A method for controlling the temperature of a smart valve

Abstract

The present application relates to flow metering technical field, specifically, it is a kind of back temperature control method of intelligent control valve.It includes the following steps: receiving back temperature control strategy and the set value of specified back temperature sent by user, and judging initial control parameter set according to back temperature control strategy;According to sampling period, real-time operation data is collected from temperature sensing module and flow meter, current time backwater temperature and flow are obtained, and back temperature deviation is calculated;Based on back temperature deviation, the control target value of intelligent control valve is calculated using model fuzzy logic predictive control algorithm;Based on control target value, valve opening is adjusted, and control parameter is dynamically corrected using back temperature control strategy, for self-adapting back temperature control.The present application fuses the composite algorithm of fuzzy logic control and model predictive control (MPC), on the basis of fuzzy inference generating preliminary control signal, the future backwater temperature change trend is multi-step predicted in combination with nonlinear system model.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of flow metering, in particular to a return temperature control method of a smart control valve. BACKGROUND

[0002] In the current heating industry, the regulation of heating still generally relies on environmental temperature changes and the experience of operating personnel, and the opening of the supply and return water valves at the heat station or user end is manually adjusted to achieve rough regulation and control of the heating temperature. This traditional manual intervention mode has many drawbacks: first, the adjustment process has strong hysteresis and cannot respond to changes in user-side load in real time, resulting in uneven heating and phenomena such as "near heat and far cold" or "overheating during the day and cold at night", which seriously affect user comfort; second, due to the lack of accurate monitoring and analysis of the hydraulic conditions of the secondary pipe network, it is difficult to achieve hydraulic balance and thermal balance, and there is a serious problem of hydraulic imbalance, i.e. "large flow, small temperature difference", which not only reduces the energy efficiency of the heating system, but also increases energy consumption at the heat source end; third, manual adjustment lacks data support and quantitative standards, and the operation is arbitrary, making it difficult to ensure the consistency of the adjustment quality, and requiring a large amount of manpower, resulting in high operation and maintenance costs.

[0003] In addition, with the continuous expansion of urban heating scale, the increasing individualized needs of users, and the promotion of policies such as peak-valley electricity prices and carbon emission control, the traditional mode cannot meet the requirements of modern smart heating systems for fine, intelligent and energy-saving operation. Especially under the background of the national "double carbon" strategy, the heating industry, as a key field of energy consumption and carbon emissions, needs to be transformed and upgraded through automation, digitization and intelligent technology. Therefore, a return temperature control method of a smart control valve is provided. SUMMARY

[0004] The purpose of the present application is to provide a return temperature control method of a smart control valve to solve the problems of low control accuracy, serious hydraulic imbalance, large energy waste and difficulty in achieving dynamic balance of the secondary network caused by the traditional heating system relying on manual experience adjustment.

[0005] To achieve the above purpose, the present application provides a return temperature control method of a smart control valve, comprising the following steps:

[0006] S1, receiving the return temperature control strategy and the set value of the specified return temperature sent by the user, and determining the initial control parameter set according to the return temperature control strategy;

[0007] S2, collecting real-time operation data from the temperature sensing module and the flow meter according to the sampling period, obtaining the current return water temperature and flow, and calculating the return temperature deviation;

[0008] S3, the control target value of the intelligent control valve is calculated by using the model fuzzy logic predictive control algorithm based on the temperature return deviation;

[0009] S4, the valve opening is adjusted based on the control target value, and the control parameters are dynamically corrected by using the temperature return control strategy for self-adaptive temperature return control.

[0010] As a further improvement of the technical solution, in S1, the initial control parameter set is determined according to the temperature return control strategy, including the following steps:

[0011] S1.1, receiving the temperature return control strategy instruction data packet from the user end or the upper computer through the communication interface, the temperature return control strategy instruction data packet at least including the target return water temperature set value , temperature return control mode, predictive control parameter;

[0012] S1.2, the temperature return control strategy instruction data packet is structured and analyzed, the control parameters are extracted, and the control parameters are checked for legality and standardized in data format;

[0013] S1.3, determining the initial control parameter set according to the extracted control parameters.

[0014] As a further improvement of the technical solution, in S2, the temperature return deviation is calculated, including the following steps:

[0015] S2.1, collecting the current return water temperature from the secondary side return water temperature sensor according to the preset sampling period , and collecting the current return water flow from the flow meter at the same time;

[0016] S2.2, intelligently dynamically filtering and abnormally damping the return water temperature and return water flow data;

[0017] S2.3, calculating the instantaneous temperature return deviation based on the collected real-time temperature and target temperature .

