Vehicle driving characteristic comparison method and device, computer equipment and storage medium

Abstract

The invention relates to a vehicle driving characteristic comparison method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring multi-dimensional target driving characteristics of at least two target vehicle sets in a preset time period; determining a target feature number corresponding to each dimension of driving feature corresponding to each target vehicle set according to a corresponding relationship between the candidate driving feature and the candidate feature number; splicing the target feature numbers corresponding to the target vehicle sets to obtain joint feature numbers corresponding to the target vehicle sets; and according to the occurrence frequency of the joint feature number corresponding to each target vehicle set in the preset time period, determining the driving feature similarity between the target vehicle sets. According to the scheme, the driving feature similarity between the target vehicle sets is determined by comparing the occurrence frequencies of the joint feature numbers corresponding to the target vehicle sets in the preset time period, and the calculation amount of calculating the driving feature similarity between the vehicles is reduced.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a vehicle driving characteristics comparison method, device, computer equipment and storage medium. Background Art

[0002] Driving characteristic similarity refers to determining the overall similarity of vehicles in different driving characteristics by comparing and analyzing the data distribution of vehicles in these characteristics. That is, the evaluation results of vehicle driving characteristics under multiple single dimensions are integrated into a comprehensive evaluation result, so as to comprehensively compare the travel, driving and operation behaviors of vehicles from multiple dimensions, so as to evaluate the performance differences and advantages and disadvantages of different vehicles.

[0003] In traditional technology, cosine similarity, root mean square error and other methods are usually used to calculate the similarity of driving characteristics, which has the problem of large amount of calculation. Summary of the invention

[0004] Based on this, it is necessary to provide a vehicle driving characteristics comparison method, device, computer equipment and storage medium to address the above technical problems, which can reduce the amount of calculation for calculating the similarity of driving characteristics between vehicles.

[0005] In a first aspect, the present application provides a vehicle driving characteristics comparison method, the method comprising:

[0006] Acquire multi-dimensional target driving characteristics of at least two target vehicle sets within a preset time period; wherein the multi-dimensional target driving characteristics at least include vehicle speed, acceleration, engine speed, engine torque, total mass of the target vehicle, gear position, terrain and road type;

[0007] According to the correspondence between the candidate driving features and the candidate feature numbers, the target feature numbers corresponding to the driving features of each dimension corresponding to each target vehicle set are determined;

[0008] The target feature numbers corresponding to each target vehicle set at each moment in the preset time period are concatenated to obtain the joint feature numbers corresponding to each target vehicle set;

[0009] The driving feature similarities between the target vehicle sets are determined according to the frequencies of occurrence of the joint feature numbers corresponding to the target vehicle sets within a preset time period.

[0010] In one embodiment, determining the driving feature similarity between each target vehicle set according to the frequency of occurrence of each joint feature number corresponding to each target vehicle set within a preset time period includes:

[0011] Determine the probability of each joint feature number corresponding to each target vehicle set appearing within the preset time period according to the frequency of each joint feature number corresponding to each target vehicle set appearing within the preset time period;

[0012] The driving feature similarities between the target vehicle sets are determined according to the probability of each joint feature number corresponding to each target vehicle set appearing within a preset time period.

[0013] In one embodiment, the method of determining the probability of each joint feature number corresponding to each target vehicle set appearing within a preset time period according to the frequency of each joint feature number corresponding to each target vehicle set appearing within a preset time period includes:

[0014] For each target vehicle set, the ratio between the frequency of each joint feature number appearing at each moment in the preset time period and the sum of the frequencies of each joint feature number appearing at each moment in the preset time period is taken as the probability of each joint feature number appearing in the preset time period.

[0015] In one embodiment, determining the driving feature similarity between each target vehicle set according to the probability of each joint feature number corresponding to each target vehicle set appearing within a preset time period includes:

[0016] Selecting two target vehicle sets from each target vehicle set; wherein the selected two target vehicle sets are a first target vehicle set and a second target vehicle set;

[0017] For each joint feature number corresponding to the first target vehicle set, if there is a target joint feature number that is the same as the joint feature number corresponding to the first target vehicle set among the joint feature numbers corresponding to the second target vehicle set, the minimum value of the probability of the target joint feature number appearing within a preset time period is taken as the probability of the target joint feature number; wherein the probability of the target joint feature number appearing within the preset time period includes the probability of the target joint feature number appearing in each joint feature number corresponding to the first target vehicle set within the preset time period, and the probability of the target joint feature number appearing in each joint feature number corresponding to the second target vehicle set within the preset time period;

[0018] The sum of the probabilities of the joint feature numbers of the targets is taken as the driving feature similarity between the first target vehicle set and the second target vehicle set.

[0019] In one embodiment, if the candidate driving features are gear, terrain and road type, the correspondence between the candidate driving features and the candidate feature numbers is established in the following manner:

[0020] For each candidate driving feature, determining each sub-feature under the candidate driving feature;

[0021] Assign a candidate feature number to each sub-feature;

[0022] According to each sub-feature and the candidate feature number corresponding to each sub-feature, a corresponding relationship between the candidate driving feature and the candidate feature number is established.

