Self-adaptive regulation and control hydraulic system based on artificial intelligence
By adopting an adaptive control method based on artificial intelligence in the hydraulic system, deeply analyzing the operating status of hydraulic system components and adaptively adjusting the control strategy, the problem of lack of scientificity and accuracy of traditional hydraulic system control methods is solved, and the regulation efficiency and performance of hydraulic system are improved.
Patent Information
- Application Number
- CN202411790776.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-06
- Publication Date
- 2025-03-11
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The traditional hydraulic system regulation methods lack in-depth analysis and accurate prediction of the operating status of internal components of the hydraulic system, and it is difficult to adapt to complex and changeable working conditions, resulting in a lack of scientificity and accuracy of regulation strategies, which can easily lead to poor regulation results or system instability.
Adaptive hydraulic system based on artificial intelligence is adopted, including hydraulic system component marking module, regulation impact analysis module and component adaptation and regulation module. Through the deep learning model, each component in the hydraulic system is deeply analyzed, the regulatory components are determined, and the regulatory impact is analyzed through the simulated regulatory model, and the regulatory strategy is adaptively adjusted.
It realizes accurate analysis and regulation of the operating status of hydraulic system components, improves the scientificity and accuracy of the regulation strategy, can flexibly adjust according to actual conditions, and improves the regulation efficiency and performance of the hydraulic system.
Smart Images
Figure CN119594089A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of hydraulic control, and more specifically, to an adaptive control hydraulic system based on artificial intelligence. Background Art
[0002] As a key component of industrial equipment and mechanical systems, the stability and efficiency of the hydraulic system's performance directly affect the operation of the entire system. Traditional hydraulic system control methods often rely on manual experience and fixed control strategies, which are difficult to adapt to complex and changing working conditions. With the rapid development of artificial intelligence technology, its application in industrial automation, control system optimization and other fields is becoming more and more extensive, providing new ideas and methods for the intelligent control of hydraulic systems.
[0003] However, the existing hydraulic system control technology still has many shortcomings. On the one hand, the traditional control method lacks in-depth analysis and accurate prediction of the operating status of the components inside the hydraulic system, resulting in the formulation of control strategies often based on empirical judgment, lacking scientificity and accuracy. On the other hand, there are many components in the hydraulic system, and the mutual influence between the components is complex. The traditional control method is difficult to fully consider the synergy between the components, which easily leads to poor control effect or system instability.
[0004] In addition, the working conditions of hydraulic systems are often changeable, such as load changes, temperature changes, etc. These factors will affect the performance of the system. Traditional fixed control strategies are difficult to adapt to such a changing environment and cannot be flexibly adjusted according to actual conditions, thus limiting the control efficiency and performance of the hydraulic system.
[0005] Based on the above background, the present invention proposes an adaptive control hydraulic system based on artificial intelligence. Summary of the invention
[0006] In view of the deficiencies in the prior art, the purpose of the present invention is to provide an adaptive control hydraulic system based on artificial intelligence.
[0007] To achieve the above object, the present invention provides the following technical solutions: Adaptive control hydraulic system based on artificial intelligence, including hydraulic system component marking module, control impact analysis module, component adaptive control module; The hydraulic system component marking module is used to set a hydraulic system detection cycle, obtain an operation demand value of each component in the hydraulic system after each hydraulic system detection cycle, and mark the component as a control component or a normal component based on a comparison result between the operation demand value and the operation demand critical value; The control impact analysis module is used to determine the control components of the hydraulic system and obtain the control impact value of each control component; The component adaptation control module is used to obtain the control adaptation value and set the control adaptation demarcation value. When the control adaptation value is greater than or equal to the control adaptation demarcation value, all control components are sorted in descending order according to the numerical values of the control influence values, and the control components in the hydraulic system are controlled in sequence according to the sorting order. When the control adaptation value is less than the control adaptation demarcation value, all control components in the hydraulic system are controlled simultaneously.
[0008] Furthermore, based on the comparison result between the operating requirement value and the operating requirement critical value, the component is marked as a regulated component or a normal component. Specifically, the operating requirement critical value is set. When the operating requirement value of the component is greater than or equal to the operating requirement critical value, the component is marked as a regulated component. When the operating requirement value of the component is less than the operating requirement critical value, the component is marked as a normal component.
