Heating control system and method for a drinking water heater

Abstract

The present invention provides a heating control system and method for a drinking water heater, which relates to the technical field of control and regulation of non-electric variables. Through the collaborative work of multiple modules such as a data acquisition and analysis module, an evaluation model construction module, and a weight selection module, the present invention realizes the dynamic optimization control of the heating power and the internal pressure compensation power. This system can not only collect water quality and system operation data in real time, accurately predict the scaling rate, but also intelligently adjust control parameters according to the prediction results, so as to achieve a balance between energy saving and efficient scaling control under different scaling states.

Description

Technical Field

[0001] The present invention relates to the technical field of control and regulation of non-electric variables, and specifically to a heating control system and method for a drinking water heater. Background Art

[0002] In high-altitude low-pressure areas, due to the low environmental pressure, a drinking water heater needs to increase the pressure in the system through a pressure compensation unit to reach the boiling point of water. However, the scaling phenomenon may significantly affect the system performance. The boiling point of water decreases in a low-pressure environment, resulting in increased energy consumption and extended heating time during the heating process. Scaling will reduce the heat conduction efficiency of the heating element, and at the same time, the deposition in the pipeline will increase the flow resistance, further exacerbating the pressure instability of the system. This situation will cause the pressure compensation unit to frequently adjust to maintain the target pressure, increasing the operating burden and wear of the equipment, thus significantly increasing the operating cost and shortening the service life of the equipment;

[0003] In the prior art, the publication number is CN108388288B, and the name is an intelligent heating system and heating method based on big data analysis of time periods, including a controller, a drinking water conduit, and a heating tank. A part of the conduit of the drinking water conduit extends into the heating tank. The heating tank is filled with a heat transfer agent. The heating tank is provided with a temperature detection device for detecting the temperature of the heat transfer agent and a heating device for heating the heat transfer agent. The controller includes: an acquisition module for acquiring the temperature detection value output by the temperature detection device; a monitoring module for judging whether the temperature detection value is less than a temperature preset value; and an execution module for controlling the heating device to start to raise the temperature of the heat transfer agent to a rated temperature value and then turn off when the temperature detection value is less than the temperature preset value.

[0004] When the prior art deals with the scaling problem in the heating process of drinking water in a low-pressure environment, it mainly relies on the pressure compensation unit to maintain the pressure in the system to ensure that the water can reach an appropriate boiling point. However, this method has significant deficiencies:

[0005] 1. The formation of scaling will continuously reduce the heat conduction efficiency of the heating element, forcing the pressure compensation unit to frequently adjust the pressure to maintain the target pressure, resulting in an increased operating burden and wear of the equipment, and thus shortening the service life of the equipment and increasing the maintenance cost;

[0006] 2. Traditional control systems often lack accurate monitoring and prediction of the real-time scaling rate, and cannot dynamically optimize control parameters according to changes in water quality and operating conditions, resulting in low energy utilization efficiency;

[0007] 3. There is an urgent need for an intelligent control system that can monitor in real time and dynamically adjust the heating power and internal pressure compensation power to effectively inhibit the occurrence of scaling, optimize energy utilization, and improve the operating stability and equipment life of the system.

[0008] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure. Therefore, it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0009] The purpose of the present invention is to provide a heating control system and method for a drinking water heater to solve the problems raised in the above background art.

[0010] To achieve the above purpose, the present invention provides the following technical solutions:

[0011] A heating control system for a drinking water heater specifically includes:

[0012] A data acquisition and analysis module: used to collect in real time the mineral ion concentration index of the water to be heated flowing into the drinking water heater, the temperature data of multiple hot zones on the surface of the heating element in the drinking water heater, and the pressure compensation index output by the internal pressure compensation unit;

[0013] Based on the mineral ion concentration index, calculate the real-time scaling rate;

[0014] Based on the temperature data of multiple hot zones, calculate the instantaneous heat transfer temperature deviation between each hot zone;

[0015] An evaluation model construction module: used to input the instantaneous heat transfer temperature deviation, the mineral ion concentration index, and the pressure compensation index as independent variables and output the real-time scaling rate of the water to be heated as the dependent variable to construct a scaling state evaluation model based on regression analysis to evaluate the scaling state of the drinking water heater in real time;

[0016] A predicted scaling rate set determination module: used to, on the basis that the mineral ion concentration index in the water to be heated is a constant value, make different combinations and adjustments to the instantaneous heat transfer temperature deviation and the pressure compensation index within their respective regulation ranges, and input these combinations into the scaling state evaluation model, and the scaling state evaluation model generates a predicted scaling rate set for these combinations;

[0017] An optimal screening module: used to determine the mineral ion concentration index in the current water to be heated and generate a real-time predicted scaling rate set according to the predicted scaling rate set determination module;

[0018] Screen out at least three of the lowest real-time predicted scaling rates from the real-time predicted scaling rate set and match the corresponding instantaneous heat transfer temperature deviation and pressure compensation index;

[0019] A control parameter generation module: used to generate control adjustment parameters for the heating power of the heating element and the pressurization power of the internal pressure compensation unit corresponding to each combination according to the at least three combinations of the instantaneous heat transfer temperature deviation and the pressure compensation index screened out;

[0020] Weight selection module: used to set the judgment threshold for real-time prediction of the scaling rate, and based on the comparison result of the judgment threshold, allocate corresponding selection weights to the heating power of the heating element and the pressurization power of the internal pressure compensation unit;

[0021] Determine the control adjustment parameter with the highest matching degree with the selection weight from three groups of control adjustment parameters according to the selection weight.

