Load-sensitive multi-way valve hydraulic system of agricultural tractor

Abstract

The invention relates to the technical field of multi-way valve hydraulic systems, in particular to a load-sensitive multi-way valve hydraulic system of an agricultural tractor. Comprising an intelligent control module, a load-sensitive variable pump, a load-sensitive multi-way valve, an execution element, a pressure sensor, a temperature sensor, a flow sensor, a temperature compensation and self-adaptive control system and an intelligent control system and intelligent overflow valve design and control optimization system. Wherein the flow and the pressure of the load-sensitive variable pump are adjusted according to the load requirement of the system. Through the optimal design of a traditional load-sensitive multi-way valve hydraulic system, pressure loss caused by an oil duct damping hole is reduced, the influence of low temperature on the system in a cold environment is solved, the response speed and control precision of the system are improved, and the service life of the system is prolonged. A more efficient oil duct structure, intelligent overflow valve control, a temperature compensation mechanism and a self-adaptive control strategy are adopted, and efficient and stable operation of the hydraulic system under various working conditions is ensured.

Description

Technical Field

[0001] The invention relates to the technical field of multi-way valve hydraulic systems, in particular to a load-sensitive multi-way valve hydraulic system for an agricultural tractor. Background Art

[0002] The load-sensing system uses closed-loop control between the load-sensing multi-way valve and the load-sensing variable pump. It has the advantages of low power loss, no load influence, and good coordination of compound actions. It is increasingly used in construction machinery.

[0003] The existing load-sensitive multi-way valve usually has two LS relief valves on each valve, and each LS relief valve is set with a different relief pressure. When the compound action is performed, the maximum LS pressure is transmitted to the variable mechanism of the load-sensitive pump through the shuttle valve, so that the pump outputs the required flow. However, the LS relief pressure is affected by the damping hole in the oil channel of the multi-way valve, resulting in inevitable pressure loss in the process of transmitting the LS pressure, especially in cold winter, which greatly affects the response speed and control accuracy of the system.

[0004] Therefore, a load-sensitive multi-way valve hydraulic system for agricultural tractors is proposed. Summary of the invention

[0005] The object of the present invention is to provide a load-sensitive multi-way valve hydraulic system for an agricultural tractor to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: a load-sensitive multi-way valve hydraulic system for an agricultural tractor, comprising an intelligent control module, a load-sensitive variable pump, a load-sensitive multi-way valve, an actuator, a pressure sensor, a temperature sensor, a flow sensor, a temperature compensation and adaptive control system and an intelligent control system, and an intelligent overflow valve design and control optimization system;

[0007] The load-sensitive variable pump: This pump adjusts the flow and pressure according to the load requirements of the system and outputs the required hydraulic power. Its control is adjusted by the pressure signal of the load-sensitive multi-way valve;

[0008] The load-sensitive multi-way valve has the main task of controlling the flow and pressure of the system by adjusting the pressure of the LS relief valve according to the load conditions, ensuring that the pressure and flow requirements of the hydraulic system match the load conditions;

[0009] The actuators: hydraulic cylinders and motors, perform tasks according to the pressure and flow output by the multi-way valve;

[0010] Intelligent control module: This module contains a real-time feedback mechanism, which monitors the system's pressure, flow, and temperature parameters through sensors to optimize the control strategy;

[0011] Temperature compensation and adaptive control system, the system integrates oil temperature sensor to monitor the temperature change of hydraulic oil in real time. The control module automatically adjusts the system pressure and flow according to the real-time oil temperature to compensate for the change of oil viscosity.

[0012] The intelligent relief valve design and control optimization system includes an electronically adjustable relief valve. The control of the relief valve is adjusted through an electronic sensor, which monitors the hydraulic oil temperature and pressure changes in real time and automatically adjusts the relief valve opening according to the working conditions.

