A method for online power matching of an electronically controlled positive flow excavator

Abstract

This invention discloses an online power matching method for an electronically controlled positive flow excavator in the field of engineering machinery control. The method includes: optimizing the power of two main pumps based on a comparison between the expected power of the two main pumps and the preset maximum engine power, obtaining the required real-time power of the two main pumps after power optimization and the actual flow demand of each main pump; using engine speed as the objective function, employing the CPSO algorithm for tracking optimization to obtain the optimal engine speed; and calculating the displacement of each main pump based on the optimal engine speed and the actual flow demand of each main pump, thus obtaining the control current of each main pump. This invention can perform real-time tracking optimization of engine speed, calculate the optimal target engine speed and main pump current, ensuring the engine's operating point falls within the optimal fuel consumption range. While guaranteeing the power required by the main pumps, it minimizes the engine's output power, achieving power matching to achieve the goal of minimum fuel consumption.

Description

Technical Field

[0001] This invention relates to an online power matching method for an electronically controlled positive flow excavator, belonging to the field of engineering machinery control technology. Background Technology

[0002] Excavators, as a type of engineering machinery, are widely used in many fields and industries. Most existing excavators are divided into single-pump and dual-pump types. Compared to traditional negative-flow hydraulic control systems, fully electronic control systems offer significant improvements in operational comfort, but overall machine performance falls short of expectations, resulting in energy and power losses. Firstly, there are friction losses as hydraulic oil passes through pipes and interfaces, and power losses due to friction pressure within the hydraulic system. Secondly, there are throttling losses; when the hydraulic excavator performs precision work, the machine cannot control the valves in real time according to the load, resulting in throttling losses. Simultaneously, during the hydraulic excavator's flow regulation, a large amount of hydraulic oil returns to the tank from the neutral position through bypass throttling, causing power losses. Thirdly, there are overflow losses; when the flow rate required by the hydraulic system is less than the pump's output flow rate, excess hydraulic oil returns to the tank through the overflow valve, leading to power losses. During the start-up and braking of the slewing mechanism, the huge rotational inertia causes the hydraulic pump to continue working, so the discharged hydraulic oil returns to the other side of the hydraulic pump through the overflow valve, causing energy losses.

[0003] Whether using a single or dual pump, fully electronically controlled excavators cannot fully absorb the engine's output power during operation. This is especially true for dual-pump tandem systems, where significant power loss occurs regardless of whether pressure-based or power-based control algorithms are employed. In the former case, when the pressure difference between the two pumps is too large, the pump with the lower pressure experiences substantial power loss due to pressure differences along the pump's path. In the latter case, during single-action operations, only half the power is utilized, with excess hydraulic oil returning to the tank via the relief valve, resulting in significant power waste. Therefore, it is necessary to optimize engine speed tracking to maintain stability, allowing for better pump absorption of engine output power. Simultaneously, employing different power distribution methods can achieve energy savings.

[0004] Currently, the power matching method for excavator systems operating with a single pump involves adding a constant power setting module. This module compares the pump displacement signal calculated using constant power with the pilot input signal, and outputs the smaller value. However, the pump's outlet pressure fluctuates significantly, and the pump flow rate also changes with the pressure, further affecting the pump's output power and causing substantial fluctuations. Excavator systems with dual pumps in series control utilize two types of constant power control algorithms: the power-sharing method and the pressure-sharing method. Both methods also suffer from significant power fluctuations due to large pressure and flow rate fluctuations. Furthermore, under varying load conditions, the pump's output power can overshoot, exacerbating power waste. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide an online power matching method for an electronically controlled positive flow excavator. It adopts a dual-pump oil supply system, with front and rear pumps supplying oil to the actuators respectively. The engine speed is tracked and optimized in real time, and the optimal target speed of the engine and the current of the main pump are calculated so that the engine's operating point falls in the optimal fuel consumption range. While ensuring the power required by the main pump, the engine's output power is minimized, thus achieving power matching to achieve the goal of minimum fuel consumption.

[0006] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0007] This invention provides a method for online power matching of an electronically controlled positive flow excavator, wherein the excavator employs a dual main pump oil supply system, and the method includes:

[0008] Based on the comparison between the expected power of the two main pumps and the preset maximum power of the engine, the power of the two main pumps is optimized to obtain the required real-time power of the two main pumps after power optimization and the actual flow requirement of each main pump.

