Multi-objective collaborative control optimization system for stop valve based on industrial data processing

Abstract

The present application relates to the technical fields of industrial fluid control and valve automation, in particular to a stop valve multi-target collaborative control optimization system based on industrial data processing, comprising a multi-dimensional data acquisition step: a multi-dimensional data acquisition center calls real-time operation parameters of the stop valve; a flow resistance characteristic coupling analysis step: a fluid state observation unit calculates the current flow coefficient and the transient pressure difference value based on the parameters; a risk discrimination and decision step: a working condition risk assessment unit quantifies the water hammer and cavitation indexes, obtains a comprehensive risk evaluation value by weighting, and outputs a smooth control or ultra-fast response signal accordingly; a trajectory planning step: a trajectory planning unit respectively generates a nonlinear damping velocity curve or a linear step velocity curve according to the signal type; a flexible execution step: a flexible execution unit drives the stop valve to act, and monitors the closing contact state to realize the switching of the velocity and torque control modes; the present application solves the problem that the traditional control ignores the fluid dynamics risk, and prolongs the service life of the equipment.

Description

Technical Field

[0001] This invention relates to the field of industrial fluid control and valve automation technology, specifically to a multi-objective collaborative control optimization system for shut-off valves based on industrial data processing. Background Technology

[0002] In industrial fluid transport and control applications, gate valves rely on precise action to ensure the safety and regulation efficiency of the pipeline system. The control system usually needs to combine the pipeline operating parameters and valve status to adjust the valve core displacement in real time in order to cut off or regulate the flow of the medium. For the opening and closing control of shut-off valves, existing solutions generally adopt a single speed control logic or a fixed open-loop control strategy. That is, the actuator is driven to act according to a preset speed curve or a simple PID command, directly pursuing the speed of valve action to meet the needs of emergency shut-off and real-time feedback. Although this solution is feasible under low pressure or non-sensitive conditions, it focuses too much on the speed of mechanical action and ignores the real-time fluid dynamic risks inside the pipeline. It also lacks a coupling analysis mechanism between mechanical displacement and fluid response. When encountering complex conditions such as high pressure difference and long-distance pipelines, simple linear inference can easily lead to the failure of the control strategy. Furthermore, traditional control methods struggle to resolve the conflict between speed and safety: simply pursuing extremely fast closure can easily induce severe pressure fluctuations, resulting in water hammer and damaging pipeline infrastructure; while adopting a conservative low-speed operation for safety can lead to the sealing surface being in a state of small opening and high flow velocity for extended periods, causing severe cavitation damage and erosion wear. Moreover, the lack of flexible monitoring of the closed contact state often results in excessive torque damage or poor sealing of the valve at the end of its stroke. Therefore, how to establish a closed-loop control architecture with fluid dynamic risk perception capabilities, effectively balancing water hammer and cavitation risks while improving the adaptability and flexible execution capability of valve actions, has become an urgent technical problem to be solved. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides a multi-objective collaborative control optimization system for shut-off valves based on industrial data processing. Specifically, the technical solution of this invention includes: The multi-objective collaborative control optimization system for shut-off valves based on industrial data processing includes: a multi-dimensional data acquisition center, a fluid state observation unit, an operating condition risk assessment unit, a trajectory planning unit, and a flexible execution unit. The multidimensional data acquisition center is used to retrieve the real-time operating parameters of the shut-off valve and send the real-time operating parameters to the fluid state observation unit for flow resistance characteristic coupling analysis to obtain the current flow coefficient and transient pressure difference value. The current flow coefficient and transient pressure difference value are then sent to the working condition risk assessment unit for fluid dynamic risk discrimination processing to obtain the water hammer risk index and cavitation damage index. The operating condition risk assessment unit is used to perform weighted quantitative analysis of the water hammer risk index and cavitation damage index to obtain a comprehensive risk assessment value. This comprehensive risk assessment value is then processed to obtain a stable control signal or a rapid response signal. When a stable control signal is generated, the trajectory planning unit is used to perform multi-objective speed optimization analysis on the stroke of the shut-off valve, generating a nonlinear damped speed curve. When a rapid response signal is generated... The trajectory planning unit is used to perform time-optimal analysis on the stroke of the shut-off valve and generate a linear step speed curve; the flexible actuator is used to drive the shut-off valve to act according to the nonlinear damped speed curve or the linear step speed curve, and to monitor the closing contact state of the shut-off valve to realize the switching between speed control mode and torque control mode.

[0004] Preferably, the flow resistance characteristic coupling analysis process is as follows: obtain the preset inherent flow characteristic data of the shut-off valve, which includes the standard flow coefficient value corresponding to different valve core displacements; obtain the real-time valve core displacement data collected by the multi-dimensional data acquisition center; and perform interpolation mapping processing on the preset inherent flow characteristic data based on the real-time valve core displacement data to obtain the current flow coefficient. At the same time, the pressure values ​​before and after the valve are acquired from the multi-dimensional data acquisition center, and the value obtained by subtracting the pressure value after the valve from the pressure value before the valve is set as the transient pressure difference value.

