Hydraulic control system and method and crane

Abstract

The invention relates to the technical field of hydraulic control, and particularly discloses a hydraulic control system and method and a crane, and the system comprises a working state judgment module which judges whether the current working state of a hydraulic system is normal or not according to the pressure, flow and temperature of hydraulic oil and displacement and angle data of an execution mechanism. If normal, the optimization control module generates an optimization control strategy based on fuzzy control and a neural network algorithm; if yes, the fault processing module calls the optimal fault processing strategy according to the exception type and the severity degree. The preliminary control module preliminarily controls the flow direction, pressure and flow of hydraulic oil according to a strategy. After preliminary control is completed, the working state prediction module determines a prediction time domain according to system dynamic characteristics and control requirements, and predicts the working state of the system based on a state space model. The stable state judgment module further controls according to a prediction result; according to the hydraulic control system, accurate judgment, optimal control, fault processing, state prediction and stable maintenance of the working state of the hydraulic system can be achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of hydraulic control, and particularly to a hydraulic control system, method and crane. Background Art

[0002] In today's highly developed industrial field, hydraulic control systems play a crucial role and are widely used in many fields such as aerospace, construction machinery, and automobile manufacturing. With the rapid development of industrial technology and the increasing complexity of production requirements, as the core component of high-end equipment, hydraulic control systems are facing higher performance and reliability requirements, and are also facing the wave of intelligent upgrading empowered by artificial intelligence technology.

[0003] In terms of judging the working state of traditional hydraulic control systems, they usually rely on only a limited number of parameters. For example, they simply rely on single data such as the pressure or flow rate of hydraulic oil. This method cannot comprehensively and accurately reflect the actual working state of the hydraulic system, and is prone to misjudgment or missed judgment. In the formulation of control strategies, most of them adopt relatively simple and fixed algorithms, which are difficult to fully adapt to complex and changeable working environments and diverse working task requirements. When a hydraulic system fails, existing technologies often lack an efficient and accurate fault diagnosis mechanism, and cannot quickly and accurately identify the fault type and evaluate the severity of the fault, so it is difficult to quickly call the most appropriate fault handling strategy. Moreover, for the prediction of the system working state, existing technical means are often not mature and perfect enough, and it is difficult to accurately and comprehensively estimate the system working state in a future period of time. This makes the system face more uncertainties during operation, unable to effectively guarantee the stability and reliability of the system, and may thus affect the normal progress of the entire production process and product quality. The technical bottlenecks of traditional hydraulic control systems in working condition cognition, control strategies and fault management urgently need to be further broken through by artificial intelligence technology.

[0004] Therefore, the present invention proposes a hydraulic control system, method and crane. Summary of the Invention

[0005] The present invention provides a hydraulic control system, method and crane, including: A working state judgment module judges whether the working state of the hydraulic system is normal through multi-faceted data to ensure accurate assessment of the system condition. An optimization control module generates an optimization strategy using fuzzy control algorithm and neural network algorithm when the system is normal to improve the system efficiency and performance. A fault handling module can call the optimal strategy according to the type and severity of the abnormality when the system is abnormal to quickly and effectively handle the fault. A preliminary control module conducts preliminary control according to the optimization strategy or the fault handling strategy to adjust the system in time. A working state prediction module predicts the working state by determining the prediction time domain and based on the state space model to provide a basis for further control. A stable state judgment module further controls based on the duration of the normal state to ensure that the system is stable and meets the requirements. This hydraulic control system can accurately judge, optimize control, handle faults, predict states and maintain stability of the working state of the hydraulic system, improving the reliability, stability and working efficiency of the system.

[0006] The present invention provides a hydraulic control system, including:

[0007] A working state judgment module, configured to judge whether the current working state of the hydraulic system is normal based on the pressure, flow rate, temperature of the hydraulic oil and the displacement and angular data of the actuator;

[0008] An optimization control module, configured to generate an optimization control strategy based on fuzzy control algorithm and neural network algorithm when the current working state of the hydraulic system is determined to be normal;

[0009] A fault handling module, configured to call the optimal fault handling strategy based on the type and severity of the abnormality when the current working state of the hydraulic system is determined to be abnormal;

[0010] A preliminary control module, configured to conduct preliminary control on the flow direction, pressure and flow rate of the hydraulic oil based on the optimization control strategy or the optimal fault handling strategy;

[0011] A working state prediction module, configured to determine the prediction time domain based on the current dynamic characteristics and control requirements of the hydraulic system when the hydraulic system completes preliminary control, and predict the working state of the hydraulic system within the prediction period based on the state space model and the prediction time domain of the hydraulic system;

[0012] A stable state judgment module, configured to further control the working state of the hydraulic system based on the duration of all normal states in the working state of the hydraulic system within the prediction period until the current normal state stability requirement of the hydraulic system is met.

[0013] Preferably, the working state judgment module includes:

[0014] The sensing and monitoring sub-module is used to collect the pressure, flow rate, temperature of the hydraulic oil and the displacement and angular data of the actuator in real time based on a variety of sensors;

[0015] The working state judgment sub-module is used to judge whether the current working state of the hydraulic system is normal based on the pressure, flow rate, temperature of the hydraulic oil and the displacement and angular data of the actuator.

[0016] Preferably, the optimization control module includes:

[0017] The optimization model generation sub-module is used to generate a composite optimization model based on the fuzzy control algorithm and the neural network algorithm when the current working state of the hydraulic system is determined to be normal;

[0018] The optimization strategy generation sub-module is used to generate an optimization control strategy according to the preset working mode, the pressure, flow rate, temperature of the hydraulic oil, the displacement and angular data of the actuator, and the composite optimization model.

