Fuzzy electro-hydraulic pile-up valve control method and system based on intelligent optimization

Abstract

According to the fuzzy electro-hydraulic integrated valve control method and system based on intelligent optimization, the Levy flight parameters of the attack prey strategy in the snake vulture algorithm are adopted to update the foraging strategy of the artificial fish swarm algorithm, so that the local optimization precision is improved; whether an escape strategy of a snake vulture algorithm or a rear-end collision behavior strategy in an artificial fish swarm algorithm is adopted is judged based on a position updating mode of an artificial fish swarm rear-end collision behavior, so that the later global search capability of the snake vulture algorithm is enhanced, and the algorithm is prevented from quickly falling into local optimum; an optimal fuzzy control parameter is predicted by adopting a snake vulture hybrid algorithm, and a fuzzy controller under the parameter is used for performing fuzzy control on energy management of the electro-hydraulic integrated valve, so that the control precision and robustness of the system are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of electro-hydraulic system control, and particularly relates to a fuzzy electro-hydraulic integrated valve control method and system based on intelligent optimization. Background Art

[0002] The statements in this part only provide background technical information related to the present invention, and do not necessarily constitute prior art.

[0003] With the booming development of modern agriculture and engineering fields, agricultural machinery and construction machinery have become the key forces driving the leap in agricultural production efficiency, ensuring food security, and accelerating infrastructure construction. Modern agricultural machinery not only promotes the mechanization process of agricultural production, but also, through the innovative application of intelligent technologies, realizes the delicate control and scientific management of the agricultural production process, improving the operation efficiency and precision control level. Similarly, the same applies to the construction machinery field, where they have also achieved refined management of the construction process and a significant improvement in operation efficiency through intelligent transformation, perfectly integrating precise control and efficient operation.

[0004] In complex operations, modern agricultural machinery and construction machinery face problems such as insufficient real-time control accuracy and low control accuracy of modular agricultural machinery integrated valve groups, which restrict the operation effect. Therefore, researching modular electro-hydraulic closed-loop self-calibration intelligent control strategies is crucial for improving the real-time control accuracy of agricultural machinery and achieving high-performance operations. Summary of the Invention

[0005] To overcome the deficiencies of the above-mentioned prior art, the present invention provides a fuzzy electro-hydraulic integrated valve control method and system based on intelligent optimization. The foraging strategy of the artificial fish swarm algorithm is updated with the Lévy flight parameters of the attack prey strategy in the secretary bird algorithm, improving the accuracy of local optimization. The position update method based on the artificial fish swarm's following behavior is used to determine whether to adopt the escape strategy of the secretary bird algorithm or the following behavior strategy of the artificial fish swarm algorithm, strengthening the global search ability of the secretary bird algorithm in the later stage and avoiding the algorithm quickly falling into local optimum. The fuzzy control parameters are optimized using the secretary bird hybrid algorithm, improving the control accuracy and robustness of the system.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a fuzzy electro-hydraulic integrated valve control method based on intelligent optimization, including:

[0008] Construct a fuzzy control model of the electro-hydraulic integrated valve;

[0009] Optimize the fuzzy control parameters in the fuzzy control model of the electro-hydraulic integrated valve using the secretary bird hybrid algorithm to obtain the optimal fuzzy control parameters, and realize the energy management of the electro-hydraulic integrated valve according to the fuzzy control strategy corresponding to the optimal fuzzy control parameters.

[0010] Among them, the secretarybird hybrid algorithm includes: updating the foraging strategy of the artificial fish swarm algorithm with the Levy flight parameters of the prey attack strategy in the secretarybird algorithm; judging whether to adopt the escape strategy of the secretarybird algorithm or the pursuit behavior strategy in the artificial fish swarm algorithm by using the position update method of the pursuit behavior in the artificial fish swarm algorithm.

[0011] In a second aspect, the present invention provides a fuzzy electro-hydraulic integrated valve control system based on intelligent optimization, including:

[0012] A construction module, which is configured to: construct a fuzzy control model of the electro-hydraulic integrated valve;

[0013] A control module, which is configured to: optimize the fuzzy control parameters in the fuzzy control model of the electro-hydraulic integrated valve by using the secretarybird hybrid algorithm to obtain the optimal fuzzy control parameters, and realize the energy management of the electro-hydraulic integrated valve according to the fuzzy control strategy corresponding to the optimal fuzzy control parameters;

[0014] Among them, the secretarybird hybrid algorithm includes: updating the foraging strategy of the artificial fish swarm algorithm with the Levy flight parameters of the prey attack strategy in the secretarybird algorithm; judging whether to adopt the escape strategy of the secretarybird algorithm or the pursuit behavior strategy in the artificial fish swarm algorithm by using the position update method of the pursuit behavior in the artificial fish swarm algorithm.