[0023] In one embodiment, if the candidate driving features are vehicle speed, acceleration, engine speed, engine torque and total mass of the target vehicle, the correspondence between the candidate driving features and the candidate feature numbers is established in the following manner:

[0024] For each candidate driving feature, determining an upper value limit, a lower value limit, a number of groups, and a grouping step length of the candidate driving feature;

[0025] Determine the grouping upper limit and grouping lower limit corresponding to each group according to the grouping quantity and the grouping step size;

[0026] determining a first difference between the candidate driving characteristic and the lower numerical limit, and a second difference between the upper numerical limit and the lower numerical limit;

[0027] Determine the grouping interval to which the ratio belongs according to the ratio between the first difference and the second difference, and the grouping upper limit and the grouping lower limit corresponding to each group;

[0028] Using the group number corresponding to the group interval as the candidate feature number of the candidate driving feature;

[0029] According to each candidate driving feature between the numerical upper limit and the numerical lower limit of the candidate driving feature, and the candidate feature number corresponding to each candidate driving feature, a corresponding relationship between the candidate driving feature and the candidate feature number is established.

[0030] In a second aspect, the present application further provides a vehicle driving characteristics comparison device, the device comprising:

[0031] An acquisition module, used to acquire multi-dimensional target driving characteristics of at least two target vehicle sets within a preset time period; wherein the multi-dimensional target driving characteristics at least include vehicle speed, acceleration, engine speed, engine torque, total mass of the target vehicle, gear position, terrain and road type;

[0032] A first determination module is used to determine the target feature number corresponding to each dimension of driving features corresponding to each target vehicle set according to the correspondence between the candidate driving features and the candidate feature numbers;

[0033] A splicing module, used for splicing the target feature numbers corresponding to the target vehicle sets to obtain the joint feature numbers corresponding to the target vehicle sets;

[0034] The second determination module is used to determine the driving feature similarity between each target vehicle set according to the joint feature number corresponding to each target vehicle set.

[0035] In a third aspect, the present application further provides a computer device, the computer device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the following steps when executing the computer program:

[0036] Acquire multi-dimensional target driving characteristics of at least two target vehicle sets within a preset time period; wherein the multi-dimensional target driving characteristics at least include vehicle speed, acceleration, engine speed, engine torque, total mass of the target vehicle, gear position, terrain and road type;

[0037] According to the correspondence between the candidate driving features and the candidate feature numbers, the target feature numbers corresponding to the driving features of each dimension corresponding to each target vehicle set are determined;

[0038] The target feature numbers corresponding to each target vehicle set at each moment in the preset time period are concatenated to obtain the joint feature numbers corresponding to each target vehicle set;

[0039] The driving feature similarities between the target vehicle sets are determined according to the frequencies of occurrence of the joint feature numbers corresponding to the target vehicle sets within a preset time period.

[0040] In a fourth aspect, the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0041] Acquire multi-dimensional target driving characteristics of at least two target vehicle sets within a preset time period; wherein the multi-dimensional target driving characteristics at least include vehicle speed, acceleration, engine speed, engine torque, total mass of the target vehicle, gear position, terrain and road type;

[0042] According to the correspondence between the candidate driving features and the candidate feature numbers, the target feature numbers corresponding to the driving features of each dimension corresponding to each target vehicle set are determined;

[0043] The target feature numbers corresponding to each target vehicle set at each moment in the preset time period are concatenated to obtain the joint feature numbers corresponding to each target vehicle set;

[0044] The driving feature similarities between the target vehicle sets are determined according to the frequencies of occurrence of the joint feature numbers corresponding to the target vehicle sets within a preset time period.

[0045] In a fifth aspect, the present application further provides a computer program product, the computer program product comprising a computer program, which implements the following steps when executed by a processor:

[0046] Acquire multi-dimensional target driving characteristics of at least two target vehicle sets within a preset time period; wherein the multi-dimensional target driving characteristics at least include vehicle speed, acceleration, engine speed, engine torque, total mass of the target vehicle, gear position, terrain and road type;

[0047] According to the correspondence between the candidate driving features and the candidate feature numbers, the target feature numbers corresponding to the driving features of each dimension corresponding to each target vehicle set are determined;

[0048] The target feature numbers corresponding to each target vehicle set at each moment in the preset time period are concatenated to obtain the joint feature numbers corresponding to each target vehicle set;

[0049] The driving feature similarities between the target vehicle sets are determined according to the frequencies of occurrence of the joint feature numbers corresponding to the target vehicle sets within a preset time period.

[0050] The vehicle driving feature comparison method, device, computer equipment and storage medium obtain multi-dimensional target driving features of at least two target vehicle sets within a preset time period; wherein the multi-dimensional target driving features at least include vehicle speed, acceleration, engine speed, engine torque, total mass of the target vehicle, gear position, terrain and road type; and determine the target feature number corresponding to each dimension of driving features corresponding to each target vehicle set according to the correspondence between the candidate driving features and the candidate feature number; and concatenate the target feature numbers corresponding to each target vehicle set to obtain the joint feature number corresponding to each target vehicle set; and then determine the driving feature similarity between each target vehicle set according to the frequency of occurrence of the joint feature number corresponding to each target vehicle set within the preset time period. The above scheme determines the driving feature similarity between each target vehicle set by comparing the frequency of occurrence of the joint feature number corresponding to each target vehicle set within the preset time period, without the need to calculate the driving feature similarity by cosine similarity, root mean square error and the like, thereby reducing the amount of calculation of the driving feature similarity between vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] FIG1 is a schematic flow chart of a vehicle driving characteristic comparison method in one embodiment;

