Heat adjusting system and adjusting method of dynamic balance type intelligent heat meter

Abstract

The invention discloses a heat adjusting system and an adjusting method of a dynamic balance type intelligent heat meter, relates to the technical field of heat adjusting systems, and aims to solve the technical problem that an existing heat adjusting system is generally insufficient in dynamic balance ability. Comprising a temperature sensor, a flow sensor and a timing device and is used for collecting historical data of temperature, flow and use time of a user side and real-time operation parameters of a heat supply pipe network in real time, and the real-time operation parameters comprise water supply temperature, water return temperature and pipe network pressure; and the central processing unit is in communication connection with the data acquisition module, receives and stores the historical data and the real-time operation parameters and forms a historical database, the central processing unit integrates a dynamic balance algorithm, and the algorithm analyzes and predicts the heat demand of each user side in real time based on the historical data and the real-time operation parameters. The method has the advantage that the dynamic balance capacity of the heat supply pipe network is improved.

Description

Technical Field

[0001] This invention relates to the field of heat regulation system technology, and more specifically, to a heat regulation system and regulation method for a dynamic balance intelligent heat meter. Background Technology

[0002] In the field of heating pipe networks, existing heat regulation systems generally suffer from insufficient dynamic balancing capabilities. Specifically, traditional systems struggle to accurately predict and coordinate heat demand at each user end based on real-time data. Uneven heat distribution and significant energy waste often result from factors such as pipe network resistance characteristics drift, multi-parameter coupling effects, and limited adjustment strategies for both near and far-end users. Furthermore, system oscillations or response lags are prone to occur during regulation, making it difficult to meet users' dual demands for heating comfort and energy efficiency. Therefore, we propose a heat regulation system and method based on a dynamically balanced smart heat meter. Summary of the Invention

[0003] The purpose of this invention is to provide a dynamic balance intelligent heat meter heat regulation system and regulation method to solve the technical problem that existing heat regulation systems generally have insufficient dynamic balance capability.

[0004] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a heat regulation system for a dynamic balance intelligent heat meter, comprising: The data acquisition module, distributed at each user end of the heating network, includes temperature sensors, flow sensors, and timing devices. It is used to collect historical data on temperature, flow rate, and usage time at the user end, as well as real-time operating parameters of the heating network, including supply water temperature, return water temperature, and network pressure. The central processing unit is communicatively connected to the data acquisition module, receives and stores historical data and real-time operating parameters to form a historical database. The central processing unit integrates a dynamic balancing algorithm, which analyzes and predicts the heat demand of each user terminal in real time based on historical data and real-time operating parameters, and generates valve adjustment parameters. The heat assessment module, connected to the central processing unit, selects a heat adjustment method based on the analysis and prediction results. The adjustment methods include: Heating operation; Flow regulation operation; Heating and flow regulation operation; The heat regulation module connects the heat judgment module and the central processing unit, calculates the heat regulation coefficient based on the selected regulation method and valve regulation parameters, and generates heat regulation instructions. The intelligent control module is connected to the heat regulation module, receives heat regulation commands and converts them into valve regulation commands; The valve actuator is installed at each user terminal and communicates with the intelligent control module. It adjusts and controls the valve opening in real time according to the valve adjustment command to achieve precise regulation of the heat supply flow.

[0005] Preferred options also include: The monitoring module connects the data acquisition module and the heat regulation module, monitors the heat supply, return water temperature and heat change information in real time, and feeds it back to the central processing unit to correct the regulation parameters. The correction module, connected to the monitoring module, corrects the heat regulation coefficient based on the heat change amplitude, including temperature change amplitude and flow rate change amplitude, thereby optimizing the prediction accuracy of the dynamic balance algorithm.

[0006] Preferably, the dynamic balancing algorithm of the central processing unit specifically includes: Historical data preprocessing involves normalizing the collected temperature, flow, and usage time data, removing outliers, and constructing a multidimensional feature dataset that includes user load characteristics and pipeline topology. The demand forecasting model is constructed by training a neural network algorithm to predict user heat demand. The input parameters include historical heat consumption data, real-time meteorological data, and user building parameters. Theoretical heat demand calculation is based on fluid mechanics principles, combined with real-time supply water temperature, return water temperature, pipeline pressure, and pipe length and diameter parameters at each user end. It calculates the friction resistance and flow distribution along the pipeline network to obtain the theoretical heat demand at each user end. The adjustment parameters are generated by comparing the theoretical heat demand with the actual heat supply measured by the data acquisition module, and the valve opening adjustment parameters are generated based on the PID control algorithm.