[0019] Preferably, the fault handling module includes:

[0020] The deviation vector generation sub-module is used to construct an abnormal deviation vector considering the coupling relationship based on the pressure, flow rate, temperature of the hydraulic oil, the displacement and angular data of the actuator, and the coupling coefficient matrix;

[0021] The abnormal degree vector generation sub-module is used to calculate the abnormal degree of the hydraulic system in each abnormal type currently based on the weight matrix, and generate an abnormal degree vector based on the abnormal degree of the hydraulic system in all abnormal types currently;

[0022] The abnormal degree enhancement function construction sub-module is used to construct an abnormal degree enhancement function G(E) based on the abnormal deviation vector considering the coupling relationship and introducing the information entropy;

[0023] The strategy utility value calculation sub-module is used to calculate the utility value of each preset fault handling strategy under the abnormal degree vector based on the abnormal degree enhancement function G(E);

[0024] The optimal strategy calling sub-module is used to screen and call the preset fault handling strategy with the maximum utility value as the optimal fault handling strategy among all the preset fault handling strategies.

[0025] Preferably, the deviation vector generation sub-module includes:

[0026] The state parameter vector generation unit is used to generate a state parameter vector of the hydraulic system based on the pressure, flow rate, temperature of the hydraulic oil and the displacement and angular data of the actuator;

[0027] An initial abnormal deviation vector generation unit, configured to generate an initial abnormal deviation vector of the hydraulic system based on the normal range of each state parameter and the state parameter vector of the hydraulic system;

[0028] A coupling relationship incorporation unit, configured to construct an abnormal deviation vector ΔS′={Δs′ 1 ,Δs′ 2 ,…,Δs′ n} considering the coupling relationship based on the coupling coefficient matrix between state parameters and the initial abnormal deviation vector, where:

[0029]

[0030] In the formula, Δs i is the i-th abnormal deviation value included in the initial abnormal deviation vector, n is the total number of state parameters included in the state parameter vector, c ij is the coupling strength representing between the i-th state parameter and the j-th state parameter in the state parameter vector in the coupling coefficient matrix, and Δs j is the j-th abnormal deviation value included in the initial abnormal deviation vector.

[0031] Preferably, the abnormal degree enhancement function construction sub-module includes:

[0032] A probability distribution statistics unit, configured to obtain the probability distribution of all abnormal types based on the ratio of the abnormal degree of the hydraulic system in each abnormal type currently to the sum of the abnormal degrees of the hydraulic system in all abnormal types currently;

[0033] An information entropy formula construction unit, configured to construct an information entropy formula based on the probability distribution of all abnormal types:

[0034]

[0035] In the formula, H is the information entropy output value, p l is the probability value of the l-th abnormal type, ln is the logarithmic function with the natural constant e as the base and the value of e is 2.71828, and m is the total number of abnormal types;

[0036] An abnormal degree function construction unit, configured to construct an abnormal degree enhancement function G(E) based on the abnormal deviation vector considering the coupling relationship and introducing the information entropy formula:

[0037]

[0038] In the formula, n is the total number of state parameters included in the state parameter vector, ω i is the weight of the abnormal deviation value of the i-th state parameter in the state parameter vector in the enhancement function, and Δs′ iThe \(i\)-th abnormal deviation value in the abnormal deviation vector considering the coupling relationship, where \(\gamma\) is the weight value of the information entropy.

[0039] Preferably, the policy utility value calculation sub-module includes:

[0040] A policy parameter acquisition unit for acquiring the control cost and the expected system recovery time of each preset fault handling policy;

[0041] A utility value calculation unit for calculating the utility value \(U(p q ,E)\) of each preset fault handling policy under the abnormal degree vector based on the control cost, the expected system recovery time of each preset fault handling policy, and the abnormal degree enhancement function \(G(E)\).

[0042] Preferably, the stable state judgment module includes:

[0043] A state stability evaluation sub-module for evaluating the current normal state stability of the hydraulic system based on all normal state duration periods in the working state of the hydraulic system within the prediction period;

[0044] A further control sub-module for further controlling the working state of the hydraulic system based on the current normal state stability of the hydraulic system until the current normal state stability requirement of the hydraulic system is met.

[0045] The present invention provides a hydraulic control method applied to any of the above hydraulic control systems, including:

[0046] Step 1: Determine whether the current working state of the hydraulic system is normal based on the pressure, flow rate, temperature of the hydraulic oil, and the displacement and angular data of the actuator;

[0047] Step 2: When the current working state of the hydraulic system is determined to be normal, generate an optimized control policy based on the fuzzy control algorithm and the neural network algorithm;

[0048] Step 3: When the current working state of the hydraulic system is determined to be abnormal, call the optimal fault handling policy based on the abnormal type and the abnormal severity;

[0049] Step 4: Preliminarily control the flow direction, pressure, and flow rate of the hydraulic oil based on the optimized control policy or the optimal fault handling policy;

[0050] Step 5: When the hydraulic system completes the preliminary control, determine the prediction time domain based on the current dynamic characteristics and control requirements of the hydraulic system, and predict the working state of the hydraulic system within the prediction period based on the state space model and the prediction time domain of the hydraulic system;

[0051] Step 6: Based on all the normal state durations in the working state of the hydraulic system during the prediction period, further control the working state of the hydraulic system until the current normal state stability requirements of the hydraulic system are met.

[0052] The present invention provides a crane, including an actuator that is provided with force and torque by any one of the above hydraulic control systems.

[0053] The beneficial effects of the present invention compared with the prior art are as follows: The working state judgment module judges whether the working state of the hydraulic system is normal through multi-faceted data to ensure accurate assessment of the system status. The optimization control module uses fuzzy control algorithms and neural network algorithms to generate optimization strategies when the system is normal, improving the system efficiency and performance. The fault handling module can call the optimal strategy according to the type and severity of the abnormality when the system is abnormal, quickly and effectively coping with the fault. The preliminary control module performs preliminary control based on the optimization strategy or the fault handling strategy, timely adjusting the system. The working state prediction module provides a basis for further control by determining the prediction time domain and predicting the working state based on the state space model. The stable state judgment module further controls based on the normal state duration to ensure that the system stably meets the requirements. This hydraulic control system can achieve accurate judgment, optimization control, fault handling, state prediction and stability maintenance of the working state of the hydraulic system, improving the reliability, stability and working efficiency of the system, becoming one of the important application scenarios of artificial intelligence, and empowering industrial development.

[0054] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in this application document.

