Intelligent control method for stop valve based on digital twinning

Abstract

The invention relates to the technical field of digital twinning, and particularly discloses a digital twinning-based stop valve intelligent control method, which comprises the following steps that a digital twinning model of a pipe network system is decomposed into a plurality of sub-models, and each sub-model corresponds to a stop valve and a corresponding associated pipe network area; monitoring data of a single stop valve are obtained, the monitoring data are transmitted to the corresponding sub-model, and the sub-model updates the model state according to the monitoring data; exchanging monitoring data among the sub-models to form a global data set, and calculating the opening degree adjustment amount of the stop valve based on the sub-models with updated model states and the global data set; and the opening degree adjustment amount is sent to a stop valve actuator, and the actuator drives the stop valve to act. According to the method, the overall hydraulic coupling state of the pipe network is sensed by dividing associated areas and exchanging data; and pressure fluctuation and hydraulic unbalance are effectively inhibited based on cooperative calculation of the valve opening adjustment amount.

Description

Technical Field

[0001] This invention relates to the field of digital twin technology, and more specifically to a smart control method for shut-off valves based on digital twins. Background Technology

[0002] A gate valve, also known as a stop valve or shut-off valve, is a type of valve that uses the up-and-down movement of a valve disc along the centerline of the valve seat to cut off or regulate the flow of media in a pipeline. Gate valves are commonly used and reliable shut-off and regulating components in industrial pipelines, chemical plants, heating systems, shipbuilding, and metallurgy.

[0003] In large-scale pipeline systems, multiple shut-off valves often employ independent control modes, with each valve adjusting its opening based on local operating conditions, lacking a holistic consideration of the overall hydraulic characteristics of the pipeline network. Under this control method, when a shut-off valve in one area actuates, the resulting flow change is transmitted to other areas through the pipeline network's fluid coupling effect. Since other valves cannot perceive the global hydraulic state in real time, they struggle to make synchronized adjustments, leading to uneven pressure distribution, sudden increases or decreases in local pressure, and other hydraulic imbalances. This not only affects the stability of the medium supply to end users (e.g., fluctuating water pressure in the water supply system) but may also cause pipeline vibration and valve sealing surface damage due to excessive pressure fluctuations, increasing system operational risks and maintenance costs. Summary of the Invention

[0004] The purpose of this invention is to provide a smart control method for shut-off valves based on digital twins, thereby solving the aforementioned technical problems.

[0005] The objective of this invention can be achieved through the following technical solutions: The intelligent control method for shut-off valves based on digital twins includes the following steps: The digital twin model of the pipeline network system is decomposed into several sub-models, each sub-model corresponding to a shut-off valve and the corresponding associated pipeline area; Acquire monitoring data for a single shut-off valve, including the current shut-off valve opening value and measurement data of the associated pipeline area. Transmit the monitoring data to the corresponding sub-model, and the sub-model updates the model state based on the monitoring data. The sub-models exchange monitoring data to form a global dataset. The opening adjustment of the shut-off valve is calculated based on the sub-models after updating their model states and the global dataset. The opening adjustment amount is sent to the shut-off valve actuator, which drives the shut-off valve to operate.

[0006] As a further aspect of the present invention: obtaining the associated pipeline network area includes: Obtain the node location table, pipeline connection table, gate valve installation location table, pressure measurement node location table, and flow measurement node location table of the pipeline network system; Map the node locations, shut-off valve installation locations, pressure measurement node locations, and flow measurement node locations to the vertices of the graph, and the pipes to the edges of the graph. Assign directions to the edges according to the fluid design flow direction to obtain a directed topology graph. Starting from the vertex where the stop valve a is located, perform a breadth-first traversal along the directed edges until the termination condition is met. Record all vertices visited during the traversal to obtain the associated pipeline area of ​​the stop valve a.

[0007] As a further aspect of the present invention, the termination conditions include: Condition 1: Encounter the vertex corresponding to another shut-off valve; Condition 2: Encounter the vertex corresponding to the pressure monitoring node and / or flow monitoring node; Condition 3: Reach the preset maximum traversal depth.

