A control optimization method for locking a remote control integrated anti-theft thermostatic valve

Abstract

This invention relates to the field of thermal control valve technology, and discloses a control optimization method for a remote-controlled, anti-theft thermal control valve. The method includes constructing a three-dimensional thermal field simulation model; collecting real-time temperature field data of the valve surface and internal flow channels using distributed temperature sensors to form a dynamic sequence; acquiring velocity and pressure field data of the internal medium using a multiphase flow monitoring device to generate a flow field characteristic map; establishing an energy efficiency evaluation model by combining the above data and the simulation model; and using a dynamic adaptive optimization algorithm to optimize parameters and generate a control strategy to drive the actuator to complete the coordinated adjustment of valve core opening and flow velocity. This method achieves comprehensive monitoring and accurate evaluation of the temperature and flow fields, improves the targeting and response speed of control parameter optimization, and optimizes the synergy between anti-theft and heat exchange efficiency.

Description

Technical Field

[0001] This invention relates to the field of thermal control valve technology, specifically to a control optimization method for locking a remote-controlled integrated anti-theft thermal control valve. Background Technology

[0002] Current control technologies for remote-controlled integrated anti-theft thermal regulating valves rely heavily on simplified models based on empirical formulas for energy efficiency assessment. These models simply substitute discrete temperature and flow data into calculations without considering the dynamic changes in the temperature field and the comprehensive characteristics of the flow field. Parameter optimization typically employs algorithms with fixed logic, adjusting control parameters according to preset rules, lacking the ability to dynamically adapt to the valve's real-time operating conditions.

[0003] Existing technologies, lacking integration of dynamic temperature field sequences and flow field characteristic maps, rely solely on single parameters, failing to accurately map the complex thermal and fluid coupling processes within valves. This results in significant discrepancies between the output energy efficiency assessment results and actual operating conditions. Fixed-mode optimization algorithms cannot adjust their optimization logic based on dynamic changes in the energy efficiency assessment model. Consequently, the generated control parameter adjustment strategies lag behind operational fluctuations, making it difficult to match valve opening and medium flow rate adjustments to real-time requirements. This often leads to insufficient adjustment accuracy and slow response, impacting system operational stability.

[0004] By effectively integrating the core data of the dynamic temperature field sequence and flow field characteristic spectrum into the three-dimensional thermal field simulation model, an evaluation model that can truly reflect the energy efficiency of valve operation is constructed, which is the key to improving the accuracy of the evaluation. In order to address the problem of poor adaptability of traditional algorithms, an optimization mechanism that can dynamically track and optimize the energy efficiency evaluation model has been developed to generate control parameter adjustment strategies that are highly matched with real-time operating conditions, thereby avoiding the lag and blindness of control decisions. This is the technical bottleneck that needs to be overcome. Summary of the Invention

[0005] The purpose of this invention is to provide a control optimization method for locking a remote-controlled integrated anti-theft thermal regulating valve, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a control optimization method for locking a remote-controlled integrated anti-theft thermal regulating valve, the method comprising:

[0007] A three-dimensional thermal field simulation model is constructed, and real-time temperature field data of the surface and internal flow channel of the thermal regulating valve are collected by distributed temperature sensors to form a dynamic sequence of temperature field.

[0008] A multiphase flow monitoring device is used to acquire velocity field distribution and pressure field fluctuation data of the medium inside the thermal regulating valve, and a flow field characteristic map is generated.

[0009] Based on the aforementioned temperature field dynamic sequence and flow field characteristic spectrum, an energy efficiency evaluation model for the thermal control valve is established in conjunction with a three-dimensional thermal field simulation model.

[0010] The energy efficiency assessment model is optimized using a dynamic adaptive optimization algorithm to generate a control parameter adjustment strategy.

[0011] According to the control parameter adjustment strategy, the actuator of the thermal regulating valve is driven to complete the coordinated adjustment of valve core opening and flow rate.

[0012] Preferably, the construction of the three-dimensional thermal field simulation model includes:

[0013] The temperature distribution data of the outer surface of the thermal regulating valve is collected by an infrared thermal imager, and the temperature readings of key nodes inside the valve body are obtained by an insertion thermocouple.

[0014] A three-dimensional mesh model is established based on the geometric parameters of the thermal regulating valve, and the temperature distribution data is mapped to the mesh nodes.

[0015] The heat conduction flux between grid cells is calculated using the finite volume method to generate a dynamic sequence of the temperature field.

[0016] The spatiotemporal variation characteristics of the velocity and pressure fields are extracted by simulating the flow state of the medium in the flow channel using computational fluid dynamics software.

[0017] By coupling temperature field and flow field data, a three-dimensional thermal field simulation model reflecting the actual working state of the thermal regulating valve is established.

[0018] Preferably, the energy efficiency evaluation model for the thermal regulating valve includes:

[0019] The heat loss coefficient and heat transfer efficiency index are extracted from the three-dimensional thermal field simulation model.

[0020] The turbulence intensity and energy dissipation rate of the medium flow are calculated based on the flow field characteristic map.

[0021] A multi-objective optimization algorithm is used to weight and fuse the heat loss coefficient, heat transfer efficiency, turbulence intensity, and energy dissipation rate;

[0022] Establish a nonlinear mapping relationship with valve core opening degree as the independent variable and comprehensive energy efficiency evaluation value as the dependent variable;

[0023] The parameters of the nonlinear mapping relationship are calibrated using experimental data to form an energy efficiency assessment model.

[0024] Preferably, the generation control parameter adjustment strategy includes:

[0025] Set the target range for the comprehensive energy efficiency evaluation value and the safe operating range for the valve core opening;

[0026] The particle swarm optimization algorithm is used to search for valve core opening combinations that allow the comprehensive energy efficiency evaluation value to fall into the target range within the safe operating range.

[0027] Calculate the corresponding flow rate adjustment based on the valve core opening combination obtained from the search;

[0028] Establish the timing relationship between valve core opening and flow rate regulation to form a control parameter adjustment strategy.

[0029] Preferably, the actuator driving the thermal regulating valve performs coordinated regulation of the valve core opening and flow rate by:

[0030] The valve core opening command sequence in the control parameter adjustment strategy is analyzed. The valve core is precisely positioned according to the command sequence by controlling the stepper motor, and the output frequency of the variable frequency pump is adjusted synchronously to achieve precise matching of flow rate.

[0031] Real-time monitoring of feedback data from valve core position sensors and flow meters forms a closed-loop control system.

[0032] Preferably, the method further includes dynamic calibration of the coordinated adjustment process, specifically:

[0033] Real-time changes in the dynamic sequence of the temperature field and the characteristic spectrum of the flow field are collected during the adjustment process;

[0034] Input real-time change data into the energy efficiency assessment model to calculate the actual comprehensive energy efficiency evaluation value;

[0035] Compare the deviation between the actual comprehensive energy efficiency evaluation value and the target range;

[0036] When the deviation exceeds the allowable range, the dynamic adaptive optimization algorithm is restarted to generate a correction strategy.

[0037] Preferably, the method of restarting the dynamic adaptive optimization algorithm to generate a correction strategy includes:

[0038] Calculate the absolute value of the deviation between the actual comprehensive energy efficiency evaluation value and the median of the target interval, and adjust the search step size parameter of the dynamic adaptive optimization algorithm according to the magnitude of the absolute value of the deviation;

[0039] Initialize the algorithm population, where each individual represents a combination of valve opening degree and flow rate regulation; use the energy efficiency assessment model to iteratively evaluate the comprehensive energy efficiency evaluation value of each individual, and select individuals whose comprehensive energy efficiency evaluation value falls within the target interval as candidate solutions;

[0040] The solution with the smallest change in valve core opening is selected from the candidate solution set as the basis for the correction strategy; the execution timing of valve core opening and flow rate regulation in the correction strategy is adjusted according to the real-time status data of the current actuator.

