Internet of vehicles data processing method and device, computer equipment, readable storage medium and program product

Abstract

The invention relates to an Internet of Vehicles data processing method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring an original data signal of the Internet of Vehicles, and naming the original data signal according to a preset naming rule; determining a data storage type of the original data signal according to a preset storage rule; determining a measurement unit used by the original data signal according to a preset unit rule; under the condition that it is determined that the original data signal needs to be analyzed according to a preset analysis rule, analyzing the original data signal according to the preset analysis rule to obtain an analysis result; under the condition that it is determined that the analysis result needs to be flattened according to a preset flattening rule, the analysis result is flattened, and a flattening result is obtained; and storing the flattening result according to the data storage type and the measurement unit. By adopting the method, the Internet of Vehicles data can be unified in multiple dimensions, and the efficiency and accuracy of subsequent data analysis are improved.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle data processing, and in particular to a method, apparatus, computer equipment, computer-readable storage medium and computer program product for vehicle network data processing. Background Art

[0002] As the trend of data intelligence in the automotive industry accelerates, Internet of Vehicles big data technology has gradually become an important part of the automotive industry. Through the interconnection of vehicles, sensors, application software and other devices, Internet of Vehicles realizes the real-time collection and storage of data such as vehicle driving status and driving behavior.

[0003] However, due to chaotic data management, there are many problems such as mixed storage of parsed and unparsed messages, inconsistent units, irregular naming, etc., which restrict the effective use and value mining of subsequent data and cause developers to spend too much time processing Internet of Vehicles data. Summary of the invention

[0004] Based on this, it is necessary to provide a vehicle network data processing method, device, computer equipment, computer-readable storage medium and computer program product that can unify the standards of various dimensions of vehicle network data in response to the above-mentioned technical problems.

[0005] In a first aspect, the present application provides a method for processing Internet of Vehicles data, comprising:

[0006] Obtaining original data signals of the Internet of Vehicles, and naming the original data signals according to a preset naming rule;

[0007] Determining the data storage type of the original data signal according to a preset storage rule;

[0008] Determine the measurement unit used by the original data signal according to a preset unit rule;

[0009] When it is determined according to the preset analysis rule that the original data signal needs to be analyzed, analyzing the original data signal according to the preset analysis rule to obtain an analysis result;

[0010] When it is determined according to a preset flattening rule that the parsing result needs to be flattened, flattening the parsing result to obtain a flattened result;

[0011] The flattened result is stored according to the data storage type and the measurement unit.

[0012] In one embodiment, the naming of the original data signal according to a preset naming rule includes:

[0013] Acquire the data table type of the original data signal and the function type of the signal field in the original data signal;

[0014] Determining a naming prefix of the original data signal according to the data table type;

[0015] Determining a naming structure of the original data signal according to the function type;

[0016] The original data signal is named according to the naming prefix and the naming structure.

[0017] In one embodiment, determining the data storage type of the original data signal according to a preset storage rule includes:

[0018] Acquire a physical feature of the original data signal; the physical feature is a data type of the original data signal;

[0019] The data storage type of the original data signal is determined according to the physical characteristics; the data storage type includes integer storage, floating point storage and string storage.

[0020] In one embodiment, determining the measurement unit used by the original data signal according to a preset unit rule includes:

[0021] Acquiring a correspondence between the physical characteristics of the original data signal and the data unit;

[0022] According to the corresponding relationship, the data with the same attributes in the original data signal are matched to a unified measurement unit, and the data in the original data signal that needs to improve the numerical precision is matched to a measurement unit with a preset precision.

[0023] In one embodiment, parsing the original data signal according to the preset parsing rule to obtain the parsing result includes:

[0024] Obtaining a data format corresponding to the parsing rule; the data format includes binary, octal, decimal and hexadecimal;

[0025] The data format of the original data signal is converted into a data format corresponding to the parsing rule.

[0026] In one embodiment, flattening the parsing result to obtain the flattened result includes:

[0027] The data structure type of the parsing result is converted into a flattened structure, wherein the flattened structure consists of a one-dimensional array or an object.