[0005] Preferably, the fluid dynamics risk assessment process is as follows: obtain the preset pipe length parameters and fluid wave velocity parameters of the pipeline network where the shut-off valve is located, perform time-dependent differential operation on the transient pressure difference value to obtain the pressure difference change gradient, calculate the product of the pressure difference change gradient, the preset pipe length parameters and the fluid wave velocity parameters, and normalize the product based on a preset benchmark scaling ratio, and set the processed value as the water hammer risk index. Simultaneously, the preset saturated vapor pressure of the fluid is obtained, the difference between the downstream pressure and the preset saturated vapor pressure is calculated, and the difference is set as the pressure margin value. The quotient of the transient pressure difference value divided by the pressure margin value is calculated, and the quotient is set as the cavitation damage index. The lower limit of the pressure margin value is configured as a preset non-zero minimum value to avoid division by zero.

[0006] Preferably, the weighted quantitative analysis and discrimination process is as follows: obtain the preset water hammer weight coefficient and the preset cavitation weight coefficient, multiply the water hammer risk index by the preset water hammer weight coefficient to obtain the water hammer component value, multiply the cavitation damage index by the preset cavitation weight coefficient to obtain the cavitation component value, and add the water hammer component value and the cavitation component value to obtain the comprehensive risk assessment value. The comprehensive risk assessment value is compared and analyzed with the preset safety threshold. If the comprehensive risk assessment value is greater than or equal to the preset safety threshold, a stable control signal is generated; if the comprehensive risk assessment value is less than the preset safety threshold, an ultra-fast response signal is generated.

[0007] Preferably, the multi-objective speed optimization analysis process is as follows: obtain the full stroke distance of the shut-off valve, divide the full stroke distance into an acceleration segment, a constant speed segment, and a buffer segment; construct a multi-objective optimization model that includes the action time objective function, the pressure overshoot objective function, and the erosion loss objective function; Based on the steady control signal, weight coefficients are set in the multi-objective optimization model so that the weight coefficients of the pressure overshoot objective function and the erosion loss objective function are greater than the weight coefficients of the action time objective function. The corresponding velocity value in the buffer section is calculated through iterative calculation. The curves generated by fitting the corresponding velocity values ​​of the acceleration section, the constant speed section and the calculated buffer section are set as nonlinear damped velocity curves.

[0008] Preferably, the process of obtaining the erosion loss objective function is as follows: obtain the preset flow velocity distribution data of the shut-off valve at different opening degrees, perform interpolation mapping on the preset flow velocity distribution data based on the current flow coefficient, determine the local flow velocity value of the sealing surface, calculate the product of the cube of the local flow velocity value and the preset density value of the fluid medium, set the obtained product value as the erosion power density, and perform integration processing on the erosion power density over the action time to obtain the erosion loss objective function.

[0009] Preferably, the control mode switching process of the flexible actuator is as follows: the remaining stroke value of the shut-off valve is obtained, and the remaining stroke value is compared with the preset sealing contact threshold; if the remaining stroke value is greater than the preset sealing contact threshold, the speed control mode is maintained, and the output speed of the actuator is adjusted according to the nonlinear damping speed curve or the linear step speed curve. If the remaining stroke value is less than or equal to the preset sealing contact threshold, the system switches to torque control mode, obtains the preset sealing pressure target value, converts the sealing pressure target value into a target torque value based on the valve stem structure parameters of the shut-off valve, and generates a constant output torque command based on the target torque value until the collected actuator feedback torque reaches the preset shut-off torque threshold.

[0010] Preferably, it also includes an adaptive correction unit; the adaptive correction unit is configured to: after the shut-off valve has finished operating, obtain the actual pressure peak value and actual operating time during the current operation, and calculate the difference between the actual pressure peak value and the preset ideal pressure limit value to obtain the pressure deviation coefficient; The preset water hammer weight coefficient in the working condition risk assessment unit is corrected and updated based on the pressure deviation coefficient: if the pressure deviation coefficient is positive, the preset water hammer weight coefficient is increased; if the pressure deviation coefficient is negative, the current preset water hammer weight coefficient is maintained or the preset water hammer weight coefficient is decreased according to the preset step size.

[0011] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention constructs a closed-loop control architecture from multi-dimensional physical perception to fluid state observation and then to flexible execution, effectively solving the problem of the separation between mechanical action and fluid response in traditional control; through flow resistance characteristic coupling analysis and real-time differential pressure calculation, the system uses digital twin technology to accurately map the mechanical position of the valve to fluid dynamic parameters, providing a high-fidelity data foundation for subsequent risk assessment, avoiding the error caused by linear inference based solely on the opening degree, and ensuring the physical authenticity of the input source of the control model; 2. This invention introduces a multi-objective collaborative control mechanism based on fluid dynamics risk assessment, breaking the contradiction between speed and safety in traditional valve control. The system can perform weighted quantitative analysis of water hammer risk index and cavitation damage index, and dynamically generate nonlinear damped velocity curves or linear step velocity curves based on the comprehensive risk assessment value. This mechanism can achieve rapid shut-off within physical limits under emergency conditions, and can effectively suppress pressure overshoot and reduce cavitation erosion under high-risk conditions through multi-objective velocity optimization, significantly extending the service life of valves and pipeline equipment. 3. This invention establishes a microscopic loss quantification model based on erosion power density, realizing the active suppression of physical wear on the sealing surface; by integrating the physical properties of fluid medium density with the cube of local flow velocity, the system transforms the abstract material wear mechanism into a calculable control optimization objective, guiding the valve to quickly pass through the high-velocity, high-loss opening region; this strategy significantly reduces the erosion damage of the sealing surface by the fluid without changing the existing hardware structure and configuration, thereby maintaining the long-term sealing performance of the valve. 4. This invention adopts a dual-mode flexible drive strategy that seamlessly switches between speed control and torque control, perfectly resolving the contradiction between sealing performance and mechanical protection at the valve's closing end. By monitoring the remaining stroke in real time, the system automatically switches from speed mode to torque mode when approaching closure and generates a constant output torque command based on the target value of the sealing specific pressure. This method can ensure sufficient clamping force to prevent media leakage, and also prevent valve stem bending and sealing surface crushing caused by overshoot or excessive torque, thus achieving a soft landing closure of the valve. Attached Figure Description

