Signal analysis method, device and server

Abstract

The application discloses a signal analysis method, device and server. The method comprises the following steps: receiving a message signal output by a vehicle, segmenting the message signal to obtain a plurality of segmented signals. According to a set conversion relationship, vehicle data corresponding to each segmented signal of the plurality of segmented signals is obtained. According to the arrangement order of the plurality of segmented signals, the vehicle data corresponding to each segmented signal of the plurality of segmented signals is spliced to obtain state data of the vehicle. The signal analysis method, device and server provided by the application have high analysis efficiency.

Description

Technical Field

[0001] This invention relates to Signal Analysis The field of technology, in particular, relates to a signal analysis method, apparatus, and server. Background Technology

[0002] Currently, the traditional method for parsing signals is to convert the signal into a binary stream and then parse the binary stream by traversing the hard-coded data in byte order.

[0003] Taking vehicle collision signals as an example, these signals are used to acquire vehicle data before, during, and after a collision event, facilitating collision analysis. With the development of electric vehicle technology, vehicle signal data has become increasingly diverse and complex. Currently, electric vehicle collision signals are long hexadecimal signals composed of 26 sub-signals, totaling tens of thousands of bytes. Traditional parsing methods for such long signals take tens of minutes, resulting in very low parsing efficiency. Summary of the Invention

[0004] This application provides a signal parsing method, apparatus, and server to solve the technical problem of low efficiency in long signal parsing in the prior art.

[0005] In view of the above problems, this application is made in order to provide a signal analysis method, apparatus and server that overcomes or at least partially solves the above problems.

[0006] Firstly, a signal analysis method is provided, including:

[0007] Receive the message signal output by the vehicle, segment the message signal to obtain multiple segmented signals;

[0008] Based on the set conversion relationship, obtain the vehicle data corresponding to each segment signal of the multiple segment signals;

[0009] Based on the arrangement order of multiple segmented signals, the vehicle data corresponding to each segmented signal is spliced ​​together to obtain the vehicle status data.

[0010] Optionally, the conversion relationship can be obtained using the following method:

[0011] Using a set conversion formula, a one-to-one mapping is made between a preset signal set and a preset dataset. The signal set is a collection of multiple identification signals, and the dataset is a collection of multiple vehicle data.

[0012] Based on the established conversion relationship, obtain the vehicle data corresponding to each segment signal of the multiple segment signals, including:

[0013] Based on the one-to-one mapping relationship between the signal set and the dataset, vehicle data corresponding to each segment of the multiple segmented signals is obtained.

[0014] Optionally, vehicle data includes: vehicle health status data, driving behavior data, and intelligent driving data.

[0015] Optionally, a predefined conversion formula is used to perform a one-to-one mapping between a preset signal set and a preset dataset, including:

[0016] Using JavaScript expressions, a one-to-one mapping is established between signal sets and datasets to obtain the JavaScript execution engine configuration table;

[0017] Based on the one-to-one mapping relationship between signal sets and datasets, vehicle data corresponding to each segment of multiple segmented signals is obtained, including:

[0018] Convert JavaScript expressions into a JavaScript execution engine;

[0019] Using N JavaScript execution engines, and based on the JavaScript execution engine configuration table, vehicle data corresponding to each segment signal of multiple segment signals is obtained, where N is a positive integer greater than 0.

[0020] Optionally, N JavaScript execution engines are used. Based on the JavaScript execution engine configuration table, vehicle data corresponding to each segment signal of multiple segmented signals is obtained, including:

[0021] At any given moment, each of the N JavaScript execution engines receives one or more segmented signals and outputs the vehicle data corresponding to the segmented signals according to the JavaScript execution engine configuration table.

[0022] Optionally, N JavaScript execution engines are used. Based on the JavaScript execution engine configuration table, vehicle data corresponding to each segment signal of multiple segmented signals is obtained, including:

[0023] The JavaScript execution engine compares the received segmented signals with the signal set in the JavaScript execution engine configuration table. If it is determined that there is an identifier signal in the signal set that corresponds to the segmented signal, the engine outputs vehicle data that has a mapping relationship with the identifier signal corresponding to the segmented signal.