[0022] A heating control method for a drinking water heater, which is used to execute the heating control system of the drinking water heater, including:

[0023] Step S1: Real-time collect the mineral ion concentration index of the water to be heated flowing into the drinking water heater, the temperature data of multiple hot zones on the surface of the heating element in the drinking water heater, and the pressure compensation index output by the internal pressure compensation unit;

[0024] Calculate the real-time scaling rate based on the mineral ion concentration index;

[0025] Calculate the instantaneous heat transfer temperature deviation between each hot zone based on the temperature data of multiple hot zones;

[0026] Step S2: Use the instantaneous heat transfer temperature deviation, the mineral ion concentration index, and the pressure compensation index as independent variables and the real-time scaling rate of the water to be heated as the dependent variable to construct a scaling state evaluation model based on regression analysis to evaluate the scaling state of the drinking water heater in real time;

[0027] Step S3: On the basis that the mineral ion concentration index in the water to be heated is a constant value, make different combined adjustments to the instantaneous heat transfer temperature deviation and the pressure compensation index within their respective regulation ranges, and input these combinations into the scaling state evaluation model. The scaling state evaluation model generates a set of predicted scaling rates for these combinations;

[0028] Step S4: Determine the mineral ion concentration index of the current water to be heated, and generate a set of real-time predicted scaling rates according to the predicted scaling rate set determination module;

[0029] Screen out at least three of the lowest real-time predicted scaling rates from the set of real-time predicted scaling rates, and match the corresponding instantaneous heat transfer temperature deviation and pressure compensation index;

[0030] Step S5: Generate control adjustment parameters for the heating power of the heating element and the pressurization power of the internal pressure compensation unit corresponding to each combination according to the at least three combinations of the instantaneous heat transfer temperature deviation and the pressure compensation index selected;

[0031] Step S6: Set the judgment threshold for the real-time predicted scaling rate, and based on the comparison result of the judgment threshold, assign corresponding selection weights to the heating power of the heating element and the pressurization power of the internal pressure compensation unit;

[0032] According to the selection weights, determine the control adjustment parameters with the highest matching degree with the selection weights from the three groups of control adjustment parameters.

[0033] Compared with the prior art, the beneficial effects of the present invention are as follows: Through the collaborative work of multiple modules such as the data acquisition and analysis module, the evaluation model construction module, and the weight selection module, the dynamic optimization control of the heating power and the internal pressure compensation power is realized; This system can not only collect water quality and system operation data in real time, accurately predict the scaling rate, but also intelligently adjust the control parameters according to the prediction results, so as to achieve the balance between energy saving and efficient scaling control under different scaling states; It has significant advantages in the accuracy of scaling control and the energy utilization efficiency, can effectively extend the equipment life, reduce the operation cost, and improve the overall performance and reliability of the drinking water heater in a low-pressure environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of the overall system module of the present invention;

[0035] Figure 2 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0036] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the following further describes the present invention in detail with reference to specific embodiments.

[0037] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meaning understood by those of ordinary skill in the field to which the present invention belongs. The "first", "second", and similar terms used in the present invention do not indicate any order, quantity, or importance, but are only used to distinguish different components. The terms such as "including" or "comprising" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left", "right", etc. are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly. Embodiment

[0038] Please refer to Figure 1 , the present invention provides a technical solution:

[0039] A heating control system for a drinking water heater, which is applied to a drinking water heater including an internal pressure compensation unit, specifically includes:

[0040] A data acquisition and analysis module: used to collect in real time the mineral ion concentration index of the water to be heated flowing into the drinking water heater, the temperature data of multiple hot zones on the surface of the heating element in the drinking water heater, and the pressure compensation index output by the internal pressure compensation unit;

[0041] Further explanation: The internal pressure compensation unit is used to ensure that the water to be heated can reach the target boiling point by increasing the internal pressure of the heating chamber corresponding to the heating element in the high-altitude low-pressure area;

[0042] It should be noted that: A high-precision pressure sensor is installed at the pressure output end of the internal pressure compensation unit; the pressure sensor is directly connected to the heating chamber and is located in the section of the output pipeline of the pressure compensation unit closest to the heating chamber to ensure the accurate reflection of real-time pressure data;

[0043] The following are the detailed operation steps and technical explanations of the internal pressure compensation unit:

[0044] The internal pressure compensation unit adopts dynamic pressure regulation technology to make up for the decrease in the boiling point in the high-altitude low-pressure environment by increasing the internal pressure of the heating chamber; the pressure output from the internal pressure compensation unit directly affects the heat transfer efficiency in the heating chamber and the setting of the water boiling point temperature;

[0045] The pressure compensation unit calibrates the corresponding relationship between the output pressure and the target boiling point of the water to be heated, and the calibration relationship is based on the following formula: ;

[0046] Where, is the target boiling point after pressure compensation, is the standard temperature of the normal boiling point, The pressure increment output by the pressure compensation unit will be characterized as the pressure compensation index; is the calibration coefficient related to the thermodynamic properties of water; is the real-time air pressure value of the current environment, is the standard pressure under atmospheric pressure;

[0047] The beneficial effects of this embodiment are as follows: Traditional drinking water heating equipment usually ignores the influence of the high-altitude low-pressure environment on the heating efficiency, while this solution realizes the optimization of adaptation to special environments through the precise control of the internal pressure compensation unit, significantly improving the applicable range of the heating system.

[0048] By analyzing the conductivity data of the water to be heated to deduce the mineral ion concentration index, the specific deduction steps are as follows:

[0049] The conductivity data is measured and obtained through a conductivity sensor; the conductivity sensor is installed at the water inlet of the drinking water heater to measure the conductivity of the water to be heated in real time, and the acquisition frequency is set to once per minute. The original conductivity data collected is converted into a digital signal through an analog-to-digital conversion module; subsequently, using a pre-calibrated calibration curve, the conductivity value is converted into a mineral ion concentration index;

[0050] 1.1) Select typical water quality samples for experimental calibration:

[0051] From the water sources actually used by the drinking water heater, select several representative samples to ensure that these samples cover the common mineral ion concentration ranges; the typical sample ranges include:

[0052] Surface water: river water, lake water;

[0053] Groundwater: deep well water;

[0054] Treated water: tap water, purified water;

[0055] At the same time, set a series of concentration gradients (10mg / L, 50mg / L, 100mg / L...) according to the samples. These samples are professionally determined for their mineral ion concentrations by a high-precision analysis laboratory. In this embodiment, ICP-MS or chemical titration method is used for determination to obtain accurate mineral ion concentration indicators, and the standardized data is used as a reference;

[0056] 1.2) Measure the conductivity of the water samples:

[0057] Place the water samples whose mineral ion concentrations have been determined in the calibration experimental device, and use the same conductivity sensor as in the drinking water heater to measure the conductivity values of each experimental water sample to ensure that the collected conductivity data is consistent with the original sensor signal collected in the actual device;