[0013] Preferably, the intelligent overflow valve design and control optimization system comprises the following steps:

[0014] Step 1: Establish the control model of the overflow valve:

[0015] Flow rate Q of relief valve valve The relationship between the opening x is as follows:

[0016]

[0017] Among them, C v is the flow coefficient of the relief valve, ΔP is the pressure difference, f(x) is the function between the opening of the relief valve and the flow rate, x is the opening, and the value range is [0,1];

[0018] Step 2: Overflow valve control algorithm: Use PID controller to adjust the opening of the overflow valve according to the pressure and flow feedback of the hydraulic system. PID control algorithm:

[0019]

[0020] Among them, e(t) = Pset-P(t) is the error, that is, the difference between the set pressure and the current pressure, u(t) is the control signal, Kp, Ki, Kd are proportional, integral, and differential gains respectively;

[0021] Step 3: Realize real-time control of the opening. According to the control signal u(t)u(t), calculate the required opening of the relief valve x(t)x(t), so that the flow of the relief valve meets the load demand. When the system pressure deviates from the set pressure, the electronic relief valve adjusts the valve body opening according to the PID control algorithm.

[0022] Step 4: Overflow valve control based on temperature compensation, temperature sensor feedback and oil viscosity compensation, and optimization of adaptive control algorithm.

[0023] Preferably, the temperature sensor feedback and oil viscosity compensation, oil temperature sensors are installed at the pump inlet and the oil tank to monitor the temperature of the hydraulic oil in real time. When the oil temperature is T, the viscosity μ(T) of the oil will change with the change of temperature. The viscosity usually has the following relationship: μ(T)=μ0·e -αT

[0024] Among them, μ0 is the viscosity at room temperature, α is the temperature coefficient, and T is the temperature;

[0025] Establish a temperature compensation function, add the temperature compensation factor to the relief valve control, and set the pressure setting of the relief valve, that is, the set pressure P set (T) varies with oil temperature;

[0026] Temperature compensation function:

[0027] P set (T) = P set0 (1+β(T-T0))

[0028] Among them, P set0 is the set pressure at room temperature, β is the temperature compensation coefficient, T0 is room temperature;

[0029] Dynamically adjust the pressure setting of the relief valve. Each time the relief valve is adjusted, the intelligent control module dynamically adjusts the pressure setting value based on the data of the temperature sensor.

[0030] Preferably, the optimization of the adaptive control algorithm introduces an adaptive PID control algorithm, and adjusts the PID parameters according to the real-time measured pressure, flow and temperature signals. The controller gains Kp, Ki, Kd can be dynamically adjusted by an online identification algorithm (such as the least squares method);

[0031] Dynamically adjust PID parameters:

[0032] According to the working state of the system, that is, load change and temperature change, the PID gain is dynamically adjusted by the following formula:

[0033] K p (t) = K p0 +δK p (T,ΔP)

[0034] K i (t) = K i0 +δK i (T,ΔP)

[0035] K d (t) = K d0 +δK d (T,ΔP)

[0036] Among them, K p0 ,K i0 ,K d0 is the initial gain, δK p ,δK i ,δK d is the adjustment amount, T is the temperature, and ΔP is the pressure change.

[0037] Preferably, the temperature compensation and adaptive control system specifically comprises the following steps:

[0038] Step 1: Install multiple oil temperature sensors at the pump suction port, the bottom of the oil tank, and the overflow valve to monitor the temperature change of the oil in real time and input the temperature T(t) data into the intelligent control module;

[0039] Step 2: Establish an adaptive temperature model. Based on the temperature data collected by the sensor, establish an adaptive temperature control model for the hydraulic system. The temperature change rate Related to the changes in oil viscosity μ(T) and flow rate:

[0040] Where α is the thermal conductivity coefficient Q pump is the flow rate of the pump;

[0041] Step 3: Adaptive compensation control strategy, introduce adaptive PID control algorithm, adjust control parameters according to oil temperature in real time, the impact of temperature change on system performance will be calculated in real time and fed back to the control module, adjust the pump flow and the pressure setting of the relief valve, the goal of adaptive control is to minimize the negative impact of temperature change, through the following objective function:

[0042]

[0043] The objective function describes the relationship between the system output pressure P(t) and the target pressure P set The adaptive control compensates for the temperature change by minimizing the difference between the two.