[0024] Optionally, the message signal can be segmented to obtain multiple segmented signals, including:

[0025] Convert the message signal into a binary string;

[0026] The binary string is split into multiple binary substrings.

[0027] Secondly, a signal analysis device is provided, comprising:

[0028] The signal segmentation unit is used to acquire message signals, segment the message signals, and obtain multiple segmented signals.

[0029] The parsing unit is used to obtain the vehicle data corresponding to each segment signal of multiple segment signals according to the set conversion relationship;

[0030] The splicing unit is used to splice the vehicle data corresponding to each segment signal according to the arrangement order of multiple segment signals to obtain the vehicle status data.

[0031] Thirdly, this application also provides a server, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the server performs the method provided in the first aspect.

[0032] Fourthly, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the computer to perform the method provided in the first aspect.

[0033] Fifthly, this application also provides a computer program product, including a computer program that, when run, causes a computer to perform the method provided in the first aspect.

[0034] The technical solution provided in this application has at least the following technical effects or advantages:

[0035] The signal parsing method, apparatus, and server provided in this application segment a message signal into multiple segmented signals. Based on a predefined conversion relationship, the vehicle data corresponding to each segmented signal is obtained. The vehicle data is then concatenated according to the order of the segmented signals, thus achieving message signal parsing and obtaining complete vehicle status data. The signal parsing method provided in this application can achieve parallel parsing of multiple segmented signals, thereby improving parsing efficiency. For long signals that traditionally require tens of minutes to parse, the time can be reduced to within one minute.

[0036] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0037] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0038] Figure 1 This is a schematic diagram of signal analysis methods in the prior art;

[0039] Figure 2 This is a flowchart of the signal parsing method in an embodiment of this application;

[0040] Figure 3 This is a flowchart illustrating the analysis of vehicle collision signals in an embodiment of this application;

[0041] Figure 4 This is a schematic diagram of the signal analysis device in an embodiment of this application;

[0042] Figure 5 This is a schematic diagram of the server in an embodiment of this application. Detailed Implementation

[0043] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings.

[0044] The accompanying drawings illustrate various structural schematics according to embodiments of this application. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0045] To better understand the above technical solutions, the following will describe the above technical solutions in detail with reference to specific implementation methods. It should be understood that the embodiments of this disclosure and the specific features in the embodiments are detailed descriptions of the technical solutions of this application, rather than limitations on the technical solutions of this application. In the absence of conflict, the embodiments of this application and the technical features in the embodiments can be combined with each other.

[0046] Traditional operations for analyzing signals are as follows: Figure 1 As shown, the signal is converted into a binary stream, and then the binary stream is traversed in byte order and hard-coded. The extracted bytes are hard-coded into signal values ​​and parsed. Finally, the output individual signal values ​​are concatenated in byte order to obtain the parsed information.

[0047] The messages output by the vehicle contain status information of various systems, such as engine speed, vehicle speed, fuel level, coolant temperature, and air conditioning status. Understanding and interpreting these messages is crucial for vehicle diagnosis, repair, and modification.

[0048] Taking the vehicle collision signal output from an electric vehicle collision as an example, the collision signal is used to acquire vehicle data before, during, and after the collision, facilitating collision analysis. However, with the development of electric vehicle technology, vehicle signal data has become increasingly diverse and complex. Currently, the electric vehicle collision signal is a long hexadecimal signal carrying information including vehicle speed, acceleration, yaw rate, steering angle, gear position, engine valve position and percentage of full opening, brake pedal position, driver's seatbelt status, driver's front airbag deployment time, driver's side curtain airbag deployment time, driver's seatbelt pretensioner deployment time, motor speed per minute, front passenger front airbag deployment time, front passenger side curtain airbag deployment time, front passenger seatbelt pretensioner deployment time, etc., totaling tens of thousands of bytes. Traditional parsing methods for such long signals take tens of minutes, resulting in very low parsing efficiency. Furthermore, the existing signal parsing method, which uses byte-order traversal and hard encoding, is custom-developed and lacks reusability.