[0007] Preferably, the dynamic balancing algorithm of the central processing unit further includes: The multivariable coupling analysis module constructs a temperature-flow coupling model based on the principles of fluid thermodynamics. The model takes the water supply temperature, heating medium flow rate, pipeline pressure, user end heat dissipation area, and pipeline heat loss coefficient as input parameters and outputs the synergistic relationship between the temperature regulation and flow regulation of each user end. The real-time pipeline resistance calculation unit dynamically calculates the friction resistance coefficient by collecting pressure sensor data at the beginning and end of the pipeline and combining it with real-time flow data. The friction resistance coefficient is used to correct the pipeline flow distribution model and eliminate the influence of resistance characteristic drift caused by pipe scaling and valve aging. The near-far coordinated adjustment strategy adopts a "flow priority adjustment + temperature compensation" mechanism for near-far users and a "temperature benchmark adjustment + flow gain" mechanism for far-far users. The differential temperature adjustment coefficient and flow adjustment coefficient ratio are calculated through a coupled model.

[0008] Preferably, the temperature-flow coupling model constructed by the multivariate coupling analysis module is specifically as follows: Establish a mapping relationship between heat transfer and regulation: First, calculate the actual heat supply through the heat transfer equation, and then generate temperature and flow regulation based on the deviation between demand and actual measurement. The specific workflow of the real-time pipeline resistance calculation unit is as follows: real-time acquisition of pipeline inlet pressure, user end pressure and heating medium flow rate in the pipeline, and calculation of friction resistance coefficient based on fluid mechanics formulas.

[0009] Preferably, the heat regulation module includes: Coefficient acquisition unit: acquires temperature regulation coefficient and flow regulation coefficient, which are used for proportional calculation of temperature and flow regulation, respectively; The temperature regulation coefficient is calibrated based on the type and area of ​​the radiator at the user end, while the flow regulation coefficient is determined based on the valve flow characteristic curve and the nominal diameter of the pipe. Proportional adjustment unit: Uses a composite adjustment formula to dynamically adjust the heat supply and return heat; Water volume regulation unit: It monitors the heat coefficient after regulation in real time through a comparator and triggers corresponding commands; The coupled control unit dynamically adjusts the weight ratio of temperature and flow rate regulation output from the coupled model to avoid coupling errors caused by single parameter adjustment.

[0010] Preferably, the adjustment logic of the coupling adjustment unit is as follows: When the temperature difference between the supply and return water at the user end exceeds the preset temperature difference threshold, the temperature priority adjustment mode is activated. When the pipeline pressure fluctuates beyond the preset range, the weight of the flow regulation coefficient is automatically increased to ensure that pressure balance is achieved first through flow regulation.

[0011] Preferably, the correction module includes: Amplitude acquisition unit: accurately acquires the real-time change amplitude of temperature and flow rate as the basis for calculating the correction coefficient; Initial adjustment coefficient acquisition unit: Acquires the initial heat adjustment coefficient based on design parameters from the central processing unit; Correction coefficient determination unit: generates the final heat consumption coefficient by combining the temperature and flow rate variation range; Coupling effect correction unit: performs secondary correction based on the deviation between the actual heat supply and the predicted value of the coupling model; Resistance characteristic correction unit: linearly compensates the initial adjustment coefficient based on the resistance correction factor; Predictive correction unit: Uses a time-varying trend model to pre-correct the future adjustment coefficient.

[0012] A method for regulating the heat of a dynamically balanced smart heat meter, comprising: S1. Data acquisition: Through data acquisition modules distributed at various user terminals of the heating network, real-time data such as temperature, flow rate, usage time, and real-time operating parameters of the heating network are collected. S2. Data Analysis: The central processing unit receives historical data and real-time operating parameters, analyzes the heat demand of each user terminal through a dynamic balancing algorithm, and generates valve adjustment parameters. S3. Adjustment Method Selection: The heat judgment module selects one of the following adjustment methods based on the analysis results: Heating operation; Flow regulation operation; Heating and flow regulation operation; S4. Coefficient Calculation: The heat regulation module calculates the heat regulation coefficient based on the selected regulation method and valve regulation parameters. S5. Command issuance: The intelligent control module converts the heat regulation command into a valve regulation command and sends it to the valve actuator. S6. Execute regulation: The valve actuator adjusts the valve opening to achieve heat regulation in the following ways: If heating is required, the linked heat source equipment will increase the water supply temperature. If it is a flow regulation operation, increase the valve opening to increase the flow rate; If it is a heating and flow regulation operation, both temperature increase and flow rate increase are performed simultaneously; S7. Monitoring and feedback: The monitoring module collects information on temperature change, flow rate change, and heat change in real time and feeds it back to the central processing unit. S8. Parameter correction: The correction module adjusts the adjustment coefficient according to the magnitude of heat change, thereby optimizing the prediction accuracy of the dynamic balance algorithm.

[0013] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention integrates a dynamic balance algorithm with a multivariate coupled analysis model to achieve real-time prediction and precise adjustment of heat demand at each user end, thereby improving the accuracy of heat flow regulation, significantly enhancing the dynamic balance capability of the heating network, and solving the problem of uneven heat distribution in the existing system.

[0014] 2. By utilizing a real-time pipeline resistance calculation unit and a coordinated adjustment strategy between near and far ends, the system can dynamically correct resistance characteristic drift caused by factors such as pipe scaling and valve aging, reduce resistance errors during long-term operation, further optimize the accuracy of adjustment parameters, and improve the system's adaptability to complex pipeline environments.