[0041] Preferably, the dynamic adaptive optimization algorithm is implemented using an improved particle swarm optimization algorithm, and includes the following steps:

[0042] Set the size of the particle swarm and the upper limit of the number of iterations. Initialize the position and velocity vectors of each particle. The position vector represents the valve core opening value, and the velocity vector represents the opening change rate.

[0043] Calculate the fitness value of each particle, output the comprehensive energy efficiency evaluation value through the energy efficiency assessment model; update the individual historical best position and global historical best position of each particle;

[0044] Monitor the convergence status of the particle swarm optimization algorithm and dynamically adjust the inertia weight factor and social learning factor.

[0045] A new generation of particle swarms is generated based on the updated position and velocity vectors; the iteration process is repeated until the termination condition is met, and the valve opening value corresponding to the global historical best position is output.

[0046] Preferably, calculating the fitness value of each particle includes:

[0047] Input the valve core opening, represented by the particle position vector, into the energy efficiency evaluation model; extract the current heat loss coefficient, heat transfer efficiency index, turbulence intensity, and energy dissipation rate from the energy efficiency evaluation model; combine the heat loss coefficient, heat transfer efficiency index, turbulence intensity, and energy dissipation rate into a comprehensive energy efficiency evaluation value using a weighted summation method; calculate the distance between the comprehensive energy efficiency evaluation value and the median of the target interval, and use the reciprocal of the distance value as the fitness value.

[0048] Preferably, the weighted summation method includes:

[0049] Based on the working priority of the thermal control valve, set the weighting coefficients for the heat loss coefficient, heat transfer efficiency index, turbulence intensity, and energy dissipation rate; multiply the heat loss coefficient by its weighting coefficient to obtain the weighted heat loss value, multiply the heat transfer efficiency index by its weighting coefficient to obtain the weighted heat transfer value, multiply the turbulence intensity by its weighting coefficient to obtain the weighted turbulence value, and multiply the energy dissipation rate by its weighting coefficient to obtain the weighted dissipation value; sum the weighted heat loss value, weighted heat transfer value, weighted turbulence value, and weighted dissipation value to obtain the comprehensive energy efficiency evaluation value.

[0050] Compared with the prior art, the beneficial effects of the present invention are:

[0051] This approach uses dynamic temperature field sequences and flow field characteristic maps as core inputs to construct an energy efficiency evaluation model for thermal control valves based on a three-dimensional thermal field simulation model. Conventional techniques often rely on single temperature or flow parameters, using simplified formulas to assess energy efficiency, which fails to reflect the complex thermal and fluid coupling state inside the valve. This solution uses a three-dimensional thermal field simulation model to build a data fusion platform, deeply integrating multi-dimensional temperature change data captured by distributed temperature sensors with velocity field distribution and pressure field fluctuation information obtained from multiphase flow monitoring devices. This fully reconstructs the interaction process between the temperature field and flow field inside the valve. This combination of multi-dimensional data and simulation models allows energy efficiency evaluation to overcome the limitations of isolated parameters, forming a systematic judgment based on complete operating conditions. The evaluation results highly match the actual energy efficiency state of the valve, avoiding the evaluation distortion problems of conventional techniques.

[0052] A dynamic adaptive optimization algorithm is used to optimize the parameters of an energy efficiency assessment model, thereby generating control parameter adjustment strategies. Traditional parameter optimization relies on fixed logic algorithms, outputting strategies according to preset rules, which is difficult to adapt to the dynamic fluctuations of valve operating conditions. This algorithm can track the output changes of the energy efficiency assessment model in real time, and autonomously adjust the optimization direction and step size according to the dynamic characteristics of the temperature and flow fields, breaking the fixed mode limitations of conventional algorithms. The dynamic linkage between the algorithm and the energy efficiency assessment model ensures that the parameter optimization process closely follows the actual operating state of the valve, and the generated control parameter adjustment strategies can accurately match the real-time operating requirements. When driving the actuator to adjust, it can achieve instant coordination between valve core opening and medium flow velocity, avoiding the problems of adjustment lag and insufficient accuracy in conventional technologies, enhancing valve operating stability, and reducing energy loss caused by parameter mismatch. Attached Figure Description

[0053] Figure 1 This is a schematic diagram illustrating the working principle of the control optimization method for locking a remote-controlled integrated anti-theft thermal regulating valve as described in this invention.

[0054] Figure 2 Flowchart for constructing a three-dimensional thermal field simulation model;

[0055] Figure 3 A flowchart for establishing an energy efficiency assessment model;

[0056] Figure 4 This is a diagram illustrating the control and adjustment process of the thermal regulating valve.

[0057] Figure 5 This is a diagram of the dynamic adaptive optimization calibration process. Detailed Implementation

[0058] 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.

[0059] Please see Figure 1 This invention provides a control optimization method for a remote-controlled, anti-theft thermal control valve. The method includes: constructing a three-dimensional thermal field simulation model. This model continuously collects temperature data through a distributed temperature sensor network deployed on the surface and internal flow channels of the thermal control valve, forming a dynamic sequence of temperature fields that reflects the spatiotemporal changes in temperature. Simultaneously, using multiphase flow monitoring devices, such as ultrasonic flow meters and differential pressure transmitters, the flow velocity profile and pressure pulsation information of the medium inside the valve are acquired in real time, thereby generating a flow field characteristic map depicting the flow characteristics. Based on the aforementioned dynamic temperature field sequence and flow field characteristic map, data assimilation and state estimation are performed using the established three-dimensional thermal field simulation model to construct an energy efficiency evaluation model that can accurately assess the energy utilization efficiency of the thermal control valve under current operating conditions. Subsequently, a dynamic adaptive optimization algorithm, such as an improved particle swarm optimization algorithm, is used to iteratively solve the energy efficiency evaluation model to find the optimal combination of control parameters that optimizes the energy efficiency evaluation value, thereby generating a control parameter adjustment strategy that includes the target valve core opening and the desired flow velocity. The strategy is sent to the execution unit, which drives the electric actuator of the thermal control valve to adjust the valve core opening and links the variable frequency pump to adjust the pipeline flow rate, thereby achieving coordinated optimization of valve core opening and medium flow rate and improving the overall energy efficiency of the system.

[0060] Example 1: See Figure 2In practical implementation, the construction of the three-dimensional thermal field simulation model involves the coordinated execution of multiple precise steps. Data acquisition, as the initial step, employs a high-precision infrared thermal imager to comprehensively scan the outer surface of the lockable remote-controlled integrated anti-theft thermal regulating valve. The scanning process ensures coverage of all exposed areas of the valve. The infrared thermal imager generates a surface temperature distribution image, and the image data is stored in matrix form, with each pixel associated with a temperature value and spatial coordinate information. To obtain temperature data of areas inside the lockable remote-controlled integrated anti-theft thermal regulating valve that cannot be directly observed, several insertable armored thermocouples are pre-embedded at key nodes in the valve body. These key nodes include the area near the valve seat and the inflection points of the flow channel. The insertable armored thermocouples are implanted inside the valve body through drilling and fixing. The leads of the insertable armored thermocouples are connected to the data acquisition system, which records temperature readings at a set sampling frequency and includes timestamps. In some embodiments, the scanning resolution of the infrared thermal imager is set to no less than 640x480 pixels to ensure detailed capture of temperature distribution, and the temperature measurement accuracy of the insert-type sheathed thermocouple is required to be within ±0.5 degrees Celsius to ensure data reliability. The data acquisition system synchronizes and aligns the surface temperature data of the infrared thermal imager with the internal temperature readings of the insert-type sheathed thermocouple, forming a raw temperature dataset based on a unified time reference.