[0015] In a third aspect, the present invention provides an electronic device, including a memory, a processor, and computer instructions stored on the memory and running on the processor. When the computer instructions are run by the processor, the method described in the first aspect is completed.

[0016] In a fourth aspect, the present invention provides a computer-readable storage medium for storing computer instructions. When the computer instructions are executed by the processor, the method described in the first aspect is completed.

[0017] In a fifth aspect, the present invention provides a computer program product, including a computer program. When the computer program is executed by the processor, the method described in the first aspect is implemented.

[0018] The above one or more technical solutions have the following beneficial effects:

[0019] In the present invention, the foraging strategy of the artificial fish swarm algorithm is updated with the Levy flight parameters of the prey attack strategy in the secretarybird algorithm, which improves the accuracy of local optimization. Based on the position update method of the artificial fish swarm pursuit behavior, it is judged whether to adopt the escape strategy of the secretarybird algorithm or the pursuit behavior strategy in the artificial fish swarm algorithm, which strengthens the global search ability in the later stage of the secretarybird algorithm and avoids the algorithm quickly falling into local optimum.

[0020] In the present invention, a snake-vulture hybrid algorithm is used to predict the optimal fuzzy control parameters, and the fuzzy controller under these parameters is used to perform fuzzy control on the energy management of the electro-hydraulic integrated valve, improving the control accuracy and robustness of the system.

[0021] Advantages of additional aspects of the present invention will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings forming a part of this specification are used to provide a further understanding of the present invention. The schematic embodiments and descriptions thereof of the present invention are used to explain the present invention and do not constitute an improper limitation to the present invention.

[0023] Figure 1 It is a flowchart of a fuzzy electro-hydraulic integrated valve control method based on intelligent optimization in Embodiment 1 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] It should be noted that the following detailed description is exemplary and is 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 of ordinary skill in the technical field to which the present invention belongs.

[0025] 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.

[0026] Without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0027] Embodiment 1

[0028] This embodiment discloses a fuzzy electro-hydraulic integrated valve control method based on intelligent optimization, including:

[0029] According to the actual working condition parameters of the electro-hydraulic integrated valve, a fuzzy control model of the electro-hydraulic integrated valve is constructed;

[0030] The snake-vulture hybrid algorithm is used to optimize the fuzzy control parameters in the fuzzy control model of the electro-hydraulic integrated valve to obtain the optimal fuzzy control parameters, and the energy management of the electro-hydraulic integrated valve is realized according to the fuzzy control strategy corresponding to the optimal fuzzy control parameters;

[0031] Among them, the snake-vulture hybrid algorithm includes: using the Levy flight parameters of the prey attack strategy in the snake-vulture algorithm to update the foraging strategy of the artificial fish swarm algorithm; using the position update method of the following behavior in the artificial fish swarm algorithm to judge whether to adopt the escape strategy of the snake-vulture algorithm or the following behavior strategy in the artificial fish swarm algorithm.

[0032] In this embodiment, the probability density function of the Weibull distribution is used as the membership function in the fuzzy control, and the parameters m and η of the probability density function of the Weibull distribution are optimized by the secretary-vulture hybrid algorithm, thereby obtaining the corresponding fuzzy membership function:

[0033]

[0034] Among them, x is a random variable, representing the physical quantity that needs to be controlled, such as pressure; m>0 is a proportional parameter, which determines the degree of expansion of the membership function along the x-axis; η>0 is a shape parameter, which determines the shape of the membership function; when f(x)≥1, f(x)=1.

[0035] In the specific implementation process, the Secretary Vulture hybrid algorithm is used to randomly generate an initial population containing multiple individuals within a certain range of values. Each individual corresponds to a set of possible Weibull distribution parameters m and η values, and the relevant parameters such as the size of the population are set; the improved parameters m and η are put into the fuzzy control strategy, and then based on the Amesim simulation of the dynamic and economic parameters of the operation device, the economic performance is used as the fitness value on the premise of satisfying the dynamic performance, and the parameters corresponding to the individual with the best fitness in the population are obtained, which are the parameters of the fuzzy membership function optimized by the Secretary Vulture hybrid algorithm. Agricultural machinery and engineering machinery operate based on the above fuzzy control according to actual conditions.

