CN110826145A - Automobile multi-parameter operation condition design method based on heuristic Markov chain evolution - Google Patents

Abstract

The invention discloses a method for designing multi-parameter operating conditions of automobiles based on hyper-heuristic Markov chain evolution. The method takes the Markov chain evolution method as a research benchmark, and introduces hyper-heuristic architecture into the Markov chain evolution method. middle. Firstly, based on the operator satisfying the Markov property, combined with the function and diversity of the strategy, define the boundary variables of the strategy and design multiple strategy factors; then, based on the expected operating conditions, formulate the allocation mechanism and working conditions of the strategy factors and the sequence of operating conditions. Finally, the genetic algorithm is selected as the high-level heuristic algorithm, and the process of applying multi-strategy factors to the operating condition sequence population is used as a low-level heuristic algorithm to establish an efficient multi-parameter algorithm. Condition design method. The adaptability of the proportion of strategy factors in the design architecture further significantly improves the operation efficiency compared with the Markov chain evolution method. The method is highly portable and convenient for practical operation and use by automotive engineers.

Description

Design method of automobile multi-parameter operating conditions based on hyperheuristic Markov chain evolution

technical field

The invention relates to a method for designing an automobile operating condition, in particular to a method for designing a multi-parameter operating condition of an automobile based on a hyper-heuristic Markov chain evolution.

Background technique

The generation of vehicle representative operating conditions is the basic requirement of current research on vehicle testing, evaluation, control and prediction. In order to improve the adaptability, it is necessary to consider the actual influencing factors in many aspects. For example, the selection of the size of the power components of the hybrid vehicle is sensitive to the road gradient, so the design of the working conditions including the road gradient needs to be considered. For another example, when simulating and verifying the operating conditions, only the representative operating conditions of the two parameters of speed and acceleration are considered. Due to the fixed shifting strategy in the vehicle powertrain simulation software, there is a gap with the actual driver's shifting style, which leads to the design of It is difficult to meet the requirements of engine load consistency under working conditions. Therefore, it is necessary to consider the design of operating conditions including gear, speed and torque parameters. In conclusion, the need to design multi-parameter vehicle operating conditions becomes strong.

The current multi-parameter working condition design method includes two mainstream methods based on Markov chain method and its derived intelligent method. Although the existing literature is widely used to establish a Markov chain model with three parameters of speed, acceleration and slope, it is applied to evaluate the rated power of the energy source of hybrid electric vehicles and optimize the power train. However, due to the randomly generated nature of Markov chains, the advantage of this method for designing high-dimensional operating cases is not so obvious. For the problem that the simulation time of the multi-dimensional Markov chain model is too long, although the number of states can be reduced by increasing the parameter step size, for example, the step size of velocity, acceleration and gradient in the three-dimensional Markov chain model is relative to the two-dimensional velocity and gradient. The step size is appropriately increased to shorten the simulation time, but it will result in a significantly fluctuating sequence of operating conditions. In addition, with the design requirements of higher design accuracy and more parameter conditions, the Markov chain design method is far from being able to cope. A more potential method for designing high-dimensional operating conditions is the Markov chain evolution method, which combines the Markov chain with the evolution method to design an operating strategy that satisfies the Markov property, and makes the process design process directional. Approach the target condition; compared to the Markov chain method, this method can significantly improve the operating efficiency of the design condition. However, there are two problems in the Markov chain evolution method. One is that the operator ratio cannot be adjusted, that is, it lacks adaptive performance; the other is that the algorithm has poor flexibility, that is, when engineering applications, the designed new strategy factors need to be transplanted to traditional genetics. algorithms or other functions. Therefore, it is necessary to continue to improve the existing Markov chain evolution methods.

SUMMARY OF THE INVENTION

The technical problem to be solved by the present invention is to provide an automobile multi-parameter operating condition design method based on hyperheuristic Markov chain evolution, which can effectively solve the problems of self-adaptive adjustment of the proportion of strategy factors and flexibility of engineering application, broaden the current The parameter dimension of the method design case, and it has great potential to improve the design efficiency.