[0061] The model building phase relies on the precise geometric parameters of the integrated anti-theft thermal control valve with remote locking. These parameters are extracted from the 3D CAD design drawings of the valve, including key geometric features such as the flow channel shape and contour, valve body wall thickness, and flange connection dimensions. Using pre-processing software such as ANSYSICEM or similar tools, the geometric model is imported and meshed to generate a 3D mesh model containing tetrahedral or hexahedral elements. The mesh size is configured according to computational accuracy requirements and resources; a denser mesh is used in high-gradient regions, such as near the valve seat, while a sparser mesh is used in uniform regions to balance computational efficiency. In practice, surface temperature data acquired by an infrared thermal imager is interpolated onto the outer surface nodes of the valve body in the 3D mesh model using a coordinate mapping algorithm. This algorithm involves converting the image pixel coordinate system to the world coordinate system of the 3D mesh. Simultaneously, internal key point temperature data read by the insert-type armored thermocouple is directly assigned to the corresponding node positions in the 3D mesh model. For the initial temperature values ​​of non-measurement points in the 3D mesh model, a distance-based weighted interpolation method is used for calculation. The weighted interpolation method uses known temperature nodes as references and assigns temperature values ​​according to the inverse weight of spatial distance to complete the construction of the initial temperature field of the entire 3D mesh model.

[0062] In the computational analysis phase, the finite volume method (FVM) is used as the core numerical calculation method. The FVM treats each grid cell in the 3D mesh model as a control volume, establishing an energy conservation equation for each control volume. This equation considers the heat conduction effect between grid cells and the heat convection effect between the grid cells and the flowing medium. In practice, the heat conduction flux is calculated based on Fourier's law, using a central difference scheme to discretize the temperature gradient. The heat convection term is processed using an upwind scheme based on the medium velocity to ensure numerical stability. The energy equation for the entire computational domain is solved iteratively, updating the temperature values ​​of the grid nodes in each iteration until the residual converges below a set threshold, thus generating a dynamic sequence of the temperature field evolving over time. This dynamic sequence stores the historical temperature data of each grid node in units of time steps. Optionally, an adaptive time step strategy can be introduced during the calculation process, dynamically adjusting the time step according to the rate of temperature change to improve computational efficiency. The flow field simulation was implemented using computational fluid dynamics (CFD) software. The CFD software sets the boundary conditions for the medium flow, including inlet velocity conditions, outlet pressure conditions, and wall boundary conditions. The wall boundary conditions utilize standard wall functions to handle the near-wall region flow. The CFD software solves the Navier-Stokes equations, which include the mass conservation equation, momentum conservation equation, and energy conservation equation. The velocity vector components and pressure scalar values ​​of each grid node within the flow channel are extracted to obtain the spatiotemporal variation characteristics of the velocity field distribution and pressure field fluctuations.

[0063] The temperature field calculation results and flow field simulation results are integrated through a bidirectional coupled solver. The bidirectional coupled solver alternately solves the temperature field and flow field equations. Within each time step, the flow field is solved first to obtain the velocity distribution. Then, the velocity field is substituted into the temperature field equation to calculate the temperature distribution. Next, fluid properties such as density and viscosity are updated based on temperature changes, and the updated properties are fed back to the flow field equation for the next iteration. In practice, the coupling process ensures that the direct impact of flow on heat transfer is considered, such as changes in the convective heat transfer coefficient, and the reaction of fluid property changes caused by temperature differences on the flow, such as velocity adjustments due to thermal expansion. It can be understood that the bidirectional coupled solver requires multiple data exchange iterations until both the temperature field and flow field reach convergence, thereby establishing a three-dimensional thermal field simulation model that can realistically reflect the thermodynamic state of the remote-controlled anti-theft thermal regulating valve under complex operating conditions. To handle transient conditions, the three-dimensional thermal field simulation model supports unsteady-state simulation by setting time-dependent boundary conditions to simulate dynamic processes.

[0064] In practical implementation, sensor deployment during the data acquisition phase must consider actual installation constraints. The installation location of the infrared thermal imager should avoid obstructions, ensuring the field of view covers the entire outer surface of the integrated anti-theft thermal regulating valve with remote control. Ambient lighting conditions must remain stable during scanning to reduce interference. The pre-embedding depth of the insert-type armored thermocouple is precisely calculated to reflect the true internal temperature of the valve body. The response time constant of the thermocouple is matched with the sampling rate of the data acquisition system to avoid signal distortion. During the model building phase, the generation of the 3D mesh requires mesh independence verification. By comparing the calculation results under different mesh densities, the mesh size is selected to ensure that the solution no longer changes significantly, guaranteeing mesh independence. The choice of the finite volume method discretization scheme in the calculation and analysis phase affects numerical diffusion. A second-order upwind scheme is typically used to balance accuracy and stability, and an algebraic multigrid method is used for iterative solutions to accelerate convergence. Boundary conditions for flow field simulation are set based on actual operating condition measurements. For example, the inlet velocity is calibrated by a flow meter, the outlet pressure is fed back by a pressure sensor, and the changes in medium physical properties such as specific heat capacity and thermal conductivity with temperature are incorporated into the equations. The implementation of the coupled solver relies on commercial software or custom code. The data exchange interface ensures a consistent mapping between temperature and flow field variables, and the number of coupled iterations is dynamically controlled based on the problem's nonlinearity. Understandably, the entire construction process requires high-performance computing resources, especially for large-scale mesh models, where parallel computing techniques are used to shorten simulation time. Optionally, the model verification stage involves comparing simulation results with experimental data, such as using additional thermocouple measurement points for error analysis, to calibrate model parameters. In some embodiments, for specific media such as steam, the effects of phase change need to be considered, and latent heat terms need to be added to the energy equation to accurately simulate condensation or evaporation processes. The three-dimensional thermal field simulation model can output detailed thermodynamic states of the remote-controlled anti-theft thermal regulating valve under different valve core opening degrees, providing a quantitative basis for control optimization.

[0065] Example 2: See Figure 3In practical implementation, the establishment of the energy efficiency assessment model begins with extracting quantitative energy efficiency indicators from the output of the three-dimensional thermal field simulation model. The heat loss coefficient is calculated based on the ratio of the total heat dissipation from the valve body's outer surface to the environment to the total heat carried by the medium flowing through the valve. The total heat dissipation is obtained by integrating the surface heat transfer coefficient of the valve body's outer surface and multiplying it by the temperature difference. The total heat carried by the medium is calculated using the medium's mass flow rate, specific heat capacity, and inlet / outlet temperature difference. The heat transfer efficiency index is determined by evaluating the ratio of the actual temperature drop after the medium flows through the lockable remote-controlled integrated anti-theft thermal regulating valve to the theoretical maximum possible temperature drop under ideal adiabatic conditions. The actual temperature drop is directly calculated from the measurements of temperature sensors placed upstream and downstream of the valve, while the theoretical maximum temperature drop is calculated using thermodynamic formulas based on the initial state and final pressure of the medium. In practical implementation, post-processing analysis is performed based on the flow field characteristic maps acquired in real time by the multiphase flow monitoring device. These maps include velocity vector field and pressure scalar field data. Turbulence intensity is calculated using the ratio of the root mean square value of velocity fluctuations to the local time-averaged velocity. Velocity fluctuation data is obtained through Reynolds decomposition. Energy dissipation rate is calculated based on the velocity gradient tensor of the flow field, estimated through the double-point product of the viscous stress and deformation rate tensors. These four indicators characterize the energy utilization of the integrated anti-theft thermal control valve from both thermodynamic and hydrodynamic perspectives.