[0018] As a further improvement of the technical solution, in S2.2, the return water temperature and return water flow data are intelligently dynamically filtered and abnormally damped, including the following steps:

[0019] S2.21, calculating the average value and dynamic fluctuation threshold of the return water temperature according to the historical data of the last period of time h; when the newly collected return water temperature and return water flow When the data instantaneously deviates from the current average value by more than the dynamic fluctuation threshold, the return water temperature and the return water flow are marked as suspected abnormal values;

[0020] S2.22, check whether the change trend of the current data point is continuous and smooth with the data points of the historical period, to determine whether the data is a normal value;

[0021] S2.23, for suspected abnormal values, output the last period effective value plus the maximum allowed change; for normal values, use sliding average filtering.

[0022] As a further improvement of the technical solution, in S3, the control target value of the intelligent control valve is calculated based on the return temperature deviation using a model-based fuzzy logic predictive control algorithm, including the following steps:

[0023] S3.1, obtain the current return temperature deviation, the actually measured return water temperature and flow, and use them as inputs of the model-based fuzzy logic predictive control algorithm;

[0024] S3.2, fuzzify the return water temperature , perform fuzzy reasoning and aggregation based on the fuzzy rule base, and generate a preliminary valve control signal through defuzzification;

[0025] S3.3, use the preliminary valve control signal as input, predict the future system response through the prediction model, optimize the objective function combined with the constraint conditions, generate the optimal sequence of valve opening degree, and select the valve opening degree at the next sampling time as the control target value .

[0026] As a further improvement of the technical solution, in S3.2, the return water temperature is fuzzified, fuzzy reasoning and aggregation are performed based on the fuzzy rule base, and a preliminary valve control signal is generated through defuzzification, including the following steps:

[0027] S3.21, map the return water temperature to fuzzy sets;

[0028] S3.22, calculate the membership degree of each return water temperature value in each fuzzy set using a triangular membership function;

[0029] S3.23, according to the pre-defined fuzzy rule base, perform reasoning on the membership degrees of the input variables to generate corresponding output fuzzy sets;

[0030] S3.24, perform aggregation operation on the fuzzy sets output by all rules to form the overall fuzzy output;

[0031] S3.25. Using a weighted average method, convert the aggregated fuzzy output into a preliminary valve control signal. .

[0032] As a further improvement to this technical solution, in step S3.3, an optimal valve opening sequence is generated, and the valve opening at the next sampling time is selected as the control target value. This includes the following steps:

[0033] S3.31, Initial valve control signal As the initial input to the model predictive controller;

[0034] S3.32 Predicting the Future Based on Nonlinear System Models The changing trends of return water temperature and return temperature deviation within each sampling period;

[0035] S3.33. Construct an optimization objective function and solve for the optimal valve opening sequence using a quadratic programming algorithm;

[0036] S3.34. Select the valve opening at the next sampling time from the optimal sequence as the current control target value. .

[0037] As a further improvement to this technical solution, in S3.32, the future is predicted based on a nonlinear system model. The trend of return water temperature and return temperature deviation within a sampling period includes the following steps:

[0038] Establish discrete-time state equations based on the nonlinear system model;

[0039] Current system status and initial valve control signals As the initial condition for prediction, and to set the prediction step size. With control time domain Determine the number of future forecast cycles;

[0040] For each future sampling period Iteratively predict future states to obtain the future The sequence of return water temperature and return temperature deviation for each sampling period.

[0041] As a further improvement to this technical solution, step S4, which adjusts the valve opening based on the control target value, involves the following specific steps:

[0042] Target valve opening Converted into the corresponding valve opening signal Based on the equal percentage flow characteristics of the valve, the target flow rate through the valve is calculated. Based on target traffic The corresponding execution signal is generated to drive the electric actuator to adjust the valve to the corresponding opening position.