[0049] In view of this, this application provides a signal parsing method that segments the message signal into multiple segmented signals, obtains the vehicle data corresponding to the segmented signals according to a set conversion relationship, and concatenates the vehicle data according to the order of the segmented signals, thereby realizing the parsing of the message signal and obtaining complete vehicle status data. The signal parsing method provided by this application can realize the parallel parsing of multiple segmented signals to improve parsing efficiency. For long signals that traditionally require tens of minutes to parse, it can be shortened to within 1 minute.

[0050] Please refer to Figure 2 , Figure 2 This is a flowchart of a signal parsing method in an embodiment of this application. The method includes:

[0051] S201 receives the message signal output by the vehicle, segments the message signal, and obtains multiple segmented signals.

[0052] S202, based on the set conversion relationship, obtain the vehicle data corresponding to each segment signal of the multiple segment signals.

[0053] S203: Based on the arrangement order of multiple segmented signals, the vehicle data corresponding to each segmented signal is spliced ​​together to obtain the vehicle status data.

[0054] In some optional implementations, the conversion relationship set in S202 is obtained using the following method:

[0055] Using a set conversion formula, a one-to-one mapping is made between a preset signal set and a preset dataset. The signal set is a collection of multiple identification signals, and the dataset is a collection of multiple vehicle data.

[0056] The operation of S202 specifically includes: acquiring vehicle data corresponding to each segment of multiple segmented signals based on the one-to-one mapping relationship between the signal set and the dataset. Vehicle data includes: vehicle health status data, driving behavior data, and intelligent driving data.

[0057] In some feasible implementations, JavaScript expressions can be used to map signal sets and datasets one-to-one, obtaining a JavaScript execution engine configuration table to establish the defined conversion relationship. The operation of obtaining vehicle data corresponding to each segment of multiple signal segments based on the one-to-one mapping relationship between signal sets and datasets specifically includes:

[0058] The JavaScript expression is converted into a JavaScript execution engine. N JavaScript execution engines are used, and according to a JavaScript execution engine configuration table, vehicle data corresponding to each segment signal from multiple segmented signals is obtained, where N is a positive integer greater than 0. For example, at the same time, each of the N JavaScript execution engines receives more than one segmented signal and outputs the vehicle data corresponding to the segmented signal according to the JavaScript execution engine configuration table. Specifically, the JavaScript execution engine compares the received segmented signal with the signal set in the JavaScript execution engine configuration table. If it determines that there is an identifier signal in the signal set corresponding to the segmented signal, it outputs the vehicle data in the data set that has a mapping relationship with the identifier signal corresponding to the segmented signal.

[0059] JavaScript is a scripting programming language that enables complex functionalities on web pages. It defines the conversion relationships between signals and information using JavaScript expressions, resulting in a JavaScript engine. This engine boasts fast execution, cross-platform compatibility, and is extremely user-friendly for development engineers, significantly improving development efficiency. The JavaScript execution engine configuration table can be expanded at any time with one-to-one associations between identifier signals and vehicle data. Furthermore, the configuration table itself can be reused across multiple JavaScript execution engines, thus exhibiting scalability and reusability. For similar signal parsing, no code needs to be developed; simply using the JavaScript execution engine configuration table is sufficient to retrieve the corresponding vehicle data, resulting in high parsing efficiency.

[0060] The following example uses the analysis of vehicle collision signals, combined with... Figure 3 This paper introduces how to implement flexible parsing of vehicle collision signals based on parallel segmentation and a JavaScript engine. Figure 3 This is a flowchart illustrating the analysis of vehicle collision signals in an embodiment of this application.

[0061] After receiving a vehicle collision signal, such as Figure 3 As shown, the S201 operation is executed first to convert the vehicle collision signal into a binary string. Then, the binary string is divided (i.e. segmented) to obtain binary substrings 1, 2...M, where M is a positive integer greater than 1.