[0058] Instrument calibration: Before the experiment, accurately calibrate the conductivity sensor to reduce noise interference and drift error; the reference standard is calibrated using a KCl experimental solution; the KCl experimental solution is used to characterize the standard conductivity values at different concentrations;

[0059] Measure multiple water samples: Measure the conductivity of each experimental water sample one by one, record the mineral ion concentration indicators (mg / L) and the corresponding conductivity values (μS / cm) of different water samples; and record the mineral ion concentration indicator as ; Record the conductivity value as E;

[0060] 1.3) Construct a calibration curve:

[0061] The mineral ion concentration index and the conductivity value E are fitted into a calibration curve model according to the following relationship; Among them, the relationship function is a linear relationship or a non-linear distribution, and the fitting model is selected according to the actual data; common fitting models include: if the conductivity is proportional to the mineral ion concentration index, the following linear fitting is used: ; is the corresponding slope, representing the coefficient for converting unit conductivity to the mineral ion concentration index; is the intercept;

[0062] If the conductivity and the mineral ion concentration index are non-proportional, quadratic or cubic polynomial fitting is used: ; among them, , , are coefficients obtained by fitting based on experimental data;

[0063] When there is a mutation between the low mineral ion concentration index and the high mineral ion concentration index within the coverage range of the conductivity, the calibration curve is segmented and linearly or non-linearly fitted respectively;

[0064] Finally, the calibration curve form and the fitting coefficients are saved in the internal database of the device as the conversion model during operation;

[0065] 1.4) Real-time conversion of conductivity value to mineral ion concentration index:

[0066] Retrieve the calibration curve model: In the data acquisition and analysis module, according to the pre-calibrated and stored calibration curve model , load the corresponding fitting model type. The fitting model types include linear, quadratic or segmented models, as well as fitting parameters. Substitute the conductivity value E collected from the water to be heated into the calibration curve model for calculation to obtain the real-time mineral ion concentration index ; Re-calibrate the device through the user interface or an external device, input new water sample ion concentration and conductivity data, and update the calibration model; when it is detected that the conductivity value E deviates from the applicable range of the existing calibration curve for a long time, the device will first prompt the user. Automatically integrate the data of multiple offline manual calibrations into a cumulative curve and dynamically update the default calibration model of the device.

[0067] The pre-calibrated calibration curve is established based on experimental water samples, truly reflects the water quality environment faced by the drinking water heater, and the data is reliable;

[0068] Through the adaptive calibration function, the device can continuously optimize the calibration curve to cope with water quality changes in different environments.

[0069] In summary, through the calibration curve of mineral ion concentration established by preliminary experiments, the device can stably and effectively calculate the real-time mineral ion concentration index of the water to be heated, laying a solid data foundation for the evaluation of the scaling rate;

[0070] The beneficial effects of "deriving the mineral ion concentration index by analyzing the conductivity data of the water to be heated" in this embodiment are described as follows:

[0071] There are the following problems in directly measuring the mineral ion concentration index of the water to be heated:

[0072] Directly measuring the mineral ion concentration usually requires professional experimental equipment, such as high-precision instruments, reagents, etc., and requires trained personnel to operate; this type of measurement method requires a large amount of reagents and consumables.

[0073] Directly measuring the mineral ion concentration takes a certain amount of time, ranging from a few minutes to several hours, and does not have the ability of dynamic measurement and real-time response;

[0074] The solution of deriving the mineral ion concentration index through conductivity data has significant advantages in the heating control system of a drinking water heater, including:

[0075] The sensor has a simple structure, low cost, small volume, and is easy to integrate.

[0076] It has strong real-time performance and supports dynamic monitoring and instant feedback.

[0077] It has high adaptability and can flexibly adjust based on the calibration curve to support multiple water qualities.

[0078] It has high economy, good user experience, and is easy to maintain.

[0079] Further explanation: Calculate the real-time scaling rate based on the mineral ion concentration index;

[0080] Determine the experimental calibration coefficient of the real-time scaling rate ;

[0081] Experimental calibration coefficient The specific determination steps are as follows:

[0082] Determine that the heating element in the drinking water heater works in the heating cavity;

[0083] Prepare water samples to be heated containing different types of mineral ions:

[0084] Calcium carbonate type water quality (CaCO3): Commonly found in groundwater or hard water;

[0085] Calcium sulfate type water quality (CaSO4): Commonly found in some tap water and industrial water;

[0086] Magnesium-containing water quality (Mg²+ ): The scaling situation caused by the combination of magnesium ions and calcium ions needs to be considered.

[0087] Mixed component water quality: Such as containing carbonate (CO3² - ), sulfate (SO4² - ), magnesium ions (Mg² + ), etc.

[0088] The mineral ion concentration of each water sample is selected according to the actual working conditions, such as 100mg / L, 200mg / L, 500mg / L, 1000mg / L, etc., increasing gradually.

[0089] Prepare the above various simulated water samples and simultaneously detect their mineral ion concentration indicators ;

[0090] Load a pre-weighed heating element in the experimental simulation heating chamber.

[0091] The heating element needs to be completely clean and record the initial mass .

[0092] The material of the heating element is the same as that of the actual drinking water heater used;

[0093] Select typical heating temperatures: The boiling point of ordinary drinking water is 100°C;

[0094] Running time: The continuous heating time for each experiment is 2 hours, simulating the key stage of the actual cumulative scaling process;

[0095] Pressure regulation: Simulate the conditions of low pressure 1atm and high pressure 2atm in different experimental groups to observe the influence of environmental pressure on the scaling rate.

[0096] When heating in the heating chamber, continuously circulate the water sample to be measured, and simultaneously calculate the mineral ion concentration index of the water to be heated in real time .

[0097] After heating is completed, after the heating element cools to room temperature, gently rinse it with deionized water to remove loose attachments.