[0055] The technical solutions of the present invention will be further described in detail below through the accompanying drawings and embodiments. Description of the Drawings

[0056] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification, and are used to explain the present invention together with the embodiments of the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0057] Figure 1 It is a schematic diagram of the hydraulic control system in the embodiment of the present invention;

[0058] Figure 2 It is a flowchart of the hydraulic control method in the embodiment of the present invention. Detailed Embodiments

[0059] The preferred embodiments of the present invention will be described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only for explaining and illustrating the present invention, and are not used to limit the present invention.

[0060] Embodiment 1:

[0061] Referring to Figure 1 , the present invention provides a hydraulic control system, comprising:

[0062] A working state judgment module, configured to judge whether the current working state of the hydraulic system is normal based on the pressure, flow rate, temperature of the hydraulic oil and the displacement rotation and angular data of the actuator;

[0063] An optimization control module, configured to generate an optimization control strategy based on a fuzzy control algorithm and a neural network algorithm when the current working state of the hydraulic system is determined to be normal;

[0064] A fault handling module, configured to call an optimal fault handling strategy based on the type of abnormality and the severity of the abnormality when the current working state of the hydraulic system is determined to be abnormal;

[0065] A preliminary control module, configured to preliminarily control the flow direction, pressure, and flow rate of the hydraulic oil based on the optimization control strategy or the optimal fault handling strategy;

[0066] A working state prediction module, configured to determine a prediction time domain based on the current dynamic characteristics and control requirements of the hydraulic system when the preliminary control of the hydraulic system is completed, and predict the working state of the hydraulic system within the prediction period based on the state space model and the prediction time domain of the hydraulic system;

[0067] A stable state judgment module, configured to further control the working state of the hydraulic system based on all normal state duration periods in the working state of the hydraulic system within the prediction period until the current normal state stability requirement of the hydraulic system is met.

[0068] In this embodiment, the pressure, flow rate, and temperature of the hydraulic oil: The pressure of the hydraulic oil refers to the magnitude of the pressure generated by the oil in the hydraulic system. For example, the pressure of the hydraulic oil at a certain point in the system is 20 MPa. The flow rate is the volume of hydraulic oil passing through a certain cross-section per unit time. For example, the flow rate passing through per second is 50 liters. The temperature is the current temperature value of the hydraulic oil, such as 50 degrees Celsius.

[0069] In this embodiment, the displacement rotation and angular data of the actuator: refer to data such as the distance moved and the angle rotated by the actuator (such as a hydraulic cylinder, a hydraulic motor, etc.). For example, the length of the hydraulic cylinder extending is 10 cm, and the angle rotated by the hydraulic motor is 30 degrees.

[0070] In this embodiment, the current working state of the hydraulic system: It is a comprehensive description of the current operating conditions of the hydraulic system, including whether it is operating normally, whether there are faults, and the level of working efficiency, etc. For example, the system is currently in a normal and efficient operating state, or there is a fault state with unstable pressure.

[0071] In this embodiment, the optimization control strategy: It is a control method and parameter setting generated according to the current situation and requirements of the system through specific algorithms (such as fuzzy control and neural network algorithms) for improving system performance, efficiency, stability, etc. For example, specific control schemes such as adjusting the output flow rate of the hydraulic pump and changing the opening degree of the valve.

[0072] In this embodiment, the type of abnormality: It refers to the types of abnormal situations that occur in the hydraulic system, such as too high pressure, insufficient flow rate, too high temperature, etc.

[0073] In this embodiment, the severity of the abnormality: It describes the severity of the abnormal situation of the hydraulic system, which can be divided from light to heavy into slight, moderate, severe, etc. For example, a slight pressure exceeding the normal range is a slight abnormality, while a too high temperature causing component damage is a severe abnormality.

[0074] In this embodiment, the optimal fault handling strategy: Among various possible methods for dealing with hydraulic system faults, it is the most suitable handling method determined after evaluation and selection for the current type and severity of the abnormality. For example, for a severe insufficient flow rate fault, the optimal strategy may be to immediately stop the machine for maintenance and replace relevant components.

[0075] In this embodiment, the preliminary control of the flow direction, pressure, and flow rate of the hydraulic oil based on the optimization control strategy or the optimal fault handling strategy: According to the generated optimization or fault handling strategy, initially adjust and control the flow direction, pressure magnitude, and flow rate of the hydraulic oil.

[0076] In this embodiment, the current dynamic characteristics and control requirements of the hydraulic system: The dynamic characteristics include system performance such as the response speed and stability to input changes, such as the response time of the pressure when the load changes. The control requirements are the expected operating state and performance requirements for the system, for example, requiring the system to reach a certain pressure value within a specific time.

[0077] In this embodiment, the prediction time domain: When predicting the working state of the hydraulic system, the set future time range. For example, predicting the working state of the system within the next 5 seconds, and these 5 seconds are the prediction time domain.

[0078] In this embodiment, the state - space model of the hydraulic system: A model that describes the relationship between internal state variables (such as pressure, flow rate, etc.) of the hydraulic system and inputs and outputs in a mathematical way.

[0079] In this embodiment, the working state of the hydraulic system within the prediction period: the prediction of the operation of the hydraulic system within the set prediction time domain, including the changes in parameters such as pressure, flow rate, and temperature.

[0080] In this embodiment, the normal state duration: the continuous time period during which the hydraulic system is in a normal working state.

[0081] In this embodiment, the current normal state stability requirement of the hydraulic system: the specific standards and requirements for the stability of the current normal operation state of the hydraulic system, such as the pressure fluctuation range within ±1 MPa.

[0082] The beneficial effects of the above technologies are as follows: The working state judgment module judges whether the working state of the hydraulic system is normal through multi-faceted data to ensure accurate assessment of the system status. The optimization control module uses fuzzy control algorithms and neural network algorithms to generate optimization strategies when the system is normal, improving the system efficiency and performance. The fault handling module can call the optimal strategy according to the type and severity of the abnormality when the system is abnormal, and respond to the fault quickly and effectively. The preliminary control module performs preliminary control based on the optimization strategy or the fault handling strategy, and adjusts the system in a timely manner. The working state prediction module provides a basis for further control by determining the prediction time domain and predicting the working state based on the state space model. The stable state judgment module further controls based on the normal state duration to ensure that the system is stable and meets the requirements. This hydraulic control system can achieve accurate judgment, optimization control, fault handling, state prediction, and stability maintenance of the working state of the hydraulic system, improving the reliability, stability, and working efficiency of the system.