[0052] Figure 2A schematic diagram of a process for determining the similarity of driving characteristics between target vehicle sets in one embodiment;

[0053] Figure 3 A schematic diagram of a flow chart for determining the similarity of driving characteristics between target vehicle sets in another embodiment;

[0054] Figure 4 A schematic diagram of a flow chart of establishing a correspondence between enumerated candidate driving features and candidate feature numbers in one embodiment;

[0055] Figure 5 A schematic diagram of a flow chart of establishing a correspondence between continuous candidate driving features and candidate feature numbers in one embodiment;

[0056] Figure 6 is a flow chart of a vehicle driving characteristic comparison method in another embodiment;

[0057] Figure 7 is a structural block diagram of a vehicle driving characteristic comparison device in one embodiment;

[0058] Figure 8 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0060] The vehicle driving characteristics comparison method provided in the embodiment of the present application can be applied to the application scenario of determining the similarity of driving characteristics between vehicles, and is specifically applied to the scenario of producing and operating vehicles according to the similarity of driving characteristics between vehicles. The method provided in the embodiment of the present application can be executed by a server, or by a terminal with a certain computing power. Among them, the server can be implemented as an independent server or a server cluster composed of multiple servers. The terminal can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc.

[0061] In one embodiment, as shown in FIG1 , a vehicle driving feature comparison method is provided, and the method is applied to a server as an example for explanation, including the following steps:

[0062] S101, obtaining multi-dimensional target driving characteristics of at least two target vehicle sets within a preset time period.

[0063] Exemplarily, the target vehicles are vehicles with the same purpose, such as vehicles used to carry goods or passengers. The target vehicle set is a combination including at least one target vehicle. The multi-dimensional target driving characteristics of each target vehicle within a preset time period can be acquired by the Internet of Vehicles equipment on each target vehicle. The multi-dimensional target driving characteristics at least include data such as vehicle speed, acceleration, engine speed, engine torque, total mass of the target vehicle, gear position, terrain and road type.

[0064] For example, obtain the Global Positioning System (GPS) time (in seconds), GPS longitude (in degrees), GPS latitude (in degrees), GPS speed (in kilometers per hour), instrument speed (in kilometers per hour), and vehicle acceleration (in meters per second) collected by the Internet of Vehicles equipment on each target vehicle. 2 ), engine speed (in rpm), engine torque percentage (in %), gear (dimensionless), total mass (in t), terrain (dimensionless) and road type (dimensionless). The above vehicle driving characteristics may come from any source, and may come from a telematics box (TBOX), or other software, algorithms, databases or hardware devices.

[0065] It should be noted that before determining the target feature numbers corresponding to the various dimensional driving features corresponding to each target vehicle set, the multi-dimensional target driving features of at least two target vehicle sets obtained within a preset time period may first be cleaned to facilitate data processing.

[0066] Exemplarily, data with empty GPS time can be deleted row by row; for data rows with repeated GPS timestamps, only the data that appears for the first time is retained; the mean μ and standard deviation σ are calculated for each driving feature, and data outside μ±3σ are eliminated; and data in each dimension of driving feature data that is greater than or equal to the upper limit of the value corresponding to the driving feature data of each dimension and less than the lower limit of the value is eliminated; when the GPS speed exceeds 1.1 times the instrument speed, the instrument speed is used instead of the GPS speed, and the corrected GPS speed is used as the vehicle speed; the units of each driving feature are regularized, and non-standard unit data are converted into standard unit data, such as converting the vehicle speed from m / s to km / h.

[0067] S102, determining the target feature number corresponding to each dimension of the driving features corresponding to each target vehicle set according to the correspondence between the candidate driving features and the candidate feature numbers.

[0068] Furthermore, a correspondence between each candidate driving feature and a candidate feature number may be established in advance to determine the target feature number corresponding to each dimension of driving features corresponding to each target vehicle set. For example, for each target driving feature corresponding to each target vehicle set, first determine the candidate driving feature that is the same as the target driving feature, and use the candidate feature number corresponding to the candidate driving feature as the target feature number of the target driving feature.

[0069] S103, concatenating the target feature numbers corresponding to the target vehicle sets at each moment in the preset time period to obtain joint feature numbers corresponding to the target vehicle sets.

[0070] Furthermore, for each target vehicle set, the target feature numbers corresponding to each moment within a preset time period are spliced ​​in a preset order. For example, the target feature numbers corresponding to each target driving feature are spliced ​​in the order of vehicle speed, acceleration, engine speed, engine torque, total mass of the target vehicle, gear position, terrain and road type to obtain the joint feature numbers corresponding to each target vehicle set.

[0071] For example, if at a certain moment, the target feature numbers corresponding to the speed, acceleration, engine speed, engine torque, total mass of the target vehicle, gear position, terrain and road type of one of the target vehicle sets are 01, 06, 4, 01, 08, 02, 1 and 1 respectively, then the joint feature number at that moment is '0106401080211' (single quotes indicate that the code is in string format).

[0072] S104, determining the driving feature similarities between the target vehicle sets according to the frequencies of occurrence of the joint feature numbers corresponding to the target vehicle sets within a preset time period.

