Hydraulic loading system control method based on fuzzy control algorithm

Abstract

The invention provides a hydraulic loading system control method based on a fuzzy control algorithm, and relates to the technical field of system control, and the method comprises the steps: 1, obtaining real-time hydraulic data of a target hydraulic loading system based on a preset sensor, and obtaining first control hydraulic data; 2, an initial fuzzy control algorithm is obtained based on hydraulic characteristics of a target hydraulic loading system, algorithm training is carried out based on historical hydraulic data and historical target hydraulic pressure, and a first fuzzy control algorithm is obtained; 3, comparing the first control hydraulic pressure data with preset target hydraulic pressure, inputting the first control hydraulic pressure data into a first fuzzy control algorithm based on a comparison result, and performing hydraulic control; and 4, the real-time operation state of the target hydraulic loading system is monitored in real time, the stability of the system is determined, and state display early warning is carried out. The control precision of the hydraulic loading system is higher, and the control is more timely and effective.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control, and particularly relates to a control method for a hydraulic loading system based on a fuzzy control algorithm. Background Art

[0002] At present, hydraulic loading systems are widely used in medium-speed roller mills in coal power plants, chemical industries, coal-to-oil plants, cement plants, steel plants, etc. The coal mill crushes coal blocks and grinds them into pulverized coal. The hydraulic loading system provides a grinding pressure for the grinding rollers of the coal mill that changes with the load (coal quantity of the coal feeder). The magnitude is achieved by the proportional overflow valve controlling the pressure of the hydraulic oil system according to the command signal that changes with the load (coal quantity of the coal feeder).

[0003] However, the existing control of the hydraulic loading system still has problems such as poor control performance and inaccurate control.

[0004] Therefore, the present invention provides a control method for a hydraulic loading system based on a fuzzy control algorithm. Summary of the Invention

[0005] The present invention provides a control method for a hydraulic loading system based on a fuzzy control algorithm to solve the problems of untimely and inaccurate control of the hydraulic loading system in the prior art.

[0006] The present invention provides a control method for a hydraulic loading system based on a fuzzy control algorithm, including: Step 1: Obtain real-time hydraulic data of the target hydraulic loading system based on a preset sensor to obtain first control hydraulic data; Step 2: Obtain a corresponding initial fuzzy control algorithm based on the hydraulic characteristics of the target hydraulic loading system, and train the initial fuzzy control algorithm based on the historical hydraulic data and historical target hydraulic pressure of the target hydraulic loading system to obtain a first fuzzy control algorithm for the target hydraulic loading system; Step 3: Compare the first control hydraulic data with a preset target hydraulic pressure, thereby input the first control hydraulic data into the first fuzzy control algorithm based on the comparison result, and perform hydraulic control on the target fuzzy controller based on the first fuzzy control algorithm; Step 4: Real-time monitor the real-time operating state of the target hydraulic loading system, thereby determine the stability of the target hydraulic loading system, and perform status display or status warning.

[0007] According to the present invention, obtaining the first control hydraulic data includes: Step 11: Screen the critical hydraulic pressure of the hydraulic loading system that matches the type of the target hydraulic loading system from the hydraulic loading database to determine the critical hydraulic pressure of the target hydraulic loading system; Step 12: Obtain the real-time hydraulic data of the target hydraulic loading system based on a preset sensor, and compare the real-time hydraulic data with the critical hydraulic pressure; If the real-time hydraulic data is less than the critical hydraulic pressure, it is determined that the target hydraulic loading system is in a relatively stable state, and the real-time hydraulic data is used as the first control hydraulic data of the target hydraulic loading system; If the real-time hydraulic data is not less than the critical hydraulic pressure, it is determined that the target hydraulic loading system is not in a relatively stable state, and a status warning is given.

[0008] According to the first fuzzy control algorithm for obtaining the target hydraulic loading system provided by the present invention, it includes: Step 21: Obtain the corresponding initial fuzzy control algorithm based on the hydraulic characteristics of the target hydraulic loading system; Step 22: Obtain the historical hydraulic data of the target hydraulic loading system and the historical target hydraulic pressure of the historical hydraulic control process, so as to obtain the historical hydraulic data table of the target hydraulic loading system; Step 23: Input each historical hydraulic data and the corresponding historical target hydraulic pressure in the historical hydraulic data table into the initial fuzzy control algorithm, so as to obtain the first control parameter of the initial fuzzy control algorithm; Step 24: Obtain a first parameter set based on the first control parameter of the target hydraulic loading system, and thus classify each first control parameter in the first parameter set, so as to obtain a first classification parameter set, wherein each first classification parameter subset contains each first control sub-parameter of the same parameter type; Step 25: Exclude the corresponding maximum control parameter value and minimum control parameter value in each first classification parameter subset in the first classification parameter set, and thus obtain a second classification parameter subset based on the control parameter values of the remaining first control sub-parameters in the current first classification parameter subset; Step 26: Sort the first control sub-parameters in each second classification parameter subset, so as to obtain an ordered second classification parameter subset, and extract the median of the first control sub-parameters in the current second classification parameter subset as the control parameter value of the corresponding parameter type of the current second classification parameter subset, so as to obtain the first control parameter value; Step 27: Optimize the corresponding control parameter of the initial fuzzy control algorithm based on each first control parameter value to obtain the second control parameter value, so as to obtain the first fuzzy control algorithm of the target hydraulic loading system.