[0044] Preferably, the step 2, the adaptive temperature model further includes introducing a neural network model, training a multi-layer perceptron neural network by inputting multi-dimensional data of temperature, pressure and flow rate, and outputting the viscosity of the hydraulic oil:

[0045] μ(T,P,Q)=f(T,P,Q)

[0046] Where T is the oil temperature, P is the system pressure, Q is the flow rate, and μ(T,P,Q) is the viscosity of the oil.

[0047] Preferably, the step 2, the adaptive temperature model also includes fuzzy control and reinforcement learning technology for optimization, and the fuzzy control introduces adaptive fuzzy control so that the fuzzy rule base is automatically updated with the feedback data during system operation;

[0048] When the temperature changes, reinforcement learning adjusts the system parameters in real time, by learning the optimal control strategy related to the temperature and pressure state:

[0049]

[0050] Among them, Q(s t ,a t ) is the value of taking action at in state st, rt is the immediate reward, and γ is the discount factor.

[0051] Preferably, the step 2, the adaptive temperature model further includes introducing a multi-physics field coupling model influenced by load; the multi-physics field coupling model: establishing a dynamic coupling equation:

[0052]

[0053] Where T is the oil temperature, Q in and Q out are the input and output heat flows, ρ is the oil density, c p is the specific heat capacity and α is the thermal diffusion coefficient.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] 1. Through the optimization design of the traditional load-sensitive multi-way valve hydraulic system, the pressure loss caused by the oil channel damping hole is reduced, the influence of low temperature on the system in cold environment is solved, and the response speed and control accuracy of the system are improved. This application adopts a more efficient oil channel structure, intelligent overflow valve control, temperature compensation mechanism and adaptive control strategy to ensure the efficient and stable operation of the hydraulic system under various working conditions;

[0056] Improve system response speed: The optimized adaptive control algorithm can adjust the operating parameters of the pump and relief valve in real time to maintain fast response under load and temperature fluctuations.

[0057] Enhanced control accuracy: Through machine learning-based dynamic viscosity prediction and multi-physics field coupling models, the nonlinear temperature-viscosity-load relationship in the system can be accurately captured to improve control accuracy.

[0058] Adapt to load fluctuations and extreme temperatures: Introducing the influencing factors of load changes and deep learning algorithms enables the system to adapt to load fluctuations and extreme temperatures. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0060] Figure 1 The flowchart of the intelligent overflow valve design and control optimization system of the present invention;

[0061] Figure 2 It is a diagram of the multi-way valve hydraulic system of the present invention;

[0062] Figure 3 It is a flow chart of the temperature compensation and adaptive control system of the present invention. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] See also Figures 1 to 3 , the present invention provides a technical solution:

[0065] A load-sensitive multi-way valve hydraulic system for an agricultural tractor, comprising an intelligent control module, a load-sensitive variable pump, a load-sensitive multi-way valve, an actuator, a pressure sensor, a temperature sensor, a flow sensor, a temperature compensation and adaptive control system and an intelligent control system, and an intelligent overflow valve design and control optimization system;

[0066] Intelligent control module: This module contains a real-time feedback mechanism, which monitors the system's pressure, flow, temperature and other parameters through sensors, optimizes the control strategy, and automatically adjusts the operating parameters of the hydraulic system, especially in low temperature environments;

[0067] The load-sensitive variable pump: This pump adjusts the flow and pressure according to the load requirements of the system and outputs the required hydraulic power. Its control is adjusted by the pressure signal of the load-sensitive multi-way valve;

[0068] The load-sensitive multi-way valve has the main task of controlling the flow and pressure of the system by adjusting the pressure of the LS relief valve according to the load conditions, ensuring that the pressure and flow requirements of the hydraulic system match the load conditions;

[0069] The actuators: hydraulic cylinders and motors, perform tasks according to the pressure and flow output by the multi-way valve;

[0070] Intelligent control module: This module contains a real-time feedback mechanism, which monitors the system's pressure, flow, and temperature parameters through sensors to optimize the control strategy;

[0071] Temperature compensation and adaptive control system, the system integrates oil temperature sensor to monitor the temperature change of hydraulic oil in real time. The control module automatically adjusts the system pressure and flow according to the real-time oil temperature to compensate for the change of oil viscosity.