[0009] Using engine speed as the objective function, the CPSO algorithm is employed for tracking and optimization to obtain the optimal engine speed.

[0010] Based on the engine's optimal speed and the actual flow requirements of each main pump, the displacement of each main pump is calculated, and the control current of each main pump is obtained.

[0011] Furthermore, the dual-pump oil supply system of the present invention uses front and rear dual pumps (i.e., the first main pump and the second main pump) to supply oil to the actuators. For example, the first main pump and the second main pump jointly supply oil for the boom retraction, boom swing, and boom raising; the first main pump supplies oil alone for the boom lowering, bucket retraction, and bucket swing; and the second main pump supplies oil alone for left slewing and right slewing.

[0012] Furthermore, the power optimization of the two main pumps based on the comparison between the expected power of the two main pumps and the preset maximum engine power includes:

[0013] When the expected power of the two main pumps does not exceed the preset maximum engine power, the required real-time power after power optimization of the two main pumps is equal to the expected power of the two main pumps.

[0014] When the expected power of the two main pumps exceeds the preset maximum engine power, the required real-time power after power optimization of the two main pumps is equal to the preset maximum engine power.

[0015] Furthermore, the method for calculating the expected power of the two main pumps includes:

[0016] The system collects real-time pressure data from the excavator's first and second main pumps, as well as the range of motion of the excavator's joystick.

[0017] Calculate the required flow rates of the first and second main pumps based on the range of motion of the handle.

[0018] The required total power is calculated based on the real-time pressure of the first and second main pumps and the required flow rate, which is the expected power of the two main pumps.

[0019] Furthermore, the calculation of the required total power based on the real-time pressure of the first and second main pumps and the required flow rate includes:

[0020] Total power required = Power required by the first main pump + Power required by the second main pump;

[0021] Power required for the first main pump = Real-time pressure of the first main pump * Flow rate required by the first main pump;

[0022] The power required for the second main pump = the real-time pressure of the second main pump * the flow rate required by the second main pump.

[0023] Furthermore, the method for calculating the actual flow requirement of each main pump includes:

[0024] Actual flow requirement of the first main pump = Flow required to be provided by the first main pump * (Real-time power required after power optimization of the two main pumps / Expected power of the two main pumps);

[0025] Actual flow requirement of the second main pump = Flow rate required by the second main pump * (Real-time power required after power optimization of the two main pumps / Expected power of the two main pumps).

[0026] Furthermore, the step of using engine speed as the objective function and employing the CPSO algorithm for tracking and optimization to obtain the optimal engine speed includes:

[0027] Determine the known inputs of the algorithm: the required real-time power after power optimization of the two main pumps, the real-time speed of the engine, the desired speed of the engine, and the universal characteristic curve of the engine;

[0028] Initialize the settings to include the maximum allowed number of iterations, inertia weights, and learning factor.

[0029] Based on the required real-time power and universal characteristic curves after power optimization of the two main pumps, the position and velocity of the chaotic particles are initialized according to the Logistic equation; where the position and velocity of the chaotic particles correspond to the engine speed.

[0030] According to formula v i =ω*v i +c1*rand*(Px i)+c2*rand*(Gx i ) and formula x i =x i +v i Update the velocity and position of each particle to obtain the optimal position P. g =(P g1 ,P g2 ,…,P gD ); where i = 1, 2…N, N is the total number of particles, v i Let x be the velocity of the particle. i Let c1 and c2 be the current position of the particle, rand be a random number between 0 and 1, ω be the inertia weight, P be the optimal position of the i-th particle, and G be the optimal position of the entire swarm.

[0031] For the optimal position P g =(P g1 ,P g2 ,…,P gD Perform chaos optimization;

[0032] Output the global optimal position to obtain the engine's optimal speed.

[0033] Furthermore, the initialization of the chaotic particle position and velocity based on the Logistic equation includes:

[0034] In formula B n+1 =μB n (1-B n In this context, μ is a control parameter, taken as μ = 4, and 0 ≤ B. n If the initial value B is ≤1, the system is in a chaotic state. n ∈[0,1], iterate to obtain a defined time series B1,B2,B3,…;

[0035] Randomly generate an n-dimensional vector where each component has a value between 0 and 1, B1 = (B 11 B 12 ,…,B 1n ), where n is the number of variables, and N vectors are obtained from the equation;

[0036] B n The component carriers are assigned to the value range of the corresponding variables, the fitness value of the particle swarm is calculated, and the M solutions with better performance are selected from the N initial populations as initial solutions, and M initial velocities are randomly generated.

