Intelligent control method, system and device for warmer

Abstract

The invention relates to the technical field of intelligent control of heat supply systems, in particular to an intelligent control method, system and device for a warmer, and the method specifically comprises the steps that indoor temperature data, warmer power data and fan rotating speed data in the working process of the warmer are collected; the change condition of the indoor temperature in the working process of the warmer is analyzed, and a temperature response adjusting coefficient is built; a temperature feedback control coefficient is constructed by analyzing the cooperative relation among the indoor temperature, the heater power and the fan rotating speed in the working process of the heater and the lag response time of the temperature and combining the temperature response adjustment coefficient; the deviation in the PID controller is adjusted through the temperature feedback control coefficient, the problem that it is difficult to adjust the actual temperature to the target temperature rapidly and stably through a traditional PID control algorithm is solved, the temperature intelligent control effect in the working process of the warmer is optimized, and the warming experience of a user is improved.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent control of heating systems, and in particular to an intelligent control method, system and device for heaters. Background Art

[0002] As an indispensable household appliance in winter life, the performance and intelligence of heaters are directly related to the user's heating experience and comfort. Most traditional heaters use simple switch controls and temperature adjustment knobs, and users need to manually adjust to maintain a suitable indoor temperature. However, this manual control method is not only cumbersome to operate, but also difficult to achieve precise temperature control, resulting in poor heating effect or energy waste. With the advancement of technology, heaters are gradually developing in the direction of intelligence. By integrating advanced sensors, microprocessors and communication modules, more accurate temperature monitoring and control, as well as convenient functions such as remote control and timer switches are achieved. It brings users a more comfortable and energy-saving heating experience.

[0003] Among the many functions of a heater, temperature control is the most important one, which is directly related to the user's heating experience. Among them, the PID control algorithm is widely used in the temperature control of heaters due to its advantages of simple algorithm and strong adaptability. However, in actual applications, since the temperature control system of the heater is a nonlinear, thermal inertia, and hysteresis system, it is difficult for the traditional PID control algorithm to quickly and stably adjust the actual temperature to the target temperature. In the process of temperature control, serious overshoot and oscillation often occur due to the nonlinearity, thermal inertia, and hysteresis of the temperature, which in turn affects the user's heating experience. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide an intelligent control method, system and device for a heater. The technical solutions adopted are as follows: In a first aspect, an embodiment of the present application provides an intelligent control method for a heater, the method comprising the following steps: Collect the power, fan speed and indoor temperature at each moment during the operation of the heater; Based on the change trend of the temperature data in the neighborhood at the current moment and the indoor temperature at the current moment, the temperature response slowness at the current moment is constructed; based on the fluctuation of the data in the historical neighborhood temperature data at the current moment, the temperature oscillation coefficient at the current moment is constructed; based on the temperature response slowness and the temperature oscillation coefficient, the temperature response adjustment coefficient at the current moment is constructed; In the historical adjacent time of the current moment, the time series corresponding to each parameter is obtained, and the delay time of the indoor temperature at the current moment for the changes of the other two parameters is constructed based on the difference between the time series of different parameters; based on the correlation between the time series of different parameters, the delay time and the temperature response adjustment coefficient are combined to construct the temperature feedback control coefficient at the current moment; A deviation adjustment value at the current moment is constructed based on the temperature feedback control coefficient and the current indoor temperature; based on the deviation adjustment value, the heater is controlled in combination with the PID controller of the heater.

[0005] In one embodiment, the process of obtaining the temperature response slowness is: Record the sequence of temperature data at all times in the neighborhood of any time as a neighborhood temperature sequence; perform linear fitting on all data in the neighborhood temperature sequence at any time through a fitting algorithm, and record the obtained fitting straight line as the first straight line; obtain the absolute value of the difference between the indoor temperature at the current time and the set target temperature; The temperature response slowness at the current moment is calculated according to the absolute value of the difference at the current moment and the absolute value of the slope of the first straight line, wherein the temperature response slowness at the current moment is positively correlated with the absolute value of the difference at the current moment, and negatively correlated with the absolute value of the slope at the current moment.