[0083] Embodiment 2:

[0084] Based on Embodiment 1, the working state judgment module includes:

[0085] The sensing and monitoring sub-module is used to collect the pressure, flow rate, temperature of the hydraulic oil, and the displacement and angular data of the actuator in real time based on a variety of sensors;

[0086] The working state judgment sub-module is used to judge whether the current working state of the hydraulic system is normal based on the pressure, flow rate, temperature of the hydraulic oil, and the displacement and angular data of the actuator.

[0087] In this embodiment, a variety of sensors refer to multiple different types of sensors used to detect different parameters in the hydraulic system. For example, a pressure sensor is used to detect the pressure of the hydraulic oil, a flow sensor is used to detect the flow rate, a temperature sensor is used to detect the temperature, a displacement sensor is used to detect the displacement of the actuator, and an angle sensor is used to detect the rotation angle of the actuator, etc. For example, in a hydraulic system, a pressure sensor (model: XXX) is used to measure the pressure and a flow sensor (model: YYY) is used to measure the flow rate at the same time.

[0088] In this embodiment, determining whether the current working state of the hydraulic system is normal based on the pressure, flow rate, temperature of the hydraulic oil and the displacement and angular data of the actuator means comprehensively analyzing and determining whether the current working condition of the hydraulic system is normal or abnormal according to the specific data information such as the pressure value, flow rate, temperature of the hydraulic oil, the displacement distance and rotation angle of the actuator. For example, if the pressure, flow rate and temperature of the hydraulic oil are all within the normal range, and the displacement and rotation angle of the actuator also meet the expectations, it is determined that the system is working normally; otherwise, if one or more parameters exceed the normal range, it is determined that the system is not working properly.

[0089] Advantages of the above technical solutions: The sensing and monitoring sub-module uses a variety of sensors to collect key data in real time, ensuring the comprehensiveness and timeliness of the data. The working state judgment sub-module makes state judgments based on accurate data from multiple aspects, improving the accuracy and reliability of the judgment. The working state judgment module can accurately and timely determine the current working state of the hydraulic system, providing an accurate basis for subsequent control and processing.

[0090] Embodiment 3:

[0091] On the basis of Embodiment 1, the control module is optimized, including:

[0092] An optimized model generation sub-module, which is used to generate a composite optimization model based on the fuzzy control algorithm and the neural network algorithm when the current working state of the hydraulic system is determined to be normal;

[0093] An optimized strategy generation sub-module, which is used to generate an optimized control strategy according to the preset working mode, the pressure, flow rate, temperature of the hydraulic oil, the displacement and angular data of the actuator, and the composite optimization model.

[0094] In this embodiment, generating a composite optimization model based on the fuzzy control algorithm and the neural network algorithm means integrating the characteristics of the fuzzy control algorithm that is good at dealing with fuzzy and uncertain information, and the powerful learning and adaptive capabilities of the neural network algorithm to create an optimization model that can combine the advantages of both. For example, in the control of a hydraulic system, combining the fuzzy judgment of the temperature range by the fuzzy control and the learning of the pressure change trend by the neural network to generate a composite optimization model that can consider multiple factors at the same time.

[0095] In this embodiment, generating an optimized control strategy based on a preset working mode, the pressure, flow rate, temperature of the hydraulic oil, the displacement and angular data of the actuator, and the composite optimization model means that according to the preset system working mode (such as heavy load, light load, etc.), combined with the specific data of the pressure, flow rate, temperature of the hydraulic oil and the displacement and rotation angle of the actuator monitored in real time, and then using the previously generated composite optimization model to formulate a control strategy that can make the system reach the best working state. For example, if the preset working mode is heavy load and the currently monitored hydraulic oil pressure is low and the flow rate is large, according to the composite optimization model, an optimized control strategy for increasing the output power of the oil pump is generated.

[0096] Advantages of the above technical solutions: The optimization model generation sub-module generates a composite optimization model by integrating the fuzzy control algorithm and the neural network algorithm, giving full play to the advantages of the two algorithms and improving the accuracy and adaptability of the model. The optimization strategy generation sub-module generates a control strategy based on the preset working mode, multi-faceted data and the composite optimization model, making the optimized control strategy more in line with the actual working requirements and improving the performance and efficiency of the system. The optimized control module can generate accurate and effective optimized control strategies for the hydraulic system in the normal working state, ensuring the efficient operation of the system.

[0097] Embodiment 4:

[0098] Based on Embodiment 1, the fault handling module includes:

[0099] The deviation vector generation sub-module is used to construct an abnormal deviation vector considering the coupling relationship based on the pressure, flow rate, temperature of the hydraulic oil and the displacement and angular data of the actuator, and the coupling coefficient matrix;

[0100] The abnormal degree vector generation sub-module is used to calculate the abnormal degree of the hydraulic system in each abnormal type based on the weight matrix, and generate an abnormal degree vector based on the abnormal degree of the hydraulic system in all abnormal types;

[0101] The abnormal degree enhancement function construction sub-module is used to construct an abnormal degree enhancement function G(E) based on the abnormal deviation vector considering the coupling relationship and introducing information entropy;

[0102] The strategy utility value calculation sub-module is used to calculate the utility value of each preset fault handling strategy under the abnormal degree vector based on the abnormal degree enhancement function G(E);

[0103] The optimal strategy calling sub-module is used to screen and call the preset fault handling strategy with the maximum utility value as the optimal fault handling strategy among all preset fault handling strategies.

[0104] In this embodiment, the coupling coefficient matrix is a matrix used to describe the degree of mutual influence between different state parameters in a hydraulic system. For example, the coupling coefficient between pressure and flow rate is 0.8, indicating a strong mutual influence between them. The coupling coefficient matrix is usually obtained through a large number of experiments and theoretical analyses of the relationships between system parameters. First, study the mutual influence of different parameter combinations separately, measure and record the data, and then process these data through mathematical methods to determine the coupling coefficients between parameters and construct the coupling coefficient matrix.

