CN117493896A - Vehicle bus data comparison method and device, electronic equipment and storage medium - Google Patents
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Abstract
The application discloses a vehicle bus data comparison method, a device, electronic equipment and a storage medium, wherein the vehicle bus data comparison method comprises the following steps: acquiring first bus data of a first vehicle under a target working condition and second bus data of a second vehicle under the target working condition; acquiring signal similarity between signals in the first bus data and signals in the second bus data; acquiring load similarity between the load rate of the bus channel in the first bus data and the load rate of the bus channel in the second bus data; and determining data similarity between the first bus data and the second bus data based on the signal similarity and the load similarity. The method utilizes the signal data and the load rate data in the bus data of the two vehicles to respectively compare, so that the comparison is performed not only in terms of the behavior of the vehicles, but also in terms of the performance of the vehicles, and the reliability of comparison results is improved.
Description
Technical Field
The present disclosure relates to the field of vehicle data analysis technologies, and in particular, to a method and apparatus for comparing vehicle bus data, an electronic device, and a storage medium.
Background
The development process of automobiles is a very complex process, and is typically performed by a plurality of manufacturers. In different vehicles of the same model, the same controller may come from different manufacturers, and different production batches of the same controller may also use different chips. This means that even with the same model, there may be minor differences in the performance and function of different vehicles. In the development, production and use of automobiles, problems of very low probability and difficult investigation often occur.
The implementation of the automobile functions is realized through data interaction among various systems in the automobile, and the interaction is mainly realized through an on-board network and a bus communication protocol. Current automotive network architectures typically employ a multi-level architecture, which results in inefficiency when the vehicles are analyzed manually to obtain differences between the vehicles.
Disclosure of Invention
In view of the above, the application provides a vehicle bus data comparison method, a device, an electronic device and a storage medium.
In a first aspect, an embodiment of the present application provides a method for comparing vehicle bus data, where the method includes: acquiring first bus data of a first vehicle under a target working condition and second bus data of a second vehicle under the target working condition; acquiring signal similarity between signals in the first bus data and signals in the second bus data; acquiring load similarity between the load rate of the bus channel in the first bus data and the load rate of the bus channel in the second bus data; and determining data similarity between the first bus data and the second bus data based on the signal similarity and the load similarity.
In an alternative embodiment, the acquiring the signal similarity between the signal in the first bus data and the signal in the second bus data includes: acquiring a first signal sequence corresponding to each signal in the first bus data and a second signal sequence corresponding to each signal in the second bus data, wherein the first signal sequence and the second signal sequence comprise signal values ordered according to time; for each continuous signal in the first bus data and the second bus data, according to the change trend of the first signal sequence and the second signal sequence corresponding to each continuous signal, obtaining the similarity between each continuous signal in the first bus data and each continuous signal in the second bus data as the similarity corresponding to each continuous signal; and aiming at each discrete signal in the first bus data and the second bus data, according to signal value change points in signal values of the first signal sequence and the second signal sequence, acquiring the similarity between each discrete signal in the first bus data and each discrete signal in the second bus data as the similarity corresponding to each discrete signal.
In an optional embodiment, the obtaining, for each continuous signal in the first bus data and the second bus data, a similarity between the each continuous signal in the first bus data and the each continuous signal in the second bus data according to a trend of variation of the first signal sequence and the second signal sequence corresponding to each continuous signal, as the similarity corresponding to each continuous signal, includes: dividing the first signal sequence corresponding to each continuous signal according to the change trend of the first signal sequence corresponding to each continuous signal to obtain a plurality of first signal subsequences corresponding to each continuous signal; dividing the second signal sequence corresponding to each continuous signal according to the change trend of the second signal sequence corresponding to each continuous signal to obtain a plurality of second signal subsequences corresponding to each continuous signal; aligning the plurality of first signal subsequences and the plurality of second signal subsequences corresponding to each of the continuous signals; for each continuous signal, obtaining the similarity between each aligned first signal subsequence and each aligned second signal subsequence, and obtaining a plurality of subsequence similarities corresponding to each continuous signal; and determining the similarity corresponding to each continuous signal according to the multiple subsequence similarities corresponding to each continuous signal.
In an optional embodiment, the obtaining, for each discrete signal in the first bus data and the second bus data, a similarity between the each discrete signal in the first bus data and the each discrete signal in the second bus data as a similarity corresponding to the each discrete signal according to a signal value change point in signal values of the first signal sequence and the second signal sequence includes: acquiring a first signal value change point set in a first signal sequence corresponding to each discrete signal and a second signal value change point set in a second signal sequence; and determining the similarity corresponding to each discrete signal according to the editing distance corresponding to the first signal value change point set and the second signal value change point set corresponding to each discrete signal.
In an optional embodiment, the obtaining the load similarity between the load rate of the bus channel in the first bus data and the load rate of the bus channel in the second bus data includes: acquiring a first load sequence corresponding to each path of bus channel in the first bus data and a second load sequence corresponding to each path of bus channel in the second bus data, wherein the first load sequence and the second load sequence comprise load rates ordered according to time; dividing the first load sequence corresponding to each bus channel according to the change trend of the first load sequence corresponding to each bus channel to obtain a plurality of first load subsequences corresponding to each bus channel; dividing the second load sequence corresponding to each bus channel according to the change trend of the second load sequence corresponding to each bus channel to obtain a plurality of second load subsequences corresponding to each bus channel; aligning the first load subsequences and the second load subsequences corresponding to each bus channel; aiming at each path of bus channel, obtaining the similarity between each aligned first load subsequence and each aligned second load subsequence, and obtaining a plurality of subsequence similarities corresponding to each path of bus channel; and determining the similarity corresponding to each path of bus channel according to the multiple sub-sequence similarities corresponding to each path of bus channel.
In an alternative embodiment, the signal similarity includes a similarity corresponding to each signal, the load similarity includes a similarity corresponding to each bus channel, and determining the data similarity between the first bus data and the second bus data based on the signal similarity and the load similarity includes: determining the overall signal similarity between the first bus data and the second bus data according to the signal similarity corresponding to each signal and the signal weight corresponding to each signal; determining the overall load similarity between the first bus data and the second bus data according to the load similarity corresponding to each bus channel and the number of bus channels; and determining the data similarity between the first bus data and the second bus data according to the overall signal similarity, the first similarity weight corresponding to the overall signal similarity, the overall load similarity and the second similarity weight corresponding to the overall load similarity.