[0006] In one embodiment, the process of obtaining the temperature oscillation coefficient is: The sequence composed of all temperature data in the historical adjacent time window at the current moment is recorded as the local temperature data sequence at the current moment; the local temperature data sequence is obtained by the peak search algorithm to obtain the maximum values ​​in the local temperature data sequence; the temperature oscillation coefficient at the current moment is recorded as , The expression is: , where represents the mean of all maximum values ​​in the local temperature data sequence at the current moment; N represents the number of maximum values ​​in the local temperature data sequence at the current moment; and They respectively represent the i-th and i-1-th maximum values ​​in the local temperature data sequence at the current moment.

[0007] In one embodiment, the temperature response adjustment coefficient is: the product of the temperature response slowness at the current moment and the temperature oscillation coefficient.

[0008] In one embodiment, the delay time is obtained by: The time series of indoor temperature and heater power is used as the input of the cross-correlation algorithm, and the output is the delay time of the indoor temperature for the change of heater power; the time series of indoor temperature and fan speed is used as the input of the cross-correlation algorithm, and the output is the delay time of the indoor temperature for the change of fan speed.

[0009] In one embodiment, the process of obtaining the temperature feedback control coefficient is: The correlation between the time series of the indoor temperature and the heater power at the current moment is calculated by the correlation algorithm, which is recorded as ; The correlation between the current indoor temperature and the time series of the fan speed is calculated by the correlation algorithm, which is recorded as ; The temperature feedback control coefficient at the current moment is recorded as , The expression is: , where Indicates the temperature response adjustment coefficient of the heater at the current moment; Indicates the delay time of the indoor temperature to the heater power change at the current moment; Indicates the delay time of the indoor temperature to the fan speed change at the current moment.

[0010] In one embodiment, the process of obtaining the deviation adjustment value is: The product of the normalized value of the temperature feedback control coefficient at that moment and the absolute value of the difference is used as the adjustment value of the deviation in the PID controller at the current moment.

[0011] In one embodiment, the heater control is performed based on the deviation adjustment value in combination with a PID controller of the heater, specifically: The deviation in the PID controller of the heater at the current moment is adjusted to the deviation adjustment value, and the heater is feedback-regulated through the adjusted controller.

[0012] In a second aspect, the embodiment of the present application further provides an intelligent control system for a heater, comprising: Data collection module: collects the power, fan speed and indoor temperature at each moment during the operation of the heater; Temperature response module: construct the temperature response slowness at the current moment based on the change trend of the temperature data in the neighborhood at the current moment and the indoor temperature at the current moment; construct the temperature oscillation coefficient at the current moment based on the fluctuation of the data in the historical neighborhood temperature data at the current moment; construct the temperature response adjustment coefficient at the current moment based on the temperature response slowness and the temperature oscillation coefficient; Temperature feedback module: in the historical adjacent time of the current moment, the time series corresponding to each parameter is obtained, and the delay time of the indoor temperature at the current moment for the changes of the other two parameters is constructed based on the difference between the time series of different parameters; based on the correlation between the time series of different parameters, the delay time and the temperature response adjustment coefficient are combined to construct the temperature feedback control coefficient at the current moment; Heater control module: constructs a deviation adjustment value at the current moment based on the temperature feedback control coefficient and the current indoor temperature; controls the heater based on the deviation adjustment value in combination with the heater's PID controller.

[0013] In a third aspect, an embodiment of the present application also provides an intelligent control device for a heater, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the method described in the first aspect when executing the computer program.

