Energy-saving intelligent switching valve body operation method and system

Abstract

The invention relates to the technical field of intelligent valve body control, and discloses an energy-saving intelligent switching valve body operation method and system. The method comprises the following steps: acquiring historical operation data of an intelligent switching valve body, and generating a structured feature set according to preset dimensions such as a fluid pressure fluctuation curve; constructing a multi-dimensional dynamic feature matrix containing a time axis and the like; determining initial operation constraint conditions according to the valve body life distribution model and the switching mode library; generating an initial energy consumption curve through dynamic switching simulation; and carrying out iterative correction by using a self-adaptive optimization algorithm to generate a multi-dimensional energy-saving operation strategy. The system comprises a data processing module, a matrix construction module, a condition determination module, a simulation module, an optimization module and the like to cooperatively realize the method. The method can accurately analyze the operation state of the valve body, optimizes the operation strategy, effectively reduces the energy consumption, improves the operation performance and the equipment management level, is suitable for various industrial scenes, and has a good application prospect.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent valve body control, and specifically provides an energy-saving intelligent switching valve body operation method and system. Background Art

[0002] In modern industrial production, as a key component of the fluid transportation system, intelligent switching valve bodies are widely used in many fields such as petrochemical, power, and metallurgy. The quality of their performance directly affects the stability, safety, and energy utilization efficiency of the production process.

[0003] With the continuous expansion of industrial production scale and the increasing complexity of production processes, the operating requirements for intelligent switching valve bodies are also getting higher and higher. Most traditional valve body operation methods rely on manual experience or simple control logic and are difficult to adapt to complex and changeable working conditions. On the one hand, it is impossible to accurately conduct comprehensive regulation based on multiple factors such as fluid pressure fluctuations, spool wear, medium temperature changes, and actuator energy consumption. As a result, during actual operation, problems such as frequent leakage and slow response of the valve body often occur, which not only affects production efficiency but also increases equipment maintenance costs and safety risks. For example, in petrochemical production, if the valve body cannot be switched in a timely and accurate manner, it may lead to an imbalance in fluid pressure in the pipeline, triggering serious accidents such as material leakage and even explosion.

[0004] On the other hand, the energy issue has become the focus of global attention. As the main field of energy consumption, energy conservation and emission reduction in industrial production are extremely urgent. However, there are many unreasonable aspects in the existing valve body operation methods in terms of energy utilization. Due to the lack of effective monitoring and optimization of actuator energy consumption and the failure to fully consider the impact of factors such as spool wear on energy consumption, the valve body consumes a large amount of unnecessary energy during operation. Taking the power industry as an example, a large number of intelligent switching valve bodies cause huge energy waste due to excessive energy consumption during long-term operation, increasing the power generation cost, which runs counter to the concept of sustainable development.

[0005] In addition, with the development trend of industrial automation and intelligence, the existing valve body operation methods and systems are difficult to meet the needs of intelligent production. It is impossible to achieve in-depth mining and analysis of valve body operation data and provide strong support for production decision-making. In the information age, enterprises need real-time and accurate data to optimize production processes and improve production management levels. However, traditional valve body operation methods cannot provide these key data, restricting the intelligent upgrade of enterprises. Summary of the Invention

[0006] The purpose of the present invention is to provide an energy-saving intelligent switching valve body operation method and system to solve the problems raised in the above background art.

[0007] To achieve the above object, the present invention provides the following technical solution: An energy-saving intelligent switching valve body operation method, the method comprising: Obtain the historical operation data of the intelligent switching valve body, and classify the data according to preset dimensions to generate a structured feature set; the preset dimensions include fluid pressure fluctuation curve, spool wear degree, medium temperature change, and actuator energy consumption spectrum; Construct a dynamic feature matrix, define a multi-dimensional analysis coordinate system based on the classification dimensions of the feature set, and the coordinate system includes a time axis, a fluid axis, a mechanical axis, and an energy consumption axis; Determine the initial operation constraint conditions according to the valve body life distribution model and the switching mode library, and the constraint conditions include leakage probability threshold, switching response time rule, and energy consumption sensitivity classification; Perform dynamic switching simulation on the feature set based on the dynamic feature matrix, simulate the switching behavior of the valve body under various working conditions through a multi-modal simulation engine, and generate an initial energy consumption curve; Use an adaptive optimization algorithm to iteratively correct the initial energy consumption curve, adjust the spool wear accumulation weight and the energy consumption coupling coefficient, and generate a multi-dimensional energy-saving operation strategy.

[0008] Preferably, the constructing of the dynamic feature matrix includes: Divide the time axis into switching stages synchronized with the medium transportation cycle, and each stage is associated with a pressure transient compensation factor; Define a time-varying response function of the spool displacement based on the fluid axis, and integrate the medium temperature lag characteristic; Embed the energy consumption spectrum density distribution of the actuator in the mechanical axis, and dynamically associate the switching times with the mechanical efficiency correction coefficient.