[0066] A weighted summation method from a multi-objective optimization algorithm is used to fuse four indicators: heat loss coefficient, heat transfer efficiency, turbulence intensity, and energy dissipation rate. This method requires pre-assigning a weight coefficient to each indicator. The weight coefficient allocation is determined based on the operational priority of the integrated anti-theft thermal regulating valve in the specific application scenario. For example, in district heating systems where insulation performance is crucial, a higher weight is assigned to the heat loss coefficient; in processes requiring stable flow, a higher weight is assigned to turbulence intensity. In practice, the weight coefficients are assigned through a configurable weight matrix. This matrix stores preset weight values ​​for different operating conditions, and operators can select or fine-tune the weight values ​​through a human-machine interface according to actual needs. The weighted calculation process is as follows: The real-time calculated heat loss coefficient is multiplied by its corresponding weighting coefficient to obtain a dimensionless weighted heat loss evaluation value; the heat transfer efficiency index is multiplied by its corresponding weighting coefficient to obtain a weighted heat transfer evaluation value; the turbulence intensity is multiplied by its corresponding weighting coefficient to obtain a weighted turbulence evaluation value; the energy dissipation rate is multiplied by its corresponding weighting coefficient to obtain a weighted dissipation evaluation value; finally, the weighted heat loss evaluation value, weighted heat transfer evaluation value, weighted turbulence evaluation value, and weighted dissipation evaluation value are algebraically added together, and the sum is the comprehensive energy efficiency evaluation value representing the overall energy efficiency level of the lockable remote-controlled integrated anti-theft thermal regulating valve in its current state. In some embodiments, the sum of the weighting coefficients is normalized to 1 to maintain the relative consistency of the comprehensive energy efficiency evaluation value.

[0067] Establishing a nonlinear mapping relationship with valve core opening as the independent variable and comprehensive energy efficiency evaluation value as the dependent variable is the core of the energy efficiency assessment model. This nonlinear mapping relationship typically manifests as a complex implicit function, difficult to express analytically. In practice, the construction of the nonlinear mapping relationship is based on a large amount of historical operating data or controlled experimental data. The data points cover different valve core openings within the entire safe operating range of the integrated anti-theft thermal control valve. Each data point includes a valve core opening value and the comprehensive energy efficiency evaluation value calculated using the weighted summation method described above at that opening. Using these data points, nonlinear regression algorithms, such as multinomial fitting, support vector regression, or artificial neural networks, are employed to approximate the complex relationship between variables, thereby determining the specific mathematical form and internal parameters of the nonlinear mapping relationship. Optionally, the artificial neural network structure can be designed to include an input layer, several hidden layers, and an output layer. The input layer nodes correspond to the valve core opening, and the output layer nodes correspond to the comprehensive energy efficiency evaluation value. The network weights are trained using a backpropagation algorithm. Once the model parameters are calibrated, the resulting energy efficiency assessment model becomes a black box or gray box function. By inputting any valve core opening value, the model can quickly output a predicted comprehensive energy efficiency evaluation value.

[0068] In the stage of generating control parameter adjustment strategies, it is first necessary to set clear operating boundaries and objectives. The target range for the comprehensive energy efficiency evaluation value is set according to the system energy efficiency requirements. The target range is a closed interval, representing a satisfactory energy efficiency level range. The safe operating range of the valve core opening is determined based on the mechanical design limits and process safety requirements of the integrated anti-theft thermal control valve with remote locking, preventing the actuator from exceeding physical limits or causing process disturbances. In specific implementation, a particle swarm optimization algorithm is used to perform a global search within the safe operating range of the valve core opening. The initialization process of the particle swarm optimization algorithm randomly generates a set of particles. The position vector of each particle represents a candidate valve core opening value, and the velocity vector of each particle represents the change and direction of its position during the iteration process. During the iteration process, for each particle in the population, the valve core opening value represented by its position vector is input into the energy efficiency evaluation model. The energy efficiency evaluation model calculates and returns the predicted comprehensive energy efficiency evaluation value at that opening. The particle swarm optimization algorithm compares the relationship between this evaluation value and the target range. The objective function of the particle swarm optimization algorithm is set to evaluate whether the comprehensive energy efficiency evaluation value corresponding to the particle's position falls within the target interval and is as close as possible to the median of the interval. Particles update their velocity and position based on their individual historical best position and the swarm's global historical best position. Through multiple iterations, the particle swarm gradually converges to the optimal or satisfactory region in the search space. In some embodiments, the convergence condition of the particle swarm optimization algorithm can be set as the change in the global optimal solution being less than a threshold or reaching the maximum number of iterations for several consecutive generations.

[0069] For the valve opening combinations obtained by the particle swarm optimization algorithm that allow the comprehensive energy efficiency evaluation value to fall within the target range, it is necessary to further calculate the corresponding flow rate regulation. The calculation process is based on the pipeline characteristic equation and pump characteristic curve in fluid mechanics. The pipeline characteristic equation describes the relationship between flow rate and resistance loss in the pipeline, while the pump characteristic curve describes the relationship between pump head and flow rate. Changing the valve opening is equivalent to changing the local resistance coefficient of the pipeline, thereby causing a shift in the operating point of the entire system. By simultaneously solving the pipeline characteristic equation and the pump characteristic curve, the new operating flow rate required to maintain stable system operation under the new valve opening can be determined. The difference between the current flow rate and the new operating flow rate is the flow rate regulation. Optionally, when calculating the flow rate regulation, a variable frequency pump can be considered for continuous adjustment, and the flow rate regulation can be converted into the change in the frequency setpoint of the variable frequency pump. Finally, a timing coordination relationship between valve opening and flow rate regulation is established. This timing coordination needs to consider the response time of the actuator of the integrated anti-theft thermal control valve and the response time of the variable frequency pump to avoid large water hammer or pressure fluctuations during regulation. In specific implementation, the timing coordination relationship can be designed so that valve opening adjustment and flow rate regulation are performed simultaneously, or in different modes such as valve opening acting first and flow rate following, forming the final control parameter adjustment strategy. The control parameter adjustment strategy is stored in the form of an instruction sequence, including information such as time point, target valve opening, and target flow rate value. It can be understood that the generation of the control parameter adjustment strategy is a dynamic optimization process that can adapt to different initial operating conditions and optimization objectives.

[0070] Example 3: In specific implementation, the execution of the control parameter adjustment strategy relies on the drive system of the integrated remote-controlled anti-theft thermal control valve. The control unit receives and parses the valve core opening instruction sequence encoded in the control parameter adjustment strategy. The valve core opening instruction sequence contains a series of target opening values ​​arranged in chronological order and corresponding timestamp information. The parsing process converts the target opening value into pulse signal parameters required to drive the stepper motor. The pulse signal parameters include pulse frequency and pulse quantity. The pulse frequency determines the rotation speed of the stepper motor, and the pulse quantity determines the rotation angle of the stepper motor, thereby precisely controlling the linear or angular displacement of the valve core. In specific implementation, the stepper motor is connected to the valve stem of the integrated remote-controlled anti-theft thermal control valve through a reduction mechanism. After receiving the pulse signal, the stepper motor generates a corresponding angular displacement. The angular displacement is converted into the linear motion of the valve core through a transmission mechanism such as a lead screw or gear, ultimately positioning the valve core to the opening position specified by the instruction. Synchronously, the control unit generates a frequency setting command for the variable frequency pump based on the flow rate adjustment amount calculated in the control parameter adjustment strategy. This command is sent to the frequency converter via an analog output module or fieldbus communication. The frequency converter adjusts the frequency of its output power supply according to the command, thereby changing the speed of the AC asynchronous motor driving the pump. The change in motor speed directly leads to a change in the pump's flow output, achieving precise matching of the medium flow rate in the pipeline. In some embodiments, the synchronization of the valve opening command sequence and the flow rate adjustment command is ensured by a precise timing controller. The timing controller ensures that the phase difference between the stepper motor start-up time and the frequency converter frequency change time is controlled within milliseconds, thus achieving coordinated adjustment of the valve opening and flow rate.