[0014] The embodiments of the present application have at least the following beneficial effects: The present application collects indoor temperature data, heater power data and fan speed data during the working process of the heater, analyzes the changes in indoor temperature during the working process of the heater, and constructs a temperature response adjustment coefficient, taking into account the nonlinearity and thermal inertia characteristics of the indoor temperature, and better reflecting the overshoot and oscillation of the indoor temperature during the working process of the heater; by analyzing the coordinated relationship between the indoor temperature and the heater power and the fan speed during the working process of the heater, as well as the lag response time of the temperature and combining the temperature response adjustment coefficient, a temperature feedback control coefficient is constructed, taking into account the coordinated change relationship between the indoor temperature and the heater power and the fan speed, and accurately reflecting the abnormal changes in the indoor temperature during the working process of the heater; the deviation in the PID controller is adjusted through the temperature feedback control coefficient, so as to better realize the intelligent temperature control during the working process of the heater and improve the user's heating experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 A flowchart of a method for intelligently controlling a heater according to an embodiment of the present application; Figure 2 Schematic diagram of the process of obtaining the temperature response slowness; Figure 3 It is a structural schematic diagram of the intelligent control system for the heater. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the intelligent control method, system and device for the heater proposed in the present application, its specific implementation, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0019] The specific scheme of the intelligent control method, system and device for a heater provided by the present application is described in detail below with reference to the accompanying drawings.

[0020] See also Figure 1 , which shows a flowchart of a method for intelligent control of a heater provided by an embodiment of the present application, the method comprising the following steps: Step S1, collecting the power, fan speed and indoor temperature at each moment during the operation of the heater.

[0021] This application optimizes the temperature control of a multifunctional air-heating heater in a bathroom. Since the heater is installed on the top of the bathroom, the overall temperature in the bathroom is raised by heating and circulating the air. The power of the heating element directly affects the heating efficiency, and the speed of the fan determines the circulation speed and coverage of the warm air. The stability of the voltage and the size of the current during the operation of the heater directly affect the heating power of the heater. Therefore, this application uses a voltage sensor, a current sensor, a speed sensor, and a temperature sensor to respectively collect the voltage, current, fan speed, and indoor temperature data during the operation of the heater in real time.

[0022] Preferably, for data collection of various parameters, in the embodiment of the present application, the data collection time interval of various parameters is set to As other embodiments of the present application, the implementer can set the data collection time interval according to the actual situation.

[0023] In order to prevent data loss in the collected data, all the collected voltage, current, fan speed and indoor temperature data are filled with missing data by the mean filling method. The mean filling method is a well-known technology and the specific process will not be repeated here.

[0024] The product of the voltage and current at the current moment is calculated to obtain the power of the heater at the current moment during its operation.

[0025] Step S2, constructing the temperature response slowness at the current moment based on the change trend of the temperature data in the neighborhood at the current moment and the indoor temperature at the current moment; constructing the temperature oscillation coefficient at the current moment based on the fluctuation of the data in the historical neighborhood temperature data at the current moment; constructing the temperature response adjustment coefficient at the current moment based on the temperature response slowness and the temperature oscillation coefficient.

[0026] When the heater is working, the air is heated by the heating element and blown into the bathroom, thereby raising the overall temperature of the bathroom. If the fan speed is too low, the hot air will be concentrated near the heater and the overall indoor temperature cannot be raised evenly. Secondly, the size of the heater power directly affects the temperature rise speed in the bathroom. At the same time, due to the characteristics of temperature nonlinearity, thermal inertia, and hysteresis, there will be overshoot and oscillation in the temperature adjustment process of the heater.

[0027] (1) The temperature response has nonlinear and thermal inertia characteristics. When the difference between the indoor temperature and the set target temperature is larger, the temperature response speed is slower; when the difference between the indoor temperature and the set target temperature is smaller, the temperature response speed is faster.

[0028] Therefore, taking the current moment as an example, record the current moment as moment t, analyze the temperature change trend in the neighborhood at the current moment and the indoor temperature at the current moment, and calculate the temperature response slowness at the current moment, specifically: Construct the neighborhood of the current moment. Preferably, in one embodiment of the present application, the neighborhood of the current moment is set to the previous As other embodiments of the present application, the implementer can set the neighborhood of the current time according to the actual situation.