[0008] As a further aspect of the present invention: obtaining the adjacent pipeline area includes: Obtain the set of all vertices in the associated pipeline region of the shut-off valve, and obtain the set Da corresponding to shut-off valve a and the set Db corresponding to shut-off valve b; If the intersection of set Da and set Db is not empty, then the associated pipeline region of stop valve a is the adjacent pipeline region of stop valve b, and the associated pipeline region of stop valve b is the adjacent pipeline region of stop valve a.

[0009] As a further aspect of the present invention: the sub-model updates the model state based on monitoring data, including: The measurement data includes pressure data and flow data, where the pressure data is the pressure value of the pressure monitoring node and the flow data is the flow value of the flow monitoring node, respectively. For the sub-model corresponding to shut-off valve a, the opening value of shut-off valve a in the monitoring data of shut-off valve a is used to cover the original opening value of shut-off valve a. The pressure value in the monitoring data of shut-off valve a is used to overwrite the original pressure value of the corresponding pressure monitoring node; The flow rate value from the monitoring data of shut-off valve a is used to overwrite the original flow rate value of the corresponding flow monitoring node.

[0010] As a further aspect of the present invention: obtaining the global dataset includes: If the intersection of set Da and set Db is not empty, then stop valve a and stop valve b are adjacent valves. The vertices in the intersection of set Da and set Db are taken as common vertices. If the common vertex is a pressure monitoring node or a flow monitoring node, then the common vertex is recorded as the target vertex. The measured value of the target vertex corresponding to the shut-off valve b is sent to the shut-off valve a, and the opening value of the shut-off valve b is sent to the shut-off valve a. The measured values ​​include pressure and flow rate. After all adjacent valves of shut-off valve a have undergone the above steps, the global dataset of shut-off valve a is obtained.

[0011] As a further aspect of the present invention: after the actuator drives the shut-off valve to operate, it further includes: Calculate the target opening degree M1 = M0 + ΔM, where M0 represents the opening degree of the shut-off valve before it is activated, and ΔM represents the opening degree adjustment amount. After the action is completed, the opening value M2 of the shut-off valve is obtained. If |M1-M2|>Mys, an error message is sent to report an error. Mys represents the preset error threshold.

[0012] The beneficial effects of this invention compared to the prior art are as follows: 1) This invention decouples the pipeline network system into associated sub-regions covering the dynamic propagation path of fluid by dividing the associated regions of a directed topology graph. This ensures that the control range of each shut-off valve includes key monitoring nodes (such as pressure / flow nodes) that its operation may affect. The sub-models cover local states with real-time monitoring data, accurately capturing the current hydraulic conditions. More importantly, by exchanging common monitoring point data and opening values ​​between adjacent valves, each sub-model can synchronously acquire real-time pressure, flow rate, and adjacent valve operation status in the coupled region. This allows the shut-off valves to no longer rely on isolated local data, but rather to dynamically perceive the hydraulic coupling effect of the pipeline network (such as the transmission path of flow changes and pressure fluctuation trends) based on a global dataset, providing a complete operating condition basis for coordinated control. 2) Based on global perception, the valve opening adjustment is calculated using the updated sub-model and global dataset, enabling the shut-off valve to not only respond to local conditions but also predict and counteract coupling interference from adjacent areas. When the valve A opening adjustment causes a change in flow rate, its associated sub-model predicts the transmission effect of this change on the valve B region through the common node pressure data sent by the adjacent valve B. Valve B then synchronously calculates its own compensation adjustment based on the received valve A opening and common point data. This distributed parallel collaborative mechanism enables multiple valves to form a dynamic balance system: local actions trigger synchronous compensation of associated valves through real-time data exchange, effectively suppressing uneven pressure distribution and sudden rises / falls. Attached Figure Description

[0013] The invention will now be further described with reference to the accompanying drawings.

[0014] Figure 1 This is a flowchart illustrating the intelligent control method for shut-off valves based on digital twins according to the present invention. Detailed Implementation

[0015] 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.