[0037] If the fitness of a chaotic particle is better than its individual extreme value P, then P is set as the new position.

[0038] If the fitness of a chaotic particle is better than its individual extreme value G, then G is set as the new position.

[0039] Furthermore, the optimal position P g =(P g1 ,P g2 ,…,P gD Chaos optimization includes:

[0040] P gi (i = 1, 2, ..., D) is mapped to the domain [0, 1] of the logical equation. The equation is iterated to generate a sequence of chaotic variables. The generated sequence of chaotic variables is then returned to the original solution space through inverse mapping.

[0041] For each feasible solution experienced by the chaotic variable in the original solution space, the fitness value is calculated to obtain the best-performing feasible solution P. * ;

[0042] Use P * Replace the position of any chaotic particle in the current swarm. If the stopping condition is met, output the globally optimal position; otherwise, return to the step of initializing the chaotic particle's position and velocity.

[0043] Furthermore, based on the engine's optimal speed and the actual flow requirement of each master pump, the displacement of each master pump (the displacement being the amount of oil provided per revolution of the master pump) is calculated, and the control current of each master pump is obtained, including:

[0044] The displacement of the first master pump = the actual flow requirement of the first master pump / the optimal speed of the engine;

[0045] The displacement of the second main pump = the actual flow requirement of the second main pump / the optimal speed of the engine;

[0046] Then, based on the main pump drawings and current displacement table, the control current corresponding to each main pump is obtained.

[0047] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0048] The method described in this invention tracks and optimizes the engine speed, ensuring that the speed always operates within the optimal range according to load changes, thereby further reducing losses. At the same time, it tracks the pump's output power more accurately, further improving the power matching between the pump and the engine, and better reducing the excavator's energy consumption.

[0049] This invention utilizes the engine universal characteristic curve and CPSO algorithm to perform real-time tracking and optimization of engine speed, calculate the optimal engine target speed and main pump current, so that the engine operating point falls in the optimal fuel consumption range, and achieves the minimum engine output power while ensuring the power required by the main pump, thus completing power matching to achieve the goal of minimum fuel consumption.

[0050] The method described in this invention makes a power optimization judgment based on the expected power of the dual pumps, and adopts two modes to ensure that the power can be absorbed by the pumps to the maximum extent. Attached Figure Description

[0051] Figure 1 This is a flowchart of an online power matching method for an electronically controlled positive flow excavator provided in an embodiment of the present invention. Detailed Implementation

[0052] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations thereof. In the absence of conflict, the embodiments and technical features in the embodiments can be combined with each other.

[0053] This invention provides a method for online power matching of an electronically controlled positive flow excavator, wherein the excavator employs a dual main pump oil supply system, and the method includes:

[0054] Based on the comparison between the expected power of the two main pumps and the preset maximum power of the engine, the power of the two main pumps is optimized to obtain the required real-time power of the two main pumps after power optimization and the actual flow requirement of each main pump.

[0055] Using engine speed as the objective function, the CPSO algorithm is employed for tracking and optimization to obtain the optimal engine speed.

[0056] Based on the engine's optimal speed and the actual flow requirements of each main pump, the displacement of each main pump is calculated, and the control current of each main pump is obtained.

[0057] Figure 1 This is a flowchart illustrating an online power matching method for an electronically controlled positive flow excavator, as described in a specific application embodiment of the present invention. This flowchart merely shows the logical sequence of the method described in this embodiment; however, in other possible embodiments of the present invention, different methods may be used, provided there are no conflicts. Figure 1 Complete the steps shown or described in the order indicated.

[0058] The online power matching method for electrically controlled positive flow excavators provided in this embodiment can be applied to a terminal and can be executed by an online power matching device for electrically controlled positive flow excavators. This device can be implemented by software and / or hardware and can be integrated into the terminal, such as any smartphone, tablet computer, or computer device with communication capabilities.