[0098] Weigh the final mass of the heating element with a precision electronic balance ; Calculate the scaling amount through the following formula :

[0099] ; According to the scaling amount obtained from the experiment , calculate the scaling rate per hour , expressed in the scaling mass per unit time: ;

[0100] where t is the experimental running time, and in this embodiment, t = 2h; 2h represents 2 hours; the scaling rate obtained from the experiment and the corresponding mineral ion concentration index establish a functional relationship: ; By fitting the experimental data through linear regression method, the experimental calibration coefficient value is obtained.

[0101] This embodiment gives the following examples:

[0102] If mg / L, and the experiment measures then: ;

[0103] Table 1 Determination of experimental calibration coefficient:

[0104]

[0105] The heating control system stores a predefined scaling rate calculation formula: ;

[0106] Read the corresponding experimental calibration coefficient from the internal configuration file in real time according to the mineral ion type of the water to be heated currently ; Substitute the mineral ion concentration index calculated in real time into the scaling rate calculation formula to calculate the real-time scaling rate ;

[0107] Through automatic calibration it can flexibly adapt to different water qualities, and has stronger adaptability compared with the traditional fixed parameter method.

[0108] High real-time performance and accuracy: Real-time collect and process conductivity data, and combined with the calibration curve model and experimental calibration coefficient, it can quickly and accurately calculate the real-time scaling rate.

[0109] Based on the temperature data of multiple heating zones, calculate the instantaneous heat transfer temperature deviation between each heating zone;

[0110] Further explanation: The temperature data of each heating zone on the surface of the heating element are collected in real time by a multi-point infrared temperature sensor, and after denoising and calibration processing, the temperature value of each heating zone is obtained , where n represents the total number of heating zones on the surface of the heating element;

[0111] Use the following formula to calculate the average temperature of multiple heating zones on the surface of the heating element : Among them, is the temperature value of heating zone i, and n is the total number of heating zones;

[0112] Calculate the temperature deviation of heating zone i , the specific formula is: ; Define the formula for calculating the instantaneous heat transfer temperature deviation as follows: ;

[0113] Where is the instantaneous heat transfer temperature deviation between each heating zone; represents the absolute value of the temperature deviation of heating zone i; it is used to calculate the absolute average value of the deviation to avoid the cancellation of positive and negative temperatures inside; The larger the value, the greater the deviation degree of the surface heat transfer consistency of the heating element.

[0114] Evaluation model construction module: used to input the instantaneous heat transfer temperature deviation, mineral ion concentration index, and pressure compensation index as independent variables, and output the real-time scaling rate of the water to be heated as the dependent variable, to construct a scaling state evaluation model based on regression analysis to evaluate the scaling state of the drinking water heater in real time;

[0115] Further explanation: Obtain the following real-time data from the data acquisition and analysis module:

[0116] Instantaneous heat transfer temperature deviation ; Mineral ion concentration index ; Pressure compensation index ; Real-time scaling rate ;

[0117] Establish a structured database to store the above four types of data in real time according to the time series, and perform data preprocessing on these data to obtain a preprocessed data set;

[0118] It should be noted that in this embodiment, the relational database management system (RDBMS) technology, specifically MySQL or PostgreSQL, is selected to construct a structured database;

[0119] The preprocessed data set includes standardizing the independent variables , , . The standardization process uses Z-score standardization to eliminate the influence of different dimensions on model training;

[0120] Based on the preprocessed data set, calculate the Pearson correlation coefficients between the independent variables , , and the dependent variable ;

[0121] Based on the calculated Pearson correlation coefficients, determine the influence of each independent variable on the real-time scaling rate The degree of influence is sorted according to the absolute value of the Pearson correlation coefficient to identify the main driving factors. It should be noted that in this embodiment, the independent variables with the absolute value of the Pearson correlation coefficient greater than 0.6 are set as the main driving factors;

[0122] The preprocessed data set is divided into a training set (80%) and a test set (20%) according to the time series to ensure that the model is not affected by future data during the training process;

[0123] Furthermore: Using the training set data, fit a multiple linear regression model to establish the independent variables , , and the dependent variable The linear relationship between them is: ; is the intercept, , , are the regression coefficients.

[0124] Optimize the hyperparameters using cross-validation to ensure the stability and generalization ability of the model on different data sets;

[0125] Furthermore: Apply the trained regression model on the test set to predict the real-time fouling rate ;

[0126] Use the mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination as the model performance evaluation indicators to comprehensively measure the prediction accuracy and interpretability of the model;

[0127] Calculate the MSE, RMSE, and between the predicted value and the actual value to evaluate the accuracy of the model.

[0128] Export the trained multiple linear regression model into a format suitable for embedded systems, including ONNX and PMML, to ensure efficient deployment in the heating control system.

[0129] The fouling rate prediction set determination module: Based on the constant value of the mineral ion concentration index in the water to be heated, different combinations of the instantaneous heat transfer temperature deviation and the pressure compensation index are adjusted within their respective control ranges, and these combinations are input into the fouling state evaluation model, and the fouling state evaluation model generates a set of predicted fouling rates for these combinations; Further explanation: Determine the control range of the pressure compensation index :

[0130] Set the standard atmospheric pressure to 101.3 kPa as a reference benchmark;

[0131] The real-time air pressure value of the current environment is monitored in real time through an atmospheric pressure sensor ;

[0132] Based on the calculation formula ;

[0133] Based on the minimum and maximum pressure capacity output values of the internal pressure compensation unit, set the pressure compensation index The regulation range is ;

[0134] is the lower limit value of the pressure compensation index; is the upper limit value of the pressure compensation index;

[0135] According to the equipment specification manual, set the minimum output pressure value and the maximum output pressure value ;

[0136] Set the regulation range of the instant heat transfer temperature deviation as ; Obtain the maximum heating power and the minimum heating power ;

[0137] is the lower limit value of the instant heat transfer temperature deviation; Set as the instant heat transfer temperature deviation of the heating element at the minimum heating power; Ensure that the heating efficiency is not lower than the basic requirements;

[0138] is the upper limit value of the instant heat transfer temperature deviation; Set as the instant heat transfer temperature deviation of the heating element at the maximum heating power;

[0139] Establish a linear or non-linear relationship between the heating power and in order to precisely adjust at different power settings;

[0140] Set and The step sizes are respectively and ;

[0141] Within the range of to with a step size of Iteratively generate all values;

[0142] Within the range of to within the range, with a step size generate all values;

[0143] Use the Cartesian product to generate all combinations, ensuring that each pair of combinations is within its respective regulation range;

[0144] Remove obviously unreasonable or extreme combinations through pre-screening to ensure that the generated combination set is both comprehensive and safe;

[0145] Input the combinations into the fouling state evaluation model and generate a set of predicted fouling rates; the specific implementation steps are as follows:

[0146] Set the mineral ion concentration index to a constant value to ensure the consistency of the model input;

[0147] For each combination, construct an input vector containing , , :

[0148] ;

[0149] Load the trained regression model parameters: for each input vector, apply the regression formula to calculate the predicted fouling rate: ; Store the value corresponding to each combination in the set of predicted fouling rates.

