Gearbox parameter optimization method and system based on combined intelligent algorithm

Abstract

The present invention provides a transmission parameter optimization method and system based on a combined intelligent algorithm, wherein the transmission ratios of each gear distributed in a geometric progression are obtained according to the number of gears in the transmission, the maximum transmission ratio and the minimum transmission ratio; the transmission ratios of each gear distributed in a geometric progression are subjected to a certain value range and a value step length to obtain a combination scheme of the transmission ratios of the transmission gearbox; according to the obtained combination scheme of the transmission ratios of the transmission gearbox and a preset dynamic performance model and a fuel economy model, a multi-objective optimization is performed based on a combined algorithm of an ant colony algorithm and an artificial fish swarm algorithm to determine a Pareto optimal solution, and the best transmission ratio scheme of the transmission gearbox is obtained according to different selections of requirements; the present invention combines the ant colony algorithm that can quickly converge to a local optimal value and the artificial fish swarm algorithm that can obtain a wide range of robustness advantages, so as to form a combined intelligent algorithm, take dynamic performance and fuel economy into consideration, realize more accurate and rapid multi-objective optimization, and obtain the optimal matching scheme of the transmission gearbox efficiently and accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of gearbox design optimization, and in particular to a gearbox parameter optimization method and system based on a combined intelligent algorithm. Background Art

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

[0003] The gearbox can change the speed and torque of the power output by the internal combustion engine. The matching of the gearbox ratio directly determines the power and fuel economy of the whole vehicle. Due to the complex actual operating conditions, heavy commercial vehicles and tractors have a large number of gears, generally up to 20 to 40 gears, which makes the combination of gearbox ratios increase exponentially.

[0004] The inventors found that in the existing gearbox design process, engineers mostly determine gearbox parameters based on experiments and formula calculations, which takes a long time to develop and has a high error rate, greatly increasing the difficulty of obtaining the optimal gearbox matching solution. Summary of the invention

[0005] In order to address the deficiencies in the prior art, the present invention provides a gearbox parameter optimization method and system based on a combined intelligent algorithm, which integrates the ant colony algorithm with the artificial fish swarm algorithm, and combines the advantages of the ant colony algorithm that can quickly converge to the local optimal value and the artificial fish swarm algorithm that can obtain extensive robustness to form a combined intelligent algorithm. Multi-objective optimization is performed considering power and fuel economy, and the optimal matching solution for the gearbox is obtained efficiently and accurately.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] A first aspect of the present invention provides a transmission parameter optimization method based on a combined intelligent algorithm.

[0008] A gearbox parameter optimization method based on a combined intelligent algorithm includes the following processes:

[0009] Determine the number of gears, maximum transmission ratio and minimum transmission ratio of the gearbox;

[0010] According to the number of gears in the gearbox, the maximum transmission ratio and the minimum transmission ratio, the transmission ratio of each gear is obtained in a geometric progression;

[0011] According to a certain value range and value step length, the transmission ratio combination scheme of the gearbox is obtained by assigning the transmission ratio of each gear position of equal ratio series;

[0012] According to the obtained transmission ratio combination scheme and the preset dynamic model and fuel economy model, multi-objective optimization is performed based on the combined algorithm of ant colony algorithm and artificial fish swarm algorithm to determine the Pareto optimal solution, and the best transmission ratio scheme is obtained according to different selections of needs.

[0013] As an optional implementation method, based on the combination scheme of each gearbox ratio, an acceleration performance simulation test and a 100km fuel consumption performance simulation test are carried out to obtain power performance samples and fuel economy performance samples under each gearbox ratio scheme;

[0014] The power performance samples are used to train the support vector machine to obtain the power model; the fuel economy performance samples are used to train the support vector machine to obtain the fuel economy model.

[0015] As an optional implementation, in the combined algorithm based on the ant colony algorithm and the artificial fish swarm algorithm, the foraging behavior includes:

[0016] When the artificial fish searches and finds other positions X in the field of view k Nutrient concentration Y k X than the current position j When it is high, it moves to X k Randomly walk a certain distance in the direction of

[0017] If there are multiple locations X that meet the conditions within the field of view of the artificial fish e , combine the fish swarm algorithm concentration Y with the ant colony algorithm to calculate the transfer probability of each point:

[0018]

[0019] in, From X j Click to X e The transition probability, From X j Click to X e The pheromone concentration, Y e For X e Point nutrient concentration, For X j With X e The distance between them, α is the pheromone importance factor, β is the nutrient concentration and distance importance factor;

[0020] The destination of foraging behavior is all feasible points X e In , the point corresponding to the position with the largest transition probability is:

[0021]

[0022] From this, the direction of foraging behavior is obtained;

[0023] The artificial fish performed foraging behaviors including:

[0024]

[0025] Among them, X next is the next position of the artificial fish, X j is the current position of the artificial fish, For X j With X k The distance between them, step is the maximum step length of the artificial fish, and rand is a random number between [0,1].