[0012] The present invention will be further explained below with reference to the accompanying drawings and embodiments: Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0014] Example 1: Please see Figure 1 The multi-objective collaborative control optimization system for shut-off valves based on industrial data processing includes a multi-dimensional data acquisition center, a fluid state observation unit, an operating condition risk assessment unit, a trajectory planning unit, and a flexible execution unit. The multidimensional data acquisition center is used to retrieve the real-time operating parameters of the shut-off valve and send the real-time operating parameters to the fluid state observation unit for flow resistance characteristic coupling analysis to obtain the current flow coefficient and transient pressure difference value. The current flow coefficient and transient pressure difference value are then sent to the working condition risk assessment unit for fluid dynamic risk discrimination processing to obtain the water hammer risk index and cavitation damage index. The working condition risk assessment unit is used to perform weighted quantitative analysis of the water hammer risk index and cavitation damage index to obtain a comprehensive risk assessment value. The comprehensive risk assessment value is then processed to obtain a stable control signal or a rapid response signal. When a stable control signal is generated, the trajectory planning unit is used to perform multi-objective speed optimization analysis on the stroke of the shut-off valve to generate a nonlinear damped speed curve. When a rapid response signal is generated, the trajectory planning unit is used to perform time-optimal analysis on the stroke of the shut-off valve to generate a linear step speed curve. The flexible actuator is used to drive the shut-off valve to operate based on the nonlinear damping speed curve or the linear step speed curve, and to monitor the closing contact state of the shut-off valve, thereby switching between speed control mode and torque control mode.

[0015] This embodiment details the physical architecture and logical topology of the system, aiming to address the problem of traditional shut-off valve control that prioritizes switching speed while neglecting fluid dynamic risks. The multi-dimensional data acquisition center, acting as the system's sensing endpoint, uses a high-frequency sensor array to retrieve real-time operating parameters of the shut-off valve and its associated pipeline network. These parameters include, but are not limited to, valve stem displacement, pipeline pressure, and fluid temperature, providing the original physical basis for subsequent analysis. The fluid state observation unit receives these parameters and performs flow resistance characteristic coupling analysis. This process utilizes digital twin technology to map mechanical displacement into fluid dynamic parameters, calculating the key bridge connecting mechanical action and fluid response: the current flow coefficient and the transient pressure difference. As the core of decision-making, the working condition risk assessment unit performs fluid dynamic risk discrimination processing on the above fluid parameters, quantifies the water hammer risk index and cavitation damage index, and further performs weighted quantitative analysis to generate a comprehensive risk assessment value. In response to the comparison result of this assessment value and the safety threshold, the system intelligently outputs a strategy signal: if the risk is high, a stable control signal is generated; if the risk is controllable, an ultra-fast response signal is generated. The trajectory planning unit generates a corresponding velocity curve according to the signal type. For the stable control signal, it performs multi-objective velocity optimization analysis to generate a nonlinear damped velocity curve; for the ultra-fast response signal, it performs time-optimal analysis to generate a linear step velocity curve. The linear step velocity curve here is specifically defined as a trapezoidal velocity waveform. Its rising and falling edges are not theoretically vertical jumps, but rather linear ramps limited by the maximum physical acceleration of the actuator. That is, it rises rapidly to the target velocity with a constant slope under the physically permissible limit acceleration, maintains a short period of uniform velocity, and then falls with a limit deceleration, thus approximating the time-optimal effect of the step response at the physical level. The flexible actuator drives the shut-off valve to act according to the generated curve and has dual-mode control capability, monitoring the closing contact state in real time to achieve seamless switching from speed control mode to torque control mode.

[0016] Example 2: The coupled analysis process of flow resistance characteristics is as follows: The preset inherent flow characteristic data of the shut-off valve is obtained. The preset inherent flow characteristic data includes the standard flow coefficient value corresponding to different valve core displacements. The real-time valve core displacement data collected by the multi-dimensional data acquisition center is obtained. Based on the real-time valve core displacement data, the preset inherent flow characteristic data is interpolated and mapped to obtain the current flow coefficient. At the same time, the pressure values ​​before and after the valve are acquired by the multi-dimensional data acquisition center, and the value obtained by subtracting the pressure value after the valve from the pressure value before the valve is set as the transient pressure difference value. This embodiment specifically defines the process of coupled analysis of flow resistance characteristics, which is a key step connecting the mechanical domain and the fluid domain. The system acquires the preset inherent flow characteristic data of the shut-off valve. This data comes from laboratory calibration or CFD simulation and is represented by a lookup table or fitting function that records the correspondence between different valve core displacements and standard flow coefficient values. The fluid state observation unit acquires the real-time valve core displacement data collected by the multi-dimensional data acquisition center and performs interpolation mapping processing on the preset inherent flow characteristic data based on the displacement data. In this process, considering the nonlinearity of valve characteristics, this embodiment employs cubic spline interpolation to ensure a smooth and continuous estimate between discrete data points, thereby obtaining the current flow coefficient. Simultaneously, for differential pressure calculation, the system acquires the upstream pressure value and the downstream pressure value and performs a subtraction operation, as shown in the following formula: ; in, The data source is the inlet pressure sensor, and its physical meaning is the real-time pressure value before the valve. The data source is the outlet pressure sensor, and its physical meaning is the real-time pressure value after the valve. The source is the calculation result, and the physical meaning is the transient pressure difference value; the calculated transient pressure difference value is used as the basic input for subsequent risk assessment.