The technical solution adopted by the present invention to solve the above-mentioned technical problems is to provide a method for designing a multi-parameter operating condition of an automobile based on the evolution of the hyper-heuristic Markov chain, which includes the following steps: S1) an operation strategy based on the evolution method of the Markov chain , by defining the strategy boundary variables, select multiple strategy factors that satisfy the Markov property; S2) design the allocation mechanism and update mechanism of strategy factors and the sequence of operating conditions, and then generate an evaluation function based on the expected operating conditions; S3) adopt As a high-level control algorithm with a hyper-heuristic architecture, the traditional genetic algorithm evolves and iterates the strategy factor population composed of strategy factor sequences, and finally obtains the operating conditions of the expected evaluation value.

Further, the step S1 includes:

Step S11: Based on the actual collected data of the vehicle, set the step size of each parameter, divide each parameter state, count the multi-parameter state transition probability matrix P, and use the Markov chain stochastic simulation method to generate the target operating condition length L and The start-stop state is the candidate working condition of idle speed;

Step S12: Based on the multi-parameter state transition probability matrix calculated in step S11, design a crossover operator strategy that generates an individual sequence that must satisfy the Markov chain state transition relationship when any two operating condition sequences are exchanged with an equal-length crossover segment, and the The process of randomly selecting a sequence of working conditions to replace the sequence of working conditions to be mutated based on the candidate working conditions in step S11 is used as the mutation operator strategy;

Step S13: Define the state position where any two working condition sequences are exchanged as the strategy boundary variable R, and set the strategy boundary variable vector R=[R ₁ , R ₂ ,...,R _i ,...,R _j ,...,R _n ], i,j∈[1,n], and R _i ≠R _j , n is the number of different Rs;

Step S14: According to the vector R in step S13, firstly divide all the commutative segments of equal length based on the two operator strategies satisfying the Markov property in step S12 into multiple different n+1 groups, n+1 groups. The equal-length exchangeable segment generates n+1 equal-length crossover operator strategies, that is, n+1 equal-length strategy factors that can exert local search and global search capabilities in the evolution process. Secondly, according to the design conditions, the length deviation is kept at Within the range of a certain threshold T _r and the non-equal-length exchangeable segments of the vector R and n+1 groups, n+1 non-equal-length strategy factors are generated; finally, there are 2n+3 mutation operator strategies including the ability to update the state of the working condition. a strategic factor;

Step S15: Based on the 2n+3 strategy factors obtained in step S14, set the proportion pr of each strategy factor, and randomly form a strategy factor sequence of length _t Y(θ)=[θ ¹ ,θ ² ,... ,θ ⁱ ,...,θ ^t ], where t>(2n+3), θ∈[θ _e1 ,θ _e2 ,…,θ _e(n+1) ,θ _ne1 ,θ _ne2 ,…,θ _{ne (n+1)} ,θ _m ], θ _e1 ,θ _e2 ,…,θ _e(n+1) correspond to equal-length crossover strategy factors, θ _ne1 ,θ _ne2 ,…,θ _ne(n+1) correspond to non-equal length Long crossover strategy factor, θ _m is a complete mutation operator; at the same time, a population Pop( ² ){X}=[X ₁ ; X ₂ ;... ; X _t ], where X is the state sequence of working conditions, under the condition of the initial population Pop( ² ){X} of the working condition sequence, set the evaluation index of the working condition and the relative deviation threshold T _r , with the help of the satisfaction criterion, calculate The initial function value F{X}=[F(X ₁ ), F(X ₂ ),..., F(X _t )] of the working condition sequence population, and the maximum value of the working condition sequence population is obtained by formula (1). The optimal function value F(X _* ) and the optimal operating condition sequence X _* ;

F(X _* )=min(F{X})(1).

Further, the parameters in the step S11 include speed, acceleration and road gradient, and the value of L is 1800s; the value of n in the step S13 is 6, and the value of R is [10, 20, 50, 100, 150, 200]; In the step _S13 , the value of Tr is 10%; in the step _S13 , the value of pr is (1:1:1:2:3:3:3:1:1:1:2:3: 3:3:2), the value of t is 30.

Further, the step S2 includes:

计算工况序列种群

的函数值

Step S21: Under the current k-th strategy factor sequence Y(θ), perform a roulette operation on the function value F{X} of the individual working condition sequence in step S15, and generate a population sorted by the individual working condition sequence Assign the working condition sequences in the population to the strategy factors in the strategy factor sequence Y(θ) one-to-one, and perform the strategy operation to generate the working condition sequence subpopulation.

