Excavator engine rotating speed optimizing method based on particle swarm optimization, excavator rotating speed adjusting method, electronic equipment and storage medium

Abstract

The invention provides an excavator engine rotating speed optimizing method based on a particle swarm algorithm, an excavator rotating speed adjusting method, electronic equipment and a storage medium, and belongs to the technical field of engine control. According to the scheme, the method comprises the steps that operation parameter sets of an excavator in different working stages in the excavation circulation process are obtained; according to the operation parameter set of each working stage, initial three-dimensional particles corresponding to the working stage are set, and initialization processing of a particle swarm algorithm is carried out; an evaluation function is constructed according to the fuel consumption rate of the engine, the anti-load capacity of the whole vehicle, the volumetric efficiency of the two hydraulic pumps and the constant flow proportion of the two hydraulic pumps, and an initial global optimal solution of each working stage is obtained through calculation; and performing iterative optimization of an initial global optimal solution on each working stage based on a particle swarm algorithm. According to the excavator engine rotating speed optimization method based on the particle swarm optimization, the rotating speed displacement point with the best economical efficiency can be optimized.

Description

Technical Field

[0001] The present application relates to the field of engine control technology, and in particular to an excavator engine speed optimization method based on a particle swarm algorithm, an excavator speed adjustment method, an electronic device, and a computer-readable storage medium. Background Art

[0002] Fuel efficiency is an important indicator for measuring excavator performance. The existing excavator strategy is a constant speed control strategy. In the same driving cycle, the speed and displacement are fixed. However, in the actual working process, the load is constantly changing. In the existing speed control logic, speed adjustment is mainly based on manual calibration. Determining the optimal speed adjustment value requires a large number of repetitive calibration experiments. In addition, the speed adjustment value after the external load changes will cause new problems. Adaptive adjustment cannot be achieved, resulting in low overall fuel efficiency of the excavator.

[0003] The Chinese patent application document with the publication number CN114357880A discloses a method for optimizing the working point of a hydraulic excavator engine in stages, which divides a working cycle into five working stages according to the pilot pressure on the outlet pressure waveform curve diagram of the front pump and the rear pump, takes the waveform curve values ​​of the outlet pressure of the front pump and the rear pump in the initial time period of the working stage as the characteristic value, and uses a multi-classification algorithm to establish a classification model according to the divided working stages. The fuel consumption model of the engine, the efficiency model of the hydraulic pump and the adaptability evaluation function of the load are established, and the engine output torque is greater than the minimum torque size actually required for the work in different working stages as a constraint condition, and the multi-objective optimization algorithm is used to obtain the optimal working point of the engine in different working stages. A gear feedback mechanism is established so that the driver can select the appropriate gear to avoid the engine from stalling due to insufficient power. However, this scheme needs to use multiple evaluation functions as constraint conditions for calculation, and also requires the driver to perform gear shifting operations, and cannot adaptively perform speed optimization control.

[0004] The Chinese patent application document with the publication number CN116186467A discloses a method for optimizing the working point of an excavator based on a comprehensive evaluation model. The method comprehensively considers the fuel consumption of the excavator engine, the efficiency of the hydraulic pump and the load change of the excavator for stabilizing the working point by controlling the displacement of the hydraulic pump. The relationship between the fuel consumption of the engine and its speed and torque is determined according to the engine universal characteristic curve; the relationship between the efficiency of the hydraulic pump and its speed, pressure difference and displacement ratio is determined according to the experimental data of the hydraulic pump; and the pressure value of the engine load is converted and fitted into the torque value of the engine. The working speed of the excavator is selected, and the optimization objectives are low fuel consumption of the engine during actual work, high efficiency of the hydraulic pump, and small fluctuation of the engine working point during actual work. A comprehensive evaluation optimization model is established, and the importance of engine fuel consumption and load fluctuation is determined by setting weight coefficients. The upper and lower limits of the working point torque are determined according to the efficiency of the hydraulic pump, and finally the torque of the actual working point of the engine is determined. However, this scheme cannot realize the adaptive adjustment of the excavator speed for different working conditions.