[0009] According to the present invention, obtaining the second control parameter value includes: Obtain the second control parameter value ; ; wherein, is the second control parameter value of the i-th first control sub-parameter in the first fuzzy D control algorithm for the target hydraulic loading system; is the first control parameter value of the i-th first control sub-parameter; is the parameter influence degree of the parameter type corresponding to the j-th first control sub-parameter on the parameter type corresponding to the current i-th first control parameter value; is the type conversion coefficient of the j-th first control sub-parameter; is the type influence weight; is the similarity degree between the historical working environment corresponding to the j-th first control sub-parameter and the historical working environment corresponding to the first control sub-parameter corresponding to the current i-th first control parameter value; is the degree conversion coefficient of the j-th first control sub-parameter; is the parameter matching weight; m is the number of control sub-parameters in the first fuzzy control algorithm; n is the number of remaining control sub-parameters in the first fuzzy control algorithm except the current i-th control sub-parameter; exp[] is the exponential function with e as the base.

[0010] Performing hydraulic control on the target fuzzy controller according to the first fuzzy control algorithm provided by the present invention, including: Step 31: Compare the first control hydraulic data with the preset target hydraulic pressure; If the first control hydraulic data is less than the preset target hydraulic pressure, no hydraulic control is required at the current moment; If the first control hydraulic data is not less than the preset target hydraulic pressure, input the first control hydraulic data into the first fuzzy control algorithm to determine the hydraulic control parameters of the target hydraulic loading system; Step 32: Transmit the hydraulic control parameters to the target fuzzy controller to determine the hydraulic control instruction and perform hydraulic control on the target hydraulic loading system.

[0011] Comparing the first control hydraulic data with the preset target hydraulic pressure includes: Step 311: Obtain the hydraulic performance parameters of the target hydraulic loading system and simultaneously obtain the initial target hydraulic pressure of the target hydraulic loading system; Step 312: Extract the first hydraulic deviation corresponding to the current hydraulic performance parameters from the performance influence database; Step 313: Combine the first hydraulic deviation with the initial target hydraulic pressure to determine the preset target hydraulic pressure of the target hydraulic loading system.

[0012] According to the present invention, the real-time operating state of the target hydraulic loading system is monitored in real time to determine the stability of the target hydraulic loading system and perform status display or status warning, including: Step 41: Monitor the real-time operating status of the target hydraulic loading system at each moment within the current hydraulic control cycle; Step 42: Classify the real-time operating status within the current hydraulic control cycle according to different status types, so as to obtain the first classified operating status set; Step 43: Sort each first classified operating status in the first classified operating status set in chronological order to obtain the ordered second classified operating status; Step 44: Input each second classified operating status into the same coordinate system, so as to obtain the second status curve corresponding to each second classified operating status, thereby determining the stability of the target hydraulic loading system, and then performing status display and status warning.

[0013] Determining the stability of the target hydraulic loading system according to the present invention, and then performing status display and status warning, including: Step 441: Input each second classified operating status into the same coordinate system, so as to obtain the second status curve corresponding to each second classified operating status; Step 442: Compare the difference between the curve maximum point and the curve minimum point of the second status curve, so as to determine the curve fluctuation degree of the second status curve; Step 443: Combine the curve fluctuation degree of each second status curve of the target hydraulic loading system with the corresponding status type to determine the comprehensive stability of the target hydraulic loading system; Step 444: Compare the comprehensive stability with the preset hydraulic stability; If the comprehensive stability is greater than the preset hydraulic stability, perform status display on the real-time operating status of the target hydraulic loading system; If the comprehensive stability is not greater than the preset hydraulic stability, perform status warning on the real-time operating status of the target hydraulic loading system.

[0014] Compared with the prior art, the beneficial effect of the present invention is: A hydraulic loading system control method based on a fuzzy control algorithm provided by the present invention determines the first control hydraulic data by acquiring the real-time hydraulic data of the target hydraulic loading system, and trains the initial fuzzy control algorithm based on the historical hydraulic data and the historical target hydraulic pressure to obtain the first fuzzy control algorithm, so as to input the first control hydraulic data into the first fuzzy control algorithm to make the hydraulic control more accurate, thereby realizing accurate and effective hydraulic stability control. Description of the Drawings

[0015] To more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the attached drawings required in the description of the embodiments or the prior art. Obviously, the attached drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other attached drawings can also be obtained based on these drawings.

[0016] Figure 1 It is a flowchart of a control method for a hydraulic loading system based on a fuzzy control algorithm provided by an embodiment of the present invention. Specific implementation manners

[0017] To make the objectives, technical solutions and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention in conjunction with the attached drawings in the present invention. Obviously, the described embodiments are some embodiments of the present invention, rather than all embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0018] Embodiment 1: The embodiment of the present invention provides a control method for a hydraulic loading system based on a fuzzy control algorithm, as Figure 1 shown, including: Step 1: Obtain the real-time hydraulic data of the target hydraulic loading system based on a preset sensor to obtain the first control hydraulic data; Step 2: Obtain the corresponding initial fuzzy control algorithm based on the hydraulic characteristics of the target hydraulic loading system, and train the initial fuzzy control algorithm based on the historical hydraulic data and historical target hydraulic of the target hydraulic loading system to obtain the first fuzzy control algorithm of the target hydraulic loading system; Step 3: Compare the first control hydraulic data with a preset target hydraulic pressure, and thus input the first control hydraulic data into the first fuzzy control algorithm based on the comparison result, and perform hydraulic control on the target fuzzy controller based on the first fuzzy control algorithm; Step 4: Real-time monitor the real-time operating state of the target hydraulic loading system, thereby determine the stability of the target hydraulic loading system, and perform status display or status warning.

