Multi-cylinder hydraulic device data interaction system based on digital twin driving

Abstract

The application discloses a multi-cylinder hydraulic equipment data interaction system based on digital twin driving and relates to the technical field of industrial automation control. In order to solve the problems that data acquisition and processing lack flexibility and pertinence, model construction and optimization are not perfect, and simulation optimization and real-time feedback are lacked in the prior art, the application realizes accurate perception of equipment state and fault early warning by optimizing sensor layout and adjusting acquisition frequency, accurately describes equipment physical structure and working principle by using a digital twin model, scientifically evaluates and effectively predicts the current and future operation states of the equipment by combining an evaluation index system of a state analysis unit, provides a basis for early maintenance, reduces equipment failure risk, simulates the implementation effect of a control strategy by using the digital twin model, optimizes control instructions according to simulation evaluation, dynamically adjusts by monitoring the execution effect in real time, ensures accurate control of the equipment, improves the stability and reliability of equipment operation, and improves production efficiency.

Description

Technical Field

[0001] This invention relates to the field of industrial automation control technology, and in particular to a data interaction system for multi-cylinder hydraulic equipment based on digital twin drive. Background Technology

[0002] Traditional monitoring and control systems for multi-cylinder hydraulic equipment rely primarily on manual experience and simple sensor detection, failing to provide a comprehensive and real-time understanding of equipment operation. Patent application CN118455440A discloses a digital twin-driven interactive system for multi-cylinder hydraulic presses. This system includes: a parametric modeling module for the multi-cylinder hydraulic press, simplifying its structure and building a twin model; a real-time data acquisition module, acquiring information such as pressure and displacement; a data preprocessing module, primarily involving data cleaning, integration, transformation, and reduction; a twin-driven module for the multi-cylinder hydraulic press, enabling real-time deep data interaction between the virtual twin and the physical entity, completing the construction of an edge-cloud interactive model; and a parameter adaptive module, implementing parameter adaptive correction of the twin model to predict the behavior of the physical model. This patent application solves the problems of stress analysis, metal fatigue analysis, and real-time monitoring being impossible on physical multi-cylinder hydraulic presses, while also addressing the issues of excessive costs or risks associated with verifying control algorithm experiments.

[0003] Although the aforementioned patent application has optimized some functions using digital twin technology, the following problems still exist:

[0004] In existing technologies, there is a lack of dynamic adjustment mechanism for data acquisition frequency, data acquisition and processing lack flexibility and targeting, and the digital twin model cannot be updated efficiently. It is difficult to quickly and accurately reflect the actual state changes of the equipment, which reduces the accuracy and reliability of the model. Furthermore, it is impossible to detect potential problems in the control strategy in advance, making it difficult to achieve precise control and continuous stable operation of the equipment. Summary of the Invention

[0005] The purpose of this invention is to provide a data interaction system for multi-cylinder hydraulic equipment based on digital twin drive, which can realize comprehensive monitoring, intelligent analysis and precise control of multi-cylinder hydraulic equipment, improve the operating efficiency and reliability of the equipment, and reduce production costs, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A data interaction system for multi-cylinder hydraulic equipment based on digital twin drive includes:

[0008] The data acquisition module is configured to deploy sensors at key parts of the multi-cylinder hydraulic equipment to collect the operating data of the multi-cylinder hydraulic equipment in real time. At the same time, the data acquisition frequency of the multi-cylinder hydraulic equipment is adjusted according to the operating status and changing trends of the equipment.

[0009] The twin model construction module is configured to build a digital twin model of a multi-cylinder hydraulic equipment. Based on the collected operating data of the multi-cylinder hydraulic equipment, the physical structure and working principle of the multi-cylinder hydraulic equipment are analyzed to obtain the equipment status information of the multi-cylinder hydraulic equipment.

[0010] The interactive control module is configured to determine whether there is an abnormality in the multi-cylinder hydraulic equipment based on the analysis results of the digital twin model. If an abnormality is found, it generates control commands according to the preset control strategy and sends the control commands to the actuator of the multi-cylinder hydraulic equipment.

[0011] Furthermore, the data acquisition module also includes:

[0012] The sensor optimization unit is configured to analyze the importance and correlation of data from various parts of the multi-cylinder hydraulic equipment based on its structural characteristics and working properties, determine the key installation positions of the sensors, and optimize the sensor layout.

[0013] The data acquisition frequency adjustment unit is configured to monitor the equipment operating status of multi-cylinder hydraulic equipment in real time and adjust the preset data acquisition frequency according to the changing trend of the equipment operating status.

[0014] The equipment monitoring unit is configured to collect operating environment data of multi-cylinder hydraulic equipment and provide early warning of equipment failure based on the correlation between operating environment data and operating data.

[0015] Furthermore, the sampling frequency adjustment unit specifically includes:

[0016] The collected operational data is acquired, and key features are extracted based on the data type. The extracted key features are then integrated to obtain pressure operational data sets, displacement operational data sets, oil temperature operational data sets, and oil flow operational data sets for multi-cylinder hydraulic equipment.

[0017] The target values ​​of the pressure operation data group, displacement operation data group, oil temperature operation data group, and oil flow operation data group are compared with the preset threshold range of each data group. The equipment operation status and change trend of the multi-cylinder hydraulic equipment are judged by combining the data fluctuation range of each data group.