[0015] 3. The present invention also uses a correction module to provide real-time feedback and secondary correction of the temperature and flow rate changes, combined with a time-varying trend model to pre-correct the future adjustment coefficient. This allows the system to reduce the heat prediction error during long-term operation, thereby achieving precise regulation and reducing heating energy consumption, further solving the technical pain point of energy waste in traditional systems. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the system framework of the present invention. Detailed Implementation

[0017] To facilitate understanding of the technical solution of the present invention by those skilled in the art, the technical solution of the present invention will now be further described in conjunction with the accompanying drawings.

[0018] Example 1, such as Figure 1 As shown, the present invention provides a heat regulation system for a dynamic balance intelligent heat meter, comprising: The data acquisition module, distributed at each user end of the heating network, includes temperature sensors, flow sensors, and timing devices. It is used to collect historical data on temperature, flow rate, and usage time at the user end, as well as real-time operating parameters of the heating network (including supply water temperature, return water temperature, and network pressure). The central processing unit is communicatively connected to the data acquisition module, receives and stores historical data and real-time operating parameters to form a historical database. The central processing unit integrates a dynamic balancing algorithm, which analyzes and predicts the heat demand of each user terminal in real time based on historical data and real-time operating parameters, and generates valve adjustment parameters. The heat assessment module, connected to the central processing unit, selects a heat adjustment method based on the analysis and prediction results. The adjustment methods include: Heating operation (adjusting water supply temperature); Flow regulation operation (adjusting the flow rate of the heating medium, which is water in this invention); Heating and flow regulation operation (simultaneous adjustment of temperature and flow); The heat regulation module connects the heat judgment module and the central processing unit, calculates the heat regulation coefficient based on the selected regulation method and valve regulation parameters, and generates heat regulation instructions. The intelligent control module is connected to the heat regulation module, receives heat regulation commands and converts them into valve regulation commands; The valve actuator is installed at each user terminal and is connected to the intelligent control module. It adjusts and controls the valve opening in real time according to the valve adjustment command to achieve precise adjustment of the heat supply flow. The monitoring module connects the data acquisition module and the heat regulation module, monitors the heat supply, return water temperature and heat change information in real time, and feeds it back to the central processing unit to correct the regulation parameters. The correction module, connected to the monitoring module, corrects the heat regulation coefficient based on the heat change amplitude (including temperature change amplitude and flow rate change amplitude) to optimize the prediction accuracy of the dynamic balance algorithm.

[0019] In an embodiment of the present invention, the dynamic balancing algorithm of the central processing unit specifically includes: Historical data preprocessing involves normalizing the collected temperature, flow, and usage time data, removing outliers, and constructing a multidimensional feature dataset that includes user load characteristics and pipeline topology. The normalization process uses the minimum-maximum normalization method, as shown in the following formula: ; in, This is the original data. and These are the minimum and maximum values ​​in the data set, respectively. The data is after normalization; The demand forecasting model is constructed by training a neural network algorithm to predict user heat demand. The input parameters include historical heat consumption data, real-time meteorological data (outdoor temperature and humidity), and user building parameters (area and thermal insulation performance). Its prediction model is expressed as: ; in, For the predicted heat demand, For the first Each input feature For the corresponding weights, For bias terms, For activation function, The number of input features; Theoretical heat demand calculation is based on fluid mechanics principles, combined with real-time supply water temperature, return water temperature, pipeline pressure, and pipe length and diameter parameters at each user end. It calculates the friction resistance and flow distribution along the pipeline network to obtain the theoretical heat demand at each user end. Friction resistance Calculated using the Darcy-Weisbach formula: ; in, This is the friction coefficient. For the length of the pipe, The inner diameter of the pipe. For fluid velocity, It is the acceleration due to gravity; Flow distribution is solved simultaneously using the flow balance equation and energy equation of the pipeline nodes. The node flow balance equation is as follows: ; in, Inflow or outflow node The The flow rate of the pipe For nodes A collection of interconnected pipes, For nodes Traffic demand; After solving for the actual flow rate of each pipe using the energy equation, the heat calculation formula is then applied. Calculate the theoretical heat demand, where, For specific heat capacity, For quality flow, For the temperature difference between supply and return water; Adjustment parameters are generated by comparing theoretical heat demand. The actual heat supply measured by the data acquisition module Valve opening adjustment parameters are generated based on the PID control algorithm; The PID control algorithm formula is: ; in, This represents the output valve opening adjustment amount. This is the proportionality coefficient. The integral time constant is... The differential time constant is For error, It is the integral variable.