[0009] Preferably, the adaptive optimization algorithm uses an improved random forest-long short-term memory hybrid model, including: Encode the spool displacement deviation and the actuator energy consumption decay rate as a multi-objective optimization function, and define a loss function to evaluate the energy consumption sensitivity matching degree and the mechanical parameter drift cost; Extract high-dimensional feature interaction terms through a gradient boosting decision tree, and use a time series memory unit to capture dynamic dependence relationships, and output an energy-saving prediction value integrating the energy consumption trend.

[0010] Preferably, the division of the time axis includes: Based on the periodic characteristics of the historical operation data, divide the time axis into dynamic sliding windows, and each window is associated with the start and stop marks of the medium transportation stage; Introduce a time decay factor to dynamically adjust the weight distribution of each stage, and use a hidden Markov model to predict the switching demand of future time segments.

[0011] Preferably, the definition of the fluid axis includes: Define the non - linear regression constraint equation of the spool displacement according to the change rate of the medium viscosity, and optimize the flow velocity distribution function based on the fluid - structure interaction analysis; Extract the fluid pulsation energy spectrum through the frequency - domain integration algorithm, and dynamically correct the response delay parameter of the medium temperature hysteresis characteristic.

[0012] Preferably, the definition of the mechanical axis includes: Use the wavelet packet energy entropy algorithm to quantify the high - frequency components of the vibration signal of the actuator, and real - time correlate the vibration energy with the mechanical wear rate; Dynamically adjust the attenuation gradient of the mechanical efficiency correction coefficient according to the stress distribution model of the sealing component.

[0013] Preferably, the definition of the energy consumption axis includes: Based on the spatio - temporal distribution characteristics of historical energy consumption data, establish a hierarchical clustering model of the energy consumption benchmark curve; Identify energy consumption abnormal events through the mutation detection mechanism, and use the adaptive threshold segmentation algorithm to generate the dynamic interval division of the energy consumption sensitivity.

[0014] Preferably, the division of the dynamic sliding window further includes: Adaptive adjust the window length according to the pipeline pressure fluctuation amplitude, and introduce the gradient descent method to optimize the iteration step of the time decay factor; Align the window boundary with the valve body action timing sequence to generate an energy consumption evaluation matrix with joint calibration of the time axis - mechanical axis.

[0015] Preferably, the iterative correction includes: Construct a dual - stream attention network model with the spool friction energy, the current distortion rate of the actuator, and the medium corrosion index as input features; Screen key energy consumption factors through the adaptive feature selection mechanism, and use the multi - head attention layer to fuse cross - axis domain correlation features to generate a corrected energy consumption confidence interval.

[0016] Preferably, the present invention further includes an energy - saving intelligent switching valve body operating system, and the system includes: A data processing module, which is used to obtain the historical operation data of the intelligent switching valve body, and generate a structured feature set classified according to the preset dimensions of the fluid pressure fluctuation curve, the spool wear degree, the medium temperature change, and the actuator energy consumption spectrum; A matrix construction module, which is used to define a multi - dimensional analysis coordinate system including the time axis, the fluid axis, the mechanical axis, and the energy consumption axis based on the classification dimensions of the feature set, and construct a dynamic feature matrix; A condition determination module, which is used to determine the initial operation constraint conditions including the leakage probability threshold, the switching response time rule, and the energy consumption sensitivity classification according to the valve body life distribution model and the switching mode library; A simulation module, which is used to perform dynamic switching simulation on a feature set based on a dynamic feature matrix, simulate the switching behavior of a valve body under various working conditions through a multimodal simulation engine, and generate an initial energy consumption curve; An optimization module, which is used to iteratively correct the initial energy consumption curve by using an adaptive optimization algorithm, adjust the cumulative weight of spool wear and the energy consumption coupling coefficient, and generate a multi-dimensional energy-saving operation strategy.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: In terms of data utilization and analysis, by obtaining the historical operation data of an intelligent switching valve body and generating a structured feature set according to preset dimensions such as fluid pressure fluctuation curves, spool wear degree, medium temperature change, and actuator energy consumption spectrum, the comprehensive collection and in-depth collation of valve body operation information are realized. This lays a solid foundation for the subsequent accurate analysis of the valve body operation state. Compared with traditional methods that only rely on single or a small number of parameters for judgment, the accuracy and comprehensiveness of the evaluation of the valve body operation condition are greatly improved. Based on these structured feature sets, a dynamic feature matrix is constructed, and using a multi-dimensional analysis coordinate system, the changing trends of different operation parameters over time and their mutual relationships can be clearly displayed, enabling operators to more intuitively and deeply understand the operation rules of the valve body, thus providing strong support for formulating reasonable operation strategies.