[0018] Based on the judgment results of the equipment's operating status and changing trends, a corresponding data acquisition frequency adjustment strategy is generated to adjust the data acquisition frequency of the multi-cylinder hydraulic equipment.

[0019] Furthermore, adjust the data acquisition frequency for multi-cylinder hydraulic equipment, specifically including:

[0020] Extract the target values ​​from the pressure operation data group, displacement operation data group, oil temperature operation data group, and oil flow operation data group;

[0021] Extract the preset threshold ranges corresponding to the pressure operation data group, displacement operation data group, oil temperature operation data group, and oil flow operation data group;

[0022] The target values ​​of the pressure operation data group, displacement operation data group, oil temperature operation data group, and oil flow operation data group are compared with their corresponding preset threshold ranges to obtain the standard deviation values ​​of the pressure operation data group, displacement operation data group, oil temperature operation data group, and oil flow operation data group. The standard deviation values ​​of the pressure operation data group, displacement operation data group, oil temperature operation data group, and oil flow operation data group are the parameter values ​​used to represent the data fluctuation range of each data group.

[0023] Compare the standard deviation values ​​corresponding to the pressure operation data group, displacement operation data group, oil temperature operation data group and oil flow operation data group with the preset standard deviation threshold.

[0024] If there is no data set whose standard deviation exceeds the preset standard deviation threshold, the data acquisition frequency of the multi-cylinder hydraulic equipment will not be adjusted.

[0025] When there are data groups whose standard deviation values ​​exceed the preset standard deviation threshold, the data groups whose standard deviation values ​​exceed the preset standard deviation threshold are selected as the target data groups.

[0026] The composite standard deviation of the target data group is obtained by using a weighted average of the standard deviation values ​​corresponding to the target data group.

[0027] The frequency of data acquisition for multi-cylinder hydraulic equipment is adjusted using the comprehensive standard deviation value corresponding to the target data set.

[0028] Furthermore, the frequency of data acquisition for multi-cylinder hydraulic equipment is adjusted using the comprehensive standard deviation value corresponding to the target data set, specifically including:

[0029] Retrieve the composite standard deviation value corresponding to the target data set;

[0030] Retrieve the maximum permissible standard deviation value for each data set contained in the target data set;

[0031] The average value of the maximum permissible standard deviation corresponding to the target data group is obtained by using the maximum permissible standard deviation value corresponding to each data group contained in the target data group;

[0032] The ratio of the comprehensive standard deviation value corresponding to the target data set to the average value of the maximum permissible standard deviation of the target data set is processed to obtain the standard deviation ratio coefficient corresponding to the target data set.

[0033] The frequency of data acquisition for multi-cylinder hydraulic equipment is adjusted using the standard deviation ratio coefficient corresponding to the target data set.

[0034] Furthermore, the twin model building module specifically includes:

[0035] The model building unit is configured to construct a basic digital twin model of the multi-cylinder hydraulic equipment based on the design drawings and physical characteristics of the multi-cylinder hydraulic equipment, which is used to describe the working principle of the multi-cylinder hydraulic equipment.

[0036] The data fusion unit is configured to fuse the acquired operational data with the basic digital twin model to update the state variables and initial parameters of the basic digital twin model of the multi-cylinder hydraulic equipment.

[0037] The status analysis unit is configured to establish an equipment status assessment index system. Based on the output results of the updated digital twin model of the multi-cylinder hydraulic equipment, it extracts key features of the equipment's operating status and judges the equipment's operating status based on the key features and assessment indexes.

[0038] Furthermore, the condition analysis unit establishes an equipment condition assessment index system, specifically including:

[0039] Acquire historical operating data of multi-cylinder hydraulic equipment under different working conditions and health states, extract features from the historical operating data, and obtain an initial feature set of the equipment operating status based on the extraction results;

[0040] Key features related to the evaluation indicators are retrieved from the initial feature set and integrated into a subset of key features to determine the categories of equipment status evaluation indicators and establish labels;

[0041] Based on the digital twin model of the multi-cylinder hydraulic equipment, the attribute information of each key component of the multi-cylinder hydraulic equipment, the structural information of the equipment, and the preset working mode information are obtained, and the weight value of each key component in the overall operation of the equipment is determined.

[0042] Based on the weight value of each key component, a corresponding quantitative standard and grading threshold are set for each evaluation indicator, and a correspondence between indicator level and equipment operating status is established.

[0043] Furthermore, the state analysis unit also includes:

[0044] The output results at the current moment are obtained from the digital twin model, including real-time operating data of pressure, displacement, oil temperature, and oil flow of each cylinder, as well as the equipment operating status judgment results of the multi-cylinder hydraulic equipment;

[0045] By combining historical operating data of multi-cylinder hydraulic equipment and arranging them according to time series, a time series dataset is constructed.

[0046] The dynamic features of the time series dataset are extracted based on the sliding window technique, the data distribution of the time series dataset is detected, and the key features in the time series dataset are determined based on the equipment status assessment index system.

[0047] The operating data of multi-cylinder hydraulic equipment is predicted based on the time series prediction model. Based on the prediction results and combined with the equipment status assessment index system, the operating status of the multi-cylinder hydraulic equipment at future time is determined.

[0048] Furthermore, the twin model building module also includes:

[0049] The model parameter update unit is configured to compare the real-time acquired operating data with the output data of the digital twin model during the operation of the multi-cylinder hydraulic equipment, and dynamically update the model parameters based on the comparison results using the latest acquired operating data, adjusting the weight and value of each parameter in the digital twin model.