[0036] The electro-hydraulic integrated valve can be applied to agricultural machinery and equipment such as high-horsepower tractors and large grain combine harvesters, and can also be applied to engineering machinery such as excavators, cranes, and loaders. The fuzzy control strategy applied to the electro-hydraulic integrated valve can change its flow rate, pressure and other parameters to meet actual operating requirements. The fuzzy membership function with Weibull distribution characteristics obtained by the above method is particularly suitable for the working characteristics of non-road machinery such as agricultural machinery or engineering machinery.

[0037] By simulating the capture, attack, and escape behaviors of secretary birds, and drawing on the characteristics of snakes' flexible swimming and exploration of new areas to expand the search space, we can update and iterate the individuals in the population, guide the population to evolve in a direction with higher fitness, and refer to the characteristics of secretary birds that are good at using a global perspective to find high-quality resources to accelerate convergence to a better solution, thereby continuously updating the parameter values m and η represented by each secretary bird individual.

[0038] Secretary bird algorithm: According to the habits of secretary bird, the survival skills of secretary bird are divided into hunting strategy and escape strategy; hunting strategy includes prey finding strategy and prey attack strategy.

[0039] Initialize the position of the secretary bird. The initial position can be expressed as:

[0040] X i,j =lb j +r×(ubj -lb j )

[0041] i = 1, 2, ..., M, j = 1, 2, ..., Dim

[0042] where X i,j represents the current position of the i-th secretarybird in the j-th dimension; r represents a random number between 0 and 1; lb j and ub j are the lower and upper bounds of the search space respectively; M is the number of secretarybirds; Dim is the dimension.

[0043] Prey Search Strategy: Simulate the secretarybird's search for prey, using its incredibly sharp eyesight to track and discover snakes hidden in the grass. Implement the prey search strategy of the secretarybird algorithm, and the position update method is shown by the following formula:

[0044]

[0045] where t represents the current iteration number; T represents the maximum iteration number; represents the new state of the i-th secretarybird in the first stage; x i,j represents the current position of the i-th secretarybird in the j-th dimension; xrandom_1 and xrandom_2 are the random candidate solution positions in the first stage iteration; R1 represents an array with a dimension of 1×Dim randomly generated in the interval [0, 1], where Dim is the dimension of the solution space; is the current position of the i-th secretarybird in the j-th dimension in the first stage; F i new,P1 represents the fitness value of the objective function in the new state; F i represents the fitness value of the objective function in the previous state.

[0046] Prey Attack Strategy: The secretarybird doesn't rush to fight. It observes the snake with agile steps and mobility, gradually provoking and exhausting its stamina by keenly judging the snake's movements, and then launches an attack when the snake is exhausted. Implement the prey attack strategy of the secretarybird algorithm, and the position update method is shown by the following formula:

[0047]

[0048] RL = 0.5×Levy(Dim)

[0049]

[0050] where s is a fixed constant of 0.01; η1 is a fixed constant of 1.5; u and v are random numbers in the interval [0, 1]; Γ represents the gamma function, x best is the current optimal position of the secretarybird; Levy(Dim) is the Levy flight parameter, and Dim is the dimension.

[0051] Escape strategy: Secretary birds have many natural enemies, such as eagles, foxes, and jackals. When encountering threats, secretary birds will escape or disguise themselves to avoid attacks from natural enemies. Implementing the escape strategy of the secretary bird algorithm, the position update method is shown by the following formula:

[0052]

[0053] K = round(1 + rand(1, 1))

[0054] where RB is Brownian motion; r = 0.5; R2 represents an array of dimension 1×Dim randomly generated from a normal distribution; x random represents the random candidate of the current iteration; K represents a random selection of the integer 1 or 2, is the new state of the i-th secretary bird in the second stage; r i > 0.5.

[0055] Artificial fish swarm algorithm: An artificial fish swarm containing n fish can be represented as the set {AF1, AF2,..., AF n}, and the state of the i-th artificial fish can be represented as the vector XF i = (xf i,1 , xf i,2 ,..., xf i,m ), where xf i,j represents the optimization variable of the j-th (j = 1, 2,..., m) dimension. Denote f as the objective function (food degree function), and Y i = f(XF i ) represents the food degree corresponding to the state XF i . step represents the step size, that is, the maximum distance that an artificial fish moves each time; visual represents the field of view, that is, the perception range of an artificial fish; δ represents the crowding factor, which is used to control the density of the artificial fish swarm to avoid the fish swarm being too concentrated in a local optimal domain; Try_number is the maximum number of trials when an artificial fish conducts foraging behavior.