[0026] As an optional implementation method, in the combined algorithm based on the ant colony algorithm and the artificial fish swarm algorithm, the swarming behavior includes:

[0027] The pheromone concentration of the ant colony algorithm is integrated into the clustering behavior of the fish swarm algorithm, and the center position of the artificial fish swarm is X c Nutrient concentration Y c Than the nutrient concentration Y at the current location j High, the artificial fish is based on the number of artificial fish n within its field of view f Make a grouping behavior judgment. If there are other artificial fish in the field of vision and their central position has a higher concentration of the influencing substance and meets the grouping conditions, they will swim towards the central position;

[0028] The conditions for clustering behavior are:

[0029] Y c / n f >Y j ·δ

[0030] Among them, n f is the number of artificial fish within its field of view, Y c is the nutrient concentration at the center of the fish school, Y j is the nutrient concentration at the current location, and δ is the crowdedness of the location itself;

[0031] If the condition Y for grouping behavior is met c / n f >Y j ·δ, then the center position of the artificial fish school is X c Without crowding, the artificial fish performed schooling behavior;

[0032] Group behavior, including:

[0033]

[0034] Among them, X nextis the next position of the artificial fish, X j is the current position of the artificial fish, For X j With X c The distance between them, step is the maximum step length of the artificial fish, and rand is a random number between [0,1]. is the pheromone concentration;

[0035] Calculate the foraging behavior target point X respectively k Nutrient concentration Y k and the clustering behavior target point X c Nutrient concentration Y c , if Y k >Y c , the artificial fish will forage for food; otherwise, the artificial fish will flock together.

[0036] As an optional implementation, the transmission ratios of the gears allocated in geometric progression are:

[0037]

[0038] Among them, i max is the maximum transmission ratio, i min is the minimum transmission ratio, m is the number of gears in the gearbox, and k is the gear number;

[0039] Determine the value range of each gear as [0.7i k ,1.3i k ], with a step size of 0.1i k , filter and keep the ones that meet The gearbox ratio scheme of the inequality is a combination scheme of gearbox ratios.

[0040] A second aspect of the present invention provides a gearbox parameter optimization system based on a combined intelligent algorithm.

[0041] A gearbox parameter optimization system based on a combined intelligent algorithm, comprising:

[0042] The initial parameter determination module is configured to: determine the number of gears of the transmission, the maximum transmission ratio and the minimum transmission ratio;

[0043] The transmission ratio geometric progression allocation module is configured to obtain the transmission ratio of each gear of the geometric progression allocation according to the number of gears of the gearbox, the maximum transmission ratio and the minimum transmission ratio;

[0044] The combination scheme acquisition module is configured to: obtain a combination scheme of the gearbox transmission ratio according to a certain value range and value step length for each gear ratio allocated with equal ratio progression;

[0045] The optimal gearbox transmission ratio scheme generation module is configured as follows: based on the acquired gearbox transmission ratio combination scheme and the preset dynamic model and fuel economy model, a multi-objective optimization is performed based on the combined algorithm of the ant colony algorithm and the artificial fish swarm algorithm to determine the Pareto optimal solution, and the optimal gearbox transmission ratio scheme is obtained according to different selections of needs.

[0046] A third aspect of the present invention is a computer-readable storage medium having a program stored thereon, wherein when the program is executed by a processor, the program implements the steps in the gearbox parameter optimization method based on a combined intelligent algorithm as described in the first aspect of the present invention.

[0047] The fourth aspect of the present invention provides an electronic device, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the transmission parameter optimization method based on a combined intelligent algorithm as described in the first aspect of the present invention are implemented.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. The gearbox parameter optimization method and system of the present invention is based on a combined intelligent algorithm, which integrates the ant colony algorithm and the artificial fish swarm algorithm, combines the ant colony algorithm to quickly converge to the local optimal value and the artificial fish swarm algorithm to obtain a wide range of robustness advantages, and forms a combined intelligent algorithm. It takes into account power and fuel economy, realizes more accurate and rapid multi-objective optimization, and obtains the optimal matching solution for the gearbox efficiently and accurately.