[0050] The model evaluation unit is configured to perform a reliability assessment of the digital twin model based on the comparison results between the actual collected running data and the data output by the digital twin model, and to determine whether optimization and training are needed.

[0051] Furthermore, the interactive control module also includes:

[0052] The generated control commands are input into the digital twin model, which simulates the equipment's operating state after the control strategy is implemented. During the simulation, the digital twin model adjusts its own state variables and parameters according to the control commands.

[0053] The simulation results are used to evaluate whether the key performance indicators of the multi-cylinder hydraulic equipment have returned to the normal range, and to check whether any new potential problems have emerged during the simulation process.

[0054] Based on the simulation evaluation results, determine whether the current control strategy and control commands need to be optimized, and based on the judgment results, send the optimized control commands to the actuators of the multi-cylinder hydraulic equipment;

[0055] Real-time monitoring of the execution status of the actuators and the actual operating status of multi-cylinder hydraulic equipment; comparison of equipment status changes before and after executing control commands; and assessment of the actual execution effect of the control strategy.

[0056] Compared with the prior art, the beneficial effects of the present invention are:

[0057] By optimizing sensor layout and adjusting acquisition frequency, comprehensive data on equipment operation and environment is collected, enabling precise perception of equipment status and fault early warning. A digital twin model is used to accurately describe the physical structure and working principle of the equipment. Combined with the evaluation index system of the status analysis unit, the current and future operating status of the equipment is scientifically evaluated and effectively predicted, providing a basis for early maintenance and reducing the risk of equipment failure. The interactive control module uses the digital twin model to simulate the implementation effect of control strategies, optimizes control commands based on simulation evaluation, and dynamically adjusts them by monitoring the execution effect in real time, ensuring precise control of the equipment, improving the stability and reliability of equipment operation, and ultimately improving production efficiency, reducing maintenance costs, and enhancing the adaptability of the equipment under complex working conditions. Attached Figure Description

[0058] Figure 1 This is a block diagram of the data interaction system for a multi-cylinder hydraulic device based on digital twin drive, according to the present invention. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] To address the shortcomings of existing technologies, such as the lack of flexibility and specificity in data acquisition and processing, inadequate model building and optimization, lack of simulation optimization and real-time feedback, and inability to proactively identify potential problems in control strategies, thus hindering precise control and continuous stable operation of equipment, please refer to [link to relevant documentation]. Figure 1 This embodiment provides the following technical solution:

[0061] A data interaction system for multi-cylinder hydraulic equipment based on digital twin drive includes:

[0062] The data acquisition module is configured to deploy sensors at key locations of the multi-cylinder hydraulic equipment to collect real-time operating data, including pressure, displacement, oil temperature, and oil flow rate of each cylinder. It also preprocesses the collected data, such as through noise reduction and filtering, to improve data quality. Furthermore, it adjusts the data acquisition frequency based on the equipment's operating status and trends. Other features include:

[0063] The sensor optimization unit is configured to analyze the importance and correlation of data from various parts of the multi-cylinder hydraulic equipment based on the structural characteristics and working characteristics of the multi-cylinder hydraulic equipment, such as cylinder layout and connection method, hydraulic pipeline routing and branching, location and function of key components, working cycle and action sequence, load change law, etc., determine the key installation position of the sensor, and optimize the sensor layout to ensure that key data can be collected comprehensively and accurately.

[0064] The data acquisition frequency adjustment unit is configured to monitor the operating status of multi-cylinder hydraulic equipment in real time. It adjusts the preset data acquisition frequency according to the changing trend of the equipment's operating status. When the equipment's operating status is stable, the acquisition frequency is appropriately reduced to reduce the amount of data processing. When the equipment's operating status fluctuates abnormally or is in a critical working stage, the acquisition frequency is increased to obtain detailed data in a timely manner.

[0065] The equipment monitoring unit is configured to collect operating environment data of multi-cylinder hydraulic equipment. The operating environment data includes temperature and humidity, dust concentration, electromagnetic interference intensity, etc. of the space where the equipment is located. Based on the correlation between the operating environment data and the operating data, the unit will issue an early warning of equipment failure. For example, when the ambient humidity is high and the water content of the equipment oil is increasing, an early warning will be issued in time to indicate the potential risk of equipment corrosion.

[0066] The twin model construction module is configured to build a digital twin model of a multi-cylinder hydraulic equipment, including the geometry and connection relationships of each cylinder. Based on the collected operating data of the multi-cylinder hydraulic equipment, the physical structure and working principle of the multi-cylinder hydraulic equipment are analyzed to obtain the equipment status information of the multi-cylinder hydraulic equipment.

[0067] The interactive control module is configured to analyze the results of the digital twin model to determine whether there are any abnormalities in the multi-cylinder hydraulic equipment. If an abnormality is found, it generates control commands according to the preset control strategy and sends the control commands to the actuators of the multi-cylinder hydraulic equipment to achieve precise control of the equipment, such as adjusting the working pressure, displacement and other parameters of each cylinder to ensure stable operation of the equipment.

