A safety management and control method for hazardous chemical vehicles based on big data

Abstract

The present invention relates to the field of vehicle speed control technology, and more specifically, to a method for safety control of chemical hazardous vehicles based on big data, the method comprising: obtaining vehicle speed data and engine power data during vehicle operation; regulating the maximum vehicle speed once every preset time period, and in any regulation process, using the average stability of the vehicle speed data during the regulation period to correct the preset temperature threshold, the correction value is the product of the average stability and the temperature threshold, and based on the correction value, using a simulated annealing algorithm to find the optimal solution for the maximum vehicle speed during the regulation period, so as to control the vehicle speed based on the optimal solution. The present invention can accurately control the vehicle speed.

Description

Technical Field

[0001] The present invention relates to the field of vehicle speed control technology. More specifically, the present invention relates to a safety management and control method for chemical hazardous vehicles based on big data. Background Art

[0002] With the rapid development of the economy and the chemical industry, the transportation of hazardous chemicals has become a key industry link. Since hazardous chemicals are dangerous, flammable, and explosive, the safety requirements for transportation vehicles are extremely strict. In particular, vehicles transporting hazardous chemicals can easily cause serious traffic accidents if they exceed the speed limit. Speeding not only significantly increases the risk of accidents, but may also lead to more serious consequences due to factors such as insufficient driver reaction time and unstable vehicle control. Therefore, effective control of the maximum speed of vehicles transporting hazardous chemicals has become a key measure to ensure safety.

[0003] The simulated annealing (SA) algorithm is a global optimization algorithm based on randomized search. The application of this algorithm in the intelligent control of the maximum speed of vehicles has a strong global optimization capability and can effectively handle complex constraints, multi-dimensional optimization and dynamic changes. In the optimization process, it can not only avoid falling into the local optimal solution, but also flexibly adapt to complex environments and uncertainties. Therefore, the optimization results output by this algorithm can be used to control the speed of the vehicle.

[0004] In the related art, for example, a patent document with publication number CN113625548A discloses a speed control method for a meta-action unit based on a simulated annealing algorithm and fuzzy PID. The method includes: first, before speed control, the whole machine is decomposed into subsystems, and then the subsystems are decomposed into meta-action units for control; the speed of the worm rotation meta-action unit is collected by a speed sensor, the motor speed is set by a controller, and the control rule table, fuzzy domain, fuzzy language value, and membership function of the fuzzy ID controller algorithm are designed to realize the setting of integral coefficients and differential coefficients; and the proportional factor of the speed P controller is intelligently optimized using a simulated annealing algorithm.

[0005] However, the above scheme uses the traditional simulated annealing algorithm when performing intelligent optimization. The traditional simulated annealing algorithm relies on a fixed annealing temperature threshold, and the size of the threshold has a direct impact on the number of iterations of the algorithm, which in turn determines the efficiency and effect of the optimization process. However, since the actual collected data often shows a dynamic trend and is easily disturbed by noise, using a fixed annealing temperature threshold may not be able to adapt to such variable data characteristics. This may cause the algorithm to deviate during the optimization process, making it impossible to accurately achieve the control target. Summary of the invention

[0006] In order to solve the problem that the efficiency and effect of optimizing by using simulated annealing algorithm are poor, and the target cannot be accurately controlled, the present invention provides a safety control method for chemical hazardous vehicles based on big data. The method comprises:

[0007] Obtain vehicle speed data and engine power data during vehicle operation;

[0008] The maximum speed of the vehicle is adjusted once every preset time. During any adjustment process, the preset temperature threshold is corrected using the average stability of the speed data during the adjustment period. The correction value is the product of the average stability and the temperature threshold. Based on the correction value, the simulated annealing algorithm is used to find the optimal solution for the maximum speed of the adjustment period, so as to control the speed based on the optimal solution. The temperature threshold is used to determine the number of iterations when the simulated annealing algorithm is optimized. The method for obtaining the stability of any speed includes:

[0009] Calculate the stability of any vehicle speed data, which is negatively correlated with the difference between the vehicle speed data and adjacent vehicle speed data, and the difference between the vehicle speed data and the average vehicle speed data of the control period;

[0010] Stability: ; For the The stability of vehicle speed data; For the The stability of vehicle speed data; , Respectively The correlation between the vehicle speed data in the control period and the vehicle speed data in the target period and the engine power data; For the The target period is the stability of the engine power data collected at the same time as the vehicle speed data; The vehicle speed data is in the control period except the The vehicle speed data corresponds to a time period other than the time.