[0028] In a second aspect, the present application also provides a vehicle networking data processing device, including:

[0029] A naming module, used to obtain the original data signal of the Internet of Vehicles and name the original data signal according to a preset naming rule;

[0030] A determination module, used to determine the data storage type of the original data signal according to a preset storage rule;

[0031] The determination module is further used to determine the measurement unit used by the original data signal according to a preset unit rule;

[0032] A parsing module, configured to, when it is determined according to a preset parsing rule that the original data signal needs to be parsed, parse the original data signal according to the preset parsing rule to obtain a parsing result;

[0033] A flattening module, used for flattening the parsing result to obtain a flattened result when it is determined according to a preset flattening rule that the parsing result needs to be flattened;

[0034] A storage module is used to store the flattening result according to the data storage type and the measurement unit.

[0035] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0036] Obtaining original data signals of the Internet of Vehicles, and naming the original data signals according to a preset naming rule;

[0037] Determining the data storage type of the original data signal according to a preset storage rule;

[0038] Determine the measurement unit used by the original data signal according to a preset unit rule;

[0039] When it is determined according to the preset analysis rule that the original data signal needs to be analyzed, analyzing the original data signal according to the preset analysis rule to obtain an analysis result;

[0040] When it is determined according to a preset flattening rule that the parsing result needs to be flattened, flattening the parsing result to obtain a flattened result;

[0041] The flattened result is stored according to the data storage type and the measurement unit.

[0042] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0043] Obtaining original data signals of the Internet of Vehicles, and naming the original data signals according to a preset naming rule;

[0044] Determining the data storage type of the original data signal according to a preset storage rule;

[0045] Determine the measurement unit used by the original data signal according to a preset unit rule;

[0046] When it is determined according to the preset analysis rule that the original data signal needs to be analyzed, analyzing the original data signal according to the preset analysis rule to obtain an analysis result;

[0047] When it is determined according to a preset flattening rule that the parsing result needs to be flattened, flattening the parsing result to obtain a flattened result;

[0048] The flattened result is stored according to the data storage type and the measurement unit.

[0049] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0050] Obtaining original data signals of the Internet of Vehicles, and naming the original data signals according to a preset naming rule;

[0051] Determining the data storage type of the original data signal according to a preset storage rule;

[0052] Determine the measurement unit used by the original data signal according to a preset unit rule;

[0053] When it is determined according to the preset analysis rule that the original data signal needs to be analyzed, analyzing the original data signal according to the preset analysis rule to obtain an analysis result;

[0054] When it is determined according to a preset flattening rule that the parsing result needs to be flattened, flattening the parsing result to obtain a flattened result;

[0055] The flattened result is stored according to the data storage type and the measurement unit.

[0056] The above-mentioned Internet of Vehicles data processing method, device, computer equipment, computer-readable storage medium and computer program product first obtain the original data signal of the Internet of Vehicles, name the original data signal according to the preset naming rules; determine the data storage type of the original data signal according to the preset storage rules; determine the measurement unit used by the original data signal according to the preset unit rules; when it is determined according to the preset parsing rules that the original data signal needs to be parsed, the original data signal is parsed according to the preset parsing rules to obtain the parsing result; when it is determined according to the preset flattening rules that the parsing result needs to be flattened, the parsing result is flattened to obtain the flattened result; the flattened result is stored according to the data storage type and the measurement unit. In this way, Internet of Vehicles data of different structures and different granularities are automatically identified and standardized according to the preset rules to form a unified data format, which reduces the time for developers to process data and improves the efficiency and accuracy of data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0058] Figure 1 This is an application environment diagram of a vehicle networking data processing method in an embodiment;

[0059] Figure 2 A schematic diagram of a flow chart of a method for processing Internet of Vehicles data in one embodiment;

[0060] Figure 3 A schematic diagram of a flow chart of a method for processing Internet of Vehicles data in another embodiment;

[0061] Figure 4 is a structural block diagram of a vehicle networking data processing device in an embodiment;

[0062] Figure 5 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the present application more clearly understood, 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.