[0068] In this embodiment, by deploying sensors at key parts of the multi-cylinder hydraulic equipment, comprehensive and accurate data on equipment operation and environment can be collected. A digital twin model constructed based on this data reflects the equipment status in real time, enabling timely judgment and precise control of equipment anomalies. This ensures stable equipment operation and improves the reliability and safety of equipment operation. By establishing an equipment status assessment index system and combining historical and real-time data, the current operating status of the equipment can be scientifically assessed, and the future operating status can be effectively predicted. Potential problems can be identified in advance, facilitating the formulation of maintenance plans, reducing equipment downtime, lowering maintenance costs, and improving the overall operating efficiency of the equipment. During the control process, the control strategy is simulated and evaluated through the digital twin model, and dynamic adjustments are made based on the actual execution effect, forming an efficient feedback mechanism. This continuously improves the accuracy and effectiveness of equipment control, adapts to complex and ever-changing working scenarios, and enhances the adaptability and stability of the equipment.

[0069] In this embodiment, the sampling frequency adjustment unit specifically includes:

[0070] The collected operational data is acquired, and key features are extracted based on the data type. The extracted key features are then integrated to obtain pressure operational data sets, displacement operational data sets, oil temperature operational data sets, and oil flow operational data sets for multi-cylinder hydraulic equipment.

[0071] In this embodiment, for pressure operation data, key features such as average pressure, peak pressure, and fluctuation range are extracted; for displacement operation data, features such as displacement change rate and maximum displacement value are extracted; for oil temperature operation data, features such as oil temperature change trend and maximum oil temperature are extracted; and for oil flow operation data, features such as flow stability and average flow rate are extracted.

[0072] The target values ​​of the pressure operation data group, displacement operation data group, oil temperature operation data group, and oil flow operation data group are compared with the preset threshold range of each data group. The equipment operation status and change trend of the multi-cylinder hydraulic equipment are judged by combining the data fluctuation range of each data group.

[0073] Based on the judgment results of the equipment's operating status and changing trends, a corresponding data acquisition frequency adjustment strategy is generated to adjust the data acquisition frequency of the multi-cylinder hydraulic equipment.

[0074] In this embodiment, when the target value of a data group exceeds a preset threshold range, or the data fluctuation range exceeds a certain standard, it is determined that the multi-cylinder hydraulic equipment may be in an abnormal state. The changing trends of each data group over a period of time are analyzed, such as a continuous increase in pressure or a sharp rise in oil temperature, to determine the development direction of the multi-cylinder hydraulic equipment's operating status—whether it is trending towards stability, the abnormality is intensifying, or it is about to enter a critical working stage. When the equipment's operating status is stable, and the target values ​​of each data group are within the preset threshold range with small data fluctuations, the data acquisition frequency of each data group is appropriately reduced to decrease the amount of data processing. When the equipment's operating status exhibits abnormal fluctuations, one or more data groups exceed the threshold range with large fluctuations, or the equipment is in a critical working stage, the acquisition frequency is increased to 2-3 times the original frequency to obtain detailed data in a timely manner and accurately grasp the equipment's status.

[0075] Alternatively, the following adjustment strategies can be used to adjust the frequency of data acquisition that is already in operation;

[0076] Specifically, adjust the data acquisition frequency of multi-cylinder hydraulic equipment, including:

[0077] Extract the target values ​​from the pressure operation data group, displacement operation data group, oil temperature operation data group, and oil flow operation data group;

[0078] Extract the preset threshold ranges corresponding to the pressure operation data group, displacement operation data group, oil temperature operation data group, and oil flow operation data group;

[0079] The target values ​​of the pressure operation data group, displacement operation data group, oil temperature operation data group, and oil flow operation data group are compared with their corresponding preset threshold ranges to obtain the standard deviation values ​​of the pressure operation data group, displacement operation data group, oil temperature operation data group, and oil flow operation data group. The standard deviation values ​​of the pressure operation data group, displacement operation data group, oil temperature operation data group, and oil flow operation data group are the parameter values ​​used to represent the data fluctuation range of each data group.

[0080] Compare the standard deviation values ​​corresponding to the pressure operation data group, displacement operation data group, oil temperature operation data group and oil flow operation data group with the preset standard deviation threshold.

[0081] If there is no data set whose standard deviation exceeds the preset standard deviation threshold, the data acquisition frequency of the multi-cylinder hydraulic equipment will not be adjusted.

[0082] When there are data groups whose standard deviation values ​​exceed the preset standard deviation threshold, the data groups whose standard deviation values ​​exceed the preset standard deviation threshold are selected as the target data groups.

[0083] The composite standard deviation of the target data group is obtained by using a weighted average of the standard deviation values ​​corresponding to the target data group.

[0084] The frequency of data acquisition for multi-cylinder hydraulic equipment is adjusted using the comprehensive standard deviation value corresponding to the target data set.