[0056] Suppose the state of the artificial fish AF i is XF i , and the corresponding food degree is Y i . Randomly find a state XF i ' within its field of view visual, and the corresponding food degree is Y i '. If Y i ' < Y i , then AF i moves towards XF i"Take a step forward, otherwise randomly search for the next state and determine whether the forward condition is satisfied. If it is still not satisfied after repeating Try_number times, take a step forward to a random state within the field of view. The foraging behavior simulates the process of an artificial fish searching for a state with a better food degree within its own field of view. The state change process is shown as follows:

[0057]

[0058] where Rand() refers to a random function that follows a uniform distribution, and the parentheses (0,1) refer to the value range; step represents the step size, that is, the maximum distance that an artificial fish moves each time.

[0059] Let the state of the artificial fish AF i be XF i , the number of artificial fish within its field of view visual be n f , and the state of the optimal artificial fish be XF max . If Y max / n f <δY i , indicating that the food degree of the partner is better and the crowding degree is not high, then AF i moves one step towards XF max . The chasing behavior simulates the process of an artificial fish searching for and approaching the artificial fish with the optimal food degree within the field of view. The state change process is shown as follows:

[0060]

[0061] where Rand() refers to a random function that follows a uniform distribution, and the parentheses (0,1) refer to the value range; step represents the step size, that is, the maximum distance that an artificial fish moves each time; δ represents the crowding degree factor, which is used to control the density of the artificial fish swarm to avoid the fish swarm being too concentrated in a local optimal domain; Y max is the maximum food degree concentration.

[0062] In this embodiment, the strategy of the secretary bird algorithm for finding prey is selected. After finding the prey, the strategy of the secretary bird algorithm for attacking the prey is executed to find the optimal value X best ; the foraging strategy of the artificial fish swarm is improved by Levy(Dim) in the strategy of the secretary bird algorithm for attacking the prey. Specifically:

[0063]

[0064] where XF i is the state of the artificial fish AF i ; Y i is the food degree corresponding to the state XF i ; XF i ' is the artificial fish AF iA state randomly searched within its visual range, Y i ' is the state XF i ' corresponding food degree.

[0065] The optimal value of this process is denoted as X′ best .

[0066] Select X best and X′ best The optimal value between the two Judge Whether the continuous number of times exceeds the first set value, that is, NY times. In this implementation, the first set value is taken as NY = 20; if not, return to the attack prey strategy of the secretary bird algorithm until The continuous number of times is greater than the first set value. At this time, the algorithm falls into a local optimum and executes the jump-out strategy.

[0067] In this embodiment, the execution of the jump-out strategy is specifically as follows: Determine whether to adopt the escape strategy of the secretary bird algorithm or the pursuit behavior strategy in the artificial fish swarm algorithm through the position update method of the pursuit behavior in the artificial fish swarm algorithm, and then update the position, which has strong early global search ability and avoids falling into local optimum. The position update is specifically shown as the following formula:

[0068]

[0069] Among them, xf best is the optimal secretary bird position after position update; nf f is the number of artificial fish within the visual range visual of the artificial fish after position update; YF max is the maximum food degree concentration after position update; YF i is the food degree concentration corresponding to the artificial fish AF i after position update, and δ represents the crowding factor.

[0070] Execute this strategy NZ times until NZ > the second set value, that is, Tt. In this embodiment, Tt = 20 is taken. At this time, the optimal value is found, and thus the secretary bird hybrid algorithm is obtained.

[0071] In this embodiment, by constructing a fuzzy control strategy, the above secretary bird hybrid algorithm is used to train and predict the parameters of the optimal fuzzy control membership function, and the fuzzy controller under this parameter is used to perform fuzzy control on the energy management of the electro-hydraulic integrated valve, improving the control accuracy and robustness of the system.