[0043] As a further improvement to this technical solution, in step S4, the control parameters are dynamically corrected using a temperature recovery control strategy, involving the following specific steps:

[0044] Real-time acquisition of primary side supply water temperature, secondary side return water temperature, and current flow rate data; when the secondary side return water temperature is detected to continuously deviate from the target return water temperature setpoint... Repeat steps S2 to S4 to adjust the valve opening and flow rate; when the secondary return water temperature reaches the target return water temperature set value... After stabilizing, it enters a waiting monitoring state to continuously detect temperature difference changes, which is used for adaptive optimization of valve control strategies and closed-loop temperature control stability under different operating conditions.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0046] 1. The present invention relates to a method for controlling the return water temperature of an intelligent control valve. By integrating a composite algorithm of fuzzy logic control and model predictive control (MPC), based on the initial control signal generated by fuzzy inference, the method combines a nonlinear system model to make multi-step predictions on the future return water temperature change trend. By optimizing the objective function to solve for the optimal valve opening sequence, the method effectively balances the nonlinear characteristics and dynamic response performance of the system, significantly improving the accuracy and anti-interference capability of the return water temperature control. At the same time, by collecting operating data in real time and dynamically correcting the control parameters, the method achieves adaptive adjustment for different operating conditions, enhancing the stability and robustness of the system under complex environments such as load fluctuations and external disturbances.

[0047] 2. The present invention relates to a method for controlling the return temperature of an intelligent control valve. In the data acquisition stage, an intelligent dynamic filtering and abnormal damping processing mechanism is introduced. By statistically analyzing the historical data of return water temperature and flow rate, a dynamic fluctuation threshold is constructed. Combined with trend continuity judgment, abnormal data is identified. A filtering strategy combining effective value compensation and light moving average is adopted to effectively suppress abnormal interference such as sensor noise and data jumps, ensuring the accuracy and smoothness of the input data of the control algorithm. This avoids misadjustment or frequent valve operation caused by data distortion, and improves the operational stability of the entire heating system and the service life of the valve actuator. Attached Figure Description

[0048] Figure 1 This is a flowchart of the overall method of the present invention. Detailed Implementation

[0049] 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0050] Example: Please refer to Figure 1 As shown, this embodiment provides a method for controlling the temperature return of an intelligent control valve, including the following steps:

[0051] S1. Receive the user's temperature recovery control strategy and specified temperature recovery setpoint, and determine the initial control parameter set based on the temperature recovery control strategy;

[0052] In this embodiment, the intelligent control valve includes at least an actuator and a control unit;

[0053] The control unit integrates a model-based fuzzy logic predictive control algorithm, which is used to calculate and output the valve's control target value in real time based on the input control signal. The secondary side recirculation control strategy can be implemented using a model-fuzzy logic predictive control algorithm, which combines model predictive control and fuzzy logic control to calculate the control target value of the valve.

[0054] The actuator is used to calculate the control target value. To actually adjust the valve opening;

[0055] The process of determining the initial control parameter set based on the temperature recovery control strategy includes the following steps:

[0056] S1.1 Receive the return water control strategy instruction data packet from the user terminal or host computer through the communication interface. The return water control strategy instruction data packet includes at least the target return water temperature setpoint. Temperature recovery control modes (such as constant temperature control, dynamic adaptive control, or time-sharing control), predictive control parameters (including prediction step size) Control Time Domain The data includes constraint thresholds, fuzzy logic rule base numbers, and membership function types. The control unit parses the instruction data package to extract control parameters related to algorithm calculation.

[0057] S1.2 The control unit performs structured parsing of the temperature recovery control strategy instruction data packet, extracts control parameters (including at least the set temperature, control mode, prediction parameters, and fuzzy rule base index), and performs legality verification and data format standardization on the control parameters to ensure that the input parameters meet the algorithm calculation requirements. Specifically, it first checks the numerical range and type of each parameter to ensure that it meets the system design requirements, such as the temperature value being within the allowable physical range, the sampling period being positive, the valve opening constraint not exceeding the mechanical limit, and the fuzzy rule base number corresponding to a valid library. At the same time, it standardizes the data format, including unifying the numerical units (such as degrees Celsius and seconds), converting integers / floating-point numbers into data types that the algorithm can recognize, and filling or correcting missing or outlier values ​​by default, to ensure that all input parameters can be directly used for the calculation of the model fuzzy logic predictive control algorithm without triggering anomalies or errors.