[0085] The technical effects of the above solution are as follows: By extracting target values ​​and preset threshold ranges for operating data sets such as pressure, displacement, oil temperature, and oil flow rate, and comparing them, the fluctuation of each data set can be accurately analyzed, and the corresponding standard deviation value can be calculated to accurately represent the fluctuation range of the data. This helps to accurately grasp the changes of key parameters of multi-cylinder hydraulic equipment under different operating conditions, providing accurate data support for subsequent frequency adjustments. Comparing the standard deviation value of each data set with the preset standard deviation threshold can determine whether the data fluctuation is abnormal. When there is a data set exceeding the threshold, the target data set is selected and its comprehensive standard deviation value is calculated, and then the operating data acquisition frequency is adjusted using this value. This adaptive adjustment method can dynamically optimize the data acquisition frequency according to the actual operating conditions of the equipment. When the data fluctuation is large, the acquisition frequency is increased to capture changes in the operating status of the equipment more timely and accurately; when the data fluctuation is small, the acquisition frequency is reduced to avoid unnecessary data acquisition and processing, improving data acquisition efficiency and equipment operating performance. By monitoring and adjusting the data acquisition frequency in real time, potential problems in equipment operation can be detected in a timely manner. Adjusting the data acquisition frequency based on data fluctuations ensures that the collected data accurately reflects the equipment's operating status without causing data redundancy or missing information due to excessively high or low acquisition frequencies. This helps improve data quality and validity, providing a more reliable data foundation for equipment performance analysis, fault diagnosis, and optimized control, thereby further enhancing the overall performance and operating efficiency of the equipment.

[0086] Specifically, adjusting the data acquisition frequency of multi-cylinder hydraulic equipment using the comprehensive standard deviation value corresponding to the target data set includes:

[0087] Retrieve the composite standard deviation value corresponding to the target data set;

[0088] Retrieve the maximum permissible standard deviation value for each data set contained in the target data set;

[0089] The average value of the maximum permissible standard deviation corresponding to the target data group is obtained by using the maximum permissible standard deviation value corresponding to each data group contained in the target data group;

[0090] The ratio of the comprehensive standard deviation value corresponding to the target data set to the average value of the maximum permissible standard deviation of the target data set is processed to obtain the standard deviation ratio coefficient corresponding to the target data set.

[0091] The standard deviation ratio coefficient corresponding to the target data set is used to adjust the data acquisition frequency of the multi-cylinder hydraulic equipment. The adjusted data acquisition frequency is obtained by the following formula:

[0092] F = F base *[1+α*tanh(β*(xr))]

[0093] Where F represents the adjusted operating data acquisition frequency; F base The formula represents the data acquisition frequency before adjustment; α represents the adjustment amplitude coefficient, used to control the maximum amplitude of frequency change, with a value range of 0.5-1.6; β represents the curve steepness factor, used to control the response speed of frequency change, with a value range of 2.2-3.1; x represents the standard deviation ratio coefficient corresponding to the target data group; r represents the coefficient reference value corresponding to the operating data of multi-cylinder hydraulic equipment, with a value range of 0.3-0.8. The above formula uses the hyperbolic tangent function tanh, making the adjustment of the acquisition frequency non-linear. Compared to linear adjustment, it can adjust the acquisition frequency more flexibly and delicately according to the standard deviation ratio coefficient x. When x is small, the frequency adjustment amplitude is small; when x increases to a certain extent, the frequency adjustment amplitude changes faster, enabling a faster response to increased fluctuations in equipment operating data, achieving adaptive adjustment, and optimizing data acquisition performance. The limited value ranges of the adjustment amplitude coefficient α, the curve steepness factor β, and r ensure that the adjustment range and trend of the acquisition frequency are within a controllable range. To avoid unreasonable data acquisition frequency due to excessive adjustment range or too fast or too slow change speed, ensure that the data acquisition frequency is always within an appropriate range, maintain stable and reliable operation of the equipment data acquisition system, and improve data acquisition quality and equipment operating performance indicators.

[0094] The technical effects of the above solution are as follows: By calculating the ratio of the comprehensive standard deviation of the target data set to the average of the maximum permissible standard deviation (standard deviation ratio coefficient), the frequency of data acquisition can be dynamically and accurately adjusted according to the degree of fluctuation in the actual operating data of the equipment. When the data fluctuation is large, the acquisition frequency can be increased in a timely manner to closely monitor the equipment status; when the fluctuation is small, the acquisition frequency can be reasonably reduced to avoid resource waste and improve data acquisition efficiency. The adjustment amplitude coefficient α can control the maximum amplitude of frequency change, and the curve steepness factor β can control the response speed of frequency change. Different values ​​can adapt to different working conditions and equipment characteristics, making the acquisition frequency adjustment more flexible and adaptable when facing complex and ever-changing operating scenarios, ensuring stable equipment operation while optimizing data acquisition results. Adjusting the acquisition frequency based on data fluctuation can detect abnormal trends in equipment operation in advance, adjust the monitoring intensity in a timely manner, help maintenance personnel discover potential fault hazards in advance, take measures to avoid faults, ensure the safe and stable operation of multi-cylinder hydraulic equipment, reduce downtime and maintenance costs, and improve the overall performance and service life of the equipment.

[0095] In this embodiment, the twin model construction module specifically includes:

[0096] The model building unit is configured to construct a basic digital twin model of a multi-cylinder hydraulic equipment based on the design drawings (size, material properties, connection methods, etc. of each cylinder) and physical characteristics. This model is used to describe the precise geometry of each cylinder, the connection relationship between components, and the working principle of the hydraulic system.

[0097] The data fusion unit is configured to fuse the acquired operational data with the basic digital twin model, update the state variables and initial parameters of the basic digital twin model of the multi-cylinder hydraulic equipment, including determining the geometric parameters, material property parameters, flow coefficient, and pressure loss coefficient of each cylinder, so that the model can track the operational changes of the equipment in real time.