In an alternative embodiment, after the determining the data similarity between the first bus data and the second bus data based on the signal similarity and the load similarity, the method further includes: if the data similarity does not reach the data similarity threshold corresponding to the data similarity, determining a difference data type between the first vehicle and the second vehicle according to the signal similarity and the first similarity threshold and the load similarity and the second similarity threshold; and determining the difference information between the first vehicle and the second vehicle according to the data corresponding to the difference data type.
In a second aspect, an embodiment of the present application provides a device for comparing vehicle bus data, the device including: the bus data acquisition module is used for acquiring first bus data of a first vehicle under a target working condition and second bus data of a second vehicle under the target working condition; a signal similarity obtaining module, configured to obtain a signal similarity between a signal in the first bus data and a signal in the second bus data; the load similarity acquisition module is used for acquiring load similarity between the load rate of the bus channel in the first bus data and the load rate of the bus channel in the second bus data; and the data similarity determining module is used for determining the data similarity between the first bus data and the second bus data based on the signal similarity and the load similarity.
In a third aspect, an embodiment of the present application provides an electronic device, including: one or more processors; a memory; one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications configured to perform the method of comparing vehicle bus data provided in the first aspect above.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium having program code stored therein, the program code being callable by a processor to perform the method for comparing vehicle bus data provided in the first aspect.
According to the scheme, signal data and load rate data in bus data of two vehicles are utilized for comparison respectively, comparison is carried out not only from the behavior of the vehicles, but also from the performance aspect of the vehicles, the reliability of comparison results is improved, the comparison time consumed by manual comparison is reduced, and the comparison efficiency of the bus data among the vehicles is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly introduced below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart illustrating a comparison method of vehicle bus data according to an embodiment of the present application.
Fig. 2 is a flow chart illustrating a method for comparing vehicle bus data according to another embodiment of the present application.
Fig. 3 shows a detailed flowchart of step S203 in another embodiment of the present application.
Fig. 4 shows a trend chart and a cumulative sum control chart of a first signal sequence according to another embodiment of the present application.
Fig. 5 shows a schematic diagram of segmentation of a first signal sequence and a second signal sequence according to another embodiment of the present application.
Fig. 6 shows a schematic alignment of a sub-sequence of a first signal sequence with a sub-sequence of a second signal sequence in another embodiment of the present application.
Fig. 7 is a detailed flowchart of step S204 in another embodiment of the present application.
Fig. 8 shows a block diagram of a comparison device for vehicle bus data according to an embodiment of the present application.
Fig. 9 shows a block diagram of an electronic device for performing a comparison method of vehicle bus data according to an embodiment of the present application.
Detailed Description
In order to enable those skilled in the art to better understand the present application, the following description will make clear and complete descriptions of the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application.
The inventor further researches a bus data comparison scheme, wherein the bus data comparison scheme is aimed at collecting whole bus data, importing a database file by means of current mainstream bus analysis equipment such as CANoe, CANalyzer and the like, replaying the collected real bus data log, and manually analyzing the data content obtained by tool analysis to know the behavior of the vehicle. In the comparison scheme, the bus analysis equipment is high in price, so that the bus data analysis cost is high, the bus analysis equipment can only judge whether vehicles are similar or not, and can not quantify the similarity of the bus data among the vehicles, so that a developer can not locate the difference among the vehicles.
Aiming at the background technology and the technical problems, the inventor provides a comparison method, a device, electronic equipment and a storage medium for bus data of vehicles, which are used for respectively comparing signal data and load rate data in the bus data of two vehicles, so that the comparison is performed not only in terms of vehicle behavior, but also in terms of vehicle performance, the reliability of comparison results is improved, the comparison time consumed by manual comparison is reduced, and the comparison efficiency of bus data among vehicles is improved.
Referring to fig. 1, fig. 1 is a flow chart illustrating a comparison method of vehicle bus data according to an embodiment of the present application. In a specific embodiment, the vehicle bus data comparing method is applied to the vehicle bus data comparing device 200 shown in fig. 8 and the electronic apparatus provided with the vehicle bus data comparing device 200.
The following will describe the flow chart shown in fig. 1 in detail, and the comparison method of the vehicle bus data may specifically include the following steps:
step S110: and acquiring first bus data of the first vehicle under the target working condition and second bus data of the second vehicle under the target working condition.
The working condition refers to the working condition of the vehicle. The target working condition may be a working condition specified by a user, or may be a working condition corresponding to the vehicle currently, and the determination mode of the target working condition is not specifically limited here. The working condition may be that the vehicle speed from the start of the vehicle to the vehicle is 100km/h and the running is carried out for 5min.
The bus data refers to data that the vehicle interacts with each component in the on-board network. The bus data may be acquired through real-time acquisition by a plurality of sensors, or may be acquired directly from a server or a data storage device at a vehicle end, or may be acquired by acquiring log files corresponding to all bus data of a first vehicle and a second vehicle, and analyzing the log files through database files corresponding to the bus data, so as to obtain the first bus data corresponding to the first vehicle and the second bus data corresponding to the second vehicle. A database file is information used to decode raw bus data into physical values or human-readable forms. The database file formats used by the different bus types are different. For example, the controller area network Database (DBC) file is a Database file of a controller area network (ContrllerArea Network, can) bus, the logical data file (Logical Data File, LDF) file is a Database file of a local connectivity network (LocalInterconnectNetwork, LIN) bus, and the extensible markup language file (AUTomotive Open Systems ARchitecture Extensible Markup Language, ARXML) ARXML of the automotive software architecture is a Database file of an on-board ethernet. In the embodiment of the present application, the obtaining manner of the bus data is preferably that a log file of the bus data is obtained, and the log file is parsed by using a database file, so as to obtain the first bus data and the second bus data.
And acquiring the bus data of the first vehicle and the second vehicle under the same target vehicle condition in a specific bus data acquisition mode so as to realize bus data comparison between the two vehicles by using the bus data.