[0105] In this embodiment, the abnormal deviation vector considering the coupling relationship is a vector describing abnormal conditions obtained by comprehensively considering the mutual influence relationships between parameters when analyzing abnormalities in a hydraulic system. For example, when considering the coupling of pressure and flow rate simultaneously, the obtained abnormal deviation vector is [0.5, -0.3].

[0106] In this embodiment, the weight matrix is a matrix used to assign different degrees of importance to different abnormal types or parameters. For example, a weight of 0.6 is assigned to pressure abnormality and a weight of 0.4 is assigned to flow rate abnormality. The weight matrix is obtained based on expert experience, historical fault data statistics, and system importance assessment. Experts assign weights to different abnormal types based on their in-depth understanding of the system; analyze historical fault data to determine the weights according to the impact of each abnormal type on the system; and also adjust the weights of abnormal types according to the importance of different parts of the system.

[0107] In this embodiment, the degree of abnormality of the hydraulic system for each abnormal type refers to the specific severity of the hydraulic system currently in various different types of abnormalities (such as too high pressure, too low temperature, etc.). For example, the degree of abnormality of the current too high pressure is severe, and the degree of abnormality of the too low temperature is moderate. The degree of abnormality of each abnormal type in the hydraulic system is obtained by monitoring relevant parameters. First, set the normal range of each parameter, and then collect data in real time. Compare the collected values with the normal range, and the greater the deviation, the higher the degree of abnormality. By comprehensively considering the deviation situations of multiple parameters, the specific degree of abnormality of each abnormal type is obtained.

[0108] In this embodiment, generating an abnormal degree vector based on the degree of abnormality of the hydraulic system for all abnormal types combines the degrees of abnormality of the system in various abnormal types into a vector form. For example, if the degrees of the current system in three abnormal types of too high pressure, insufficient flow rate, and too low temperature are severe, mild, and moderate respectively, the generated abnormal degree vector may be [3, 1, 2], where the numbers represent different degrees.

[0109] In this embodiment, the preset fault handling strategy is the processing methods and steps set in advance for various possible fault situations. For example, the preset strategy for too high pressure is to adjust the relief valve, and the preset strategy for insufficient flow rate is to check the oil pump and pipeline.

[0110] In this embodiment, the utility value of each preset fault handling strategy under the abnormal degree vector measures the numerical value of the effect and value that each preset fault handling strategy can produce under the current abnormal degree of the system. For example, the utility value of a certain preset strategy under the current abnormal degree vector is 80, indicating that this strategy is expected to have a good handling effect under such abnormal conditions.

[0111] Advantages of the above technical solutions: The deviation vector generation sub-module constructs an abnormal deviation vector considering the coupling relationship, which more comprehensively and accurately reflects the abnormal situation of the system. The abnormal degree vector generation sub-module generates an abnormal degree vector, which quantifies the severity of different abnormal types. The abnormal degree enhancement function construction sub-module introduces an information entropy construction function to further enhance the accuracy of the evaluation of the abnormal degree. The policy utility value calculation sub-module calculates the utility value of each preset fault handling strategy, providing a quantitative basis for policy selection. The optimal policy call sub-module filters and calls the strategy with the maximum utility value to ensure the adoption of the most effective fault handling measures. The fault handling module can accurately evaluate the abnormal degree, scientifically calculate the policy utility, and thus quickly call the optimal fault handling strategy, improving the ability and efficiency of the hydraulic system to handle faults.

[0112] Embodiment 5:

[0113] Based on Embodiment 4, the deviation vector generation sub-module includes:

[0114] A state parameter vector generation unit, configured to generate a state parameter vector of the hydraulic system based on the pressure, flow rate, temperature of the hydraulic oil, and the displacement and angular data of the actuator;

[0115] An initial abnormal deviation vector generation unit, configured to generate an initial abnormal deviation vector of the hydraulic system based on the normal range of each state parameter and the state parameter vector of the hydraulic system;

[0116] A coupling relationship incorporation unit, configured to construct an abnormal deviation vector ΔS′={Δs′ 1 ,Δs′ 2 ,…,Δs′ n} considering the coupling relationship based on the coupling coefficient matrix between state parameters and the initial abnormal deviation vector, where:

[0117]

[0118] In the formula, Δs i is the i-th abnormal deviation value included in the initial abnormal deviation vector, n is the total number of state parameters included in the state parameter vector, c ijis the coupling strength between the $i$-th state parameter and the $j$-th state parameter in the state parameter vector included in the coupling coefficient matrix, $\Delta s$ j is the $j$-th abnormal deviation value included in the initial abnormal deviation vector.

[0119] In this embodiment, a state parameter vector of the hydraulic system is generated based on the pressure, flow rate, temperature of the hydraulic oil, and the displacement and angular data of the actuator: these data of the hydraulic oil are integrated and processed into a vector form that can comprehensively describe the system state. For example, combining pressure values, flow rate values, etc. into a vector such as $[P, Q, T, X, Y]$.

[0120] In this embodiment, an initial abnormal deviation vector of the hydraulic system is generated based on the normal range of each state parameter and the state parameter vector of the hydraulic system: first, determine the normal value range of each state parameter, then compare the current state parameter vector, calculate the difference from the normal range, and form a vector describing the abnormal deviation. For example, if the normal pressure range is $[10, 20]$ and the current pressure is 25, the deviation is 5, and the deviations of multiple parameters form the initial abnormal deviation vector.

[0121] Advantages of the above technical solutions: The state parameter vector generation unit generates a state parameter vector, providing basic data for subsequent analysis. The initial abnormal deviation vector generation unit generates an initial deviation vector by comparing with the normal range, which can intuitively reflect the abnormal degree of the parameters. The coupling relationship incorporation unit constructs an abnormal deviation vector considering the coupling relationship between state parameters, making the deviation evaluation more in line with the actual situation of the system and improving the accuracy and comprehensiveness of the evaluation. The deviation vector generation sub-module can accurately and comprehensively construct an abnormal deviation vector considering the coupling relationship, providing strong support for accurately evaluating the abnormal situation of the system.