[0059] The excavator described in this embodiment adopts a dual-pump oil supply system, with front and rear dual pumps (i.e., the first main pump and the second main pump) supplying oil to the actuators respectively. Specifically, the first and second main pumps jointly supply oil for the stick retraction, stick swing, and boom raising, while the first main pump supplies oil alone for the boom lowering, bucket retraction, and bucket swing, and the second main pump supplies oil alone for the left and right swing.

[0060] See Figure 1 The method in this embodiment specifically includes the following steps:

[0061] S1. Real-time pressure of the first and second main pumps of the excavator is collected by pressure sensors; based on the collected excavator handle movement amplitude, the flow rate required by the first and second main pumps is analyzed and calculated; based on the real-time pressure and the required flow rate, the real-time total power required by the main pumps, i.e. the expected power of the two main pumps, can be calculated.

[0062] Power optimization is performed on the calculated required power: Based on the operating mode, the maximum power of the engine corresponding to different modes is set (i.e., the preset maximum engine power). When the expected power of the two main pumps does not exceed the maximum engine power, the dual pumps output according to the expected power, and the required real-time power after power optimization of the two main pumps is equal to the expected power of the two main pumps. When the expected power of the two main pumps exceeds the maximum engine power, the required real-time power after power optimization of the two main pumps is equal to the preset maximum engine power. Simultaneously, the actual flow demand of each main pump is allocated proportionally, calculated as follows:

[0063] Actual flow requirement of the first main pump = Calculated flow required by the first main pump * (Real-time power required after power optimization of the two main pumps / Expected power of the two main pumps);

[0064] Actual flow requirement of the second main pump = Calculated flow required by the second main pump * (Required real-time power after power optimization of the two main pumps / Expected power of the two main pumps).

[0065] In this embodiment, the flow rates required by the first and second main pumps are calculated based on the handle's movement amplitude, which can be done using the following method:

[0066] In this method, the two handles are electric handles, each handle has four directions: front, back, left, and right. In each direction, according to the magnitude of the action, it can be converted into an analog signal H of 0-1000. Q is the required total flow rate of the main pump corresponding to different operations, and k is the ratio of the handle action to the corresponding flow rate, which is the test value. A direct proportional correspondence or other correspondence methods can also be used, and this application does not impose any restrictions.

[0067] Different operations for excavators:

[0068] When the boom moves alone, the required total flow rate of the main pump is Q1 = k1 * H1. The flow rate required by each main pump is further allocated by specific logic to obtain the flow rates pump1_arm and pump2_arm of each pump.

[0069] When the boom rises in a single motion, the total flow rate of the main pump required is Q2 = k2 * H2. The flow rate required by each main pump is further allocated by specific logic to obtain the flow rates pump1_boomup and pump2_boomup of each pump.

[0070] When the bucket operates alone, the flow rate required by the first main pump is pump1_bucket = k3 * H3, and the flow rate required by the second main pump is 0.

[0071] When the boom is lowered in a single motion, the flow rate required by the first main pump is pump1_boomdown = k4 * H4, and the flow rate required by the second main pump is 0.

[0072] When the rotary pump is in operation, the flow rate required by the second main pump is pump2_swing = k5 * H5, and the flow rate required by the first main pump is 0.

[0073] When performing a combined operation: the required flow rate of the first main pump = pump1_arm + pump1_boomup + pump1_boomdown + pump1_bucket.

[0074] The required flow rate of the second main pump = pump2_arm + pump2_boomup + pump2_swing.

[0075] In this embodiment, the real-time total power required by the main pump is calculated based on the real-time pressure and the required flow rate, which can be done using the following method:

[0076] Total power required = Power required by the first main pump + Power required by the second main pump;

[0077] Power required for the first main pump = Real-time pressure of the first main pump * Flow rate required by the first main pump;

[0078] The power required for the second main pump = the real-time pressure of the second main pump * the flow rate required by the second main pump.

[0079] S2. Using engine speed as the objective function, the CPSO algorithm is employed for tracking optimization. The known input quantities of the algorithm are determined, including: the required real-time power after optimization of the two main pumps, the real-time engine speed, the desired engine speed, and the engine's universal characteristic curve as known quantities for the algorithm. The basic parameters of the algorithm are initialized, including the maximum allowed number of iterations, the algorithm inertia weight, and the learning factor. In this embodiment, the inertia weight is set to 1, the learning factor is set to 2, and the maximum number of iterations is 100.

