Fuzzy Control Method, Device and Medium Applicable to PTC Heating System

Abstract

The present invention relates to the technical field of control or regulation, and particularly relates to a fuzzy control method, device and medium applicable to a PTC heating system. The method includes: obtaining historical temperature data of the PTC heating system and determining the target heating task each time in the historical temperature data; using the temperature data sequence in the target heating task to determine the heat dissipation expectation factor and the system medium loss coefficient of the target heating task; inputting the system medium loss coefficient as a fuzzy rule parameter into the inference engine of the fuzzy controller to output the real-time heating power corresponding to the real-time temperature data; and using the real-time heating power to perform heating control on the PTC heating system. By comprehensively considering the heat dissipation path and the medium deterioration factor, the present invention determines the medium loss coefficient of the heating task, and then precisely controls the heating power, significantly improving the heating effect of the PTC heating system.

Description

Technical Field

[0001] The present invention relates to the technical field of control or regulation, and particularly relates to a fuzzy control method, device, and medium applicable to a PTC heating system. Background Art

[0002] A PTC (Positive Temperature Coefficient) heating system utilizes the temperature-dependent resistance characteristic of PTC materials to achieve self-regulation of temperature and is applicable to application scenarios with high requirements for heating stability.

[0003] In a PTC heating system, with the complex changes in the working environment, external factors can cause the resistance of PTC materials to change non-linearly with temperature. This leads to the difficulty in predicting the change of its resistance due to the temperature dependence of PTC materials, thus affecting the thermal response and current regulation of the heating system. Eventually, it is difficult for the PTC heating system to adapt to the temperature fluctuations of PTC materials in a timely manner, which may lead to a decline in heating effect and even a risk of overheating. Summary of the Invention

[0004] In order to solve the technical problem that it is difficult for a PTC heating system to adapt to the temperature fluctuations of PTC materials in a timely manner and easily leads to poor heating effect, the purpose of the present invention is to provide a fuzzy control method, device, and medium applicable to a PTC heating system. The specific technical solutions adopted are as follows:

[0005] The present invention provides a fuzzy control method applicable to a PTC heating system, and the method includes:

[0006] Obtain the historical temperature data of the PTC heating system and determine the target heating task each time in the historical temperature data;

[0007] Utilize the temperature data sequence in the target heating task to determine the heat dissipation expectation factor and the system medium loss coefficient of the target heating task;

[0008] Take the system medium loss coefficient as a fuzzy rule parameter and input it into the inference engine of the fuzzy controller to output the real-time heating power corresponding to the real-time temperature data;

[0009] Use the real-time heating power to control the heating of the PTC heating system;

[0010] Among them, the heat dissipation expectation factor characterizes the possible expectation of the heat dissipation efficiency generated during the heating process by the characteristics of the target heating task itself; the system medium loss coefficient characterizes the loss degree of the heating medium.

[0011] Further, using the temperature data sequence in the target heating task to determine the heat dissipation expectation factor of the target heating task includes:

[0012] Decompose the temperature data sequence in the target heating task to obtain the heat dissipation temperature data of the target heating task;

[0013] Use the heat dissipation temperature data to determine the heat dissipation expectation factor of the target heating task.

[0014] Further, decomposing the temperature data sequence in the target heating task to obtain the heat dissipation temperature data of the target heating task includes:

[0015] Determine the soaking stage temperature data in the temperature data sequence;

[0016] Perform non - negative matrix factorization on the soaking stage temperature data to obtain multiple data decomposition combinations;

[0017] Determine the periodicity coefficient and kurtosis of the first data component in each data decomposition combination;

[0018] Use the periodicity coefficient and kurtosis to determine the optimal decomposition coefficient of each data decomposition combination;

[0019] Determine the maximum value in the optimal decomposition coefficients and its corresponding target data decomposition combination;

[0020] Determine the second data component in the target data decomposition combination as the heat dissipation temperature data of the target heating task;

[0021] Among them, the soaking stage represents the stage where the heating energy and the heat dissipation energy are in balance.

[0022] Further, after determining the second data component in the target data decomposition combination as the heat dissipation temperature data of the target heating task, it further includes:

[0023] Use the ICA algorithm to decompose the heat dissipation temperature data of the target heating task to obtain the heat dissipation temperature data of the target heating task on different heat dissipation paths.