[0072] The intelligent relief valve design and control optimization system includes an electronically adjustable relief valve. The control of the relief valve is adjusted through an electronic sensor, which monitors the hydraulic oil temperature and pressure changes in real time and automatically adjusts the relief valve opening according to the working conditions.

[0073] Working of load-sensing multi-way valve: There are multiple LS relief valves in the system, each valve determines the load condition by sensing different pressures in the oil channel. When the system load changes, the adjustment of the LS relief valve can ensure that the pump output matches the load demand.

[0074] Optimization of pressure transmission: The pressure transmission in the traditional system will be affected by the oil channel damping hole, resulting in a certain pressure loss. By optimizing the oil channel design, the pressure loss generated when the hydraulic oil flows through the damping hole is reduced, and the feedback control mechanism is automatically adjusted to ensure that the LS overflow pressure is accurately transmitted to the load-sensitive pump.

[0075] Specifically, the intelligent overflow valve design and control optimization system includes the following steps:

[0076] Step 1: Establish the control model of the overflow valve:

[0077] Flow rate Q of relief valve valve The relationship between the opening x is as follows:

[0078]

[0079] Among them, C v is the flow coefficient of the relief valve, ΔP is the pressure difference, f(x) is the function between the opening of the relief valve and the flow rate, x is the opening, and the value range is [0,1];

[0080] Step 2: Overflow valve control algorithm: Use PID controller to adjust the opening of the overflow valve according to the pressure and flow feedback of the hydraulic system. PID control algorithm:

[0081]

[0082] Among them, e(t) = Pset-P(t) is the error, that is, the difference between the set pressure and the current pressure, u(t) is the control signal, Kp, Ki, Kd are proportional, integral, and differential gains respectively;

[0083] Step 3: Realize real-time control of the opening. According to the control signal u(t)u(t), calculate the required opening of the relief valve x(t)x(t), so that the flow of the relief valve meets the load demand. When the system pressure deviates from the set pressure, the electronic relief valve adjusts the valve body opening according to the PID control algorithm.

[0084] Step 4: Overflow valve control based on temperature compensation, temperature sensor feedback and oil viscosity compensation, and optimization of adaptive control algorithm.

[0085] Specifically, the temperature sensor feedback and oil viscosity compensation, the oil temperature sensor is installed at the pump inlet and the oil tank, and the temperature of the hydraulic oil is monitored in real time. When the oil temperature is T, the viscosity of the oil μ(T) will change with the change of temperature. The viscosity usually has the following relationship: μ(T) = μ0·e -αT

[0086] Among them, μ0 is the viscosity at room temperature, α is the temperature coefficient, and T is the temperature;

[0087] Establish a temperature compensation function. When the oil temperature changes, the viscosity of the hydraulic oil will affect the efficiency of the pump and the adjustment accuracy of the valve. Therefore, a temperature compensation factor is added to the overflow valve control, so that the pressure setting of the overflow valve, that is, the set pressure P set (T) varies with oil temperature;

[0088] Temperature compensation function:

[0089] P set (T) = P set0 (1+β(T-T0))

[0090] Among them, P set0 is the set pressure at room temperature, β is the temperature compensation coefficient, T0 is room temperature;

[0091] Dynamically adjust the pressure setting of the relief valve. Each time the relief valve is adjusted, the intelligent control module dynamically adjusts the pressure setting value based on the data of the temperature sensor.

[0092] Specifically, the optimization of the adaptive control algorithm introduces an adaptive PID control algorithm, and adjusts the PID parameters according to the real-time measured pressure, flow and temperature signals. The controller gains Kp, Ki, Kd can be dynamically adjusted by an online identification algorithm (such as the least squares method);

[0093] Dynamically adjust PID parameters:

[0094] According to the working state of the system, that is, load change and temperature change, the PID gain is dynamically adjusted by the following formula:

[0095] K p (t) = K p0 +δK p (T,ΔP)

[0096] K i (t) = K i0 +δK i (T,ΔP)

[0097] K d (t) = K d0 +δKd (T,ΔP)

[0098] Among them, K p0 ,K i0 ,K d0 is the initial gain, δK p ,δK i ,δK d is the adjustment amount, T is the temperature, and ΔP is the pressure change.

