A diesel engine intelligent design method and system based on combined intelligent algorithm

Abstract

The application provides a diesel engine intelligent design method and system based on a combined intelligent algorithm, an ellipsoid unit neural network model is constructed, structure design parameters and operation parameters are taken as inputs, fuel consumption rate and NO x / soot emission are taken as outputs, the ellipsoid unit neural network model is trained by using a data set, a diesel engine matching design model is obtained, a black eagle algorithm and an alpha wolf in a grey wolf algorithm are combined based on a particle swarm algorithm principle, local optimization is carried out, when falling into local optimization, migration in the black eagle algorithm and a beta wolf are combined to jump out of local optimization, a black eagle fusion algorithm is obtained, the obtained diesel engine matching design model is optimized by using the black eagle fusion algorithm, a Pareto solution is obtained, a diesel engine design scheme is determined based on design optimization requirements, and the application can balance the problems of fuel consumption rate and emission of the diesel engine, and suitable diesel engine design parameters can be selected according to design requirements.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of intelligent engine design, and particularly relates to a diesel engine intelligent design method and system based on a combined intelligent algorithm. BACKGROUND

[0002] The statements in this section merely provide background information related to the application and do not necessarily constitute prior art.

[0003] Large-bore high-speed diesel engines have the characteristics of high power density, good reliability and low comprehensive cost. Large-bore means larger piston area and longer stroke, which can inhale and compress more air in each working cycle, thereby burning more fuel to generate more power. This makes large-bore diesel engines very suitable for applications requiring high power output, and is widely used in large power machinery such as large agricultural machinery (such as tractors), ship power, engineering machinery, mining machinery, and power locomotives.

[0004] While large-bore diesel engines bring convenience, under the premise of ensuring power, economy and emissions are often contradictory. Diesel engines have good economy under high-temperature and high-pressure combustion conditions, but are prone to produce nitrogen oxides and soot emissions, polluting the environment. How to balance economy and emissions has become a difficult problem in the optimization design of diesel engines. SUMMARY

[0005] To solve the above problems, the application provides a diesel engine intelligent design method and system based on a combined intelligent algorithm. The application constructs an ellipsoid unit neural network model for diesel engine design matching, inputs relevant parameters, trains using the ellipsoid unit neural network, and obtains an intelligent design model. The grey wolf algorithm is improved based on the principle of particle swarm optimization, the alpha wolf in the grey wolf algorithm is combined with the capture in the black eagle algorithm for optimization, the beta wolf in the grey wolf algorithm is combined with the migration step in the black eagle algorithm to prevent falling into a local optimal solution, thereby obtaining a black eagle hybrid algorithm. The black eagle hybrid algorithm is used to optimize the intelligent design model to obtain a Pareto solution and obtain a diesel engine intelligent design scheme. The diesel engine fuel consumption rate and emission problems can be balanced, and appropriate diesel engine design parameters can be selected.

[0006] According to some embodiments, the application adopts the following technical scheme:

[0007] A diesel engine intelligent design method based on a combined intelligent algorithm, comprising the following steps:

[0008] Obtain diesel engine structure design parameters and operating parameters, calculate the fuel consumption rate, NO x / soot emission data, and construct a data set;

[0009] An ellipsoid unit neural network model is constructed, with structural design parameters and operating parameters as inputs, fuel consumption rate and NO x / soot emission as outputs, the ellipsoid unit neural network model is trained by using a data set to obtain a diesel engine matching design model;

[0010] Based on the principle of particle swarm algorithm, the capturing in the black eagle algorithm and the alpha wolf in the grey wolf algorithm are combined for local optimization. When falling into local optimization, the migration in the black eagle algorithm and the beta wolf are combined to jump out of local optimization, thereby obtaining a black eagle hybrid algorithm.

[0011] The black eagle hybrid algorithm is used to optimize the obtained diesel engine matching design model to obtain a Pareto solution. Based on design optimization requirements, a diesel engine design scheme is determined.

[0012] As an optional implementation, the operating parameters include rotational speed and torque, and the structural design parameters include intake swirl, combustion chamber diameter and injection pressure. According to different operating parameters and structural design parameters, fuel consumption rate and NO x / soot emission under this condition are obtained by simulation design, thereby constructing a data set.

