Excavator Engine Speed Optimization Method Based on Particle Swarm Algorithm, Excavator Speed Regulation Method, Electronic Device and Storage Medium

Abstract

The present application provides an optimization method for the engine speed of an excavator based on a particle swarm algorithm, an excavator speed adjustment method, an electronic device, and a storage medium, belonging to the technical field of engine control. The solution includes: obtaining a set of operation parameters for different working stages during the excavation cycle of the excavator; setting an initial three-dimensional particle corresponding to each working stage according to the set of operation parameters for each working stage, and performing initialization processing of the particle swarm algorithm; constructing an evaluation function based on the engine fuel consumption rate, the vehicle's anti-load capacity, the volumetric efficiency of two hydraulic pumps, and the constant flow rate ratio of two hydraulic pumps, and calculating the initial global optimal solution for each working stage; and performing iterative optimization of the initial global optimal solution for each working stage based on the particle swarm algorithm. The optimization method for the engine speed of an excavator based on the particle swarm algorithm in the present application can find the speed displacement point with the best economy.

Description

Technical Field

[0001] The present application relates to the technical field of engine control. Specifically, it relates to a method for optimizing the speed of an excavator engine based on a particle swarm algorithm, an excavator speed adjustment method, an electronic device, and a computer-readable storage medium. Background Technique

[0002] Fuel consumption efficiency is an important indicator for measuring the performance of an excavator. The existing excavator strategy is a constant speed control strategy, and the adjustment of speed and displacement is fixed within the same driving cycle. However, during the actual working process, the load is constantly changing. In the existing speed control logic, the speed adjustment is mainly based on manual calibration. A large number of repetitive calibration experiments are required to determine the optimal speed adjustment value, and new problems will occur with the speed adjustment value after the external load changes. It is impossible to achieve adaptive adjustment, resulting in low overall fuel efficiency of the excavator.

[0003] The Chinese patent application document with the publication number CN114357880A discloses an optimization method for the working point of a hydraulic excavator engine in stages. It divides an operation cycle into five working stages based on the pilot pressure on the outlet pressure waveform curves of the front pump and the rear pump, uses the waveform curve values of the outlet pressure of the front pump and the rear pump in the initial period of a working stage as characteristic values, and establishes a classification model using a multi-classification algorithm according to the divided working stages. An engine fuel consumption model, a hydraulic pump efficiency model, and a load adaptability evaluation function are established. Taking the engine output torque being greater than the minimum torque required for actual work in different working stages as a constraint condition, the optimal working point of the engine in different working stages is obtained using a multi-objective optimization algorithm. A gear position feedback mechanism is established to enable the driver to select a suitable gear position to avoid the engine from stalling due to insufficient power. However, this solution requires the use of multiple evaluation functions as constraint conditions for calculation, and also requires the driver to perform gear shifting operations, and cannot perform adaptive 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. Aiming at the method of stabilizing the working point by controlling the displacement of the hydraulic pump, it comprehensively considers the fuel consumption of the excavator engine, the efficiency of the hydraulic pump, and the load change of the excavator. The relationship between the fuel consumption of the engine and its speed and torque is determined according to the universal characteristic curve of the engine; 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; 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 comprehensive evaluation optimization model is established with the low fuel consumption of the engine, high efficiency of the hydraulic pump, and small fluctuation of the engine working point during actual operation as the optimization goal. The importance of engine fuel consumption and load fluctuation is determined by setting the weight coefficient, 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 solution cannot achieve the adaptive adjustment of the excavator's speed for different working conditions.

[0005] Therefore, there is an urgent need to develop a new method for optimizing the speed of the excavator engine, with the optimization goals of reducing fuel consumption and improving work efficiency, and realizing the adaptive adjustment of the excavator's speed for different working conditions. Summary of the Invention

[0006] This application aims to solve at least one of the technical problems in the related art to some extent. For this purpose, this application provides a method for optimizing the speed of an excavator engine based on the particle swarm algorithm, an excavator speed adjustment method, an electronic device, and a computer-readable storage medium. Optimizing the control of the speed based on the particle swarm algorithm can reduce fuel consumption, improve work efficiency, and ensure good control effects at the same time.