[0018] Regarding the valve body life and operation reliability, the initial operation constraint conditions are determined according to the valve body life distribution model and the switching mode library, including leakage probability thresholds, switching response time rules, energy consumption sensitivity grading, etc. This measure effectively avoids the problems of premature damage to the valve body or increased leakage risk caused by improper operation. By strictly controlling the leakage probability threshold, it ensures that the valve body operates in a safe and reliable state, reducing material losses and environmental pollution caused by leakage. At the same time, reasonable switching response time rules ensure that the valve body can act quickly and accurately when switching is required, improving the stability and continuity of the production process. For example, in some pharmaceutical production lines with extremely high requirements for flow control accuracy, the present invention can ensure that the valve body responds to flow change instructions in a timely manner, guaranteeing the quality stability of drug production. The setting of energy consumption sensitivity grading provides a clear direction and basis for the formulation of energy-saving operation strategies.

[0019] In terms of energy-saving optimization, dynamic switching simulation is performed based on the dynamic feature matrix to generate the initial energy consumption curve, and an adaptive optimization algorithm is used to iteratively correct it. This method can fully consider factors such as the cumulative weight of spool wear and the energy consumption coupling coefficient. By adjusting these parameters, the operation strategy of the valve body is optimized, achieving significant energy-saving effects. Taking the fluid transportation system of a large steel enterprise as an example, after applying the technology of the present invention, a large amount of energy costs can be saved every year, reducing the production costs of the enterprise and improving the market competitiveness of the enterprise. In addition, by identifying energy consumption abnormal events and dynamically dividing the sensitive interval of energy consumption, the problem of excessive energy consumption can be discovered and solved in a timely manner, further tapping the energy-saving potential.

[0020] From the perspective of improving the overall operating performance, the improved models in the adaptive optimization algorithm, such as encoding the spool displacement deviation and the energy consumption decay rate of the actuator into a multi-objective optimization function, and using gradient boosting decision trees and time series memory units, can accurately capture the dynamic changes and complex relationships in the operation of the valve body. By screening key energy consumption factors through the adaptive feature selection mechanism and fusing cross-axis domain correlation features, not only a more accurate energy consumption confidence interval is generated, but also a multi-dimensional energy-saving operation strategy is formulated. This enables the valve body to maintain efficient operation under different working conditions, extends the service life of the valve body, reduces the frequency of equipment maintenance and replacement, improves the operating efficiency and reliability of the entire fluid transportation system, and provides a strong guarantee for the stable and efficient operation of industrial production. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is the working principle diagram of the operation method of the energy-saving intelligent switching valve body described in the present invention; Figure 2 is the flow chart of the adaptive optimization algorithm; Figure 3 is the flow chart of the time axis division; Figure 4 is the flow chart of the energy consumption axis determination. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0023] Please refer to Figures 1 - 4 , the present invention relates to an operation method of an energy-saving intelligent switching valve body, and its specific implementation manners will be elaborated in detail below.

[0024] Obtain the historical operation data of the intelligent switching valve body, and classify the data according to preset dimensions to generate a structured feature set. Among them, the preset dimensions include the fluid pressure fluctuation curve, the spool wear degree, the medium temperature change, and the actuator energy consumption spectrum. These data are the basis for subsequent analysis and decision-making. By collecting and organizing the historical operation data, the operation characteristics of the valve body under different working conditions can be deeply understood. For example, obtain the fluid pressure fluctuation conditions in different time periods from long-term operation records, accurately record the wear degree of the spool in each stage, monitor the change trend of the medium temperature over time, and the energy consumption of the actuator under different operations.

[0025] Construct a dynamic feature matrix, define a multi-dimensional analysis coordinate system based on the classification dimensions of the feature set. This coordinate system includes a time axis, a fluid axis, a mechanical axis, and an energy consumption axis. The multi-dimensional analysis coordinate system provides a powerful tool for comprehensively analyzing the operation state of the valve body. The time axis is used to record the time sequence of the valve body operation, the fluid axis reflects the parameter changes related to the fluid, the mechanical axis reflects the operation characteristics of mechanical components, and the energy consumption axis focuses on the analysis of energy consumption data. By integrating data from different dimensions into a matrix, the mutual relationships between various factors can be observed more intuitively, providing comprehensive data support for subsequent analysis and decision-making.

[0026] Determine the initial operation constraint conditions according to the valve body life distribution model and the switching mode library. The constraint conditions include the leakage probability threshold, the switching response time rule, and the energy consumption sensitivity grading. The valve body life distribution model is established based on a large amount of experimental data and theoretical analysis, and is used to predict the life of the valve body under different usage conditions. The switching mode library stores a variety of verified switching modes. The initial operation constraint conditions determined based on this information can ensure the safe and reliable operation of the valve body, while meeting the requirements of energy conservation and high efficiency. For example, set a reasonable leakage probability threshold to ensure that the valve body operates within a certain leakage risk range; formulate a switching response time rule to ensure that the valve body can respond in a timely manner when switching is required; classify the energy consumption sensitivity to more targeted reduce energy consumption when optimizing the operation strategy.