[0013] As an optional implementation, the process of combining the capturing in the black eagle algorithm and the alpha wolf in the grey wolf algorithm includes: first, the black eagle algorithm is used for optimization to obtain a best position Then, the alpha wolf in the grey wolf algorithm and the capturing process in the black eagle algorithm are combined based on the principle of particle swarm algorithm to obtain a best position Two values are obtained each time, and the better position is selected by comparison

[0014] As an optional implementation, the alpha wolf in the grey wolf algorithm and the capturing process in the black eagle algorithm are combined, specifically as follows:

[0015]

[0016] wherein, is an updated agent position of the alpha wolf, is a black eagle position obtained by combining the black eagle algorithm and the capturing process in the grey wolf algorithm, is a current best position, D1 and D2 are position adjustment factors, represents a process variable, s0 is a column vector with a dimension of d, and the elements are between 0.5 and 1, represents the position of the i-th black eagle at the t-th update.

[0017] As an optional implementation, based on the principle of particle swarm algorithm, the formula of the black eagle grey wolf algorithm after fusion is combined with the particle swarm to obtain the following formula:

[0018]

[0019] wherein, is the position of the i th update of the combined algorithm at the t+1 th time, ω is the weight; k is the current iteration number; c1 and c2 are learning factors; rand is a random number between 0 and 1, is the optimal value of this update, is the optimal value obtained by the t th black eagle algorithm optimization;

[0020] comparison and find the optimal value judgment whether the number of consecutive times less than the set value exceeds the predetermined number of times, if yes, fall into local optimum, if not, return to the optimization process until falling into local optimum.

[0021] As an optional implementation, when the optimization process falls into local optimum, the migration in the black eagle algorithm is combined with the beta wolf to jump out of the process of local optimum, which is: the beta wolf in the grey wolf algorithm is integrated into the migration step in the black eagle algorithm to obtain the migration fusion formula:

[0022]

[0023] wherein, z(f) is a migration function, is the updated agent position of the beta wolf, is the position obtained after migration, the migration fusion formula is obtained, s1 is a column vector with dimension d, the elements of which are between-1 and 1, t2 is a random number from 0.4 to 1 formed by tent hybrid mapping;

[0024] Thus, the black eagle fusion algorithm is obtained.

[0025] As an optional implementation, the diesel engine matching design model constructed is optimized by using the obtained black eagle fusion algorithm to obtain the Pareto solution; and a design scheme is given according to different requirements of the diesel engine designer for economy or emission.

[0026] A diesel engine intelligent design system based on a combined intelligent algorithm, comprising:

[0027] A data set construction module configured to obtain diesel engine structure design parameters and operation parameters, calculate the data of fuel consumption rate, NO x / soot emission, and construct a data set;

[0028] A model construction module configured to construct an ellipsoid unit neural network model, taking the structure design parameters and operation parameters as inputs, and taking the fuel consumption rate and NO xThe carbon smoke emission is output, and the ellipsoid unit neural network model is trained by using the data set to obtain a diesel engine matching design model;

[0029] The algorithm fusion module is configured to combine the capturing in the black eagle algorithm and the alpha wolf in the grey wolf algorithm based on the particle swarm algorithm principle to perform local optimization, and combine the migration in the black eagle algorithm and the beta wolf to jump out of the local optimal solution when falling into the local optimal solution, so as to obtain the black eagle hybrid algorithm.

[0030] The optimization calculation module is configured to use the black eagle hybrid algorithm to optimize the obtained diesel engine matching design model to obtain a Pareto solution, and determine a diesel engine design scheme based on design optimization requirements.

[0031] A computer readable storage medium for storing computer instructions, which, when executed by a processor, completes the steps in the above method.

[0032] An electronic device comprising a memory and a processor, and computer instructions stored on the memory and running on the processor, which, when executed by the processor, complete the steps in the above method.