[0005] It can be seen that there is an urgent need to develop a new method for optimizing the excavator engine speed, with the optimization goals of reducing fuel consumption and improving work efficiency, and to achieve adaptive adjustment of the excavator speed for different working conditions. Summary of the invention

[0006] The present application aims to solve at least one of the technical problems in the related art to a certain extent. To this end, the present application provides an excavator engine speed optimization method based on a particle swarm algorithm, an excavator speed adjustment method, an electronic device and a computer-readable storage medium. The speed optimization control based on the particle swarm algorithm can reduce fuel consumption, improve work efficiency and ensure a better control effect.

[0007] To achieve the above-mentioned objectives, in the first aspect, the present application provides an excavator engine speed optimization method based on a particle swarm algorithm, comprising: obtaining a set of operating parameters of the excavator at different working stages during the excavation cycle; according to the set of operating parameters of each working stage, setting the initial three-dimensional particles corresponding to the working stage and performing initialization processing of the particle swarm algorithm, wherein the initial three-dimensional particles include three dimensions: speed, hydraulic pump displacement one and hydraulic pump displacement two; constructing an evaluation function based on the engine fuel consumption rate, the load resistance of the whole vehicle, the volumetric efficiency of the two hydraulic pumps and the constant flow ratio of the two hydraulic pumps, and calculating the initial global optimal solution for each working stage; performing iterative optimization of the initial global optimal solution for each working stage based on the particle swarm algorithm, and generating a global optimal solution for each working stage.

[0008] Preferably, the different working stages include a preparation stage, an excavation stage, a rotation stage, and a discharge stage, and the operating parameter set includes rotation speed, pump pressure, and torque data.

[0009] Preferably, the division of different working stages includes: dividing into a preparation stage when the boom-down pilot pressure and the rotation pilot pressure of the excavator are both greater than the corresponding preset values, dividing into an excavation stage when the boom-retraction pilot pressure and the bucket-retraction pilot pressure of the excavator are both greater than the corresponding preset values, dividing into a material shifting stage when the boom-lifting pilot pressure and the rotation pilot pressure of the excavator are both greater than the corresponding preset values, and dividing into an unloading stage when the boom-over pilot pressure and the bucket-overturn pilot pressure of the excavator are both greater than the corresponding preset values.

[0010] Preferably, the constant flow ratio of the two hydraulic pumps refers to the ratio of the product of the rotational speed and displacement of the two hydraulic pumps before and after adjustment. The closer the constant flow ratio is to 1, the better the controllability. The load resistance of the whole vehicle is expressed by calculating the percentage of the current load torque to the maximum torque.

[0011] Preferably, the step of constructing an evaluation function includes normalizing the engine fuel consumption rate, the load-bearing capacity of the whole vehicle, the volumetric efficiency of the two hydraulic pumps and the constant flow ratio of the two hydraulic pumps within the range of [0,1], and obtaining the evaluation function P after weighted processing as follows: P=K1×A+K2×(1-B)+K3×(1-C)+K4×(1-D); wherein A represents the engine fuel consumption rate, B represents the load-bearing capacity of the whole vehicle, C represents the average volumetric efficiency of the two hydraulic pumps, and D represents the constant flow ratio of the two hydraulic pumps; K1, K2, K3, and K4 respectively represent the allocation weights of the corresponding evaluation indicators.

[0012] Preferably, the step of performing initialization processing of the particle swarm algorithm includes: setting the number of particles, the moving range and the moving speed range.

[0013] Preferably, the step of iteratively optimizing the initial global optimal solution includes: continuously calculating the positions of newly generated particles and obtaining their evaluation function values; when the number of iterations preset by the particle swarm algorithm is reached or the improvement of the global optimal solution converges to a preset threshold, the iterative process ends.

[0014] Preferably, the position of the newly generated particle is expressed as:

[0015] X i =X (i-1) +C1×rand×(P i -X (i-1) )+C2×rand×(gX (i-1) )+C3V (i-1) ; Among them, X i represents the position of the newly generated particle, X (i-1) represents the last particle position, C1, C2, and C3 are learning factors, and C1 controls the particle's local optimal solution P iC2 controls the degree of following of the particle to the global optimal solution g, C3 represents the inertia weight, rand is the generated random number, V (i-1) Represents the last velocity of the particle.

[0016] In the second aspect, the present application provides a method for adjusting the speed of an excavator, comprising: identifying the current working stage of the excavator; obtaining a global optimal solution corresponding to the working stage, wherein the global optimal solution is obtained by optimizing the excavator engine speed according to any of the above-mentioned methods; and adjusting the speed and displacement of the engine according to the global optimal solution.