[0011] The present invention can adaptively adjust the temperature threshold of the corresponding adjustment period according to the changing characteristics of the vehicle speed data in any control period, thereby avoiding the problems of poor efficiency and effect in the optimization process of the simulated annealing algorithm caused by using a fixed temperature threshold, ensuring the accuracy of the determined optimal maximum vehicle speed, and thus achieving precise control of the vehicle.

[0012] Preferably, the smoothness of any vehicle speed data satisfies the following relationship:

[0013] ;

[0014] In the formula, For the The stability of vehicle speed data; For the The value of vehicle speed data; For the The average vehicle speed data of the control period during which the vehicle speed data is located; For the The difference between the vehicle speed data and the left adjacent vehicle speed data; For the The difference between the vehicle speed data and the right adjacent vehicle speed data; is the absolute value symbol; To preset hyper parameters; is a natural exponential function.

[0015] The present invention combines the outstanding situation of any vehicle speed data in the control period, measures the change characteristics of the vehicle speed data, and can accurately analyze the stability of each vehicle speed data, thereby ensuring the accuracy of the determined stability.

[0016] Preferably, the method for obtaining the difference between any vehicle speed data and adjacent vehicle speed data includes:

[0017] The absolute value of the difference between any vehicle speed data and the left adjacent vehicle speed data, as well as the absolute value of the difference between the vehicle speed data and the right adjacent vehicle speed data are calculated, and the sums are taken to obtain the difference between the vehicle speed data and the adjacent vehicle speed data.

[0018] Preferably, adjusting the maximum speed of the vehicle once every preset time period includes:

[0019] At the start time of any control period, the optimal solution for the maximum vehicle speed in the control period is determined, and the vehicle speed at each moment in the control period is controlled based on the optimal solution until the start time of the next control period.

[0020] The present invention can achieve safety control of the vehicle during driving.

[0021] Preferably, before calculating the product of the average stability of the vehicle speed data during the control period and the preset temperature threshold, the method further includes:

[0022] The average stability is normalized to obtain a normalized value of the average stability.

[0023] The present invention limits the range of the average stability of the vehicle speed data in any control period to 0-1, which can reduce the amount of calculation of the correction value of the preset temperature threshold and reduce the difficulty of calculation.

[0024] Preferably, the method for obtaining the correlation includes:

[0025] The Pearson correlation coefficient or the Spearman rank correlation coefficient of the vehicle speed data and the engine power data in the corresponding time period is calculated to obtain the correlation between the vehicle speed data and the engine power data in the corresponding time period.

[0026] Preferably, after obtaining the vehicle speed data and the engine power data during the operation of the vehicle, the method further includes:

[0027] Performing curve fitting on the vehicle speed data or the engine power data within the sampling period to obtain a vehicle speed fitting curve or a power fitting curve;

[0028] Based on the vehicle speed fitting curve or the power fitting curve, the vehicle speed data or the engine power data within the sampling period is divided into a number of data segments with the same length as the preset time length.

[0029] The present invention can reduce the difficulty of data analysis by performing curve fitting on vehicle speed data or engine power data within a sampling period.

[0030] Preferably, the smoothness of the engine power data is determined in the same manner as the smoothness of the vehicle speed data.

[0031] The present invention has the following effects:

[0032] 1. The present invention can avoid the problems of poor optimization efficiency and effect caused by the use of a fixed temperature threshold by adaptively adjusting the temperature threshold of the simulated annealing algorithm when searching for the best solution in each control period, thereby ensuring the accuracy of the optimal maximum vehicle speed in each control period, and thus accurately controlling the vehicle speed and achieving safe vehicle control.