[0072] Embodiment 2

[0073] The purpose of this embodiment is to provide a fuzzy electro-hydraulic integrated valve control system based on intelligent optimization, including:

[0074] A building module, configured to: build a fuzzy control model of an electro-hydraulic integrated valve;

[0075] A control module, configured to: optimize the fuzzy control parameters in the fuzzy control model of the electro-hydraulic integrated valve by using a secretary vulture hybrid algorithm to obtain the optimal fuzzy control parameters, and control the electro-hydraulic integrated valve according to the fuzzy control strategy corresponding to the optimal fuzzy control parameters;

[0076] Wherein, the secretary vulture hybrid algorithm includes: updating the foraging strategy of the artificial fish swarm algorithm by using the Lévy flight parameter of the prey attack strategy in the secretary vulture algorithm; judging whether to adopt the escape strategy of the secretary vulture algorithm or the pursuit behavior strategy in the artificial fish swarm algorithm by using the position update method of the pursuit behavior in the artificial fish swarm algorithm.

[0077] In more embodiments, there is also provided:

[0078] An electronic device, including a memory, a processor, and computer instructions stored on the memory and running on the processor. When the computer instructions are run by the processor, the method described in Embodiment 1 is completed. For the sake of brevity, it will not be elaborated here.

[0079] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0080] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A part of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0081] A computer-readable storage medium for storing computer instructions, which, when executed by a processor, complete the method described in Embodiment 1.

[0082] The method in Embodiment 1 can be directly embodied as being executed and completed by a hardware processor, or by a combination of hardware and software modules in the processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method. To avoid repetition, it will not be described in detail here.

[0083] A computer program product includes a computer program which, when executed by a processor, implements the method described in Embodiment 1.

[0084] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program modules, which are executed in a device on a target real or virtual processor to perform the process / method as described above. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In various embodiments, the functions of program modules can be combined or divided as needed. The machine-executable instructions for program modules can be executed within a local or distributed device. In a distributed device, program modules can be located in local and remote storage media.

[0085] The computer program code for implementing the method of the present invention can be written in one or more programming languages. This computer program code can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program code is executed by the computer or other programmable data processing device, the functions / operations specified in the flowchart and / or block diagram are implemented. The program code can be executed entirely on the computer, partially on the computer, as a stand-alone software package, partially on the computer and partially on a remote computer, or entirely on a remote computer or server.

[0086] In the context of the present invention, the computer program code or related data can be carried by any suitable carrier so that a device, apparatus, or processor can perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, etc. Examples of signals can include electrical, optical, radio, acoustic, or other forms of propagated signals, such as carrier waves, infrared signals, etc.

[0087] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in conjunction with this embodiment can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0088] Although the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, it is not a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications or deformations that can be made without creative efforts on the basis of the technical solutions of the present invention are still within the protection scope of the present invention.

Claims

1. A fuzzy electro-hydraulic integrated valve control method based on intelligent optimization, characterized in that, Including: Constructing a fuzzy control model of an electro-hydraulic integrated valve; Optimizing the fuzzy control parameters in the fuzzy control model of the electro-hydraulic integrated valve by using the vulture hybrid algorithm to obtain the optimal fuzzy control parameters, and controlling the electro-hydraulic integrated valve according to the fuzzy control strategy corresponding to the optimal fuzzy control parameters; Wherein, the vulture hybrid algorithm includes: updating the foraging strategy of the artificial fish swarm algorithm by using the Levy flight parameters of the prey attack strategy in the vulture algorithm; judging whether to adopt the escape strategy of the vulture algorithm or the pursuit behavior strategy in the artificial fish swarm algorithm by using the position update method of the pursuit behavior in the artificial fish swarm algorithm.

2. The fuzzy electro-hydraulic integrated valve control method based on intelligent optimization according to claim 1, characterized in that, Executing the prey attack strategy of the vulture algorithm, and the position update is realized by the following formula: RL = 0.5×Levy(Dim) where t represents the current iteration number; T represents the maximum iteration number; is the current position of the i-th secretarybird in the j-th dimension at the first stage; x best is the current optimal position of the secretarybird; x i,j represents the current position of the i-th secretarybird in the j-th dimension; represents the new state of the i-th secretarybird at the first stage; F i new,P1 represents the fitness value of the objective function in the new state; F i represents the fitness value of the objective function in the previous state; u and v are random numbers within the interval [0, 1]; Γ represents the gamma function; s is a fixed constant of 0.01; η1 is a fixed constant of 1.5; Levy(Dim) is the Levy flight parameter.