[0017] In a third aspect, the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to implement any of the above-mentioned methods for optimizing the excavator engine speed.

[0018] In a fourth aspect, the present application provides a computer-readable storage medium, comprising a computer program, which, when run on a computer or a processor, enables the electronic device to execute any of the above-described methods for optimizing the excavator engine speed.

[0019] Based on the above technical solution, it can be seen that the excavator engine speed optimization method based on particle swarm algorithm of the present application has at least one of the following beneficial effects compared with the prior art:

[0020] This application uses a particle swarm algorithm to optimize the speed. On the one hand, optimizing the speed has less impact on the driver's controllability than torque control. On the other hand, the introduction of the particle swarm algorithm can optimize the speed and displacement point with the best fuel efficiency, and the calculation speed of finding the global optimal solution will be faster;

[0021] This application constructs an evaluation function based on the engine fuel consumption rate, the load-bearing capacity of the vehicle, the volumetric efficiency of the two hydraulic pumps and the constant flow ratio of the two hydraulic pumps, so that the fuel consumption, load-bearing capacity, transmission loss and the impact on the efficiency of the whole vehicle during the excavation process are fully considered in the optimization process, and the overall controllability can be guaranteed to be the best while ensuring the lowest fuel consumption.

[0022] Other features and advantages of the present application will be described in the subsequent description, and in part will become apparent from the description, or it may be understood through the implementation of the present application that the objects and other advantages of the present application can be realized and obtained through the written description and the structures particularly pointed out in the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0024] Figure 1 This is a flow chart of the method for optimizing the excavator engine speed based on the particle swarm algorithm of the present application. DETAILED DESCRIPTION

[0025] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0026] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the embodiments of the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application are also intended to include plural forms, unless the context clearly indicates other meanings.

[0027] In view of the deficiencies in the prior art, the purpose of this application is to provide an excavator engine speed optimization method, adjustment method, equipment, and medium, aiming to overcome the problem that the current excavator control system cannot achieve adaptive adjustment of the excavator speed for different working conditions.

[0028] The basic idea of ​​this application is that due to the wide adjustment range of speed and displacement, the use of exhaustive or segmented methods will result in incomplete coverage and fall into local optimal solutions. The introduction of the particle swarm algorithm will make the coverage of the entire vehicle wider and easier to find the global optimum. Therefore, this application introduces a particle swarm algorithm to find the speed and displacement point with the best economy. At the same time, the excavator working conditions are divided into stages, and different working parameters corresponding to the global optimal solution are introduced in different stages, which can achieve the highest fuel efficiency while meeting the economy.

[0029] Embodiment 1

[0030] In order to develop an engine speed optimization control method that can adaptively adjust the speed according to different working conditions, the inventors have conducted in-depth research on the particle swarm algorithm and proposed an excavator engine speed optimization method, an excavator speed adjustment method, an electronic device and a computer-readable storage medium based on the particle swarm algorithm.

[0031] Specifically, Figure 1 As shown, a method for optimizing the engine speed of an excavator based on a particle swarm algorithm is provided, comprising the following steps:

[0032] S1. Obtaining a set of operating parameters of the excavator at different working stages during the excavation cycle;

[0033] S2. According to the set of operating parameters of each working stage, the initial three-dimensional particles corresponding to the working stage are set and the particle swarm algorithm is initialized, wherein the initial three-dimensional particles include three dimensions: rotation speed, hydraulic pump displacement 1 and hydraulic pump displacement 2;

[0034] S3, constructing an evaluation function based on the engine fuel consumption rate, the load resistance capacity of the whole vehicle, the volumetric efficiency of the two hydraulic pumps and the constant flow ratio of the two hydraulic pumps, and calculating the initial global optimal solution for each working stage;

[0035] S4. Based on the particle swarm algorithm, the initial global optimal solution is iteratively optimized for each working stage to generate the global optimal solution for each working stage.

[0036] In the above step S1, the operating parameter set of the excavator at different working stages during the excavation cycle is obtained. It can be the operating parameter set of different working stages during the previous excavation cycle, or it can be the operating parameter set of different working stages during the previous excavation cycles. The specific selection of the previous few times can be set according to actual work needs.