[0058] S1.3. Based on the extracted control parameters, determine the initial control parameter set, which should include at least the model predicted control parameters (such as prediction time domain, control time domain, weighting coefficients, valve opening change constraints), fuzzy membership function parameters, and fuzzy rule weights. This initial control parameter set is used for the subsequent temperature recovery closed-loop control calculations in steps S2 to S4. Specifically, the control unit first determines the control strategy type based on the temperature recovery control mode (such as constant temperature control, dynamic adaptive control, or time-segmented control), and combines this with the prediction step size in the predicted control parameters. Control Time Domain The system retrieves parameter templates matching the strategy from the system database or preset model parameter table, based on the constraint threshold and fuzzy logic rule base number; then, it sets the actual temperature accordingly. Adaptive correction is performed based on the current system operating conditions (including initial return water temperature and flow rate). The weight coefficients, constraint boundaries, and control gain of the model predictive control part are initialized, and the corresponding fuzzy membership functions and fuzzy rule weights are loaded to form a complete set of initial control parameters.

[0059] S2. Collect real-time operating data from the temperature sensing module and flow meter according to the sampling cycle, obtain the current return water temperature and flow rate, and calculate the return temperature deviation;

[0060] In this embodiment, the calculation of the temperature return deviation includes the following steps:

[0061] S2.1, According to the preset sampling period The current return water temperature is collected from the secondary side return water temperature sensor. At the same time, the current return water flow rate is collected from the flow meter. ;

[0062] S2.2, Regarding the return water temperature and return water flow The data undergoes intelligent dynamic filtering and abnormal damping processing to eliminate instantaneous noise and abnormal fluctuations, smooth signal changes, and thus provide stable and reliable real-time data input for subsequent temperature return deviation calculation and valve control.

[0063] In hot water secondary loops or industrial heating systems, return water temperature and flow are easily affected by instantaneous load changes, pump and valve regulation disturbances, sensor measurement errors, or external interference, leading to sudden changes or noise signals. If directly used for valve control, this can result in frequent valve opening and closing, control oscillations, or excessive temperature fluctuations, affecting system stability and comfort. Therefore, intelligent filtering and damping are needed to smooth the data and ensure stable and reliable closed-loop control. Regarding return water temperature... and return water flow The main advantage of intelligent dynamic filtering and abnormal damping of data is that it can effectively suppress sensor noise, instantaneous spikes and abnormal fluctuations, ensure the stability and continuity of the input control algorithm, thereby improving the prediction accuracy of the model fuzzy logic predictive control algorithm and the response smoothness of valve regulation, and achieving high precision and robustness of temperature recovery control.

[0064] For return water temperature and return water flow The data undergoes intelligent dynamic filtering and anomaly damping processing, including the following steps:

[0065] S2.21. Based on historical data of h over a recent period, calculate the average value and dynamic fluctuation threshold of the return water temperature (to determine the normal fluctuation range); when the newly collected return water temperature... and return water flow When the data momentarily deviates from the current average value by more than the dynamic fluctuation threshold, the return water temperature will be... and return water flow The data was marked as a suspected outlier;

[0066] Specifically: First, within a time window h, the collected return water temperature... ( =1…h) Calculate the average value Use this as the current reference temperature, and then calculate the standard deviation of the temperature within this window. or maximum range of change And based on empirical coefficients (Value range is 1.0-2.5, determined through on-site parameter adjustment) Generate dynamic fluctuation threshold. or To determine whether the newly collected temperature is within the normal fluctuation range, i.e., if If it is, it is considered a normal value; otherwise, it is marked as a suspected outlier.

[0067] S2.22. Check whether the trend of the current data point is continuous and smooth with the data points of the historical period to determine whether the data is a normal value; if the direction of change of a data point (such as a sudden and significant increase) is seriously contrary to the trend of the previous few periods, damping processing is initiated (damping processing refers to not directly using the abnormal value as the control input when a momentary abnormality or sudden change in return water temperature or flow is detected, but limiting its change range, adjusting the output value to the effective value of the previous period plus the maximum allowable change, thereby weakening the sudden change or spike in the signal, making the system input transition smoothly, avoiding the valve to adjust violently or control to oscillate due to momentary abnormalities, thereby improving the stability and robustness of the return temperature control).

[0068] Specifically: the control unit receives the current return water temperature and traffic Next, the rate of change or trend direction of historical data for the most recent sampling periods (e.g., the past n periods) is calculated, and a historical smooth trend line is obtained through weighted averaging or moving average fitting. Then, the direction and magnitude of the increase or decrease of the current data point are compared with the continuity of the historical trend line. If the direction of change of the current point is seriously inconsistent with the historical trend (e.g., the signs of increase or decrease are opposite and the magnitude of change exceeds a preset threshold), the change is considered complete. Preset threshold If the maximum change in return water temperature or flow rate data allowed by the system within a continuous sampling period is considered a transient anomaly or sudden change, damping processing is applied to that data point to limit the output value to the effective value of the previous period plus the maximum allowable change, thereby reducing spike impacts and ensuring smooth and continuous data.