[0019] In this embodiment, the preset sensor refers to a sensor pre-installed or configured in the target hydraulic loading system for real-time monitoring and obtaining the hydraulic data of the system, such as pressure, flow rate, position, etc.

[0020] In this embodiment, the target hydraulic loading system refers to a system that needs to perform hydraulic control, which may be an industrial machine, a construction machine or other equipment that requires hydraulic power.

[0021] In this embodiment, the real-time hydraulic data refers to the hydraulic data that is acquired by the sensor in real time and transmitted to the control system, and is used to monitor and control the system.

[0022] In this embodiment, the first control hydraulic data refers to the hydraulic data that is acquired by a preset sensor and is used to initially control the target hydraulic loading system.

[0023] In this embodiment, the hydraulic characteristics refer to the variation laws of physical characteristics such as pressure, flow rate, and temperature that are exhibited by the target hydraulic loading system during operation.

[0024] In this embodiment, the initial fuzzy control algorithm refers to the algorithm that is designed based on fuzzy control theory and is used to initially control the target hydraulic loading system.

[0025] In this embodiment, the historical hydraulic data refers to the hydraulic data that is recorded during the past operation of the target hydraulic loading system and is used to train and optimize the fuzzy control algorithm.

[0026] In this embodiment, the historical target hydraulic pressure refers to the hydraulic pressure value that the target hydraulic loading system should reach or has reached during past operation, and is used to compare with the real-time hydraulic data to evaluate the performance of the system.

[0027] In this embodiment, the first fuzzy control algorithm refers to the fuzzy control algorithm that is used to actually control the target hydraulic loading system after being trained and optimized.

[0028] In this embodiment, the preset target hydraulic pressure refers to the ideal hydraulic pressure value that the target hydraulic loading system should reach, and is used to compare with the real-time acquired hydraulic data to evaluate the control effect of the system.

[0029] In this embodiment, the comparison result refers to the result obtained by comparing the first control hydraulic data with the preset target hydraulic pressure, and is used to determine whether it is necessary to adjust the control strategy.

[0030] In this embodiment, the target fuzzy controller: refers to the controller that is designed based on the first fuzzy control algorithm and is used to actually control the target hydraulic loading system.

[0031] In this embodiment, hydraulic control refers to the process of precisely controlling the hydraulic pressure value of the target hydraulic loading system by adjusting means such as the output of the hydraulic pump and the opening degree of the control valve.

[0032] In this embodiment, the real-time operating state refers to various state information exhibited by the target hydraulic loading system during operation, such as pressure, flow rate, temperature, vibration, etc.

[0033] In this embodiment, stability refers to the ability of the target hydraulic loading system to maintain a constant or stable state during operation, and is one of the important indicators for evaluating the performance of the system.

[0034] In this embodiment, status display refers to presenting the real-time operating status of the target hydraulic loading system in a visual manner so that the operator can intuitively understand the operating status of the system.

[0035] In this embodiment, status warning means that when the operating status of the target hydraulic loading system is abnormal or about to be abnormal, the system can automatically issue an alarm to remind the operator to take corresponding measures for handling.

[0036] The beneficial effects of the above technical solution are as follows: The first control hydraulic data is determined by obtaining the real-time hydraulic data of the target hydraulic loading system, and the initial fuzzy control algorithm is trained based on the historical hydraulic data and the historical target hydraulic pressure to obtain the first fuzzy control algorithm. Then, the first control hydraulic data is input into the first fuzzy control algorithm to make the hydraulic control more accurate, thereby achieving accurate and effective hydraulic stability control.

[0037] Embodiment 2: Based on Embodiment 1, the first control hydraulic data is obtained, including: Step 11: Screen the critical hydraulic pressure of the hydraulic loading system that matches the type of the target hydraulic loading system from the hydraulic loading database, so as to determine the critical hydraulic pressure of the target hydraulic loading system; Step 12: Obtain the real-time hydraulic data of the target hydraulic loading system based on the preset sensor, and compare the real-time hydraulic data with the critical hydraulic pressure; If the real-time hydraulic data is less than the critical hydraulic pressure, it is determined that the target hydraulic loading system is in a relatively stable state, and the real-time hydraulic data is used as the first control hydraulic data of the target hydraulic loading system; If the real-time hydraulic data is not less than the critical hydraulic pressure, it is determined that the target hydraulic loading system is not in a relatively stable state, and status warning is performed.

[0038] In this embodiment, the hydraulic loading database is a database that stores relevant data of various types of hydraulic loading systems. For example, these data include hydraulic values, performance parameters, system characteristics, etc. of different systems under different conditions. The purpose of database design is to facilitate users to query, analyze and compare the performance of different types of hydraulic loading systems.

[0039] In this embodiment, the target hydraulic loading system refers to a specific hydraulic loading system that is currently being monitored, controlled or studied. It is the focus of all operations and data analysis.

[0040] In this embodiment, the critical hydraulic pressure refers to the maximum hydraulic pressure that the hydraulic loading system can withstand under normal operating conditions. Exceeding this value may cause the system to become unstable or malfunction. The critical hydraulic pressure is an important parameter for system design and safe operation.

[0041] In this embodiment, the preset sensors are pre-installed in the target hydraulic loading system to continuously monitor the hydraulic data of the system. They can convert hydraulic pressure into electrical signals or other measurable forms for data processing and analysis.