[0017] Example 3: The fluid dynamics risk assessment and processing procedure is as follows: The preset pipe length parameters and fluid wave velocity parameters of the pipeline network where the shut-off valve is located are obtained. The transient pressure difference value is differentiated over time to obtain the pressure difference change gradient. The product of the pressure difference change gradient, the preset pipe length parameters, and the fluid wave velocity parameters is calculated. The product is normalized based on a preset benchmark scaling ratio. The processed value is set as the water hammer risk index. Simultaneously, the preset saturated vapor pressure of the fluid is obtained, the difference between the downstream pressure and the preset saturated vapor pressure is calculated, and the difference is set as the pressure margin value. The quotient of the transient pressure difference value divided by the pressure margin value is calculated, and the quotient is set as the cavitation damage index. The lower limit of the pressure margin value is configured as a preset non-zero minimum value to avoid division by zero.

[0018] This embodiment mathematically models and quantifies the fluid dynamics risk assessment process, aiming to transform abstract physical risks into calculable numerical indicators. To quantify the impact risk caused by fluid inertia, the system obtains the preset pipe length parameters and fluid wave velocity parameters of the pipeline network where the shut-off valve is located, performs a time-dependent differential operation on the transient pressure difference value, and obtains the pressure difference change gradient, denoted as... Calculate the product of the pressure difference gradient, the preset pipe length parameter, and the fluid wave velocity parameter, and normalize the product based on a preset benchmark scaling ratio. Set the processed value as the water hammer risk index; the formula is: ; in, The source is differential calculation; its physical meaning is the pressure gradient; and its unit is... ; The source is preset pipeline parameters, and the physical meaning is characteristic pipe length, with units of... ; The source is a fluid property; its physical meaning is the propagation speed of pressure waves, and the unit is... ; The source is a preset value, and its physical meaning is the rated pressure limit value of the pipeline system, in units of; The preset dynamic correction coefficient is defined as the square of the fluid wave velocity parameter a and the preset safety redundancy coefficient. The product of, i.e. This construction makes the physical dimensions of the denominator... With molecules By maintaining consistent physical dimensions, a dimensionless ratio that intuitively reflects impact strength can be calculated, and its physical essence is approximately the same as... ; Simultaneously, to quantify the erosion risk caused by phase change, the system obtains the preset saturated vapor pressure of the fluid and calculates the difference between the downstream pressure and this vapor pressure to obtain the pressure margin value; during this process, to prevent erosion under severe cavitation conditions... This causes the denominator to become negative, which in turn causes the risk index to decrease in the opposite direction, triggering the system to execute forced clamping logic: Calculate the original difference ,like Then the pressure margin value will be forcibly set to a preset non-zero minimum value. ,For example To ensure that the denominator is always positive and extremely small, the calculated cavitation damage index tends to positive infinity, which accurately reflects the current extremely dangerous working conditions; the quotient of the transient pressure difference value divided by the pressure margin value is calculated and set as the cavitation damage index. The larger the index value, the higher the probability of choking flow. This embodiment introduces a mathematical model that includes physical parameters such as pipe length and wave velocity, giving the system the ability to predict and perceive pressure fluctuations. In particular, the differential operation of the pressure gradient enables the system to keenly capture the trend of pressure changes. The correction of unit dimensions and the protection design for abnormal divisors significantly improve the physical rigor and robustness of the algorithm.

[0019] Example 4: The weighted quantization analysis and discrimination process is as follows: The preset water hammer weight coefficient and preset cavitation weight coefficient are obtained. The water hammer risk index is multiplied by the preset water hammer weight coefficient to obtain the water hammer component value. The cavitation damage index is multiplied by the preset cavitation weight coefficient to obtain the cavitation component value. The water hammer component value and the cavitation component value are added together to obtain the comprehensive risk assessment value. The comprehensive risk assessment value is compared and analyzed with the preset safety threshold. If the comprehensive risk assessment value is greater than or equal to the preset safety threshold, a stable control signal is generated; if the comprehensive risk assessment value is less than the preset safety threshold, an ultra-fast response signal is generated.