[0033] Compared with the prior art, the beneficial effects of the present application are:

[0034] The present application combines the capturing and migration mode of the black eagle algorithm with the grey wolf algorithm based on the particle swarm algorithm principle, can improve the global and local search ability, can prevent falling into the local optimal solution, jump out of the local optimal solution, is more likely to find the global optimal solution, effectively balances the search ability of the algorithm at different stages, enhances the local development ability, makes it faster to find the target solution in the middle and later stages of algorithm iteration, can promote the smooth transition of global and local search of the algorithm, improves the optimization performance and stability of the algorithm, and ensures the timeliness and accuracy of calculation.

[0035] The present application comprehensively considers the balance of diesel engine economy, fuel consumption and emission, monitors the changes of various performances, selects appropriate diesel engine design parameters, and has great positive effects on reducing pollutant emission, protecting the environment and improving economic benefits.

[0036] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the following preferred embodiments are described in detail below, and the accompanying drawings are described as follows. BRIEF DESCRIPTION OF DRAWINGS

[0037] The drawings accompanying the specification of the present application form a part of the present application, and the schematic embodiments of the present application and the description thereof are used to explain the present application, and do not constitute an improper limitation on the present application.

[0038] Figure 1The present invention is a flowchart of a method according to an embodiment. DETAILED DESCRIPTION

[0039] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0040] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0041] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0042] In the absence of conflict, the embodiments and features in the embodiments of this application can be combined with each other.

[0043] Example 1

[0044] A diesel engine intelligent design method based on combined intelligent algorithm, such as Figure 1 As shown, the following steps are included:

[0045] Obtain the diesel engine structural design parameters and operating parameters, calculate the fuel consumption rate, NO x / Carbon smoke emission data, build a data set;

[0046] Construct an ellipsoid unit neural network model, taking the structural design parameters and operating parameters as input, fuel consumption rate and NO x / soot emissions are output, and the ellipsoidal unit neural network model is trained using the data set to obtain a diesel engine matching design model;

[0047] Based on the principle of particle swarm optimization, the capture in the Black Hawk algorithm is combined with the α wolf in the Gray Wolf algorithm to perform local optimization. When trapped in the local optimum, the migration in the Black Hawk algorithm is combined with the β wolf to jump out of the local optimum, thus obtaining the Black Hawk fusion algorithm.

[0048] The Black Hawk fusion algorithm is used to optimize the obtained diesel engine matching design model to obtain a Pareto solution, and the diesel engine design scheme is determined based on the design optimization requirements.

[0049] The key points in the above process are:

[0050] Based on the principles of the particle swarm optimization (PSO), the capture and migration methods of the Black Hawk algorithm are integrated with the Gray Wolf algorithm to create the Black Hawk Fusion Algorithm. Based on the Black Hawk Fusion Algorithm, appropriate parameters are selected, specifically speed, torque, intake swirl, combustion chamber diameter, and injection pressure, to obtain the corresponding Pareto solution and select the appropriate solution.

[0051] The Black Hawk algorithm process mainly includes searching, capturing and migrating;

[0052] The Black Hawk search formula in the Black Hawk algorithm is:

[0053]

[0054]

[0055] Where: X r t is a random position in the search space updated at the tth time, X i t is the tth update of the position of a random black hawk, X t best It is the best position at present. t and i are in D-dimensional space, t is the number of cycles, i is the number of black hawks, and D is X t best The farthest distance to the search boundary, r1 is a random number from 0 to 1, t1 is a random number from 0 to 1 formed by tent mapping, ub and lb are the upper and lower limits of the search space respectively. All are intermediate variables, and minimizefitness is to minimize fitness;

[0056] The Black Hawk capture formula in the Black Hawk algorithm is:

[0057]

[0058] Where: D1 is the position adjustment factor 1; D2 is the position adjustment factor 2, X i * Represents the position after the first adjustment, s0 is a column vector of dimension d, and its elements are between (0.5, 1).

[0059] The Black Hawk migration formula in the Black Hawk algorithm is:

[0060]

[0061] in, f bestis the current best fitness value, f(j) is the fitness value of the jth individual. s1 is a column vector of dimension d with elements between -1 and 1, t2 is a random number from 0.4 to 1 formed by the tent hybrid mapping.