For example, the target condition is that a person enters the vehicle and starts the vehicle, and then stops the vehicle after traveling for 2 km. The vehicle is provided with ten paths of high-speed CAN buses, two paths of LIN buses and three paths of vehicle-mounted Ethernet buses. Then all data of ten high-speed CAN buses, two-way LIN buses and three-way vehicle-mounted ethernet buses of the first vehicle and the second vehicle to be compared under the target working condition need to be collected.
In some embodiments, the method for comparing vehicle bus data provided in the embodiments of the present application may also be used for comparing bus data of a plurality of vehicles to be compared. The reference vehicle needs to be determined before the bus data comparison can be made. The reference vehicle may be determined from a plurality of vehicles to be compared, and at this time, the remaining plurality of vehicles to be compared respectively compare the bus data with the reference vehicle. The reference vehicle may be designated by the user, and in this case, the plurality of vehicles to be compared need to be compared with the reference vehicle. The determination of the reference vehicle may be performed before or after the bus data of the plurality of vehicles to be compared are acquired, and the determination method of the reference vehicle and the determination time of the reference vehicle are not specifically limited. The reference vehicle is determined firstly, and then the vehicle to be compared is compared with the reference vehicle, so that the phenomenon of confusion of the comparison data caused by the absence of the reference vehicle can be avoided, and the comparison efficiency of bus data comparison for the vehicle to be compared is improved.
Step S120: and acquiring the signal similarity between the signal in the first bus data and the signal in the second bus data.
The signal refers to the driving intention or state information of the bus data under the target working condition. According to the characteristics of the signals and the application scene, the signals can be divided into continuous signals and discrete signals. The continuous signal includes, but is not limited to, a vehicle speed signal, an oil amount signal, an electric quantity signal, and the like. Discrete signals include, but are not limited to, switching signals, status signals, and the like. The vehicle signals may be classified into bus signals such as CAN bus signals, LIN bus signals, and on-vehicle ethernet bus signals according to the type of bus. The specific classification of the signals is not limited herein.
The calculation of the signal similarity between the first bus data and the second bus data may be the calculation of the similarity of only one signal of the first bus data and the second bus data, or the comprehensive calculation of the similarity of all kinds of signals of the first bus data and the second bus data. The manner of calculating the signal similarity between the first bus data and the second bus data is not particularly limited here.
The difference between the behaviors of the first vehicle and the second vehicle under the same target vehicle condition can be obtained by comparing the first bus data with the second bus data from the signal level, namely, comparing the first vehicle and the second vehicle under the same target vehicle condition by using the running intention and/or the state information of the vehicles.
Step S130: and acquiring the load similarity between the load rate of the bus channel in the first bus data and the load rate of the bus channel in the second bus data.
The load rate is the ratio of the actual data transmitted in unit time in each path of bus channel to the theoretical maximum data amount which can be transmitted, and is used for measuring the utilization degree of the transmission capacity of the bus channel. The higher the value of the load factor, the more fully utilized the bandwidth of the bus channel, but when the load factor exceeds the carrying capacity of the bus, problems such as increased transmission delay, reduced performance, or data loss may result.
The calculation formula of the load factor is as follows:
the data amount actually transmitted refers to the data amount passing through the bus channel in a given time period, and the unit is Bit.
For example, a certain bus channel transmits CAN standard frames, and the baud rate is 500Kbps, i.e. 500×1000=500000 bits CAN be transmitted in 1 second, i.e. 500 bits CAN be transmitted in 1 ms. At this time, the load rate calculation formula of the bus channel is as follows:
wherein 111 refers to the actual length of a CAN standard frame for transmitting a frame of data frame, where the actual length includes a start frame (1 Bit) +an arbitration field (12 Bit) +a control field (6 Bit) +a data field (64 Bit) +a CRC field (16 Bit) +an ACK field (2 Bit) +an end frame (7 Bit) +itm (3 Bit) =111 Bit, and n is the number of packets transmitted by a certain bus channel within 1 ms.
The similarity between the first bus data and the second bus data is compared from the load rate direction, namely the similarity between the first vehicle and the second vehicle under the same target working condition is compared from the bus performance corresponding to each bus channel, so that the comparison error caused by vehicle bus data comparison only from vehicle signals is avoided, and the reliability of the comparison result between the first vehicle and the second vehicle is improved.
Step S140: and determining data similarity between the first bus data and the second bus data based on the signal similarity and the load similarity.
The data similarity between the first bus data and the second bus may be calculated by setting the same or different proportions for the signal similarity and the load similarity, or the same or different weights may be configured for the signal similarity and the load similarity, so as to calculate the data similarity between the first vehicle and the second vehicle. The method for obtaining the data similarity is not particularly limited, and can be set by a user in a self-defining way.
According to the scheme provided by the embodiment of the application, the signal data and the load rate data in the bus data of the two vehicles are utilized for comparison respectively, so that the comparison is performed not only from the behavior of the vehicles, but also from the performance aspect of the vehicles, the reliability of comparison results is improved, the comparison time consumed by manual comparison is reduced, and the comparison efficiency of the bus data among the vehicles is improved.
Referring to fig. 2, fig. 2 is a flow chart illustrating a method for comparing vehicle bus data according to another embodiment of the present application. The following will describe the flow chart shown in fig. 2 in detail, and the comparison method of the vehicle bus data may specifically include the following steps:
step S201: and acquiring first bus data of the first vehicle under the target working condition and second bus data of the second vehicle under the target working condition.
The detailed description of step S201 is referred to step S110, and will not be repeated here.
Step S202: and acquiring a first signal sequence corresponding to each signal in the first bus data and a second signal sequence corresponding to each signal in the second bus data, wherein the first signal sequence and the second signal sequence comprise signal values ordered according to time.
The obtaining of the signals in the first bus data and the signals in the second bus data may be directly splitting the log files corresponding to all the bus data according to each signal, or splitting the log files corresponding to all the bus data according to each path of bus channel to obtain the log files corresponding to each path of bus channel, and then analyzing the log files of each path of bus channel to obtain each signal in each path of bus channel, where the obtaining mode of the signals is not specifically limited.
After the signals in the first bus data and the signals in the second bus data are obtained, the signal values corresponding to the signals in the first bus data and the signal values corresponding to the signals in the second bus data are ordered according to time, so that a first signal sequence corresponding to each signal in the first bus data and a second signal sequence corresponding to each signal in the second bus data are obtained.