[0020] This embodiment specifies the weighted quantitative analysis and discrimination process to comprehensively consider different types of risks and make optimal decisions. The system acquires pre-stored preset water hammer weight coefficients and preset cavitation weight coefficients. These two coefficients are set according to the specific application scenario of the pipeline network. For example, long-distance pipelines are more concerned about water hammer, while precision chemical pipelines are more concerned about corrosion. The system performs weighted calculations, multiplying the water hammer risk index by the water hammer weight coefficient to obtain the water hammer component value, and multiplying the cavitation damage index by the cavitation weight coefficient to obtain the cavitation component value. The two are then added together to obtain the comprehensive risk assessment value. The formula is as follows: ; in, The source is the calculation result, and the physical meaning is the comprehensive risk assessment value; The source is the calculation result of the previous steps, and the physical meaning is the water hammer risk index; The source is a preset parameter, and its physical meaning is the water hammer weight coefficient; The source is the calculation result of the previous steps, and the physical meaning is the cavitation damage index; The source is a preset parameter, and its physical meaning is the cavitation weighting coefficient; The system compares and analyzes the comprehensive risk assessment value with the preset safety threshold. The preset safety threshold is not set arbitrarily, but is obtained by consulting the SN fatigue curve database of valve materials. The specific value is the critical risk value corresponding to the fatigue limit of the valve material at the current operating temperature multiplied by a safety factor, such as 0.8. This factor is selected according to the safety margin provisions of the pressure-temperature rating in the general valve standards such as GB / T12224, and is used as the physical boundary to distinguish between the stable mode and the high-speed mode. When the comprehensive risk assessment value is greater than or equal to the preset safety threshold, the system determines that it is currently in a high-risk operating condition and generates a stable control signal to force the valve to enter the optimized closing mode; when the comprehensive risk assessment value is less than the preset safety threshold, the system determines that it is currently in a low-risk operating condition and generates an ultra-fast response signal to allow the valve to operate at its maximum capacity. This embodiment achieves multi-dimensional insight into complex operating conditions through a weighted fusion mechanism. Instead of using control logic based on a single fixed threshold, it makes dynamic decisions based on the severity of real-time risks. This mechanism optimizes the dynamic response performance of the system while ensuring the absolute safety of the pipeline network, avoids unnecessary low-speed operation under safe conditions, and improves the system's adaptability to operating conditions.

[0021] Example 5: The multi-objective velocity optimization analysis process is as follows: The full stroke distance of the shut-off valve is obtained, and the full stroke distance is divided into an acceleration segment, a constant speed segment, and a buffer segment; A multi-objective optimization model is constructed, which includes an objective function for action time, an objective function for pressure overshoot, and an objective function for erosion loss. Based on a stationary control signal, weighting coefficients are set in the multi-objective optimization model so that the weighting coefficients of the objective functions for pressure overshoot and erosion loss are greater than the weighting coefficient of the objective function for action time. The corresponding velocity values ​​in the buffer section are calculated through iterative calculation. The curves generated by fitting the corresponding velocity values ​​of the acceleration section, the constant velocity section, and the calculated buffer section are set as nonlinear damped velocity curves.

[0022] This embodiment details the multi-objective velocity optimization analysis process, which is the core step in generating the nonlinear damped velocity curve. The system obtains the full stroke distance of the shut-off valve and logically divides it into an acceleration segment, a constant speed segment, and a buffer segment, where the buffer segment typically corresponds to the region with the most drastic changes in flow resistance characteristics. The system constructs a multi-objective optimization model, which includes three competing objective functions: a shortest action time objective function, a minimum pressure overshoot objective function, and a minimum cumulative erosion loss objective function. To fully disclose the construction details of the optimization model, this embodiment further clarifies the mathematical expressions of each objective function: Action-time objective function Defined as the integral of the action duration, the formula is: ; in, For time variables, This represents the total valve operating time. The length of the buffer segment. The speed of the buffer segment is optimized, which is the optimization variable in this embodiment; The sum of the inherent motion durations of the acceleration phase and the constant velocity phase is calculated using the following formula: ; in, The preset constant speed segment speed, The maximum allowable acceleration of the actuator. The length of the uniform velocity segment; during the optimization process Treat it as a constant term; Pressure overshoot objective function Defined as the second integral of the pressure exceeding the limit, the formula is: ; in, The transient pressure difference value calculated in Example 2; The preset safety differential pressure threshold; As the penalty coefficient, when The value is 1 if the condition is met, otherwise it is 0. To ensure the optimization model is mathematically closed-loop, this embodiment explicitly defines the overall objective function. The linear weighted sum of the sub-objective functions: ; in, These are preset action time reference values, pressure overshoot reference values, and erosion loss reference values, which are used to convert each sub-objective function into dimensionless values, thus solving the problem of inconsistent physical dimensions making weighting impossible. Let be the corresponding dimensionless weight coefficient, and satisfy . It should be noted that this set of coefficients is specifically used for speed optimization iteration, and is different from the weighting coefficients used for risk assessment in Example 4. They are independent of each other; Furthermore, regarding the calculation of the gradient of the overall objective function The problem of variable attribution in the process, namely how to establish mechanical velocity. With fluid pressure difference The system incorporates a modified valve-pipeline state equation to support partial derivative calculations, enabling dynamic correlation between valves and pipelines. ; in, The normalized dimensionless flow coefficient, therefore The physical unit is ; The valve stem speed, in units of ; Transient pressure difference, unit: ; At this point, a dimensional analysis is performed on the first term on the right side of the equation: normalized flow coefficient gradient. The unit is valve stem speed The unit is Transient pressure difference The unit is The dimension combination of the product of the three is: ; Due to the differential pressure difference on the left side of the equation The physical units are the same In order to maintain the uniformity of dimensions on both sides of the equation, this embodiment will... Defined as a dimensionless fluid stiffness gain coefficient for pipe networks, its value is determined solely by the volumetric characteristics of the pipe network; simultaneously... The damping coefficient of the fluid system is given by the unit [unit not specified]. To ensure the damping term The unit is also This allows for a correct reflection of the physical process of pressure difference changes; To address the inconsistency between the optimization model assumptions and the execution strategy, this system will... The points are treated as control points for the B-spline curve; the specific iterative calculation steps include: Step 1, initializing the buffer segment velocity control points. Step two, utilizing the current Spline interpolation is performed between the velocity points of the preceding and following segments to generate a continuous nonlinear velocity trajectory. and its control points partial derivatives Step 3: Construct and solve the sensitivity differential equation to accurately calculate the gradient: ; in, For sensitivity variables; Step four: Calculate the total gradient using the chain rule, given the action-time objective function. For control variables Since there is an explicit dependency, this embodiment completes the direct gradient term to achieve true multi-objective optimization: ; Step 5: Utilize the calculated complete gradient Update in the negative direction Step 6: Determine whether the decrease in the objective function of this iteration is less than the preset convergence threshold. If the condition is met, stop the iteration and output the current optimal speed value; perform spline fitting on the speed values ​​of the acceleration segment, the constant speed segment, and the optimized buffer segment, and the generated curve is the nonlinear damped speed curve; This embodiment resolves the conflict between the control objectives of speed of action and stability of pressure at the mathematical level through a multi-objective optimization algorithm. The generated nonlinear curve enables differentiated control by performing deceleration suppression in the high-risk range and fast response in the low-risk range. In particular, it implements precise damping control in the fluid dynamics-sensitive buffer section, effectively suppressing the generation of water hammer waves at the physical level and realizing the refinement of the control strategy.