[0098] The status analysis unit is configured to establish an equipment status assessment index system, extract key features of the equipment's operating status based on the output of the updated digital twin model of the multi-cylinder hydraulic equipment, and determine the equipment's operating status based on the key features and assessment indexes. It also includes:

[0099] The output results at the current moment are obtained from the digital twin model, including real-time operating data of pressure, displacement, oil temperature, and oil flow of each cylinder, as well as the equipment operating status judgment results of the multi-cylinder hydraulic equipment;

[0100] By combining historical operating data of multi-cylinder hydraulic equipment and arranging them according to time series, a time series dataset is constructed.

[0101] The sliding window technique is used to extract dynamic features from time series datasets, calculate the average rate of change of cylinder pressure, displacement acceleration, and oil temperature fluctuation amplitude over a period of time, reflect the changing trend of equipment operating status, detect the data distribution of time series datasets, such as mean, variance, and standard deviation, and determine the key features in time series datasets based on the equipment status assessment index system.

[0102] The operating data of multi-cylinder hydraulic equipment is predicted based on the time series prediction model. According to the prediction results, the predicted values ​​of parameters such as pressure, displacement, oil temperature, and oil flow rate of each cylinder at future time are obtained. Combined with the equipment status assessment index system, the operating status of multi-cylinder hydraulic equipment at future time is judged.

[0103] In this embodiment, the status analysis unit establishes a device status assessment index system, specifically including:

[0104] Acquire historical operating data of multi-cylinder hydraulic equipment under different working conditions and health states, including pressure, displacement, oil temperature, oil flow rate of each cylinder and operating environment data, extract features from the historical operating data, and obtain an initial feature set of the equipment operating status based on the extraction results;

[0105] Key features related to the evaluation indicators are retrieved from the initial feature set and integrated into a key feature subset. The categories of equipment status evaluation indicators are determined and labels are established, including pressure stability indicators, displacement accuracy indicators, oil temperature change rate indicators, oil cleanliness indicators, etc.

[0106] Based on the digital twin model of the multi-cylinder hydraulic equipment, the attribute information of each key component of the multi-cylinder hydraulic equipment, the structural information of the equipment, and the preset working mode information are obtained, and the weight value of each key component in the overall operation of the equipment is determined.

[0107] Based on the weight value of each key component, a corresponding quantitative standard and grading threshold are set for each evaluation indicator, and a correspondence between indicator level and equipment operating status is established. For example, a stable state corresponds to the normal level of the indicator, and an abnormal state corresponds to the mild, moderate or severe abnormal level of the indicator.

[0108] In this embodiment, the pressure stability index is divided into four levels according to the pressure fluctuation range: stable, slightly fluctuating, moderately fluctuating, and severely fluctuating, and corresponding pressure fluctuation percentage thresholds are set for each level.

[0109] In this embodiment, by extracting and integrating key features of operational data, the data collection frequency is intelligently adjusted according to the equipment's operating status, improving data processing efficiency and avoiding resource waste. The status analysis unit establishes a comprehensive equipment status assessment index system. Combining historical and real-time operational data, it can not only accurately determine the current operating status of multi-cylinder hydraulic equipment, but also effectively predict future status through time series prediction models. It monitors the equipment from multiple dimensions, discovers potential problems in advance, provides strong support for preventive maintenance and fault warning of the equipment, and ensures stable operation of the equipment.

[0110] In this embodiment, the twin model construction module further includes:

[0111] The model parameter update unit is configured to compare the real-time acquired operating data with the output data of the digital twin model during the operation of the multi-cylinder hydraulic equipment. Based on the comparison results, it dynamically updates the model parameters using the latest acquired operating data, adjusting the weights and values ​​of each parameter in the digital twin model to more accurately reflect the real-time status of the equipment. For example, based on the actual changes in pressure and displacement of each cylinder under different operating conditions, it dynamically adjusts the parameters in the digital twin model regarding hydraulic system flow distribution and pressure loss, improving the model's accuracy.

[0112] The model evaluation unit is configured to perform a reliability assessment of the digital twin model based on the comparison results between the actual collected running data and the data output by the digital twin model, and to determine whether optimization and training are needed.

[0113] In this embodiment, real-time operating data is compared with the output of the digital twin model during equipment operation. The model parameters are dynamically adjusted based on the differences, which can closely follow the actual changes in the equipment's status, more accurately reflect the real-time situation of the equipment, greatly improve the accuracy of the model, enhance the model's adaptability to different working conditions, ensure that the equipment can be effectively simulated under various complex working conditions, and conduct reliability assessments on the digital twin model. This allows for the timely detection and resolution of potential deviations or failures in the model, ensuring the reliability and stability of the digital twin model.

[0114] In this embodiment, the interactive control module further includes:

[0115] The generated control commands are input into the digital twin model. The digital twin model simulates the equipment operating state after the control strategy is implemented. During the simulation, it adjusts its own state variables and parameters according to the control commands, such as changing the simulated values ​​of pressure, displacement, oil temperature, and oil flow rate of each cylinder.

[0116] Based on the simulation results, evaluate whether the key performance indicators of the multi-cylinder hydraulic equipment have returned to the normal range, such as whether the pressure stability index has returned to the stable level, whether the displacement accuracy meets the preset standard, and check whether new potential problems have occurred during the simulation process, such as whether the stress of some components exceeds the safe range, or whether it will cause abnormal fluctuations in other cylinders.