[0062] Then, the S202 operation is executed, which uses N JavaScript execution engines to parse according to the JavaScript execution engine configuration table, where N is a positive integer greater than 0.

[0063] It is understood that the signal parsing method provided in this application embodiment can realize parallel parsing of segmented signals. The number of segmented signals obtained by segmenting a signal will directly affect the parsing efficiency. Figure 3 Each binary substring in the binary substrings 1, 2...M represents a segment signal. Each segment signal can be parsed to obtain one piece of vehicle data. One piece of vehicle data can contain more than one information value. When the number of segment signals obtained from segmentation is large, such that one piece of vehicle data contains only one information value, a JavaScript execution engine performs the S202 operation to obtain the corresponding vehicle data as one information value. When the number of segment signals obtained from segmentation is small, such that one piece of vehicle data contains multiple information values, the JavaScript execution engine performs the S202 operation, which also needs to parse it in byte order using a hard-coded method to obtain the corresponding vehicle data as multiple information values.

[0064] For example, a 1000-section vehicle collision signal contains 1000 information values. If the existing method of traversing hard-coded bytes in byte order is used for parsing, it is necessary to read the bytes 1000 times.

[0065] If the signal parsing method provided in this application is adopted, the vehicle collision signal is first divided into four segments of 250 bytes each. Each segment includes 250 information values. These four segments can be parsed by one or more JavaScript execution engines at the same time. Although the JavaScript execution engine needs to read each segment 250 times in a loop, since it can parse the four segments at the same time, its overall time consumption can be reduced to 25% of the existing parsing method that traverses hard-coded segments in byte order.

[0066] In other words, if the vehicle collision signal is divided into 100 segments, each segment containing 10 information values, the JavaScript execution engine only needs to parse each segment 10 times. However, since it can parse all 100 segments simultaneously, its overall time consumption can be reduced to 10% of the time required to parse 1000 segments.

[0067] In other words, if the vehicle collision signal is divided into 1,000 segments, each containing one information value, the JavaScript execution engine can parse all 1,000 segments simultaneously. Including the time for concatenating the information values ​​according to the segment sequence, the signal parsing time can be reduced to less than 10%.

[0068] The JavaScript execution engine can simultaneously compare multiple segmented signals with the signal set in the JavaScript execution engine configuration table to obtain vehicle data that has a mapping relationship with the identification signals corresponding to each of the multiple segmented signals. For example, such as... Figure 3 As shown, JavaScript execution engine 1 parses binary substring 1 and binary substring 2 simultaneously, while JavaScript execution engine N parses binary substring M separately, finally obtaining vehicle data 1, 2...M.

[0069] A JavaScript execution engine is controlled by one operator. When a vehicle collision signal contains information such as vehicle speed, acceleration, yaw rate, steering angle, gear, engine valve position, percentage of fully open position, brake pedal position, driver's seat belt status, driver's front airbag deployment time, driver's side curtain airbag deployment time, driver's seat belt pretensioner deployment time, motor speed per minute, front passenger front airbag deployment time, front passenger side curtain airbag deployment time, and front passenger seat belt pretensioner deployment time, after the vehicle collision signal is segmented into multiple segments, multiple operators, each controlling a JavaScript execution engine, group and parse the segmented signals, thereby facilitating the classification and analysis of the parsed information.

[0070] Finally, the operation S203 is executed, concatenating the vehicle data 1, 2...M according to the binary string arrangement order 1, 2...M to obtain the vehicle status data. The vehicle status data is used to analyze the vehicle's operating status when a collision event occurs, thereby facilitating the diagnosis of the event's cause and targeted improvements to the vehicle.