[0073] Furthermore, the frequency of occurrence of each joint feature number corresponding to each target vehicle set within a preset time period can be calculated, and the driving feature similarity between each target vehicle set can be determined based on the overlap between the joint feature numbers corresponding to each target vehicle set and the frequency of occurrence of each joint feature number corresponding to each target vehicle set within a preset time period.

[0074] The above vehicle driving feature comparison method obtains multi-dimensional target driving features of at least two target vehicle sets within a preset time period; wherein the multi-dimensional target driving features at least include vehicle speed, acceleration, engine speed, engine torque, total mass of the target vehicle, gear position, terrain and road type; and according to the correspondence between the candidate driving features and the candidate feature numbers, the target feature numbers corresponding to the driving features of each dimension corresponding to each target vehicle set are determined; and the target feature numbers corresponding to each target vehicle set are spliced ​​to obtain the joint feature numbers corresponding to each target vehicle set; and then according to the frequency of occurrence of the joint feature numbers corresponding to each target vehicle set within the preset time period, the driving feature similarity between each target vehicle set is determined. The above scheme determines the driving feature similarity between each target vehicle set by comparing the frequency of occurrence of the joint feature numbers corresponding to each target vehicle set within the preset time period, without the need to calculate the driving feature similarity by means of cosine similarity, root mean square error, etc., thereby reducing the amount of calculation of the driving feature similarity between vehicles.

[0075] In some optional implementations, see Figure 2 , Figure 2 A flow chart of determining the similarity of driving characteristics between target vehicle sets is provided, which specifically includes the following steps:

[0076] S201, determining the probability of each joint feature number corresponding to each target vehicle set appearing within a preset time period according to the frequency of each joint feature number corresponding to each target vehicle set appearing within a preset time period.

[0077] Exemplarily, the number of times each joint feature number corresponding to each target vehicle set appears within a preset time period can be first determined, and the number of times each joint feature number corresponding to each target vehicle set appears within the preset time period can be used as the frequency of occurrence of each joint feature number corresponding to each target vehicle set within the preset time period.

[0078] Furthermore, the frequency of occurrence of each joint feature number corresponding to each target vehicle set within the preset time period can be determined based on the proportional relationship between the frequencies of occurrence of each joint feature number corresponding to each target vehicle set within the preset time period.

[0079] S202, determining the driving feature similarities between the target vehicle sets according to the probability of each joint feature number corresponding to each target vehicle set appearing within a preset time period.

[0080] Furthermore, the driving feature similarities between the target vehicle sets may be determined based on the overlap between the joint feature numbers corresponding to the target vehicle sets and the probability of the joint feature numbers corresponding to the target vehicle sets appearing within a preset time period.

[0081] In an embodiment of the present application, the frequency of occurrence of each joint feature number corresponding to each target vehicle set within a preset time period is calculated, and then the probability of occurrence of each joint feature number corresponding to each target vehicle set within the preset time period is determined. The driving feature similarity between each target vehicle set can be determined based on the probability of occurrence of each joint feature number corresponding to each target vehicle set within the preset time period and the overlap of each joint feature number corresponding to each target vehicle set. There is no need to calculate the driving feature similarity by using methods such as cosine similarity and root mean square error, thereby reducing the amount of calculation of the driving feature similarity between vehicles.

[0082] Furthermore, S201 in the above embodiment can be implemented in the following manner:

[0083] For each target vehicle set, the ratio of the frequency of each joint feature number appearing in the preset time period to the sum of the frequencies of each joint feature number appearing in the preset time period can be used as the probability of each joint feature number appearing in the preset time period. , the frequency of occurrence of this joint feature number is , the sum of the frequencies of occurrence of each joint feature number within the preset time period is ,in, , M is the number of non-repeated joint feature numbers in the target vehicle set. Then the joint feature number The probability of occurrence within a preset time period for .

[0084] In an embodiment of the present application, the probability of each joint feature number appearing within the preset time period is determined based on the ratio of the frequency of each joint feature number appearing within the preset time period to the sum of the frequencies of each joint feature number appearing within the preset time period, so as to further determine the driving feature similarity between each target vehicle set.

[0085] Further, see Figure 3 , Figure 3 Another flow chart for determining the similarity of driving characteristics between target vehicle sets is provided, which specifically includes the following steps:

[0086] S301, randomly select two target vehicle sets from each target vehicle set.

[0087] Exemplarily, two target vehicle sets may be selected from each target vehicle set, and the driving characteristic similarities between the two target vehicle sets may be compared; wherein the selected two target vehicle sets are recorded as a first target vehicle set and a second target vehicle set.

[0088] S302, for each joint feature number corresponding to the first target vehicle set, if there is a target joint feature number that is the same as the joint feature number corresponding to the first target vehicle set among the joint feature numbers corresponding to the second target vehicle set, then the minimum value of the probability of the target joint feature number appearing within the preset time period is taken as the probability of the target joint feature number.

[0089] Exemplarily, for each joint feature number corresponding to the first target vehicle set, it is determined whether there is a joint feature number identical to the joint feature number corresponding to the first target vehicle set among the joint feature numbers corresponding to the second target vehicle set; if so, the joint feature number identical to the joint feature number corresponding to the first target vehicle set among the joint feature numbers corresponding to the second target vehicle set is used as the target joint feature number.