[0042] In this embodiment, the real-time hydraulic data is the hydraulic pressure value of the target hydraulic loading system obtained in real time through the preset sensors. These data reflect the current operating state of the system and are an important basis for condition monitoring, early warning, and control.

[0043] In this embodiment, the relatively stable state means that when the real-time hydraulic data of the target hydraulic loading system is less than the critical hydraulic pressure, the system can be considered to be in a relatively stable state. This means that the system can operate normally under the current conditions and will not malfunction due to excessive hydraulic pressure.

[0044] In this embodiment, the first control hydraulic data refers to the real-time hydraulic data used as the first control hydraulic data when the target hydraulic loading system is in a relatively stable state. These data can be used for further system control, optimization, or as a benchmark for subsequent analysis.

[0045] In this embodiment, the status warning means that when the real-time hydraulic data is not less than the critical hydraulic pressure, the system will trigger a status warning. This means that the target hydraulic loading system may be about to enter an unstable state or has already malfunctioned, and emergency measures need to be taken to prevent further problems.

[0046] The beneficial effects of the above technical solution are: By obtaining the real-time hydraulic data of the target hydraulic loading system to determine the first control hydraulic data, and then training the initial fuzzy control algorithm in combination with historical hydraulic data and historical target hydraulic pressures, the first fuzzy control algorithm is obtained, making the hydraulic control more accurate.

[0047] Embodiment 3: Based on Embodiment 2, the first fuzzy control algorithm for the target hydraulic loading system is obtained, including: Step 21: Obtain the corresponding initial fuzzy control algorithm based on the hydraulic characteristics of the target hydraulic loading system; Step 22: Obtain the historical hydraulic data of the target hydraulic loading system and the historical target hydraulic pressures in the historical hydraulic control process, so as to obtain the historical hydraulic data table of the target hydraulic loading system; Step 23: Based on each historical hydraulic data and its corresponding historical target hydraulic pressure in the historical hydraulic data table, initialize the fuzzy control algorithm, thereby obtaining the first control parameter of the initial fuzzy control algorithm; Step 24: Obtain the first parameter set based on the first control parameter of the target hydraulic loading system, and thereby classify each first control parameter in the first parameter set to obtain the first classification parameter set. Among them, each first classification parameter subset contains each first control sub-parameter of the same parameter type; Step 25: Exclude the maximum control parameter value and the minimum control parameter value corresponding to each first classification parameter subset in the first classification parameter set, and thereby obtain the second classification parameter subset based on the control parameter values of the remaining first control sub-parameters in the current first classification parameter subset; Step 26: Sort the first control sub-parameters in each second classification parameter subset to obtain an ordered second classification parameter subset, and extract the median of the first control sub-parameters in the current second classification parameter subset as the control parameter value of the corresponding parameter type of the current second classification parameter subset, thereby obtaining the first control parameter value; Step 27: Optimize the corresponding control parameter of the initial fuzzy control algorithm based on each first control parameter value to obtain the second control parameter value, thereby obtaining the first fuzzy control algorithm of the target hydraulic loading system.

[0048] In this embodiment, the hydraulic characteristics refer to the physical characteristics or behavior patterns exhibited by the target hydraulic loading system under specific conditions (such as pressure, temperature, flow rate, etc.). These characteristics are the basis for designing and optimizing the control system.

[0049] In this embodiment, the initial fuzzy control algorithm: Fuzzy control is a control method based on fuzzy logic. It does not require an accurate mathematical model but relies on a set of rules to describe the behavior of the system. The initial fuzzy control algorithm refers to the set of fuzzy control rules initially determined in the design stage for controlling the target hydraulic loading system.

[0050] In this embodiment, the historical hydraulic data refers to the hydraulic pressure values recorded during the past operation of the target hydraulic loading system.

[0051] In this embodiment, the historical target hydraulic pressure is the hydraulic pressure value that the system expects to reach at the moment corresponding to the historical hydraulic data. These values are usually used to evaluate the performance of the control system.

[0052] In this embodiment, the historical hydraulic data table organizes the historical hydraulic data and its corresponding historical target hydraulic pressure into a table form for subsequent data analysis and processing.

[0053] In this embodiment, the first control parameters are the parameters in the initial fuzzy control algorithm, and these parameters determine the specific implementation manner of the fuzzy control rules, such as the membership functions of the input variables, the conditional statements in the rule base, etc.

[0054] In this embodiment, the first parameter set is a set containing all the first control parameters. These parameters may involve different types, such as the parameters of the membership function, the weights of the rule base, etc.

[0055] In this embodiment, the first classified parameter subset is a subset obtained by classifying the parameters in the first parameter set according to their types. Each subset contains all the parameters of the same type.

[0056] In this embodiment, the maximum control parameter value and the minimum control parameter value are the two extreme values with the largest and smallest parameter values in the first classified parameter subset. These values may be generated due to noise, abnormal data, or extreme states of the system, and usually need to be excluded during the optimization process.

[0057] In this embodiment, the second classified parameter subset is a subset composed of the remaining parameters after excluding the maximum and minimum control parameter values. The parameter values in this set are more concentrated and can better reflect the normal behavior of the system.

[0058] In this embodiment, the median is the value located in the middle after sorting all the parameter values in the second classified parameter subset by size. The median is a robust statistic that is not sensitive to extreme values and is often used to estimate the central tendency of data.