[0033] 2. When calculating the stability of any vehicle speed data, the present invention can not only capture the changing characteristics of the vehicle speed data within the control period, but also rely on the correlation between the vehicle speed data and the engine power data in the same period or at the same moment, eliminate the interference of noise data, thereby ensuring the accuracy of the stability, and then accurately correct the preset temperature threshold. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0035] Figure 1 It is a schematic diagram of the steps of a chemical hazardous vehicle safety management and control method based on big data in an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0037] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0038] Reference Figure 1 A chemical hazardous vehicle safety management and control method based on big data includes steps S1 and S2, which are as follows:

[0039] S1: Obtain vehicle speed data and engine power data during vehicle operation.

[0040] Specifically, the vehicle speed data and engine power data can be collected by using the GPS system and OBD-II (on-board diagnostic system) at a certain collection frequency, such as once per second, during the same sampling period. It should be noted that the sampling period is the entire driving distance of the vehicle.

[0041] In an exemplary embodiment of the present invention, after obtaining the vehicle speed data and the engine power data during the operation of the vehicle, the following steps are also included:

[0042] Perform curve fitting on the vehicle speed data or engine power data within the sampling period to obtain a vehicle speed fitting curve or a power fitting curve; based on the vehicle speed fitting curve or the power fitting curve, divide the vehicle speed data or engine power data within the sampling period into a number of data segments with the same length as the preset time length.

[0043] Optionally, the least square method can be used to perform curve fitting on the vehicle speed data or engine power data within the sampling period; the weighted least square method can also be used to perform curve fitting; of course, a suitable method can also be selected for curve fitting according to specific circumstances. This embodiment does not specifically limit the method of curve fitting.

[0044] Optionally, the vehicle speed data or the engine power data within the sampling period is divided into a number of data segments with the same length as the preset time length, which can provide a data basis for each control period.

[0045] S2: The maximum speed of the vehicle is adjusted once at preset intervals. During any adjustment process, the preset temperature threshold is corrected using the average stability of the speed data during the adjustment period. The correction value is the product of the average stability and the temperature threshold. Based on the correction value, a simulated annealing algorithm is used to find the optimal solution for the maximum speed of the adjustment period, so as to control the vehicle speed based on the optimal solution.

[0046] The temperature threshold is used to determine the number of iterations when the simulated annealing algorithm is optimized.

[0047] It should be noted that when applying the simulated annealing algorithm to the intelligent control of the maximum speed of the vehicle, the traditional method usually adopts the same temperature threshold in different control periods. However, the size of the temperature threshold in the simulated annealing algorithm directly affects the number of optimization iterations in each control period. The fixed temperature threshold has poor adaptability: if the temperature threshold is too low, the number of iterations in the optimization process is large, resulting in reduced algorithm efficiency; if the temperature threshold is too high, the number of iterations is small, which may lead to insufficient accuracy of the optimal solution. Therefore, the present invention improves the traditional simulated annealing algorithm by adaptively adjusting the temperature threshold in the corresponding control period according to the average stability of the vehicle speed data in each control period, thereby improving the efficiency and accuracy of determining the optimal solution for the maximum vehicle speed in each control period.

[0048] Optionally, the preset time length may be set to 30 seconds, that is, the maximum speed of the vehicle is adjusted every 30 seconds in the present invention, that is, the duration of each adjustment period is 30 seconds. This embodiment does not specifically limit the size of the preset time length.

[0049] Specifically, in any regulation, first, it is necessary to calculate the stability of any vehicle speed data within the regulation period, and then use the average stability of the vehicle speed data within the regulation period to correct the preset temperature threshold, so as to obtain the temperature threshold when the simulated annealing algorithm searches for the optimal solution for the highest vehicle speed in the corresponding regulation period.

[0050] The stability of any vehicle speed data within any control period can be determined by the following steps:

[0051] Step 1: Calculate the stability of any vehicle speed data. The stability is negatively correlated with the difference between the vehicle speed data and the adjacent vehicle speed data, and the difference between the vehicle speed data and the average vehicle speed data of the control period.

[0052] Among them, the stability refers to the data that measures the stability of any vehicle speed data in any control period. If any vehicle speed data in any control period is significantly different from the adjacent vehicle speed data and the overall level of the vehicle speed data in the control period, the stability of the vehicle speed data is low; on the contrary, if the vehicle speed data is slightly different from the adjacent vehicle speed data and the overall level of the vehicle speed data in the control period, the stability of the vehicle speed data is high.