[0009] Furthermore, the operation requirement value of the component is obtained through the following steps: collecting actual operation data of various monitoring data of the component in the hydraulic system detection cycle, extracting and integrating the actual operation data features of various monitoring data into an actual operation feature set, obtaining the operation requirement model of the component, using the actual operation feature set as input data of the operation requirement model, and the operation requirement model outputs the operation requirement value of the component.
[0010] Furthermore, the control influence value of the control component is obtained through the following steps: constructing a hydraulic system simulation control model, the hydraulic system simulation control model simulates the control of the control component, and during the simulation control process, after each hydraulic system detection cycle, the operation influence value of the hydraulic system detection cycle is obtained, and the total number of hydraulic system detection cycles is marked as Cycb, and all hydraulic system detection cycles are sorted in order of chronological order, and the operation influence values of two adjacent hydraulic system detection cycles after sorting are summed and averaged to obtain the operation influence duration value, and the operation influence duration threshold is set. When the operation influence duration value is greater than or equal to the operation influence duration threshold, the operation continuous disappointment times are increased by one, and the operation continuous disappointment times are marked as Basp, and the formula is used. The control impact value Mdrt of the control component is obtained, where da is the detection cycle quantity coefficient and db is the operation continuous disappointment coefficient.
[0011] Furthermore, the hydraulic system simulation control model is constructed as follows: collect the actual operation data of various monitoring data of a control component in the hydraulic system detection cycle, determine the expected operation data of various monitoring data of the control component in the hydraulic system detection cycle, mark the remaining components except the control component in the hydraulic system as influencing components, obtain the actual operation data of various monitoring data of the influencing component in the hydraulic system detection cycle, and construct a hydraulic system simulation control model based on the actual operation data of various monitoring data in the control component, the expected operation data, and the actual operation data of various monitoring data in the influencing component.
[0012] Furthermore, the operation impact value of the hydraulic system detection cycle is obtained by the following steps: obtain the operation demand value Dgt of each influencing component after the hydraulic system detection cycle, g=1, 2, ..., G, g is the number of the influencing component, G is the total number of influencing components, set the operation demand coefficient to kt, and use the formula The operation impact value Bds of the hydraulic system detection cycle is obtained.
[0013] Furthermore, the regulation fitness value is obtained by the following steps: marking the total number of regulation components as THeg, matching all regulation components into a regulation group, obtaining the regulation influence gap value of the regulation group, setting the regulation influence gap threshold, and when the regulation influence gap value of the regulation group is greater than or equal to the regulation influence gap threshold, increasing the number of regulation gaps by one; when the regulation influence gap value of the regulation group is less than the regulation influence gap threshold, no further processing is performed, and the number of regulation gaps is marked as Bmsr. Get the regulation adaptation value Lgzs.
[0014] Furthermore, the control influence gap value of the control group is obtained by the following steps: the control influence values of two control components in the control group are difference-calculated and the absolute value is taken to obtain the control influence gap value of the control group.
[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. Set up a hydraulic system component marking module and a control impact analysis module, conduct in-depth analysis of the operation of each component in the hydraulic system through a deep learning model, accurately and efficiently analyze the control components in the hydraulic system, and after determining the control components, simulate the control of each control component by building a hydraulic system simulation control model. Through simulated control, comprehensively analyze the impact of the control of the control component on the hydraulic system, and simultaneously simulate the control method of the control component to ensure that the control components of the hydraulic components are subsequently reliably and reasonably controlled; 2. Set up the component adaptation control module to conduct in-depth analysis on the impact of each control component in the hydraulic system, and then control the control components in the hydraulic system in the order of influence or at the same time, and adaptively adjust the control strategy according to the actual situation in the hydraulic system to comprehensively ensure the control efficiency of the hydraulic system. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] Figure 1 It is a system operation flow chart of the adaptive control hydraulic system based on artificial intelligence; Figure 2 A flowchart for obtaining the operation requirement value of the component; Figure 3 This is the system module diagram of the adaptive control hydraulic system based on artificial intelligence. DETAILED DESCRIPTION
[0017] Reference Figures 1 to 3 , an adaptive control hydraulic system based on artificial intelligence, including a hydraulic system component marking module, a control impact analysis module, and a component adaptive control module.