[0064] The vehicle networking data processing method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the original signal data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car devices, projection devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The head-mounted device can be a virtual reality (VR) device, an augmented reality (AR) device, smart glasses, etc. The server 104 can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0065] In an exemplary embodiment, Figure 2 As shown, a method for processing vehicle network data is provided, and the method is applied to Figure 1 The terminal 102 in the example is used as an example to illustrate, including the following steps 202 to 212. Among them:

[0066] Step 202, obtaining the original data signal of the Internet of Vehicles, and naming the original data signal according to a preset naming rule.

[0067] In some embodiments, a terminal acquires an original data signal of the Internet of Vehicles, obtains the data table type of the original data signal and the functional type of the signal field in the original data signal; determines a naming prefix of the original data signal according to the data table type; determines a naming structure of the original data signal according to the functional type; and names the original data signal according to the above-mentioned naming prefix and naming structure.

[0068] Step 204: Determine the data storage type of the original data signal according to a preset storage rule.

[0069] Specifically, the physical characteristics of the original data signal are acquired, and the data storage type of the original data signal is determined according to the physical characteristics.

[0070] Among them, the physical feature is the data type of the original data signal, which can be specifically defined by the user based on common sense and work experience, and the embodiment of the present application does not limit this; the data storage type includes integer storage, floating-point storage and string storage, and can also be other data storage types, which the embodiment of the present application does not limit this.

[0071] Step 206: Determine the measurement unit used by the original data signal according to a preset unit rule.

[0072] In some embodiments, the correspondence between the physical characteristics of the original data signal and the data unit is obtained, and based on the correspondence, the data with the same attributes in the original data signal are matched to a unified unit of measurement, and the data in the original data signal that needs to improve numerical precision is matched to a unit of measurement with a preset precision.

[0073] Step 208: When it is determined according to the preset analysis rule that the original data signal needs to be analyzed, the original data signal is analyzed according to the preset analysis rule to obtain an analysis result.

[0074] In some embodiments, when it is determined according to a preset parsing rule that the original data signal needs to be parsed, a data format corresponding to the parsing rule is obtained, and the original data signal is converted into a data format corresponding to the parsing rule.

[0075] The data format includes but is not limited to binary, octal, decimal and hexadecimal data formats, which are not limited in the embodiments of the present application.

[0076] Step 210 , when it is determined according to a preset flattening rule that the parsing result needs to be flattened, the parsing result is flattened to obtain a flattened result.

[0077] In some embodiments, when it is determined according to a preset flattening rule that the parsing result needs to be flattened, the data structure type of the parsing result is converted into a flattened structure.

[0078] Among them, the flattened structure consists of a one-dimensional array or object.

[0079] Step 212: Store the flattened result according to the data storage type and measurement unit.

[0080] In some embodiments, the terminal stores the flattening result in a database according to the determined storage type and measurement unit.

[0081] In the above-mentioned vehicle network data processing method, the original data signal of the vehicle network is obtained, and the original data signal is named according to the preset naming rules; the data storage type of the original data signal is determined according to the preset storage rules; the measurement unit used by the original data signal is determined according to the preset unit rules; when it is determined according to the preset analysis rules that the original data signal needs to be analyzed, the original data signal is analyzed according to the preset analysis rules to obtain the analysis result; when it is determined according to the preset flattening rules that the analysis result needs to be flattened, the analysis result is flattened to obtain the flattened result; the flattened result is stored according to the data storage type and the measurement unit. In this way, the vehicle network data of different structures and different granularities are automatically identified and standardized according to the preset rules to form a unified data format, which reduces the time for developers to process data and improves the efficiency and accuracy of data analysis.

[0082] In an exemplary embodiment, the original data signal is named according to a preset naming rule, including: obtaining the data table type in which the original data signal is located and the functional type of the signal field in the original data signal; determining the naming prefix of the original data signal according to the data table type; determining the naming structure of the original data signal according to the functional type; and naming the original data signal according to the naming prefix and the naming structure.

[0083] In actual implementation, the terminal will obtain the data table type where the original data signal is located and the function type of the signal field in the original data signal, and determine the naming prefix of the original signal according to the data table type.