[0150] This technical solution systematically realizes the generation of an efficient and accurate set of predicted fouling rates based on the combination adjustment of under the condition of a fixed mineral ion concentration index by refining each operation step of the predicted fouling rate set determination module; ensuring that the system can maintain an intelligent and real-time fouling state evaluation ability under changing environments and working conditions.

[0151] Optimal screening module: used to determine the mineral ion concentration index in the current water to be heated, and generate a real-time predicted fouling rate set according to the predicted fouling rate set determination module;

[0152] Screen out at least three of the lowest real-time predicted fouling rates from the real-time predicted fouling rate set, and match the corresponding instantaneous heat transfer temperature deviation and pressure compensation indicators;

[0153] Further explanation: Transfer the mineral ion concentration index obtained in real time in the current water to be heated to the predicted fouling rate set determination module;

[0154] The predicted fouling rate set determination module is based on the input , and Combine to generate a corresponding set of real-time predicted fouling rates;

[0155] The set of real-time predicted fouling rates contains data records for each group ;

[0156] For all values in the set of real-time predicted fouling rates, sort them in ascending order to ensure that the lowest real-time fouling rate is at the front;

[0157] Select at least three of the lowest values from the sorted set of real-time predicted fouling rates to form an optimal candidate set ;

[0158] Extract the corresponding combinations from the selected lowest real-time predicted fouling rates to form the following optimal control parameter set .

[0159] The beneficial effects of this embodiment are as follows:

[0160] Adopt an efficient sorting and screening algorithm to ensure that the lowest fouling rate combination can be quickly and accurately screened out in a large-scale dataset, improving the system's response speed and prediction accuracy.

[0161] Tightly integrate the optimal screening module with the predicted fouling rate set determination module and the heating control system to form an intelligent closed-loop control system, significantly improving the overall intelligence level and operation efficiency of the system.

[0162] Control parameter generation module: used to generate control adjustment parameters for the heating power of the heating element and the pressurization power of the internal pressure compensation unit corresponding to each combination according to at least three combinations of the instant heat transfer temperature deviation and pressure compensation indicators screened out;

[0163] Further explanation: Establish a mapping relationship model between the heating power of the heating element and the instant heat transfer temperature deviation to generate the heating power of the corresponding heating element at different values ;

[0164] Based on experimental data, use linear regression or nonlinear fitting methods to establish the mathematical relationship model between and :

[0165] where , , are fitting parameters;

[0166] Verify the accuracy of the mapping model using independent test data to ensure that can accurately reflect changes;

[0167] Establish a mapping relationship model between the pressurization power and the pressure compensation index in the internal pressure compensation unit to generate the pressurization power of the internal pressure compensation unit under different ; ;

[0168] Specifically establish the and mathematical relationship model between: ; where , , are fitting parameters;

[0169] Verify the accuracy of the mapping model using independent test data to ensure that can accurately reflect changes;

[0170] Denote any combination in the optimal control parameter set as , and use the established mapping relationship model above to calculate the corresponding ; ; where , j is the index of the combination in the optimal control parameter set; successively represent the corresponding combinations in the following optimal control parameter set: ; Store the corresponding to each group of as the following control adjustment parameter set: .

[0171] Weight selection module: used to set the judgment threshold for real-time prediction of the fouling rate, and based on the comparison result of the judgment threshold, assign corresponding selection weights to the heating power of the heating element and the pressurization power of the internal pressure compensation unit;

[0172] Determine the control adjustment parameter with the highest matching degree with the selection weight from the three groups of control adjustment parameters according to the selection weight.

[0173] Further explanation: Analyze the historical data of system operation to determine the normal range and abnormal range of the real-time predicted fouling rate ;

[0174] Based on the statistical analysis results, set the judgment threshold for the real-time predicted fouling rate to ;

[0175] In this embodiment, is set as the 95th percentile of the historical data to ensure that the normal and abnormal fouling rates can be effectively distinguished;

[0176] It is specifically determined by the entropy weight method;

[0177] Under different working conditions, verify through simulation operation of its effectiveness to ensure that it can accurately reflect the scaling state of the actual system;

[0178] The weight distribution ratio between the heating power of the heating element and the pressurizing power of the internal pressure compensation unit is , ;

[0179] When , preferentially reduce the heating power , and the weight distribution ratio value q1 is ;

[0180] This embodiment sets ;

[0181] The above settings have the following advantages:

[0182] Energy conservation: The direct effect of reducing the heating power. When the scaling rate is within a safe range, reducing the heating power can reduce energy consumption and achieve the energy-saving goal; this not only helps to reduce the operating cost but also meets the requirements of environmental protection.

[0183] Controlling temperature rise: Reducing the risk of excessive temperature. Reducing the heating power helps to control the temperature in the heating chamber and prevent the aggravation of mineral precipitation and scaling due to excessive temperature.

[0184] Improving equipment life: Reducing equipment wear. Reducing the heating power not only saves energy but also reduces the working load of the heating element, prolongs the service life of the equipment, and reduces the maintenance cost.

[0185] When , preferentially increase the pressurizing power of the internal pressure compensation unit , and the weight distribution ratio value q2 is ; q1 > q2;

[0186] q1 and q2 are determined by the entropy weight method;

[0187] The above settings have the following advantages:

[0188] Increasing the boiling point: Delaying mineral precipitation. Increasing the air pressure in the heating chamber will increase the boiling point of water, so that water remains liquid at a higher temperature. This helps to delay the precipitation of minerals and the formation of scaling, thus effectively inhibiting the scaling rate;

[0189] Optimizing heat transfer efficiency: Enhancing the heat transfer process, a higher internal air pressure can improve the heat transfer efficiency within the heating chamber, enabling more uniform heat distribution, reducing local overheating, and lowering the risk of scaling.