[0069] S2.23. For suspected outliers, instead of using them directly, the output is the effective value from the previous period (referring to the return water temperature or flow rate value that has been confirmed as normal and usable for control calculations after intelligent filtering and anomaly detection in the previous sampling period) plus the maximum allowable change (referring to the maximum range of safe and reasonable change in return water temperature or flow rate under system physical conditions within a continuous sampling period, used to limit the impact of abnormal or sudden data on control) to smooth out spike noise; for normal values, a moving average filter is used (the structure of a lightweight moving average filter refers to, within each sampling period, comparing the current collected value with the previous period's value). The average of historical valid values ​​is calculated using an equal-weighted or weighted method. (where the moving average window length is used to generate the output value), smoothing out minor fluctuations while ensuring real-time data processing.

[0070] S2.3, Based on real-time temperature data With target temperature Calculate the instantaneous temperature return deviation :

[0071] ;

[0072] Among them, if This indicates that the current return water temperature is lower than the target temperature, and the return water temperature needs to be increased; if This indicates that the current return water temperature is higher than the target, and the return water temperature needs to be lowered.

[0073] S3. Calculate the control target value of the intelligent control valve based on the model fuzzy logic predictive control algorithm using the temperature return deviation.

[0074] In this embodiment, the model-fuzzy logic predictive control algorithm is a hybrid control algorithm that combines model predictive control and fuzzy logic control to achieve rapid adjustment of valve opening.

[0075] The control target value of the intelligent control valve is calculated based on the temperature return deviation using a model-based fuzzy logic predictive control algorithm, including the following steps:

[0076] S3.1 Obtain the current return temperature deviation and the actual measured return water temperature and flow rate, and use them as inputs to the model fuzzy logic predictive control algorithm;

[0077] S3.2, Regarding the return water temperature Fuzzification is performed, fuzzy reasoning and aggregation are carried out based on the fuzzy rule base, and preliminary valve control signals are generated after defuzzification;

[0078] Among them, the return water temperature The process involves fuzzification, fuzzy inference and aggregation based on a fuzzy rule base, and defuzzification to generate a preliminary valve control signal. This includes the following steps:

[0079] S3.21, Set the return water temperature Mapping to fuzzy sets (e.g., small, medium, large);

[0080] S3.22 Calculate each return water temperature using triangular membership functions. The membership degree of the input value in each fuzzy set provides the basis for fuzzy inference. Specifically, it involves defining several fuzzy sets (such as small, medium, and large) for each input variable (e.g., return water temperature or heat deviation), and assigning a corresponding triangular membership function to each fuzzy set, with its vertices corresponding to the minimum, center, and maximum values ​​of the fuzzy set, respectively; then, the input value... Substitute each triangle function and calculate the membership degree based on the relationship between the input value and the vertex position. ,like If it is located on the left hypotenuse of the triangle, then ( Let be the left endpoint of the triangle, representing the lower bound of the fuzzy set. (The vertex (highest point) of the triangle represents the center value or most typical value of the fuzzy set); if it is located on the right hypotenuse, then... ( (where is the right endpoint of the triangle, representing the upper limit of the fuzzy set); if at the vertex... ,but This method yields the membership degree of the input value across all fuzzy sets, providing a foundation for subsequent fuzzy inference.

[0081] S3.23. Based on the predefined fuzzy rule base (the predefined fuzzy rule base refers to a set of "if-then" fuzzy control rules established during the system design phase, used to describe the empirical or engineering logic relationship between input variables (such as return temperature deviation and return water flow) and output control variables (such as valve opening adjustment); each rule performs fuzzy inference based on the input membership degree, reflecting control suggestions for the valve under different operating conditions, for example: if... It is "low" and If it is "low", then It is a "small increase", among which, This refers to the valve opening adjustment amount. Due to temperature deviation, (For the rate of change of flow rate), reason about the membership degree of the input variable, and generate the corresponding output fuzzy set by using minimal fuzzy logic operation for each rule, which reflects the fuzzy response of valve control;

[0082] S3.24. Aggregate the fuzzy sets of all rule outputs to form an overall fuzzy output that comprehensively reflects the control suggestions of multiple rules;