[0080] S3. Based on the required real-time power and universal characteristic curves after the power optimization of the two main pumps, initialize the position and velocity of the chaotic particles according to the Logistic equation; where the position and velocity of the chaotic particles correspond to the engine speed;

[0081] In formula B n+1 =μB n (1-B n In this context, μ is a control parameter, taken as μ = 4, and 0 ≤ B. n If the initial value B is ≤1, the system is in a chaotic state. n ∈[0,1], iterate to obtain a defined time series B1,B2,B3,…;

[0082] Randomly generate an n-dimensional vector where each component has a value between 0 and 1, B1 = (B 11 B 12 ,…,B 1n ), where n is the number of variables, and N vectors are obtained from the equation;

[0083] B n The component carriers are assigned to the value range of the corresponding variables, the fitness value of the particle swarm is calculated, and the M solutions with better performance are selected from the N initial populations as initial solutions, and M initial velocities are randomly generated.

[0084] According to S2, this embodiment determines the dimension to be 1, the number of chaotic particles to be 100, the number of iterations to be 100, the maximum value to be 2200 r / min, the particle velocity range to be 0≤v≤10, and the position range to be 0≤x≤10.

[0085] S4. If the fitness of the chaotic particle is better than the individual extreme value P, set P as the new position;

[0086] S5. If the fitness of the chaotic particle is better than the individual extreme value G, set G as the new position;

[0087] S6. According to the formula

[0088] v i =ω*v i +c1*rand*(Px i )+c2*rand*(Gx i );

[0089] x i =x i +v i ;

[0090] Update the velocity and position of each particle to obtain the optimal position P. g =(P g1 ,Pg2 ,…,P gD ); where i = 1, 2…N, N is the total number of particles, v i Let x be the velocity of the particle. i Let c1 and c2 be the current position of the particle, rand be a random number between 0 and 1, ω be the inertia weight, P be the optimal position of the i-th particle, and G be the optimal position of the entire swarm.

[0091] Finally, the historical optimal position and the global optimal position of the particle are determined, such as P = G = (8.5, 7.6).

[0092] S7. For the optimal position P g =(P g1 ,P g2 ,…,P gD Perform chaos optimization;

[0093] P gi (i = 1, 2, ..., D) is mapped to the domain [0, 1] of the logical equation. The equation is iterated to generate a sequence of chaotic variables. The generated sequence of chaotic variables is then returned to the original solution space through inverse mapping.

[0094]

[0095] For each feasible solution experienced by the chaotic variable in the original solution space, the fitness value is calculated to obtain the best-performing feasible solution P. * , such as P * =(8.0,7.5);

[0096] S8. Use P * Replace the position of any chaotic particle in the current group. If the stopping condition is met, output the globally optimal position, which is the optimal speed of the engine; otherwise, return to S3.

[0097] S9. Based on the engine's optimal speed and the actual flow requirement of each pump, calculate the displacement of each main pump, and then obtain the control current of each main pump. The calculation method is as follows:

[0098] The displacement of the first master pump = the actual flow requirement of the first master pump / the optimal speed of the engine;

[0099] The displacement of the second main pump = the actual flow requirement of the second main pump / the optimal speed of the engine;

[0100] Then, based on the main pump drawings and current displacement table, the control current corresponding to each main pump is obtained.

[0101] The displacement of the main pump is the amount of oil supplied in one revolution of the main pump.

[0102] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0103] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0104] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0105] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0106] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for online power matching of an electronically controlled positive flow excavator, characterized in that, The excavator employs a dual main pump oil supply system, and the method includes: Based on a comparison of the expected power of the two main pumps with the preset maximum engine power, the power of the two main pumps is optimized to obtain the required real-time power of the two main pumps after power optimization and the actual flow requirement of each main pump; including: When the expected power of the two main pumps does not exceed the preset maximum engine power, the required real-time power after power optimization of the two main pumps is equal to the expected power of the two main pumps. When the expected power of the two main pumps exceeds the preset maximum engine power, the required real-time power after power optimization of the two main pumps is equal to the preset maximum engine power. The calculation method for the actual flow requirement of each main pump includes: Actual flow requirement of the first main pump = Flow required by the first main pump * (Real-time power required after power optimization of the two main pumps / Expected power of the two main pumps). Actual flow requirement of the second main pump = Flow required by the second main pump * (Real-time power required after power optimization of the two main pumps / Expected power of the two main pumps). Using engine speed as the objective function, the CPSO algorithm is employed for tracking and optimization to obtain the optimal engine speed. Based on the engine's optimal speed and the actual flow requirements of each main pump, the displacement of each main pump is calculated, and the control current of each main pump is obtained.