[0029] The sequence of all temperature data in the neighborhood at the current moment in ascending time order is recorded as the neighborhood temperature sequence at the current moment. All temperature data in the neighborhood temperature sequence at the current moment are used as input of the least squares linear fitting algorithm, and the temperature data is linearly fitted, and the output fitting straight line is recorded as the first straight line. Among them, the least squares linear fitting algorithm is a well-known technology, and the specific process is not repeated here.

[0030] It should be noted that for the linear fitting of all data in the sequence, this application only provides one linear fitting method. There are many existing linear fitting methods, and implementers can also use other linear fitting algorithms to perform linear fitting on all data in the sequence. This application does not make specific restrictions.

[0031] Get the slope of the first straight line, recorded as Slope The smaller it is, the slower the temperature response at the current moment, and the worse the user's heating experience.

[0032] The temperature response slowness at the current moment is calculated based on the absolute value of the difference between the indoor temperature at the current moment and the set target temperature and the absolute value of the slope of the first straight line, wherein the temperature response slowness at the current moment is positively correlated with the absolute value of the difference at the current moment, and negatively correlated with the absolute value of the slope of the first straight line at the current moment.

[0033] It can be understood that the positive correlation and negative correlation in this application refer to the relationship between the independent variable and the dependent variable. The positive correlation means that the independent variable increases (decreases) as the dependent variable increases (decreases); the negative correlation means that the independent variable decreases (increases) as the dependent variable increases (decreases); the specific positive correlation and negative correlation can be determined according to actual conditions during the application process, and this application does not impose any special restrictions.

[0034] Preferably, in one embodiment of the present application, the expression of the temperature response slowness at the current moment can be: , where Indicates the temperature response slowness at the current moment; Indicates the absolute value of the difference between the current indoor temperature and the set target temperature; represents the slope of the first straight line at the current moment; It is a positive number preset artificially to prevent the denominator from being 0. Preferably, in one embodiment of the present application, the value of 𝛽 is set to 0.01. As other embodiments of the present application, the implementer can set it according to the actual situation. The value of .

[0035] In other embodiments of the present application, the expression of the temperature response slowness at the current moment can be: , where is the normalization function.

[0036] When using a heater for heating, the greater the difference between the current indoor temperature and the set target temperature, and the rate of change of the current indoor temperature The smaller the value, the slower the temperature response calculated at the current moment. The larger the value, the slower the temperature response at the current moment.

[0037] (2) Due to the nonlinearity of temperature and thermal inertia, there is a more serious oscillation phenomenon when controlling and adjusting the indoor temperature. The larger the temperature oscillation amplitude, the smaller the difference between two adjacent oscillation amplitudes, and the longer it takes for the indoor temperature to reach the set target temperature. This indicates that when the indoor temperature is maintained by the heater, the stability of controlling the indoor temperature at the target temperature is poor.

[0038] Therefore, a historical adjacent time window of the current moment is set. Preferably, in one embodiment of the present application, the time interval within 1 minute before the current moment is used as the historical adjacent time window of the current moment. As other embodiments of the present application, the implementer can set the historical adjacent time window of the current moment according to actual conditions.

[0039] The sequence of all temperature data in the historical adjacent time window at the current moment in ascending time order is recorded as the local temperature data sequence at the current moment, and the local temperature data sequence is used as the input of the automatic multi-scale peak search algorithm, and the output is the maximum values ​​of the temperature data in the local temperature data sequence. Among them, the automatic multi-scale peak search algorithm is a well-known technology, and the specific process is not repeated here.

[0040] It should be noted that for obtaining the maximum value in the sequence, this application only provides a peak search algorithm. There are many existing peak search algorithms, and implementers can also use other peak search algorithms to obtain the maximum value in the sequence. This application does not make specific restrictions.