Step S203: and aiming at each continuous signal in the first bus data and the second bus data, according to the change trend of the first signal sequence and the second signal sequence corresponding to each continuous signal, acquiring the similarity between each continuous signal in the first bus data and each continuous signal in the second bus data as the similarity corresponding to each continuous signal.
The continuous signal is continuous in time parameter, that is, the continuous signal is defined at any point in time, so that the similarity between the first signal sequence and the second signal sequence cannot be determined from the signal value of a certain time point of one signal, and if the similarity between the first signal sequence and the second signal sequence is determined from the signal value of a certain time point alone, the influence caused by the mutation of the local signal value is emphasized, and the influence caused by the whole signal value is weakened. Therefore, the signal similarity between the signal in the first bus data and the signal in the second bus data should be determined by using the continuous signal through the similarity between the signal change trends between the two, thereby obtaining the similarity between the overall signal values between the two.
Optionally, referring to fig. 3, for each continuous signal in the first bus data and the second bus data, according to a trend of variation of the first signal sequence and the second signal sequence corresponding to each continuous signal, a similarity between each continuous signal in the first bus data and each continuous signal in the second bus data is obtained, and the similarity is used as a similarity corresponding to each continuous signal, and specifically includes steps S2301 to S2305, which are described in detail below:
step S2031: and dividing the first signal sequence corresponding to each continuous signal according to the change trend of the first signal sequence corresponding to each continuous signal to obtain a plurality of first signal subsequences corresponding to each continuous signal.
The trend of change refers to the direction and rate of change of a signal over time at a point in time. The change trend of the first signal sequence may be obtained by derivative calculation, or may be obtained by calculation such as differential calculation or fourier transformation, or may be obtained by normalizing the first signal sequence and then obtaining the normalized change trend. The method for acquiring the variation trend in the embodiment of the present application is determined by the user, and the method for acquiring the variation trend is not specifically limited herein.
The normalization processing may be that the signal value of the first signal sequence is mapped in the [0,1] interval, so as to obtain the data point corresponding to the normalized first signal sequence, and then the change trend corresponding to the first signal sequence is obtained according to the mapped data point.
The mapping formula is:
and (3) mapping the signal values in the first signal sequence into the [0,1] interval according to the formula (3) so as to reduce the calculation difficulty of the signal values in the first signal sequence in the subsequent calculation.
The first signal sequence is segmented according to the change trend of the first signal sequence, the segmentation mode can be used for segmenting the first signal sequence with fixed duration, or a special signal value is used as a segmentation point for segmentation, or the change trend is further processed to obtain a transformation inflection point in the change trend, and the signal sequence is segmented according to the change inflection point.
In the embodiment of the application, the segmentation method preferably further processes the change trend according to a preset change trend processing method, so as to segment the change trend according to a change inflection point. The preset change trend processing mode may be a cumulative sum. The running sum uses the current and recent data to check for minor changes or variability in the process mean, and the running sum looks equally important to the current and recent data.
Dividing the trend of the signal sequence using the running sum includes: and calculating a sequence average value corresponding to the first signal sequence. And calculating the corresponding accumulated sum of each signal value in the first signal sequence according to the sequence average value. And calculating the sequence extreme points in the cumulative sum. And dividing the first signal sequence according to the signal value corresponding to the sequence extreme point.
The sequence average value formula corresponding to the first signal sequence is calculated as follows:
wherein i is the signal value index in the first signal sequence, and n is the number of signal values in the first signal sequence.
And (3) obtaining a sequence average value corresponding to the first signal sequence according to the formula (4). And then calculating the corresponding accumulated sum of each signal value in the first signal sequence according to the sequence average value, wherein the accumulated sum has the following calculation formula:
and (3) calculating the accumulated sum corresponding to each signal value in the first signal sequence according to the formula (5), and acquiring the accumulated sum sequence corresponding to the first signal sequence according to the accumulated sum. Determining sequence extreme points in the accumulated sum sequence according to the accumulated sum sequence, and dividing the first signal sequence according to signal values in the first signal sequence corresponding to the sequence extreme points, wherein the normalized first signal sequence is X_sets= { [ X ] 1 ，…，x i ]，[x i+1 ，…，x j ]，…，[x k ，…，x M ]}。
Illustratively, the first signal sequence is x= [ X1, X2, …, xi, …, xM ], and mapping by equation (3) yields a normalized first signal sequence x_d= [ x1_d, x2_d, …, xi_d, …, xm_d ].
Referring to fig. 4, a trend graph corresponding to the normalized first signal sequence is shown in fig. 4 (1), and an accumulation and control graph of the normalized first signal sequence is shown in fig. 4 (2). As can be seen from fig. 4, the curve in fig. 4 (2) has two sequence extreme points, p1 and p2, respectively. According to the signal value corresponding to p1 and the signal value corresponding to p2, the normalized first signal sequence is divided to obtain 3 subsequences, namely, subsequence 1, subsequence 2 and subsequence 3, and the details can be seen from (1) of fig. 5.
Step S2032: and dividing the second signal sequence corresponding to each continuous signal according to the change trend of the second signal sequence corresponding to each continuous signal to obtain a plurality of second signal subsequences corresponding to each continuous signal.
In the embodiment of the present application, the specific step of dividing the second signal sequence may refer to step S2031, which is not described herein. And the normalized second signal sequence is Y_sets= { [ Y 1 ，…，y i ]，[y i+1 ，…，y j ]，…，[y k ，…，y M ]}。
For example, the normalized second signal sequence is divided to obtain the corresponding subsequence 1, subsequence 2, and subsequence 3 in fig. 5 (2).
Step S2033: and aligning the plurality of first signal subsequences and the plurality of second signal subsequences corresponding to each continuous signal.
The first signal subsequences and the second signal subsequences corresponding to each continuous signal are aligned, so that data omission phenomenon during data comparison can be avoided, and the similarity obtained after comparison is more in line with actual conditions. The comparison mode may be alignment according to the signal value position in the first signal subsequence, or alignment according to the signal value position in the second signal subsequence, or alignment according to time between the first signal subsequence and the second signal subsequence, or alignment according to an average value corresponding to the signal value in each first signal subsequence and an average value corresponding to the signal value in the second signal subsequence. In the sub-sequence alignment method in the embodiment of the present application, the average value corresponding to the signal value in each first signal sub-sequence is preferably aligned with the average value corresponding to the signal value in the second signal sub-sequence, and the second signal sub-sequence corresponding to the average value of the second signal sub-sequence closest to the average value corresponding to the first signal sub-sequence is selected for alignment, or the second signal sub-sequence with the difference value between the average value corresponding to the first signal sub-sequence and the average value corresponding to the second signal sub-sequence within the preset range is selected for alignment, and the method for performing correspondence according to the average value of the signal sub-sequences and the preset range are not specifically limited herein.