[0020] In an embodiment of the present invention, the dynamic balancing algorithm of the central processing unit further includes: The multivariable coupling analysis module constructs a temperature-flow coupling model based on the principles of fluid thermodynamics. The model takes the water supply temperature, heating medium flow rate, pipeline pressure, user end heat dissipation area, and pipeline heat loss coefficient as input parameters and outputs the synergistic relationship between the temperature regulation and flow regulation of each user end. The real-time pipeline resistance calculation unit dynamically calculates the friction resistance coefficient by collecting pressure sensor data at the beginning and end of the pipeline and combining it with real-time flow data. The friction resistance coefficient is used to correct the pipeline flow distribution model and eliminate the influence of resistance characteristic drift caused by pipe scaling and valve aging. The near-far coordinated adjustment strategy adopts a "flow priority adjustment + temperature compensation" mechanism for near-far users and a "temperature benchmark adjustment + flow gain" mechanism for far-far users. The differential temperature adjustment coefficient and flow adjustment coefficient ratio are calculated through a coupled model.

[0021] In an embodiment of the present invention, the temperature-flow coupling model constructed by the multivariate coupling analysis module is specifically as follows: Establish a mapping relationship between heat transfer and regulation: First, calculate the actual heat supply through the heat transfer equation, and then generate temperature and flow regulation based on the deviation between demand and actual measurement. Heat transfer equation: ; in, The actual heat supplied to the user end. The overall heat transfer coefficient includes the pipe heat loss coefficient. For the heat dissipation area of ​​the user end, For water supply temperature, The return water temperature, For the flow rate of the heating medium, The pressure difference between the pipeline inlet and the user end; Equation for generating the adjustment quantity: ; in, For temperature regulation, For flow regulation, , The parameters of the nonlinear mapping function, trained based on historical data, are determined by fitting using the least squares method. Introducing time-varying factors For the overall heat transfer coefficient and flow pressure coefficient Perform dynamic correction: ; in, , These are the initial parameters. This was determined through trend analysis of long-term pipeline network operation data.

[0022] In an embodiment of the present invention, the specific workflow of the real-time pipeline resistance calculation unit is as follows: Real-time collection of pipeline inlet pressure User-side pressure and the flow rate of the heating medium in the pipeline The friction coefficient is calculated based on fluid mechanics formulas; Real-time friction coefficient calculation: ; in, The inner diameter of the pipe. For the length of the pipe, For the density of the heating medium, The pressure difference between the pipeline inlet and the user end; Resistance correction factor generation: in, To design the drag coefficient, Used to update the pipeline flow allocation model, when This indicates increased resistance, and vice versa. Establish a time-varying trend model for the resistance correction factor: ; in, This represents the change in the resistance correction factor between adjacent time points. The Kalman filter smoothing coefficients are determined through training with historical data.

[0023] In an embodiment of the present invention, the heat regulation module includes: Coefficient acquisition unit: acquires temperature regulation coefficient and flow regulation coefficient, which are used for proportional calculation of temperature and flow regulation, respectively; Temperature regulation coefficient Flow regulation coefficient is determined by calibrating the type and area of ​​the user-side radiator. Determined based on the valve flow characteristic curve and the nominal diameter of the pipe; Proportional adjustment unit: Employs a composite adjustment formula to dynamically adjust the heat supply and return water heat. ; in, For heat supply detection coefficient, For the temperature change range, The magnitude of the flow rate change; Water volume regulation unit: Monitors the heat coefficient after regulation in real time via a comparator and triggers corresponding commands. when At that time, a command to increase the flow rate is sent, and the valve opening is adjusted accordingly. for: ; in, Adjust the step size based on the base. This is a resistance correction factor; when At that time, a heating command is sent, and the water supply temperature is adjusted. for: ; in, Base temperature adjustment step size, This refers to the temperature regulation amount.

[0024] In an embodiment of the present invention, the heat regulation module further includes: The coupled control unit dynamically adjusts the weight ratio of temperature and flow rate regulation output by the coupled model to avoid coupling errors caused by single parameter regulation. Weighting formula: ; in, As the weight of the temperature regulation coefficient, For the weight of the flow regulation coefficient, The weighted sensitivity coefficient is determined based on the network load characteristics. Pressure fluctuation response mechanism: When the pipeline pressure fluctuation rate At that time, pressure balance is prioritized through flow regulation coefficient. To preset the pressure fluctuation threshold, the flow regulation coefficient is corrected as follows: ; in, The pressure response coefficient was determined through fluid dynamics simulation. Smooth transition processing: Introducing a smoothing factor The weight changes satisfy: ; Avoid system oscillations caused by sudden changes in adjustment parameters.

[0025] In an embodiment of the present invention, the adjustment logic of the coupling adjustment unit is as follows: When the temperature difference between the supply and return water at the user end At this time, the temperature priority adjustment mode is activated. To preset the temperature difference threshold, the weighting of the temperature adjustment coefficient satisfies the following: ; When pipeline pressure fluctuations exceed the preset range At that time, the weight of the flow regulation coefficient is automatically increased to: ; in, For weight adjustment step size, For symbolic functions, This indicates the current real-time pressure value of the pipeline network. This indicates the set pressure value of the pipeline network, ensuring that pressure balance is achieved primarily through flow regulation.