[0071] For example, for vehicle collision signals, the segmentation rules of the signal and the JavaScript parsing engine expressions after segmentation are stored in a table, with the table structure shown below:

[0072] CREATE TABLE`veh_event_data_record`(

[0073] `ID`bigint NOT NULL COMMENT'Auto-incrementing primary key',

[0074] `BYTE_SERIAL_NUMBER`varchar(255)DEFAULT NULL COMMENT'Serial number is separated by ~ for multiple bytes: 23 to 25, and a single byte is a single number: 1',

[0075] `CONVERSION_FORMULA`varchar(2000)DEFAULT NULL COMMENT'Conversion Formula',

[0076] `EMPTY_VALUE`varchar(255)DEFAULT NULL COMMENT'Value retrieved',

[0077] `INVALID_VALUE`varchar(255)DEFAULT NULL COMMENT'Invalid value',

[0078] `NAME`varchar(255)DEFAULT NULL COMMENT'Name',

[0079] `SINGLE_EVENT_LEN`int DEFAULT NULL COMMENT 'Length of a single event signal, in bytes',

[0080] `SINGLE_EVENT_NUM`int DEFAULT NULL COMMENT 'Number of single event signals',

[0081] `SINGLE_SIGNAL_LEN`int DEFAULT NULL COMMENT 'Length of a single signal, in bytes',

[0082] `SINGLE_SIGNAL_LEN_BIT`int DEFAULT NULL COMMENT 'Length of a single signal, in bits',

[0083] `UNIT`varchar(255)DEFAULT NULL COMMENT'Unit',

[0084] `ASCII`tinyint(1)DEFAULT'0'COMMENT'Is it ASCII',

[0085] PRIMARY KEY(`ID`)

[0086] )ENGINE=InnoDB DEFAULT CHARSET=utf8mb3 COMMENT='EDR Data Parsing Rules Configuration Table'.

[0087] In the table structure above, the length of a single signal (SINGLE_EVENT_LEN) is clearly defined using a table, so that when encountering other similar signals, signal segmentation can be reused.

[0088] In this way, by segmenting the original binary signal and configuring the parsing through a table structure, scalability and reusability are achieved. For similar signals, the same table configuration can be used to solve the problem without developing new code.

[0089] The conversion formula for each signal is stored in the field (CONVERSION_FORMULA).

[0090] Here is an example of data:

[0091] INSERT INTO VEH_EVENT_DATA_RECORD(ID,

[0092] BYTE_SERIAL_NUMBER,CONVERSION_FORMULA,

[0093] EMPTY_VALUE,INVALID_VALUE,NAME,

[0094] SINGLE_EVENT_LEN,SINGLE_EVENT_NUM,

[0095] SINGLE_SIGNAL_LEN,SINGLE_SIGNAL_LEN_BIT,UNIT,

[0096] ASCII)VALUES(4,'28','function transfrom(n){

[0097] if (n == 255) {

[0098] The message "Data not available" appears. (The signal is invalid.)

[0099] }else if(n==254){

[0100] return "Data invalid";

[0101] }else{

[0102] return n;

[0103] }

[0104] }','255','254','Longitudinal clipping',1,1,1,8,'ms',false);

[0105] The longitudinally clipped signal here has a length of 8, and the parsing method is function.

[0106] transform(n){

[0107] if (n == 255) {

[0108] The message "Data not available" was returned.

[0109] }else if(n==254){

[0110] return "Data invalid";

[0111] }else{

[0112] return n;

[0113] }

[0114] }

[0115] The parsing method determines whether the binary value of the signal is a special number, such as 255 or 254. If not, it returns the original value.

[0116] After obtaining the original signal string, the following code is used to implement segmentation and parallel parsing:

[0117]

[0118]

[0119] In this way, if the parsing method for segmented signals is the same, the same JavaScript conversion formula can be reused, and the formula can be converted into a unified execution engine. This solution can significantly improve parsing efficiency by using parallel parsing after segmentation. For vehicle collision signals of tens of thousands of bytes, the parsing process that originally took tens of minutes can be shortened to less than 1 minute using the parallel parsing scheme.

[0120] For example, when a gear position signal exists within the segmented signal, the gear position signal is stored in the signal set, and the gear position information is stored in the dataset. A JavaScript expression is used to create a one-to-one mapping between the gear position signal and the gear position information, and this mapping is stored in the JavaScript execution engine configuration table. The JavaScript engine parses the gear position signal as follows:

[0121] The vehicle gear position is obtained from the input binary string (a segmented signal represented by n).