Calculate the population of the sequence of operating conditions

the function value of

的最佳函数值

和最佳序列

Step S22: Update the population Pop ⁽²⁾ {X} and the function value F{X}, and update the offspring population and function value Replace the parent population Pop ⁽²⁾ {X} and the population function value F{X} respectively; obtain the daughter population of the working condition sequence according to formula (2)

The best function value of

and the best sequence

通过将

赋给X_*更新最佳工况序列X_*，通过将

赋给F(X_*)更新最佳函数值F(X_*)；when

by putting

Assign X _* to update the optimal case sequence X _* by putting

Assign F(X _* ) to update the best function value F(X _* );

时，将X_*赋到种群Pop⁽²⁾{X}中在

位置，将对应函数值

赋到函数值F(X_*)的相同位置，实现最佳工况序列X_*和最佳函数值F(X_*)的更新；when

, assign X _* to the population Pop ⁽²⁾ {X} exist

position, which will correspond to the function value

Assign it to the same position of the function value F(X _* ) to realize the update of the optimal operating condition sequence X _* and the optimal function value F(X _* );

Step S23: Output the optimal function value F(X _* ) under the current k-th strategy sequence Y(θ).

Further, the step S3 includes:

计算策略因子序列个体函数值

Step S31: Based on the strategy factor sequence of step S15, randomly form an initial strategy factor sequence population of size _Kmax

Calculate the individual function value of the policy factor sequence

Step S32: According to the strategy factor sequence function value F{Y}, according to the default elite probability p _d , select and retain the best K _max ×p _d elite individuals in the current population, and then perform the traditional selection operator operation on the sequence individuals;

Step S33: According to the default crossover probability p _c , select (K _max -K _max ×p _d )×p _c sequence individuals from the (K _max -K _max ×p _d ) remaining individuals to perform random two-point crossover operation to generate (K _max -K _max ×p _d ) × p _c crossover individuals;

Step S34: Perform single-point mutation operation on the remaining K _max -K _max ×p _d -(K _max -K _max ×p _d )×pc individuals to generate K _max -K _max ×p _d - ₍ K _max -K _max ×p _d )×p _c variant individuals;

Step S35: A new strategy factor sequence population is formed by the elite individuals in step S32, the crossover individuals in step S33 and the mutant individuals in step S34;

Step S36: Determine whether the output best evaluation value F(Y _* ) reaches the expected threshold Tr range, if F(Y _{* ) ≤Tr} , then F(Y _* )=F(X _* ), where Y _* is For the best strategy sequence combination, go to step S37, otherwise, go back to step S32;

Step S37: Decode the optimal operating condition state sequence X _* , and output the multi-parameter expected operating operating condition time series.

Further, the value of K _max in the step S31 is 24.

Compared with the prior art, the present invention has the following beneficial effects: the present invention combines the hyperheuristic architecture with the Markov chain evolution method, and effectively solves the problem of self-adaptive adjustment and program encapsulation of the ratio of strategy factors for the design of automobile operating conditions. The problem is that the present invention can widen the parameter dimension of the design conditions of the current method, and has a great potential to improve the design efficiency. In addition, the framework proposed by the present invention is highly portable, and provides support for the operation and use of automobile engineers.

Description of drawings

Fig. 1 is the block diagram of the super-heuristic Markov chain evolution method of the present invention;

Fig. 2 is the change curve of the speed of the design result of the present invention with time;

Fig. 3 is the change curve of the acceleration with time of the design result of the present invention;

Fig. 4 is the change curve of the gradient with time of the design result of the present invention;

Fig. 5 is a histogram of the distribution of the average number of strategy factors when the output expected operating conditions of 10 random strategy factor populations of the present invention;

FIG. 6 is a histogram of the running time of 10 design experiments each of the hyperheuristic Markov chain evolution method and the Markov chain evolution method of the present invention.

Detailed ways

The present invention will be further described below with reference to the accompanying drawings and embodiments.

FIG. 1 is a block diagram of the hyperheuristic Markov chain evolution method of the present invention.

Referring to Fig. 1, the method for designing a multi-parameter operating condition of an automobile based on the evolution of the hyper-heuristic Markov chain provided by the present invention includes the following steps:

Step S1: Based on the operation strategy of the Markov chain evolution method, by defining strategy boundary variables, a plurality of strategy factors satisfying the Markov property are designed, and the specific process includes steps S11 to S14.