[0026] In an embodiment of the present invention, the correction module includes: Amplitude acquisition unit: accurately acquires the real-time change amplitude of temperature and flow rate as the basis for calculating the correction coefficient; Temperature variation range: ; in, This represents the temperature value measured in real time within the heating system. It is a target temperature value preset according to heating demand; Flow rate variation: ; in, This refers to the current actual flow rate of the medium in the heating network. It is the flow rate value obtained through theoretical calculation or model prediction based on system operating parameters; Initial adjustment coefficient acquisition unit: Acquires the initial heat adjustment coefficient based on design parameters from the central processing unit. This coefficient is related to the user-side design heat load. and the rated flow rate of the pipeline network Related: ; The water supply temperature under design conditions. The return water temperature under design conditions; Correction coefficient determination unit: Generates the final heat consumption coefficient by combining the temperature and flow rate variation amplitudes. ; in, , Adjust the weights for temperature and flow rate dynamically based on season and load factor.

[0027] In an embodiment of the present invention, the correction module further includes: Coupling effect correction unit: performs secondary correction based on the deviation between the actual heat supply and the predicted value of the coupling model. The deviation calculation formula is as follows: ; in, The actual heat supplied to users is collected through the monitoring module. Predicted heat supply calculated by temperature-flow coupling model when When this happens, a correction is triggered: ; in, The coupling correction coefficient is determined by fitting historical coupling deviation data; Drag characteristic correction unit: based on drag correction factor Linear compensation is applied to the initial adjustment coefficient: ; in, This is the resistance compensation coefficient, which is related to the pipe material and service life. Prediction correction unit: utilizing a time-varying trend model Pre-adjustment of future adjustment coefficients: ; in, This is the drag correction factor for the next time step predicted by Kalman filtering.

[0028] Example 2: A method for regulating the heat of a dynamically balanced smart heat meter, comprising: S1. Data acquisition: Through data acquisition modules distributed at various user terminals of the heating network, real-time data such as temperature, flow rate, usage time, and real-time operating parameters of the heating network are collected. S2. Data Analysis: The central processing unit receives historical data and real-time operating parameters, analyzes the heat demand of each user terminal through a dynamic balancing algorithm, and generates valve adjustment parameters. S3. Adjustment Method Selection: The heat judgment module selects one of the following adjustment methods based on the analysis results: Heating operation (increases water supply temperature separately); Flow regulation operation (increasing the flow rate of the heating medium separately); Heating and flow regulation operation (simultaneously increasing temperature and flow rate); S4. Coefficient Calculation: The heat regulation module calculates the heat regulation coefficient based on the selected regulation method and valve regulation parameters. S5. Command issuance: The intelligent control module converts the heat regulation command into a valve regulation command and sends it to the valve actuator. S6. Execute regulation: The valve actuator adjusts the valve opening to achieve heat regulation in the following ways: If heating is required, the linked heat source equipment will increase the water supply temperature. If it is a flow regulation operation, increase the valve opening to increase the flow rate; If it is a heating and flow regulation operation, both temperature increase and flow rate increase are performed simultaneously; S7. Monitoring and feedback: The monitoring module collects information on temperature change, flow rate change, and heat change in real time and feeds it back to the central processing unit. S8. Parameter correction: The correction module adjusts the adjustment coefficient according to the magnitude of heat change, thereby optimizing the prediction accuracy of the dynamic balance algorithm.

[0029] Example 3: Heat regulation for near-end users Scene parameters: Pipe length L = 100m, pipe diameter D = 80mm (0.08m), design resistance coefficient water supply temperature Return water temperature ,flow Heat dissipation area Design heat load .

[0030] Technical solution execution process: 1. Dynamic equilibrium algorithm calculation Normalization: a certain temperature value Historical extreme values , ; ; Neural network prediction: input , , Output ; Theoretical heat: flow rate Friction along the path Water column; ; PID control: Actual measurement ,error , ; (Valve opening adjustment amount); 2. Coupled Model and Resistance Correction Coupling parameters: , ; ; Resistance calculation: , , ; 3. Adjustment and correction of execution Adjustment method: Near-end "traffic priority" ; Adjustment coefficient: ; ; Correction factor: , , ; .

[0031] I. Minimum-Maximum Normalization Formula: Original source: A data preprocessing method in mathematics used to linearly transform data to the [0,1] interval.

[0032] This application formula ; Derivation process: The classic min-max normalization method is directly adopted without modification. By subtracting the minimum value and dividing by the range, the original data is mapped to the standard interval, which facilitates subsequent neural network training and model calculation.

[0033] Logical relationship: As the first step in data preprocessing, it provides standardized input for subsequent demand forecasting models.

[0034] II. Neural Network Prediction Model Formula: Original source: Multilayer perceptron model in machine learning, a general neural network structure.