[0084] Exemplarily, data table types may include regular tables, temporary tables, dimension tables and the like. For example, prefixes include temporary tables tmp, primary key prefix pk, business domain rear axle business is named axle, subject domain fault diagnosis domain is named diagnosis, common structure engine is named engine, and common data category speed is named speed.

[0085] According to the function type, determine the naming structure of the original data signal; divide the signal fields into primary key fields, foreign key fields, vehicle operation fields, vehicle configuration parameter fields, date fields, road fields, etc. according to different functions, and use different structures in naming. For example, vehicle operation data is named according to "Structure Affiliation_Data Category".

[0086] Furthermore, the original data signal is named according to the naming prefix and the naming structure, for example, the vehicle operation data is named "engine_speed".

[0087] In the above embodiment, the original data signal is named according to a preset naming rule, ensuring that the category of the table can be directly determined by the name, thereby improving the readability and maintainability of the database.

[0088] In some exemplary embodiments, the data storage type of the original data signal is determined according to preset storage rules, including: acquiring physical characteristics of the original data signal; the physical characteristics are the data type of the original data signal; based on the physical characteristics, the data storage type of the original data signal is determined; the data storage types include integer storage, floating point storage and string storage.

[0089] In actual implementation, the physical characteristics of the original data signal are obtained, and the data storage type is determined according to the physical characteristics of the original data signal.

[0090] Among them, the data storage type includes but is not limited to integer storage, floating point storage and character storage, which is not limited in the embodiments of the present application.

[0091] Exemplarily, Du Yu specifies the storage precision and value range for numeric data, and specifies the character type and storage length for string data.

[0092] For data signals whose data can be accurate to integer digits and whose data range does not exceed the maximum allowable range of integer variables; for status data signals whose specific data values ​​are used to express status bits; for switch data signals whose specific data values ​​are 0 or 1, integer storage is required.

[0093] For data signals that have accuracy requirements but cannot be accurate to integer digits, floating-point storage is used based on the specific number of accurate digits and value range.

[0094] For signals stored in the form of Chinese characters, or in the form of a combination of Chinese characters and English letters, numbers, and special symbols; for data signals or switch signals that do not specify specific status bits or do not use 0 or 1 to represent the status of a switch; for signals whose numerical range or precision exceeds the upper limit specified for integer or floating-point types and there is no conversion method to adjust them to be stored in integer or floating-point types, string storage is required.

[0095] In the above embodiment, each type of data is stored accordingly, so that the efficiency of data analysis is improved.

[0096] In some exemplary embodiments, the measurement unit used by the original data signal is determined according to a preset unit rule, including: obtaining the correspondence between the physical characteristics of the original data signal and the data unit; according to the correspondence, matching the data with the same attributes in the original data signal to a unified measurement unit, and matching the data in the original data signal that needs to improve the numerical precision to the measurement unit of the preset precision.

[0097] In actual implementation, the correspondence between the physical characteristics of the original data signal and the data unit is obtained; based on the correspondence, the data with the same attributes in the original data signal are matched to a unified unit of measurement, and the data in the original data signal that needs to improve numerical accuracy is matched to a unit of measurement with a preset accuracy.

[0098] Among them, data with the same attribute is defined as data that point to the same physical quantity in physics, such as sensor temperature and atmospheric temperature, which belong to the physical quantity of temperature in physics.

[0099] For example, the actual urea injection amount should be stored in L / h as a unified data unit. However, since the accuracy requirement for data analysis is 0.01 ml / h, using a unified unit will cause data accuracy loss. Therefore, the data is stored in ml / h.

[0100] In the above embodiment, the original data signal is named according to the preset unit rule, which ensures that the numerical precision of the data meets the needs of data analysis and improves the accuracy and efficiency of data analysis.

[0101] In some exemplary embodiments, the original data signal is parsed according to preset parsing rules to obtain parsing results, including: obtaining a data format corresponding to the parsing rule; the data format includes binary, octal, decimal and hexadecimal; and converting the data format of the original data signal into a data format corresponding to the parsing rule.

[0102] In actual implementation, the data format corresponding to the parsing rule is obtained, and the data format of the original data signal is converted into the data format corresponding to the parsing rule. The data format includes but is not limited to binary, octal, decimal and hexadecimal, which is not limited in the embodiment of the present application.