[0023] Example 6: The process of obtaining the objective function for erosion loss is as follows: The preset flow velocity distribution data of the shut-off valve at different opening degrees is obtained. Based on the current flow coefficient, the preset flow velocity distribution data is interpolated and mapped to determine the local flow velocity value of the sealing surface. The product of the cube of the local flow velocity value and the preset density value of the fluid medium is calculated. The obtained product value is set as the erosion power density. The erosion power density is integrated over the action time to obtain the erosion loss objective function.

[0024] This embodiment physically models the principle of obtaining the objective function of erosion loss, aiming to quantify the micro-cutting process of fluid kinetic energy on the material surface; it obtains the preset flow velocity distribution data of the shut-off valve at different opening degrees, which comes from the CFD simulation database and describes the local maximum flow velocity when the fluid flows through the valve core sealing surface; Based on the current flow coefficient, interpolation mapping is performed on the preset velocity distribution data to obtain the baseline velocity factor under the square root of unit pressure difference. ; Obtain the current transient pressure difference value Perform pressure correction calculation This determines the actual local flow velocity value of the sealing surface; based on the erosion wear principle, the product of the cube of the local flow velocity value and the preset density value of the fluid medium is calculated, and the resulting product is set as the erosion power density, with the following formula: ; in, The source is the calculation result, and the physical meaning is... Erosion power density at any given moment; The source is preset fluid parameters, and its physical meaning is the density of the medium; The source is the calculation result after interpolation and pressure correction; its physical meaning is... The local flow velocity at the sealing surface at a given time; it should be noted that this symbol... The velocity of the fluid medium is used in Example 5 to refer to the mechanical velocity of the valve stem. For different physical quantities, the two are expressed through the flow formula. By performing nonlinear correlation, conflicts in the physical meaning of the optimization variables are avoided; The source is the system clock, and its physical meaning is a time variable; The erosion power density is integrated over the entire action time to obtain the erosion loss objective function. The specific integration formula, integration variables, and integration limits are as follows: ; Among them, the integral variable Lower limit of points The upper limit of integration at the start of valve action This is the moment when the valve action ends; during the optimization process, the system strives to find a velocity path that minimizes this integral value. This embodiment creatively transforms the microscopic material wear mechanism into a macroscopic control optimization objective. By minimizing the integral function, the control system can guide the valve to quickly pass through high-loss opening zones that would lead to high-flow-rate scouring. This strategy significantly reduces physical wear on the sealing surface, thereby maintaining the valve's long-term sealing performance while maintaining the existing physical conditions.

[0025] Example 7: The control mode switching process of the flexible actuator is as follows: Obtain the remaining stroke value of the shut-off valve and compare it with the preset sealing contact threshold. If the remaining stroke value is greater than the preset sealing contact threshold, the speed control mode is maintained, and the output speed of the actuator is adjusted according to the nonlinear damping speed curve or the linear step speed curve. If the remaining stroke value is less than or equal to the preset sealing contact threshold, the system switches to torque control mode, obtains the preset sealing pressure target value, converts the sealing pressure target value into a target torque value based on the valve stem structure parameters of the shut-off valve, and generates a constant output torque command based on the target torque value until the collected actuator feedback torque reaches the preset shut-off torque threshold.