[0035] Formula for this application: ; Derivation process: Following the standard form of neural networks, the input features are weighted and summed, and then output through an activation function. In this application, real-time meteorological data and user building parameters have been added to the input parameters, expanding the dimensions of traditional heat consumption data.

[0036] Logical relationship: Based on the normalized data, a heat demand prediction model is obtained through training, which provides a reference for theoretical heat calculation.

[0037] III. Darcy-Weisbach Formula (for calculating friction loss): Original source: Classical fluid mechanics formulas used to calculate pipe friction resistance.

[0038] Formula for this application: ; Derivation process: Darcy's formula is directly used without modification. Friction resistance is calculated using pipe length, inner diameter, and flow velocity parameters, and is used for pipeline flow distribution analysis.

[0039] Logical relationship: It is the basis for calculating the friction resistance along the pipeline network, and provides resistance parameters for subsequent flow distribution and theoretical heat demand calculation.

[0040] IV. Node Flow Balance Equation: Original source: The continuity equation in fluid mechanics, used for pipeline flow analysis.

[0041] Formula for this application: ; Derivation process: Following the classical node flow balance principle, the sum of the inflow and outflow of the node is equal to the node's demand flow. In this application, the actual flow is solved by combining the energy equation.

[0042] Logical relationship: In conjunction with the energy equation, it is used to determine the actual flow rate of each pipe in the pipeline network, and then calculate the theoretical heat demand.

[0043] V. Heat Calculation Formula: Original source: Fundamental formulas of thermodynamics .

[0044] Formula for this application: (in (for the temperature difference between supply and return water) Derivation process: The thermodynamic formula is directly quoted without modification. The heat is calculated using specific heat capacity, mass flow rate and temperature difference, and used for theoretical heat demand and actual heat supply calculation.

[0045] Logical relationship: After the flow allocation calculation, the theoretical heat demand of each user terminal is obtained by combining the temperature parameters.

[0046] VI. PID Control Algorithm Formula: Original source: Classical PID control algorithm in automatic control theory.

[0047] Formula for this application: ; Derivation process: Following the standard form of PID control, the error term is defined as the difference between the theoretical heat and the measured heat. The valve opening adjustment is calculated through proportional, integral, and derivative components to achieve closed-loop control.

[0048] Logical relationship: Based on the deviation between theoretical and measured heat, valve adjustment parameters are generated, which is the core control algorithm of the adjustment system.

[0049] VII. Heat transfer equations in the temperature-flow coupling model: Original source: Derived based on the principles of heat transfer and fluid mechanics.

[0050] Formula for this application: ; Derivation process: The heat transfer equation With the flow-pressure effect item Considering the impact of pipeline heat loss and pressure difference on heating supply, a time-varying factor is introduced. Dynamically correct the overall heat transfer coefficient K and flow-pressure coefficient .

[0051] Logical relationships: used to describe the coupling relationship between heat supply and parameters such as temperature, flow rate, and pressure, providing a model basis for generating regulation quantities.

[0052] VIII. Equation for the generation of the adjustment quantity: Original source: Deviation analysis based on heat transfer equations.

[0053] Formula for this application: ; Derivation process: Based on the deviation between demand and measured heat, temperature and flow rate regulation are generated through a nonlinear mapping function (least square fitting based on historical data training), taking into account the coupled effects of multiple parameters such as flow rate, pressure, and temperature.

[0054] Logical relationship: Based on the deviation of the heat transfer equation, specific temperature and flow rate adjustment commands are generated to achieve multi-variable coordinated regulation.

[0055] IX. Real-time calculation equation for pipeline resistance: Original source: The formula for calculating resistance in fluid mechanics, derived from Bernoulli's equation.

[0056] Formula for this application: Real-time friction coefficient: ,in ; Resistance correction factor: ; Time-varying trend model: ; Derivation process: The real-time resistance coefficient is calculated by back-calculating parameters such as the pressure difference and flow rate at the beginning and end of the pipeline network. The correction factor is obtained by comparing it with the design resistance coefficient, and a Kalman filter smoothing coefficient is introduced. A time-varying model is constructed to dynamically correct the drag characteristic drift.

[0057] Logical relationship: Used for real-time monitoring of pipeline resistance changes, correction of flow distribution model, and improvement of regulation accuracy.

[0058] 10. Composite Regulation Formula (Heat Regulation Module): Original source: Based on the principle of proportional control for heat regulation.

[0059] Formula for this application: ; Derivation process: Based on the basic heating supply test value, the temperature adjustment coefficient is used... and flow regulation coefficient For the range of temperature change and the magnitude of flow change Weighted adjustments are made to achieve composite regulation.

[0060] Logical relationship: Based on the adjustment method (temperature / flow / load), calculate the final adjusted heat and guide the valve actuator to operate.

[0061] XI. Weighting formula (coupling adjustment unit): Original source: Based on the concept of weight allocation in control theory.

[0062] Formula for this application: Temperature weighting: ; Traffic weight: ; Derivation process: The Sigmoid function is used to dynamically adjust the weights of temperature and flow rate regulation. The weight ratio is determined based on the deviation between the measured and detected heat (through the sensitivity coefficient k) to avoid coupling errors caused by single parameter regulation.