Step S11: Based on the actual collected data of the vehicle, including parameters such as speed, acceleration and road gradient, set the step size and maximum value of the speed, acceleration and road gradient, where Δv=0.5m/s, Δa=0.1m/s ² , Δg= ¹ %, vmin=0m/s, _vmax =35m/s, _amin = _-2m /s2, _amax = ^2m /s2, _gmin =-10% and _gmax =10%, divided For each parameter state, the state transition probability matrix P of the three parameters is counted according to formula (3), and the Markov chain stochastic simulation method is used to generate the candidate working condition with the target working condition length L and the starting and ending state is idle speed; where L is 1800s;

为一维空间状态个数,N_i′j′为一维空间状态s_i′到一维空间状态s_j′的转移次数，N_i′为一维空间状态s_i′到其他状态的转移次数和，p_i′j′为一维空间状态s_i′到一维空间状态s_j′的转移概率，S为空间状态集合；where:

is the number of one-dimensional space states, N _i'j' is the transition times from one-dimensional space state _si' to one-dimensional space state s _j ', and N _i' is the transition times from one-dimensional space state _si' to other states and, p _i'j' is the transition probability from one-dimensional space state _si' to one-dimensional space state s _j' , S is the set of space states;

The state s _w at time w is calculated according to formula (4)

s _w =m _w +(u _w -1)×M+(h _w -1)×M×N (4)

In the formula: the speed state at the time of m _w -w, the acceleration state at the time of u _w -w, the slope state at the time of h _w -w, M - the number of speed states, N - the number of acceleration states, and the calculation methods are shown in the formula ( 5) to formula (9).

Step S12: Based on the three-parameter state transition probability matrix P calculated in Step S11, design a crossover operator strategy that generates an individual sequence that must satisfy the Markov chain state transition relationship when any two operating condition sequences are exchanged with equal-length crossover segments, and The process of randomly selecting a working condition sequence to replace the working condition sequence to be mutated based on the candidate working conditions in step S11 is used as the mutation operator strategy;

Step S13: Define the state position where any two working condition sequences are exchanged as the strategy boundary variable R, and set the strategy boundary variable vector R=[R ₁ , R ₂ ,...,R _i ,...,R _j ,...,R _n ], i,j∈[1,n], and R _i ≠R _j , n is the number of different R; where R is [10, 20, 50, 100, 150, 200], n is 6;

Step S14: According to the vector R in step S13, divide all the commutative segments of equal length based on the two operator strategies satisfying the Markov property in step S12 into multiple different n+1 groups, n+1 groups, etc. The long exchangeable segment generates n+1 equal-length crossover operator strategies, that is, n+1 equal-length strategy factors that can exert local search and global search capabilities in the evolution process. Secondly, the length deviation is kept at a certain threshold according to the design conditions. Within the range of T _r and the vector R, the non-equal-length exchangeable segments of n+1 groups generate n+1 non-equal-length strategy factors; finally, there are 2n+3 mutation operator strategies including the ability to update the state of the working condition. Strategy factors; that is, corresponding to 15 strategy factors, which are the intersections at [1:10]s, [10:20]s, [20:50]s, [50:100]s, [100:150]s , [150:200]s, [>200]s interval isometric crossover strategy factor, and crossover segments at [1:10]s, [10:20]s, [20:50]s, [50:100 ]s, [100:150]s, [150:200]s, [>200]s interval non-isometric crossover strategy factor and full mutation strategy factor, T _r is 10%;

Step S15: Based on the 2n+3 strategy factors obtained in Step S14, set the proportion pr of each strategy factor, and randomly form a strategy factor sequence of length _t Y(θ)=[θ ¹ ,θ ² ,...,θ ⁱ ,...,θ ^t ], where t>(2n+3), θ∈[θ _e1 ,θ _e2 ,...,θ _e(n+1) ,θ _ne1 ,θ _ne2 ,..., θ _ne(n+1) ,θ _m ], θ _e1 ,θ _e2 ,...,θ _e(n+1) correspond to equal-length crossover strategy factors, θ _ne1 ,θ _ne2 ,...,θ _{ne(n +1)} Corresponding to the non-equal-length crossover strategy factor, θ _m is a complete mutation operator; at the same time, construct a working condition sequence population Pop ⁽²⁾ {X}=[X ₁ , which is randomly composed of working condition state sequences and whose population size is t; X ₂ ;...;X _t ], where X is the state sequence of working conditions, under the condition of the initial population Pop ⁽²⁾ {X} of the working condition sequence, set the evaluation index of the working condition and the relative deviation threshold _Tr , with the help of the satisfaction function criterion, calculate the initial function value F{X}=[F(X ₁ ), F(X ₂ ),..., F(X _t )] of the population of the working condition sequence, and by formula (10) Obtain the optimal function value F(X _* ) of the population of the working condition sequence and the optimal working condition sequence X _* ; where p _r is (1:1:1:2:3:3:3:1:1:1:2 :3:3:3:2), t is 30;