[0053] Specifically, the stability of any vehicle speed data satisfies the following relationship:

[0054] ;

[0055] In the formula, For the The stability of vehicle speed data; For the The value of vehicle speed data; For the The average vehicle speed data of the control period during which the vehicle speed data is located; For the The difference between the vehicle speed data and the left adjacent vehicle speed data; For the The difference between the vehicle speed data and the right adjacent vehicle speed data; is the absolute value symbol; To preset hyper parameters, in this embodiment = 0.001, setting the hyperparameter is to prevent There is a zero situation; is the natural exponential function, where the natural exponential function is a natural constant An exponential function with base .

[0056] in, Reflects the The difference between the first vehicle speed data and the adjacent vehicle speed data. The larger the value, the The greater the difference between the individual vehicle speed data and the average vehicle speed data of the control period, the greater the credibility, thereby ensuring the accuracy of the determined stability.

[0057] It should be noted that the sampling time of any vehicle speed data and the adjacent vehicle speed data are continuous in time sequence. For example, when the sampling time of any vehicle speed data is When , the sampling time of the left adjacent data of the vehicle speed data is , the sampling time of the right adjacent data of the vehicle speed data is .

[0058] In another embodiment, the smoothness of the vehicle speed data may also be calculated in other ways, such as by multiplying first and then taking the inverse instead of calculating by multiplying first and then taking the inverse.

[0059] In an exemplary embodiment of the present invention, the difference between any vehicle speed data and adjacent vehicle speed data may be determined by the following steps:

[0060] The absolute value of the difference between any vehicle speed data and the left adjacent vehicle speed data, as well as the absolute value of the difference between the vehicle speed data and the right adjacent vehicle speed data are calculated, and the sums are taken to obtain the difference between the vehicle speed data and the adjacent vehicle speed data.

[0061] Optionally, the ratio of any vehicle speed data to the left adjacent vehicle speed data and the ratio of the vehicle speed data to the right adjacent vehicle speed data may be calculated and summed to obtain the difference between the vehicle speed data and the adjacent vehicle speed data. This embodiment does not specifically limit the method for determining the difference.

[0062] Step 2: Calculate the stability of the vehicle speed data.

[0063] It should be noted that since there may be noise data in the vehicle speed data within the sampling period, and the noise data has similar values ​​to the normal data, the smoothness of the calculated vehicle speed data may be biased. In the application scenario of the present invention, there is usually a certain correlation between the vehicle speed data and the engine power data, which is specifically manifested as follows: the engine power directly affects the acceleration performance and maximum speed of the vehicle. The greater the power, the stronger the power that the vehicle can provide, thereby reaching a higher speed in a shorter time. Therefore, the present invention combines the correlation between the vehicle speed data and the engine power data to correct the smoothness of the vehicle speed data, thereby reducing the interference of the noise data.

[0064] Specifically, the stability of any vehicle speed data satisfies the following relationship:

[0065] ;

[0066] In the formula, For the The stability of vehicle speed data; For the The stability of vehicle speed data; , Respectively The correlation between the vehicle speed data and the engine power data in the control period and the target period, where the target period is The vehicle speed data is in the control period except the The speed data corresponds to a period other than the time; For the The smoothness of the engine power data collected at the same time as the vehicle speed data.

[0067] in, Quantified the The smaller the value, the smaller the consistency of the vehicle speed data and engine power data at the sampling time. The greater the consistency between the vehicle speed data corresponding to the sampling moment and the engine power data, the smaller the possibility that the vehicle speed data belongs to noise data, and the greater the stability of the vehicle speed data, the greater the credibility, and the corresponding stability of the vehicle speed data is relatively greater.

[0068] The larger the The stability of the engine power data collected at the same time as the vehicle speed data is high, which means that in the quantification of the When the vehicle speed data corresponding to the sampling time is consistent with the changes in the engine power data, the credibility of the obtained consistency value is relatively high.