[0063] Logical relationship: In multivariate coupled regulation, the regulation weights of temperature and flow are dynamically allocated according to the real-time deviation to optimize the regulation effect.

[0064] 12. Correct the relevant formulas in the module: Temperature variation range: ; Source: Directly defined as the absolute difference between the measured temperature and the set temperature, used to quantify temperature deviation.

[0065] Flow rate variation: ; Source: Defined as the percentage relative deviation between real-time traffic and computed traffic, quantifying traffic fluctuations.

[0066] Initial adjustment coefficient: ; Source: Derived based on design heat load, rated flow rate and design temperature difference, reflecting the heat regulation benchmark under design conditions.

[0067] Final calorie expenditure coefficient: ; Derivation process: The initial adjustment coefficient is corrected by multiplying the temperature and flow rate changes by a correction weight, and the final coefficient is limited to no more than 2.0 to avoid over-adjustment.

[0068] Coupling effect correction ,in ; Derivation process: Based on the deviation between measured and predicted heat, the final adjustment coefficient is corrected a second time to improve the model's adaptability.

[0069] Drag characteristic correction: ; Derivation process: Based on resistance correction factor The initial adjustment coefficient is linearly compensated to reflect the impact of changes in pipeline resistance.

[0070] Forecast revision: ; Derivation process: Using the time-varying trend model of the resistance correction factor, the resistance situation at the next moment is predicted, and the adjustment coefficient is pre-corrected to adapt to the resistance change in advance.

[0071] The embodiments disclosed in this invention are preferred embodiments, but are not limited thereto. Those skilled in the art can easily understand the spirit of this invention based on the above embodiments and make different extensions and variations, but as long as they do not depart from the spirit of this invention, they are all within the protection scope of this invention.

Claims

1. A heat regulation system for a dynamic balance intelligent heat meter, characterized in that, include: The data acquisition module, distributed at each user end of the heating network, includes temperature sensors, flow sensors, and timing devices. It is used to collect historical data on temperature, flow rate, and usage time at the user end, as well as real-time operating parameters of the heating network, including supply water temperature, return water temperature, and network pressure. The central processing unit is communicatively connected to the data acquisition module, receives and stores historical data and real-time operating parameters to form a historical database. The central processing unit integrates a dynamic balancing algorithm, which analyzes and predicts the heat demand of each user terminal in real time based on historical data and real-time operating parameters, and generates valve adjustment parameters. The heat assessment module, connected to the central processing unit, selects a heat adjustment method based on the analysis and prediction results. The adjustment methods include: Heating operation; Flow regulation operation; Heating and flow regulation operation; The heat regulation module connects the heat judgment module and the central processing unit, calculates the heat regulation coefficient based on the selected regulation method and valve regulation parameters, and generates heat regulation instructions. The intelligent control module is connected to the heat regulation module, receives heat regulation commands and converts them into valve regulation commands; The valve actuator is installed at each user terminal and communicates with the intelligent control module. It adjusts and controls the valve opening in real time according to the valve adjustment command to achieve precise regulation of the heat supply flow.

2. The heat regulation system of a dynamic balance intelligent heat meter according to claim 1, characterized in that, Also includes: The monitoring module connects the data acquisition module and the heat regulation module, monitors the heat supply, return water temperature and heat change information in real time, and feeds it back to the central processing unit to correct the regulation parameters. The correction module, connected to the monitoring module, corrects the heat regulation coefficient based on the heat change amplitude, including the temperature change amplitude and the flow rate change amplitude, thereby optimizing the prediction accuracy of the dynamic balance algorithm.

3. The heat regulation system of a dynamic balance intelligent heat meter according to claim 1, characterized in that, The dynamic balancing algorithm of the central processing unit specifically includes: Historical data preprocessing involves normalizing the collected temperature, flow, and usage time data, removing outliers, and constructing a multidimensional feature dataset that includes user load characteristics and pipeline topology. The demand forecasting model is constructed by training a neural network algorithm to predict user heat demand. The input parameters include historical heat consumption data, real-time meteorological data, and user building parameters. Theoretical heat demand calculation is based on fluid mechanics principles, combined with real-time supply water temperature, return water temperature, pipeline pressure, and pipe length and diameter parameters at each user end. It calculates the friction resistance and flow distribution along the pipeline network to obtain the theoretical heat demand at each user end. The adjustment parameters are generated by comparing the theoretical heat demand with the actual heat supply measured by the data acquisition module, and the valve opening adjustment parameters are generated based on the PID control algorithm.