2. The online power matching method for electronically controlled positive flow excavators according to claim 1, characterized in that, The method for calculating the expected power of the two main pumps includes: The system collects real-time pressure data from the excavator's first and second main pumps, as well as the range of motion of the excavator's joystick. Calculate the required flow rates of the first and second main pumps based on the range of motion of the handle. The required total power is calculated based on the real-time pressure of the first and second main pumps and the required flow rate, which is the expected power of the two main pumps.

3. The online power matching method for an electronically controlled positive flow excavator according to claim 2, characterized in that, The calculation of the required total power based on the real-time pressure of the first and second main pumps and the required flow rate includes: Total power required = Power required by the first main pump + Power required by the second main pump; The power required for the first main pump = the real-time pressure of the first main pump * the flow rate required by the first main pump; The power required for the second main pump = the real-time pressure of the second main pump * the flow rate required by the second main pump.

4. The online power matching method for electronically controlled positive flow excavators according to claim 1, characterized in that, The process of using engine speed as the objective function and employing the CPSO algorithm for tracking and optimization to obtain the optimal engine speed includes: Determine the known inputs of the algorithm: the required real-time power after power optimization of the two main pumps, the real-time speed of the engine, the desired speed of the engine, and the universal characteristic curve of the engine; Initialize the settings to include the maximum allowed number of iterations, inertia weights, and learning factor. Based on the required real-time power and universal characteristic curves after power optimization of the two main pumps, the position and velocity of the chaotic particles are initialized according to the Logistic equation; where the position and velocity of the chaotic particles correspond to the engine speed. According to the formula and formula Update the velocity and position of each particle to obtain the optimal position. ;in, =1,2…N, where N is the total number of particles. For the velocity of the particle, This represents the particle's current position. and The learning factor is rand, which is a random number between 0 and 1. As inertia weight, For the first The optimal position of each particle. This is the optimal position for the entire particle swarm. For the optimal position Perform chaos optimization; Output the global optimal position to obtain the engine's optimal speed.

5. The online power matching method for an electronically controlled positive flow excavator according to claim 4, characterized in that, The initialization of the chaotic particle position and velocity based on the Logistic equation includes: In the formula middle, It is a control parameter, taking ,set up The system is in a chaotic state, with arbitrary initial values. Iterate to obtain a defined time series ; Randomly generate an n-dimensional vector where each component has a value between 0 and 1. n is the number of variables, and N vectors are obtained according to the equation; Will The component carriers are assigned to the value range of the corresponding variables, the fitness value of the particle swarm is calculated, and the M solutions with better performance are selected from the N initial populations as initial solutions, and M initial velocities are randomly generated. If the fitness of a chaotic particle is better than its individual extreme value P, then P is set as the new position. If the fitness of a chaotic particle is better than its individual extreme value G, then G is set as the new position.

6. The online power matching method for an electronically controlled positive flow excavator according to claim 4, characterized in that, The optimal position Chaos optimization includes: Will Mapping to the domain [0,1] of the logical equation, iterating through the equation generates a sequence of chaotic variables. Then, the generated sequence of chaotic variables is returned to the original solution space via inverse mapping, yielding... ； For each feasible solution experienced by the chaotic variable in the original solution space, the fitness value is calculated to obtain the best-performing feasible solution. ; use Replace the position of any chaotic particle in the current swarm. If the stopping condition is met, output the globally optimal position; otherwise, return to the step of initializing the chaotic particle's position and velocity.

7. The online power matching method for an electronically controlled positive flow excavator according to claim 1, characterized in that, The process involves calculating the displacement of each main pump based on the engine's optimal speed and the actual flow requirement of each main pump, and obtaining the control current for each main pump, including: The displacement of the first master pump = the actual flow requirement of the first master pump / the optimal speed of the engine; The displacement of the second main pump = the actual flow requirement of the second main pump / the optimal speed of the engine; Then, based on the main pump drawings and current displacement table, the control current corresponding to each main pump is obtained.

Images