[0122] Embodiment 6:

[0123] Based on Embodiment 4, the abnormal degree enhancement function construction sub-module includes:

[0124] A probability distribution statistics unit, configured to obtain the probability distribution of all abnormal types based on the ratio of the abnormal degree of the hydraulic system in each abnormal type to the sum of the abnormal degrees of the hydraulic system in all abnormal types;

[0125] An information entropy formula construction unit, configured to construct an information entropy formula based on the probability distribution of all abnormal types:

[0126]

[0127] In the formula, $H$ is the information entropy output value, $p$ lis the probability value of the l-th anomaly type, ln is the logarithmic function with the natural constant e as the base and the value of e is 2.71828, and m is the total number of anomaly types;

[0128] Anomaly degree function construction unit, which is used to construct an enhanced anomaly degree function G(E) based on the anomaly deviation vector considering the coupling relationship and introducing the information entropy formula:

[0129]

[0130] In the formula, n is the total number of state parameters included in the state parameter vector, ω i is the weight of the anomaly deviation value of the i-th state parameter in the state parameter vector in the enhanced function, and Δs′ i is the i-th anomaly deviation value in the anomaly deviation vector considering the coupling relationship, and γ is the weight value of the information entropy.

[0131] In this embodiment, based on the ratio of the anomaly degree of the hydraulic system in each anomaly type currently to the sum of the anomaly degrees of the hydraulic system in all anomaly types currently, the probability distribution of all anomaly types is obtained: by calculating the proportion of the anomaly degree of each anomaly type in the total anomaly degree, the possibility of various anomaly types occurring is determined to form a probability distribution. For example, if the anomaly degree of a certain type is 10 and the total anomaly degree is 50, then its probability is 0.2.

[0132] In this embodiment, the weight of the anomaly deviation value of the i-th state parameter in the state parameter vector in the enhanced function: refers to the importance coefficient assigned to the anomaly deviation value at a specific position in the state parameter vector in the constructed function for enhancing anomaly evaluation.

[0133] In this embodiment, the weight value of the information entropy: is the influence degree coefficient set for the information entropy, which reflects the uncertainty, when constructing the enhanced anomaly degree function.

[0134] The beneficial effects of the above technical solutions: The probability distribution statistics unit obtains the probability distribution by calculating the ratio, providing basic data for the construction of the information entropy. The information entropy formula constructed by the information entropy formula construction unit can quantify the uncertainty and complexity of the anomaly type. The anomaly degree function construction unit introduces the information entropy formula to construct the enhanced anomaly degree function, more comprehensively and accurately evaluating the anomaly degree. The enhanced anomaly degree function construction sub-module can scientifically and effectively construct the function, enhancing the evaluation ability of the anomaly degree of the hydraulic system and providing a more reliable basis for the subsequent selection of fault handling strategies.

[0135] Embodiment 7:

[0136] On the basis of Embodiment 4, the strategy utility value calculation sub-module includes:

[0137] A policy parameter acquisition unit, configured to acquire the control cost and the expected system recovery time of each preset fault handling policy;

[0138] A utility value calculation unit, configured to calculate the utility value of each preset fault handling policy under the abnormal degree vector based on the control cost, the expected system recovery time of each preset fault handling policy, and the abnormal degree enhancement function G(E):

[0139]

[0140] In the formula, U(p q ,E) is the utility value of the q-th preset fault handling policy under the abnormal degree vector, m is the total number of abnormal types, v ql is the influence weight of the l-th abnormal type on the utility value of the q-th preset fault handling policy and e is the natural constant and the value of e is 2.71828, α is the slope of the control function, β is the central position of the control function, E l is the abnormal degree of the l-th abnormal type, U(p q ,E) is the utility value of the q-th preset fault handling policy under the abnormal degree vector E, θ 1 is the weight coefficient of the abnormal degree, θ 2 is the weight coefficient of the control cost, θ 3 is the weight coefficient of the expected system recovery time, Cost(p q ) is the control cost of the q-th preset fault handling policy, RecoveryTime(p q ) is the expected system recovery time of the q-th preset fault handling policy.

[0141] In this embodiment, acquiring the control cost and the expected system recovery time of each preset fault handling policy means determining the cost required during the implementation of each pre-set strategy for dealing with faults and the duration expected to restore the system to normal. For example, the control cost of strategy A is x yuan, and the expected system recovery time is 2 hours.

[0142] In this embodiment, the influence weight of the l-th abnormal type on the utility value of the q-th preset fault handling policy represents the degree of influence of different abnormal types on the effectiveness value that a specific fault handling policy can produce. For example, the influence weight of abnormal type 1 on strategy 1 is 0.7.

[0143] In this embodiment, the slope of the control function is a parameter representing the rate of change in the control function graph, which determines the steepness of the growth or decline of the function.

[0144] In this embodiment, the central position of the control function is the position of the point with special significance or key role in the control function.

[0145] In this embodiment, the weight coefficient of the abnormal degree is a coefficient used to measure the importance of the abnormal degree in the overall evaluation.

[0146] In this embodiment, the weight coefficient of controlling cost represents the proportion of the importance of controlling cost when calculating the utility value of the fault handling strategy.

[0147] In this embodiment, the weight coefficient of the expected system recovery time reflects the relative importance of the expected system recovery time for evaluating the effectiveness of the fault handling strategy.

[0148] Beneficial effects of the above technical solutions: The policy parameter acquisition unit acquires key parameters such as controlling cost and expected system recovery time, providing comprehensive data support for utility value calculation. The utility value calculation unit calculates the utility value by comprehensively considering the controlling cost, expected system recovery time, and abnormal degree enhancement function, and can accurately quantify the comprehensive effect of each preset fault handling strategy. The policy utility value calculation sub-module can scientifically and objectively evaluate the utility of each preset fault handling strategy under specific abnormal conditions, providing a reliable basis for screening the optimal strategy, and helping to improve the efficiency and economy of fault handling.

[0149] Embodiment 8:

[0150] Based on Embodiment 1, the stable state judgment module includes:

[0151] The state stability evaluation sub-module is used to evaluate the current normal state stability of the hydraulic system based on all normal state duration periods in the working state of the hydraulic system within the prediction period.