4. The heat regulation system of a dynamic balance intelligent heat meter according to claim 3, characterized in that, The dynamic balancing algorithm of the central processing unit further includes: The multivariable coupling analysis module constructs a temperature-flow coupling model based on the principles of fluid thermodynamics. The model takes the water supply temperature, heating medium flow rate, pipeline pressure, user end heat dissipation area, and pipeline heat loss coefficient as input parameters and outputs the synergistic relationship between the temperature regulation and flow regulation of each user end. The real-time pipeline resistance calculation unit dynamically calculates the friction resistance coefficient by collecting pressure sensor data at the beginning and end of the pipeline and combining it with real-time flow data. The friction resistance coefficient is used to correct the pipeline flow distribution model and eliminate the influence of resistance characteristic drift caused by pipe scaling and valve aging. The near-far coordinated adjustment strategy adopts a "flow priority adjustment + temperature compensation" mechanism for near-far users and a "temperature benchmark adjustment + flow gain" mechanism for far-far users. The differential temperature adjustment coefficient and flow adjustment coefficient ratio are calculated through a coupled model.

5. The heat regulation system of a dynamic balance intelligent heat meter according to claim 4, characterized in that, The temperature-flow coupling model constructed by the multivariate coupling analysis module is specifically as follows: Establish a mapping relationship between heat transfer and regulation: First, calculate the actual heat supply through the heat transfer equation, and then generate temperature and flow regulation based on the deviation between demand and actual measurement. The specific workflow of the real-time pipeline resistance calculation unit is as follows: real-time acquisition of pipeline inlet pressure, user end pressure and heating medium flow rate in the pipeline, and calculation of friction resistance coefficient based on fluid mechanics formulas.

6. The heat regulation system of a dynamic balance intelligent heat meter according to claim 1, characterized in that, The heat regulation module includes: Coefficient acquisition unit: acquires temperature regulation coefficient and flow regulation coefficient, which are used for proportional calculation of temperature and flow regulation, respectively; The temperature regulation coefficient is calibrated based on the type and area of ​​the radiator at the user end, while the flow regulation coefficient is determined based on the valve flow characteristic curve and the nominal diameter of the pipe. Proportional adjustment unit: Uses a composite adjustment formula to dynamically adjust the heat supply and return heat; Water volume regulation unit: It monitors the heat coefficient after regulation in real time through a comparator and triggers corresponding commands; The coupled control unit dynamically adjusts the weight ratio of temperature and flow rate regulation output from the coupled model to avoid coupling errors caused by single parameter adjustment.

7. The heat regulation system of a dynamic balance intelligent heat meter according to claim 6, characterized in that, The adjustment logic of the coupling adjustment unit is as follows: When the temperature difference between the supply and return water at the user end exceeds the preset temperature difference threshold, the temperature priority adjustment mode is activated. When the pipeline pressure fluctuates beyond the preset range, the weight of the flow regulation coefficient is automatically increased to ensure that pressure balance is achieved first through flow regulation.

8. The heat regulation system of a dynamic balance intelligent heat meter according to claim 2, characterized in that, The correction module includes: Amplitude acquisition unit: accurately acquires the real-time change amplitude of temperature and flow rate as the basis for calculating the correction coefficient; Initial adjustment coefficient acquisition unit: Acquires the initial heat adjustment coefficient based on design parameters from the central processing unit; Correction coefficient determination unit: generates the final heat consumption coefficient by combining the temperature and flow rate variation range; Coupling effect correction unit: performs secondary correction based on the deviation between the actual heat supply and the predicted value of the coupling model; Resistance characteristic correction unit: linearly compensates the initial adjustment coefficient based on the resistance correction factor; Predictive correction unit: Uses a time-varying trend model to pre-correct the future adjustment coefficient.

9. A method for regulating a heat regulation system applied to a dynamic balance intelligent heat meter as described in claim 1, characterized in that, include: S1. Data acquisition: Through data acquisition modules distributed at various user terminals of the heating network, real-time data such as temperature, flow rate, usage time, and real-time operating parameters of the heating network are collected. S2. Data Analysis: The central processing unit receives historical data and real-time operating parameters, analyzes the heat demand of each user terminal through a dynamic balancing algorithm, and generates valve adjustment parameters. S3. Adjustment Method Selection: The heat judgment module selects one of the following adjustment methods based on the analysis results: Heating operation; Flow regulation operation; Heating and flow regulation operation; S4. Coefficient Calculation: The heat regulation module calculates the heat regulation coefficient based on the selected regulation method and valve regulation parameters. S5. Command issuance: The intelligent control module converts the heat regulation command into a valve regulation command and sends it to the valve actuator. S6. Execute regulation: The valve actuator adjusts the valve opening to achieve heat regulation in the following ways: If heating is required, the linked heat source equipment will increase the water supply temperature. If it is a flow regulation operation, increase the valve opening to increase the flow rate; If it is a heating and flow regulation operation, both temperature increase and flow rate increase are performed simultaneously; S7. Monitoring and feedback: The monitoring module collects information on temperature change, flow rate change, and heat change in real time and feeds it back to the central processing unit. S8. Parameter correction: The correction module adjusts the adjustment coefficient according to the magnitude of heat change, thereby optimizing the prediction accuracy of the dynamic balance algorithm.

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