[0027] Perform dynamic switching simulation on the feature set based on the dynamic feature matrix. Simulate the switching behavior of the valve body under various working conditions through a multi-modal simulation engine to generate an initial energy consumption curve. The multi-modal simulation engine can comprehensively consider various factors, such as the flow characteristics of the fluid, the mechanical properties of mechanical components, and the change law of energy consumption, to simulate the actual switching process of the valve body under different working conditions. Through a large number of simulation experiments, the initial energy consumption curves under different working conditions can be obtained. These curves reflect the energy consumption of the valve body under the current operating conditions, providing a reference basis for subsequent optimization.

[0028] An adaptive optimization algorithm is used to iteratively correct the initial energy consumption curve, adjust the cumulative weight of spool wear and the energy consumption coupling coefficient, and generate a multi-dimensional energy-saving operation strategy. The adaptive optimization algorithm can continuously adjust the optimization parameters according to the actual situation to achieve the optimal energy-saving effect. By adjusting the cumulative weight of spool wear and the energy consumption coupling coefficient, the operation strategy of the valve body can be optimized, unnecessary energy consumption can be reduced, and the service life of the valve body can be extended. For example, the weight of the spool in the energy consumption calculation can be dynamically adjusted according to the wear degree of the spool, so that the operation strategy is more in line with the actual situation; the energy consumption coupling coefficient is optimized to improve the energy utilization efficiency and achieve multi-dimensional energy-saving goals.

[0029] The specific implementation manners of the present invention will be further described in detail through six embodiments below.

[0030] Embodiment 1: In the process of constructing the dynamic feature matrix, the division of the time axis is involved. Specifically, based on the periodic characteristics of the historical operation data, the time axis is divided into dynamic sliding windows. This is because the operation of the valve body often has a certain periodicity. For example, in industrial production, the transportation of the medium may be carried out according to a certain time rule. By analyzing the historical operation data, this periodic characteristic can be determined, and then the time axis can be divided into switching stages synchronized with the medium transportation cycle. Each window is associated with the start and stop marks of the medium transportation stage, so that the actual production stage corresponding to each window can be clarified, which is convenient for subsequent analysis. For example, in a chemical production process, the medium transportation is divided into stages such as feeding, reaction, and discharging, and the time and operation requirements of each stage are different. By marking the start and stop times, the operation conditions of the valve body in different stages can be accurately understood.

[0031] A time decay factor is introduced to dynamically adjust the weight distribution of each stage. As time goes by, the influence of the data in the earlier stage on the current decision may gradually decrease, so it is necessary to introduce a time decay factor. The value range of the time decay factor is usually between 0 and 1, and its calculation formula is: , where represents the time decay factor, is the decay coefficient, which is determined according to the actual situation, represents the time interval between the current time and the data generation time. By adjusting The value can control the attenuation speed. The hidden Markov model is used to predict the switching requirements for future time segments. The hidden Markov model is a statistical model that can predict future states based on historical data. In this embodiment, through the training of historical operation data, the hidden Markov model can learn the rules of valve body switching, so as to predict possible future switching requirements. For example, according to the switching frequency and time interval of the valve body under different working conditions in the past period, the model can predict whether the valve body needs to be switched in the next few hours and at what time point the switching is most appropriate. This can make preparations in advance, optimize the operation of the valve body, and reduce energy consumption.

[0032] In addition, the window length is adaptively adjusted according to the amplitude of pipeline pressure fluctuation. When the amplitude of pipeline pressure fluctuation is large, it indicates that the working condition changes violently, and it is necessary to observe the operation state of the valve body more finely, so the window length is appropriately shortened; on the contrary, when the amplitude of pressure fluctuation is small, the window length can be appropriately extended. The gradient descent method is introduced to optimize the iteration step size of the time decay factor. The gradient descent method is a commonly used optimization algorithm that gradually reduces the objective function by continuously adjusting the values of parameters. In this embodiment, the objective function can be a function related to energy consumption. By adjusting the iteration step size of the time decay factor, the time decay factor can converge to the optimal value faster, so as to more accurately adjust the weight distribution of each stage. Finally, the window boundary is aligned with the valve body action time sequence to generate an energy consumption evaluation matrix with joint calibration of the time axis and the mechanical axis. This can ensure that when evaluating energy consumption, the impacts of time and mechanical factors can be comprehensively considered, and the energy consumption situation of the valve body can be analyzed more accurately.

[0033] Embodiment 2: For the definition of the fluid axis, a non-linear regression constraint equation for the spool displacement is defined according to the change rate of the medium viscosity. The medium viscosity is one of the important factors affecting fluid flow and spool operation. In actual operation, the medium viscosity may change with factors such as temperature and pressure. Let the change rate of the medium viscosity be , and the spool displacement be . Through a large number of experiments and data analysis, the following non-linear regression constraint equation can be established: , where is the coefficient fitted according to the experimental data. This equation reflects the relationship between the change rate of the medium viscosity and the spool displacement. In actual operation, the reasonable spool displacement can be calculated through this equation according to the real-time monitored change rate of the medium viscosity to ensure the normal operation of the valve body.