For example, referring to fig. 6, fig. 6 shows a schematic alignment diagram of signal subsequences provided in an embodiment of the present application. Step S2034: and aiming at each continuous signal, acquiring the similarity between each aligned first signal subsequence and each aligned second signal subsequence, and obtaining a plurality of subsequence similarities corresponding to each continuous signal.
The similarity between each first signal subsequence and each second signal subsequence after alignment is calculated, which may be obtained through an algorithm, or may be obtained through calculation through a neural network, where the manner of calculating the similarity between each first signal subsequence and each second signal subsequence after alignment is not specifically limited. In the embodiment of the present application, the similarity between each aligned first signal subsequence and each second signal subsequence is preferably obtained by using a dynamic time warping algorithm (Dynamic Time Warping, DTW). The DTW algorithm is applicable to sequences of different lengths or slightly offset in time axis. The DTW algorithm mainly obtains the greatest possible similarity by automatically warping the two sequences and performing local scaling alignment on the time axis so that the morphologies thereof are as consistent as possible. The specific formula for calculating the similarity by using the DTW algorithm is as follows:
D(i，j)＝Dist(i，j)+min[D(i-1，j)，D(i，j-1)，D(i-1，j-1)] (6)
Wherein D (i, j) represents the similarity of the current first signal subsequence signal value to the current second signal subsequence signal value, D (i-1, j) represents the similarity of the last first signal subsequence signal value to the current second signal subsequence signal value, D (i, j-1) represents the similarity of the current first signal subsequence signal value to the last second signal subsequence signal value, and D (i-1, j-1) represents the similarity of the last first signal subsequence signal value to the last second signal subsequence signal value.
Calculating and obtaining the similarity { d } between each aligned first signal subsequence and second signal subsequence according to formula (6) 1 ，d 2 ，…，d M }。
Step S2035: and determining the similarity corresponding to each continuous signal according to the multiple subsequence similarities corresponding to each continuous signal.
In the embodiment of the present application, a similarity formula corresponding to each continuous signal is determined according to the similarity of a plurality of subsequences as follows:
wherein d i Representing subsequence similarity values, log1_l i Is the length of the subsequence, log1_l is the length of the signal sequence to which such signal corresponds.
And (3) calculating the similarity corresponding to each continuous signal according to the formula (7), so as to obtain the similarity of each continuous signal of the first vehicle and the second vehicle.
Step S204: and aiming at each discrete signal in the first bus data and the second bus data, according to signal value change points in signal values of the first signal sequence and the second signal sequence, acquiring the similarity between each discrete signal in the first bus data and each discrete signal in the second bus data as the similarity corresponding to each discrete signal.
The discrete signal is a state signal generated at a specific time, and is generated only when the vehicle is operated or the driver is operated. For example, a switching signal may be generated in response to a driver only when the driver has controlled a switching button. Therefore, the similarity between the first signal sequence and the second signal sequence can be determined from the signal value corresponding to a certain signal at a certain time point, so that the influence caused by the mutation of the local signal value is emphasized by using the discrete signal.
The signal value change point refers to that the signal value of a certain signal in the discrete signal is different from the signal value of the last unit time, and the signal value is the signal finger change point.
Optionally, referring to fig. 7, for each discrete signal in the first bus data and the second bus data, according to a signal value change point in a signal value of the first signal sequence and a signal value of the second signal sequence, a similarity between each discrete signal in the first bus data and each discrete signal in the second bus data is obtained, and the similarity is used as a similarity corresponding to each discrete signal, and specifically includes steps S2401 to S2402, which are described in detail below:
Step S2041: and acquiring a first signal value change point set in the first signal sequence corresponding to each discrete signal and a second signal value change point set in the second signal sequence.
The signal value change point set refers to a set of signal values corresponding to all change points in the signal sequence and signal values corresponding to the change points before the change. It should be noted that if all the signal values in the signal sequence are the same, the signal value change point set corresponding to the signal sequence is the signal value, and only one signal value is included in the signal value change point set.
Step S2042: and determining the similarity corresponding to each discrete signal according to the editing distance corresponding to the first signal value change point set and the second signal value change point set corresponding to each discrete signal.
The edit distance refers to the minimum number of single character edits (insertions, deletions, or substitutions) between two sets. In the embodiment of the application, the method is used for measuring the editing distance between the first signal value change point set and the second signal value change point set, and determining the similarity between the first signal value change point set and the second signal value change point set according to the editing distance. The similarity formula is determined according to the edit distance as follows:
Where len (dor1_p) represents the length of the first signal value change point set, and len (dor2_p) represents the length of the second signal value change point set.
The signal values in the first signal sequence are all 0, and the corresponding first signal value change point set is {0}. And a certain signal value in the second signal sequence is 1, and the rest signal values are 0, wherein the corresponding second signal value change point set is {0,1,0}. By acquiring the edit distance between the {0} changes to {0,1,0}, the similarity corresponding to the discrete signal is determined according to formula (8). It should be noted that if there are a plurality of continuous identical signal values in the signal sequence, the signal value change points are recorded only once, so as to reduce the calculation amount.
Step S205: acquiring a first load sequence corresponding to each path of bus channel in the first bus data and a second load sequence corresponding to each path of bus channel in the second bus data, wherein the first load sequence and the second load sequence comprise load rates ordered according to time.
The acquiring manner of the first load sequence and the second load sequence may refer to the acquiring manner of the signal sequence, which is not described herein.
Step S206: and dividing the first load sequence corresponding to each bus channel according to the change trend of the first load sequence corresponding to each bus channel, and obtaining a plurality of first load subsequences corresponding to each bus channel.
Step S207: and dividing the second load sequence corresponding to each bus channel according to the change trend of the second load sequence corresponding to each bus channel, and obtaining a plurality of second load subsequences corresponding to each bus channel.