[0103] For example, during the data analysis process, the data to be analyzed is binary, but the system can only analyze decimal data. In this case, the binary data needs to be parsed into decimal data.

[0104] In the above embodiment, it is automatically identified whether the current data signal needs to be analyzed, and then the data is analyzed and processed, ensuring that the data after normalization can be directly provided to data analysis and application developers for use without the need for data conversion again.

[0105] In some exemplary embodiments, flattening the parsing result to obtain the flattened result includes: converting the data structure type of the parsing result into a flattened structure, where the flattened structure consists of a one-dimensional array or an object.

[0106] In actual implementation, when the parsing result needs to be flattened, the parsing result is flattened, and the data structure type of the parsing result is converted into a flattened structure.

[0107] For example, for composite type data such as struct structure and map mapping, it is converted into data consisting of a one-dimensional array or object.

[0108] In the above embodiment, the data analysis process is made more efficient by simplifying the complex analysis results.

[0109] To explain the method for processing vehicle network data in this application in detail, an embodiment is described below. The specific flow chart is as follows: Figure 3 As shown, exemplarily, the present application is described with a method for processing Internet of Vehicles data in a specific Internet of Vehicles scenario.

[0110] First, the terminal obtains the original data signal, and obtains the data table type where the original data signal is located and the function type of the signal field in the original data signal, and determines the naming prefix of the original signal according to the data table type.

[0111] Exemplarily, data table types may include regular tables, temporary tables, dimension tables and the like. For example, prefixes include temporary tables tmp, primary key prefix pk, business domain rear axle business is named axle, subject domain fault diagnosis domain is named diagnosis, common structure engine is named engine, and common data category speed is named speed.

[0112] According to the function type, determine the naming structure of the original data signal; divide the signal fields into primary key fields, foreign key fields, vehicle operation fields, vehicle configuration parameter fields, date fields, road fields, etc. according to different functions, and use different structures in naming. For example, vehicle operation data is named according to "Structure Affiliation_Data Category".

[0113] Furthermore, the original data signal is named according to the naming prefix and the naming structure, for example, the vehicle operation data is named "engine_speed".

[0114] The physical characteristics of the original data signal are obtained, and the data storage type is determined according to the physical characteristics of the original data signal.

[0115] Exemplarily, Du Yu specifies the storage precision and value range for numeric data, and specifies the character type and storage length for string data.

[0116] For data signals whose data can be accurate to integer digits and whose data range does not exceed the maximum allowable range of integer variables; for status data signals whose specific data values ​​are used to express status bits; for switch data signals whose specific data values ​​are 0 or 1, integer storage is required.

[0117] For data signals that have accuracy requirements but cannot be accurate to integer digits, floating-point storage is used based on the specific number of accurate digits and value range.

[0118] For signals stored in the form of Chinese characters, or in the form of a combination of Chinese characters and English letters, numbers, and special symbols; for data signals or switch signals that do not specify specific status bits or do not use 0 or 1 to represent the status of a switch; for signals whose numerical range or precision exceeds the upper limit specified for integer or floating-point types and there is no conversion method to adjust them to be stored in integer or floating-point types, string storage is required.

[0119] Obtain the correspondence between the physical characteristics of the original data signal and the data unit; according to the correspondence, match the data with the same attributes in the original data signal to a unified unit of measurement, and match the data in the original data signal that needs to improve the numerical accuracy to a unit of measurement with a preset accuracy.

[0120] Among them, data with the same attribute is defined as data that point to the same physical quantity in physics, such as sensor temperature and atmospheric temperature, which belong to the physical quantity of temperature in physics.

[0121] For example, the actual urea injection amount should be stored in L / h as a unified data unit. However, since the accuracy requirement for data analysis is 0.01 ml / h, using a unified unit will cause data accuracy loss. Therefore, the data is stored in ml / h.

[0122] The data format corresponding to the parsing rule is obtained, and the data format of the original data signal is converted into the data format corresponding to the parsing rule.

[0123] For example, during the data analysis process, the data to be analyzed is binary, but the system can only analyze decimal data. In this case, the binary data needs to be parsed into decimal data.