[0083] S3.25. Using a weighted average method, convert the aggregated fuzzy output into a preliminary valve control signal. It is used as the input for model predictive control to achieve initial adjustment of valve opening;

[0084] S3.3. Using the initial valve control signal as input, predict the future system response through a predictive model, optimize the objective function based on constraints, generate the optimal valve opening sequence, and select the valve opening at the next sampling time as the control target value. ;

[0085] In this embodiment, model predictive control calculations predict the system state over a future period using a predictive model, and optimize the objective function based on this prediction. This pre-calculates the optimal valve opening sequence, achieving predictive and forward-looking control of valve actions and avoiding the lag issues that may arise from simple feedback control. By combining model predictive control with fuzzy logic control, fine-tuning of valve openings can be achieved, enabling the return water temperature to quickly and accurately track the setpoint, thus achieving precise secondary return water temperature control. Real-time fuzzy inference of actual measured flow and temperature difference values ​​quickly determines the current state and generates preliminary valve control signals; this rule-based method offers fast response. The fuzzy logic preprocessing section performs real-time fuzzy inference and quantification of actual measured heat values, effectively addressing uncertainties such as system parameter changes and external environmental disturbances, improving system robustness and dynamic response speed, and thus enhancing the control speed of the intelligent valve. Furthermore, by establishing a predictive model to predict future system responses and optimizing the objective function based on constraints to obtain the optimal valve opening sequence, it helps to plan ahead and precisely adjust valve openings, thereby improving dynamic response speed and control accuracy.

[0086] Furthermore, an optimal valve opening sequence is generated, and the valve opening at the next sampling time is selected as the control target value. This includes the following steps:

[0087] S3.31, Initial valve control signal As the initial input to the model predictive controller;

[0088] S3.32 Predicting the Future Based on Nonlinear System Models Return water temperature within each sampling period and temperature return deviation The core of this process is the nonlinear system model, which establishes a dynamic causal relationship between valve opening and system state (return water temperature and return water flow rate). This nonlinear system model is based on the first law of thermodynamics (energy conservation) and fluid mechanics principles. Based on the first law of thermodynamics, the heat exchange system is considered a dynamic energy balance process between energy input (supply side) and energy output (return water side). By modeling the energy change per unit time within the control volume, a nonlinear energy conservation equation is constructed for the return water temperature as a function of valve opening, flow rate, and heat exchange power. Simultaneously, based on fluid mechanics principles, the valve is considered a fluid throttling element, and the relationship between valve opening and system flow rate is established through valve characteristic curves, pressure drop-flow rate equations, and pipeline resistance models. The nonlinear mapping between these parameters, combined with the influence of flow rate on heat exchange rate, forms a dynamic causal chain of "valve opening, flow rate, energy exchange, and return water temperature." Finally, based on the above thermodynamic and fluid coupling equations, the continuous-time model is discretized to obtain the state equations. );

[0089] Among them, predicting the future based on nonlinear system models The trend of return water temperature and return temperature deviation within a sampling period includes the following steps:

[0090] Establish the discrete-time state equations based on the nonlinear system model: , It is a two-dimensional state vector that includes at least the return water temperature. Return water flow rate ; This is the valve opening control input; the state equation describes the system at the next moment. status (Right now How to change from the current state and control input Joint decision;

[0091] Current system status and initial valve control signals As the initial condition for prediction, and to set the prediction step size. With control time domain Determine the number of future forecast cycles;

[0092] For each future sampling period Iterative prediction of future states (including predicting the system state for the next period based on the state equation) From the predicted future state vector Extracting the predicted return water temperature: And calculate the predicted temperature return deviation: The future obtained Return water temperature in each sampling period Temperature deviation sequence;

[0093] S3.33 Constructing the Optimization Objective Function (Optimize the objective function) This method is used to solve for the optimal valve opening sequence under physical constraints (to minimize predicted temperature return deviation and valve regulation energy consumption). The optimal valve opening sequence is solved using a quadratic programming algorithm. (The solution process is as follows: given the system state and initial control signal, the controller predicts the change trend of return water temperature over several future sampling periods and calculates the objective function value corresponding to each possible valve opening sequence.) And, under the premise of satisfying the valve opening range, rate of change and system physical constraints, the opening sequence that minimizes the temperature return deviation and valve adjustment amplitude is selected as the optimal solution).