Step S208: and aligning the first load subsequences and the second load subsequences corresponding to each bus channel.
Step S209: and aiming at each path of bus channel, acquiring the similarity between each aligned first load subsequence and each aligned second load subsequence, and obtaining a plurality of subsequence similarities corresponding to each path of bus channel.
Step S210: and determining the similarity corresponding to each path of bus channel according to the multiple sub-sequence similarities corresponding to each path of bus channel.
The specific calculation process for obtaining the similarity corresponding to each bus channel may refer to the calculation process for calculating the similarity corresponding to each signal, which is not described herein.
Step S211: and determining the overall signal similarity between the first bus data and the second bus data according to the signal similarity corresponding to each signal and the signal weight corresponding to each signal.
In the embodiment of the present application, the signal weights may be set by a user in a user-defined manner, which is not specifically limited herein.
The calculation formula for calculating the overall signal similarity may be:
where ci is the similarity corresponding to each signal, wi is the weight corresponding to each signal,is the sum of the weights of all kinds of signals.
The formula (9) is a calculation formula of weighted average, and when the weighted average calculates the overall signal similarity, a weighted average mode is adopted to make the calculated overall signal similarity more in line with the actual situation.
Step S212: and determining the overall load similarity between the first bus data and the second bus data according to the load similarity corresponding to each bus channel and the number of the bus channels.
In the embodiment of the present application, the calculation formula of the overall load similarity may be:
where n is the number of bus lanes.
When the overall load similarity is calculated through the load similarity of each bus channel, the performance difference between vehicles can be presented by the load similarity of each bus channel, and therefore, the overall load similarity of the bus channels is calculated through a formula (10).
Step S213: and determining the data similarity between the first bus data and the second bus data according to the overall signal similarity, the first similarity weight corresponding to the overall signal similarity, the overall load similarity and the second similarity weight corresponding to the overall load similarity.
The first similarity weight may be the same as or different from the second similarity weight, and the first similarity weight and the second similarity weight may be set by a user.
In some embodiments, when the user only pays attention to the load similarity, the first similarity weight may be set to 0, at which time the calculated data similarity is only related to the load of the bus channel.
Similarly, when the user only pays attention to signal similarity, the second similarity weight is set to 0, and the data similarity is only related to signal similarity in the bus channel.
In other embodiments, after determining the data similarity between the first bus data and the second bus data, the method for comparing vehicle bus data further includes determining a differential data type between the first vehicle and the second vehicle according to the signal similarity and the first similarity threshold and the load similarity and the second similarity threshold if the data similarity does not reach the data similarity threshold corresponding to the data similarity; and determining the difference information between the first vehicle and the second vehicle according to the data corresponding to the difference data type.
The data similarity threshold, the first similarity threshold and the second similarity threshold may be the same, may be different, and specific values are set by the user.
The determining of the type of the differential data between the first vehicle and the second vehicle according to the signal similarity and the first similarity threshold and the load similarity and the second similarity threshold may determine whether the differential data between the first vehicle and the second vehicle is signal data or load factor by acquiring a difference between the signal similarity and the first similarity threshold and whether a difference between the load similarity and the second similarity threshold reaches the difference threshold. Whether the data with the difference between the first vehicle and the second vehicle is the signal data or the load rate can be judged according to whether the signal similarity reaches the first similarity threshold or whether the load similarity reaches the similarity threshold. The difference threshold is not specifically defined here as to how the type of difference data between the first vehicle and the second vehicle is determined.
The discrepancy information includes, but is not limited to, the cause of the discrepancy, the discrepancy data, and the solution.
And analyzing the data corresponding to the difference data type so as to determine the difference information between the first vehicle and the second vehicle. The analysis of the data corresponding to the differential data type may be performed by an analysis program or by an engineer, and it is not particularly limited how the data corresponding to the differential data type is analyzed.
Illustratively, the data similarity threshold is 95%, the first similarity threshold is 92%, and the second similarity threshold is 90%. When the data similarity reaches 95%, judging that the behaviors and the performances of the first vehicle and the second vehicle are highly consistent. And judging whether the signal similarity reaches 92% and the load similarity reaches 90% when the data similarity is smaller than 95%. If the signal similarity is not 92% but the load similarity is 90%, at this time, the load rates between the first vehicle and the second vehicle are judged to be highly similar, and the difference data type is the signal data, the signal data are analyzed to obtain that the difference data between the vehicles under the same target working condition are the vehicle speeds, and the reason for the difference is that the sensor is damaged, the signal is received by mistake, and the solution is to replace the sensor.
When the signal similarity reaches 92% and the load similarity does not reach 90%, judging that the signal data between the first vehicle and the second vehicle are similar in height at the moment, and the load rates are different. The method comprises the steps of analyzing the load rates corresponding to a first vehicle and a second vehicle, acquiring time points with different load rates, determining data loss in a CAN bus channel according to the time points, restarting a server corresponding to the CAN, and initializing the CAN bus channel.
According to the scheme provided by the embodiment of the application, the similarity of bus data among vehicles is quantized by utilizing the weights corresponding to different data, so that developers can check and position data with differences between two workshops according to the quantized similarity, the problem of difference caused by analyzing, checking and positioning the two workshops is solved, and bus analysis equipment is not used when bus data are compared between the two workshops, so that the bus data analysis cost of the vehicles is reduced.
Referring to fig. 8, a block diagram of a comparison device 200 for vehicle bus data according to an embodiment of the present application is shown. The device 200 for comparing vehicle bus data is applied to an electronic device, and the device 200 for comparing vehicle bus data comprises: a bus data acquisition module 210, configured to acquire first bus data of a first vehicle under a target working condition, and second bus data of a second vehicle under the target working condition; a signal similarity obtaining module 220, configured to obtain a signal similarity between the signal in the first bus data and the signal in the second bus data; a load similarity obtaining module 230, configured to obtain a load similarity between a load rate of a bus channel in the first bus data and a load rate of a bus channel in the second bus data; a data similarity determining module 240, configured to determine a data similarity between the first bus data and the second bus data based on the signal similarity and the load similarity.