[0099] Specifically, the temperature compensation and adaptive control system comprises the following steps:

[0100] Step 1: Install multiple oil temperature sensors at the pump suction port, the bottom of the oil tank, and the overflow valve to monitor the temperature change of the oil in real time and input the temperature T(t) data into the intelligent control module;

[0101] Step 2: Establish an adaptive temperature model. Based on the temperature data collected by the sensor, establish an adaptive temperature control model for the hydraulic system. The temperature change rate Related to the changes in oil viscosity μ(T) and flow rate:

[0102] Where α is the thermal conductivity coefficient Q pump is the flow rate of the pump;

[0103] Step 3: Adaptive compensation control strategy, introduce adaptive PID control algorithm, adjust control parameters according to oil temperature in real time, the impact of temperature change on system performance will be calculated in real time and fed back to the control module, adjust the pump flow and the pressure setting of the relief valve, the goal of adaptive control is to minimize the negative impact of temperature change, through the following objective function:

[0104]

[0105] The objective function describes the relationship between the system output pressure P(t) and the target pressure P set The adaptive control compensates for the temperature change by minimizing the difference between the two.

[0106] In the prior art, the temperature change of hydraulic oil is usually estimated using a simple exponential relationship:

[0107] μ(T)=μ0·e -αT

[0108] This model ignores the dynamic effect of temperature changes on oil viscosity and only relies on a single physical property of the oil, failing to consider the interaction of multiple factors (such as pressure, flow rate, and temperature) under complex working conditions.

[0109] In order to more accurately capture the nonlinear relationship between oil viscosity and temperature, we can use neural networks (ANN) to predict viscosity in real time. By collecting data under various working conditions (such as different temperatures, pressures, flow rates, etc.), a dynamic viscosity prediction model is trained with higher adaptability and accuracy.

[0110] The step 2, the adaptive temperature model also includes introducing a neural network model, training a multi-layer perceptron neural network by inputting multi-dimensional data of temperature, pressure and flow rate, and outputting the viscosity of the hydraulic oil:

[0111] μ(T,P,Q)=f(T,P,Q)

[0112] Where T is the oil temperature, P is the system pressure, Q is the flow rate, and μ(T,P,Q) is the viscosity of the oil.

[0113] Specifically, most of the existing temperature compensation methods rely on preset linear relationships or static adjustment rules, and lack the ability to dynamically adapt to system load fluctuations and temperature changes, resulting in a delayed response of the hydraulic system under complex working conditions, affecting the control accuracy. The second step, the adaptive temperature model also includes fuzzy control and reinforcement learning technology for optimization, and the fuzzy control introduces adaptive fuzzy control, so that the fuzzy rule base is automatically updated with the feedback data during system operation;

[0114] When the temperature changes, reinforcement learning adjusts the system parameters in real time, by learning the optimal control strategy related to the temperature and pressure state:

[0115]

[0116] Among them, Q(s t ,a t ) is the value of taking action at in state st, rt is the immediate reward, and γ is the discount factor.

[0117] Implementation steps:

[0118] Design a control strategy: Define the control objective (such as minimizing pressure fluctuations or maximizing system efficiency) and the reward function.

[0119] Real-time data feedback: By collecting temperature, pressure, flow and other data in real time, it is input into the RL model as state.

[0120] Learning and Optimization: Continuously optimize system control strategies through RL algorithms to adapt to different temperature and load conditions.

[0121] Specifically, when optimizing the temperature compensation model, in addition to considering the effects of temperature and oil viscosity, we also need to consider the effects of load changes and flow changes on system temperature and viscosity. To this end, we can introduce a multi-physics coupling model, taking into account multiple factors such as fluid dynamics, thermodynamics, and heat transfer, to establish a more accurate load-temperature-viscosity coupling model:

[0122] Step 2: The adaptive temperature model also includes a multi-physics coupling model that introduces load influence;

[0123] Multiphysics coupling model: Establish a dynamic coupling equation:

[0124]

[0125] Where T is the oil temperature, Q in and Q out are the input and output heat flows, ρ is the oil density, c p is the specific heat capacity and α is the thermal diffusion coefficient.