[0059] In this embodiment, the first control parameter value is the control parameter value determined in each second classified parameter subset and representing the parameter type of the subset.

[0060] In this embodiment, the second control parameter value is the value obtained by optimizing the corresponding control parameters of the initial fuzzy control algorithm based on the first control parameter value. These optimized parameter values make the fuzzy control algorithm more adaptable to the actual behavior of the target hydraulic loading system.

[0061] In this embodiment, the first fuzzy control algorithm is a fuzzy control algorithm after parameter optimization, which more accurately reflects the characteristics and behavior patterns of the target hydraulic loading system and can be used to achieve more precise control.

[0062] The beneficial effects of the above technical solution are: By training the fuzzy control algorithm with historical data, optimizing the control parameters, excluding extreme values and taking the median to determine the control parameter values, the precise optimization of the initial fuzzy control algorithm is realized, and the stability and precision of the hydraulic control of the target hydraulic loading system are improved.

[0063] Embodiment 4: Based on Embodiment 3, the second control parameter value is obtained, including: Obtain the second control parameter value ; ; where is the second control parameter value of the i-th first control sub-parameter in the first fuzzy D control algorithm of the target hydraulic loading system; is the first control parameter value of the i-th first control sub-parameter; is the parameter influence degree of the parameter type corresponding to the j-th first control sub-parameter on the parameter type corresponding to the current i-th first control parameter value; is the type conversion coefficient of the j-th first control sub-parameter; is the type influence weight; is the similarity degree between the historical working environment corresponding to the j-th first control sub-parameter and the historical working environment corresponding to the first control sub-parameter corresponding to the current i-th first control parameter value; is the degree conversion coefficient of the j-th first control sub-parameter; is the parameter matching weight; m is the number of control sub-parameters in the first fuzzy control algorithm; n is the number of remaining control sub-parameters in the first fuzzy control algorithm except the current i-th control sub-parameter; exp[] is the exponential function with e as the base.

[0064] The beneficial effects of the above technical solution are: By training the fuzzy control algorithm with historical data, optimizing the control parameters, and taking the median after removing extreme values to determine the control parameter value, the precise optimization of the initial fuzzy control algorithm is realized, and the stability and accuracy of the hydraulic control of the target hydraulic loading system are improved.

[0065] Embodiment 5: Based on Embodiment 3, hydraulic control is performed on the target fuzzy controller based on the first fuzzy control algorithm, including: Step 31: Compare the first control hydraulic data with the preset target hydraulic pressure; If the first control hydraulic data is less than the preset target hydraulic pressure, no hydraulic control is required at the current moment; If the first control hydraulic data is not less than the preset target hydraulic pressure, input the first control hydraulic data into the first fuzzy control algorithm to determine the hydraulic control parameters of the target hydraulic loading system; Step 32: Transmit the hydraulic control parameters to the target fuzzy controller to determine the hydraulic control instruction and perform hydraulic control on the target hydraulic loading system.

[0066] In this embodiment, the first controlled hydraulic data refers to the current hydraulic data of the target hydraulic loading system obtained in a certain way (such as sensor measurement, calculation, etc.), which reflects the current hydraulic state of the system.

[0067] In this embodiment, the preset target hydraulic pressure refers to the expected hydraulic pressure value preset in the system design or operation process. It represents the hydraulic level that the system should reach or maintain. The preset target hydraulic pressure is usually determined based on the performance requirements, safety standards or operating conditions of the system.

[0068] In this embodiment, hydraulic control refers to the process of monitoring, adjusting and controlling the hydraulic pressure value of the hydraulic loading system. The purpose is to ensure that the hydraulic pressure of the system remains within the preset target hydraulic pressure range to meet the performance requirements of the system and ensure safety and stability.

[0069] In this embodiment, the first fuzzy control algorithm is a control algorithm based on fuzzy logic, which is used to handle uncertainties and ambiguities. When the first controlled hydraulic data is not less than the preset target hydraulic pressure, this algorithm is used to determine the hydraulic control parameters of the target hydraulic loading system. The fuzzy control algorithm outputs control parameters through a series of fuzzy rules and reasoning processes.

[0070] In this embodiment, the hydraulic control parameter refers to the control parameter calculated by the first fuzzy control algorithm according to the first controlled hydraulic data and the preset target hydraulic pressure. It represents the specific value or instruction required to adjust the system to achieve the preset target hydraulic pressure. The hydraulic control parameters may include pressure adjustment amount, flow rate adjustment amount, etc.

[0071] In this embodiment, the target fuzzy controller refers to the controller used to implement fuzzy control. It receives the hydraulic control parameters as inputs and generates specific hydraulic control instructions based on these parameters. A series of fuzzy rules and reasoning mechanisms may be included inside the target fuzzy controller to convert the hydraulic control parameters into actual control instructions.

[0072] In this embodiment, the hydraulic control instruction refers to the instruction generated by the target fuzzy controller for hydraulic control of the target hydraulic loading system. These instructions may include specific operations such as opening or closing valves, adjusting the output of pumps, etc., aiming to make the hydraulic pressure value of the system reach or approach the preset target hydraulic pressure.

[0073] The beneficial effects of the above technical solution are: By accurately comparing the hydraulic data and performing hydraulic regulation as needed, the control parameters can be determined through the fuzzy algorithm, enabling precise hydraulic control of the target hydraulic loading system.