[0007] To achieve the above object, in the first aspect, this application provides a method for optimizing the speed of an excavator engine based on the particle swarm algorithm, including: obtaining the set of operation parameters of the excavator in different working stages during the excavation cycle; setting the initial three-dimensional particle corresponding to each working stage according to the set of operation parameters of each working stage and performing the initialization process of the particle swarm algorithm, where the initial three-dimensional particle includes three dimensions: speed, hydraulic pump one displacement, and hydraulic pump two displacement; constructing an evaluation function based on the engine fuel consumption rate, the vehicle's anti-load capacity, 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 of each working stage; performing iterative optimization of the initial global optimal solution for each working stage based on the particle swarm algorithm to generate the global optimal solution of each working stage.

[0008] Preferably, the different working stages include the preparation stage, the excavation stage, the slewing stage, and the unloading stage, and the set of operation parameters includes 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 iThe degree of following, 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, and V (i-1) represents the previous velocity of the particle.

[0016] In a second aspect, the present application provides an excavator speed regulation method, including: identifying the working stage where the excavator is currently located; obtaining the global optimal solution corresponding to the working stage, where the global optimal solution is obtained by optimizing according to any one of the above-mentioned excavator engine speed optimization methods; 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, including: a memory, a processor, and a computer program stored on the memory and capable of running on the processor, and the processor executes the program to implement any one of the above-mentioned excavator engine speed optimization methods.

[0018] In a fourth aspect, the present application provides a computer-readable storage medium, including a computer program, and when the computer program is run on a computer or a processor, the electronic device is enabled to execute any one of the above-mentioned excavator engine speed optimization methods.

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

[0020] The present application uses the particle swarm algorithm to optimize the speed control. On the one hand, optimizing the speed control has less impact on the driver's operability compared to torque control. On the other hand, introducing the particle swarm algorithm can optimize the speed and displacement point with the best fuel consumption efficiency, and the calculation speed of obtaining the global optimal solution will be faster;

[0021] The present application constructs an evaluation function based on the engine fuel consumption rate, the vehicle's anti-load capacity, the volumetric efficiency of the two hydraulic pumps, and the constant flow ratio of the two hydraulic pumps, so that it fully considers the fuel consumption, anti-load capacity, transmission loss, and the impact on the overall vehicle efficiency during the optimization process, and can ensure the best overall controllability while ensuring the lowest fuel consumption.

[0022] Other features and advantages of the present application will be described in the subsequent specification, and, in part, will become apparent from the specification, or can be understood by implementing the present application. The objectives and other advantages of the present application can be achieved and obtained through the structures specifically pointed out in the written specification and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings forming a part of this application are used to provide a further understanding of this application. The schematic embodiments and descriptions thereof of this application are used to explain this application and do not constitute an improper limitation of this application. In the drawings:

[0024] Figure 1 It is a schematic flowchart of the excavator engine speed optimization method based on the particle swarm algorithm of this application. Specific embodiments

[0025] In order to make the purpose, technical solutions and advantages of this application clearer, the following will further describe this application in detail with reference to specific embodiments and the accompanying drawings.

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

[0027] Aiming at the deficiencies of the prior art, the purpose of this application is to provide an excavator engine speed optimization method, adjustment method, device and medium, aiming to overcome the problem that the current excavator control system cannot realize the 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, using the exhaustive or segmented method will result in incomplete coverage and falling into local optimal solutions. Introducing the particle swarm algorithm will make the overall vehicle coverage wider and easier to find the global optimum. Therefore, this application introduces the particle swarm algorithm, which can optimize the speed and displacement point with the best economy. At the same time, the excavator working conditions are divided into stages, and different global optimal solution corresponding working parameters are introduced in different stages, which can achieve the highest fuel efficiency while meeting the economy.