[0034] Optimize the flow velocity distribution function based on fluid-structure interaction analysis. Fluid-structure interaction analysis takes into account the interaction between fluids and solids. During the operation of the valve body, the flow of the fluid will exert forces on solid components such as the valve core, and at the same time, the movement of the solid components will also affect the flow state of the fluid. Through fluid-structure interaction analysis, a more accurate flow velocity distribution can be obtained, thereby optimizing the flow velocity distribution function. For example, the finite element analysis method is used to simulate the flow field inside the valve body, considering factors such as the shape and position of the valve core and the physical properties of the fluid, to obtain the flow velocity distribution data under different working conditions. Based on these data, a flow velocity distribution function is established , where represents the flow velocity,[[]] represents the spatial coordinates,[[]] represents time. By optimizing this function, the flow efficiency of the fluid can be improved and energy consumption can be reduced.

[0035] Extract the fluid pulsation energy spectrum through the frequency-domain integration algorithm and dynamically correct the response delay parameter of the medium temperature lag characteristic. Fluid pulsation will affect the operation of the valve body. Through the frequency-domain integration algorithm, the fluid pulsation signal in the time domain can be converted to the frequency domain to extract its energy spectrum. Let the fluid pulsation signal be , and its frequency spectrum is obtained through Fourier transform. Then the fluid pulsation energy spectrum can be calculated through frequency-domain integration: , where and are the integration frequency ranges. According to the extracted energy spectrum, the frequency and energy distribution of fluid pulsation can be analyzed. The medium temperature lag characteristic means that the change of the medium temperature has a certain delay relative to the change of the external conditions. By analyzing the fluid pulsation energy spectrum, the response delay parameter of the medium temperature lag characteristic can be dynamically corrected to make the monitoring and control of the medium temperature change more accurate, and then optimize the operation strategy of the valve body.

[0036] Example 3: In terms of the definition of the mechanical shaft, the wavelet packet energy entropy algorithm is used to quantify the high-frequency components of the vibration signal of the actuator. The actuator will generate vibration during operation, and the vibration signal contains rich information, reflecting the operation state of the actuator. The wavelet packet energy entropy algorithm is an effective signal analysis method that can perform multi-resolution decomposition on the signal and extract the energy information of different frequency bands. Let the vibration signal of the actuator be , after wavelet packet decomposition, the signal components of different frequency bands , are obtained. The energy of each frequency band can be calculated as: , where and is the time range of integration. Then, calculate the wavelet packet energy entropy . The wavelet packet energy entropy The larger it is, the higher the uncertainty of the signal and the richer the high-frequency components. By monitoring the change of the wavelet packet energy entropy in real time, the vibration energy can be correlated with the mechanical wear rate in real time. When the value increases, it indicates that the vibration energy of the actuator increases, which may lead to an accelerated mechanical wear rate. At this time, corresponding measures need to be taken, such as adjusting the operating parameters or performing equipment maintenance.

[0037] Dynamically adjust the attenuation gradient of the mechanical efficiency correction coefficient according to the stress distribution model of the sealing component. The sealing component is an important part of the valve body, and its stress distribution will affect the mechanical efficiency and sealing performance. By establishing a stress distribution model of the sealing component, the stress distribution of the sealing component under different working conditions can be analyzed. Let the stress of the sealing component under a certain working condition be , and obtain the relationship between the stress and the mechanical efficiency correction coefficient according to the stress distribution model. When the stress exceeds a certain threshold, the mechanical efficiency correction coefficient will decay, and its attenuation gradient can be dynamically adjusted according to the stress distribution model. For example, when the stress is large, appropriately increase the attenuation gradient , so that the mechanical efficiency correction coefficient decays faster to reflect the actual decrease in mechanical efficiency. In this way, when calculating and optimizing the operation strategy of the valve body, the influence of mechanical factors can be considered more accurately, improving the energy-saving effect and the service life of the valve body.

[0038] Example 4: For the definition of the energy consumption axis, based on the spatio-temporal distribution characteristics of historical energy consumption data, establish a hierarchical clustering model of the energy consumption benchmark curve. The historical energy consumption data contains the energy consumption information of the valve body under different time and space conditions. Spatio-temporal distribution characteristic analysis can help understand the change law of energy consumption, such as the energy consumption differences in different seasons, different production periods, and different working areas. Use the hierarchical clustering algorithm to process these data, and this algorithm can divide the data into different hierarchical structures according to similarity. Let the historical energy consumption data be , where represents the th energy consumption data point. First, regard each data point as a separate class, and then calculate the distance between classes. The calculation method of the distance can adopt common distance measurement methods such as Euclidean distance. For example, the Euclidean distance and between two data points . Merge the two closest classes into a new class according to the distance, and continuously repeat this process until all data points are merged into a large class, so as to obtain the hierarchical clustering result of the energy consumption benchmark curve. Through this model, the energy consumption benchmark level under different working conditions can be determined, providing a reference for subsequent energy consumption analysis and optimization.