[0041] Then the expression of the temperature oscillation coefficient at the current moment is: , where Indicates the temperature oscillation coefficient at the current moment; represents the mean of all maximum values ​​in the local temperature data sequence at the current moment; N represents the number of maximum values ​​in the local temperature data sequence at the current moment; and They respectively represent the i-th and i-1-th maximum values ​​in the local temperature data sequence at the current moment.

[0042] Since the indoor temperature approaches the set target temperature in the form of oscillation during the heating process, the average value of all maximum indoor temperature data within 1 minute before the current moment is The larger the value, the greater the difference between two adjacent maxima. The smaller it is, the longer it takes to reach the preset temperature. The longer the temperature is controlled, the more unstable the temperature control is. The temperature oscillation coefficient at the current moment is calculated. The larger the value, the more unstable the indoor temperature control; on the contrary The smaller, The larger the value is, the shorter the time to reach the preset temperature and the shorter the time to control the temperature, and the more stable the temperature control is.

[0043] Furthermore, the product of the temperature response delay and the temperature oscillation coefficient at the current moment is used as the temperature response adjustment coefficient of the heater at the current moment. The greater the temperature response delay, the slower the response of the indoor temperature when the heater adjusts the indoor temperature, the more difficult it is to adjust the indoor temperature, and the greater the temperature response adjustment coefficient; the greater the temperature oscillation stability coefficient, the more difficult it is to stabilize the indoor temperature near the set temperature, the greater the temperature response adjustment coefficient, and thus the worse the user's heating experience.

[0044] Step S3, in the historical adjacent time of the current moment, obtain the time series corresponding to each parameter, and construct the delay time of the indoor temperature at the current moment for the changes of the other two parameters based on the difference between the time series of different parameters; based on the correlation between the time series of different parameters, combined with the delay time and the temperature response adjustment coefficient, construct the temperature feedback control coefficient at the current moment.

[0045] When using a heater for heating, the temperature response has a hysteresis, that is, the indoor temperature change always lags behind the change in heater power and fan speed, and the greater the difference between the indoor temperature and the set target temperature, the more serious the hysteresis effect. Therefore, this application further analyzes the hysteresis change of indoor temperature and heater power and fan speed.

[0046] (1) First, obtain the indoor temperature data, heater power data, and fan speed data in the historical adjacent time window at the current moment, and record the sequences composed of various types of data in the historical adjacent time window as the local temperature data sequence, local heater power sequence, and local fan speed data sequence at the current moment, respectively.

[0047] Secondly, the local temperature data sequence and the local heater power sequence at the current moment are used as the input of the cross-correlation algorithm, and the output is the delay time of the indoor temperature to the heater power change at the current moment, which is recorded as Similarly, the local temperature data sequence and the local fan speed data sequence at the current moment are used as the input of the cross-correlation algorithm, and the output is the delay time of the indoor temperature at the current moment for the fan speed change, which is recorded as The cross-correlation algorithm is a well-known technology, and the specific process is not repeated here. The delay time of the heater power change and the fan speed change , The larger it is, the more serious the hysteresis effect of indoor temperature is.

[0048] (2) In the temperature control process of the heater, the relationship between the heater power, fan speed and indoor temperature has significant synergy. That is, in actual operation, when the power is large but the wind speed is low, the hot air may be concentrated around the heater, resulting in uneven indoor temperature distribution; when the wind speed is too high but the power is insufficient, the heating rate may be slowed down due to insufficient heat. This synergy makes it difficult to achieve the ideal temperature control effect by simply adjusting the power or wind speed, which may cause the temperature response to be too slow or oscillating. Therefore, this application further analyzes the synergistic changes between the heater power, fan speed and indoor temperature.

[0049] Calculate the normalized value of the inverse of the DTW distance between the local temperature data sequence and the local heater power sequence at the current moment, and record it as the first correlation Similarly, the normalized value of the inverse of the DTW distance between the local temperature data sequence and the local fan speed data sequence at the current moment is calculated and recorded as the second correlation The calculation of the DTW distance is a well-known technique, and the specific process will not be described in detail.