[0071] Throughout the adjustment process, a closed-loop control mechanism operates continuously to ensure adjustment accuracy. A valve core position sensor monitors the actual displacement of the valve core in real time. This sensor typically employs a high-precision potentiometer or a linear variable differential transformer, converting the valve core's mechanical position into a proportional electrical signal that is fed back to the control unit. An electromagnetic or ultrasonic flow meter monitors the instantaneous flow rate in the pipeline in real time, converting the flow signal into a standard current signal or digital signal and transmitting it to the control unit. The control unit internally compares the actual valve core position signal with the target valve core opening value in the command sequence to calculate the position deviation; simultaneously, it compares the actual flow rate signal with the target flow velocity value calculated in the control parameter adjustment strategy to calculate the flow deviation. The control unit uses a proportional-integral-derivative (PID) control algorithm to process the deviation signal. The output of the PID algorithm is used to correct the pulse signal sent to the stepper motor and the frequency setting command sent to the frequency converter in real time, forming a negative feedback closed-loop control system. This ensures that the adjustment results of the valve core opening and flow velocity stably track the command values, suppressing deviations caused by external disturbances or changes in internal system parameters. It is understandable that the proportional gain, integral time, and derivative time parameters of the closed-loop control system need to be tuned according to the dynamic characteristics of the locking remote control integrated anti-theft thermal regulating valve and the actuator in order to obtain the best regulation quality, such as fast response and no overshoot.

[0072] The dynamic calibration mechanism is a key element in addressing fluctuations in operating conditions. After the adjustment action begins, the distributed temperature sensor network deployed on and inside the integrated anti-theft thermal control valve continues to operate, collecting temperature data at a set sampling period. The multiphase flow monitoring device also continuously acquires flow field characteristic data. This real-time data is transmitted to the data processing unit, which performs preprocessing operations such as filtering, noise reduction, and feature extraction on the raw data to form a new dynamic temperature field sequence and flow field characteristic map. The preprocessed real-time data is immediately input into the established energy efficiency assessment model. Based on the latest input data, the energy efficiency assessment model recalculates the actual comprehensive energy efficiency evaluation value of the integrated anti-theft thermal control valve under the current state. The system compares the calculated actual comprehensive energy efficiency evaluation value with a preset target range, which is typically set with an upper limit and a lower limit. The comparison process calculates the deviation between the actual comprehensive energy efficiency evaluation value and the upper and lower limits of the target range. Deviation assessment follows a clear logical judgment rule: if the actual comprehensive energy efficiency evaluation value is within the target range (greater than or equal to the lower limit and less than or equal to the upper limit), the current control parameters are considered valid, and the system continues to operate under the existing control parameter adjustment strategy. If the actual comprehensive energy efficiency evaluation value deviates from the target range (less than the lower limit or greater than the upper limit), and the absolute value of this deviation exceeds a pre-set allowable threshold, then the current operating conditions are determined to have changed significantly, and the existing control parameter adjustment strategy is no longer optimal.

[0073] When the deviation exceeds the allowable range, the system automatically triggers the dynamic calibration program, restarting the dynamic adaptive optimization algorithm. The algorithm uses the current actual operating data as new initial conditions and recalculates within a search space near the current valve opening and flow rate. The goal of the dynamic adaptive optimization algorithm is to quickly find a combination of valve opening and flow rate that brings the actual comprehensive energy efficiency evaluation value back to the target range under new operating constraints. After running, the algorithm outputs a new set of optimization results, i.e., the corrected control parameters. The control unit generates a correction strategy based on these corrected parameters. The correction strategy is immediately integrated into the currently executing control parameter adjustment strategy, replacing some of the original instructions, thereby correcting the actuator's actions in real time. The entire dynamic calibration process is automated and cyclical, requiring no manual intervention, ensuring that the integrated anti-theft thermal control valve can adapt to changes in external load or internal characteristic drift, maintaining high energy efficiency over the long term. It is understandable that the trigger frequency of dynamic calibration needs to be set appropriately; excessively frequent calibration may cause system oscillation, while excessively long calibration intervals may lead to decreased energy efficiency. Optionally, the system can introduce a dead-zone delay judgment, triggering calibration only after the deviation has continuously exceeded a threshold for a certain period of time, thereby improving the system's anti-interference capability. In some embodiments, the mathematical expression of the deviation can be defined as the normalized distance between the actual comprehensive energy efficiency evaluation value and the midpoint of the target interval, calculated as follows:

[0074]

[0075] in: Represents normalization bias. Represents the actual comprehensive energy efficiency evaluation value. This represents the median of the target interval. and These represent the upper and lower limits of the target interval, respectively. When Greater than the preset threshold When the deviation exceeds the allowable range, it is considered to be within the acceptable range.

[0076] See Figure 4This diagram illustrates the complete adjustment process of a remote-controlled, lockable, anti-theft thermal regulating valve when executing control parameter adjustment strategies. The chart uses a dual Y-axis to simultaneously present the dynamic changes of two key control variables: valve core opening adjustment and flow rate matching. The left vertical axis displays the percentage value of the valve core opening, reflecting the precise control of the valve core position by the actuator via a stepper motor. The target valve core opening curve represents the command sequence parsed by the control unit, while the actual valve core opening curve shows the real position change after considering system response characteristics and position sensor feedback. The slight difference between the two curves reflects the positional deviation in the closed-loop control system, which is corrected in real time using a proportional-integral-derivative control algorithm. The right vertical axis shows the change in medium flow rate; the target flow rate is an ideal value calculated based on an energy efficiency assessment model, while the actual flow rate is achieved by adjusting the motor speed using a variable frequency pump. The coordinated change between the flow rate curve and the valve core opening curve is clearly visible, demonstrating the synchronous adjustment effect ensured by the timing controller at millisecond-level precision. When the valve core opening changes, the flow rate can respond quickly and track the target value, ensuring that the thermal control valve maintains its optimal operating condition under different operating conditions. The entire regulation process is divided into multiple stages, including a rapid regulation stage, a stable operation stage, and a dynamic adjustment stage. Each stage demonstrates the control system's adaptability to different operating conditions. The smooth transition of the curve indicates that the system has good dynamic characteristics, effectively suppressing fluctuations caused by external disturbances and changes in internal parameters, ensuring the stability and accuracy of the regulation process.