[0069] In an exemplary embodiment of the present invention, the correlation between the vehicle speed data and the engine power data may be determined by the following steps:

[0070] The Pearson correlation coefficient or the Spearman rank correlation coefficient of the vehicle speed data and the engine power data in the corresponding time period is calculated to obtain the correlation between the vehicle speed data and the engine power data in the corresponding time period.

[0071] Optionally, a suitable similarity measurement method can be selected according to specific circumstances. This embodiment does not specifically limit the method for measuring the similarity between data. It should be noted that the calculation method of the Pearson correlation coefficient or the Spearman rank correlation coefficient between data is a prior art, and this embodiment will not be described in detail.

[0072] In an exemplary embodiment of the present invention, the calculation method of the smoothness of the engine power data is the same as that of the vehicle speed data. It should be noted that the present invention has elaborated in detail the calculation formula of the smoothness of the vehicle speed data, and the calculation formula of the smoothness of the engine power data will not be repeated.

[0073] Furthermore, after determining the stability of each vehicle speed data in any control period, the average stability of all vehicle speed data in the control period can be calculated to obtain the average stability of the vehicle speed data in the control period.

[0074] In an exemplary embodiment of the present invention, after calculating the average stability of the vehicle speed data in any control period, the following steps are also included:

[0075] The average stability is normalized to obtain a normalized value of the average stability.

[0076] Specifically, the normalized value of the average stability of the vehicle speed data within any control period satisfies the following relationship:

[0077] ;

[0078] In the formula, For the The normalized value of the average stability of vehicle speed data within a control period; For the During the control period The stability of vehicle speed data; is the number of vehicle speed data in any control period; for the summation symbol; is the normalization function. Reflects the The average stability of vehicle speed data during a control period.

[0079] Optionally, a hyperbolic tangent function may be used to normalize the average stability of the vehicle speed data within any control period. This embodiment does not specifically limit the normalization method.

[0080] Furthermore, after determining the normalized value of the average stability of the vehicle speed data in any control period, the product of the normalized value and the preset temperature threshold can be used as a correction value of the preset temperature threshold in the control period.

[0081] Specifically, the correction value of the preset temperature threshold in any control period satisfies the following relationship:

[0082] ;

[0083] In the formula, The preset temperature threshold is Correction value within a control period; For the The normalized value of the average stability of vehicle speed data within a control period; is the preset temperature threshold. =50.

[0084] After determining the correction value of the preset temperature threshold in each control period, the simulated annealing algorithm can be used to find the optimal solution for the maximum vehicle speed in the corresponding control period. The specific process is:

[0085] First, the initial parameters of the simulated annealing algorithm are set, such as setting the initial temperature to 80°C, setting the cooling coefficient to 0.96, setting the random perturbation range in each iteration to within 2 km / h of the maximum speed setting value in the previous annealing process, and setting the initial solution of the maximum speed to 60 km / h. Of course, appropriate initial parameters can also be set according to specific circumstances, and this embodiment does not specifically limit the setting of the initial parameters.

[0086] Then, construct the objective function: ; In the formula, is the objective function value corresponding to the current vehicle speed data; The time required to travel the entire journey at the current speed data. This value can be obtained from the vehicle's historical driving data. The larger the value, the smaller the preference of the current speed data, and the larger the corresponding objective function value; The braking distance of the vehicle on the road surface with the corresponding bumpiness at the current moment based on the current vehicle speed data, wherein the bumpiness of the vehicle at any moment can be determined by a vibration sensor installed on the vehicle body; it should be noted that The larger the value is, the lower the preference of the current vehicle speed data is, and the larger the corresponding objective function value is, so the most suitable maximum vehicle speed value can be obtained according to the road surface with different bumpiness at different times; is a normalized function. It should be noted that the objective function constructed by the present invention is intended to find the optimal solution by minimizing the objective function value. That is, the smaller the objective function value, the greater the possibility that the corresponding vehicle speed data is the optimal solution.

[0087] Finally, based on , using the simulated annealing algorithm to determine the optimal solution for the maximum vehicle speed in each control period. It should be noted that the process of using the simulated annealing algorithm to find the optimal solution is a prior art, and this embodiment will not be described in detail here.