[0050] It should be noted that for the calculation of correlation between sequences, this application only provides one correlation calculation method. There are many existing correlation calculation methods. Implementers can also use other correlation algorithms to calculate the correlation between sequences. This application does not make specific restrictions.

[0051] (3) Based on the above analysis and combined with the temperature response adjustment coefficient, the temperature feedback control coefficient at the current moment is constructed, and the expression is: In the formula, Indicates the temperature feedback control coefficient of the heater at the current moment; Indicates the temperature response adjustment coefficient of the heater at the current moment; Indicates the delay time of the indoor temperature to the heater power change at the current moment; Indicates the delay time of the indoor temperature to the fan speed change at the current moment; , Respectively represent the first correlation and the second correlation at the current moment.

[0052] Therefore, in the process of using the heater for heating, the greater the difference between the current indoor temperature and the set target temperature, the greater the calculated temperature response adjustment coefficient The larger the difference between the indoor temperature and the set target temperature, the more serious the temperature lag effect, and the lag time of the indoor temperature at the current moment to the change of heater power and fan speed. In addition, since there is a certain synergy between indoor temperature change, heater power and fan speed, the worse the synergy between indoor temperature change, heater power and fan speed, the slower the indoor temperature response rate is, and the calculated The smaller the value, the smaller the calculated temperature feedback control coefficient at the current moment. The larger it is, the more serious the lag effect of the indoor temperature at the current moment is, and the worse the user's heating experience is.

[0053] Step S4, constructing a deviation adjustment value at the current moment based on the temperature feedback control coefficient and the current indoor temperature; and controlling the heater in combination with the PID controller of the heater based on the deviation adjustment value.

[0054] The data monitoring period is set. Preferably, in the embodiment of the present application, the data monitoring period is set to 1 minute. As other embodiments of the present application, the implementer can set the data monitoring period according to the actual situation.

[0055] Temperature feedback control coefficient The nonlinear response characteristics of the temperature during the operation of the heater and the coordinated change relationship between the indoor temperature, heater power and fan speed are taken into account. Therefore, the temperature feedback control coefficient can accurately reflect the abnormal changes in the indoor temperature during the heating process. During the operation of the heater, the more serious the hysteresis effect and nonlinear changes of the indoor temperature are, the more likely the calculated temperature feedback control coefficient is. The larger it is, the worse the user's heating experience will be.

[0056] Therefore, the present application uses a feedback correction mechanism to correct the temperature feedback control during the operation of the heater according to the calculated temperature feedback control coefficient. The specific correction process is: The current moment is taken as the last moment of the data monitoring cycle, and the temperature feedback control coefficient at all moments in the data monitoring cycle is normalized to the maximum-minimum value, thereby realizing the normalization of the temperature feedback control coefficient at the current moment.

[0057] The normalized value of the temperature feedback control coefficient at the current moment is used as the feedback correction weight at the current moment.

[0058] Combined with the difference between the current indoor temperature and the set target temperature, the deviation e in the PID controller is adjusted. The adjustment value of the deviation e at the current moment is: , where represents the adjustment value of the deviation e in the PID controller at the current moment, represents the feedback correction weight at the current moment, It represents the absolute value of the difference between the current indoor temperature and the set target temperature. The deviation in the PID (Proportional Integral Derivative) controller of the PLC (Programmable Logic Controller) regulation system of the heater at the current moment is adjusted to the adjustment value of the deviation e, and the heater is feedback-regulated by the adjusted controller. The PLC regulation system and PID controller are well-known technologies, and the specific process will not be repeated here.

[0059] The schematic diagram of the process of obtaining the temperature response delay is as follows: Figure 2 shown.