[0122] function transform(n){

[0123] if (n == 255) {

[0124] The message "Data not available" was returned.

[0125] }else if(n==254){

[0126] return "Data invalid";

[0127] }else if(n==0){

[0128] return "P";

[0129] }else if(n==1){

[0130] return "R";

[0131] }else if(n==2){

[0132] return "N";

[0133] }else if(n==3){

[0134] return "D";

[0135] }

[0136] }

[0137] As shown above, when the input n is 255 (11111111), it means that the current vehicle gear cannot be obtained.

[0138] When the input n is 254 (11111110), it indicates that the current vehicle gear is invalid, and both of these cases indicate that the current gear is faulty. When the input n is 0 (00000000), it indicates that the current vehicle gear is P (Park). When the input n is 1 (00000001), it indicates that the current vehicle gear is R (Reverse). When the input n is 2 (00000010), it indicates that the current vehicle gear is N (Neutral). When the input n is 3 (00000011), it indicates that the current vehicle gear is D (Drive).

[0139] Therefore, if different vehicle collision signals are segmented to obtain gear signals, the same JavaScript conversion formula can be reused. This formula can be converted into a unified execution engine to parse different vehicle collision signals. The signal conversion expression is a JavaScript expression, which, after conversion, forms a JavaScript engine. This engine features fast execution and cross-platform compatibility, making it extremely user-friendly for R&D engineers and greatly improving development efficiency.

[0140] This application also provides a signal analysis device, such as... Figure 4 As shown, the signal analysis device 400 includes:

[0141] The signal segmentation unit 401 is used to acquire the message signal, segment the message signal, and obtain multiple segmented signals.

[0142] The parsing unit 402 is used to obtain vehicle data corresponding to each segment signal of multiple segment signals according to the set conversion relationship.

[0143] The splicing unit 403 is used to splice the vehicle data corresponding to each segment signal of the multiple segment signals according to the arrangement order of the multiple segment signals to obtain the vehicle status data.

[0144] The signal parsing device 400 provided in this application embodiment can be applied to execute the signal parsing methods S201 to S203 provided in the above embodiments. It segments the message signal into multiple segmented signals, obtains the vehicle data corresponding to the segmented signals according to a set conversion relationship, and concatenates the vehicle data according to the order of the segmented signals, thus achieving message signal parsing and obtaining complete vehicle status data. The signal parsing device 400 provided in this application can realize parallel parsing of multiple segmented signals to improve parsing efficiency. For long signals that traditionally require tens of minutes to parse, it can shorten the parsing time to within one minute.

[0145] In some optional embodiments, the signal parsing device 400 further includes a mapping unit, used to perform a one-to-one mapping between a preset signal set and a preset dataset using a set conversion formula. Specifically, it uses a JavaScript expression to perform a one-to-one mapping between the signal set and the dataset to obtain a JavaScript execution engine configuration table. The signal set is a collection of multiple identifier signals, and the dataset is a collection of multiple vehicle data, including vehicle health status data, driving behavior data, and intelligent driving data.

[0146] The signal segmentation unit 401 is specifically used to convert the message signal into a binary string; it then segments the binary string to obtain multiple binary substrings. The segmented signal is the binary substring. The mapping unit also maps the identification signal, in the form of a binary substring, to the vehicle data one-to-one, and stores the one-to-one mapped identification signal and vehicle data in the JavaScript execution engine configuration table, forming a database used to support the operation of the parsing unit 402.

[0147] Parsing unit 402 is further configured to convert JavaScript expressions into JavaScript execution engines, and to use N JavaScript execution engines to obtain vehicle data corresponding to each segment signal of multiple segmented signals according to a JavaScript execution engine configuration table, where N is a positive integer greater than 0. For example, at the same time, each of the N JavaScript execution engines receives more than one segmented signal and outputs the vehicle data corresponding to the segmented signal according to the JavaScript execution engine configuration table. Specifically, the JavaScript execution engine compares the received segmented signal with the signal set in the JavaScript execution engine configuration table. If it determines that there is an identifier signal in the signal set corresponding to the segmented signal, it outputs the vehicle data in the data set that has a mapping relationship with the identifier signal corresponding to the segmented signal.

