse.

[0093] In one embodiment of the present application, storing the first data in a data warehouse based on a preset format includes: storing the first data in a first layer of the data warehouse based on a json format.

[0094] In one embodiment of the present application, a second parameter corresponding to the business information is determined based on the vehicle business information, the first data stored based on a preset format is called in the data warehouse, the field corresponding to the second parameter is identified in the first data stored based on the preset format, and the second data is obtained by parsing the field, including: obtaining the vehicle business information and determining the second parameter corresponding to the business information; calling the first data stored based on json format in the data warehouse according to a pre-written SQL statement, identifying the field corresponding to the second parameter in the first data, parsing the field to obtain the second data, and storing the second data in the second layer of the data warehouse.

[0095] In one embodiment of the present application, the processing result of the vehicle business information is determined based on the second data, including: based on the second data stored in the second layer of the data warehouse, calculating the intermediate processing result of the vehicle business information, and storing the intermediate processing result in the third layer of the data warehouse; based on the intermediate processing result stored in the third layer of the data warehouse, calculating the final processing result of the vehicle business information, and storing the final processing result in the fourth layer of the data warehouse; and sending the final processing result to the vehicle end based on the fourth layer of the data warehouse.

[0096] In one embodiment of the present application, receiving a first parameter sent from the cloud includes: receiving a first message sent from the cloud, wherein the cloud determines a first parameter corresponding to the vehicle end according to the vehicle model, and configures the first message based on the first parameter.

[0097] According to the specific embodiments provided in this application, the technical solution provided in this application can have the following advantages: through the TSP cloud configuration center, the reporting message can be dynamically configured according to the vehicle model, or it can be configured according to the single vehicle, reducing the reporting of unnecessary field information and saving computing resources and storage resources. In the big data warehouse, since the reporting message is configured according to the needs, storage resources are saved, and the storage format is json, only the required field data can be parsed as needed, saving storage and computing resources. The intelligent driving situation of each car series or each car can be calculated, which can indirectly reflect the safe driving situation of the vehicle, making driving easier and safer.

[0098] The same or similar parts between the above embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0099] It should be noted that the embodiments of the present application may involve the use of user data. In actual applications, user-specific personal data can be used in the scheme described in this article within the scope permitted by applicable laws and regulations, provided that the applicable laws and regulations of the country are met (for example, the user's explicit consent, effective notification to the user, and the user's explicit authorization, etc.).

[0100] According to an embodiment of the present application, the present application also provides a computer device and a computer-readable storage medium. The present application also provides a computer device, including at least one processor and a memory in communication with the at least one processor; wherein the memory stores computer instructions executable by the at least one processor, and the computer instructions are executed by the at least one processor so that the at least one processor can execute the intelligent vehicle data processing method of any of the above embodiments.

[0101] like Figure 5, is a block diagram of a computer device according to an embodiment of the present application. The computer device is intended to represent various forms of digital computers or mobile devices. The digital computer may include a desktop computer, a portable computer, a workbench, a personal digital assistant, a server, a mainframe computer, and other suitable computers. The mobile device may include a tablet computer, a smart phone, a wearable device, etc.

[0102] like Figure 5 As shown, the computer device 500 includes a computing unit 501, a ROM 502, a RAM 503, a bus 504, and an input / output (I / O) interface 505, and the computing unit 501, the ROM 502, and the RAM 503 are connected to each other through the bus 504. The input / output (I / O) interface 505 is also connected to the bus 504.

[0103] The computing unit 501 can perform various processes in the method embodiment of the present application according to the computer instructions stored in the read-only memory (ROM) 502 or the computer instructions loaded from the storage unit 508 to the random access memory (RAM) 503. The computing unit 501 can be various general and / or special processing components with processing and computing capabilities. The computing unit 501 may include, but is not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. In some embodiments, the method provided in the embodiment of the present application can be implemented as a computer software program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 508.

[0104] RAM 503 may also store various programs and data required for the operation of device 500. Part or all of the computer program may be loaded and / or installed on device 500 via ROM 502 and / or communication unit 509.

[0105] The input unit 506, output unit 507, storage unit 508 and communication unit 509 in the computer device 500 can be connected to the I / O interface 505. The input unit 506 can be, for example, a keyboard, a mouse, a touch screen, a microphone, etc. The output unit 507 can be, for example, a display, a speaker, an indicator light, etc. The device 500 can exchange information, data, etc. with other devices through the communication unit 509.

[0106] It should be noted that the device may also include other components necessary for normal operation, or may only include components necessary for implementing the solution of the present application, rather than all the components shown in the figure.

[0107] Various implementations of the systems and techniques described herein can be realized in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof.