[0060] See also Figure 3 , Figure 3 is a schematic diagram of the structure of the intelligent control system for a heater provided in an embodiment of the present application. In this embodiment, each unit included in the terminal is used to execute each step in the embodiment corresponding to the intelligent control method for a heater. Figure 3 , the intelligent control system includes: Data collection module: collects the power, fan speed and indoor temperature at each moment during the operation of the heater; Temperature response module: construct the temperature response slowness at the current moment based on the change trend of the temperature data in the neighborhood at the current moment and the indoor temperature at the current moment; construct the temperature oscillation coefficient at the current moment based on the fluctuation of the data in the historical neighborhood temperature data at the current moment; construct the temperature response adjustment coefficient at the current moment based on the temperature response slowness and the temperature oscillation coefficient; Temperature feedback module: in the historical adjacent time of the current moment, the time series corresponding to each parameter is obtained, and the delay time of the indoor temperature at the current moment for the changes of the other two parameters is constructed based on the difference between the time series of different parameters; based on the correlation between the time series of different parameters, the delay time and the temperature response adjustment coefficient are combined to construct the temperature feedback control coefficient at the current moment; Heater control module: constructs a deviation adjustment value at the current moment based on the temperature feedback control coefficient and the current indoor temperature; controls the heater based on the deviation adjustment value in combination with the heater's PID controller.

[0061] Based on the same inventive concept as the above method, an embodiment of the present application also provides an intelligent control device for a heater, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above-mentioned intelligent control methods for a heater when executing the computer program.

[0062] In summary, the embodiment of the present application provides an intelligent control method for a heater, which collects indoor temperature data, heater power data and fan speed data during the working process of the heater, analyzes the changes in the indoor temperature during the working process of the heater to construct a temperature response adjustment coefficient, takes into account the nonlinearity and thermal inertia characteristics of the indoor temperature, and better reflects the overshoot and oscillation of the indoor temperature during the working process of the heater; by analyzing the coordinated relationship between the indoor temperature, heater power and fan speed during the working process of the heater and the lag response time of the temperature and combining the temperature response adjustment coefficient, a temperature feedback control coefficient is constructed, which takes into account the coordinated change relationship between the indoor temperature, heater power and fan speed, and accurately reflects the abnormal changes in the indoor temperature during the working process of the heater; the deviation in the PID controller is adjusted through the temperature feedback control coefficient, so as to better realize the intelligent temperature control during the working process of the heater and improve the user's heating experience.

[0063] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. The above-mentioned specific embodiments of the present application are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0064] The various embodiments in the present application are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0065] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. An intelligent control method for a heater, characterized in that: The method comprises the following steps: Collect the power, fan speed and indoor temperature at each moment during the operation of the heater; Based on the change trend of the temperature data in the neighborhood at the current moment and the indoor temperature at the current moment, the temperature response slowness at the current moment is constructed; based on the fluctuation of the data in the historical neighborhood temperature data at the current moment, the temperature oscillation coefficient at the current moment is constructed; based on the temperature response slowness and the temperature oscillation coefficient, the temperature response adjustment coefficient at the current moment is constructed; In the historical adjacent time of the current moment, the time series corresponding to each parameter is obtained, and the delay time of the indoor temperature at the current moment for the changes of the other two parameters is constructed based on the difference between the time series of different parameters; based on the correlation between the time series of different parameters, the delay time and the temperature response adjustment coefficient are combined to construct the temperature feedback control coefficient at the current moment; A deviation adjustment value at the current moment is constructed based on the temperature feedback control coefficient and the current indoor temperature; based on the deviation adjustment value, the heater is controlled in combination with the PID controller of the heater.

2. The intelligent control method for a heater according to claim 1, characterized in that: The process of obtaining the temperature response slowness is as follows: Record the sequence of temperature data at all times in the neighborhood of any time as a neighborhood temperature sequence; perform linear fitting on all data in the neighborhood temperature sequence at any time through a fitting algorithm, and record the obtained fitting straight line as the first straight line; obtain the absolute value of the difference between the indoor temperature at the current time and the set target temperature; The temperature response slowness at the current moment is calculated according to the absolute value of the difference at the current moment and the absolute value of the slope of the first straight line, wherein the temperature response slowness at the current moment is positively correlated with the absolute value of the difference at the current moment, and negatively correlated with the absolute value of the slope at the current moment.