[0152] The further control sub-module is used to further control the working state of the hydraulic system based on the current normal state stability of the hydraulic system until the current normal state stability requirement of the hydraulic system is met.

[0153] In this embodiment, all normal state duration periods in the working state of the hydraulic system within the prediction period refer to the continuous time periods in the normal state after predicting the working state of the hydraulic system for a future period of time (prediction period). For example, if the prediction period is 10 minutes and there are 3 consecutive minutes in the normal state, these 3 minutes are the normal state duration periods.

[0154] In this embodiment, based on all normal state duration periods in the working state of the hydraulic system within the prediction period, the current normal state stability of the hydraulic system is evaluated according to the length of the above-mentioned normal state duration periods and other conditions to judge and measure the stability degree of the current normal working state of the hydraulic system. If the normal state duration period is long and stable, it indicates better stability.

[0155] In this embodiment, based on the stability of the current normal state of the hydraulic system, the working state of the hydraulic system is further controlled until the stability requirements of the current normal state of the hydraulic system are met. According to the evaluated stability situation, finer adjustment and control are performed on the operation of the hydraulic system, so that the system can always maintain the standard of meeting the set normal stable state. For example, the stability is enhanced by adjusting parameters such as pressure and flow rate.

[0156] Advantages of the above technical solutions: The state stability evaluation sub-module can accurately evaluate the stability of the current normal state of the hydraulic system by analyzing the duration of the normal state, providing a reliable basis for subsequent control. The further control sub-module accurately controls the working state of the system according to the evaluation results, ensuring that the system stably meets the requirements and improving the reliability and stability of the system operation. The stable state judgment module can effectively monitor and regulate the stable state of the hydraulic system, ensuring the safe and efficient operation of the system.

[0157] Embodiment 9:

[0158] Reference Figure 2 , the present invention provides a hydraulic control method, which is applied to any one of the hydraulic control systems in Embodiments 1 to 8, and includes:

[0159] Step 1: Determine whether the current working state of the hydraulic system is normal based on the pressure, flow rate, temperature of the hydraulic oil and the displacement and angular data of the actuator.

[0160] Step 2: When the current working state of the hydraulic system is determined to be normal, an optimized control strategy is generated based on the fuzzy control algorithm and the neural network algorithm.

[0161] Step 3: When the current working state of the hydraulic system is determined to be abnormal, the optimal fault handling strategy is called based on the type of abnormality and the severity of the abnormality.

[0162] Step 4: Based on the optimized control strategy or the optimal fault handling strategy, preliminary control is performed on the flow direction, pressure, and flow rate of the hydraulic oil.

[0163] Step 5: When the hydraulic system completes the preliminary control, the prediction time domain is determined based on the current dynamic characteristics and control requirements of the hydraulic system, and the working state of the hydraulic system within the prediction period is predicted based on the state space model and the prediction time domain of the hydraulic system.

[0164] Step 6: Based on all the normal state duration periods in the working state of the hydraulic system within the prediction period, the working state of the hydraulic system is further controlled until the stability requirements of the current normal state of the hydraulic system are met.

[0165] Advantages of the above technical solutions: Step 1 can comprehensively collect data and accurately judge the working state of the hydraulic system, providing a prerequisite for subsequent processing. Step 2 generates an optimized control strategy when the system is normal, which helps to improve the performance and efficiency of the system. Step 3 calls the optimal fault handling strategy when the system is abnormal, and can quickly and effectively respond to the fault situation. Step 4 performs preliminary control based on the strategy and timely adjusts the key parameters of the system. Step 5 predicts the working state through the prediction time domain and state space model, providing a basis for further precise control. Step 6 further controls according to the duration of the normal state to meet the stability requirements and ensure the stable and reliable operation of the system. This hydraulic control method has a clear process and rigorous logic, and can achieve comprehensive, precise and efficient control of the hydraulic system, ensuring that the system can operate stably and reliably under various conditions.

[0166] Example 10:

[0167] The present invention provides a crane, including an actuator that receives force and torque provided by any one of the hydraulic control systems in Examples 1 to 8.

[0168] In this embodiment, the actuator that receives force and torque provided by the hydraulic control system refers to the specific component or device that is driven by the hydraulic control system and outputs force and torque. For example, in a crane, it may be a hydraulic cylinder, a hydraulic motor, etc. They receive instructions and power from the hydraulic control system, convert hydraulic energy into mechanical energy, and achieve the force and torque output required for actions such as lifting heavy objects and extending the robotic arm.

[0169] Advantages of the above technical solutions: For the crane adopting this hydraulic control system, its actuator can obtain more accurate and optimized force and torque output, thereby improving the working performance and stability of the crane. The accurate judgment and timely adjustment of the working state by the hydraulic control system can effectively reduce the downtime of the crane caused by hydraulic system failures and improve the operation efficiency. The application of the optimized control strategy and the fault handling strategy can extend the service life of the crane's hydraulic system and reduce the maintenance cost. This crane can better adapt to different working conditions in a complex working environment, ensuring the safety and reliability of the operation. It significantly improves and guarantees the crane in terms of force and torque output, operation efficiency, maintenance cost, safety, etc.

[0170] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these changes and modifications.

Claims

1. A hydraulic control system, characterized in that: include: A working state judgment module is used to judge whether the current working state of the hydraulic system is normal based on the pressure, flow, temperature of the hydraulic oil and the displacement and angle data of the actuator; An optimization control module, used for generating an optimization control strategy based on a fuzzy control algorithm and a neural network algorithm when the current working state of the hydraulic system is determined to be normal; A fault handling module, used for invoking an optimal fault handling strategy based on the type and severity of the abnormality when the current working state of the hydraulic system is determined to be abnormal; A preliminary control module, used to perform preliminary control of the flow direction, pressure and flow rate of the hydraulic oil based on an optimized control strategy or an optimal fault handling strategy; A working state prediction module is used to determine the prediction time domain based on the current dynamic characteristics and control requirements of the hydraulic system when the hydraulic system completes preliminary control, and predict the working state of the hydraulic system within the prediction period based on the state space model of the hydraulic system and the prediction time domain; The stable state judgment module is used to further control the working state of the hydraulic system based on all normal state duration periods in the working state of the hydraulic system within the prediction period until the current normal state stability requirement of the hydraulic system is met.