[0018] Hydraulic system component marking module: set the hydraulic system detection cycle (the hydraulic system detection cycle is a time period, and the hydraulic system detection cycle is infinitely looped). After each hydraulic system detection cycle, obtain the operating demand value of each component in the hydraulic system (the components in the hydraulic system include hydraulic pumps, control valve groups, oil filters, accumulators, etc.), set the operating demand critical value (the operating demand critical value is the preset value set by the system), when the operating demand value of the component is greater than or equal to the operating demand critical value, mark the component as a control component, and when the operating demand value of the component is less than the operating demand critical value, mark the component as a normal component.
[0019] The operation requirement value of the component is obtained through the following steps: collecting the actual operation data of various monitoring data of the component in the hydraulic system detection cycle, extracting and integrating the actual operation data features of various monitoring data into an actual operation feature set, obtaining the operation requirement model of the component, using the actual operation feature set as the input data of the operation requirement model, and the operation requirement model outputs the operation requirement value of the component.
[0020] In the hydraulic system, each component corresponds to an operation demand model, and each operation demand model is constructed based on a deep learning model. The difference between different operation demand models lies in the difference in training data. This embodiment will introduce the construction process of the operation demand model of the hydraulic pump: collect the actual operation feature sets of j hydraulic pumps, build a deep learning model, use the actual operation feature sets as training data for the deep learning model, assign an operation demand value to each training data, and the value range of the operation demand value is (1.9~4.9). The larger the value of the operation demand value, the more abnormal the actual operation data of the hydraulic pump is, and the smaller the value of the operation demand value, the more normal the actual operation data of the hydraulic pump is. The training data is divided into 70% training set, 15% validation set and 15% test set, and the training set, validation set and test set are trained. After the training is completed, the operation demand model of the hydraulic pump is obtained.
[0021] If the operation demand model of the control valve group is constructed, the actual operation feature sets of j control valve groups are collected. The larger the value of the operation demand value, the more abnormal the actual operation data of the control valve group is, and the smaller the value of the operation demand value, the more normal the actual operation data of the control valve group is.
[0022] Control impact analysis module: determine the control components of the hydraulic system and obtain the control impact value of each control component.
[0023] The control influence value of the control component is obtained through the following steps: collecting the actual operation data of various monitoring data of a control component in the hydraulic system detection cycle, determining the expected operation data of various monitoring data of the control component in the hydraulic system detection cycle (the expected operation data is the operation data of the influencing component under normal operation), marking the remaining components except the control component in the hydraulic system as influencing components (the remaining components include the remaining control components and the remaining normal components), obtaining the actual operation data of various monitoring data of the influencing component in the hydraulic system detection cycle, and constructing a hydraulic system simulation control model based on the actual operation data of various monitoring data in the control component, the expected operation data and the actual operation data of various monitoring data in the influencing component. The hydraulic system simulation control model simulates the control of the control component. During the simulation control process, after each hydraulic system detection cycle (due to hydraulic The regulation of components in the system requires a certain amount of time, generally between a few minutes and a few hours, so the set hydraulic system detection cycle is usually set within one minute to ensure that the simulated regulation of the control component includes multiple hydraulic system detection cycles), obtain the operation impact value of the hydraulic system detection cycle, mark the total number of hydraulic system detection cycles as Cycb, sort all hydraulic system detection cycles in order, sum and average the operation impact values of the two adjacent hydraulic system detection cycles after sorting, obtain the operation impact duration value, set the operation impact duration threshold (the operation impact duration threshold is the preset value set by the system), when the operation impact duration value is greater than or equal to the operation impact duration threshold, increase the number of operation continuous disappointments by one, when the operation impact duration value is less than the operation impact duration threshold, no further processing is performed, and the number of operation continuous disappointments is marked as Basp, using the formula The control impact value Mdrt of the control component is obtained, where da is the detection cycle quantity coefficient, db is the operation continuous disappointment coefficient, the value of da is 0.78, and the value of db is 0.86.