[0117] Based on the simulation evaluation results, determine whether the current control strategy and control commands need to be optimized, and based on the judgment results, send the optimized control commands to the actuators of the multi-cylinder hydraulic equipment;

[0118] Real-time monitoring of the execution status of the actuators and the actual operating status changes of multi-cylinder hydraulic equipment; comparison of equipment status changes before and after executing control commands; and judgment of the actual execution effect of the control strategy.

[0119] In this embodiment, by inputting control commands into a digital twin model for simulation, the operating status of the equipment after the implementation of the control strategy is evaluated in advance. A comprehensive assessment of whether key performance indicators have returned to normal ranges is conducted. This allows potential problems to be identified and resolved before the actual execution of control commands, optimizing the control strategy and commands. This significantly improves the reliability and effectiveness of the control strategy, preventing damage to the equipment due to improper control. Real-time monitoring of execution and changes in the actual operating status of the equipment enables timely adjustments to the control strategy based on the actual response of the equipment. This achieves precise control of multi-cylinder hydraulic equipment, ensuring that the equipment operates in the expected state, improving the stability and accuracy of equipment operation. If poor execution results or new problems are found, the control strategy can be adjusted promptly to quickly handle abnormal situations occurring during equipment operation, effectively reducing the probability of equipment failure, enhancing the system's fault prevention and handling capabilities, and ensuring the continuous and stable operation of the equipment.

[0120] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A data interaction system for multi-cylinder hydraulic equipment based on digital twin drive, characterized in that, include: The data acquisition module is configured to deploy sensors at key parts of the multi-cylinder hydraulic equipment to collect the operating data of the multi-cylinder hydraulic equipment in real time. At the same time, the data acquisition frequency of the multi-cylinder hydraulic equipment is adjusted according to the operating status and changing trends of the equipment. The twin model construction module is configured to build a digital twin model of a multi-cylinder hydraulic equipment. Based on the collected operating data of the multi-cylinder hydraulic equipment, the physical structure and working principle of the multi-cylinder hydraulic equipment are analyzed to obtain the equipment status information of the multi-cylinder hydraulic equipment. The interactive control module is configured to determine whether there is an abnormality in the multi-cylinder hydraulic equipment based on the analysis results of the digital twin model. If there is an abnormality, it generates control commands according to the preset control strategy and sends the control commands to the actuator of the multi-cylinder hydraulic equipment. Adjusting the data acquisition frequency of multi-cylinder hydraulic equipment, specifically including: Extract the target values ​​from the pressure operation data group, displacement operation data group, oil temperature operation data group, and oil flow operation data group; Extract the preset threshold ranges corresponding to the pressure operation data group, displacement operation data group, oil temperature operation data group, and oil flow operation data group; The target values ​​of the pressure operation data group, displacement operation data group, oil temperature operation data group, and oil flow operation data group are compared with their corresponding preset threshold ranges to obtain the standard deviation values ​​of the pressure operation data group, displacement operation data group, oil temperature operation data group, and oil flow operation data group. The standard deviation values ​​of the pressure operation data group, displacement operation data group, oil temperature operation data group, and oil flow operation data group are the parameter values ​​used to represent the data fluctuation range of each data group. Compare the standard deviation values ​​corresponding to the pressure operation data group, displacement operation data group, oil temperature operation data group and oil flow operation data group with the preset standard deviation threshold. If there is no data set whose standard deviation exceeds the preset standard deviation threshold, the data acquisition frequency of the multi-cylinder hydraulic equipment will not be adjusted. When there are data groups whose standard deviation values ​​exceed the preset standard deviation threshold, the data groups whose standard deviation values ​​exceed the preset standard deviation threshold are selected as the target data groups. The composite standard deviation of the target data group is obtained by using a weighted average of the standard deviation values ​​corresponding to the target data group. The frequency of data acquisition for multi-cylinder hydraulic equipment is adjusted using the comprehensive standard deviation value corresponding to the target data set. Adjusting the data acquisition frequency of multi-cylinder hydraulic equipment using the comprehensive standard deviation value corresponding to the target data set specifically includes: Retrieve the composite standard deviation value corresponding to the target data set; Retrieve the maximum permissible standard deviation value for each data set contained in the target data set; The average value of the maximum permissible standard deviation corresponding to the target data group is obtained by using the maximum permissible standard deviation value corresponding to each data group contained in the target data group; The ratio of the comprehensive standard deviation value corresponding to the target data set to the average value of the maximum permissible standard deviation of the target data set is processed to obtain the standard deviation ratio coefficient corresponding to the target data set. The standard deviation ratio coefficient corresponding to the target data set is used to adjust the data acquisition frequency of the multi-cylinder hydraulic equipment. The adjusted data acquisition frequency is obtained by the following formula: Where F represents the adjusted operating data acquisition frequency; F base The value represents the frequency of data acquisition before adjustment; α represents the adjustment amplitude coefficient, used to control the maximum amplitude of frequency change, with a value range of 0.5-1.6; β represents the curve steepness factor, used to control the response speed of frequency change, with a value range of 2.2-3.1; x represents the standard deviation ratio coefficient corresponding to the target data set; r represents the coefficient reference value corresponding to the operating data of multi-cylinder hydraulic equipment, with a value range of 0.3-0.