[0039] Identify energy consumption abnormal events through the mutation detection mechanism, and use the adaptive threshold segmentation algorithm to generate the dynamic interval division of energy consumption sensitivity. The mutation detection mechanism is used to monitor sudden changes in energy consumption data. When there are abnormal fluctuations in energy consumption data, it may mean that there are problems with the valve body operation or significant changes in working conditions. For example, the cumulative sum control chart (CUSUM) method is used for mutation detection. Let the energy consumption data sequence be , define the cumulative sum statistic , where is the average energy consumption under normal conditions. When exceeds a certain threshold, it is determined that an energy consumption mutation event has occurred. For the identified energy consumption abnormal events, the adaptive threshold segmentation algorithm is used to generate the dynamic interval division of energy consumption sensitivity. The adaptive threshold segmentation algorithm automatically adjusts the threshold according to the data distribution. For example, the Otsu algorithm is used. This algorithm determines the optimal threshold by maximizing the between-class variance. Let the gray level of the energy consumption data be , the number of pixels for each gray level is , and the total number of pixels is . Define the probability of the gray level . Assume that the gray levels are divided into two classes and , the threshold is , then the between-class variance , where , . By calculating the between-class variances at different thresholds, find the threshold that maximizes the between-class variance, and use this to divide the dynamic interval of energy consumption sensitivity. In this way, the energy consumption sensitivity can be more accurately evaluated according to the changes in energy consumption, providing a basis for formulating energy-saving operation strategies.

[0040] Example 5: When using the adaptive optimization algorithm to iteratively correct the initial energy consumption curve, a dual-stream attention network model is constructed. This model takes the spool friction energy, actuator current distortion rate, and medium corrosion index as input features. The spool friction energy reflects the energy loss generated by friction during the movement of the spool, the actuator current distortion rate reflects whether the actuator is operating normally, and the medium corrosion index indicates the corrosion degree of the valve body components by the medium. These features are all closely related to the energy consumption of the valve body.

[0041] Screen key energy consumption factors through an adaptive feature selection mechanism. The adaptive feature selection mechanism can automatically select features that have a greater impact on energy consumption according to the characteristics of the data and the requirements of the model. For example, the feature importance evaluation method in the random forest algorithm is adopted. Random forest is an ensemble learning model composed of multiple decision trees. By calculating the importance scores of each feature in the decision tree, the importance of the feature is evaluated. Let the importance score of feature be . The calculation method is the sum of the contributions of feature to node splitting in all decision trees. Select features with higher importance scores as key energy consumption factors. Use a multi-head attention layer to fuse cross-axis domain correlation features to generate a corrected energy consumption confidence interval. The multi-head attention layer can simultaneously focus on the relationships between different features and fuse cross-axis domain correlation features from multiple perspectives. For example, among the time axis, fluid axis, mechanical axis, and energy consumption axis, there may be mutual influences between the features on different axes. Through the multi-head attention layer, these cross-axis domain correlation features can be fused to obtain more comprehensive information. Let the input of the multi-head attention layer be , and the output be . Its calculation formula is: , where is the number of heads, , and are learnable weight matrices, and is the dimension of the key vector. Through the fusion of the multi-head attention layer, more accurate energy consumption information can be obtained, and then a corrected energy consumption confidence interval can be generated to provide support for formulating more precise energy-saving operation strategies.

[0042] Example 6: The present invention also relates to an energy-saving intelligent switching valve body operating system. The data processing module of the system is used to obtain the historical operation data of the intelligent switching valve body and generate a structured feature set classified according to the preset dimensions of the fluid pressure fluctuation curve, spool wear degree, medium temperature change, and actuator energy consumption spectrum. In practical applications, the data processing module can be connected to various sensors and data acquisition devices to collect various data during the operation of the valve body in real time. For example, fluid pressure fluctuation data is collected through a pressure sensor, spool wear degree data is obtained through a wear detection device, medium temperature change data is monitored through a temperature sensor, and actuator energy consumption spectrum data is recorded through an energy consumption monitoring device. Then, these original data are cleaned, sorted, and classified, and converted into a structured feature set for subsequent analysis and processing.