[0037] Among them, setting the initial three-dimensional particles corresponding to the working stage and performing the initialization processing of the particle swarm algorithm includes: selecting the number of particles Num, selecting the iteration period T of the particles, selecting the movement range of the particles [Nmin, Nmax], selecting the movement speed range of the particles [Vmin, Vmax], setting the initial three-dimensional particles, the three dimensions include the speed, the first displacement of the pump, and the second displacement of the pump (the front pump and the rear pump). For example, setting the number of particles to 100, the optimization period to 20 times, and setting the initial particles to X i , the moving speed is V i When the number of iterations is reached, the optimal particle setting speed and displacement are obtained. If the number of particles is insufficient, 100 random numbers Rand1 between [0,1] can be randomly generated, and then the formula Rand1×[Nmin,Nmax] can be used to obtain the initial particle position X i ; Randomly generate 100 random numbers Rand2 in [0,1], Rand2×[Vmin,Vmax], and get the initial particle speed V i .

[0038] After the excavation cycle is detected to be enabled, the controller collects the basic signals such as the rotation speed, pump pressure, torque, etc. after the excavation preparation stage. After the preparation stage is completed, the evaluation function of the corresponding initial three-dimensional particle is calculated. The initial local optimal solution P of each particle is obtained according to the evaluation function of each initial three-dimensional particle. iAt the same time, the initial global optimal solution g is obtained according to the optimal solution that satisfies the evaluation function among all the initial three-dimensional particles. Similarly, in the three stages of excavation, rotation, and unloading, the controller stores the basic signals such as the speed, pump pressure, and torque of each stage, and then performs iterative optimization of the particle swarm algorithm. After the optimization is completed, the speed and displacement of the corresponding stage are adjusted when the corresponding stage is passed again.

[0039] This solution uses a particle swarm algorithm to optimize the speed. On the one hand, optimizing the speed has less impact on the driver's controllability than torque control. On the other hand, the introduction of the particle swarm algorithm can optimize the speed and displacement point with the best fuel efficiency, and the calculation speed of finding the global optimal solution will be faster, which can greatly improve the immediacy of the speed optimization response.

[0040] Furthermore, the different working stages include a preparation stage, an excavation stage, a rotation stage, and a discharge stage, and the set of operating parameters includes basic signals such as rotation speed, pump pressure, and torque data. Preferably, the division of different working stages includes: the preparation stage is divided according to the fact that the boom lowering pilot pressure and the rotation pilot pressure of the excavator are both greater than the corresponding preset values, the excavation stage is divided according to the fact that the arm retraction pilot pressure and the bucket retraction pilot pressure of the excavator are both greater than the corresponding preset values, the material transfer stage is divided according to the fact that the boom lifting pilot pressure and the rotation pilot pressure of the excavator are both greater than the corresponding preset values, and the discharge stage is divided according to the fact that the arm flipping pilot pressure and the bucket flipping pilot pressure of the excavator are both greater than the corresponding preset values. Among them, the load in the preparation stage and unloading stage is relatively low, while the load in the excavation stage and the return transfer stage is relatively high. Therefore, the optimization process requires the adjustment of the unloading stage and the preparation stage to reduce the speed and increase the displacement, and at the same time, the flow before and after the adjustment should be kept consistent as much as possible, that is, the original flow of the original speed × the original displacement is the same as the new flow of the new speed × the new displacement, so that the controllability before and after the adjustment is close to the same; at the same time, the optimization process should increase the protection for the lowest speed of adjustment to ensure that the pump can still meet the flow demand under full discharge at this speed. Correspondingly, the excavation stage and the return transfer stage are adjusted to increase the speed and reduce the displacement, and the flow of the original speed × the original displacement before the change is also ensured to be the same as the flow of the new speed × the new displacement after the change.

[0041] Preferably, the constant flow ratio of the two hydraulic pumps refers to the ratio of the product of the rotational speed and displacement of the two hydraulic pumps before and after adjustment. The closer the constant flow ratio is to 1, the better the controllability. The load resistance of the whole vehicle is expressed by calculating the percentage of the current load torque to the maximum torque.