[0090] Then, the probability of the target joint feature number appearing in the preset time period is determined, wherein the probability of the target joint feature number appearing in the preset time period includes the probability of the target joint feature number appearing in each joint feature number corresponding to the first target vehicle set in the preset time period, and the probability of the target joint feature number appearing in each joint feature number corresponding to the second target vehicle set in the preset time period. For example, the target joint feature number is and ,in, is the target joint feature number in the first target vehicle set, is the target joint feature number in the second target vehicle set; the probability of the target joint feature number appearing in each joint feature number corresponding to the first target vehicle set in the preset time period is ; The probability of the target joint feature number appearing in each joint feature number corresponding to the second target vehicle set within the preset time period is ; The minimum value of the probability of the target joint feature number appearing in the preset time period is taken as the probability of the target joint feature number, that is, the probability of the target joint feature number .

[0091] If there is no joint feature number identical to the joint feature number corresponding to the first target vehicle set among the joint feature numbers corresponding to the second target vehicle set, then the probability of determining that the joint feature number corresponding to the first target vehicle set is 0.

[0092] S303: taking the sum of the probabilities of the joint feature numbers of the targets as the driving feature similarity between the first target vehicle set and the second target vehicle set.

[0093] Furthermore, the sum of the probabilities of the joint feature numbers of each target is used as the driving feature similarity between the first target vehicle set and the second target vehicle set. For example, if there are M non-repeating joint feature numbers in the first target vehicle set, then the driving feature similarity between the first target vehicle set and the second target vehicle set is .

[0094] Furthermore, if the similarity of driving characteristics between the first target vehicle set and the second target vehicle set exceeds a similarity threshold, the first target vehicle set and the second target vehicle set can adopt the same production and operation and maintenance method. For example, the body structure of the vehicle can be set according to the load conditions, and how to maintain the vehicle can be determined under the same load and vehicle operation data.

[0095] In the embodiment of the present application, the overlapping of the joint feature numbers corresponding to the two target vehicle sets is compared, and the driving feature similarity between the two target vehicle sets is determined based on the overlapping situation and the probability of the overlapping joint feature numbers. It does not involve calculations such as cosine similarity and root mean square error, thereby reducing the amount of calculation for calculating the driving feature similarity between vehicles.

[0096] In some optional implementations, if the candidate driving feature is an enumeration type, such as gear position, terrain and road type, the corresponding relationship between the candidate driving feature and the candidate feature number is established in the manner described in Figure 4 , Figure 4 A flowchart for establishing a correspondence between enumerated candidate driving features and candidate feature numbers is provided, which specifically includes the following steps:

[0097] S401: for each candidate driving feature, determine each sub-feature under the candidate driving feature.

[0098] Exemplarily, for each candidate driving feature, each sub-feature under the candidate driving feature is determined. For example, if the candidate driving feature is gear, then each sub-feature under the candidate driving feature includes R gear, N gear, 1 gear, 2 gear, 3 gear, 4 gear, 5 gear, 6 gear, 7 gear, 8 gear, 9 gear, 10 gear, 11 gear and 12 gear; if the candidate driving feature is terrain, then each sub-feature under the candidate driving feature includes plain, mountainous area, hill and plateau; if the candidate driving feature is road type, then each sub-feature under the candidate driving feature includes highway, national road, provincial road, city and others.

[0099] S402: assign a candidate feature number to each sub-feature.

[0100] For example, a candidate feature number may be assigned to each sub-feature, and whether the number needs to be padded with zeros on the left is determined according to the maximum value of the number. The candidate feature number assigned to each sub-feature is shown in Table 1.

[0101] Table 1:

[0102]

[0103] S403: Establish a correspondence between the candidate driving feature and the candidate feature number according to each sub-feature and the candidate feature number corresponding to each sub-feature.

[0104] Furthermore, a correspondence relationship between the candidate driving feature and the candidate feature number can be established according to each sub-feature and the candidate feature number corresponding to each sub-feature. The established correspondence relationship is shown in Table 1.

[0105] In the embodiment of the present application, for the enumerated candidate driving features, a corresponding candidate feature number is assigned to each sub-feature, and then a corresponding relationship between the candidate driving features and the candidate feature numbers is established, so as to facilitate the determination of the target feature number corresponding to the target driving feature.

[0106] In some optional implementations, if the candidate driving features are continuous, such as vehicle speed, acceleration, engine speed, engine torque, and the total mass of the target vehicle, the corresponding relationship between the candidate driving features and the candidate feature numbers is established in the following manner: Figure 5 , Figure 5 A flow chart of establishing a correspondence between continuous candidate driving features and candidate feature numbers is provided, which specifically includes the following steps:

[0107] S501: for each candidate driving feature, determine the upper limit of the value, the lower limit of the value, the number of groups, and the step length of the grouping of the candidate driving feature.

[0108] For example, for each candidate driving feature, determine the lower limit, upper limit, number of groups, and grouping step of the candidate driving feature. The upper limit and lower limit can be set according to the actual physical system characteristics; the number of groups can be determined according to expert experience or empirical formulas (such as Sturges' Empirical Formula, Sturges formula, etc.); the grouping step can be determined according to the actual physical system characteristics; the number of groups can be determined according to expert experience or empirical formulas (such as Sturges' Empirical Formula, Sturges formula, etc.); the number of groups ... the actual physical system characteristics; the number of groups can be determined according to the actual physical system characteristics; the number of groups can be determined according to the actual physical system characteristics; the number of groups can be determined according to , where round(*,4) means rounding the number to 4 decimal places. The group coding table includes group numbers , Group lower limit and group limit , where k represents the kth group, and the group number yes The integers in it are padded with zeros on the left.