[0077] Example 4: In specific implementation, referring to Table 1, the process of generating the correction strategy by the dynamic adaptive optimization algorithm begins with deviation analysis. When the deviation between the actual comprehensive energy efficiency evaluation value and the target interval exceeds the allowable range, the system first calculates the absolute value of the deviation, which is the absolute value of the difference between the actual comprehensive energy efficiency evaluation value and the median of the target interval. Based on the calculated magnitude of the absolute value of the deviation, the search step size parameter of the dynamic adaptive optimization algorithm is dynamically adjusted. When the absolute value of the deviation is large, a larger search step size is initialized to quickly locate the satisfactory region within a broad solution space; when the absolute value of the deviation is small, a smaller search step size is used for fine searching to improve the accuracy of the solution. The correspondence between the search step size parameter and the absolute value of the deviation is determined through a preset lookup table, which defines the initial step size values ​​corresponding to different deviation ranges. Table 1: Correspondence between Absolute Value of Deviation and Initial Search Step Size

[0078] absolute value range of deviation Initial search step size setting (0,0.05] 0.01 (0.05,0.1] 0.02 (0.1,0.2] 0.05 >0.2 0.1

[0079] In its implementation, the dynamic adaptive optimization algorithm initializes a population. The population size is set according to the problem complexity and computational resources. Each individual in the population is a solution vector, containing two dimensions: the first dimension represents a candidate valve opening value, and the second dimension represents the flow rate adjustment associated with that valve opening value. Population initialization typically occurs near the currently executing valve opening and flow rate adjustment. Centered on the current value, a set of candidate solution individuals is randomly generated within a certain range according to the set initial search step size. The dynamic adaptive optimization algorithm then enters the iterative evaluation phase. For each individual in the population, the algorithm extracts the valve opening value from its solution vector and inputs it into the energy efficiency evaluation model. Based on the latest temperature field dynamic sequence and flow field characteristic spectrum data, the energy efficiency evaluation model calculates and returns the predicted comprehensive energy efficiency evaluation value under that valve opening. The algorithm checks whether this predicted value falls within a preset target interval and filters out all individual solution vectors that can make the comprehensive energy efficiency evaluation value fall within the target interval, forming a candidate solution set. It can be understood that the candidate solution set may contain multiple feasible solutions.

[0080] When selecting the final correction strategy from the candidate solution set, the principle of minimum action amplitude is adopted. The absolute difference between the valve core opening value represented by each individual solution vector in the candidate solution set and the current actual valve core opening value is calculated. All these differences are compared, and the individual solution vector with the smallest difference is selected as the base solution. After selecting the base solution, the execution timing needs to be fine-tuned based on the real-time status data of the actuator of the integrated anti-theft thermal regulating valve with remote control. The real-time status data includes the real-time position feedback from the valve core position sensor, the real-time torque current of the stepper motor, and the real-time output frequency of the variable frequency pump. The purpose of timing adjustment is to ensure that the valve core opening adjustment action and the flow rate adjustment action can be smoothly connected to avoid excessive hydraulic impact or mechanical stress. For example, when a large resistance to valve core movement is detected, the issuance time of the flow rate adjustment command can be appropriately delayed, or the large change in valve core opening can be decomposed into multiple small steps and executed gradually, with simultaneous fine-tuning of the flow rate after each step. The final correction strategy not only includes the new target valve core opening value and target flow rate value, but also includes a detailed action timing plan to achieve these target values. In some embodiments, timing planning can be represented as a time-instruction curve.

[0081] The improved particle swarm optimization algorithm is the core implementation of the dynamic adaptive optimization algorithm. When the improved PSO algorithm starts running, a set of control parameters needs to be set, including the size of the particle swarm and the upper limit of the number of algorithm iterations. During the initialization phase, the position vector and velocity vector of each particle are randomly generated. In the control problem of a remote-controlled anti-theft thermal regulating valve, the position vector is a one-dimensional vector, and its value represents a specific valve opening value. The velocity vector is also a one-dimensional vector, and its value represents the rate and direction of change of the valve opening during the iteration process. Each particle flies in the search space, and its performance is evaluated by a fitness function. The fitness function inputs the particle's current position vector (i.e., the valve opening value) into the energy efficiency evaluation model to obtain a comprehensive energy efficiency evaluation value. Then, it calculates the distance between this comprehensive energy efficiency evaluation value and the median of the target interval. The reciprocal of the distance value is used as the particle's fitness value; a higher fitness value indicates a better particle position.

[0082] In each iteration, the improved particle swarm optimization algorithm updates the individual historical best position of each particle and the global historical best position of the entire population. The individual historical best position is the position with the highest fitness reached by each particle in all iterations, while the global historical best position is the position with the highest fitness found by the entire population in all iterations. The particle state update formula not only considers the particle's own historical experience and social experience but also introduces dynamic monitoring of the algorithm's convergence state. The convergence state is determined by calculating the rate of change of the population diversity index or the historical best fitness value. When premature convergence or getting stuck in a local optimum is detected, the inertia weight factor and social learning factor are dynamically adjusted. For example, the inertia weight factor is increased to maintain the particle's exploration ability, or the social learning factor is temporarily increased to enhance information exchange between particles to escape the local optimum. Optionally, a random perturbation strategy can be introduced, such as resetting the positions of some particles with a certain probability. The particles generate a new generation of positions based on the updated velocity vector and position vector formula, completing one iteration. The iteration process is repeated until a termination condition is met. The termination condition is usually reaching the maximum number of iterations or the improvement of the global historical best fitness value for several consecutive generations being less than a set threshold. After the algorithm terminates, it outputs the valve core opening value corresponding to the globally optimal historical position as the optimization result. This result will be used to generate the valve core opening command in the control parameter adjustment strategy. The improved particle swarm optimization algorithm effectively balances global exploration and local development capabilities through a dynamic parameter adjustment mechanism, improving the efficiency and reliability of the control parameter optimization problem for the integrated anti-theft thermal regulating valve with remote control and unlocking.

[0083] In practical implementation, the mapping relationship between the absolute value of the deviation and the search step size needs to be calibrated according to the actual characteristics of the integrated anti-theft thermal regulating valve with remote locking and control. This mapping relationship determines the trade-off between the algorithm's response speed and convergence accuracy. The setting of the population initialization range directly affects search efficiency; an excessively large range may lead to slow convergence, while an excessively small range may miss the global optimum. The construction of the fitness function is crucial. The fitness function needs to accurately distinguish the energy efficiency under different valve core opening degrees. The inverse distance form ensures that the fitness value monotonically increases as it approaches the target median, providing good guidance. Particle state update is the core operation of the improved particle swarm optimization algorithm. Each update relies on information from the individual's historical optimum and the global historical optimum, as well as dynamically adjusted inertia weights and social learning factors. The inertia weight factor controls the degree to which the particle's current velocity affects the velocity of the next generation; a larger value is beneficial for global exploration, while a smaller value is beneficial for local development. The social learning factor controls the degree to which the particle learns towards the optimal position of the population. Convergence monitoring is a continuous process to ensure that the algorithm does not loop infinitely or wander in invalid regions. It is understandable that the parameter settings of the improved particle swarm optimization algorithm, such as population size, upper limit of iteration count, and inertia weight adjustment strategy, need to be tested multiple times in offline simulations to determine the optimal combination, thereby ensuring performance in online applications. The entire process of generating the correction strategy is automatic, fast, and highly targeted, ensuring that the integrated anti-theft thermal regulation valve control system with locking and remote control can respond to dynamically changing working conditions in a timely and effective manner.