[0062] At the same time, the original black hawk algorithm is optimized to obtain the best position Based on the principle of particle swarm optimization algorithm, the alpha wolf in the grey wolf algorithm is combined with the capture process in the black hawk algorithm to optimize and obtain the best position Two values are obtained each time, and the better position is selected by comparison, which is:

[0063] The alpha wolf is combined with the capture process of the black hawk algorithm, and the formula is as follows:

[0064]

[0065] Among them is the updated agent position of alpha wolf, is the black hawk position obtained by combining the capture process of the black hawk algorithm and the grey wolf algorithm.

[0066] Then based on the principle of particle swarm optimization algorithm, the formula of the combined black hawk grey wolf algorithm is combined with the particle swarm to obtain the following formula:

[0067]

[0068] Among them, is the position of the combined algorithm at the i-th update t+1 time, ω is the weight; ω is the weight; k is the current iteration number; c1 and c2 are learning factors, also known as acceleration constants, usually c1=c2=2; rand is a random number between 0 and 1, is the best value obtained by optimization based on the principle of particle swarm optimization algorithm, is the best value obtained by optimization of the black hawk algorithm at the tth time.

[0069] Compare and find the best value

[0070] Select the optimal value of both Judge whether the continuous number of times exceeds the first set value, i.e. NP times, NP can be determined according to the demand value;

[0071] If not, return to the optimization process of the capture link calculation according to the black hawk algorithm and the black hawk fusion algorithm, until The continuous number of times is greater than NP, at this time the algorithm falls into local optimum, and the migration step of the grey wolf algorithm is integrated into the black hawk algorithm.

[0072] In order to prevent falling into the local optimal solution, the Gray Wolf Algorithm is now integrated into the migration step of the Black Hawk Algorithm to obtain the migration fusion formula:

[0073] Bring in beta wolf position:

[0074]

[0075] Where z(f) is the migration function, Is the beta wolf updating agent position, is the position obtained after migration, and the migration fusion formula is obtained. t2 is a random number from 0.4 to 1.

[0076] It is manifested as updating the individual position of the black hawk according to the black hawk algorithm, updating the individual position of the β wolf according to the gray wolf algorithm, and bringing the β wolf individual into the black hawk migration formula to perform the migration operation.

[0077] This strategy is executed NF times until NF> the second set value, i.e. Tt, at which point the optimal value is found, thus obtaining the Black Hawk fusion algorithm.

[0078] The obtained Black Hawk fusion algorithm is used to optimize the constructed diesel engine design matching model, thereby obtaining the Pareto solution. Based on the Pareto solution, the required diesel engine design scheme is obtained from the perspective of economy or emission.

[0079] Example 2

[0080] A diesel engine intelligent design system based on a combined intelligent algorithm, comprising:

[0081] The data set construction module is configured to obtain the diesel engine structural design parameters and operating parameters, calculate the fuel consumption rate, NO x / Carbon smoke emission data, build a data set;

[0082] The model building module is configured to build an ellipsoidal unit neural network model, taking the structural design parameters and operating parameters as input, fuel consumption rate and NO x / soot emissions are output, and the ellipsoidal unit neural network model is trained using the data set to obtain a diesel engine matching design model;

[0083] The algorithm fusion module is configured to combine the capture in the Black Hawk algorithm with the α wolf in the Gray Wolf algorithm based on the principle of the particle swarm algorithm to perform local optimization. When trapped in the local optimum, the migration in the Black Hawk algorithm is combined with the β wolf to jump out of the local optimum, thus obtaining the Black Hawk fusion algorithm.

[0084] The optimization calculation module is configured to use the Black Hawk fusion algorithm to optimize the obtained diesel engine matching design model to obtain a Pareto solution, and determine the diesel engine design scheme based on the design optimization requirements.

[0085] Embodiment three

[0086] Embodiment three of the present application provides a computer readable storage medium, having stored thereon a program, which when executed by a processor implements the steps of the diesel engine intelligent design method based on combined intelligent algorithm as described in embodiment one of the present application.

[0087] Embodiment four

[0088] Embodiment four of the present application provides an electronic device, comprising a memory and a processor, and computer instructions stored in the memory and running on the processor, which when executed by the processor implements the steps of the diesel engine intelligent design method based on combined intelligent algorithm as described in embodiment one of the present application.