[0016] Please see Figure 1 As shown, this invention is an intelligent control method for a shut-off valve based on digital twins, comprising the following steps: Step 1: Decompose the digital twin model of the pipeline network system into several sub-models; In a preferred embodiment of the present invention, the digital twin of the pipeline system is divided into several sub-models according to each valve, because each shut-off valve can only directly affect the hydraulic state within a certain range around it. To determine this range, basic data such as node locations, pipe connections, valve installation locations, pressure measurement points, and flow measurement points are collected first. Here, node locations refer to any location that can generate a hydraulic boundary, including but not limited to the connection point of two pipe sections, the bifurcation point of a tee or cross, etc. The hydraulic boundary refers to the boundary condition node that the fluid inside the network can perceive, that is, those locations that separate the pipe network from the external environment or other pipe network systems. All physical coordinates (node ​​locations, shut-off valve installation locations, pressure measurement node locations, and flow measurement node locations) are abstracted into vertices of the graph, and each pipe section is abstracted into an edge. The edges are assigned a direction according to the design flow direction shown in the drawing. The purpose is to ensure that the traversal direction of the graph is consistent with the actual flow direction of the water. For example, the direction of the pipe section from the pump station to the water branch always points to the user side. In addition to the downstream breadth-first traversal, a symmetrical breadth-first traversal is performed starting from valve a along the reverse directed edge until the upstream pump outlet or known boundary conditions are encountered; this reverse path is used to capture the influence range of the pressure wave propagating back to the source side. Starting from the vertex where the shut-off valve a (i.e., any shut-off valve) is located, perform a breadth-first traversal and expand downstream layer by layer. Expanding downstream means that in the directed topology graph where each pipe has been assigned a direction according to the design flow direction, starting from the vertex where the shut-off valve a is located, search layer by layer along only the out-degree edge that is consistent with the actual flow direction of the medium, and include all subsequent vertices that can be reached "downstream" from this vertex into the traversal queue in order of search depth. If another valve vertex is encountered during the expansion process, it means that further progress has entered the sphere of influence of another controlled object, so the process will stop here; if a vertex with a pressure gauge or flow meter is encountered, it means that the point can directly provide real-time boundary conditions and be used as a hydraulic interface for the sub-model to interact with the outside world, so the traversal can also stop here; if the number of traversed layers reaches the preset maximum depth, it means that there are no monitoring points in the surrounding area and the topology is too deep. Continuing to expand will cause the computational complexity and uncertainty to increase sharply, so the process will also stop here. When encountering another valve apex and there is a long pipe section between that valve and the current valve where no monitoring points are installed, the entire pipe section is included in the overlapping buffer zone of the two valves, and virtual monitoring nodes are automatically added at the beginning and end of the buffer zone for subsequent parameter identification and data sharing, avoiding the occurrence of "black box" sections. During the entire traversal, each vertex visited is recorded. For example, if valve a starts from vertex N20 and visits N23 and N25 in sequence until it stops at N26 where flow meter F1 is located, then these four vertices together form the associated pipe network area of ​​valve a. The principle of forming this area is based on the hydraulic logic that "the pressure gradient generated by the valve can only propagate along the flow direction until it is cut off by another valve or absorbed by known boundary conditions". The advantages of this sub-model partitioning process are that it ensures that the digital twin of each valve fully covers its actual influence range without excessively encroaching on the control domain of other valves, thus avoiding computational redundancy caused by model overlap; directed traversal ensures that the hydraulic boundary of the sub-model is consistent with the actual flow direction, thereby improving computational reliability; monitoring points or adjacent valves serve as natural boundaries, providing real-time data interfaces for subsequent model correction and facilitating the rapid exchange and sharing of key hydraulic information between different sub-models; and limiting the maximum depth takes into account the need to maintain algorithm convergence and efficiency in sparsely monitored areas. Step 2: Acquire monitoring data for a single shut-off valve, transmit the monitoring data to the corresponding sub-model, and update the model state based on the monitoring data; In another preferred embodiment of the present invention, the readings of the position sensor on the valve body and the readings of the pressure and flow sensors installed around it are packaged and sent out. For example, the current position sensor of valve a returns to a mechanical angle of 45 degrees, and is converted into a percentage opening degree accordingly. The adjacent pressure gauges provide instantaneous pressure, and the flow meter simultaneously returns instantaneous volumetric flow rate. These three types of readings