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2. The data interaction system for multi-cylinder hydraulic equipment based on digital twin drive as described in claim 1, characterized in that, The data acquisition module also includes: The sensor optimization unit is configured to analyze the importance and correlation of data from various parts of the multi-cylinder hydraulic equipment based on its structural characteristics and working properties, determine the key installation positions of the sensors, and optimize the sensor layout. The data acquisition frequency adjustment unit is configured to monitor the equipment operating status of multi-cylinder hydraulic equipment in real time and adjust the preset data acquisition frequency according to the changing trend of the equipment operating status. The equipment monitoring unit is configured to collect operating environment data of multi-cylinder hydraulic equipment and provide early warning of equipment failure based on the correlation between operating environment data and operating data.

3. The digital twin driven multi-cylinder hydraulic equipment data interaction system of claim 2, wherein, The sampling frequency adjustment unit specifically includes: The collected operational data is acquired, and key features are extracted based on the data type. The extracted key features are then integrated to obtain pressure operational data sets, displacement operational data sets, oil temperature operational data sets, and oil flow operational data sets for multi-cylinder hydraulic equipment. The target values ​​of the pressure operation data group, displacement operation data group, oil temperature operation data group, and oil flow operation data group are compared with the preset threshold range of each data group. The equipment operation status and change trend of the multi-cylinder hydraulic equipment are judged by combining the data fluctuation range of each data group. Based on the judgment results of the equipment's operating status and changing trends, a corresponding data acquisition frequency adjustment strategy is generated to adjust the data acquisition frequency of the multi-cylinder hydraulic equipment.

4. The digital twin driven multi-cylinder hydraulic equipment data interaction system of claim 3, wherein, The twin model construction module specifically includes: The model building unit is configured to construct a basic digital twin model of the multi-cylinder hydraulic equipment based on the design drawings and physical characteristics of the multi-cylinder hydraulic equipment, which is used to describe the working principle of the multi-cylinder hydraulic equipment. The data fusion unit is configured to fuse the acquired operational data with the basic digital twin model to update the state variables and initial parameters of the basic digital twin model of the multi-cylinder hydraulic equipment. The status analysis unit is configured to establish an equipment status assessment index system. Based on the output results of the updated digital twin model of the multi-cylinder hydraulic equipment, it extracts key features of the equipment's operating status and judges the equipment's operating status based on the key features and assessment indexes.

5. The digital twin driven multi-cylinder hydraulic equipment data interaction system of claim 4, wherein, The condition analysis unit establishes a system of equipment condition assessment indicators, which specifically includes: Acquire historical operating data of multi-cylinder hydraulic equipment under different working conditions and health states, extract features from the historical operating data, and obtain an initial feature set of the equipment operating status based on the extraction results; Key features related to the evaluation indicators are retrieved from the initial feature set and integrated into a subset of key features to determine the categories of equipment status evaluation indicators and establish labels; Based on the digital twin model of the multi-cylinder hydraulic equipment, the attribute information of each key component of the multi-cylinder hydraulic equipment, the structural information of the equipment, and the preset working mode information are obtained, and the weight value of each key component in the overall operation of the equipment is determined. Based on the weight value of each key component, a corresponding quantitative standard and grading threshold are set for each evaluation indicator, and a correspondence between indicator level and equipment operating status is established.

6. The digital twin driven multi-cylinder hydraulic equipment data interaction system of claim 5, wherein, The state analysis unit also includes: The output results at the current moment are obtained from the digital twin model, including real-time operating data of pressure, displacement, oil temperature, and oil flow of each cylinder, as well as the equipment operating status judgment results of the multi-cylinder hydraulic equipment; By combining historical operating data of multi-cylinder hydraulic equipment and arranging them according to time series, a time series dataset is constructed. The sliding window technique is used to extract dynamic features from time series datasets, detect the data distribution of time series datasets, and determine key features in time series datasets based on equipment status assessment index system. The operating data of multi-cylinder hydraulic equipment is predicted based on the time series prediction model. Based on the prediction results and combined with the equipment status assessment index system, the operating status of the multi-cylinder hydraulic equipment at future time is determined.

7. The data interaction system for multi-cylinder hydraulic equipment based on digital twin drive as described in claim 6, characterized in that, The twin model building module also includes: The model parameter update unit is configured to compare the real-time acquired operating data with the output data of the digital twin model during the operation of the multi-cylinder hydraulic equipment, and dynamically update the model parameters based on the comparison results using the latest acquired operating data, adjusting the weight and value of each parameter in the digital twin model. The model evaluation unit is configured to perform a reliability assessment of the digital twin model based on the comparison results between the actual collected running data and the data output by the digital twin model, and to determine whether optimization and training are needed.

8. The data interaction system for multi-cylinder hydraulic equipment based on digital twin drive as described in claim 7, characterized in that, The interactive control module also includes: The generated control commands are input into the digital twin model, which simulates the equipment's operating state after the control strategy is implemented. During the simulation, the digital twin model adjusts its own state variables and parameters according to the control commands. The simulation results are used to evaluate whether the key performance indicators of the multi-cylinder hydraulic equipment have returned to the normal range, and to check whether any new potential problems have emerged during the simulation process. Based on the simulation evaluation results, determine whether the current control strategy and control commands need to be optimized, and based on the judgment results, send the optimized control commands to the actuators of the multi-cylinder hydraulic equipment; Real-time monitoring of the execution status of the actuators and the actual operating status of multi-cylinder hydraulic equipment; comparison of equipment status changes before and after executing control commands; and assessment of the actual execution effect of the control strategy.

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