[0024] Further, using the heat dissipation temperature data to determine the heat dissipation expectation factor of the target heating task includes:

[0025] Determine the extreme points and the number of extreme points in the heat dissipation temperature data;

[0026] Starting from the maximum extreme point, determine the window area with a preset step size;

[0027] Determine the occurrence frequency and the slope difference on both sides of each extreme point in the window area in the heat dissipation temperature data;

[0028] Use the occurrence frequency and the slope difference on both sides to calculate the fluctuation characteristic coefficient of the heat dissipation temperature data;

[0029] The heat dissipation expectation factor of the target heating task is calculated using the absolute value of the difference between the maximum and minimum values in the fluctuation characteristic coefficient and the heat dissipation temperature data.

[0030] Furthermore, using the temperature data sequence in the target heating task, the system medium loss coefficient of the target heating task is determined, including:

[0031] Determine the maximum temperature and the preset steady-state temperature in the temperature data sequence and the absolute value of the difference between the two;

[0032] Determine the single required duration for the target heating task to reach the maximum temperature and the average required duration for all heating tasks with the same preset steady-state temperature in the historical temperature data to reach the maximum temperature;

[0033] Using the absolute value of the difference, the single required duration, and the average required duration, calculate the system medium deterioration coefficient of the target heating task;

[0034] Using the system medium deterioration coefficients of each group of adjacent heating tasks, determine the system medium loss coefficient of the subsequent target heating task;

[0035] Wherein, each group of adjacent heating tasks includes two consecutive target heating tasks.

[0036] Furthermore, using the system medium deterioration coefficients of each group of adjacent heating tasks, determine the system medium loss coefficient of the subsequent target heating task, including:

[0037] Using the system medium deterioration coefficients and the heat dissipation expectation factors of each group of adjacent heating tasks respectively, calculate the system medium loss coefficient of the subsequent target heating task.

[0038] Furthermore, input the system medium loss coefficient as a fuzzy rule parameter into the inference engine of the fuzzy controller to output the real-time heating power corresponding to the real-time temperature data, including:

[0039] Adjust the preset real-time heating power using the normalized system medium loss coefficient to obtain the corrected real-time heating power corresponding to the real-time temperature data.

[0040] The present invention also provides a fuzzy control device applicable to a PTC heating system. The fuzzy control device includes a processor, a storage unit, and a fuzzy control program stored on the storage unit and executable by the processor. Wherein, when the fuzzy control program is executed by the processor, the steps of the fuzzy control method applicable to the PTC heating system as described in any one of the above are implemented.

[0041] The present invention also provides a computer-readable storage medium, on which a fuzzy control program is stored. When the fuzzy control program is executed by a processor, the steps of the fuzzy control method applicable to the PTC heating system as described in any one of the above are implemented.

[0042] The present invention has the following beneficial effects:

[0043] In view of the non-linear change of the resistance of the PTC material with temperature caused by the change of the working environment in the existing PTC heating system, the present invention optimizes the temperature regulation through a fuzzy control method. First, based on the heat dissipation and heating energy balance characteristics in the soaking stage, the fuzzy rule parameters in the historical temperature data are extracted to quantify the influence of different heat dissipation paths on temperature control. At the same time, in combination with the deterioration degree of the system medium, according to different temperature change curves, the influence of medium deterioration on the heating process is quantified. By comprehensively considering the heat dissipation path and the medium deterioration factors, the medium loss coefficients at the current moment and each moment of the heating task are determined, and then the heating power is accurately controlled. Thus, by adjusting the current or voltage of the heater, accurate temperature regulation and stable heating performance can be achieved, avoiding the overshoot or undershoot phenomena of temperature control easily caused by the non-linear change of the medium resistance due to external factors in the traditional temperature control process, and significantly improving the heating effect. Description of the Drawings

[0044] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0045] Figure 1 It is a flowchart of the steps of a fuzzy control method applicable to a PTC heating system provided by an embodiment of the present invention;

[0046] Figure 2 It is a refined flowchart of step S2 in a fuzzy control method applicable to a PTC heating system provided by an embodiment of the present invention;

[0047] Figure 3 It is a refined flowchart of step S21 in a fuzzy control method applicable to a PTC heating system provided by an embodiment of the present invention;