[0088] In an exemplary embodiment of the present invention, the vehicle speed at each moment in any control period can be controlled by the following steps:

[0089] At the start time of any control period, the optimal solution for the maximum vehicle speed in the control period is determined, and the vehicle speed at each moment in the control period is controlled based on the optimal solution until the start time of the next control period.

[0090] For example, after determining the optimal solution for the maximum vehicle speed in each control period, the driver can be reminded that the vehicle speed in the corresponding period must not exceed the optimal maximum vehicle speed in that period, thereby achieving safe control of the vehicle.

[0091] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0092] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A safety management and control method for chemical hazardous vehicles based on big data, characterized in that: include: Obtain vehicle speed data and engine power data during vehicle operation; The maximum speed of the vehicle is adjusted once every preset time. During any adjustment process, the preset temperature threshold is corrected using the average stability of the speed data during the adjustment period. The correction value is the product of the average stability and the temperature threshold. Based on the correction value, a simulated annealing algorithm is used to find the optimal solution for the maximum speed of the adjustment period, so as to control the vehicle speed based on the optimal solution. The temperature threshold is used to determine the number of iterations when the simulated annealing algorithm is optimized; A method for obtaining the stability of any vehicle speed includes: The stability of any vehicle speed data is calculated. The stability is negatively correlated with the difference between the vehicle speed data and the adjacent vehicle speed data, and the difference between the vehicle speed data and the average vehicle speed data of the control period, satisfying the following relationship: ； In the formula, For the The stability of vehicle speed data; For the The value of vehicle speed data; For the The average vehicle speed data of the control period during which the vehicle speed data is located; For the The difference between the vehicle speed data and the left adjacent vehicle speed data; For the The difference between the vehicle speed data and the right adjacent vehicle speed data; is the absolute value symbol; To preset hyper parameters; is the natural exponential function Degree of stability: ; For the The stability of vehicle speed data; For the The stability of vehicle speed data; , Respectively The correlation between the vehicle speed data in the control period and the vehicle speed data in the target period and the engine power data; For the The target period is the stability of the engine power data collected at the same time as the vehicle speed data; The vehicle speed data is in the control period except the The speed data corresponds to a period other than the time; The method for obtaining the correlation includes: Calculate the Pearson correlation coefficient or the Spearman rank correlation coefficient between the vehicle speed data and the engine power data in the corresponding time period to obtain the correlation between the vehicle speed data and the engine power data in the corresponding time period; The smoothness of the engine power data is determined in the same manner as the smoothness of the vehicle speed data.

2. According to the big data-based safety management and control method for chemical hazardous vehicles of claim 1, it is characterized in that: A method for obtaining a difference between any vehicle speed data and adjacent vehicle speed data includes: The absolute value of the difference between any vehicle speed data and the left adjacent vehicle speed data, as well as the absolute value of the difference between the vehicle speed data and the right adjacent vehicle speed data are calculated, and the sums are taken to obtain the difference between the vehicle speed data and the adjacent vehicle speed data.

3. According to the big data-based safety management and control method for chemical hazardous vehicles of claim 1, it is characterized in that: The step of adjusting the maximum speed of the vehicle at a preset time interval includes: At the start time of any control period, the optimal solution for the maximum vehicle speed in the control period is determined, and the vehicle speed at each moment in the control period is controlled based on the optimal solution until the start time of the next control period.

4. According to the big data-based safety management and control method for chemical hazardous vehicles of claim 1, it is characterized in that: Before calculating the product of the average stability of the vehicle speed data during the control period and the preset temperature threshold, the method further includes: The average stability level is normalized to obtain a normalized value of the average stability level.

5. The method for safety management and control of chemical hazardous vehicles based on big data according to claim 1 is characterized in that: After obtaining the vehicle speed data and the engine power data during the operation of the vehicle, the method further includes: Performing curve fitting on the vehicle speed data or the engine power data within the sampling period to obtain a vehicle speed fitting curve or a power fitting curve; Based on the vehicle speed fitting curve or the power fitting curve, the vehicle speed data or the engine power data within a sampling period is divided into a number of data segments having the same length as the preset time length.

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