F(X _* )=min(F{X}) (10)

Among them, the set evaluation indicators include: idle speed ratio, acceleration time ratio, uniform speed time ratio, deceleration time ratio, average speed, average driving speed, driving speed standard deviation, positive acceleration kinetic energy per unit distance, average grade, average There are 11 correlation coefficients for probability distribution of downhill gradient, speed and acceleration.

Step S2: Design the allocation mechanism and update mechanism of the strategy factor and the sequence of operating conditions, and then design the evaluation function based on the expected operating conditions. The specific process includes steps S21 to S23.

并将种群中工况序列一一对应分配给策略因子序列Y(θ)中策略因子，进行策略操作，生成工况序列子代种群

计算工况序列种群

的函数值

Step S21: Under the current k-th strategy factor sequence Y(θ), perform a roulette operation on the function value F{X} of the individual working condition sequence in step S15, and generate a population sorted by the individual working condition sequence

Assign the working condition sequences in the population to the strategy factors in the strategy factor sequence Y(θ) one-to-one, and perform the strategy operation to generate the working condition sequence subpopulation.

Calculate the population of the sequence of operating conditions

the function value of

和函数值分别替换父代种群Pop⁽²⁾{X}和种群函数值F{X}；按照公式(11)获取工况序列子代种群

的最佳函数值

和最佳序列 Step S22: Update the population Pop ⁽²⁾ {X} and the function value F{X}, that is, the descendant population

and function value Replace the parent population Pop ⁽²⁾ {X} and the population function value F{X} respectively; obtain the child population of the working condition sequence according to formula (11)

The best function value of

and the best sequence

通过将

赋给X_*更新最佳工况序列X_*，通过将

赋给F(X_*)更新最佳函数值F(X_*)，当

时，将X_*赋到种群Pop⁽²⁾{X}中

在

的位置，将对应函数值

赋到函数值F(X_*)的相同位置的，实现最佳工况序列X_*和最佳函数值F(X_*)的更新；when

by putting

Assign X _* to update the optimal case sequence X _* by putting

Assign F(X _* ) to update the optimal function value F(X _* ), when

, assign X _* to the population Pop ⁽²⁾ {X}

exist

The position of , will correspond to the function value

If it is assigned to the same position of the function value F(X _* ), the update of the optimal operating condition sequence X _* and the optimal function value F(X _* ) is realized;

Step S23: Output the optimal function value F(X _* ) under the current kth strategy sequence Y(θ).

Step S3: using traditional genetic algorithm as the high-level control algorithm of the super-heuristic architecture, iterates the strategy factor population composed of strategy factor sequences, and finally obtains the operation condition of the expected evaluation value; the specific process includes steps S31 to S37.

其中K_max为24；Step S31: Based on the strategy factor sequence of step S15, randomly form an initial strategy factor sequence population of size _Kmax Calculate the individual function value of the strategy factor sequence based on step S2

where K _max is 24;

Step S32: According to the strategy factor sequence function value F{Y}, according to the default elite probability p _d , select and retain the best K _max ×p _d elite individuals in the current population, and then perform the traditional selection operator operation on the sequence individuals;

Step S33: According to the default crossover probability p _c , select (K _max -K _max ×p _d )×p _c sequence individuals from the (K _max -K _max ×p _d ) remaining individuals to perform random two-point crossover operation to generate (K _max -K _max ×p _d ) × p _c crossover individuals;

Step S34: Perform single-point mutation operation on the remaining K _max -K _max ×p _d -(K _max -K _max ×p _d )×pc individuals to generate K _max -K _max ×p _d - ₍ K _max -K _max ×p _d )×p _c variant individuals;

Step S35: A new strategy factor sequence population is formed by the elite individuals in step S32, the crossover individuals in step S33 and the mutant individuals in step S34;

Step S36: Determine whether the output best evaluation value F(Y _* ) reaches the expected threshold Tr range, if F(Y _{* ) ≤Tr} , then F(Y _* )=F(X _* ), where Y _* is For the best strategy sequence combination, go to step S37, otherwise, go back to step S32;

Step S37: Decode the optimal operating condition state sequence X _* , and output the multi-parameter expected operating operating condition time series.