[0109] For example, if the candidate driving feature is vehicle speed, the lower limit, upper limit, number of groups, and grouping step of the candidate driving feature are 0km / h, 120km / h, 12, and 0.0833 respectively; if the candidate driving feature is acceleration, the lower limit, upper limit, number of groups, and grouping step of the candidate driving feature are -1m / s respectively. 2 , 1m / s 2 , 10 and 0.0100; if the candidate driving feature is engine speed, the lower limit, upper limit, number of groups and grouping step of the candidate driving feature are 0rpm, 2000rpm, 10 and 0.0100 respectively; if the candidate driving feature is engine torque (percentage), the lower limit, upper limit, number of groups and grouping step of the candidate driving feature are 0, 100, 10 and 0.0100 respectively; if the candidate driving feature is vehicle gross mass, the lower limit, upper limit, number of groups and grouping step of the candidate driving feature are 0t, 50t, 10 and 0.0100 respectively.

[0110] Exemplarily, the numerical lower limit, numerical upper limit, number of groups and grouping step size corresponding to each candidate driving feature are shown in Table 2.

[0111] Table 2:

[0112]

[0113] S502: Determine the grouping upper limit and grouping lower limit corresponding to each group according to the grouping quantity and the grouping step.

[0114] For example, the upper and lower limits of each grouping may be determined based on the number of groups and the grouping step size. , group upper limit In particular, the maximum value of the grouping cap will be set to 1 if it is less than 1.

[0115] S503: Determine a first difference between the candidate driving feature and the lower numerical limit, and a second difference between the upper numerical limit and the lower numerical limit.

[0116] Further, a first difference between the candidate driving characteristic and the lower limit of the value, and a second difference between the upper limit of the value and the lower limit of the value are determined. For example, the first difference is , the second difference is , Represents the value of the i-th candidate driving feature at the t-th moment.

[0117] S504: Determine the grouping interval to which the ratio belongs according to the ratio between the first difference and the second difference, and the grouping upper limit and the grouping lower limit corresponding to each group.

[0118] Furthermore, the grouping interval to which the ratio belongs can be determined based on the ratio between the first difference and the second difference, and the grouping upper limit and grouping lower limit corresponding to each group. For example, if the ratio between the first difference and the second difference is The ratio between the first difference and the second difference is a value between the lower limit and the upper limit of the value corresponding to the candidate driving feature, so the group interval to which the ratio belongs can be determined according to the group upper limit and group lower limit corresponding to each group. .in, is the lower limit of the group number, The upper limit of the group number.

[0119] S505: Use the group number corresponding to the group interval as a candidate feature number of the candidate driving feature.

[0120] Exemplarily, the group numbers corresponding to the group intervals are preset, so that the group numbers corresponding to the group intervals can be used as candidate feature numbers of the candidate driving features.

[0121] S506: Establish a correspondence between the candidate driving features and the candidate feature numbers according to the candidate driving features between the upper and lower numerical limits of the candidate driving features and the candidate feature numbers corresponding to the candidate driving features.

[0122] Optionally, a correspondence between the candidate driving features and the candidate feature numbers may be established based on the candidate driving features between the upper and lower numerical limits of the candidate driving features and the candidate feature numbers corresponding to the candidate driving features. The established correspondence is shown in Table 3.

[0123] Table 3:

[0124]

[0125] In an embodiment of the present application, for continuous candidate driving features, the grouping interval to which each candidate driving feature belongs is determined, and the candidate feature number corresponding to each candidate driving feature is determined based on the grouping number corresponding to the grouping interval, and then a corresponding relationship between the candidate driving features and the candidate feature numbers is established, so as to facilitate determination of the target feature number corresponding to the target driving feature.

[0126] In some optional implementations, see Figure 6 , Figure 6 A flow chart of another vehicle driving characteristic comparison method is provided, which specifically includes the following steps:

[0127] S601, obtaining multi-dimensional target driving characteristics of at least two target vehicle sets within a preset time period.

[0128] S602: Determine the target feature number corresponding to each dimension of the driving features corresponding to each target vehicle set according to the correspondence between the candidate driving features and the candidate feature numbers.

[0129] S603, concatenating the target feature numbers corresponding to the target vehicle sets at each moment in the preset time period to obtain joint feature numbers corresponding to the target vehicle sets.

[0130] S604, determining the probability of each joint feature number corresponding to each target vehicle set appearing within the preset time period according to the frequency of each joint feature number corresponding to each target vehicle set appearing within the preset time period.

[0131] S605 , determining the driving feature similarities between the target vehicle sets according to the probability of each joint feature number corresponding to each target vehicle set appearing within a preset time period.

[0132] The specific process of S601 to S605 can refer to the description of the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0133] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0134] Based on the same inventive concept, the embodiment of the present application also provides a vehicle driving characteristic comparison device for implementing the vehicle driving characteristic comparison method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more vehicle driving characteristic comparison device embodiments provided below can refer to the limitations of the vehicle driving characteristic comparison method above, and will not be repeated here.