[0124] When the parsing result needs to be flattened, the parsing result is flattened, and the data structure type of the parsing result is converted into a flat structure.

[0125] For example, for composite type data such as struct structure and map mapping, it is converted into data consisting of a one-dimensional array or object.

[0126] The flattened results are stored in the database according to the determined storage type and measurement unit.

[0127] This application specifies multi-dimensional Internet of Vehicles data processing specifications so that Internet of Vehicles data can be stored in an orderly manner in the database according to preset rules, reducing the time developers spend on processing data and improving data analysis efficiency and accuracy.

[0128] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0129] Based on the same inventive concept, the embodiment of the present application also provides a vehicle network data processing device for implementing the vehicle network data processing method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more vehicle network data processing device embodiments provided below can refer to the limitations of the vehicle network data processing method above, and will not be repeated here.

[0130] In an exemplary embodiment, Figure 4 As shown, a vehicle networking data processing device is provided, including: a naming module 401, a determination module 402, a parsing module 403, a flattening module 404 and a storage module 405, wherein:

[0131] The naming module is used to obtain the original data signal of the Internet of Vehicles and name the original data signal according to a preset naming rule.

[0132] The determination module is used to determine the data storage type of the original data signal according to a preset storage rule.

[0133] The determination module is further configured to determine the measurement unit used by the original data signal according to a preset unit rule.

[0134] The parsing module is used to parse the original data signal according to the preset parsing rule to obtain a parsing result when it is determined that the original data signal needs to be parsed according to the preset parsing rule.

[0135] The flattening module is used to flatten the parsing result to obtain a flattened result when it is determined according to a preset flattening rule that the parsing result needs to be flattened.

[0136] A storage module is used to store the flattening result according to the data storage type and the measurement unit.

[0137] In some embodiments, the above-mentioned naming module is also used to obtain the data table type in which the original data signal is located and the functional type of the signal field in the original data signal; determine the naming prefix of the original data signal according to the data table type; determine the naming structure of the original data signal according to the functional type; and name the original data signal according to the naming prefix and the naming structure.

[0138] In some embodiments, the above-mentioned determination module is also used to obtain the physical characteristics of the original data signal; the physical characteristics are the data type of the original data signal; based on the physical characteristics, the data storage type of the original data signal is determined; the data storage type includes integer storage, floating point storage and string storage.

[0139] In some embodiments, the above-mentioned determination module is also used to obtain the correspondence between the physical characteristics of the original data signal and the data unit; according to the correspondence, the data with the same attributes in the original data signal are matched to a unified unit of measurement, and the data in the original data signal that needs to improve numerical accuracy is matched to a unit of measurement with a preset accuracy.

[0140] In some embodiments, the above-mentioned parsing module is also used to obtain the data format corresponding to the parsing rule; the data format includes binary, octal, decimal and hexadecimal; and convert the data format of the original data signal into the data format corresponding to the parsing rule.

[0141] In some embodiments, the flattening module is further used to convert the data structure type of the parsing result into a flattened structure, where the flattened structure consists of a one-dimensional array or object.

[0142] Each module in the above-mentioned vehicle networking data processing device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0143] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 5As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface, a display unit and an input device. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store Internet of Vehicles data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for processing Internet of Vehicles data is implemented.

[0144] The display unit of the computer device is used to form a visually visible picture, and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball, or touchpad provided on the computer device housing, or an external keyboard, touchpad, or mouse, etc.

[0145] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0146] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0147] Obtaining original data signals of the Internet of Vehicles, and naming the original data signals according to a preset naming rule;

[0148] Determining the data storage type of the original data signal according to a preset storage rule;

[0149] Determine the measurement unit used by the original data signal according to a preset unit rule;

[0150] When it is determined according to the preset analysis rule that the original data signal needs to be analyzed, analyzing the original data signal according to the preset analysis rule to obtain an analysis result;

[0151] When it is determined according to a preset flattening rule that the parsing result needs to be flattened, flattening the parsing result to obtain a flattened result;

[0152] The flattened result is stored according to the data storage type and the measurement unit.