[0029] Embodiment 1

[0030] In order to study an engine speed optimization control method that can adaptively adjust the speed according to different working conditions, the inventor of this application has 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, as Figure 1 shown, an excavator engine speed optimization method based on the particle swarm algorithm is provided, including the following steps:

[0032] S1. Obtain the set of operation parameters of the excavator in different working stages during the excavation cycle;

[0033] S2. Set the initial three-dimensional particles corresponding to this working stage according to the set of operation parameters for each working stage, and perform the initialization process of the particle swarm algorithm. Among them, the initial three-dimensional particles include three dimensions: rotational speed, displacement of hydraulic pump 1, and displacement of hydraulic pump 2.

[0034] S3. Construct an evaluation function based on the engine fuel consumption rate, the vehicle's anti-load capacity, the volumetric efficiency of the two hydraulic pumps, and the constant flow ratio of the two hydraulic pumps, and calculate the initial global optimal solution for each working stage.

[0035] S4. Based on the particle swarm algorithm, perform iterative optimization of the initial global optimal solution for each working stage respectively to generate the global optimal solution for each working stage.

[0036] In the above step S1, obtaining the set of operation parameters for different working stages during the excavation cycle of the excavator can be obtaining the set of operation parameters for different working stages during the previous excavation cycle, or obtaining the set of operation parameters for different working stages during the previous several excavation cycles. Specifically, how many previous times to select can be set according to actual working requirements.

[0037] Among them, setting the initial three-dimensional particles corresponding to this working stage and performing the initialization process of the particle swarm algorithm includes: selecting the number of particles Num, selecting the iteration period T of the particles, selecting the movement range [Nmin, Nmax] of the particles, selecting the movement speed range [Vmin, Vmax] of the particles, and setting the initial three-dimensional particles. The three dimensions include rotational speed, displacement of pump 1, and displacement of pump 2 (front pump and rear pump). Specifically, for example, set the number of particles to 100, the optimization period to 20 times, and set the initial particles to X i , the movement speed to V i , when the number of iterations is reached, obtain the set rotational speed and displacement of the optimal particle. If the number of particles is insufficient, 100 random numbers Rand1 between [0, 1] can be randomly generated, and then the initial particle position X can be obtained by using the formula Rand1 × [Nmin, Nmax] i ; randomly generate 100 random numbers Rand2 between [0, 1], Rand2 × [Vmin, Vmax], to obtain the initial particle velocity V i .

[0038] After detecting the excavation cycle enable, during the excavation preparation stage, the controller collects basic signals such as rotational speed, pump pressure, and torque. After the preparation stage is completed, the evaluation function of the corresponding initial three-dimensional particles is calculated. According to the evaluation function of each initial three-dimensional particle, the initial local optimal solution P of each particle is obtained i, simultaneously obtain the initial global optimal solution g according to the optimal solution of the evaluation function satisfied by all initial three-dimensional particles. Similarly, during the three stages of excavation, slewing, and unloading, after the controller stores the basic signals such as the rotational speed, pump pressure, and torque in each stage, iterative optimization of the particle swarm algorithm is performed. After the optimization is completed, when passing through the corresponding stage again, adjust the rotational speed and displacement of the corresponding stage.

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

[0040] Furthermore, the different working stages include the preparation stage, excavation stage, slewing stage, and unloading stage, and the set of operation parameters includes basic signals such as rotational speed, pump pressure, and torque data. Preferably, the division of different working stages includes: dividing into the preparation stage according to that both the boom lowering pilot pressure and the slewing pilot pressure of the excavator are greater than the corresponding preset values, dividing into the excavation stage according to that both the stick in pilot pressure and the bucket in pilot pressure of the excavator are greater than the corresponding preset values, dividing into the material transfer stage according to that both the boom raising pilot pressure and the slewing pilot pressure of the excavator are greater than the corresponding preset values, and dividing into the unloading stage according to that both the stick out pilot pressure and the bucket out pilot pressure of the excavator are greater than the corresponding preset values. Among them, the load in the preparation stage and the unloading stage is relatively low, while the load in the excavation stage and the material transfer stage is relatively high. Therefore, during the optimization process, it is necessary to adjust the rotational speed downward and increase the displacement in the unloading stage and the preparation stage, and at the same time make the flow rate before and after adjustment as consistent as possible, that is, the original flow rate of the original rotational speed × original displacement is the same as the new flow rate of the new rotational speed × new displacement. This can make the operability before and after adjustment approach consistency; at the same time, during the optimization process, protection should be added to the lowest rotational speed during adjustment to ensure that at this rotational speed, the flow rate demand can still be met when the pump is at full displacement. Correspondingly, for the excavation stage and the material transfer stage, adjust the rotational speed upward and reduce the displacement, and also ensure that the flow rate of the original rotational speed × original displacement before the change is the same as that of the new rotational speed × new displacement after the change.