Fig. 2 to Fig. 4 are the time series of speed, acceleration and road gradient of outputting expected operating conditions in a certain test of the present invention; Fig. 5 is the evolution of the present invention, which is randomly composed of 10 strategy factor populations by strategy factors until the population when expected operating conditions are output The histogram of the distribution of the average number of strategy factors in the present invention shows the characteristic that the present invention can realize the adaptive combination of strategy factors; Fig. 6 is a design experiment of 10 times each of the hyper-heuristic Markov chain evolution method and the Markov chain evolution method of the present invention The running time histogram of . According to statistics, compared with the working condition design method based on Markov chain evolution, the operation efficiency of the present invention is improved by 63.52%. In addition, the function of each part in the hyper-heuristic architecture of the present invention is easy to distinguish and encapsulate, and has strong portability, which provides support for the operation and use of automobile engineers.

Although the present invention has been disclosed above with preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications and improvements without departing from the spirit and scope of the present invention. Therefore, the present invention The scope of protection shall be defined by the claims.

Claims (6)

1. an automobile multi-parameter operating condition design method based on hyperheuristic Markov chain evolution, is characterized in that, comprises the steps: S1) Based on the operation strategy of the Markov chain evolution method, by defining the strategy boundary variables, multiple strategy factors that satisfy the Markov property are selected; S2) Design the allocation mechanism and update mechanism of the strategy factor and the sequence of operating conditions, and then generate the evaluation function based on the expected operating conditions; S3) The traditional genetic algorithm is used as the high-level control algorithm of the super-heuristic architecture, and the strategy factor population composed of the strategy factor sequence is evolved and iterated, and finally the operation condition of the expected evaluation value is obtained. 2. the automobile multi-parameter operating condition design method based on hyperheuristic Markov chain evolution as claimed in claim 1, is characterized in that, described step S1 comprises: Step S11: Based on the actual collected data of the vehicle, set the step size of each parameter, divide each parameter state, count the multi-parameter state transition probability matrix P, and use the Markov chain stochastic simulation method to generate the target operating condition length L and The start-stop state is the candidate working condition of idle speed; Step S12: Based on the multi-parameter state transition probability matrix calculated in step S11, design a crossover operator strategy that generates an individual sequence that must satisfy the Markov chain state transition relationship when any two operating condition sequences are exchanged with an equal-length crossover segment, and the The process of randomly selecting a sequence of working conditions to replace the sequence of working conditions to be mutated based on the candidate working conditions in step S11 is used as the mutation operator strategy; Step S13: Define the state position where any two working condition sequences are exchanged as the strategy boundary variable R, and set the strategy boundary variable vector R=[R ₁ , R ₂ ,...,R _i ,...,R _j ,...,R _n ], i,j∈[1,n], and R _i ≠R _j , n is the number of different Rs; Step S14: According to the vector R in step S13, firstly divide all the commutative segments of equal length based on the two operator strategies satisfying the Markov property in step S12 into multiple different n+1 groups, n+1 groups. The equal-length exchangeable segment generates n+1 equal-length crossover operator strategies, that is, n+1 equal-length strategy factors that can exert local search and global search capabilities in the evolution process. Secondly, according to the design conditions, the length deviation is kept at Within the range of a certain threshold T _r and the non-equal-length exchangeable segments of the vector R and n+1 groups, n+1 non-equal-length strategy factors are generated; finally, there are 2n+3 mutation operator strategies including the ability to update the state of the working condition. a strategic factor; Step S15: Based on the 2n+3 strategy factors obtained in step S14, set the proportion pr of each strategy factor, and randomly form a strategy factor sequence of length _t Y(θ)=[θ ¹ ,θ ² ,... ,θ ⁱ ,...,θ ^t ], where t>(2n+3), θ∈[θ _e1 ,θ _e2 ,...,θ _e(n+1) ,θ _ne1 ,θ _ne2 ,.. .,θ _ne(n+1) ,θ _m ], θ _e1 ,θ _e2 ,...,θ _e(n+1) correspond to equal-length crossover strategy factors, θ _ne1 ,θ _ne2 ,...,θ _{ne (n+1)} corresponds to a non-equal length crossover strategy factor, θ _m is a complete mutation operator; at the same time, a working condition sequence population Pop ⁽²⁾ {X} = [X ₁ ; X ₂ ;...; X _t ], where X is the state sequence of working conditions, under the condition of the initial population Pop ⁽²⁾ {X} of the working condition sequence, set the evaluation index and relative deviation threshold of the operating conditions T _r , with the help of the satisfaction criterion, calculate the initial function value F{X}=[F(X ₁ ), F( _{X 2} ), . ) to obtain the optimal function value F(X _* ) of the population of the working condition sequence and the optimal working condition sequence X _* ; F(X _* )=min(F{X}) (1). 3. the automobile multi-parameter operating condition design method based on hyperheuristic Markov chain evolution as claimed in claim 2, is characterized in that, the parameter in described step S11 comprises speed, acceleration and road gradient, the value of L is 1800s; in the step S13, the value of n is 6, and the value of R is [10, 20, 50, 100, 150, 200]; the value of Tr in the step S13 is 10%; the value of _{p r in} the step S13 The value is (1:1:1:2:3:3:3:1:1:1:2:3:3:3:2), and the value of t is 30. 4. the automobile multi-parameter operating condition design method based on hyperheuristic Markov chain evolution as claimed in claim 2, is characterized in that, described step S2 comprises: Step S21: Under the current k-th strategy factor sequence Y(θ), perform a roulette operation on the function value F{X} of the individual working condition sequence in step S15, and generate a population sorted by the individual working condition sequence