[0043] The matrix construction module defines a multidimensional analysis coordinate system including a time axis, a fluid axis, a mechanical axis and an energy consumption axis based on the classification dimension of the feature set, and constructs a dynamic feature matrix. The matrix construction module constructs a dynamic feature matrix based on the structured feature set provided by the data processing module and the definition method of the time axis, fluid axis, mechanical axis and energy consumption axis in the present invention. For example, when constructing the time axis, the division is performed according to the method in Example 1; when constructing the fluid axis, the definition method in Example 2 is used; the construction of the mechanical axis and the energy consumption axis refers to the relevant contents of Example 3 and Example 4 respectively. By constructing a dynamic feature matrix, information of different dimensions is integrated together, providing a unified data platform for subsequent analysis and decision-making.

[0044] The condition determination module determines the initial operation constraints including leakage probability threshold, switching response time rules and energy consumption sensitivity classification according to the valve body life distribution model and switching mode library. The valve body life distribution model and switching mode library can be pre-stored in the system database. The condition determination module retrieves the corresponding model and library information from the database according to the current operating status and historical data of the valve body, calculates and determines the initial operation constraints. For example, according to the service life of the valve body, operating environment and other factors, combined with the valve body life distribution model, a reasonable leakage probability threshold is determined; according to the production process requirements and past experience, a suitable switching mode is selected from the switching mode library, and the corresponding switching response time rules are formulated; according to the analysis results of the energy consumption data and the standards for energy consumption sensitivity classification, the current energy consumption sensitivity classification is determined. These initial operation constraints provide an important basis for subsequent simulation and optimization.

[0045] The simulation module performs dynamic switching simulation on the feature set based on the dynamic feature matrix, simulates the switching behavior of the valve body under various working conditions through the multimodal simulation engine, and generates an initial energy consumption curve. The simulation module uses the dynamic feature matrix generated by the matrix construction module and the feature set provided by the data processing module to perform simulation with the help of the multimodal simulation engine. The multimodal simulation engine integrates a variety of physical models and algorithms, and can comprehensively consider multiple factors such as fluid mechanics, mechanical dynamics, and thermodynamics. For example, in the process of simulating the switching of the valve body, the force of the pressure, flow rate, and viscosity changes of the fluid on the valve core, as well as the influence of the valve core movement on the flow state of the fluid are considered; at the same time, the mechanical properties and electrical properties of the actuator, as well as their interaction with the mechanical parts of the valve body are considered. By conducting a large number of simulation experiments under different working conditions, such as different medium flow, pressure, temperature conditions, different initial positions and movement speeds of the valve core, the corresponding initial energy consumption curves are generated. These curves intuitively show the changes in energy consumption during the valve body switching process under the current operating conditions, providing basic data for subsequent optimization.

[0046] The optimization module uses an adaptive optimization algorithm to iteratively correct the initial energy consumption curve, adjust the cumulative weight of spool wear and the energy consumption coupling coefficient, and generate a multi-dimensional energy-saving operation strategy. The optimization module optimizes according to the initial energy consumption curve generated by the simulation module using an adaptive optimization algorithm. For example, the improved random forest-long short-term memory hybrid model mentioned in Embodiment 5 is used as the adaptive optimization algorithm. This algorithm encodes the spool displacement deviation and the energy consumption decay rate of the actuator into a multi-objective optimization function and defines a loss function to evaluate the matching degree of energy consumption sensitivity and the mechanical parameter drift cost. High-dimensional feature interaction terms are extracted through gradient boosting decision trees, and dynamic dependencies are captured using temporal memory units to output energy-saving prediction values that integrate energy consumption trends. During the iteration process, the cumulative weight of spool wear and the energy consumption coupling coefficient are continuously adjusted to find the optimal operation strategy. After each iteration, a new simulation is performed according to the new operation strategy to obtain a new energy consumption curve, which is compared with the previous result. If the energy consumption is reduced or other optimization goals are met, the next iteration continues; otherwise, the parameters of the optimization algorithm are adjusted or other optimization strategies are adopted until the optimal multi-dimensional energy-saving operation strategy is found. The finally generated energy-saving operation strategy covers multiple dimensions such as time, fluid, machinery, and energy consumption, and can minimize energy consumption and improve the overall performance and economic benefits of the system while ensuring the normal operation of the valve body.

[0047] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device.

[0048] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An energy-saving intelligent switching valve body operation method, characterized in that: include: Obtain historical operation data of the intelligent switching valve body, and classify the data according to preset dimensions to generate a structured feature set; The preset dimensions include fluid pressure fluctuation curve, valve core wear degree, medium temperature change and actuator energy consumption spectrum; Construct a dynamic feature matrix, and define a multidimensional analysis coordinate system based on the classification dimension of the feature set, wherein the coordinate system includes a time axis, a fluid axis, a mechanical axis, and an energy consumption axis; Determining initial operation constraints according to the valve body life distribution model and the switching mode library, wherein the constraints include leakage probability threshold, switching response time rule and energy consumption sensitivity classification; Perform dynamic switching simulation on the feature set based on the dynamic feature matrix, simulate the switching behavior of the valve body under various working conditions through the multi-modal simulation engine, and generate the initial energy consumption curve; An adaptive optimization algorithm is used to iteratively correct the initial energy consumption curve, adjust the cumulative weight of valve core wear and the energy consumption coupling coefficient, and generate a multi-dimensional energy-saving operation strategy.