[0190] Controlling scaling characteristics: Altering the mineral precipitation dynamics, by adjusting the internal air pressure, the precipitation rate and precipitation pattern of minerals within the heating chamber can be changed, reducing the formation of dense scaling and facilitating subsequent cleaning and maintenance.

[0191] This embodiment sets ;

[0192] and Determine the corresponding weights through the entropy weight method;

[0193] For each group of control adjustment parameters , calculate the matching degree with the current weight distribution : : ; Select the control adjustment parameter with the largest value from ; Deliver the largest control adjustment parameter to the heating control system and the internal pressure compensation unit to guide real-time adjustment of heating power and pressurization power.

[0194] Embodiment 2:

[0195] This embodiment verifies through experiments that in the weight selection module, when , the heating power is preferentially reduced; while when , the pressurization power of the internal pressure compensation unit is preferentially increased, thereby effectively controlling the scaling rate and enhancing the system operation efficiency. The experimental design covers the influence of the adjustment of heating power and internal pressure compensation pressurization power on system performance under different scaling rates. The specific steps are as follows:

[0196] The experimental equipment includes a standardized drinking water heating system equipped with a high-precision mineral ion concentration sensor, a heating element, an internal pressure compensation unit, and a data acquisition and control system. The experimental water sample uses natural water, which is pretreated to ensure water quality consistency. Two main control parameters are set for the experiment: heating power (unit: kilowatt, kW) and the pressurization power of the internal pressure compensation unit (unit: kilowatt, kW). The real-time predicted scaling rate (unit: mg / L⋅h) is used as the main monitoring index, and is predicted and calculated through real-time sensor data.

[0197] Before the start of the experiment, all sensors are calibrated to ensure the accuracy of data acquisition. With the heating system in the non-powered state, the initial internal air pressure and the initial temperature .

[0198] By analyzing historical data and literature, determine the scaling rate judgment threshold to be 100 mg / L·h.

[0199] The experiment is divided into two main stages:

[0200] Stage 1: Real-time prediction of scaling rate ; In this stage, observe the effects on the scaling rate and system performance by reducing the heating power . Set three different reduction amplitudes (-10%, -20%, -30%) of the heating power, and record the pressurization power of the corresponding internal pressure compensation unit Real-time predict the scaling rate c.

[0201] Stage 2: Real-time prediction of scaling rate ;

[0202] In this stage, control and reduce the scaling rate by increasing the pressurization power of the internal pressure compensation unit . Set three different increase amplitudes (+10%, +20%, +30%) of the internal pressure compensation pressurization power, and record the corresponding heating power and real-time predict the scaling rate .

[0203] After each parameter adjustment, the system runs for 2 hours, and the average scaling rate data is collected after stabilization. At the same time, record the actual values of the heating power and the pressurization power of the internal pressure compensation unit to ensure the accuracy and repeatability of the data.

[0204] Organize the collected data into a table, and analyze the effects of different control parameter adjustments on the scaling rate and system performance. Through comparative analysis, verify the rationality and effectiveness of the control logic.

[0205] According to the experimental results, optimize the algorithms of the control parameter generation module and the weight selection module to ensure that the scaling rate can be flexibly and efficiently controlled in practical applications, and improve the system operation efficiency and equipment life.

[0206] The following table records the experimental data of the heating power , the pressurization power of the internal pressure compensation unit , the real-time predicted scaling rate and the total system energy consumption (unit: kilowatt-hour, kWh) under different control parameter adjustments.

[0207] Table 2 Research on the weight selection module:

[0208] Parameter Name Heating Power (kW) Internal Pressure Compensation Pressurization Power (kW) Scaling Rate (mg / L⋅h) Total System Energy Consumption (kWh) Experiment 1 - Reduce by 10% 9 5 85 14 Experiment 2 - Reduce by 20% 8 5 80 13 Experiment 3 - Reduce by 30% 7 5 78 12 Experiment 4 - Increase by 10% 10 5.5 95 15 Experiment 5 - Increase by 20% 10 6 90 16 Experiment 6 - Increase by 30% 10 6.5 85 17

[0209] From the above experimental data, the following rules can be seen:

[0210] 1. When the heating power is reduced :

[0211] In Experiments 1 to 3, as the heating power was gradually reduced from 10 kW to 7 kW, the scaling rate decreased from 85 mg / L·h to 78 mg / L·h, indicating that reducing the heating power effectively reduced the scaling rate. In addition, the total system energy consumption also decreased, from 14 kWh to 12 kWh, verifying the energy-saving effect. This shows that when the scaling rate is within a safe range, reducing the heating power can not only save energy but also further inhibit the occurrence of scaling and extend the equipment life.

[0212] 2. When the pressure compensation unit's pressurization power for internal pressure compensation is increased ;

[0213] In Experiments 4 to 6, the internal pressure compensation pressurization power increased from 5.5 kW to 6.5 kW, and the heating power remained at 10 kW. The scaling rate decreased from 95 mg / L·h to 85 mg / L·h, and the total system energy consumption increased from 15 kWh to 17 kWh. Although increasing the internal pressure compensation pressurization power slightly increased the system energy consumption, it significantly reduced the scaling rate, ensured the system operated in a stable and safe state, and effectively prevented the negative impact of scaling on the system.