[0126] Implementation steps:

[0127] Multiphysics modeling: Build fluid dynamics and heat conduction models for hydraulic systems, and consider the effects of load changes on system temperature and viscosity.

[0128] Numerical simulation: The influence of load and temperature changes on the performance of the hydraulic system is simulated by numerical simulation (such as CFD simulation or finite element method).

[0129] Adaptive control combined with load-temperature coupling: Add a feedback mechanism of load-temperature coupling to the control system and optimize the control strategy based on real-time simulation results

[0130] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A load-sensitive multi-way valve hydraulic system for agricultural tractors, characterized in that: It includes intelligent control module, load-sensitive variable pump, load-sensitive multi-way valve, actuator, pressure sensor, temperature sensor, flow sensor, temperature compensation and adaptive control system and intelligent control system, intelligent overflow valve design and control optimization system; The load-sensitive variable pump: This pump adjusts the flow and pressure according to the load requirements of the system and outputs the required hydraulic power. Its control is adjusted by the pressure signal of the load-sensitive multi-way valve; The load-sensitive multi-way valve has the main task of controlling the flow and pressure of the system by adjusting the pressure of the LS relief valve according to the load conditions, ensuring that the pressure and flow requirements of the hydraulic system match the load conditions; The actuators: hydraulic cylinders and motors, perform tasks according to the pressure and flow output by the multi-way valve; Intelligent control module: This module contains a real-time feedback mechanism, which monitors the system's pressure, flow, and temperature parameters through sensors to optimize the control strategy; Temperature compensation and adaptive control system, the system integrates oil temperature sensor to monitor the temperature change of hydraulic oil in real time. The control module automatically adjusts the system pressure and flow according to the real-time oil temperature to compensate for the change of oil viscosity. The intelligent relief valve design and control optimization system includes an electronically adjustable relief valve. The control of the relief valve is adjusted through an electronic sensor, which monitors the hydraulic oil temperature and pressure changes in real time and automatically adjusts the relief valve opening according to the working conditions.

2. The load-sensitive multi-way valve hydraulic system for agricultural tractors according to claim 1, characterized in that: The intelligent overflow valve design and control optimization system comprises the following steps: Step 1: Establish the control model of the overflow valve: Flow rate Q of relief valve valve The relationship between the opening x is as follows: Among them, C v is the flow coefficient of the relief valve, ΔP is the pressure difference, f(x) is the function between the opening of the relief valve and the flow rate, x is the opening, and the value range is [0,1]; Step 2: Overflow valve control algorithm: Use PID controller to adjust the opening of the overflow valve according to the pressure and flow feedback of the hydraulic system. PID control algorithm: Among them, e(t) = Pset-P(t) is the error, that is, the difference between the set pressure and the current pressure, u(t) is the control signal, Kp, Ki, Kd are proportional, integral, and differential gains respectively; Step 3: Realize real-time control of the opening. According to the control signal u(t)u(t), calculate the required opening of the relief valve x(t)x(t), so that the flow of the relief valve meets the load demand. When the system pressure deviates from the set pressure, the electronic relief valve adjusts the valve body opening according to the PID control algorithm. Step 4: Overflow valve control based on temperature compensation, temperature sensor feedback and oil viscosity compensation, and optimization of adaptive control algorithm.