3. The fuzzy electro-hydraulic integrated valve control method based on intelligent optimization according to claim 1 or 2, characterized in that, The foraging strategy of the artificial fish swarm algorithm is updated by using the Levy flight parameters of the prey attack strategy in the vulture algorithm, and the position update is realized by the following formula: Among them, XF i is the state of the artificial fish AF i ; Y i is the food degree corresponding to the state XF i ; XF i ' is a state randomly found by the artificial fish AF i within its visual field visual, and Y i ' is the food degree corresponding to the state XF i '; Levy(Dim) is the Levy flight parameter, and Dim is the dimension.

4. The fuzzy electro-hydraulic integrated valve control method based on intelligent optimization according to claim 3, wherein, Execute the prey-hunting strategy of the secretary bird algorithm to find the optimal value denoted as X best ; Update the foraging strategy of the artificial fish swarm algorithm using the Lévy flight parameter of the prey-hunting strategy in the secretary bird algorithm to find the optimal value of this process denoted as X b ' est ; Select X best and X b ' est and find the optimal value Judge whether the consecutive times exceed the first set value. If not, return the prey-hunting strategy of the secretary bird algorithm until the consecutive times exceed the first set value, then execute the jump-out strategy.

5. The fuzzy electro-hydraulic integrated valve control method based on intelligent optimization according to claim 4, characterized in that, The jump-out strategy is: judging whether to adopt the escape strategy of the vulture algorithm or the pursuit behavior strategy in the artificial fish swarm algorithm by using the position update method of the pursuit behavior in the artificial fish swarm algorithm, and the specific formula is: Among them, xf best is the optimal position of the secretary bird after position update; RB is Brownian motion; YF max is the maximum food concentration after position update; YF i is the food concentration corresponding to the artificial fish AF i after position update, δ represents the crowding factor; t represents the current iteration number; T represents the maximum iteration number; xf i,j represents the optimization variable of the j-th dimension; nf f is the number of artificial fish within the visual field visual of the artificial fish after position update; XF i is the state of the artificial fish AF i ; step represents the step size, that is, the maximum distance that the artificial fish moves each time; XF max is the state of the optimal artificial fish; Rand() refers to a random function that follows a uniform distribution; Executing the strategy more than the second set value to find the optimal value, thereby obtaining the vulture hybrid algorithm.

6. The fuzzy electro-hydraulic integrated valve control method based on intelligent optimization according to claim 1 or 5, characterized in that, Using the probability density function of the Weibull distribution as the membership function in fuzzy control, and optimizing the parameters m and η of the probability density function of the Weibull distribution by using the vulture hybrid algorithm, thereby obtaining the corresponding fuzzy membership function as: Wherein, x is a random variable representing the physical quantity to be controlled; m>0 is a scale parameter, which determines the extension degree of the membership function along the x-axis; η>0 is a shape parameter, which determines the shape of the membership function; Putting the improved parameters m and η into the fuzzy control strategy, based on the dynamic performance and economy of the Amesim simulation operation device, taking economy as the fitness value on the premise of meeting the dynamic performance, obtaining the parameters corresponding to the individual with the best fitness in the population at this time, which are the parameters of the fuzzy membership function optimized by the vulture hybrid algorithm, forming the best fuzzy control strategy, and agricultural machinery and construction machinery perform operations based on the above fuzzy control according to the actual situation.

7. The fuzzy electro-hydraulic integrated valve control system based on intelligent optimization is characterized in that Including: A construction module configured to construct a fuzzy control model of an electro-hydraulic integrated valve; A control module configured to optimize the fuzzy control parameters in the fuzzy control model of the electro-hydraulic integrated valve by using the vulture hybrid algorithm to obtain the optimal fuzzy control parameters, and control the electro-hydraulic integrated valve according to the fuzzy control strategy corresponding to the optimal fuzzy control parameters; Wherein, the vulture hybrid algorithm includes: updating the foraging strategy of the artificial fish swarm algorithm by using the Levy flight parameters of the prey attack strategy in the vulture algorithm; judging whether to adopt the escape strategy of the vulture algorithm or the pursuit behavior strategy in the artificial fish swarm algorithm by using the position update method of the pursuit behavior in the artificial fish swarm algorithm.

8. An electronic device, characterized in that, Including a memory, a processor, and computer instructions stored on the memory and running on the processor. When the computer instructions are run by the processor, the method according to any one of claims 1-6 is completed.

9. A computer-readable storage medium, characterized in that, For storing computer instructions, when the computer instructions are executed by a processor, the method according to any one of claims 1-6 is completed.

10. A computer program product, characterized in that, Comprising a computer program, when the computer program is executed by a processor, the method according to any one of claims 1-6 is implemented.

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