[0214] Example 3: Please refer to Figure 2 , a heating control method for a drinking water heater, which is used to execute the heating control system of the drinking water heater, including:

[0215] Step S1: Real-time collect the mineral ion concentration index of the water to be heated flowing into the drinking water heater, the temperature data of multiple hot zones on the surface of the heating element in the drinking water heater, and the pressure compensation index output by the internal pressure compensation unit;

[0216] Calculate the real-time scaling rate based on the mineral ion concentration index;

[0217] Calculate the instantaneous heat transfer temperature deviation between each hot zone based on the temperature data of multiple hot zones;

[0218] Step S2: Use the instantaneous heat transfer temperature deviation, the mineral ion concentration index, and the pressure compensation index as independent variables and the real-time scaling rate of the water to be heated as the dependent variable output to construct a scaling state evaluation model based on regression analysis to evaluate the scaling state of the drinking water heater in real time;

[0219] Step S3: On the basis that the mineral ion concentration index in the water to be heated is a constant value, different combinations of adjustments are made to the instant heat transfer temperature deviation and the pressure compensation index within their respective regulation ranges, and these combinations are input into the fouling state evaluation model, and the fouling state evaluation model generates a set of predicted fouling rates for these combinations;

[0220] Step S4: Determine the mineral ion concentration index in the current water to be heated, and generate a real-time predicted fouling rate set according to the module for determining the predicted fouling rate set;

[0221] Screen out at least three of the lowest real-time predicted fouling rates from the real-time predicted fouling rate set, and match the corresponding instant heat transfer temperature deviation and pressure compensation index;

[0222] Step S5: Generate control adjustment parameters for the heating power of the heating element and the pressurization power of the internal pressure compensation unit corresponding to each combination according to the at least three combinations of instant heat transfer temperature deviation and pressure compensation index screened out;

[0223] Step S6: Set a judgment threshold for the real-time predicted fouling rate, and based on the comparison result of the judgment threshold, assign corresponding selection weights to the heating power of the heating element and the pressurization power of the internal pressure compensation unit;

[0224] According to the selection weights, determine the control adjustment parameter with the highest matching degree with the selection weights from the three groups of control adjustment parameters.

[0225] It should be noted that: All calculation formulas in this application document adopt regression analysis including but not limited to machine learning algorithms to deeply analyze the relevant parameters collected, identify their natural trends and interrelationships. Using professional software, such as the Scikit-learn library of Python or the R language, a mathematical model matching the data is automatically generated. Then, the performance of the model is objectively evaluated through methods such as cross-validation, and combined with continuous feedback and optimization, to ensure that the created formula truly reflects the internal law of the data, thereby ensuring its effectiveness and accuracy, and ensuring that the calculation process conforms to the constraints of natural laws, rather than based on artificially set rules.

[0226] The technical solution of the present invention can be embodied in the form of a software product in essence or the part that contributes to the prior art. This computer software product can be stored in a computer-readable storage medium, such as a floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disc of a computer, etc., including several instructions to make a computer device (which can be a personal computer, server, or network device, etc.) execute the methods of various embodiments of the present invention.

[0227] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite ordered listing of executable instructions for implementing logical functions, and can be embodied specifically in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device. As used in this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device or in connection with these instruction execution systems, apparatus, or devices.

[0228] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A heating control system for a drinking water heater, characterized in that, Specifically including: Data acquisition and analysis module: used to collect in real time the mineral ion concentration index of the water to be heated flowing into the drinking water heater, the temperature data of multiple hot zones on the surface of the heating element in the drinking water heater, and the pressure compensation index output by the internal pressure compensation unit; Calculate the real-time scaling rate based on the mineral ion concentration index; Calculate the instantaneous heat transfer temperature deviation between each hot zone based on the temperature data of multiple hot zones; Evaluation model construction module: used to input the instantaneous heat transfer temperature deviation, mineral ion concentration index, and pressure compensation index as independent variables, and output the real-time scaling rate of the water to be heated as the dependent variable, construct a scaling state evaluation model based on regression analysis to evaluate the scaling state of the drinking water heater in real time; Predicted scaling rate set determination module: used to, on the basis that the mineral ion concentration index in the water to be heated is a constant value, make different combinations and adjustments to the instantaneous heat transfer temperature deviation and pressure compensation index within their respective regulation ranges, and input these combinations into the scaling state evaluation model, and the scaling state evaluation model generates a set of predicted scaling rates for these combinations; Optimal screening module: used to determine the mineral ion concentration index of the current water to be heated, and generate a real-time predicted scaling rate set according to the predicted scaling rate set determination module; Screen out at least three lowest real-time predicted scaling rates from the real-time predicted scaling rate set, and match the corresponding instantaneous heat transfer temperature deviation and pressure compensation index; Control parameter generation module: used to generate control adjustment parameters for the heating power of the heating element and the pressurization power of the internal pressure compensation unit corresponding to each combination according to the at least three combinations of the screened instantaneous heat transfer temperature deviation and pressure compensation index; Weight selection module: used to set a judgment threshold for the real-time predicted scaling rate, and based on the comparison result of the judgment threshold, assign corresponding selection weights to the heating power of the heating element and the pressurization power of the internal pressure compensation unit; Determine the control adjustment parameter with the highest matching degree with the selection weight from the three groups of control adjustment parameters according to the selection weight; The internal pressure compensation unit is used to ensure that the water to be heated can reach the target boiling point by increasing the internal pressure of the heating chamber corresponding to the heating element in the high-altitude low-pressure area; The pressure compensation unit calibrates the corresponding relationship between the output pressure and the target boiling point of the water to be heated, and the calibration relationship is based on the following formula: ； wherein, is the target boiling point after pressure compensation, is the standard temperature of the atmospheric boiling point, is the pressure increment output by the pressure compensation unit, and is characterized as the pressure compensation index; is the calibration coefficient related to the thermodynamic properties of water; is the real-time air pressure value of the current environment, is the standard pressure under atmospheric pressure; the mineral ion concentration index is deduced by analyzing the conductivity data of the water to be heated, and the specific deduction steps are as follows: The mineral ion concentration index and the conductivity value E are fitted into a calibration curve model according to the following relationship; Among them, the relational function is a linear relationship or a non-linear distribution, and the fitting model is selected according to the actual data; The conductivity value E collected from the water to be heated is substituted into the calibration curve model for calculation to obtain the real-time mineral ion concentration index .