are aligned according to timestamps and then pushed together to the digital twin sub-model corresponding to valve a. The sub-model directly replaces the valve element's recorded opening attribute with the latest 45 degrees because in hydraulic calculations, the valve's local resistance coefficient and opening degree have a one-to-one mapping relationship. Only by using the real-time opening degree can the valve's damping effect on the flow be accurately reflected. The sub-model finds the node in its own topology that corresponds to the field pressure gauge and overwrites the previously saved pressure with the latest measurement value. Similarly, it overwrites the flow attribute of the edge or node corresponding to the flow meter with the new reading. The reason is that the location of these measuring points is the interface of the hydraulic influence range of the valve, and the latest reading can present the real boundary conditions to the greatest extent. After the coverage operation is completed, all subsequent calculations of the sub-model start from the covered state, ensuring that the predicted pressure gradient and flow distribution are close to the real-time operating conditions and are not constrained by the previous lagging data. A high-speed path was established for each valve to synchronously map the real operating conditions to the virtual model, avoiding the accumulation of errors caused by the long-term dependence of the digital twin on the predicted value; by continuously covering the state of key nodes and edges, the valve sub-model always maintains the hydraulic boundary synchronized with the field, and can accurately estimate the chain effect of its own action on the entire pipeline network when calculating the opening adjustment, thus providing a reliable basis for global collaborative control. Step 3: Exchange monitoring data among sub-models to form a global dataset; Based on the previously determined vertex sets Da and Db, by traversing the elements of the sets and finding that they have an intersection, it can be determined that the associated regions of valve a and valve b overlap in physical space. This overlap means that when the two valves are adjusted, they will jointly affect the hydraulic state of a section of the pipeline network. Therefore, the two are identified as adjacent valves. Map each vertex in the intersection back to the field device type. When a vertex is found to be equipped with a pressure gauge or flow meter, it is marked as the target vertex because these monitoring points can reflect the pressure or flow at the cross-regional interface in real time. From a hydraulic mechanism perspective, they are the observation points that best reflect the coupling strength. The sub-model of valve b encapsulates its latest measurement value obtained at the target vertex along with its current opening degree into a data packet and delivers it to valve a. The principle behind this is that if valve a wants to predict the impact of its adjustment on the target vertex, it must first know the true state of the target vertex at this moment and the contribution of valve b to that state. The opening degree of valve b is a direct quantification of this contribution. After valve a repeatedly receives actions from all adjacent valves, it aligns these cross-regional measurements with local data by timestamp and organizes them into a unified structure for storage. This aggregated set is the global dataset on the valve a side, which contains real-time hydraulic readings of multiple interconnected boundaries and records the opening degree of adjacent valves, thus laying a complete input foundation for cross-regional collaboration in subsequent calculations. For valve pairs that are not directly adjacent but are coupled in series through one or more intermediate valves, a recursive forwarding strategy is adopted: when valve b sends a data packet to valve a, valve a is marked as a propagation source and forwarded to its upstream and downstream adjacent valves until the attenuation coefficient is lower than the threshold, in order to capture the chain effect. All data packets carry millisecond-level sampling timestamps. The receiving side performs linear interpolation or delay alignment based on the difference between the local clock and the sending clock to ensure that data from different sources entering the global dataset corresponds to the same virtual time, thus eliminating timing errors. This allows each valve sub-model to maintain autonomous decision-making while incorporating key monitoring information from neighboring areas and information on neighboring valve actions in real time, avoiding judgment bias caused by isolated calculations. Its advantage lies in the ability to quickly capture the coupling changes of flow and pressure in adjacent areas by explicitly sharing target vertex data, enabling each sub-model to calculate the opening adjustment based on a hydraulic boundary that is closer to the actual situation of the entire network. This fundamentally supports the multi-valve collaborative algorithm to accurately assess the impact of local actions on the global distribution. Step 4: Calculate the valve opening adjustment based on the updated sub-model and global dataset; In a specific embodiment, after obtaining the global dataset after exchanging the valve a sub-model with the adjacent valves, the real-time pressure and flow values ​​in the dataset are compared with the target interval given by the design or scheduling node by node and edge by edge, and the deviation vector of each monitoring location is calculated. The updated sub-model hydraulic equations are used—where the nodal equations ensure flow conservation at each node, the pipe segment energy equations describe the flow-pressure drop relationship using Darcy-Weisbach or Hayzen-Williams formulas, and the valves are equivalent to variable local resistances: a small disturbance analysis