[0094] in, ;

[0095] In the formula, The temperature recovery deviation weighting coefficient is dimensionless. For the current prediction time The return water temperature deviation, in units of temperature. This is a dimensionless weighting coefficient for the change in valve opening. This refers to the change in valve opening degree, that is, the change in valve opening degree at the current prediction time relative to the previous sampling time; it is a dimensionless quantity. and The specific values ​​are determined through simulation or on-site debugging based on the dynamic characteristics of the controlled system. and The ratio is selected in the range of 1:1 to 10:1 (considering the difference in dimensions) to achieve a balance between control performance and valve operating frequency;

[0096] S3.34. Select the valve opening at the next sampling time from the optimal sequence. As the current control target value It is used to adjust the valve opening and realize closed-loop control of temperature recovery.

[0097] S4. Adjust the valve opening based on the control target value, and dynamically correct the control parameters using the temperature recovery control strategy for adaptive temperature recovery control;

[0098] In this embodiment, adjusting the valve opening based on the control target value involves the following specific steps:

[0099] Target valve opening Converted into the corresponding valve opening signal Based on the equal percentage flow characteristics of the valve, the target flow rate through the valve is calculated. (In the formula, This represents the valve's maximum flow rate. These are valve-specific, equal percentage characteristic parameters (dimensionless). (where is the natural logarithm of the exponential function, a constant, approximately 2.71828), based on the target flow rate. The system generates corresponding execution signals to drive electric or pneumatic actuators to adjust the valve to the corresponding opening position, while simultaneously monitoring the valve's feedback signal and the actual flow rate measured by the flow meter in real time. ,contrast and To determine the execution deviation, when the detected flow deviation exceeds the set flow deviation threshold d, the control module automatically corrects the opening command based on the deviation direction and amplitude, iteratively fine-tuning the valve posture to gradually bring the actual flow through the valve closer to the target flow. Thus achieving control target value Precise temperature regulation and stable control.

[0100] Furthermore, dynamically adjusting the control parameters using a temperature recovery control strategy involves the following specific steps:

[0101] During system operation, the intelligent control valve collects real-time data on the primary side supply water temperature, the secondary side return water temperature, and the current flow rate. When it detects that the secondary side return water temperature continuously deviates from the target return water temperature setpoint... Repeat steps S2 to S4 to adjust the valve opening and flow rate to ensure the return water temperature gradually returns to the set range; when the secondary side return water temperature reaches the target return water temperature set value... And remain stable (i.e., within C consecutive sampling periods, the value is consistent with the set value). The deviation always remains within the allowable error range After the system has entered a stable state (when the system is considered to have entered a stable state), the intelligent control valve enters a waiting monitoring state and continuously monitors the temperature difference change. Once a new disturbance or load change is detected, steps S2 to S4 are executed again to adaptively optimize the valve control strategy and stabilize the closed-loop temperature control under different operating conditions.

[0102] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A method for controlling the temperature return of an intelligent control valve, characterized in that, Includes the following steps: S1. Receive the user's temperature recovery control strategy and specified temperature recovery setpoint, and determine the initial control parameter set based on the temperature recovery control strategy; S2. Collect real-time operating data from the temperature sensing module and flow meter according to the sampling cycle, obtain the current return water temperature and flow rate, and calculate the return temperature deviation; S3. Calculate the control target value of the intelligent control valve based on the model fuzzy logic predictive control algorithm using the temperature return deviation. S4. Adjust the valve opening based on the control target value, and dynamically correct the control parameters using the temperature recovery control strategy for adaptive temperature recovery control; In step S2, the calculation of the temperature return deviation includes the following steps: S2.1, According to the preset sampling period The current return water temperature is collected from the secondary side return water temperature sensor. At the same time, the current return water flow rate is collected from the flow meter. ; S2.2, Regarding the return water temperature and return water flow The data undergoes intelligent dynamic filtering and anomaly damping processing. S2.3, Based on real-time temperature data With target temperature Calculate the instantaneous temperature return deviation ; In step S2.2, the return water temperature... and return water flow The data undergoes intelligent dynamic filtering and anomaly damping processing, including the following steps: S2.

21. Based on historical data of h over a recent period, calculate the average value and dynamic fluctuation threshold of the return water temperature; when a new return water temperature is collected... and return water flow When the data momentarily deviates from the current average value by more than the dynamic fluctuation threshold, the return water temperature will be... and return water flow The data was marked as a suspected outlier; S2.