[0084] See Figure 5Employing a dual Y-axis design, the graph comprehensively presents the changes in two key indicators during the dynamic adaptive optimization calibration process. The left vertical axis displays the optimization trajectory of the comprehensive energy efficiency evaluation value, while the right vertical axis shows the corresponding valve opening adjustment process. Both are completely synchronized in the time dimension, clearly demonstrating the intrinsic relationship between control parameters and system performance. The comprehensive energy efficiency evaluation value curve demonstrates the adaptive adjustment capability of the improved particle swarm optimization algorithm in response to fluctuations in operating conditions. Starting from an initial low-efficiency state, the curve undergoes significant improvement during the rapid search phase, gradually entering the fine-tuning phase, and finally stabilizing within the target energy efficiency range. The target range marked by the red dashed line and the green filled area intuitively show the high-efficiency operating range that the system expects to maintain. The convergence trajectory of the energy efficiency value during the optimization process reflects the effective balance between global exploration and local development achieved by the algorithm. The valve opening curve reflects the specific control actions taken by the actuator to achieve energy efficiency optimization. The changes in the opening value are closely coordinated with the energy efficiency optimization process, with significant adjustments made in the early stages to quickly improve system performance, followed by fine-tuning to avoid mechanical shock. The coordinated changes of the two curves validate the accuracy of the energy efficiency assessment model and the effectiveness of the control strategy, demonstrating the system's rapid response to dynamic operating conditions and its adaptive optimization characteristics. The clear stage labels in the graph indicate the different phases of the optimization process, including rapid search, fine-tuning, and stable convergence, each stage reflecting the strategy of dynamically adjusting algorithm parameters. This single-graph, dual-axis design not only saves display space but, more importantly, highlights the coupling relationships and temporal coordination between key parameters, providing intuitive and effective visualization support for understanding the complex dynamic calibration mechanism.

[0085] Example 5: In specific implementation, the calculation of particle fitness value is the core step in the improved particle swarm optimization algorithm to evaluate the merits of each particle. The calculation process begins by inputting the valve opening value represented by the particle position vector into the established energy efficiency evaluation model. After receiving the input valve opening value, the energy efficiency evaluation model activates its internal calculation logic. First, it retrieves or calculates the heat loss coefficient associated with the specific valve opening value from the internal database or real-time calculation module corresponding to the current operating condition. The heat loss coefficient characterizes the heat preservation efficiency of the lockable remote control integrated anti-theft thermal regulating valve at the current opening; the smaller the value, the less heat loss. The energy efficiency evaluation model retrieves or calculates the heat transfer efficiency index. The heat transfer efficiency index reflects the effective utilization of heat when the medium flows through the lockable remote control integrated anti-theft thermal regulating valve; the closer the value is to 1, the more ideal the heat transfer process. Meanwhile, based on the latest acquired flow field characteristic data, the energy efficiency assessment model calculates the turbulence intensity of the internal flow field of the valve through built-in flow field analysis routines. Turbulence intensity quantifies the degree of flow pulsation; higher turbulence intensity typically means greater energy loss. The energy efficiency assessment model also calculates the energy dissipation rate in parallel. The energy dissipation rate describes the rate at which fluid viscous forces convert mechanical energy into heat energy and is a key parameter for assessing flow resistance loss. It can be understood that these four parameters together constitute a multi-dimensional index set for evaluating the energy efficiency status of the integrated anti-theft thermal control valve with remote locking mechanism.

[0086] After obtaining the values ​​of the four parameters—heat loss coefficient, heat transfer efficiency index, turbulence intensity, and energy dissipation rate—they need to be integrated into a single comprehensive energy efficiency evaluation value. This integration process is completed using a weighted summation method. The weighted summation method requires assigning a weight coefficient to each parameter. The allocation of these weight coefficients is strictly based on the working priority and objectives of the integrated anti-theft thermal control valve in a specific application scenario. For example, in a district heating system where energy saving and heat preservation are the primary objectives, the weight coefficient for the heat loss coefficient would be assigned a relatively high value, such as 0.4, meaning the system will pay special attention to the heat dissipation of the valve body. The weight coefficient for the heat transfer efficiency index might be set to 0.3, indicating a high requirement for the effectiveness of heat transfer. The weight coefficients for turbulence intensity and energy dissipation rate might be set to 0.15 and 0.15 respectively, reflecting the need to appropriately control flow losses while ensuring flow stability. The specific values ​​of the weight coefficients are usually determined by domain experts based on historical operating data, system design requirements, and energy efficiency standards, and can be modified through the configuration interface to adapt to different operating modes.

[0087] The weighted summation calculation process is carried out step by step according to clear mathematical rules. The first step is to multiply the value of the heat loss coefficient by its corresponding weighting coefficient. This yields the weighted heat loss value. The second step is to multiply the heat transfer efficiency index value by its corresponding weighting coefficient. This yields the weighted heat transfer value. The third step is to multiply the turbulence intensity value by its corresponding weighting coefficient. This yields the weighted turbulence values. The fourth step is to multiply the energy dissipation rate by its corresponding weighting coefficient. The weighted dissipation value is obtained. Finally, these four weighted values—weighted heat loss, weighted heat transfer, weighted turbulence, and weighted dissipation—are algebraically added together. Their sum is the comprehensive energy efficiency evaluation value, representing the overall energy efficiency level of the lock-up remote-controlled integrated anti-theft thermal control valve at that particle location. This comprehensive energy efficiency evaluation value is a dimensionless scalar; its value directly reflects the quality of the valve core opening scheme, with a higher value indicating better overall energy efficiency.

[0088] After obtaining the comprehensive energy efficiency evaluation value, it is not directly used as the fitness value of particles in the improved particle swarm optimization algorithm. A transformation operation is required. This transformation operation calculates the absolute distance between the comprehensive energy efficiency evaluation value and the median of a preset target interval, which is the arithmetic mean of the upper and lower limits of the target interval. This distance reflects the degree to which the current comprehensive energy efficiency evaluation value deviates from the ideal center point; the smaller the distance, the closer it is to the optimum. Subsequently, the reciprocal of this distance value is taken as the particle's final fitness value. Therefore, the closer the comprehensive energy efficiency evaluation value is to the median of the target interval, the smaller the distance, and the larger its reciprocal, i.e., the fitness value. This indicates that the particle is in a better position and is more worthy of retention and reference. This method of constructing the fitness function can effectively guide the particle swarm to gather towards the optimal region in the search space. In practical implementation, the distance calculation and reciprocal operation need to carefully handle boundary conditions. For example, when the comprehensive energy efficiency evaluation value is exactly equal to the median of the target interval, resulting in a distance of zero, a very large constant needs to be assigned to the fitness value to avoid the error of dividing by zero. Optionally, a fitness value truncation method can be used to limit the range of fitness values.

[0089] The specific implementation details of the weighted summation method can be adjusted as needed. The weighting coefficients are not static; the system supports dynamic adjustment of the weighting coefficients based on different operating seasons, load periods, or external ambient temperatures. For example, during the severe winter months, the weighting coefficient of the heat loss coefficient may be further increased to 0.5 to minimize heat loss; while in summer or transitional seasons, the weighting coefficient of the heat loss coefficient may be appropriately reduced, and the weighting coefficient of the heat transfer efficiency index may be increased to adapt to different operating conditions. The dynamic weighting adjustment strategy can be implemented through a predefined rule table or a simple fuzzy inference engine. The rule table takes ambient temperature, timestamps, etc., as input and outputs the corresponding combination of weighting coefficients. In some embodiments, the four parameters can be preprocessed by normalization before weighted summation, normalizing the heat loss coefficient, heat transfer efficiency index, turbulence intensity, and energy dissipation rate to the [0,1] interval to eliminate the inappropriate influence that differences in the dimensions and orders of magnitude of different parameters may have on the weighting result, making the weighted summation more fair and reasonable. The normalization process can utilize the historical maximum and minimum values ​​of the parameters for minimum-maximum scaling. Optionally, the weighted summation method doesn't have to be a simple linear weighting. In some more complex energy efficiency assessment model implementations, nonlinear weighting or utility function-based fusion methods may be used. However, linear weighted summation is widely used due to its simplicity, intuitiveness, and high computational efficiency. The entire process of calculating the particle fitness value, from parameter input to fitness value output, needs to be performed once for each particle in the population in each iteration of the improved particle swarm optimization algorithm. It is the main part of the algorithm's computational overhead, but its result directly determines the direction of the optimization search and the quality of the final solution, serving as an important bridge connecting the energy efficiency assessment model and the optimization algorithm.