[0108] The computer instructions for implementing the method of the present application can be written in any combination of one or more programming languages. These computer instructions can be provided to the computing unit 501, so that when the computer instructions are executed by the computing unit 501 such as a processor, the steps involved in the method embodiment of the present application are executed.

[0109] The present application also provides a computer-readable storage medium on which computer instructions are stored, and the computer instructions are used to enable a computer to execute the intelligent vehicle data processing method described in any of the above embodiments.

[0110] The computer-readable storage medium provided in the present application may be a tangible medium that may contain or store computer instructions for executing the steps involved in the method embodiments of the present application. The computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, and other forms of storage media.

[0111] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art 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 application should be included in the protection scope of this application.

Claims

1. A smart vehicle data processing method, applied to the cloud, characterized in that: The method includes: Determine a first parameter corresponding to the vehicle information according to the vehicle information of the vehicle end, and send the first parameter to the vehicle end; Receiving first data obtained by the vehicle terminal through screening according to the first parameter, and storing the first data in a data warehouse based on a preset format; Determine a second parameter corresponding to the business information according to the vehicle business information, call the first data stored in a preset format in the data warehouse, identify a field corresponding to the second parameter in the first data stored in the preset format, and parse the field to obtain the second data; A processing result of the vehicle service information is determined based on the second data.

2. The intelligent vehicle data processing method according to claim 1, characterized in that: The receiving the first data obtained by the vehicle terminal through screening according to the first parameter, and storing the first data in a data warehouse based on a preset format, comprises: Receiving first data obtained by the vehicle terminal according to the first parameter screening through a message queue transmission protocol, and storing the first data in a message middleware; The message middleware sends the first data to the data warehouse, and the data warehouse stores the first data based on a preset format.

3. The intelligent vehicle data processing method according to claim 2, characterized in that: The message middleware sends the first data to the data warehouse, including: The message middleware sends the first data to the data processing platform in batches based on a preset time interval; The data processing platform receives the first data and removes abnormal data from the first data; The data processing platform acquires vehicle information corresponding to the first data, and binds the first data to the corresponding vehicle information to obtain first data bound to the vehicle information; The first data bound with the vehicle information is sent to the data warehouse.

4. The intelligent vehicle data processing method according to claim 1, characterized in that: The storing the first data in a data warehouse based on a preset form includes: The first data is stored in the first layer of the data warehouse in JSON format.

5. The intelligent vehicle data processing method according to claim 4, characterized in that: The method of determining a second parameter corresponding to the business information according to the vehicle business information, calling first data stored in a preset form in the data warehouse, identifying a field corresponding to the second parameter in the first data stored in the preset form, and parsing the field to obtain the second data includes: Acquiring vehicle service information and determining a second parameter corresponding to the service information; The first data stored in the data warehouse in the JSON format is called according to a pre-written SQL statement, a field corresponding to the second parameter is identified in the first data, the field is parsed to obtain the second data, and the second data is stored in the second layer of the data warehouse.

6. The intelligent vehicle data processing method according to claim 5, characterized in that: Determining a processing result of the vehicle service information based on the second data includes: Calculating an intermediate processing result of the vehicle business information based on the second data stored in the second layer of the data warehouse, and storing the intermediate processing result in the third layer of the data warehouse; Calculating the final processing result of the vehicle business information based on the intermediate processing results stored in the third layer of the data warehouse, and storing the final processing result in the fourth layer of the data warehouse; The final processing result is sent to the vehicle end based on the fourth layer of the data warehouse.

7. The intelligent vehicle data processing method according to claim 1, characterized in that: The determining a first parameter corresponding to the vehicle information according to the vehicle information of the vehicle end, and sending the first parameter to the vehicle end, comprises: A first parameter corresponding to the vehicle end is determined according to the vehicle model, a first message is configured based on the first parameter, and the first message is sent to the vehicle end.

8. An intelligent vehicle data processing method, applied to a vehicle, characterized in that: The method includes: Receiving a first parameter sent by the cloud, wherein the first parameter is determined by the cloud according to vehicle information of the vehicle end; Based on the first parameter, first data corresponding to the first parameter is filtered and the first data is sent to the cloud. The cloud is used to store the first data in a preset format in a data warehouse, determine a second parameter corresponding to the business information according to the vehicle business information, call the first data stored in the preset format in the data warehouse, identify the field corresponding to the second parameter in the first data stored in the preset format, parse the field to obtain the second data, and determine the processing result of the vehicle business information based on the second data.

9. A computer device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores computer instructions that can be executed by the at least one processor, and the computer instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 8.

10. A computer-readable storage medium having computer instructions stored thereon, characterized in that: The computer instructions are used to cause a computer to execute the method according to any one of claims 1 to 8.

Images