22. Check whether the trend of the current data point is continuous and smooth with the data points of the historical period, in order to determine whether the data is a normal value; S2.

23. For suspected outliers, the output is the sum of the previous period's valid value and the maximum allowable change; for normal values, a moving average filter is used. In step S3, the control target value of the intelligent control valve is calculated based on the temperature return deviation using a model fuzzy logic predictive control algorithm, including the following steps: S3.1 Obtain the current return temperature deviation and the actual measured return water temperature and flow rate, and use them as inputs to the model fuzzy logic predictive control algorithm; S3.2, Regarding the return water temperature Fuzzification is performed, fuzzy reasoning and aggregation are carried out based on the fuzzy rule base, and preliminary valve control signals are generated after defuzzification; S3.

3. Using the initial valve control signal as input, predict the future system response through a predictive model, optimize the objective function based on constraints, generate the optimal valve opening sequence, and select the valve opening at the next sampling time as the control target value. ; In step S4, the control parameters are dynamically corrected using a temperature recovery control strategy, which involves the following specific steps: Real-time acquisition of primary side supply water temperature, secondary side return water temperature, and current flow rate data; when the secondary side return water temperature deviates from the target return water temperature setpoint... Repeat steps S2 to S4 to adjust the valve opening and flow rate; when the secondary return water temperature reaches the target return water temperature set value... After stabilizing, it enters a waiting monitoring state to continuously detect temperature difference changes, which is used for adaptive optimization of valve control strategies and closed-loop temperature control stability under different operating conditions.

2. The method for controlling the temperature return of the intelligent control valve according to claim 1, characterized in that: In step S1, determining the initial control parameter set based on the temperature recovery control strategy includes the following steps: S1.1 Receive the return water control strategy instruction data packet from the user terminal through the communication interface. The return water control strategy instruction data packet includes at least the target return water temperature setpoint. Temperature recovery control mode and predictive control parameters; S1.

2. Perform structured parsing on the temperature recovery control strategy instruction data packet, extract control parameters, and perform legality verification and data format standardization on the control parameters; S1.

3. Determine the initial control parameter set based on the extracted control parameters.

3. The method for controlling the temperature return of the intelligent control valve according to claim 2, characterized in that: In step S3.2, the return water temperature... The process involves fuzzification, fuzzy inference and aggregation based on a fuzzy rule base, and defuzzification to generate a preliminary valve control signal. This includes the following steps: S3.21, Set the return water temperature Mapping to a fuzzy set; S3.22 Calculate each return water temperature using triangular membership functions. The membership degree of the value in each fuzzy set; S3.

23. Based on the predefined fuzzy rule base, reason about the membership degree of the input variables and generate the corresponding output fuzzy set; S3.

24. Aggregate the fuzzy sets of all rule outputs to form a fuzzy output; S3.

25. Using a weighted average method, convert the aggregated fuzzy output into a preliminary valve control signal. .

4. The method for controlling the temperature return of the intelligent control valve according to claim 3, characterized in that: In step S3.3, an optimal valve opening sequence is generated, and the valve opening at the next sampling time is selected as the control target value. This includes the following steps: S3.31, Initial valve control signal As the initial input to the model predictive controller; S3.32 Predicting the Future Based on Nonlinear System Models The changing trends of return water temperature and return temperature deviation within each sampling period; S3.

33. Construct an optimization objective function and solve for the optimal valve opening sequence using a quadratic programming algorithm; S3.

34. Select the valve opening at the next sampling time from the optimal sequence as the current control target value. .

5. The method for controlling the temperature return of the intelligent control valve according to claim 4, characterized in that: In S3.32, the future is predicted based on a nonlinear system model. The trend of return water temperature and return temperature deviation within a sampling period includes the following steps: Establish discrete-time state equations based on the nonlinear system model; Current system status and initial valve control signals As the initial condition for prediction, and to set the prediction step size. With control time domain Determine the number of future forecast cycles; For each future sampling period Iteratively predict future states to obtain the future The sequence of return water temperature and return temperature deviation for each sampling period.

6. The method for controlling the temperature return of the intelligent control valve according to claim 1, characterized in that: In step S4, adjusting the valve opening based on the control target value involves the following specific steps: Target valve opening Converted into the corresponding valve opening signal Based on the equal percentage flow characteristics of the valve, the target flow rate through the valve is calculated. Based on target traffic The corresponding execution signal is generated to drive the electric actuator to adjust the valve to the corresponding opening position.

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