[0041] Preferably, the constant flow rate 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 rate ratio is to 1, the better the operability; the anti-load capacity of the whole vehicle is represented by calculating the percentage of the current load torque in the maximum torque.

[0042] Preferably, the steps of constructing the evaluation function include normalizing the engine fuel consumption rate, the load resistance of the whole vehicle, the volumetric efficiency of the two hydraulic pumps, and the constant flow rate ratio of the two hydraulic pumps within the range of [0, 1], and obtaining the evaluation function P after weighted processing, which is expressed as: P = K1×A + K2×(1 - B) + K3×(1 - C) + K4×(1 - D); where 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 rate ratio of the two hydraulic pumps; K1, K2, K3, and K4 respectively represent the distribution weights of the corresponding evaluation indicators, which can be specifically set according to the actual situation.

[0043] In the evaluation function of the above optimal particle, the engine fuel consumption rate A is to ensure that the optimized speed and displacement meet the optimal fuel consumption. The calculation method of the fuel consumption rate A is as follows: taking the engine speed n as the abscissa and the average torque Trq as the ordinate, the fuel consumption rate curve is obtained, forming the universal characteristic of the engine. The universal characteristic curves of different engines are different, and the final fuel consumption rate is obtained by real-time detecting 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 suddenly encountering a large load and will not affect the working efficiency due to pulling down the speed. The calculation method of the load resistance B of the whole vehicle is as follows: according to the curve of the engine output torque changing with the speed when the engine throttle opening is 100%, which represents the maximum load torque at different speeds. The external characteristic curves of different engines are different, and the load resistance is also different. The calculation method of the load resistance B is as follows: B = Trq ave / Trq max where Trq ave is the current load torque, and Trq max is the maximum torque; the percentage of the calculated current load torque accounting for the maximum torque is the load resistance. When B is smaller, it means that the remaining torque of the engine is more 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 optimized displacement meets a higher volumetric efficiency and reduce the transmission loss. The calculation method of the volumetric efficiency C of the pump is as follows: the volumetric efficiency of the pump is obtained according to the displacement percentage of the pump, the pump pressure, and the speed. The higher the volumetric efficiency of the pump, the less the loss from the engine end to the pump end of the whole vehicle.

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

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

[0048] The ratio of the original strategy rotational speed × the original strategy displacement / the new strategy rotational speed × the new strategy displacement. The closer it is to 1, the higher the consistency of the controllability with the original strategy.

[0049] Normalize the engine fuel consumption rate, the vehicle's anti-load capacity, the volumetric efficiency of the two hydraulic pumps, and the constant flow rate ratio of the two hydraulic pumps respectively within the range of [0, 1]. After normalization, perform weighted processing to obtain the final evaluation function. The smaller the value of the final evaluation function, the better the optimization effect.

[0050] Preferably, the steps for initializing the particle swarm algorithm include: setting the number of particles, the movement range, and the movement speed range.

[0051] Preferably, the steps for iterative optimization of the initial global optimal solution include: continuously calculating the positions of newly generated particles and obtaining 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 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(g-X (i-1) )+C3V (i-1) ;wherein, X i represents the position of the newly generated particle, X (i-1) represents the position of the previous particle, P i represents the local optimal solution of the current particle (i.e., the optimal position found by this particle in historical iterations), g represents the global optimal solution (i.e., the optimal position found among all particles), C1, C2, and C3 are learning factors, C1 controls the degree of following of the particle 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, and V (i-1) represents the previous speed of the particle.