[0089] Those skilled in the art will understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) containing computer usable program code.

[0090] The present application is described with reference to the flowcharts and / or block diagrams of the methods, apparatus (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions, which are executed via the processor of the computer or other programmable data processing apparatus, generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in a flow or multiple flows and / or blocks Figure 1 The means for implementing the functions specified in a flow or multiple flows and / or blocks.

[0091] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer readable memory produce a product including an instruction means, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in a flow or multiple flows and / or blocks Figure 1 The means for implementing the functions specified in a flow or multiple flows and / or blocks.

[0092] These computer program instructions can also be loaded into a computer or other programmable data processing devices, so that a series of operational steps are generated to realize the computer-implemented processes in the computer or other programmable devices, and the instructions executed in the computer or other programmable devices provide steps for implementing the functions specified in the flowchart Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0093] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Various modifications and changes can be made by those skilled in the art without departing from the spirit and principles of the present application. Any modifications, equivalent replacements, improvements, etc. made by those skilled in the art without creative labor within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A diesel engine intelligent design method based on a combined intelligent algorithm, characterized in that: The following steps are involved: Obtain the diesel engine structural design parameters and operating parameters, calculate the fuel consumption rate, / Carbon smoke emission data, build a data set; Construct an ellipsoid unit neural network model, taking the structural design parameters and operating parameters as input, and calculate the fuel consumption rate and / soot emissions are output, and the ellipsoidal unit neural network model is trained using the data set to obtain a diesel engine matching design model; Based on the principle of particle swarm optimization, the capture in the Black Hawk algorithm is combined with the α wolf in the Gray Wolf algorithm to perform local optimization. When trapped in the local optimum, the migration in the Black Hawk algorithm is combined with the β wolf to jump out of the local optimum, thus obtaining the Black Hawk fusion algorithm. Utilizing the Black Hawk fusion algorithm, the obtained diesel engine matching design model is optimized to obtain a Pareto solution, and based on the design optimization requirements, a diesel engine design solution is determined; The process of combining the capture in the Black Hawk algorithm with the α wolf in the Gray Wolf algorithm includes: firstly, using the Black Hawk algorithm to search for the best position Then, based on the particle swarm algorithm principle, the α wolf in the gray wolf algorithm is combined with the capture process in the black hawk algorithm to find the best position. , each loop gets two values, compares the two and selects the better position ; Combine the α wolf in the gray wolf algorithm with the capture process in the black hawk algorithm, specifically: in, is the α wolf updating agent position, It is the position of the black hawk obtained by combining the capture process in the black hawk algorithm and the gray wolf algorithm. It is the best position at present. 、 is the position adjustment factor, represents the process variable, The dimension is A column vector whose elements are in between, represents the position of the i-th Black Hawk at the t-th update; Based on the principle of particle swarm optimization, the formula after the fusion of Black Hawk Gray Wolf algorithm and particle swarm optimization is combined to obtain the following formula: in, is the position of the combined algorithm at the ith update t+1 time, is the weight; t is the current iteration number; and is the learning factor; is a random number between [0,1], This is the best value for this update. is the best value obtained by the tth Black Hawk algorithm optimization; Compare and Find the best value ,judge Whether the number of consecutive times that the value is less than the set value exceeds the predetermined number, if so, it falls into the local optimum, otherwise it returns to the optimization process until it falls into the local optimum; When the optimization process falls into a local optimum, the migration in the Black Hawk algorithm is combined with the β wolf to jump out of the local optimum. The process is: Integrate the β wolf in the gray wolf algorithm into the migration step in the Black Hawk algorithm, and obtain the migration fusion formula: in, is the migration function, Is the beta wolf updating agent position, is the position obtained after migration, and the migration fusion formula is obtained. The dimension is A column vector whose elements are between -1 and 1, is a random number from 0.4 to 1 formed by the tent blending map; Update the individual positions of black hawks according to the black hawk algorithm, update the individual positions of β wolves according to the gray wolf algorithm, and bring the β wolves into the black hawk migration formula to perform migration operations; This results in the Black Hawk fusion algorithm.