is performed at the current operating point, i.e., a small positive and negative increment is made to the valve a opening, and the corresponding pressure and flow changes are quickly solved, thereby obtaining the sensitivity coefficients of "opening-pressure" and "opening-flow". These sensitivity coefficients are organized into an incidence matrix according to common vertices and internal key nodes. The left side of the matrix is ​​the valve opening increment ΔM to be determined, and the right side is the pressure difference and flow difference deviation that need to be eliminated or reduced. By solving this set of linear equations, a theoretically optimal ΔM can be obtained in a single step. If the result exceeds the mechanical allowable range of the valve, it is truncated according to the extreme value. Substitute this ΔM back into the complete nonlinear model for a prediction simulation. If the prediction shows that the deviation of all target monitoring points has been compressed to the allowable range, then accept this ΔM. If there is still an error, recalculate the sensitivity based on the latest state and iterate until convergence. For example, when the sub-model finds that the pressure at the common node N26 is higher than the upper limit and the sensitivity matrix shows that "opening valve a will cause the pressure drop at this node", the algorithm will output a negative ΔM, that is, reduce the opening degree; otherwise, it will output a positive ΔM. The final ΔM, along with the timestamp, is written into the control command queue, waiting for the downstream actuator to drive the valve to move. Before solving the correlation matrix, a distributed collaborative algorithm is run among the sub-models to minimize the sum of the weighted pressure deviation and the terminal traffic deviation of the entire network as a unified goal. Consistency constraints are added to ΔM of each sub-model to avoid local optimum conflicts. Based on the pipeline wave velocity and valve opening execution rate, the time delay of pressure wave and opening change reaching adjacent nodes is predicted. This time delay is added to the sensitivity matrix as a phase compensation term to make the calculation results have feedforward characteristics and suppress wave peak superposition. While maintaining a detailed characterization of its own hydraulic behavior, the sub-model incorporates real-time state variables from neighboring zones into a unified analysis framework. Through a sensitivity matrix, it establishes a direct mathematical link between "changing itself" and "affecting global deviations," ensuring that each adjustment step addresses both local monitoring point deviations and cross-zone coupling, avoiding blindly opening or closing the valve and causing new pressure fluctuations. The iterative verification process ensures that the calculated opening degree is truly effective in the nonlinear network, while threshold truncation ensures that the command conforms to the equipment limits. The overall process makes valve regulation a predictable, verifiable, and traceable closed-loop decision, providing solid numerical support and operational safety barriers for overall network pressure balance and flow coordination. Step 5: Send the opening adjustment amount to the shut-off valve actuator, and the actuator drives the shut-off valve to operate; In another specific embodiment, after obtaining the opening adjustment amount of valve a, the increment, together with the valve's unique identifier, is packaged into a control message and sent to the valve's local control unit via the fieldbus; the local unit reads the real-time opening given by the valve position feedback device, adds the received increment to the current value to obtain the new target opening, for example, if the current stroke is 40% and the message requests an increase of 8%, then the target stroke is 48%; The local unit drives the motor or adjusts the air source pressure according to the target stroke, and drives the valve stem to move smoothly in the specified direction. During the movement, the position feedback device continuously transmits the real-time displacement. The local unit continuously compares it with the target displacement. If the difference enters the allowable range, it stops driving and applies electrical or pneumatic locking. At the same time, it reports the actual final opening degree, action time and whether the torque is abnormal to the central controller. The central controller refreshes the database and records the log accordingly. It is important to note that after the valve action command is executed, the valve a's recorded opening degree M0 is read from the real-time database before the action, and the previously calculated adjustment amount ΔM is added directly to obtain the theoretically achievable target position M1. At the same time, after the drive motor or cylinder stops, the local control unit collects the final displacement from the position feedback device, converts the displacement into a percentage opening degree, obtains the actual result M2, and uploads it to the central controller with a timestamp. After receiving the report, the central controller compares M2 with the previously saved M1 one by one. If the two are completely consistent or fall within the preset error band, the action is considered successful and a qualified record is written. If it exceeds the error band, an alarm message is generated immediately and pushed to the monitoring interface. At the same time, the valve identification, target opening degree, actual opening degree, and deviation information are recorded in the event log for maintenance personnel to trace. The alarm message also triggers a diagnostic script to guide the field to check for potential fault causes such as actuator jamming, abnormal power or air supply, and inaccurate feedback device, until the alarm is closed by manual or automatic correction.