[0050] 2. The transmission parameter optimization method and system of the present invention is based on a combined intelligent algorithm. The trained fuel economy performance SVM and power performance SVM are used as prediction targets, and a combined intelligent algorithm is used for multi-objective optimization to obtain the corresponding Pareto solution. According to the different requirements of the vehicle or tractor for power and economy, the best transmission ratio scheme of the transmission is selected and obtained, thereby improving the efficiency and accuracy of the transmission ratio scheme selection of the transmission.

[0051] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0053] Figure 1 A flow chart of a transmission parameter optimization method based on a combined intelligent algorithm provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0054] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0055] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0056] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0057] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0058] Embodiment 1:

[0059] like Figure 1 As shown, Example 1 of the present invention provides a gearbox parameter optimization method based on a combined intelligent algorithm, which combines the transfer probability of the ant colony algorithm with the foraging behavior of the artificial fish swarm algorithm, and integrates the pheromone concentration of the ant colony algorithm into the clustering behavior of the fish swarm algorithm, so as to combine the ant colony algorithm's ability to quickly converge to a local optimal value with the artificial fish swarm algorithm's ability to obtain a wide range of robustness advantages.

[0060] The ant colony algorithm is inspired by the foraging behavior of ants in nature. When searching for food, ants release a pheromone along the path they pass through. The pheromone accumulates and evaporates along the path over time. Other ants have a higher probability of choosing a direction with a high pheromone concentration and continue to increase the pheromone concentration on the path.

[0061] The pheromone concentration update formula is:

[0062]

[0063] Among them, the pheromone volatility constant of the ant colony algorithm is rho, τ mn is the pheromone concentration on the path from point m to point n, s is the serial number of the ant, q is the total number of ants, is the change in pheromone concentration on the path from point m to point n for the sth ant pair.

[0064] The artificial fish swarm algorithm is inspired by the behavior of fish in nature in finding food. The behavior of artificial fish in the optimization process includes foraging behavior and clustering behavior. The transition probability of the ant colony algorithm is combined with the foraging behavior of the artificial fish swarm algorithm, and the pheromone concentration of the ant colony algorithm is integrated into the clustering behavior of the fish swarm algorithm.

[0065] S1: The transfer probability of the ant colony algorithm is combined with the foraging behavior of the artificial fish swarm algorithm to find other positions X in the field of view when the artificial fish searches. k Nutrient concentration Y k X than the current position j When it is high, it moves to X k The artificial fish swims randomly for a certain distance in the direction of e , e is the serial number of each point that meets the conditions, e=1, 2...w, w is the total number of points that meet the conditions.

[0066] Combine the fish swarm algorithm concentration Y with the ant colony algorithm formula to calculate the transfer probability of each point:

[0067]

[0068] in, From X j Click to X e The transition probability, From X j Click to X e The pheromone concentration, Y e For X e Point nutrient concentration, For X j With X e The distance between them, α is the pheromone importance factor, β is the nutrient concentration and distance importance factor;

[0069] Then the destination of the foraging behavior is all feasible points X e In , the point corresponding to the position with the largest transition probability is:

[0070]

[0071] The direction X of the foraging behavior is obtained from this k .

[0072] The formula for the artificial fish's foraging behavior is:

[0073]

[0074] Among them, X next is the next position of the artificial fish, X j is the current position of the artificial fish, For Xj With X k The distance between them, step is the maximum step length of the artificial fish, rand is a random number between [0,1] S2: The pheromone concentration of the ant colony algorithm is integrated into the clustering behavior of the fish swarm algorithm, and the center position of the artificial fish swarm is X c Nutrient concentration Y c Than the nutrient concentration Y at the current location j High, the artificial fish is based on the number of artificial fish n within its field of view f The clustering behavior is judged. If there are other artificial fish in the field of vision and their central position has a higher concentration of the influencing substance and meets the clustering conditions, they will swim towards the central position.

[0075] The conditions for clustering behavior are:

[0076] Y c / n f >Y j ·δ (5)

[0077] Among them, n f is the number of artificial fish within its field of view, Y c is the nutrient concentration at the center of the fish school, Y j is the nutrient concentration at the current location, and δ is the crowdedness of the location itself.