[0074] Embodiment 6: Based on Embodiment 5, comparing the first controlled hydraulic data with the preset target hydraulic pressure includes: Step 311: Obtain the hydraulic performance parameters of the target hydraulic loading system, and at the same time obtain the initial target hydraulic pressure of the target hydraulic loading system; Step 312: Extract the first hydraulic deviation corresponding to the current hydraulic performance parameters from the performance impact database; Step 313: Combine the first hydraulic deviation with the initial target hydraulic pressure to determine the preset target hydraulic pressure of the target hydraulic loading system.

[0075] In this embodiment, the hydraulic performance parameters refer to the parameters that describe the hydraulic performance and characteristics of the target hydraulic loading system. For example, they include the working pressure, flow rate, efficiency, temperature, etc. of the system, which jointly reflect the hydraulic performance and state of the system.

[0076] In this embodiment, the initial target hydraulic pressure refers to the hydraulic value that the target hydraulic loading system initially expects to reach during the hydraulic control process. It may be determined based on the design requirements, operating conditions, or performance standards of the system, and serves as the benchmark for subsequent hydraulic control and adjustment.

[0077] In this embodiment, the performance impact database is a database that stores the relationships between different hydraulic performance parameters and the corresponding hydraulic deviations. It may contain a large amount of historical data and experimental results, and is used to analyze and predict the impact of different performance parameters on the hydraulic deviation of the system. The performance impact database is an important tool for designing and optimizing the hydraulic control system.

[0078] In this embodiment, the first hydraulic deviation refers to the hydraulic deviation value extracted from the performance impact database and corresponding to the current hydraulic performance parameters. The hydraulic deviation represents the difference between the actual hydraulic value and the expected hydraulic value of the system, and it reflects the degree of deviation of the system under specific performance parameters.

[0079] In this embodiment, the preset target hydraulic pressure refers to the hydraulic value that the target hydraulic loading system should reach, which is determined by combining the first hydraulic deviation and the initial target hydraulic pressure. The preset target hydraulic pressure takes into account the current performance parameters and hydraulic deviation of the system, so it is closer to the actual situation and requirements of the system.

[0080] The beneficial effects of the above technical solution are: By accurately comparing hydraulic data and performing hydraulic regulation as needed, the control parameters can be determined through a fuzzy algorithm, enabling precise hydraulic control of the target hydraulic loading system.

[0081] Embodiment 7: Based on Embodiment 6, perform status display or status warning, including: Step 41: Monitor the real-time operating status of the target hydraulic loading system at each moment during the current hydraulic control cycle; Step 42: Classify the real-time operating states within the current hydraulic control cycle according to different state types, so as to obtain the first classified operating state set; Step 43: Sort each first-classified operating state in the first-classified operating state set in chronological order to obtain the ordered second-classified operating states; Step 44: Input each second-classified operating state into the same coordinate system, so as to obtain the second state curve corresponding to each second-classified operating state, thereby determining the stability of the target hydraulic loading system, and thus performing state display and state warning.

[0082] In this embodiment, the current hydraulic control cycle refers to the time period for monitoring the state of the target hydraulic loading system, and is also called a control cycle. Within this cycle, the system will experience a series of changes in operating states, and these states will be monitored and analyzed.

[0083] In this embodiment, the real-time operating state refers to the state shown by the target hydraulic loading system at each specific moment within the current hydraulic control cycle. It may include the real-time readings of key parameters such as the hydraulic value, temperature, pressure, and flow rate of the system, as well as the change trends of these parameters.

[0084] In this embodiment, the first-classified operating state set refers to the set obtained by classifying the real-time operating states within the current hydraulic control cycle according to a certain standard (such as state type). The classification may be based on different categories such as the normal operating state, abnormal state, and fault state of the system.

[0085] In this embodiment, the chronological order refers to the order of time when events or states occur. In Step 43, each state in the first-classified operating state set is sorted in the order of the time when they occur for subsequent analysis and visualization.

[0086] In this embodiment, the second-classified operating state is the state in the first-classified operating state set after being sorted in chronological order, and they are now arranged in an ordered time sequence.

[0087] In this embodiment, the second state curve refers to the curve drawn according to the second-classified operating state in the coordinate system. Each curve represents the change trend of a specific state type of the target hydraulic loading system over a period of time.

[0088] In this embodiment, the stability refers to the ability of the target hydraulic loading system to keep its performance parameters changing stably within a certain range during operation. By analyzing the second state curve, the stability of the system can be evaluated, and possible abnormal or fault trends can be identified.

[0089] In this embodiment, status display refers to the process of presenting information such as the real-time operating status, classified status set, and status curve of the target hydraulic loading system to the user in a visual manner. Status display helps the user quickly understand the current status of the system.

[0090] In this embodiment, status warning refers to a warning signal automatically issued by the system when the status of the target hydraulic loading system reaches or exceeds a preset threshold. The purpose of status warning is to timely notify the user of possible abnormalities or failures in the system so that necessary measures can be taken for intervention.

[0091] The beneficial effects of the above technical solution are: By real-time monitoring the operating status of the hydraulic loading system, the system stability is determined, and based on the system stability, status display and warning are carried out, which can timely and effectively perform hydraulic stability control and real-time feedback control.