2. The energy-saving intelligent switching valve body operation method according to claim 1 is characterized in that: The constructing of the dynamic feature matrix comprises: The time axis is divided into switching phases synchronized with the medium delivery cycle, and each phase is associated with a pressure transient compensation factor; Define the time-varying response function of the valve core displacement based on the fluid axis and integrate the medium temperature hysteresis characteristics; The energy consumption spectrum density distribution of the actuator is embedded in the mechanical axis, and the switching times are dynamically associated with the mechanical efficiency correction factor.

3. The energy-saving intelligent switching valve body operation method according to claim 1, characterized in that: The adaptive optimization algorithm adopts an improved random forest-long short-term memory hybrid model, including: The valve core displacement deviation and actuator energy consumption decay rate are encoded into a multi-objective optimization function, and a loss function is defined to evaluate the energy consumption sensitivity matching and the mechanical parameter drift cost; High-dimensional feature interactions are extracted through the gradient boosting decision tree, and the temporal memory unit is used to capture dynamic dependencies, and the energy-saving prediction value that integrates the energy consumption trend is output.

4. The energy-saving intelligent switching valve body operation method according to claim 2 is characterized in that: The division of the time axis includes: Based on the periodic characteristics of historical operation data, the time axis is divided into dynamic sliding windows, and each window is associated with the start and stop marks of the medium transportation stage; The time decay factor is introduced to dynamically adjust the weight distribution of each stage, and the hidden Markov model is used to predict the switching demand of future time segments.

5. The energy-saving intelligent switching valve body operation method according to claim 2, characterized in that: The definition of the fluid axis includes: The nonlinear regression constraint equation of valve core displacement is defined according to the viscosity change rate of the medium, and the flow velocity distribution function is optimized based on fluid-solid coupling analysis; The fluid pulsation energy spectrum is extracted through the frequency domain integration algorithm, and the response delay parameters of the medium temperature hysteresis characteristics are dynamically corrected.

6. The energy-saving intelligent switching valve body operation method according to claim 1, characterized in that: The definition of the mechanical axis includes: The wavelet packet energy entropy algorithm is used to quantify the high-frequency components of the actuator vibration signal and correlate the vibration energy with the mechanical wear rate in real time; The attenuation gradient of the mechanical efficiency correction coefficient is dynamically adjusted according to the stress distribution model of the sealing component.

7. The energy-saving intelligent switching valve body operation method according to claim 2, characterized in that: The definition of the energy consumption axis includes: Based on the spatiotemporal distribution characteristics of historical energy consumption data, a hierarchical clustering model of energy consumption benchmark curve is established; Abnormal energy consumption events are identified through mutation detection mechanism, and adaptive threshold segmentation algorithm is used to generate dynamic interval division of energy consumption sensitivity.

8. The energy-saving intelligent switching valve body operation method according to claim 4, characterized in that: The division of the dynamic sliding window also includes: The window length is adaptively adjusted according to the pipeline pressure fluctuation amplitude, and the gradient descent method is introduced to optimize the iterative step length of the time attenuation factor; The window boundary is aligned with the valve action timing to generate an energy consumption assessment matrix with joint calibration of the time axis and mechanical axis.

9. The energy-saving intelligent switching valve body operation method according to claim 1, characterized in that: The iterative correction includes: A dual-stream attention network model is constructed, with valve core friction energy, actuator current distortion rate and medium corrosion index as input features; The key energy consumption factors are screened through an adaptive feature selection mechanism, and the cross-axis correlation features are fused using a multi-head attention layer to generate a corrected energy consumption confidence interval.

10. An energy-saving intelligent switching valve body operating system, characterized in that: include: A data processing module is used to obtain the historical operation data of the intelligent switching valve body, and generate a structured feature set according to the preset dimensions of the fluid pressure fluctuation curve, valve core wear degree, medium temperature change and actuator energy consumption spectrum; A matrix construction module is used to define a multi-dimensional analysis coordinate system including a time axis, a fluid axis, a mechanical axis, and an energy consumption axis based on the classification dimensions of the feature set, and to construct a dynamic feature matrix; A condition determination module, used to determine the initial operation constraint conditions including leakage probability threshold, switching response time rule and energy consumption sensitivity classification according to the valve body life distribution model and the switching mode library; A simulation module is used to perform dynamic switching simulation on the feature set based on the dynamic feature matrix, simulate the switching behavior of the valve body under various working conditions through a multi-modal simulation engine, and generate an initial energy consumption curve; The optimization module is used to iteratively correct the initial energy consumption curve using an adaptive optimization algorithm, adjust the cumulative weight of valve core wear and the energy consumption coupling coefficient, and generate a multi-dimensional energy-saving operation strategy.

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