[0042] Preferably, the step of constructing an evaluation function includes normalizing the engine fuel consumption rate, the load-bearing capacity of the whole vehicle, the volumetric efficiency of the two hydraulic pumps and the constant flow ratio of the two hydraulic pumps within the range of [0,1], and obtaining the evaluation function P after weighted processing as follows: P=K1×A+K2×(1-B)+K3×(1-C)+K4×(1-D); wherein A represents the engine fuel consumption rate, B represents the load-bearing capacity of the whole vehicle, C represents the average volumetric efficiency of the two hydraulic pumps, and D represents the constant flow ratio of the two hydraulic pumps; K1, K2, K3, and K4 respectively represent the allocation weights of the corresponding evaluation indicators, which can be set according to actual conditions.

[0043] In the evaluation function of the above optimal particle, the fuel consumption rate A of the engine is to ensure that the optimal speed displacement meets the optimal fuel consumption. The calculation method of the fuel consumption rate A is: take the engine speed n as the horizontal coordinate and the average torque Trq as the vertical coordinate to obtain the fuel consumption rate curve, which constitutes the universal characteristics of the engine. The universal characteristic curves of different engines are different. The final fuel consumption rate is obtained by real-time detection of the engine torque and the learned speed.

[0044] Among them, the load resistance B of the whole vehicle in the evaluation function is to ensure that the whole vehicle has a high load resistance when it encounters a sudden heavy load, and will not affect the working efficiency due to the lowering of the speed. The calculation method of the load resistance B of the whole vehicle is: according to the curve of the engine output torque changing with the speed when the engine throttle opening is 100%, it represents the maximum load torque at different speeds. Different engines have different external curves and different load resistance. The calculation method of load resistance B is as follows: B=Trq ave / Trq max , where Trq ave is the current load torque, Trq max is the maximum torque; the percentage of the current load torque to the maximum torque is calculated as the load resistance. When B is smaller, it means that the engine has more residual torque and the load resistance is stronger.

[0045] Among them, the volumetric efficiency C of the pump in the evaluation function is to ensure that the optimal displacement meets the high volumetric efficiency and reduce transmission loss. The calculation method of the volumetric efficiency C of the pump is: the volumetric efficiency of the pump is obtained according to the displacement percentage, pump pressure, and speed of the pump. The higher the volumetric efficiency of the pump, the less loss the vehicle has from the engine end to the pump end.

[0046] Among them, the constant flow ratio D of the two hydraulic pumps in the evaluation function is to represent that the closer to the constant flow, the more consistent the controllability with the original strategy.

[0047] The calculation method of the constant flow ratio D of the two hydraulic pumps is:

[0048] The closer the ratio of original strategy speed × original strategy displacement / new strategy speed × new strategy displacement is to 1, the more consistent the controllability is with the original strategy.

[0049] The engine fuel consumption rate, the load resistance of the vehicle, the volumetric efficiency of the two hydraulic pumps and the constant flow ratio of the two hydraulic pumps are normalized in the range of [0,1], and then weighted to obtain the final evaluation function. The smaller the value of the final evaluation function, the better the optimization effect.

[0050] Preferably, the step of performing initialization processing of the particle swarm algorithm includes: setting the number of particles, the moving range and the moving speed range.

[0051] Preferably, the step of iteratively optimizing the initial global optimal solution includes: continuously calculating the positions of newly generated particles and obtaining their evaluation function values; when the number of iterations preset by the particle swarm algorithm is reached or the improvement of the global optimal solution converges to a preset threshold, the iterative process ends.

[0052] Preferably, the position of the newly generated particle is expressed as:

[0053] X i =X (i-1) +C1×rand×(P i -X (i-1) )+C2×rand(gX (i-1) )+C3V (i-1) ; Among them, X i represents the position of the newly generated particle, X (i-1) Represents the last particle position, P i represents the local optimal solution of the current particle (i.e., the optimal position found by the particle in the historical iteration), g represents the global optimal solution (i.e., the optimal position found by all particles), C1, C2, and C3 are learning factors, and C1 controls the particle's response to the local optimal solution P i C2 controls the degree of following of the particle to the global optimal solution g, C3 represents the inertia weight, rand is the generated random number, V (i-1) Represents the last velocity of the particle.

[0054] In the above step S4, the initial global optimal solution is iteratively optimized for each working stage based on the particle swarm algorithm to generate the global optimal solution for each working stage. Specifically, the engine speed and displacement are adjusted accordingly based on the newly generated particle position (including three dimensions: speed, hydraulic pump displacement 1 and hydraulic pump displacement 2), and then the evaluation function at the newly generated particle position is obtained, and the process is repeated. When the number of iterations preset by the particle swarm algorithm is reached or the improvement of the global optimal solution converges to a preset threshold, the iterative process ends.