3. The load-sensitive multi-way valve hydraulic system for agricultural tractors according to claim 2, characterized in that: The temperature sensor feedback and oil viscosity compensation, install oil temperature sensors at the pump inlet and the oil tank to monitor the temperature of the hydraulic oil in real time. When the oil temperature is T, the viscosity of the oil μ(T) will change with the change of temperature. The viscosity usually has the following relationship: μ(T)=μ0·e -αT Among them, μ0 is the viscosity at room temperature, α is the temperature coefficient, and T is the temperature; Establish a temperature compensation function, add the temperature compensation factor to the relief valve control, and set the pressure setting of the relief valve, that is, the set pressure P set (T) varies with oil temperature; Temperature compensation function: P set (T)=P set0 ·(1+β(T-T0)) Among them, P set0 is the set pressure at room temperature, β is the temperature compensation coefficient, T0 is room temperature; Dynamically adjust the pressure setting of the relief valve. Each time the relief valve is adjusted, the intelligent control module dynamically adjusts the pressure setting value based on the data of the temperature sensor.

4. The load-sensitive multi-way valve hydraulic system for agricultural tractors according to claim 3, characterized in that: The optimization of the adaptive control algorithm introduces an adaptive PID control algorithm, and adjusts the PID parameters according to the real-time measured pressure, flow and temperature signals. The controller gains Kp, Ki, Kd can be dynamically adjusted through an online identification algorithm (such as the least squares method); Dynamically adjust PID parameters: According to the working state of the system, that is, load change and temperature change, the PID gain is dynamically adjusted by the following formula: K p (t)=K p0 +δK p (T,ΔP) K i (t)=K i0 +δK i (T,ΔP) K d (t)=K d0 +δK d (T,ΔP) Among them, K p0 ,K i0 ,K d0 is the initial gain, δK p ,δK i ,δK d is the adjustment amount, T is the temperature, and ΔP is the pressure change.

5. The load-sensitive multi-way valve hydraulic system for agricultural tractors according to claim 4, characterized in that: The temperature compensation and adaptive control system specifically includes the following steps: Step 1: Install multiple oil temperature sensors at the pump suction port, the bottom of the oil tank, and the overflow valve to monitor the temperature change of the oil in real time and input the temperature T(t) data into the intelligent control module; Step 2: Establish an adaptive temperature model. Based on the temperature data collected by the sensor, establish an adaptive temperature control model for the hydraulic system. The temperature change rate Related to the changes in oil viscosity μ(T) and flow rate: Where α is the thermal conductivity coefficient Q pump is the flow rate of the pump; Step 3: Adaptive compensation control strategy, introduce adaptive PID control algorithm, adjust control parameters according to oil temperature in real time, the impact of temperature change on system performance will be calculated in real time and fed back to the control module, adjust the pump flow and the pressure setting of the relief valve, the goal of adaptive control is to minimize the negative impact of temperature change, through the following objective function: The objective function describes the relationship between the system output pressure P(t) and the target pressure P set The adaptive control compensates for the temperature change by minimizing the difference between the two.

6. The load-sensing multi-way valve hydraulic system for agricultural tractors according to claim 5, characterized in that: The step 2, the adaptive temperature model also includes introducing a neural network model, training a multi-layer perceptron neural network by inputting multi-dimensional data of temperature, pressure and flow rate, and outputting the viscosity of the hydraulic oil: μ(T,P,Q)=f(T,P,Q) Where T is the oil temperature, P is the system pressure, Q is the flow rate, and μ(T,P,Q) is the viscosity of the oil.

7. The load-sensing multi-way valve hydraulic system for agricultural tractors according to claim 6, characterized in that: The step 2, the adaptive temperature model also includes fuzzy control and reinforcement learning technology for optimization, and the fuzzy control introduces adaptive fuzzy control so that the fuzzy rule base is automatically updated with the feedback data during system operation; When the temperature changes, reinforcement learning adjusts the system parameters in real time, by learning the optimal control strategy related to the temperature and pressure state: Among them, Q(s t ,a t ) is the value of taking action at in state st, rt is the immediate reward, and γ is the discount factor.

8. The load-sensitive multi-way valve hydraulic system for agricultural tractors according to claim 7, characterized in that: The step 2, the adaptive temperature model also includes introducing a multi-physics field coupling model influenced by load; Multiphysics coupling model: Establish a dynamic coupling equation: Where T is the oil temperature, Q in and Q out are the input and output heat flows, ρ is the oil density, c p is the specific heat capacity and α is the thermal diffusion coefficient.

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