[0148] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the computer to perform the method provided in the above embodiments.

[0149] This application also provides a server, such as... Figure 5 As shown, the server 500 includes a memory 501, a processor 502, and a computer program 503 stored in the memory 501 and executable on the processor 502. When the processor 502 executes the computer program 503, the server 500 performs the method provided in the above embodiments.

[0150] This application also provides a computer program product, including a computer program that, when run, causes a computer to perform the methods provided in the above embodiments.

[0151] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0152] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0153] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

Claims

1. A signal analysis method characterized by, The method comprises the following steps: receiving a message signal output by a vehicle, segmenting the message signal to obtain a plurality of segmented signals; acquiring vehicle data corresponding to each segmented signal of the plurality of segmented signals according to a set conversion relationship; splicing the vehicle data corresponding to each segmented signal of the plurality of segmented signals according to an arrangement order of the plurality of segmented signals to obtain state data of the vehicle; the set conversion relationship is obtained by using the following method: using a javascript expression to perform one-to-one mapping between a signal set and a data set to obtain a javascript execution engine configuration table, the signal set being a collection of a plurality of identification signals, and the data set being a collection of a plurality of vehicle data; the step of acquiring vehicle data corresponding to each segmented signal of the plurality of segmented signals according to a set conversion relationship comprises: converting the javascript expression into a javascript execution engine; using N javascript execution engines to acquire vehicle data corresponding to each segmented signal of the plurality of segmented signals according to the javascript execution engine configuration table, wherein N is a positive integer greater than 0.

2. The signal analysis method according to claim 1, wherein The vehicle data includes vehicle health state data, driving behavior data, and intelligent driving data.

3. The signal analysis method according to claim 1, wherein The step of using N javascript execution engines to acquire vehicle data corresponding to each segmented signal of the plurality of segmented signals according to the javascript execution engine configuration table comprises: at the same time, each javascript execution engine of the N javascript execution engines receives more than one segmented signal, and outputs vehicle data corresponding to the segmented signal according to the javascript execution engine configuration table.

4. The signal analysis method according to claim 1, wherein The step of using N javascript execution engines to acquire vehicle data corresponding to each segmented signal of the plurality of segmented signals according to the javascript execution engine configuration table comprises: the javascript execution engine compares the received segmented signal with a signal set in the javascript execution engine configuration table, and if it is determined that there is an identification signal corresponding to the segmented signal in the signal set, the vehicle data in a data set having a mapping relationship with the identification signal corresponding to the segmented signal is output.

5. The signal analysis method according to claim 1, wherein The step of segmenting the message signal to obtain a plurality of segmented signals comprises: converting the message signal into a binary string; segmenting the binary string to obtain a plurality of binary substrings.

6. A signal analysis device, characterized by The method comprises the following steps: a signal segmentation unit is configured to acquire a message signal and segment the message signal to obtain a plurality of segmented signals; an analysis unit is configured to acquire vehicle data corresponding to each segmented signal of the plurality of segmented signals according to a set conversion relationship; a splicing unit is configured to splice the vehicle data corresponding to each segmented signal of the plurality of segmented signals according to an arrangement order of the plurality of segmented signals to obtain state data of the vehicle. A mapping unit is configured to employ a JavaScript expression to perform one-to-one mapping between a signal set and a data set, to obtain a JavaScript execution engine configuration table, the signal set being a set of multiple identification signals, and the data set being a set of multiple vehicle data. The parsing unit is further configured to convert the JavaScript expression into a JavaScript execution engine, and employ N JavaScript execution engines to obtain vehicle data corresponding to each of the multiple segmented signals according to the JavaScript execution engine configuration table, where N is a positive integer greater than 0.

7. A server comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The computer program is executed by the processor, so that the server executes the method of any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 7. The computer program is executed by the processor, so that the computer executes the method of any one of claims 1 to 5.

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