[0055] This scheme uses particle swarm algorithm to optimize the speed control, which can optimize the speed displacement point with the best fuel efficiency. And the evaluation function is constructed based on the engine fuel consumption rate, the load resistance of the whole vehicle, the volumetric efficiency of the two hydraulic pumps and the constant flow ratio of the two hydraulic pumps, so that the fuel consumption, load resistance, transmission loss and the impact on the efficiency of the whole vehicle during the excavation process are fully considered in the optimization process.

[0056] Embodiment 2

[0057] The present application provides a method for adjusting the speed of an excavator, comprising: identifying the current working stage of the excavator; obtaining a global optimal solution corresponding to the working stage, wherein the global optimal solution is obtained by optimizing the excavator engine speed optimization method in the above-mentioned embodiment one; and adjusting the speed and displacement of the engine according to the global optimal solution.

[0058] Among them, with regard to identifying the current working stage of the excavator, the division of different working stages includes: the preparation stage is divided according to the excavator's boom-down pilot pressure and the rotation pilot pressure being greater than the corresponding preset values, the excavation stage is divided according to the excavator's boom-retraction pilot pressure and the bucket-retraction pilot pressure being greater than the corresponding preset values, the material-shifting stage is divided according to the excavator's boom-lifting pilot pressure and the rotation pilot pressure being greater than the corresponding preset values, and the unloading stage is divided according to the excavator's boom-over pilot pressure and the bucket-overturning pilot pressure being greater than the corresponding preset values.

[0059] This solution divides the excavation work cycle into stages, and uses the particle swarm algorithm to optimize the speed and displacement in each stage of the work interval, and finally obtains the optimal speed in each stage to maximize the overall economy. It truly realizes the self-optimization of speed and displacement according to the actual load torque to achieve the goal of minimum fuel consumption and highest efficiency.

[0060] Embodiment 3

[0061] This embodiment provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the excavator engine speed optimization method in the above-mentioned embodiment 1, and realize the following functions: adopting a particle swarm algorithm to optimize the speed control, which has less impact on the driver's controllability than torque control; and introducing a particle swarm algorithm to optimize the speed displacement point with the best fuel efficiency, and the calculation speed of finding the global optimal solution will be faster; constructing an evaluation function based on the engine fuel consumption rate, the load resistance capacity of the whole vehicle, the volumetric efficiency of the two hydraulic pumps and the constant flow ratio of the two hydraulic pumps, so that the fuel consumption, load resistance, transmission loss and impact on the efficiency of the whole vehicle during the excavation process are fully considered in the optimization process, and the overall controllability can be guaranteed to be the best on the basis of ensuring the lowest fuel consumption.

[0062] Embodiment 4

[0063] Based on the same technical concept, the embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a computer or a processor, the computer or the processor executes the steps of the above-mentioned adaptive force feedback method. And realize the following functions: adopting a particle swarm algorithm to optimize the speed control, which has less impact on the driver's controllability than torque control; and introducing a particle swarm algorithm, it can optimize the speed displacement point with the best fuel efficiency, and the calculation speed of finding the global optimal solution will be faster; construct an evaluation function based on the engine fuel consumption rate, the load resistance of the whole vehicle, the volumetric efficiency of the two hydraulic pumps and the constant flow ratio of the two hydraulic pumps, so that the fuel consumption, load resistance, transmission loss and the impact on the efficiency of the whole vehicle during the excavation process are fully considered in the optimization process, and the overall controllability can be guaranteed to be the best on the basis of ensuring the lowest fuel consumption.

[0064] The above describes specific embodiments of the present application. In addition, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0065] In the description of the embodiments of the present application, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiments of the present application. In the embodiments of the present application, the schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in the embodiments of the present application and the features of the different embodiments or examples, without contradicting each other.

[0066] In addition, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features, and do not include any ordering. Thus, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features and are used to distinguish each other. In the description of the embodiments of the present application, the meaning of "multiple" is at least two, for example, two, three, etc., unless otherwise clearly and specifically defined.