[0092] Embodiment 8: Based on Embodiment 7, the stability of the target hydraulic loading system is determined, and then status display and status warning are carried out, including: Step 441: Input each second-classified operating status into the same coordinate system to obtain the second status curve corresponding to each second-classified operating status; Step 442: Compare the difference between the curve maximum point and the curve minimum point of the second status curve to determine the curve fluctuation degree of the second status curve; Step 443: Combine the curve fluctuation degree of each second status curve of the target hydraulic loading system with the corresponding status type to determine the comprehensive stability of the target hydraulic loading system; Step 444: Compare the comprehensive stability with the preset hydraulic stability; If the comprehensive stability is greater than the preset hydraulic stability, perform status display on the real-time operating status of the target hydraulic loading system; If the comprehensive stability is not greater than the preset hydraulic stability, perform status warning on the real-time operating status of the target hydraulic loading system.

[0093] In this embodiment, the second-classified operating status refers to the operating status of the target hydraulic loading system after classification and sorting in time sequence. These statuses are organized into an ordered set for further analysis and visualization.

[0094] In this embodiment, the second status curve refers to the curve formed by connecting the second-classified operating statuses in the coordinate system. Each curve represents the change trend of a specific status type of the target hydraulic loading system over a period of time. The shape, direction, and fluctuation degree of the curve can provide important information about the system status.

[0095] In this embodiment, the difference between the maximum value point of the curve and the minimum value point of the curve refers to the vertical distance between the highest point and the lowest point on the second state curve. This difference is used to measure the fluctuation degree of the curve, that is, the range of change of the state parameter within a period of time. The greater the fluctuation degree, the more drastic the state change of the system.

[0096] In this embodiment, the curve fluctuation degree refers to the degree of fluctuation or change of the second state curve. It reflects the stability of the target hydraulic loading system under a specific state type. The fluctuation degree can be calculated by comparing the difference between the maximum value point and the minimum value point of the curve.

[0097] In this embodiment, comprehensive stability refers to the overall stability of the target hydraulic loading system under multiple state types. It is determined by combining the fluctuation degree of each second state curve with the corresponding state type. Comprehensive stability takes into account multiple aspects of the system and provides a comprehensive stability assessment.

[0098] In this embodiment, the preset hydraulic stability refers to a hydraulic stability threshold value preset according to system design requirements, operating conditions or performance standards, which is used to compare with the comprehensive stability of the target hydraulic loading system to determine whether the system is in a stable state.

[0099] In this embodiment, the status display refers to the process of displaying the real-time operating status information of the target hydraulic loading system to the user in a visual manner. The status display may include the current status, historical trends, warning information, etc. of the system, so that the user can understand the performance and status of the system.

[0100] In this embodiment, the status warning refers to a warning signal automatically issued by the system when the comprehensive stability of the target hydraulic loading system is lower than the preset hydraulic stability threshold. The purpose of the status warning is to promptly notify the user of possible risks or problems in the system so that necessary measures can be taken for intervention or maintenance.

[0101] The beneficial effect of the above technical solution is: by real-time monitoring of the operating status of the hydraulic loading system, the system stability is determined, and status display and early warning are performed based on the system stability, so that hydraulic stability control and real-time feedback control can be performed in a timely and effective manner.

[0102] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A control method for a hydraulic loading system based on a fuzzy control algorithm, characterized in that, Including: Step 1: Based on a preset sensor, obtain the real-time hydraulic data of the target hydraulic loading system to obtain the first control hydraulic data; Step 2: Obtain the corresponding initial fuzzy control algorithm based on the hydraulic characteristics of the target hydraulic loading system, and train the initial fuzzy control algorithm based on the historical hydraulic data and historical target hydraulic of the target hydraulic loading system to obtain the first fuzzy control algorithm of the target hydraulic loading system; Step 3: Compare the first control hydraulic data with the preset target hydraulic pressure, and thus input the first control hydraulic data into the first fuzzy control algorithm based on the comparison result, and perform hydraulic control on the target fuzzy controller based on the first fuzzy control algorithm; Step 4: Monitor the real-time operating state of the target hydraulic loading system in real time, so as to determine the stability of the target hydraulic loading system, and perform status display or status warning.

2. The control method of a hydraulic loading system based on a fuzzy control algorithm according to claim 1, characterized in that, Based on a preset sensor, obtain the real-time hydraulic data of the target hydraulic loading system to obtain the first control hydraulic data, including: Step 11: Screen the critical hydraulic pressure of the hydraulic loading system that matches the type of the target hydraulic loading system from the hydraulic loading database, so as to determine the critical hydraulic pressure of the target hydraulic loading system; Step 12: Based on a preset sensor, obtain the real-time hydraulic data of the target hydraulic loading system, and compare the real-time hydraulic data with the critical hydraulic pressure; If the real-time hydraulic data is less than the critical hydraulic pressure, it is determined that the target hydraulic loading system is in a relatively stable state, and the real-time hydraulic data is used as the first control hydraulic data of the target hydraulic loading system; If the real-time hydraulic data is not less than the critical hydraulic pressure, it is determined that the target hydraulic loading system is not in a relatively stable state, and a status warning is given.