[0054] In the above step S4, iterative optimization of the initial global optimal solution is performed for each working stage based on the particle swarm algorithm to generate the global optimal solution for each working stage. Specifically, based on the position of the newly generated particle (including three dimensions: rotational speed, displacement of hydraulic pump one, and displacement of hydraulic pump two), adjust the engine rotational speed and displacement accordingly, and then obtain the evaluation function at the position of the newly generated particle. Repeat this process. When the preset number of iterations of 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 solution uses the particle swarm optimization algorithm to optimize the control of the rotational speed, and can find the rotational speed displacement point with the best fuel consumption efficiency. Moreover, an evaluation function is constructed based on the engine fuel consumption rate, the anti-load capacity of the whole vehicle, the volumetric efficiency of the two hydraulic pumps, and the constant flow rate ratio of the two hydraulic pumps, so that the fuel consumption, anti-load capacity, transmission loss, and impact on the overall vehicle efficiency during the excavation process are fully considered during the optimization process.

[0056] Embodiment 2

[0057] This application provides an excavator rotational speed adjustment method, including: identifying the current working stage of the excavator; obtaining the global optimal solution corresponding to the working stage, where the global optimal solution is obtained by optimizing according to the excavator engine rotational speed optimization method in Embodiment 1 above; adjusting the rotational speed and displacement of the engine according to the global optimal solution.

[0058] Among them, regarding the identification of the current working stage of the excavator, the division of different working stages includes: dividing into the preparation stage according to that both the boom lowering pilot pressure and the swing pilot pressure of the excavator are greater than the corresponding preset values, dividing into the excavation stage according to that both the stick in pilot pressure and the bucket in pilot pressure of the excavator are greater than the corresponding preset values, dividing into the material transfer stage according to that both the boom raising pilot pressure and the swing pilot pressure of the excavator are greater than the corresponding preset values, and dividing into the unloading stage according to that both the stick out pilot pressure and the bucket out pilot pressure of the excavator are greater than the corresponding preset values.

[0059] This solution divides the excavation work cycle into stages, and for the working range of each stage, uses the particle swarm optimization algorithm to optimize the control of the rotational speed and displacement, and finally obtains the optimal rotational speed of each stage, making the overall economy the highest. It truly realizes the self-optimization of the rotational speed and displacement according to the actual load torque, so as to achieve the purpose of the lowest fuel consumption and the highest efficiency.

[0060] Embodiment 3

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

[0062] Embodiment 4

[0063] Based on the same technical concept, an embodiment of the present application further provides a computer-readable storage medium storing a computer program. When the computer program runs on a computer or a processor, the computer or the processor is caused to execute the steps of the above-mentioned adaptive force feedback method and achieve the following functions: The particle swarm algorithm is used to optimize the control of the rotational speed, which has less impact on the driver's controllability compared to torque control; and by introducing the particle swarm algorithm, the rotational speed displacement point with the best fuel consumption efficiency can be optimized, and the calculation speed of finding the global optimal solution is faster; an evaluation function is constructed based on the engine fuel consumption rate, the vehicle's anti-load capacity, the volumetric efficiency of the two hydraulic pumps, and the constant flow ratio of the two hydraulic pumps, so that the fuel consumption, anti-load capacity, transmission loss, and impact on the overall vehicle efficiency during the excavation process are fully considered during the optimization process, and the best overall controllability can be ensured on the basis of ensuring the lowest fuel consumption.

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

[0065] In the description of the embodiments of the present application, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials, or characteristics described in connection 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 embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in the embodiments of the present application and the features of different embodiments or examples.

[0066] Furthermore, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features, excluding any order. Thus, the features defined with "first" and "second" can 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 "a plurality" is at least two, such as two, three, etc., unless otherwise specifically and clearly defined.

[0067] Any process or method description depicted in the flowchart or otherwise described herein may be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a customized logical function or process, and the scope of the preferred implementation of the embodiments of the present application includes additional implementations, where the functions may be performed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the technical field to which the embodiments of the present application pertain.