[0078] If the condition Y for grouping behavior is met c / n f >Y j ·δ, then represents the center position X of the artificial fish school c Without crowding, the artificial fish performed schooling behavior.

[0079] The relevant formula for clustering behavior is:

[0080]

[0081] Among them, X next is the next position of the artificial fish, X j is the current position of the artificial fish, For X j With X c The distance between them, step is the maximum step length of the artificial fish, and rand is a random number between [0, 1]. is the pheromone concentration.

[0082] Calculate the foraging behavior target point X respectively k Nutrient concentration Y k and the clustering behavior target point X c Nutrient concentration Y c, if Y k >Y c , the artificial fish will forage for food; otherwise, the artificial fish will flock together.

[0083] Thus, a combined intelligent algorithm that integrates the ant colony algorithm and the artificial fish swarm algorithm is obtained.

[0084] In this embodiment, the vehicle or tractor needs to meet its working requirements, such as the vehicle needs to meet the requirements of climbing and high-speed driving, and the tractor needs to meet the requirements of plowing, sowing and other operations. The maximum transmission ratio i is determined according to these requirements. max , minimum transmission ratio i min , number of gearbox gears: m.

[0085] Initially, the gears are allocated in geometric progression, so the transmission ratio of each gear is

[0086]

[0087] Among them, k is the gear number.

[0088] The transmission ratio of the gearbox is optimized and the value range of each gear is determined to be [0.7i k ,1.3i k ], with a step size of 0.1i k , filter and keep the ones that meet The inequality gearbox ratio scheme is brought into the simulation software for acceleration performance and 100km fuel consumption performance tests, and the vehicle or tractor power performance and fuel economy performance samples under each gearbox ratio scheme can be obtained.

[0089] Furthermore, the support vector machine (SVM) is used to train the samples, using the radial basis kernel function, and the formula is:

[0090]

[0091] Among them, X i , X j is the input variable, σ 2 is the variance.

[0092] The samples are normalized, and 80% of the samples are selected as the training set and 20% of the samples are selected as the test set to determine whether the trained SVM meets the accuracy. If the accuracy is met, the trained power performance SVM prediction model and fuel economy performance SVM prediction model are obtained.

[0093] The trained fuel economy performance SVM and power performance SVM are used as prediction targets, and a combined intelligent algorithm is used for multi-objective optimization to obtain the corresponding Pareto solution. According to the different requirements of vehicles or tractors for power and economy, the optimal gearbox ratio scheme is selected and obtained.

[0094] Embodiment 2:

[0095] Embodiment 2 of the present invention provides a gearbox parameter optimization system based on a combined intelligent algorithm, comprising:

[0096] The initial parameter determination module is configured to: determine the number of gears of the transmission, the maximum transmission ratio and the minimum transmission ratio;

[0097] The transmission ratio geometric progression allocation module is configured to: obtain a combination scheme of the gearbox transmission ratio according to a certain value range and value step length for each gear ratio of the geometric progression allocation;

[0098] The combination scheme acquisition module is configured to: determine the value range of each gear according to the gear ratio of each gear assigned by the geometric series, and obtain the combination scheme of the gearbox transmission ratio;

[0099] The optimal gearbox transmission ratio scheme generation module is configured as follows: based on the acquired gearbox transmission ratio combination scheme and the preset dynamic model and fuel economy model, a multi-objective optimization is performed based on the combined algorithm of the ant colony algorithm and the artificial fish swarm algorithm to determine the Pareto optimal solution, and the optimal gearbox transmission ratio scheme is obtained according to different selections of needs.

[0100] The working method of the system is the same as the gearbox parameter optimization method based on the combined intelligent algorithm provided in Example 1, and will not be repeated here.

[0101] Embodiment 3:

[0102] Embodiment 3 of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the transmission parameter optimization method based on a combined intelligent algorithm as described in Embodiment 1 of the present invention.

[0103] Embodiment 4:

[0104] Embodiment 4 of the present invention provides an electronic device, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the transmission parameter optimization method based on the combined intelligent algorithm as described in Embodiment 1 of the present invention are implemented.