[0026] This embodiment specifies the control mode switching process of the flexible actuator to solve the control accuracy problem in the valve end contact stage; the system obtains the remaining stroke value of the shut-off valve in real time and compares the value with the preset sealing contact threshold; in response to the remaining stroke value being greater than the preset sealing contact threshold, the system determines that the valve has not yet contacted the valve seat, maintains the speed control mode, and strictly follows the nonlinear damped speed curve or linear step speed curve output by the trajectory planning unit to adjust the output speed of the actuator; In response to the remaining stroke value being less than or equal to a preset sealing contact threshold, the system determines that the valve has entered the contact sealing stage and immediately switches to torque control mode; in this mode, the system acquires a preset sealing specific pressure target value. The target value is then converted into a target torque value based on the valve stem structure parameters of the shut-off valve. The specific mechanical conversion formula is as follows, which calculates the required axial sealing force. : ; in, This refers to the effective sealing contact area of ​​the valve seat. Calculating the motor output torque using the principle of screw drive During this process, given the equivalent friction angle of the thread... The physical definition of a helical pair already includes the frictional loss of the helical pair, that is... Characterized the coefficient of friction The influence of tooth angle, in order to avoid introducing an efficiency coefficient This leads to redundant calculations of friction loss. Therefore, this embodiment uses a modified torque calculation formula: ; in, The valve stem thread pitch diameter is a structural parameter. This refers to the helix angle of the thread, which is also a structural parameter. This is the equivalent friction angle of the thread. ,in, The coefficient of friction, The thread angle is used; this formula directly uses the equivalent friction angle to accurately reflect the total torque required to overcome axial load and thread friction. Based on the calculated target torque value A constant output torque command is generated to drive the motor to press the valve disc with a constant torque until the collected actuator feedback torque reaches the preset shut-off torque threshold. At this point, the valve is determined to be fully closed and the power is cut off. The speed-torque dual-mode switching strategy adopted in this embodiment perfectly solves the contradiction between sealing and protection. When approaching closure, it switches to torque control, which can ensure that sufficient sealing pressure is provided to prevent leakage, and also prevent mechanical damage such as valve stem bending and sealing surface crushing caused by overshoot or excessive torque, thus achieving impact-free contact closure between valve disc and valve seat.

[0027] Example 8: This system also includes an adaptive correction unit, which is configured as follows: After the shut-off valve has finished operating, the actual peak pressure and actual operating time during this operation are obtained, and the difference between the actual peak pressure and the preset ideal pressure limit value is calculated to obtain the pressure deviation coefficient. The preset water hammer weight coefficient in the working condition risk assessment unit is corrected and updated based on the pressure deviation coefficient: if the pressure deviation coefficient is positive, the preset water hammer weight coefficient is increased. If the pressure deviation coefficient is negative, the current preset water hammer weight coefficient is maintained or the preset water hammer weight coefficient is reduced according to the preset step size.

[0028] This embodiment discloses the implementation of the adaptive correction unit, which gives the system the ability to learn itself. After the shut-off valve has finished operating, the system performs a post-evaluation process to obtain the actual pressure peak and actual operating time during this operation. The difference between the actual pressure peak and the preset ideal pressure limit value is calculated to obtain the pressure deviation coefficient. Based on the pressure deviation coefficient, the preset water hammer weight coefficient in the working condition risk assessment unit is corrected and updated. When the pressure deviation coefficient is positive, it means that the actual pressure peak exceeds the ideal value. The system increases the preset water hammer weight coefficient, so that the comprehensive risk assessment value calculated in the next operation is higher. When the pressure deviation coefficient is negative, it means that the actual pressure is far below the safety limit. The system maintains the current coefficient or slightly reduces the preset water hammer weight coefficient according to the preset step size, so as to try to release more speed potential in the next action. This embodiment solves the problem of possible deviation between model parameters and actual working conditions by introducing an iterative learning mechanism. As the number of valve actions increases, the system can automatically calibrate the risk assessment weights and gradually approach the optimal balance between safety and efficiency, enabling the control system to evolve to adapt to environmental drift such as pipeline aging and media changes.

[0029] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A multi-objective collaborative control optimization system for shut-off valves based on industrial data processing, characterized in that, It includes a multi-dimensional data acquisition center, a fluid state observation unit, a working condition risk assessment unit, a trajectory planning unit, and a flexible execution unit; The multidimensional data acquisition center is used to retrieve the real-time operating parameters of the shut-off valve and send the real-time operating parameters to the fluid state observation unit for flow resistance characteristic coupling analysis to obtain the current flow coefficient and transient pressure difference value. The current flow coefficient and transient pressure difference value are then sent to the working condition risk assessment unit for fluid dynamic risk discrimination processing to obtain the water hammer risk index and cavitation damage index. The working condition risk assessment unit is used to perform weighted quantitative analysis of water hammer risk index and cavitation damage index to obtain comprehensive risk assessment value. The comprehensive risk assessment value is then processed to obtain stable control signal or rapid response signal. When a smooth control signal is generated, the trajectory planning unit is used to perform multi-objective speed optimization analysis on the stroke of the shut-off valve and generate a nonlinear damped speed curve. When a rapid response signal is generated, the trajectory planning unit is used to perform time-optimal analysis on the stroke of the shut-off valve and generate a linear step speed curve. The flexible actuator is used to drive the shut-off valve to operate according to the nonlinear damping speed curve or the linear step speed curve, and to monitor the closing contact state of the shut-off valve, thereby switching between speed control mode and torque control mode.