[0090] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A control optimization method for locking a remote-controlled integrated anti-theft thermal regulating valve, characterized in that, The method includes: A three-dimensional thermal field simulation model is constructed, and real-time temperature field data of the surface and internal flow channel of the thermal regulating valve are collected by distributed temperature sensors to form a dynamic sequence of temperature field. A multiphase flow monitoring device is used to acquire velocity field distribution and pressure field fluctuation data of the medium inside the thermal regulating valve, and a flow field characteristic map is generated. Based on the aforementioned temperature field dynamic sequence and flow field characteristic spectrum, an energy efficiency evaluation model for the thermal control valve is established in conjunction with a three-dimensional thermal field simulation model. The energy efficiency assessment model is optimized using a dynamic adaptive optimization algorithm to generate a control parameter adjustment strategy. According to the control parameter adjustment strategy, the actuator of the thermal regulating valve is driven to complete the coordinated adjustment of valve core opening and flow rate.

2. The control optimization method for locking a remote-controlled integrated anti-theft thermal regulating valve as described in claim 1, characterized in that, The construction of the three-dimensional thermal field simulation model includes: The temperature distribution data of the outer surface of the thermal regulating valve is collected by an infrared thermal imager, and the temperature readings of key nodes inside the valve body are obtained by an insertion thermocouple. A three-dimensional mesh model is established based on the geometric parameters of the thermal regulating valve, and the temperature distribution data is mapped to the mesh nodes. The heat conduction flux between grid cells is calculated using the finite volume method to generate a dynamic sequence of the temperature field. The spatiotemporal variation characteristics of the velocity and pressure fields are extracted by simulating the flow state of the medium in the flow channel using computational fluid dynamics software. By coupling temperature field and flow field data, a three-dimensional thermal field simulation model reflecting the actual working state of the thermal regulating valve is established.

3. The control optimization method for locking a remote-controlled integrated anti-theft thermal regulating valve as described in claim 2, characterized in that, The energy efficiency evaluation model for the thermal regulating valve includes: The heat loss coefficient and heat transfer efficiency index are extracted from the three-dimensional thermal field simulation model. The turbulence intensity and energy dissipation rate of the medium flow are calculated based on the flow field characteristic map. A multi-objective optimization algorithm is used to weight and fuse the heat loss coefficient, heat transfer efficiency, turbulence intensity, and energy dissipation rate; Establish a nonlinear mapping relationship with valve core opening degree as the independent variable and comprehensive energy efficiency evaluation value as the dependent variable; The parameters of the nonlinear mapping relationship are calibrated using experimental data to form an energy efficiency assessment model.

4. The control optimization method for locking a remote-controlled integrated anti-theft thermal regulating valve as described in claim 3, characterized in that, The generation control parameter adjustment strategy includes: Set the target range for the comprehensive energy efficiency evaluation value and the safe operating range for the valve core opening; The particle swarm optimization algorithm is used to search for valve core opening combinations that allow the comprehensive energy efficiency evaluation value to fall into the target range within the safe operating range. Calculate the corresponding flow rate adjustment based on the valve core opening combination obtained from the search; Establish the timing relationship between valve core opening and flow rate regulation to form a control parameter adjustment strategy.

5. The control optimization method for locking a remote-controlled integrated anti-theft thermal regulating valve as described in claim 4, characterized in that, The actuator that drives the thermal regulating valve performs coordinated regulation of the valve core opening and flow rate, including: The valve core opening command sequence in the control parameter adjustment strategy is analyzed. The valve core is precisely positioned according to the command sequence by controlling the stepper motor, and the output frequency of the variable frequency pump is adjusted synchronously to achieve precise matching of flow rate. Real-time monitoring of feedback data from valve core position sensors and flow meters forms a closed-loop control system.

6. The control optimization method for locking a remote-controlled integrated anti-theft thermal regulating valve as described in claim 5, characterized in that, The method also includes dynamic calibration of the coordinated adjustment process, specifically: Real-time changes in the dynamic sequence of the temperature field and the characteristic spectrum of the flow field are collected during the adjustment process; Input real-time change data into the energy efficiency assessment model to calculate the actual comprehensive energy efficiency evaluation value; Compare the deviation between the actual comprehensive energy efficiency evaluation value and the target range; When the deviation exceeds the allowable range, the dynamic adaptive optimization algorithm is restarted to generate a correction strategy.

7. The control optimization method for locking a remote-controlled integrated anti-theft thermal regulating valve as described in claim 6, characterized in that, The strategy for restarting the dynamic adaptive optimization algorithm to generate corrections includes: Calculate the absolute value of the deviation between the actual comprehensive energy efficiency evaluation value and the median of the target interval, and adjust the search step size parameter of the dynamic adaptive optimization algorithm according to the magnitude of the absolute value of the deviation; Initialize the algorithm population, where each individual represents a combination of valve opening degree and flow rate regulation; use the energy efficiency assessment model to iteratively evaluate the comprehensive energy efficiency evaluation value of each individual, and select individuals whose comprehensive energy efficiency evaluation value falls within the target interval as candidate solutions; The solution with the smallest change in valve core opening is selected from the candidate solution set as the basis for the correction strategy; the execution timing of valve core opening and flow rate regulation in the correction strategy is adjusted according to the real-time status data of the current actuator.

8. The control optimization method for locking a remote-controlled integrated anti-theft thermal regulating valve as described in claim 7, characterized in that, The dynamic adaptive optimization algorithm is implemented using an improved particle swarm optimization algorithm, and includes the following steps: Set the size of the particle swarm and the upper limit of the number of iterations. Initialize the position and velocity vectors of each particle. The position vector represents the valve core opening value, and the velocity vector represents the opening change rate. Calculate the fitness value of each particle, output the comprehensive energy efficiency evaluation value through the energy efficiency assessment model; update the individual historical best position and global historical best position of each particle; Monitor the convergence status of the particle swarm optimization algorithm and dynamically adjust the inertia weight factor and social learning factor. A new generation of particle swarms is generated based on the updated position and velocity vectors; the iteration process is repeated until the termination condition is met, and the valve opening value corresponding to the global historical best position is output.

9. The control optimization method for locking a remote-controlled integrated anti-theft thermal regulating valve as described in claim 8, characterized in that, The calculation of the fitness value for each particle includes: Input the valve core opening, represented by the particle position vector, into the energy efficiency evaluation model; extract the current heat loss coefficient, heat transfer efficiency index, turbulence intensity, and energy dissipation rate from the energy efficiency evaluation model; combine the heat loss coefficient, heat transfer efficiency index, turbulence intensity, and energy dissipation rate into a comprehensive energy efficiency evaluation value using a weighted summation method; calculate the distance between the comprehensive energy efficiency evaluation value and the median of the target interval, and use the reciprocal of the distance value as the fitness value.

10. The control optimization method for locking a remote-controlled integrated anti-theft thermal regulating valve as described in claim 9, characterized in that, The weighted summation method includes: Based on the working priority of the thermal control valve, set the weighting coefficients for the heat loss coefficient, heat transfer efficiency index, turbulence intensity, and energy dissipation rate; multiply the heat loss coefficient by its weighting coefficient to obtain the weighted heat loss value, multiply the heat transfer efficiency index by its weighting coefficient to obtain the weighted heat transfer value, multiply the turbulence intensity by its weighting coefficient to obtain the weighted turbulence value, and multiply the energy dissipation rate by its weighting coefficient to obtain the weighted dissipation value; sum the weighted heat loss value, weighted heat transfer value, weighted turbulence value, and weighted dissipation value to obtain the comprehensive energy efficiency evaluation value.

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