[0135] In one embodiment, Figure 7 As shown, a vehicle driving characteristic comparison device is provided, comprising:

[0136] The acquisition module 10 is used to acquire multi-dimensional target driving characteristics of at least two target vehicle sets within a preset time period; wherein the multi-dimensional target driving characteristics at least include vehicle speed, acceleration, engine speed, engine torque, total mass of the target vehicle, gear position, terrain and road type;

[0137] A first determination module 20 is used to determine the target feature number corresponding to each dimension of the driving feature corresponding to each target vehicle set according to the correspondence between the candidate driving features and the candidate feature numbers;

[0138] A splicing module 30, for splicing target feature numbers corresponding to target vehicle sets at each moment within a preset time period to obtain joint feature numbers corresponding to target vehicle sets;

[0139] The second determination module 40 is used to determine the driving feature similarity between each target vehicle set according to the frequency of occurrence of each joint feature number corresponding to each target vehicle set within a preset time period.

[0140] The above-mentioned vehicle driving feature comparison device obtains multi-dimensional target driving features of at least two target vehicle sets within a preset time period; wherein the multi-dimensional target driving features at least include vehicle speed, acceleration, engine speed, engine torque, total mass of the target vehicle, gear position, terrain and road type; and according to the correspondence between the candidate driving features and the candidate feature numbers, the target feature numbers corresponding to the driving features of each dimension corresponding to each target vehicle set are determined; and the target feature numbers corresponding to each target vehicle set are spliced ​​to obtain the joint feature numbers corresponding to each target vehicle set; and then according to the frequency of occurrence of the joint feature numbers corresponding to each target vehicle set within the preset time period, the driving feature similarity between each target vehicle set is determined. The above-mentioned scheme determines the driving feature similarity between each target vehicle set by comparing the frequency of occurrence of the joint feature numbers corresponding to each target vehicle set within the preset time period, without the need to calculate the driving feature similarity by means of cosine similarity, root mean square error, etc., thereby reducing the amount of calculation of the driving feature similarity between vehicles.

[0141] In one embodiment, the second determining module 40 specifically includes:

[0142] A first determination unit is used to determine the probability of each joint feature number corresponding to each target vehicle set appearing within a preset time period according to the frequency of each joint feature number corresponding to each target vehicle set appearing within a preset time period;

[0143] The second determination unit is used to determine the driving feature similarity between each target vehicle set according to the probability of each joint feature number corresponding to each target vehicle set appearing within a preset time period.

[0144] In one embodiment, the first determining unit is specifically configured to:

[0145] For each target vehicle set, the ratio between the frequency of each joint feature number appearing in the preset time period and the sum of the frequencies of each joint feature number appearing in the preset time period is used as the probability of each joint feature number appearing in the preset time period.

[0146] In one embodiment, the second determining unit is specifically configured to:

[0147] Select any two target vehicle sets from each target vehicle set; wherein the selected two target vehicle sets are a first target vehicle set and a second target vehicle set; for each joint feature number corresponding to the first target vehicle set, if there is a target joint feature number that is the same as the joint feature number corresponding to the first target vehicle set among the joint feature numbers corresponding to the second target vehicle set, then the minimum value of the probability of the target joint feature number appearing within a preset time period is taken as the probability of the target joint feature number; wherein the probability of the target joint feature number appearing within the preset time period includes the probability of the target joint feature number appearing in each joint feature number corresponding to the first target vehicle set within the preset time period, and the probability of the target joint feature number appearing in each joint feature number corresponding to the second target vehicle set within the preset time period; the sum of the probabilities of each target joint feature number is taken as the driving feature similarity between the first target vehicle set and the second target vehicle set.

[0148] In one embodiment, the device further comprises a building module, and if the candidate driving characteristics are gear position, terrain and road type, the building module is used to:

[0149] For each candidate driving feature, each sub-feature under the candidate driving feature is determined; a candidate feature number is assigned to each sub-feature; and a corresponding relationship between the candidate driving feature and the candidate feature number is established according to each sub-feature and the candidate feature number corresponding to each sub-feature.

[0150] In one embodiment, if the candidate driving characteristics are vehicle speed, acceleration, engine speed, engine torque, and total mass of the target vehicle, the establishment module is used to:

[0151] For each candidate driving feature, determine the numerical upper limit, numerical lower limit, number of groups and grouping step of the candidate driving feature; determine the grouping upper limit and grouping lower limit corresponding to each group according to the number of groups and the grouping step; determine the first difference between the candidate driving feature and the numerical lower limit, and the second difference between the numerical upper limit and the numerical lower limit; determine the grouping interval to which the ratio belongs according to the ratio between the first difference and the second difference, and the grouping upper limit and grouping lower limit corresponding to each group; use the grouping number corresponding to the grouping interval as the candidate feature number of the candidate driving feature; establish a corresponding relationship between the candidate driving feature and the candidate feature number according to each candidate driving feature between the numerical upper limit and the numerical lower limit of the candidate driving feature, and the candidate feature number corresponding to each candidate driving feature.

[0152] Each module in the above-mentioned vehicle driving characteristic comparison device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each module.

[0153] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store vehicle driving characteristic data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a vehicle driving characteristic comparison method is implemented.

[0154] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0155] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the vehicle driving characteristic comparison method described in any of the above embodiments is implemented.