2. The diesel engine intelligent design method based on the combined intelligent algorithm as claimed in claim 1, characterized in that: The operating parameters include speed and torque, and the structural design parameters include intake swirl, combustion chamber diameter and injection pressure. According to different operating parameters and structural design parameters, the fuel consumption rate and / carbon smoke emissions, thereby constructing a data set.

3. The diesel engine intelligent design method based on a combined intelligent algorithm as claimed in claim 1, characterized in that: The obtained Black Hawk fusion algorithm is used to optimize the diesel engine matching design model and obtain the Pareto solution. Design solutions are provided based on the different requirements of diesel engine designers for economy or emissions.

4. A diesel engine intelligent design system based on a combined intelligent algorithm, characterized by: include: The data set construction module is configured to obtain the diesel engine structural design parameters and operating parameters, calculate the fuel consumption rate, / Carbon smoke emission data, build a data set; The model building module is configured to build an ellipsoid unit neural network model, taking the structural design parameters and operating parameters as input, fuel consumption rate and / soot emissions are output, and the ellipsoidal unit neural network model is trained using the data set to obtain a diesel engine matching design model; The algorithm fusion module is configured to combine the capture in the Black Hawk algorithm with the α wolf in the Gray Wolf algorithm based on the principle of the particle swarm algorithm to perform local optimization. When trapped in the local optimum, the migration in the Black Hawk algorithm is combined with the β wolf to jump out of the local optimum, thus obtaining the Black Hawk fusion algorithm. an optimization calculation module configured to optimize the obtained diesel engine matching design model using the Black Hawk fusion algorithm to obtain a Pareto solution, and thereby determine a diesel engine design solution based on the design optimization requirements; The process of combining the capture in the Black Hawk algorithm with the α wolf in the Gray Wolf algorithm includes: firstly, using the Black Hawk algorithm to search for the best position Then, based on the particle swarm algorithm principle, the α wolf in the gray wolf algorithm is combined with the capture process in the black hawk algorithm to find the best position. , each loop gets two values, compares the two and selects the better position ; Combine the α wolf in the gray wolf algorithm with the capture process in the black hawk algorithm, specifically: in, is the α wolf updating agent position, It is the position of the black hawk obtained by combining the capture process in the black hawk algorithm and the gray wolf algorithm. It is the best position at present. 、 is the position adjustment factor, represents the process variable, The dimension is A column vector whose elements are in between, represents the position of the i-th Black Hawk at the t-th update; Based on the principle of particle swarm optimization, the formula after the fusion of Black Hawk Gray Wolf algorithm and particle swarm optimization is combined to obtain the following formula: in, is the position of the combined algorithm at the ith update t+1 time, is the weight; t is the current iteration number; and is the learning factor; is a random number between [0,1], This is the best value for this update. is the best value obtained by the tth Black Hawk algorithm optimization; Compare and Find the best value ,judge Whether the number of consecutive times that the value is less than the set value exceeds the predetermined number, if so, it falls into the local optimum, otherwise it returns to the optimization process until it falls into the local optimum; When the optimization process falls into a local optimum, the migration in the Black Hawk algorithm is combined with the β wolf to jump out of the local optimum. The process is: Integrate the β wolf in the gray wolf algorithm into the migration step in the Black Hawk algorithm, and obtain the migration fusion formula: in, is the migration function, Is the beta wolf updating agent position, is the position obtained after migration, and the migration fusion formula is obtained. The dimension is A column vector whose elements are between -1 and 1, is a random number from 0.4 to 1 formed by the tent blending map; Update the individual positions of black hawks according to the black hawk algorithm, update the individual positions of β wolves according to the gray wolf algorithm, and bring the β wolves into the black hawk migration formula to perform migration operations; This results in the Black Hawk fusion algorithm.

5. A computer-readable storage medium, characterized in that: Used to store computer instructions, which, when executed by a processor, complete the steps of the method according to any one of claims 1 to 3.

6. An electronic device, characterized in that: The method comprises a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein the steps of the method according to any one of claims 1 to 3 are completed when the computer instructions are executed by the processor.

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