In some embodiments of the present application, the signal similarity obtaining module 220 includes: a signal sequence acquisition module, configured to acquire a first signal sequence corresponding to each signal in the first bus data and a second signal sequence corresponding to each signal in the second bus data, where the first signal sequence and the second signal sequence include signal values ordered according to time; a continuous signal similarity obtaining module, configured to obtain, for each continuous signal in the first bus data and the second bus data, a similarity between each continuous signal in the first bus data and each continuous signal in the second bus data according to a trend of variation of the first signal sequence and the second signal sequence corresponding to each continuous signal, as the similarity corresponding to each continuous signal; the discrete signal similarity obtaining module is configured to obtain, for each discrete signal in the first bus data and the second bus data, a similarity between each discrete signal in the first bus data and each discrete signal in the second bus data according to a signal value change point in a signal value of the first signal sequence and a signal value of the second signal sequence, as a similarity corresponding to each discrete signal.
In some embodiments of the present application, the continuous signal similarity acquisition module includes: the first signal subsequence acquisition module is used for dividing the first signal sequence corresponding to each continuous signal according to the change trend of the first signal sequence corresponding to each continuous signal to obtain a plurality of first signal subsequences corresponding to each continuous signal; the second signal subsequence acquisition module is used for dividing the second signal sequence corresponding to each continuous signal according to the change trend of the second signal sequence corresponding to each continuous signal to obtain a plurality of second signal subsequences corresponding to each continuous signal; an alignment module, configured to align the plurality of first signal subsequences and the plurality of second signal subsequences corresponding to each of the continuous signals; the subsequence similarity acquisition module is used for acquiring the similarity between each aligned first signal subsequence and each aligned second signal subsequence aiming at each continuous signal to obtain a plurality of subsequence similarities corresponding to each continuous signal; and the continuous signal similarity determining module is used for determining the similarity corresponding to each continuous signal according to the plurality of subsequence similarities corresponding to each continuous signal.
In some embodiments of the present application, the discrete signal similarity acquisition module includes: the change point set acquisition module is used for acquiring a first signal value change point set in the first signal sequence corresponding to each discrete signal and a second signal value change point set in the second signal sequence; and the discrete signal similarity determining module is used for determining the similarity corresponding to each discrete signal according to the editing distance corresponding to the first signal value change point set and the second signal value change point set corresponding to each discrete signal.
In some embodiments of the present application, the load similarity obtaining module 230 includes: the load sequence acquisition module is used for acquiring a first load sequence corresponding to each path of bus channel in the first bus data and a second load sequence corresponding to each path of bus channel in the second bus data, wherein the first load sequence and the second load sequence comprise load rates ordered according to time; the first load subsequence acquisition module is used for dividing the first load sequence corresponding to each path of bus channel according to the change trend of the first load sequence corresponding to each path of bus channel, so as to obtain a plurality of first load subsequences corresponding to each path of bus channel; the second load subsequence acquisition module is used for dividing the second load sequence corresponding to each path of bus channel according to the change trend of the second load sequence corresponding to each path of bus channel, so as to obtain a plurality of second load subsequences corresponding to each path of bus channel; the load sequence alignment module is used for aligning the plurality of first load subsequences and the plurality of second load subsequences corresponding to each path of bus channel; the load sub-sequence similarity acquisition module is used for acquiring the similarity between each aligned first load sub-sequence and each aligned second load sub-sequence according to each path of bus channel to obtain a plurality of sub-sequence similarities corresponding to each path of bus channel; and the load similarity determining module is used for determining the similarity corresponding to each path of bus channel according to the plurality of subsequences of the similarity corresponding to each path of bus channel.
In some embodiments of the present application, the data similarity determination module 240 includes: the overall signal similarity acquisition module is used for determining the overall signal similarity between the first bus data and the second bus data according to the signal similarity corresponding to each signal and the signal weight corresponding to each signal; the overall load similarity acquisition module is used for determining the overall load similarity between the first bus data and the second bus data according to the load similarity corresponding to each bus channel and the number of the bus channels; the data similarity determining module is configured to determine data similarity between the first bus data and the second bus data according to the overall signal similarity, a first similarity weight corresponding to the overall signal similarity, the overall load similarity, and a second similarity weight corresponding to the overall load similarity.
In some embodiments of the present application, the comparison device 200 of vehicle bus data further includes: the difference data type determining module is used for determining the difference data type between the first vehicle and the second vehicle according to the signal similarity and the first similarity threshold value and the load similarity and the second similarity threshold value if the data similarity does not reach the data similarity threshold value corresponding to the data similarity; the difference information acquisition module is used for determining the difference information between the first vehicle and the second vehicle according to the data corresponding to the difference data type.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the apparatus and modules described above may refer to the corresponding process in the foregoing method embodiment, which is not repeated herein.
In several embodiments provided herein, the coupling of the modules to each other may be electrical, mechanical, or other.
In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module. The integrated modules may be implemented in hardware or in software functional modules.
Referring to fig. 9, a block diagram of an electronic device according to an embodiment of the present application is shown. The electronic device 100 may be a switch, a computer, or a control unit with data transmission. The electronic device 100 in this application may include one or more of the following components: a processor 110, a memory 120, and one or more application programs, wherein the one or more application programs may be stored in the memory 120 and configured to be executed by the one or more processors 110, the one or more program(s) configured to perform the method as described in the foregoing method embodiments.
Processor 110 may include one or more processing cores. The processor 110 utilizes various interfaces and lines to connect various portions of the overall electronic device 100, perform various functions of the electronic device 100, and process data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 120, and invoking data stored in the memory 120. Alternatively, the processor 110 may be implemented in hardware in at least one of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 110 may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU), a graphics processor (Graphics Processing Unit, GPU), and a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for being responsible for rendering and drawing of display content; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 110 and may be implemented solely by a single communication chip.
The Memory 120 may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (Read-Only Memory). Memory 120 may be used to store instructions, programs, code, sets of codes, or sets of instructions. The memory 120 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described below, etc. The storage data area may also store data created by the electronic device 100 in use (e.g., phonebook, audiovisual data, chat log data), and the like.
Also provided in embodiments of the present application is a computer-readable storage medium having program code stored therein, the program code being callable by a processor to perform the method described in the method embodiments described above.