3. The intelligent control method for a heater according to claim 1, characterized in that: The process of obtaining the temperature oscillation coefficient is as follows: The sequence composed of all temperature data in the historical adjacent time window at the current moment is recorded as the local temperature data sequence at the current moment; the local temperature data sequence is obtained by the peak search algorithm to obtain the maximum values ​​in the local temperature data sequence; the temperature oscillation coefficient at the current moment is recorded as , The expression is: , where represents the mean of all maximum values ​​in the local temperature data sequence at the current moment; N represents the number of maximum values ​​in the local temperature data sequence at the current moment; and They respectively represent the i-th and i-1-th maximum values ​​in the local temperature data sequence at the current moment.

4. The intelligent control method for a heater according to claim 1, characterized in that: The temperature response adjustment coefficient is: the product of the temperature response slowness at the current moment and the temperature oscillation coefficient.

5. The intelligent control method for a heater according to claim 1, characterized in that: The process of obtaining the delay time is as follows: The time series of indoor temperature and heater power is used as the input of the cross-correlation algorithm, and the output is the delay time of the indoor temperature for the change of heater power; the time series of indoor temperature and fan speed is used as the input of the cross-correlation algorithm, and the output is the delay time of the indoor temperature for the change of fan speed.

6. The intelligent control method for a heater according to claim 1, characterized in that: The process of obtaining the temperature feedback control coefficient is as follows: The correlation between the time series of the indoor temperature and the heater power at the current moment is calculated by the correlation algorithm, which is recorded as ; The correlation between the current indoor temperature and the time series of the fan speed is calculated by the correlation algorithm, which is recorded as ; The temperature feedback control coefficient at the current moment is recorded as , The expression is: , where Indicates the temperature response adjustment coefficient of the heater at the current moment; Indicates the delay time of the indoor temperature to the heater power change at the current moment; Indicates the delay time of the indoor temperature to the fan speed change at the current moment.

7. The intelligent control method for a heater according to claim 2, characterized in that: The process of obtaining the deviation adjustment value is as follows: The product of the normalized value of the temperature feedback control coefficient at that moment and the absolute value of the difference is used as the adjustment value of the deviation in the PID controller at the current moment.

8. The intelligent control method for a heater according to claim 1, characterized in that: The heater control is performed based on the deviation adjustment value and in combination with the PID controller of the heater, specifically: The deviation in the PID controller of the heater at the current moment is adjusted to the deviation adjustment value, and the heater is feedback-regulated through the adjusted controller.

9. An intelligent control system for a heater, implementing the method as claimed in claim 1, characterized in that: The system comprises: Data collection module: collects the power, fan speed and indoor temperature at each moment during the operation of the heater; Temperature response module: construct the temperature response slowness at the current moment based on the change trend of the temperature data in the neighborhood at the current moment and the indoor temperature at the current moment; construct the temperature oscillation coefficient at the current moment based on the fluctuation of the data in the historical neighborhood temperature data at the current moment; construct the temperature response adjustment coefficient at the current moment based on the temperature response slowness and the temperature oscillation coefficient; Temperature feedback module: in the historical adjacent time of the current moment, the time series corresponding to each parameter is obtained, and the delay time of the indoor temperature at the current moment for the changes of the other two parameters is constructed based on the difference between the time series of different parameters; based on the correlation between the time series of different parameters, the delay time and the temperature response adjustment coefficient are combined to construct the temperature feedback control coefficient at the current moment; Heater control module: constructs a deviation adjustment value at the current moment based on the temperature feedback control coefficient and the current indoor temperature; controls the heater based on the deviation adjustment value in combination with the heater's PID controller.

10. An intelligent control device for a heater, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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