[0067] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred implementation of the embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0068] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for optimizing the engine speed of an excavator based on a particle swarm algorithm, characterized in that: include: Obtain a set of operating parameters of the excavator at different working stages during the excavation cycle; According to the set of operating parameters of each working stage, the initial three-dimensional particles corresponding to the working stage are set and the particle swarm algorithm is initialized, wherein the initial three-dimensional particles include three dimensions: speed, hydraulic pump displacement, and hydraulic pump displacement; An evaluation function is constructed based on the engine fuel consumption rate, the load resistance of the vehicle, the volumetric efficiency of the two hydraulic pumps and the constant flow ratio of the two hydraulic pumps, and the initial global optimal solution of each working stage is calculated; Based on the particle swarm algorithm, the initial global optimal solution is iteratively optimized for each working stage to generate the global optimal solution for each working stage.

2. The method according to claim 1, characterized in that The different working stages include a preparation stage, an excavation stage, a rotation stage, and an unloading stage, and the operating parameter set includes rotation speed, pump pressure, and torque data.

3. The method according to claim 2, characterized in that The division of different working stages includes: dividing into a preparation stage when the boom-down pilot pressure and the rotation pilot pressure of the excavator are both greater than the corresponding preset values, dividing into an excavation stage when the arm-retraction pilot pressure and the bucket-retraction pilot pressure of the excavator are both greater than the corresponding preset values, dividing into a material shifting stage when the boom-lifting pilot pressure and the rotation pilot pressure of the excavator are both greater than the corresponding preset values, and dividing into a unloading stage when the arm-overturning pilot pressure and the bucket-overturning pilot pressure of the excavator are both greater than the corresponding preset values.

4. The method according to claim 1, characterized in that: The constant flow ratio of the two hydraulic pumps refers to the ratio of the product of the rotational speed and displacement of the two hydraulic pumps before and after adjustment. The closer the constant flow ratio is to 1, the better the controllability. The load resistance of the whole vehicle is expressed by calculating the percentage of the current load torque to the maximum torque.

5. The method according to claim 1, characterized in that The steps of constructing the evaluation function include: The engine fuel consumption rate, the load resistance of the vehicle, the volumetric efficiency of the two hydraulic pumps and the constant flow ratio of the two hydraulic pumps are normalized in the range of [0,1], and the evaluation function P is obtained after weighted processing: P=K1×A+K2×(1-B)+K3×(1-C)+K4×(1-D); Among them, A represents the engine fuel consumption rate, B represents the load resistance of the whole vehicle, C represents the average volumetric efficiency of the two hydraulic pumps, and D represents the constant flow ratio of the two hydraulic pumps; K1, K2, K3, and K4 represent the distribution weights of the corresponding evaluation indicators respectively.

6. The method according to claim 1, characterized in that The steps of initializing the particle swarm algorithm include: setting the number of particles, the moving range and the moving speed range.

7. The method according to claim 1, characterized in that The steps for iterative optimization of the initial global optimal solution include: Continuously calculate the positions of newly generated particles and obtain their evaluation function values; When the preset number of iterations of the particle swarm algorithm is reached or the improvement of the global optimal solution converges to the preset threshold, the iteration process ends.

8. The method according to claim 7, characterized in that The position of the newly generated particle is expressed as: X i =X (i-1) +C1×rand×(P i -X (i-1) )+C2×rand×(g-X (i-1) )+C3V (i-1) ; Among them, X i represents the position of the newly generated particle, X (i-1) represents the last particle position, C1, C2, and C3 are learning factors, and C1 controls the particle's local optimal solution P i C2 controls the degree of following of the particle to the global optimal solution g, C3 represents the inertia weight, rand is the generated random number, V (i-1) Represents the last velocity of the particle.

9. A method for adjusting the speed of an excavator, characterized in that: include: Identify the current working stage of the excavator; Obtaining a global optimal solution corresponding to the working stage, wherein the global optimal solution is obtained by optimizing the excavator engine speed optimization method according to any one of claims 1 to 8; The speed and displacement of the engine are adjusted according to the global optimal solution.

10. An electronic device, characterized in that: It comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to implement the excavator engine speed optimization method based on the particle swarm algorithm as described in any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein when the computer program is run on a computer or a processor, the computer or the processor executes the excavator engine speed optimization method based on a particle swarm algorithm as described in any one of claims 1 to 8.

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