The computer readable storage medium may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read only memory), an EPROM, a hard disk, or a ROM. Optionally, the computer readable storage medium comprises a non-volatile computer readable medium (non-transitory computer-readable storage medium). The computer readable storage medium has storage space for program code to perform any of the method steps described above. The program code can be read from or written to one or more computer program products. The program code may be compressed, for example, in a suitable form.
In summary, according to the scheme provided by the application, the signal data and the load rate data in the bus data of two vehicles are utilized to be respectively compared, so that the comparison is performed not only in terms of vehicle behavior, but also in terms of vehicle performance, the reliability of comparison results is improved, the comparison time consumed by manual comparison is reduced, and the comparison efficiency of the bus data among the vehicles is improved.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limiting thereof; although the present application has been described in detail with reference to the foregoing embodiments, one of ordinary skill in the art will appreciate that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not drive the essence of the corresponding technical solutions to depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. A method of comparing vehicle bus data, the method comprising:
acquiring first bus data of a first vehicle under a target working condition and second bus data of a second vehicle under the target working condition;
Acquiring signal similarity between signals in the first bus data and signals in the second bus data;
acquiring load similarity between the load rate of the bus channel in the first bus data and the load rate of the bus channel in the second bus data;
and determining data similarity between the first bus data and the second bus data based on the signal similarity and the load similarity.
2. The method of claim 1, wherein the obtaining a signal similarity between the signal in the first bus data and the signal in the second bus data comprises:
acquiring a first signal sequence corresponding to each signal in the first bus data and a second signal sequence corresponding to each signal in the second bus data, wherein the first signal sequence and the second signal sequence comprise signal values ordered according to time;
for each continuous signal in the first bus data and the second bus data, according to the change trend of the first signal sequence and the second signal sequence corresponding to each continuous signal, obtaining the similarity between each continuous signal in the first bus data and each continuous signal in the second bus data as the similarity corresponding to each continuous signal;
And aiming at each discrete signal in the first bus data and the second bus data, according to signal value change points in signal values of the first signal sequence and the second signal sequence, acquiring the similarity between each discrete signal in the first bus data and each discrete signal in the second bus data as the similarity corresponding to each discrete signal.
3. The method according to claim 2, wherein the obtaining, for each continuous signal in the first bus data and the second bus data, a similarity between the each continuous signal in the first bus data and the each continuous signal in the second bus data as the similarity corresponding to each continuous signal according to a trend of change of the first signal sequence and the second signal sequence corresponding to each continuous signal, includes:
dividing the first signal sequence corresponding to each continuous signal according to the change trend of the first signal sequence corresponding to each continuous signal to obtain a plurality of first signal subsequences corresponding to each continuous signal;
Dividing the second signal sequence corresponding to each continuous signal according to the change trend of the second signal sequence corresponding to each continuous signal to obtain a plurality of second signal subsequences corresponding to each continuous signal;
aligning the plurality of first signal subsequences and the plurality of second signal subsequences corresponding to each of the continuous signals;
for each continuous signal, obtaining the similarity between each aligned first signal subsequence and each aligned second signal subsequence, and obtaining a plurality of subsequence similarities corresponding to each continuous signal;
and determining the similarity corresponding to each continuous signal according to the multiple subsequence similarities corresponding to each continuous signal.
4. The method according to claim 2, wherein the obtaining, for each discrete signal in the first bus data and the second bus data, a similarity between the each discrete signal in the first bus data and the each discrete signal in the second bus data as a similarity corresponding to the each discrete signal according to a signal value change point in signal values of the first signal sequence and the second signal sequence, includes:
Acquiring a first signal value change point set in a first signal sequence corresponding to each discrete signal and a second signal value change point set in a second signal sequence;
and determining the similarity corresponding to each discrete signal according to the editing distance corresponding to the first signal value change point set and the second signal value change point set corresponding to each discrete signal.
5. The method of claim 1, wherein the obtaining the load similarity between the load rate of the bus lane in the first bus data and the load rate of the bus lane in the second bus data comprises:
acquiring a first load sequence corresponding to each path of bus channel in the first bus data and a second load sequence corresponding to each path of bus channel in the second bus data, wherein the first load sequence and the second load sequence comprise load rates ordered according to time;
dividing the first load sequence corresponding to each bus channel according to the change trend of the first load sequence corresponding to each bus channel to obtain a plurality of first load subsequences corresponding to each bus channel;
Dividing the second load sequence corresponding to each bus channel according to the change trend of the second load sequence corresponding to each bus channel to obtain a plurality of second load subsequences corresponding to each bus channel;
aligning the first load subsequences and the second load subsequences corresponding to each bus channel;
aiming at each path of bus channel, obtaining the similarity between each aligned first load subsequence and each aligned second load subsequence, and obtaining a plurality of subsequence similarities corresponding to each path of bus channel;
and determining the similarity corresponding to each path of bus channel according to the multiple sub-sequence similarities corresponding to each path of bus channel.
6. The method of claim 1, wherein the signal similarities comprise similarities for each signal, the load similarities comprise similarities for each bus channel, and wherein determining data similarities between the first bus data and the second bus data based on the signal similarities and the load similarities comprises:
determining the overall signal similarity between the first bus data and the second bus data according to the signal similarity corresponding to each signal and the signal weight corresponding to each signal;
Determining the overall load similarity between the first bus data and the second bus data according to the load similarity corresponding to each bus channel and the number of bus channels;
and determining the data similarity between the first bus data and the second bus data according to the overall signal similarity, the first similarity weight corresponding to the overall signal similarity, the overall load similarity and the second similarity weight corresponding to the overall load similarity.
7. The method of any of claims 1-6, wherein after the determining the data similarity between the first bus data and the second bus data based on the signal similarity and the load similarity, the method further comprises:
if the data similarity does not reach the data similarity threshold corresponding to the data similarity, determining a difference data type between the first vehicle and the second vehicle according to the signal similarity and the first similarity threshold and the load similarity and the second similarity threshold;
and determining the difference information between the first vehicle and the second vehicle according to the data corresponding to the difference data type.
8. A device for comparing vehicle bus data, characterized in that the device comprises a functional module for implementing the method according to any one of claims 1-7.
9. An electronic device, comprising:
one or more processors;
a memory;
one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications configured to perform the method of any of claims 1-7.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein a program code, which is callable by a processor for executing the method according to any one of claims 1-7.
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