[0024] The operation impact value of the hydraulic system detection cycle is obtained by the following steps: obtain the operation demand value Dgt of each influencing component after the hydraulic system detection cycle, g=1, 2, ..., G, g is the number of the influencing component, G is the total number of influencing components, set the operation demand coefficient to kt, t=1, 2, ..., t, k1<k2<k3<...<kt, each operation demand coefficient corresponds to an operation demand value within a range, and the range of the operation demand value includes (0, Dg1], (Dg1, Dg2], ..., (Dgt-1, Dgt], when Dgt∈(0, Dg1], the operation demand coefficient is k1, and the formula is used The operation impact value Bds of the hydraulic system detection cycle is obtained.
[0025] The construction method of the hydraulic system simulation control model is as follows: select simulation software such as AMESim, Matlab / Simulink, create a new hydraulic system model in the equipment simulation software, add corresponding components to the hydraulic system model, set corresponding parameters for each component in the hydraulic system model based on the actual operating data of various monitoring data in the control component and the actual operating data of various monitoring data in the influencing component, determine the control direction of the control component based on the expected operating data of various monitoring data in the control component, and then construct a hydraulic system simulation control model. The hydraulic system simulation control model can be simulated and controlled according to the control direction.
[0026] A hydraulic system component marking module and a control impact analysis module are set up. The operation of each component in the hydraulic system is deeply analyzed through a deep learning model, and the control components in the hydraulic system are analyzed accurately and efficiently. After the control components are determined, a hydraulic system simulation control model is constructed to simulate the control of each control component. The impact of the control of the control component on the hydraulic system is comprehensively analyzed through simulated control, and the control method of the control component is simultaneously simulated to ensure that the control components of the hydraulic components are subsequently reliably and reasonably controlled.
[0027] Component adaptation control module: obtain the control adaptation value, set the control adaptation demarcation value (the control adaptation demarcation value is the preset value set by the system), when the control adaptation value is greater than or equal to the control adaptation demarcation value, sort all the control components in order from large to small according to the numerical value of the control influence value, and control the control components in the hydraulic system in the sorted order, when the control adaptation value is less than the control adaptation demarcation value, all the control components in the hydraulic system are controlled simultaneously (the control method of each control component is the control method corresponding to the simulated control of the corresponding hydraulic system simulation control model).
[0028] The control fitness value is obtained by the following steps: mark the total number of control components as THeg, match all control components into a control group, calculate the difference between the control influence values of the two control components in the control group and take the absolute value to obtain the control influence gap value of the control group, set the control influence gap threshold, and when the control influence gap value of the control group is greater than or equal to the control influence gap threshold, increase the number of control gaps by one. When the control influence gap value of the control group is less than the control influence gap threshold, no further processing is performed and the number of control gaps is marked as Bmsr. Get the regulation adaptation value Lgzs.
[0029] Set up component adaptation control module, conduct in-depth analysis on the impact of each control component in the hydraulic system, and then control the control components in the hydraulic system in the order of influence or at the same time, and adaptively adjust the control strategy according to the actual situation in the hydraulic system to comprehensively ensure the control efficiency of the hydraulic system.
[0030] The above formulas are all dimensionless and numerical calculations are performed, and the preset parameters in the formulas are set by technicians in this field according to actual conditions.
[0031] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.
[0032] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.
[0033] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.
[0034] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.
[0035] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.
[0036] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.
[0037] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.
Claims
1. An adaptive control hydraulic system based on artificial intelligence, characterized in that: It includes hydraulic system component marking module, control impact analysis module, and component adaptive control module; The hydraulic system component marking module is used to set a hydraulic system detection cycle, obtain an operation demand value of each component in the hydraulic system after each hydraulic system detection cycle, and mark the component as a control component or a normal component based on a comparison result between the operation demand value and the operation demand critical value; The control impact analysis module is used to determine the control components of the hydraulic system and obtain the control impact value of each control component; The component adaptation control module is used to obtain the control adaptation value and set the control adaptation demarcation value. When the control adaptation value is greater than or equal to the control adaptation demarcation value, all control components are sorted in descending order according to the numerical values of the control influence values, and the control components in the hydraulic system are controlled in sequence according to the sorting order. When the control adaptation value is less than the control adaptation demarcation value, all control components in the hydraulic system are controlled simultaneously.