Assign the working condition sequences in the population to the strategy factors in the strategy factor sequence Y(θ) one-to-one, and perform the strategy operation to generate the working condition sequence subpopulation.

Calculate the population of the sequence of operating conditions

the function value of

Step S22: Update the population Pop ⁽²⁾ {X} and the function value F{X}, and update the offspring population

and function value Replace the parent population Pop ⁽²⁾ {X} and the population function value F{X} respectively; obtain the daughter population of the working condition sequence according to formula (2)

The best function value of and the best sequence

when

by putting Assign X _* to update the optimal case sequence X _* by putting

Assign F(X _* ) to update the best function value F(X _* ); when

, assign X _* to the population Pop ⁽²⁾ {X}

exist position, which will correspond to the function value

Assign it to the same position of the function value F(X _* ) to realize the update of the optimal operating condition sequence X _* and the optimal function value F(X _* ); Step S23: Output the optimal function value F(X _* ) under the current k-th strategy sequence Y(θ). 5. the automobile multi-parameter operating condition design method based on hyperheuristic Markov chain evolution as claimed in claim 2, is characterized in that, described step S3 comprises: Step S31: Based on the strategy factor sequence of step S15, randomly form an initial strategy factor sequence population of size _Kmax

Calculate the individual function value of the policy factor sequence Step S32: According to the strategy factor sequence function value F{Y}, according to the default elite probability p _d , select and retain the best K _max ×p _d elite individuals in the current population, and then perform the traditional selection operator operation on the sequence individuals; Step S33: According to the default crossover probability p _c , select (K _max -K _max ×p _d )×p _c sequence individuals from the (K _max -K _max ×p _d ) remaining individuals to perform random two-point crossover operation to generate (K _max -K _max ×p _d ) × p _c crossover individuals; Step S34: Perform single-point mutation operation on the remaining K _max -K _max ×p _d -(K _max -K _max ×p _d )×pc individuals to generate K _max -K _max ×p _d - ₍ K _max -K _max ×p _d )×p _c variant individuals; Step S35: A new strategy factor sequence population is formed by the elite individuals in step S32, the crossover individuals in step S33 and the mutant individuals in step S34; Step S36: Determine whether the output best evaluation value F(Y _* ) reaches the expected threshold Tr range, if F(Y _{* ) ≤Tr} , then F(Y _* )=F(X _* ), where Y _* is For the best strategy sequence combination, go to step S37, otherwise, go back to step S32; Step S37: Decode the optimal operating condition state sequence X _* , and output the multi-parameter expected operating operating condition time series. 6 . The method for designing multi-parameter operating conditions for automobiles based on hyperheuristic Markov chain evolution according to claim 5 , wherein the value of K _max in the step S31 is 24. 7 .

Images