[0068] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. An optimization method for the engine speed of an excavator based on the particle swarm algorithm, characterized in that, Comprising: Obtaining a set of operation parameters for different working stages during the excavation cycle of an excavator; The different working stages include a preparation stage, an excavation stage, a slewing stage, and a discharging stage, and the set of operation parameters includes rotational speed, pump pressure, and torque data; wherein, the loads in the preparation stage and the discharging stage are relatively low, while the loads in the excavation stage and the slewing and discharging stages are relatively high; According to the set of operation parameters for each working stage, setting an initial three-dimensional particle corresponding to this working stage and performing initialization processing of the particle swarm algorithm, where the initial three-dimensional particle includes three dimensions: rotational speed, displacement of hydraulic pump one, and displacement of hydraulic pump two; Constructing an evaluation function based on the engine fuel consumption rate, the load resistance ability of the whole vehicle, the volumetric efficiency of two hydraulic pumps, and the constant flow rate ratio of two hydraulic pumps, and calculating the initial global optimal solution for each working stage; The steps of constructing the evaluation function include: respectively performing normalization processing within the range of [0, 1] on the engine fuel consumption rate, the load resistance ability of the whole vehicle, the volumetric efficiency of two hydraulic pumps, and the constant flow rate ratio of two hydraulic pumps, and obtaining the evaluation function P after weighted processing, which is expressed as: P = K1×A + K2×(1 - B) + K3×(1 - C) + K4×(1 - D); where A represents the engine fuel consumption rate, B represents the load resistance ability of the whole vehicle, C represents the average volumetric efficiency of two hydraulic pumps, D represents the constant flow rate ratio of two hydraulic pumps; K1, K2, K3, and K4 respectively represent the distribution weights of the corresponding evaluation indicators; The steps of performing iterative optimization of the initial global optimal solution include: continuously calculating the positions of newly generated particles and obtaining 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 a preset threshold, the iterative process ends; Based on the particle swarm algorithm, respectively performing iterative optimization of the initial global optimal solution 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 division of the different working stages includes: when the pilot pressure of the boom lowering and the pilot pressure of the slewing of the excavator are both greater than the corresponding preset values, it is divided into the preparation stage, and then when the pilot pressure of the stick in and the pilot pressure of the bucket in of the excavator are both greater than the corresponding preset values, it is divided into the excavation stage, and then when the pilot pressure of the boom raising and the pilot pressure of the slewing of the excavator are both greater than the corresponding preset values, it is divided into the material transfer stage, and then when the pilot pressure of the stick out and the pilot pressure of the bucket out of the excavator are both greater than the corresponding preset values, it is divided into the discharging stage.

3. The method according to claim 1, wherein The constant flow rate ratio of the two hydraulic pumps refers to the ratio of the product of the rotational speed and displacement before and after adjustment of the two hydraulic pumps, and the closer the constant flow rate ratio is to 1, the better the controllability; the load resistance ability of the whole vehicle is represented by calculating the percentage of the current load torque in the maximum torque.

4. The method according to claim 1, wherein The steps of performing initialization processing of the particle swarm algorithm include: setting the number of particles, the movement range, and the movement speed range.

5. The method according to claim 1, wherein 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, and X (i-1) represents the position of the previous particle. C1, C2, and C3 are learning factors. C1 controls the degree of following of the particle to the local optimal solution P i while 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, and V (i-1) represents the previous velocity of the particle.

6. A method for adjusting the rotational speed of an excavator, characterized in that, Comprising: Identifying the current working stage of the excavator; Obtaining the global optimal solution corresponding to the working stage, where the global optimal solution is optimized according to the excavator engine speed optimization method described in any one of claims 1 to 5. Adjust the engine speed and displacement according to the global optimal solution.

7. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and capable of running on the processor. The processor executes the computer program to implement the excavator engine speed optimization method based on the particle swarm algorithm according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program. When the computer program runs on a computer or a processor, the computer or the processor executes the excavator engine speed optimization method based on the particle swarm algorithm according to any one of claims 1 to 5.

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