2. The multi-objective collaborative control optimization system for shut-off valves based on industrial data processing according to claim 1, characterized in that, The flow resistance characteristic coupling analysis process is as follows: The preset inherent flow characteristic data of the shut-off valve is obtained. The preset inherent flow characteristic data includes the standard flow coefficient value corresponding to different valve core displacements. The real-time displacement data of the valve core collected by the multi-dimensional data acquisition center is obtained. Based on the real-time displacement data of the valve core, the preset inherent flow characteristic data is interpolated and mapped to obtain the current flow coefficient. At the same time, the pressure values ​​before and after the valve are acquired from the multi-dimensional data acquisition center, and the value obtained by subtracting the pressure value after the valve from the pressure value before the valve is set as the transient pressure difference value.

3. The multi-objective collaborative control optimization system for shut-off valves based on industrial data processing according to claim 1, characterized in that, The fluid dynamics risk assessment process is as follows: The preset pipe length parameters and fluid wave velocity parameters of the pipeline network where the shut-off valve is located are obtained. The transient pressure difference value is differentiated over time to obtain the pressure difference change gradient. The product of the pressure difference change gradient, the preset pipe length parameters, and the fluid wave velocity parameters is calculated. The product is normalized based on a preset benchmark scaling ratio. The processed value is set as the water hammer risk index. Simultaneously, the preset saturated vapor pressure of the fluid is obtained, the difference between the downstream pressure and the preset saturated vapor pressure is calculated, the difference is set as the pressure margin value, the quotient of the transient pressure difference value divided by the pressure margin value is calculated, and the quotient is set as the cavitation damage index. The lower limit of the pressure margin value is configured as a preset non-zero minimum value to avoid division by zero.

4. The multi-objective collaborative control optimization system for shut-off valves based on industrial data processing according to claim 1, characterized in that, The weighted quantization analysis and discrimination process is as follows: The preset water hammer weight coefficient and preset cavitation weight coefficient are obtained. The water hammer risk index is multiplied by the preset water hammer weight coefficient to obtain the water hammer component value. The cavitation damage index is multiplied by the preset cavitation weight coefficient to obtain the cavitation component value. The water hammer component value and the cavitation component value are added together to obtain the comprehensive risk assessment value. The comprehensive risk assessment value is compared and analyzed with the preset safety threshold. If the comprehensive risk assessment value is greater than or equal to the preset safety threshold, a stable control signal is generated. If the overall risk assessment value is less than the preset safety threshold, a rapid response signal will be generated.

5. The multi-objective collaborative control optimization system for shut-off valves based on industrial data processing according to claim 1, characterized in that, The multi-objective velocity optimization analysis process is as follows: The full stroke distance of the shut-off valve is obtained, and the full stroke distance is divided into an acceleration segment, a constant speed segment, and a buffer segment; a multi-objective optimization model is constructed, which includes the objective function of action time, the objective function of pressure overshoot, and the objective function of erosion loss. Based on the steady control signal, weight coefficients are set in the multi-objective optimization model so that the weight coefficients of the pressure overshoot objective function and the erosion loss objective function are greater than the weight coefficients of the action time objective function. The corresponding velocity value in the buffer section is calculated through iterative calculation. The curves generated by fitting the corresponding velocity values ​​of the acceleration section, the constant speed section and the calculated buffer section are set as nonlinear damped velocity curves.

6. The multi-objective collaborative control optimization system for shut-off valves based on industrial data processing according to claim 5, characterized in that, The process of obtaining the erosion loss objective function is as follows: The preset flow velocity distribution data of the shut-off valve at different opening degrees is obtained, and the preset flow velocity distribution data is interpolated and mapped based on the current flow coefficient to determine the local flow velocity value of the sealing surface. Calculate the product of the cube of the local velocity value and the preset density value of the fluid medium, set the resulting product as the erosion power density, and integrate the erosion power density over the action time to obtain the erosion loss objective function.

7. The multi-objective collaborative control optimization system for shut-off valves based on industrial data processing according to claim 1, characterized in that, The control mode switching process of the flexible execution unit is as follows: Obtain the remaining stroke value of the shut-off valve and compare it with the preset sealing contact threshold. If the remaining stroke value is greater than the preset sealing contact threshold, the speed control mode is maintained, and the output speed of the actuator is adjusted according to the nonlinear damping speed curve or the linear step speed curve. If the remaining stroke value is less than or equal to the preset sealing contact threshold, the system switches to torque control mode, obtains the preset sealing specific pressure target value, converts the sealing specific pressure target value into a target torque value based on the valve stem structure parameters of the shut-off valve, and generates a constant output torque command based on the target torque value until the collected actuator feedback torque reaches the preset shut-off torque threshold.

8. The multi-objective collaborative control optimization system for shut-off valves based on industrial data processing according to claim 1, characterized in that, It also includes an adaptive correction unit; the adaptive correction unit is configured as follows: After the shut-off valve has finished operating, the actual peak pressure and actual operating time during this operation are obtained, and the difference between the actual peak pressure and the preset ideal pressure limit value is calculated to obtain the pressure deviation coefficient. The preset water hammer weight coefficient in the working condition risk assessment unit is corrected and updated based on the pressure deviation coefficient: if the pressure deviation coefficient is positive, the preset water hammer weight coefficient is increased; if the pressure deviation coefficient is negative, the current preset water hammer weight coefficient is maintained or the preset water hammer weight coefficient is decreased according to the preset step size.

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