[0105] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0106] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, 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 device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0107] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0108] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0109] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0110] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A gearbox parameter optimization method based on a combined intelligent algorithm, characterized in that: The process includes: Determine the number of gears, maximum transmission ratio and minimum transmission ratio of the gearbox; According to the number of gears in the gearbox, the maximum transmission ratio and the minimum transmission ratio, the transmission ratio of each gear is obtained in a geometric progression; According to a certain value range and value step length, the transmission ratio combination scheme of the gearbox is obtained by assigning the transmission ratio of each gear position of equal ratio series; According to the obtained gearbox ratio combination scheme and the preset power model and fuel economy model, a multi-objective optimization is performed based on the combined algorithm of the ant colony algorithm and the artificial fish swarm algorithm to determine the Pareto optimal solution, and the best gearbox ratio scheme is obtained according to different selections of needs; Based on the combination scheme of each gearbox ratio, the acceleration performance simulation test and the 100km fuel consumption performance simulation test are carried out to obtain the power performance samples and fuel economy performance samples under each gearbox ratio scheme; Using the power performance samples, the support vector machine is used for training to obtain the power model; using the fuel economy performance samples, the support vector machine is used for training to obtain the fuel economy model; The transition probability of the ant colony algorithm is combined with the foraging behavior of the artificial fish swarm algorithm, and the pheromone concentration of the ant colony algorithm is integrated into the clustering behavior of the fish swarm algorithm, thus obtaining a combined algorithm that integrates the ant colony algorithm and the artificial fish swarm algorithm.

2. The gearbox parameter optimization method based on combined intelligent algorithm according to claim 1, characterized in that: In the combined algorithm based on ant colony algorithm and artificial fish swarm algorithm, foraging behavior, include: When the artificial fish searches and finds other positions X in the field of view k Nutrient concentration Y k X than the current position j When it is high, it moves to X k Randomly walk a certain distance in the direction of If there are multiple locations X that meet the conditions within the field of view of the artificial fish e , combine the fish swarm algorithm concentration Y with the ant colony algorithm to calculate the transfer probability of each point: in, From X j Click to X e The transition probability, From X j Click to X e The pheromone concentration, Y e For X e Point nutrient concentration, For X j With X e The distance between them, α is the pheromone importance factor, β is the nutrient concentration and distance importance factor; The destination of foraging behavior is all feasible points X e In , the point corresponding to the position with the largest transition probability is: From this, the direction of foraging behavior is obtained; The artificial fish performed foraging behaviors including: Among them, X next is the next position of the artificial fish, X j is the current position of the artificial fish, For X j With X k The distance between them, step is the maximum step length of the artificial fish, and rand is a random number between [0,1].

3. The gearbox parameter optimization method based on combined intelligent algorithm according to claim 2, characterized in that: In the combined algorithm based on the ant colony algorithm and the artificial fish swarm algorithm, the clustering behavior includes: The pheromone concentration of the ant colony algorithm is integrated into the clustering behavior of the fish swarm algorithm, and the center position of the artificial fish swarm is X c Nutrient concentration Y c Compared to the nutrient concentration Y at the current location j High, the artificial fish is based on the number of artificial fish n within its field of view f Make a grouping behavior judgment. If there are other artificial fish in the field of vision and their central position has a higher concentration of the influencing substance and meets the grouping conditions, they will swim towards the central position; The conditions for clustering behavior are: AND c / n f >And j ·δ Among them, n f is the number of artificial fish within its field of view, Y c is the nutrient concentration at the center of the fish school, Y j is the nutrient concentration at the current location, δ is the crowdedness of the location itself; If the condition Y for grouping behavior is met c / n f >Y j ·δ, then the center position of the artificial fish school is X c Without crowding, the artificial fish performed schooling behavior; Group behavior, including: Among them, X next is the next position of the artificial fish, X j is the current position of the artificial fish, For X j With X c The distance between them, step is the maximum step length of the artificial fish, and rand is a random number between [0,1]. is the pheromone concentration; Calculate the foraging behavior target point X respectively k Nutrient concentration Y k and the clustering behavior target point X c Nutrient concentration Y c , if Y k >Y c , the artificial fish will forage for food; otherwise, the artificial fish will flock together.

4. The gearbox parameter optimization method based on combined intelligent algorithm according to claim 1, characterized in that: The transmission ratios of each gear in geometric progression are: Among them, i max is the maximum transmission ratio, i min is the minimum transmission ratio, m is the number of gears in the gearbox, and k is the gear number; Determine the value range of each gear as [0.7i k ,1.3i k ], with a step size of 0.1i k , filter and keep the ones that meet The gearbox ratio scheme of the inequality is a combination scheme of gearbox ratios.