3. The control method of a hydraulic loading system based on a fuzzy control algorithm according to claim 2, characterized in that, Obtain the corresponding initial fuzzy control algorithm based on the hydraulic characteristics of the target hydraulic loading system, and train the initial fuzzy control algorithm based on the historical hydraulic data and historical target hydraulic of the target hydraulic loading system to obtain the first fuzzy control algorithm of the target hydraulic loading system, including: Step 21: Obtain the corresponding initial fuzzy control algorithm based on the hydraulic characteristics of the target hydraulic loading system; Step 22: Obtain the historical hydraulic data of the target hydraulic loading system and the historical target hydraulic pressure of the historical hydraulic control process, so as to obtain the historical hydraulic data table of the target hydraulic loading system; Step 23: Input each historical hydraulic data and the corresponding historical target hydraulic pressure in the historical hydraulic data table into the initial fuzzy control algorithm, so as to obtain the first control parameter of the initial fuzzy control algorithm; Step 24: Obtain the first parameter set based on the first control parameter of the target hydraulic loading system, and thus classify each first control parameter in the first parameter set to obtain the first classification parameter set, where each first classification parameter subset contains each first control sub-parameter of the same parameter type; Step 25: Eliminate the corresponding maximum control parameter value and minimum control parameter value in each first classification parameter subset in the first classification parameter set, so as to obtain the second classification parameter subset based on the control parameter values of the remaining first control sub-parameters in the current first classification parameter subset; Step 26: Sort the first control sub-parameters in each second classification parameter subset to obtain an ordered second classification parameter subset, and extract the median of the first control sub-parameters in the current second classification parameter subset as the control parameter value corresponding to the parameter type of the current second classification parameter subset, so as to obtain the first control parameter value; Step 27: Optimize the corresponding control parameters of the initial fuzzy control algorithm based on each first control parameter value to obtain the second control parameter value, so as to obtain the first fuzzy control algorithm of the target hydraulic loading system.

4. The control method of a hydraulic loading system based on a fuzzy control algorithm according to claim 3, characterized in that, Obtaining the second control parameter value includes: Obtain the second control parameter value ; ; wherein, is the second control parameter value of the i-th first control sub-parameter in the first fuzzy D control algorithm of the target hydraulic loading system; is the first control parameter value of the i-th first control sub-parameter; is the parameter influence degree of the parameter type corresponding to the j-th first control sub-parameter on the parameter type corresponding to the current i-th first control parameter value; is the type conversion coefficient of the j-th first control sub-parameter; is the type influence weight; is the similarity degree between the historical working environment corresponding to the j-th first control sub-parameter and the historical working environment corresponding to the first control sub-parameter corresponding to the current i-th first control parameter value; is the degree conversion coefficient of the j-th first control sub-parameter; is the parameter matching weight; m is the number of control sub-parameters in the first fuzzy control algorithm; n is the number of remaining control sub-parameters in the first fuzzy control algorithm except the current i-th control sub-parameter; exp[] is the exponential function with e as the base.

5. The control method of a hydraulic loading system based on a fuzzy control algorithm according to claim 3, wherein Compare the first control hydraulic data with the preset target hydraulic pressure, and based on the comparison result, input the first control hydraulic data into the first fuzzy control algorithm, and perform hydraulic control based on the corresponding target fuzzy controller of the first fuzzy control algorithm, including: Step 31: Compare the first control hydraulic data with the preset target hydraulic pressure; If the first control hydraulic data is less than the preset target hydraulic pressure, no hydraulic control is required at the current moment; If the first control hydraulic data is not less than the preset target hydraulic pressure, input the first control hydraulic data into the first fuzzy control algorithm to determine the hydraulic control parameters of the target hydraulic loading system; Step 32: Transmit the hydraulic control parameters to the target fuzzy controller to determine the hydraulic control instruction and perform hydraulic control on the target hydraulic loading system.

6. A control method for a hydraulic loading system based on a fuzzy control algorithm according to claim 5, characterized in that, Comparing the first control hydraulic data with the preset target hydraulic pressure includes: Step 311: Obtain the hydraulic performance parameters of the target hydraulic loading system and simultaneously obtain the initial target hydraulic pressure of the target hydraulic loading system; Step 312: Extract the first hydraulic deviation corresponding to the current hydraulic performance parameters from the performance impact database; Step 313: Combine the first hydraulic deviation with the initial target hydraulic pressure to determine the preset target hydraulic pressure of the target hydraulic loading system.

7. A control method for a hydraulic loading system based on a fuzzy control algorithm according to claim 5, characterized in that, Monitor the real-time operating state of the target hydraulic loading system in real time to determine the stability of the target hydraulic loading system, and perform status display or status warning, including: Step 41: Monitor the real-time operating state of the target hydraulic loading system at each moment during the current hydraulic control cycle; Step 42: Classify the real-time operating states during the current hydraulic control cycle according to different state types to obtain the first classification operating state set; Step 43: Sort each first classification operating state in the first classification operating state set in chronological order to obtain an ordered second classification operating state; Step 44: Input each second classification operating state into the same coordinate system to obtain the second state curve corresponding to each second classification operating state, so as to determine the stability of the target hydraulic loading system, and perform status display and status warning.

8. A control method for a hydraulic loading system based on a fuzzy control algorithm according to claim 7, characterized in that, Determining the stability of the target hydraulic loading system and performing status display and status warning includes: Step 441: Input each second classification operating state into the same coordinate system to obtain the second state curve corresponding to each second classification operating state; Step 442: Compare the difference between the curve maximum point and the curve minimum point of the second state curve to determine the curve fluctuation degree of the second state curve; Step 443: Combine the degree of curve fluctuation of each second state curve of the target hydraulic loading system with the corresponding state type to determine the comprehensive stability of the target hydraulic loading system; Step 444: Compare the comprehensive stability with the preset hydraulic stability; If the comprehensive stability is greater than the preset hydraulic stability, display the real-time operating state of the target hydraulic loading system; If the comprehensive stability is not greater than the preset hydraulic stability, give a state warning for the real-time operating state of the target hydraulic loading system.

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