2. The artificial intelligence-based adaptive control hydraulic system according to claim 1 is characterized in that: Based on the comparison result between the operation requirement value and the operation requirement critical value, the component is marked as a regulated component or a normal component. Specifically, the operation requirement critical value is set. When the operation requirement value of the component is greater than or equal to the operation requirement critical value, the component is marked as a regulated component. When the operation requirement value of the component is less than the operation requirement critical value, the component is marked as a normal component.
3. The artificial intelligence-based adaptive control hydraulic system according to claim 1 is characterized in that: The operation requirement value of the component is obtained through the following steps: collecting the actual operation data of various monitoring data of the component in the hydraulic system detection cycle, extracting and integrating the actual operation data features of various monitoring data into an actual operation feature set, obtaining the operation requirement model of the component, using the actual operation feature set as the input data of the operation requirement model, and the operation requirement model outputs the operation requirement value of the component.
4. The artificial intelligence-based adaptive control hydraulic system according to claim 1, characterized in that: The control influence value of the control component is obtained through the following steps: constructing a hydraulic system simulation control model, the hydraulic system simulation control model simulates the control of the control component, and during the simulation control process, after each hydraulic system detection cycle, the operation influence value of the hydraulic system detection cycle is obtained, and the total number of hydraulic system detection cycles is marked as Cycb, and all hydraulic system detection cycles are sorted in order, and the operation influence values of the two adjacent hydraulic system detection cycles after sorting are summed and averaged to obtain the operation influence duration value, and the operation influence duration threshold is set. When the operation influence duration value is greater than or equal to the operation influence duration threshold, the operation continuous disappointment times are increased once, and the operation continuous disappointment times are marked as Basp. The formula is used The control impact value Mdrt of the control component is obtained, where da is the detection cycle quantity coefficient and db is the operation continuous disappointment coefficient.
5. The artificial intelligence-based adaptive control hydraulic system according to claim 4 is characterized in that: The hydraulic system simulation control model is constructed as follows: collect the actual operation data of various monitoring data of a control component in the hydraulic system detection cycle, determine the expected operation data of various monitoring data of the control component in the hydraulic system detection cycle, mark the remaining components in the hydraulic system except the control component as influencing components, obtain the actual operation data of various monitoring data of the influencing component in the hydraulic system detection cycle, and construct a hydraulic system simulation control model based on the actual operation data of various monitoring data in the control component, the expected operation data, and the actual operation data of various monitoring data in the influencing component.
6. The artificial intelligence-based adaptive control hydraulic system according to claim 4 is characterized in that: The operation impact value of the hydraulic system detection cycle is obtained by the following steps: obtain the operation demand value Dgt of each influencing component after the hydraulic system detection cycle, g=1, 2, ..., G, g is the number of the influencing component, G is the total number of influencing components, set the operation demand coefficient to kt, and use the formula The operation impact value Bds of the hydraulic system detection cycle is obtained.
7. The artificial intelligence-based adaptive control hydraulic system according to claim 1, characterized in that: The regulation fitness value is obtained by the following steps: mark the total number of regulation components as THeg, match all regulation components into a regulation group, obtain the regulation influence gap value of the regulation group, set the regulation influence gap threshold, and when the regulation influence gap value of the regulation group is greater than or equal to the regulation influence gap threshold, increase the number of regulation gaps by one; when the regulation influence gap value of the regulation group is less than the regulation influence gap threshold, no further processing is performed, and the number of regulation gaps is marked as Bmsr. Get the regulation adaptation value Lgzs.
8. The artificial intelligence-based adaptive control hydraulic system according to claim 7, characterized in that: The control influence gap value of the control group is obtained by the following steps: the control influence values of two control components in the control group are difference-calculated and the absolute value is taken to obtain the control influence gap value of the control group.