2. The heating control system of a drinking water heater according to claim 1, characterized in that: Experimental calibration coefficient for determining the real-time fouling rate ; The heating control system stores a predefined calculation formula for the scaling rate: ; Read the corresponding experimentally calibrated coefficient from the internal configuration file in real time according to the mineral ion type of the water to be heated currently ; Substitute the mineral ion concentration index calculated in real time into the scaling rate calculation formula to calculate the real-time scaling rate ; Calculate the average temperature of multiple hot zones on the surface of the heating element : Among them, is the temperature value of the hot zone i, and n is the total number of hot zones; calculate the temperature deviation of the hot zone i , and the specific formula is:; ; Define the calculation formula for the instantaneous heat transfer temperature deviation as follows: ; Among them, is the instantaneous heat transfer temperature deviation between each hot zone; represents the absolute value of the temperature deviation of the hot zone i; The larger the value, the greater the deviation degree of the heat transfer consistency on the surface of the heating element.

3. The heating control system of a drinking water heater according to claim 2, characterized in that: Obtain the following real-time data from the data acquisition and analysis module: instantaneous heat transfer temperature deviation ; mineral ion concentration index ; pressure compensation index ; real-time scaling rate ; Establish a structured database, store the above four types of data in real time according to the time series, and perform data preprocessing on these data to obtain a preprocessed data set; Divide the preprocessed data set into a training set and a test set according to the time series to ensure that the model is not affected by future data during the training process; Using the training set data, fit a multiple linear regression model to establish the independent variables , , and the dependent variable The linear relationship between: ; among which, is the intercept, , , are the regression coefficients.

4. The heating control system of a drinking water heater according to claim 3, characterized in that: Set the pressure compensation index based on the minimum and maximum pressure capacity output values of the internal pressure compensation unit The regulation range is ; is the lower limit value of the pressure compensation index; is the upper limit value of the pressure compensation index; Set the instant heat transfer temperature deviation The regulation range is ; Obtain the maximum heating power of the heating element and the minimum heating power; ; Set as the lower limit value of the instant heat transfer temperature deviation of the heating element at the minimum heating power; Set as the upper limit value of the instant heat transfer temperature deviation of the heating element at the maximum heating power; Set and The step sizes of are respectively and ; In the range from to With a step size of Iteratively generate all values; In the range from to With a step size of Iteratively generate all values; Use the Cartesian product to generate all combinations to ensure that each pair of combinations is within its respective regulation range; For each combination corresponding value is stored in the predicted fouling rate set.

5. The heating control system of a drinking water heater according to claim 4, characterized in that: Transfer the mineral ion concentration index obtained in real time from the current water to be heated to the prediction scale formation rate set determination module; The predicted scaling rate set determination module generates a corresponding real-time predicted scaling rate set according to the input , and by combination. The real-time predicted fouling rate set contains each set of data records; for all values in the real-time predicted fouling rate set, sort them in ascending order to ensure that the lowest real-time fouling rate is at the front; Select at least three lowest values from the sorted set of real-time predicted fouling rates to form an optimal candidate set ​ values to form an optimal candidate set; extract the corresponding from the selected lowest real-time predicted fouling rate combinations to form the following optimal control parameter set .

6. The heating control system of a drinking water heater according to claim 5, characterized in that: Establish a mapping relationship model between the heating power of the heating element and the instantaneous heat transfer temperature deviation to generate the heating power of the corresponding heating element at different values; establish a mapping relationship model between the pressurization power in the internal pressure compensation unit and the pressure compensation index to generate the pressurization power of the internal pressure compensation unit at different ; denote any combination in the optimal control parameter set as and calculate the corresponding using the mapping relationship models established above; ; where ; ; among them , j is the index of the combination in the optimal control parameter set; Each group corresponding is stored as the following set of control adjustment parameters: .

7. The heating control system of a drinking water heater according to claim 6, characterized in that: Set the judgment threshold for real-time prediction of the fouling rate to be ; Denote the weight allocation ratio value between the heating power of the heating element and the pressurization power of the internal pressure compensation unit as ; When the heating power is preferentially reduced and the weight distribution ratio value q1 is set to be ; when the pressurization power of the internal pressure compensation unit is preferentially increased and the weight distribution ratio value q2 is set to be ; q1 > q2; for each set of control adjustment parameters calculate the matching degree with the current weight distribution : ; select the control adjustment parameter with the largest value from ; ; ; Transfer the maximum control adjustment parameter to the heating control system and the internal pressure compensation unit to guide the real-time adjustment of the heating power and the pressurization power.

8. A heating control method for a drinking water heater, characterized in that: The method is used to execute the heating control system of the drinking water heater described in any one of claims 1-7, including: Step S1: Collect in real time the mineral ion concentration index of the water to be heated flowing into the drinking water heater, the temperature data of multiple hot zones on the surface of the heating element in the drinking water heater, and the pressure compensation index output by the internal pressure compensation unit; Calculate the real-time scaling rate based on the mineral ion concentration index; Calculate the instantaneous heat transfer temperature deviation between each hot zone based on the temperature data of multiple hot zones; Step S2: Use the instant heat transfer temperature deviation, mineral ion concentration index, and pressure compensation index as independent variables and the real-time scaling rate of the water to be heated as the dependent variable to construct a scaling state evaluation model based on regression analysis to evaluate the scaling state of the drinking water heater in real time; Step S3: On the basis that the mineral ion concentration index in the water to be heated is a constant value, make different combinations of adjustments to the instant heat transfer temperature deviation and pressure compensation index within their respective regulation ranges, and input these combinations into the scaling state evaluation model. The scaling state evaluation model generates a set of predicted scaling rates for these combinations Step S4: Determine the mineral ion concentration index in the current water to be heated, and generate a set of real-time predicted scaling rates according to the set of predicted scaling rates determination module; Select at least three of the lowest real-time predicted scaling rates from the set of real-time predicted scaling rates, and match the corresponding instant heat transfer temperature deviation and pressure compensation index; Step S5: Generate control adjustment parameters for the heating power of the heating element and the pressurization power of the internal pressure compensation unit corresponding to each combination according to at least three combinations of the instant heat transfer temperature deviation and pressure compensation index selected; Step S6: Set a judgment threshold for the real-time predicted scaling rate, and based on the comparison result of the judgment threshold, assign corresponding selection weights to the heating power of the heating element and the pressurization power of the internal pressure compensation unit; According to the selection weights, determine the control adjustment parameter with the highest matching degree with the selection weights from the three sets of control adjustment parameters.

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