[0017] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the present invention should still fall within the scope of the present invention.

Claims

1. A method for intelligent control of a shut-off valve based on digital twins, characterized in that, Includes the following steps: The digital twin model of the pipeline network system is decomposed into several sub-models, each sub-model corresponding to a shut-off valve and the corresponding associated pipeline area; Acquire monitoring data for a single shut-off valve, including the current shut-off valve opening value and measurement data of the associated pipeline area. Transmit the monitoring data to the corresponding sub-model, and the sub-model updates the model state based on the monitoring data. The sub-models exchange monitoring data to form a global dataset. The opening adjustment of the shut-off valve is calculated based on the sub-models after updating their model states and the global dataset. The opening adjustment amount is sent to the shut-off valve actuator, which drives the shut-off valve to operate.

2. The intelligent control method for a shut-off valve based on digital twins according to claim 1, characterized in that, The associated pipeline network areas include: Obtain the node location table, pipeline connection table, gate valve installation location table, pressure measurement node location table, and flow measurement node location table of the pipeline network system; Map the node locations, shut-off valve installation locations, pressure measurement node locations, and flow measurement node locations to the vertices of the graph, and the pipes to the edges of the graph. Assign directions to the edges according to the fluid design flow direction to obtain a directed topology graph. Starting from the vertex where the stop valve a is located, perform a breadth-first traversal along the directed edges until the termination condition is met. Record all vertices visited during the traversal to obtain the associated pipeline area of ​​the stop valve a.

3. The intelligent control method for a shut-off valve based on digital twins according to claim 2, characterized in that, Termination conditions include: Condition 1: Encounter the vertex corresponding to another shut-off valve; Condition 2: Encounter the vertex corresponding to the pressure monitoring node and / or flow monitoring node; Condition 3: Reach the preset maximum traversal depth.

4. The intelligent control method for a shut-off valve based on digital twins according to claim 2, characterized in that, Obtaining adjacent pipeline network areas includes: Obtain the set of all vertices in the associated pipeline region of the shut-off valve, and obtain the set Da corresponding to shut-off valve a and the set Db corresponding to shut-off valve b; If the intersection of set Da and set Db is not empty, then the associated pipeline region of stop valve a is the adjacent pipeline region of stop valve b, and the associated pipeline region of stop valve b is the adjacent pipeline region of stop valve a.

5. The intelligent control method for a shut-off valve based on digital twins according to claim 4, characterized in that, The sub-model updates the model status based on monitoring data, including: The measurement data includes pressure data and flow data, where the pressure data is the pressure value of the pressure monitoring node and the flow data is the flow value of the flow monitoring node, respectively. For the sub-model corresponding to shut-off valve a, the opening value of shut-off valve a in the monitoring data of shut-off valve a is used to cover the original opening value of shut-off valve a. The pressure value in the monitoring data of shut-off valve a is used to overwrite the original pressure value of the corresponding pressure monitoring node; The flow rate value from the monitoring data of shut-off valve a is used to overwrite the original flow rate value of the corresponding flow monitoring node.

6. The intelligent control method for a shut-off valve based on digital twins according to claim 5, characterized in that, Retrieving the global dataset includes: If the intersection of set Da and set Db is not empty, then stop valve a and stop valve b are adjacent valves. The vertices in the intersection of set Da and set Db are taken as common vertices. If the common vertex is a pressure monitoring node or a flow monitoring node, then the common vertex is recorded as the target vertex. The measured value of the target vertex corresponding to the shut-off valve b is sent to the shut-off valve a, and the opening value of the shut-off valve b is sent to the shut-off valve a. The measured values ​​include pressure and flow rate. After all adjacent valves of shut-off valve a have undergone the above steps, the global dataset of shut-off valve a is obtained.

7. The intelligent control method for a shut-off valve based on digital twins according to claim 1, characterized in that, After the actuator drives the shut-off valve to operate, it also includes: Calculate the target opening degree M1 = M0 + ΔM, where M0 represents the opening degree of the shut-off valve before it is activated, and ΔM represents the opening degree adjustment amount. After the action is completed, the opening value M2 of the shut-off valve is obtained. If |M1-M2|>Mys, an error message is sent to report an error. Mys represents the preset error threshold.

Images