2. The hydraulic control system according to claim 1, characterized in that: The working status judgment module includes: The sensor monitoring submodule is used to collect the pressure, flow, temperature of the hydraulic oil and the displacement and angle data of the actuator in real time based on a variety of sensors; The working state judgment submodule is used to judge whether the current working state of the hydraulic system is normal based on the pressure, flow, temperature of the hydraulic oil and the displacement and angle data of the actuator.

3. The hydraulic control system according to claim 1, characterized in that: Optimized control module, including: The optimization model generation submodule is used to generate a composite optimization model based on a fuzzy control algorithm and a neural network algorithm when the current working state of the hydraulic system is determined to be normal; The optimization strategy generation submodule is used to generate an optimization control strategy based on the preset working mode, pressure, flow, temperature of the hydraulic oil, displacement and angle data of the actuator, and the composite optimization model.

4. The hydraulic control system according to claim 1, characterized in that: Fault handling module, including: Deviation vector generation submodule, used to construct abnormal deviation vector under the premise of considering coupling relationship based on the pressure, flow, temperature of hydraulic oil and displacement and angle data of actuator and coupling coefficient matrix; An abnormality degree vector generating submodule, used for calculating the abnormality degree of the hydraulic system in each abnormality type based on the weight matrix, and generating an abnormality degree vector based on the abnormality degree of the hydraulic system in all abnormality types; The abnormality enhancement function construction submodule is used to construct the abnormality enhancement function G(E) based on the abnormal deviation vector under the premise of considering the coupling relationship and introducing information entropy; A strategy utility value calculation submodule is used to calculate the utility value of each preset fault handling strategy under the abnormality degree vector based on the abnormality degree enhancement function G(E); The optimal strategy calling submodule is used to filter and call the preset fault handling strategy with the maximum utility value among all preset fault handling strategies as the optimal fault handling strategy.

5. The hydraulic control system according to claim 4, characterized in that: Deviation vector generation submodule, including: A state parameter vector generating unit, used for generating a state parameter vector of the hydraulic system based on the pressure, flow, temperature of the hydraulic oil and the displacement and angle data of the actuator; an initial abnormal deviation vector generating unit, used for generating an initial abnormal deviation vector of the hydraulic system based on a normal range of each state parameter and a state parameter vector of the hydraulic system; The coupling relationship is incorporated into the unit, which is used to construct an abnormal deviation vector ΔS′={Δs′1, Δs′2, …, Δs′ based on the coupling coefficient matrix between the state parameters and the initial abnormal deviation vector. n },in: In the formula, Δs i is the ith abnormal deviation value contained in the initial abnormal deviation vector, n is the total number of state parameters contained in the state parameter vector, c ij is the coupling strength between the i-th state parameter and the j-th state parameter in the state parameter vector contained in the coupling coefficient matrix, Δs j is the jth abnormal deviation value contained in the initial abnormal deviation vector.

6. The hydraulic control system according to claim 4, characterized in that: The abnormality enhancement function builds submodules, including: A probability distribution statistics unit, used to obtain the probability distribution of all abnormal types based on the ratio of the current abnormality degree of the hydraulic system in each abnormal type to the sum of the current abnormality degrees of the hydraulic system in all abnormal types; The information entropy formula construction unit is used to construct the information entropy formula based on the probability distribution of all abnormal types: In the formula, H is the information entropy output value, p l is the probability value of the lth abnormal type, ln is the logarithmic function with the natural constant e as the base and the value of e is 2.71828, and m is the total number of abnormal types; The abnormality degree function construction unit is used to construct the abnormality degree enhancement function G(E) based on the abnormal deviation vector under the premise of considering the coupling relationship and introducing the information entropy formula: Where n is the total number of state parameters contained in the state parameter vector, ω i is the weight of the abnormal deviation value of the i-th state parameter in the state parameter vector in the enhancement function, Δs′ i is the i-th abnormal deviation value in the abnormal deviation vector under the premise of considering the coupling relationship, and γ is the weight value of the information entropy.

7. The hydraulic control system according to claim 4, characterized in that: The strategy utility value calculation submodule includes: A strategy parameter acquisition unit, used to obtain the control cost and expected system recovery time of each preset fault handling strategy; The utility value calculation unit is used to calculate the utility value U(p) of each preset fault handling strategy under the abnormality degree vector based on the control cost, expected system recovery time and abnormality degree enhancement function G(E) of each preset fault handling strategy. q ,E).

8. The hydraulic control system according to claim 1, characterized in that: The stable state judgment module includes: A state stability evaluation submodule, used to evaluate the current normal state stability of the hydraulic system based on all normal state duration periods in the working state of the hydraulic system within the prediction period; The further control submodule is used to further control the working state of the hydraulic system based on the current normal state stability of the hydraulic system until the current normal state stability requirement of the hydraulic system is met.

9. A hydraulic control method, characterized in that: A hydraulic control system as claimed in any one of claims 1 to 8, comprising: Step 1: Determine whether the current working state of the hydraulic system is normal based on the pressure, flow, temperature of the hydraulic oil and the displacement and angle data of the actuator; Step 2: When the current working state of the hydraulic system is judged to be normal, an optimization control strategy is generated based on the fuzzy control algorithm and the neural network algorithm; Step 3: When the current working state of the hydraulic system is determined to be abnormal, the optimal fault handling strategy is called based on the abnormality type and the severity of the abnormality; Step 4: Preliminary control of the flow direction, pressure and flow rate of the hydraulic oil based on the optimization control strategy or the optimal fault handling strategy; Step 5: When the hydraulic system completes the preliminary control, the prediction time domain is determined based on the current dynamic characteristics and control requirements of the hydraulic system, and the working state of the hydraulic system within the prediction period is predicted based on the state space model of the hydraulic system and the prediction time domain; Step 6: Based on all the normal state duration periods in the working state of the hydraulic system within the prediction period, further control the working state of the hydraulic system until the current normal state stability requirement of the hydraulic system is met.

10. A crane, characterized in that: An actuator comprising a hydraulic control system according to any one of claims 1 to 8 providing force and torque.

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