[0153] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0154] Obtaining original data signals of the Internet of Vehicles, and naming the original data signals according to a preset naming rule;

[0155] Determining the data storage type of the original data signal according to a preset storage rule;

[0156] Determine the measurement unit used by the original data signal according to a preset unit rule;

[0157] When it is determined according to the preset analysis rule that the original data signal needs to be analyzed, analyzing the original data signal according to the preset analysis rule to obtain an analysis result;

[0158] When it is determined according to a preset flattening rule that the parsing result needs to be flattened, flattening the parsing result to obtain a flattened result;

[0159] The flattened result is stored according to the data storage type and the measurement unit.

[0160] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0161] Obtaining original data signals of the Internet of Vehicles, and naming the original data signals according to a preset naming rule;

[0162] Determining the data storage type of the original data signal according to a preset storage rule;

[0163] Determine the measurement unit used by the original data signal according to a preset unit rule;

[0164] When it is determined according to the preset analysis rule that the original data signal needs to be analyzed, analyzing the original data signal according to the preset analysis rule to obtain an analysis result;

[0165] When it is determined according to a preset flattening rule that the parsing result needs to be flattened, flattening the parsing result to obtain a flattened result;

[0166] The flattened result is stored according to the data storage type and the measurement unit.

[0167] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0168] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0169] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0170] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for processing Internet of Vehicles data, characterized in that: The method comprises: Obtaining original data signals of the Internet of Vehicles, and naming the original data signals according to a preset naming rule; Determining the data storage type of the original data signal according to a preset storage rule; Determine the measurement unit used by the original data signal according to a preset unit rule; When it is determined according to the preset analysis rule that the original data signal needs to be analyzed, analyzing the original data signal according to the preset analysis rule to obtain an analysis result; When it is determined according to a preset flattening rule that the parsing result needs to be flattened, flattening the parsing result to obtain a flattened result; The flattened result is stored according to the data storage type and the measurement unit.

2. The method according to claim 1, characterized in that The step of naming the original data signal according to a preset naming rule includes: Acquire the data table type of the original data signal and the function type of the signal field in the original data signal; Determining a naming prefix of the original data signal according to the data table type; Determining a naming structure of the original data signal according to the function type; The original data signal is named according to the naming prefix and the naming structure.

3. The method according to claim 1, characterized in that The determining the data storage type of the original data signal according to a preset storage rule includes: Acquire a physical feature of the original data signal; the physical feature is a data type of the original data signal; The data storage type of the original data signal is determined according to the physical characteristics; the data storage type includes integer storage, floating point storage and string storage.

4. The method according to claim 1, characterized in that The determining, according to a preset unit rule, a measurement unit used by the original data signal includes: Acquiring a correspondence between the physical characteristics of the original data signal and the data unit; According to the corresponding relationship, the data with the same attributes in the original data signal are matched to a unified measurement unit, and the data in the original data signal that needs to improve the numerical precision is matched to a measurement unit with a preset precision.

5. The method according to claim 1, characterized in that The step of parsing the original data signal according to the preset parsing rule to obtain a parsing result includes: Obtaining a data format corresponding to the parsing rule; the data format includes binary, octal, decimal and hexadecimal; The data format of the original data signal is converted into a data format corresponding to the parsing rule.

6. The method according to claim 1, characterized in that The step of flattening the parsing result to obtain a flattened result includes: The data structure type of the parsing result is converted into a flattened structure, wherein the flattened structure consists of a one-dimensional array or an object.

7. A vehicle networking data processing device, characterized in that: The device comprises: A naming module, used to obtain the original data signal of the Internet of Vehicles and name the original data signal according to a preset naming rule; A determination module, used to determine the data storage type of the original data signal according to a preset storage rule; The determination module is further used to determine the measurement unit used by the original data signal according to a preset unit rule; A parsing module, configured to, when it is determined according to a preset parsing rule that the original data signal needs to be parsed, parse the original data signal according to the preset parsing rule to obtain a parsing result; A flattening module, used for flattening the parsing result to obtain a flattened result when it is determined according to a preset flattening rule that the parsing result needs to be flattened; A storage module is used to store the flattening result according to the data storage type and the measurement unit.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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