[0156] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the vehicle driving characteristic comparison method described in any of the above embodiments is implemented.

[0157] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the vehicle driving characteristic comparison method described in any of the above embodiments is implemented.

[0158] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0159] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0160] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0161] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A vehicle driving characteristics comparison method, characterized in that: The method comprises: Acquire multi-dimensional target driving characteristics of at least two target vehicle sets within a preset time period; wherein the multi-dimensional target driving characteristics at least include vehicle speed, acceleration, engine speed, engine torque, total mass of the target vehicle, gear position, terrain and road type; According to the correspondence between the candidate driving features and the candidate feature numbers, the target feature numbers corresponding to the driving features of each dimension corresponding to each target vehicle set are determined; The target feature numbers corresponding to each target vehicle set at each moment in the preset time period are concatenated to obtain the joint feature numbers corresponding to each target vehicle set; The driving feature similarities between the target vehicle sets are determined according to the frequencies of occurrence of the joint feature numbers corresponding to the target vehicle sets within a preset time period.

2. The method according to claim 1, characterized in that The determining of the driving feature similarity between each target vehicle set according to the frequency of occurrence of each joint feature number corresponding to each target vehicle set within a preset time period includes: Determine the probability of each joint feature number corresponding to each target vehicle set appearing within the preset time period according to the frequency of each joint feature number corresponding to each target vehicle set appearing within the preset time period; The driving feature similarities between the target vehicle sets are determined according to the probability of each joint feature number corresponding to each target vehicle set appearing within a preset time period.

3. The method according to claim 2, characterized in that The method of determining the probability of each joint feature number corresponding to each target vehicle set appearing within a preset time period according to the frequency of each joint feature number corresponding to each target vehicle set appearing within a preset time period includes: For each target vehicle set, the ratio between the frequency of each joint feature number appearing in the preset time period and the sum of the frequencies of each joint feature number appearing in the preset time period is used as the probability of each joint feature number appearing in the preset time period.

4. The method according to claim 3, characterized in that The determining of the driving feature similarity between each target vehicle set according to the probability of each joint feature number corresponding to each target vehicle set appearing within a preset time period includes: Selecting two target vehicle sets from each target vehicle set; wherein the selected two target vehicle sets are a first target vehicle set and a second target vehicle set; For each joint feature number corresponding to the first target vehicle set, if there is a target joint feature number that is the same as the joint feature number corresponding to the first target vehicle set among the joint feature numbers corresponding to the second target vehicle set, the minimum value of the probability of the target joint feature number appearing within a preset time period is taken as the probability of the target joint feature number; wherein the probability of the target joint feature number appearing within the preset time period includes the probability of the target joint feature number appearing in each joint feature number corresponding to the first target vehicle set within the preset time period, and the probability of the target joint feature number appearing in each joint feature number corresponding to the second target vehicle set within the preset time period; The sum of the probabilities of the joint feature numbers of the targets is taken as the driving feature similarity between the first target vehicle set and the second target vehicle set.

5. The method according to claim 1, characterized in that If the candidate driving features are gear position, terrain and road type, the correspondence between the candidate driving features and the candidate feature numbers is established in the following manner: For each candidate driving feature, determining each sub-feature under the candidate driving feature; Assign a candidate feature number to each sub-feature; According to each sub-feature and the candidate feature number corresponding to each sub-feature, a corresponding relationship between the candidate driving feature and the candidate feature number is established.

6. The method according to claim 1, characterized in that If the candidate driving features are vehicle speed, acceleration, engine speed, engine torque and the total mass of the target vehicle, the correspondence between the candidate driving features and the candidate feature numbers is established in the following manner: For each candidate driving feature, determining an upper value limit, a lower value limit, a number of groups, and a grouping step length of the candidate driving feature; Determine the grouping upper limit and grouping lower limit corresponding to each group according to the grouping quantity and the grouping step size; determining a first difference between the candidate driving characteristic and the lower numerical limit, and a second difference between the upper numerical limit and the lower numerical limit; Determine the grouping interval to which the ratio belongs according to the ratio between the first difference and the second difference, and the grouping upper limit and the grouping lower limit corresponding to each group; Using the group number corresponding to the group interval as the candidate feature number of the candidate driving feature; According to each candidate driving feature between the numerical upper limit and the numerical lower limit of the candidate driving feature, and the candidate feature number corresponding to each candidate driving feature, a corresponding relationship between the candidate driving feature and the candidate feature number is established.

7. A vehicle driving characteristics comparison device, characterized in that: The device comprises: An acquisition module, used to acquire multi-dimensional target driving characteristics of at least two target vehicle sets within a preset time period; wherein the multi-dimensional target driving characteristics at least include vehicle speed, acceleration, engine speed, engine torque, total mass of the target vehicle, gear position, terrain and road type; A first determination module is used to determine the target feature number corresponding to each dimension of driving features corresponding to each target vehicle set according to the correspondence between the candidate driving features and the candidate feature numbers; A splicing module, used for splicing target feature numbers corresponding to each target vehicle set at each moment within a preset time period to obtain joint feature numbers corresponding to each target vehicle set; The second determination module is used to determine the driving feature similarity between each target vehicle set according to the frequency of occurrence of each joint feature number corresponding to each target vehicle set within a preset time period.

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

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

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

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