5. A gearbox parameter optimization system based on a combined intelligent algorithm, characterized in that: include: The initial parameter determination module is configured to: determine the number of gears of the transmission, the maximum transmission ratio and the minimum transmission ratio; The transmission ratio geometric progression allocation module is configured to obtain the transmission ratio of each gear of the geometric progression allocation according to the number of gears of the gearbox, the maximum transmission ratio and the minimum transmission ratio; The combination scheme acquisition module is configured to: obtain a combination scheme of the gearbox transmission ratio according to a certain value range and value step length for each gear ratio allocated with equal ratio progression; The optimal gearbox transmission ratio scheme generation module is configured to: determine the Pareto optimal solution by multi-objective optimization based on the combination algorithm of the ant colony algorithm and the artificial fish swarm algorithm according to the acquired gearbox transmission ratio combination scheme and the preset power performance model and fuel economy model, and obtain the optimal gearbox transmission ratio scheme according to different requirements; Based on the combination scheme of each gearbox ratio, the acceleration performance simulation test and the 100km fuel consumption performance simulation test are carried out to obtain the power performance samples and fuel economy performance samples under each gearbox ratio scheme; Using the power performance samples, the support vector machine is used for training to obtain the power model; using the fuel economy performance samples, the support vector machine is used for training to obtain the fuel economy model; The transition probability of the ant colony algorithm is combined with the foraging behavior of the artificial fish swarm algorithm, and the pheromone concentration of the ant colony algorithm is integrated into the clustering behavior of the fish swarm algorithm, thus obtaining a combined algorithm that integrates the ant colony algorithm and the artificial fish swarm algorithm.

6. The gearbox parameter optimization system based on combined intelligent algorithm according to claim 5, characterized in that: In the combined algorithm based on ant colony algorithm and artificial fish swarm algorithm, foraging behavior, include: When the artificial fish searches and finds other positions X in the field of view k Nutrient concentration Y k X than the current position j When it is high, it moves to X k Randomly walk a certain distance in the direction of If there are multiple locations X that meet the conditions within the field of view of the artificial fish e , combine the fish swarm algorithm concentration Y with the ant colony algorithm to calculate the transfer probability of each point: in, From X j Click to X e The transition probability, From X j Click to X e The pheromone concentration, Y e For X e Point nutrient concentration, For X j With X e The distance between The destination of foraging behavior is all feasible points X e In , the point corresponding to the position with the largest transition probability is: From this, the direction of foraging behavior is obtained; The artificial fish performed foraging behaviors including: Among them, X next is the next position of the artificial fish, X j is the current position of the artificial fish, For X j With X k The distance between them, step is the maximum step length of the artificial fish, and rand is a random number between [0,1].

7. The gearbox parameter optimization system based on combined intelligent algorithm according to claim 6, characterized in that: In the combined algorithm based on the ant colony algorithm and the artificial fish swarm algorithm, the clustering behavior includes: The pheromone concentration of the ant colony algorithm is integrated into the clustering behavior of the fish swarm algorithm, and the center position of the artificial fish swarm is X c Nutrient concentration Y c Compared to the nutrient concentration Y at the current location j High, the artificial fish is based on the number of artificial fish n within its field of view f Make a grouping behavior judgment. If there are other artificial fish in the field of vision and their central position has a higher concentration of the influencing substance and meets the grouping conditions, they will swim towards the central position; The conditions for clustering behavior are: AND c / n f >And j ·δ Among them, n f is the number of artificial fish within its field of view, Y c is the nutrient concentration at the center of the fish school, Y j is the nutrient concentration at the current location, δ is the crowdedness of the location itself; If the condition Y for grouping behavior is met c / n f >Y j ·δ, then the center position of the artificial fish school is X c Without crowding, the artificial fish performed schooling behavior; Group behavior, including: Among them, X next is the next position of the artificial fish, X j is the current position of the artificial fish, For X j With X c The distance between them, step is the maximum step length of the artificial fish, and rand is a random number between [0,1]. is the pheromone concentration; Calculate the foraging behavior target point X respectively k Nutrient concentration Y k and the clustering behavior target point X c Nutrient concentration Y c , if Y k >Y c , the artificial fish will forage for food; otherwise, the artificial fish will flock together.

8. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps in the gearbox parameter optimization method based on a combined intelligent algorithm as described in any one of claims 1 to 4 are implemented.

9. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the gearbox parameter optimization method based on the combined intelligent algorithm as described in any one of claims 1 to 4 are implemented.

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