[0048] Figure 4 It is a refined flowchart of step S2 in a fuzzy control method applicable to a PTC heating system provided by another embodiment of the present invention;

[0049] Figure 5Schematic diagram of the hardware operating environment of the fuzzy control device applicable to the PTC heating system involved in the embodiment solution of the present invention;

[0050] Figure 6 Schematic diagram of the framework structure of the fuzzy control device applicable to the PTC heating system involved in the embodiment solution of the present invention;

[0051] Figure 7 Schematic diagram of the fitting curve of the temperature data of a single heating task involved in the embodiment solution of the present invention. Detailed implementation manners

[0052] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following combines the accompanying drawings and preferred embodiments to detail the specific implementation manners, structures, features and effects of a fuzzy control method, device and medium applicable to the PTC heating system according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0054] The following specifically describes the specific solution of a fuzzy control method applicable to the PTC heating system provided by the present invention with reference to the accompanying drawings.

[0055] Embodiment 1:

[0056] For the fuzzy control method applicable to the PTC heating system provided by the present invention, please refer to Figure 1 , which shows the flowchart of the steps of the fuzzy control method applicable to the PTC heating system provided by an embodiment of the present invention.

[0057] The method includes:

[0058] Step S1, obtaining the historical temperature data of the PTC heating system and determining the target heating task each time in the historical temperature data;

[0059] In this embodiment, a thermocouple temperature sensor with high precision can be used to collect the real-time temperature of the PTC heating system; initialize the sensor and connect it to a microcontroller (such as Arduino, Raspberry Pi), and the temperature data can be read once every 1 second; at the same time, the microcontroller performs pre-operations such as noise processing and smoothing processing on the original sensor temperature data to ensure the accuracy of subsequent data analysis, so as to obtain the historical temperature data required in this embodiment; the processed historical temperature data can be stored in a local storage medium for subsequent analysis and strategy optimization; an efficient and low-latency data transmission protocol (such as I2C, SPI, MQTT) can also be used to achieve the real-time transmission of the collected data, ensuring that the data can be quickly and accurately transmitted from the sensor to the fuzzy control unit.

[0060] Based on the historical temperature data that can be stored in the local storage medium is composed of the temperature data set or sequence corresponding to two or more target heating tasks accumulated, the historical temperature data can be divided into temperature data sequences corresponding to different target heating tasks.

[0061] Step S2, using the temperature data sequence in the target heating task, determine the heat dissipation expectation factor and the system medium loss coefficient of the target heating task;

[0062] Where the temperature data sequence refers to the set of temperature data collected during any target heating task, which can be arranged in a certain order, for example, the temperature data sequence formed in the order of collection time.

[0063] Among them, the heat dissipation expectation factor characterizes the possible expectation of the heat dissipation efficiency generated during the heating process by the characteristics of the target heating task itself; the system medium loss coefficient characterizes the loss degree of the heating medium. Both the heat dissipation expectation factor and the system medium loss coefficient can be used as fuzzy rule parameters in the fuzzy control unit.

[0064] To facilitate the understanding of the fuzzy control method and the fuzzy control unit (fuzzy controller) in this embodiment, a brief introduction to the fuzzy control unit is given here:

[0065] The fuzzy controller consists of a knowledge base, an input / output interface, a fuzzification interface (converting input variables into fuzzy values), a knowledge base, an inference engine, a defuzzification interface (converting the fuzzy inference result into an accurate control signal), etc.

[0066] The knowledge base of the fuzzy controller consists of a database and a rule base:

[0067] The database contains all the historical temperature data records of the thermocouple sensor;

[0068] The rule base contains all fuzzy rule parameters extracted from the changes in historical temperature data, and the fuzzy rule parameters can be used to perform fuzzy control on the temperature of the PTC heating system.

[0069] Specifically, in one embodiment, please refer to Figure 2 , step S2 includes:

[0070] Step S21, decompose the temperature data sequence in the target heating task to obtain the heat dissipation temperature data of the target heating task;

[0071] The heat dissipation expectation factor is considered here because the resistance of the PTC material is closely related to temperature, and the change in the external temperature will interfere with its surface heat dissipation effect, thereby affecting the material resistance and causing the actual temperature rise curve to deviate from the expectation. The prior art only adjusts the current during the temperature rise process through the real-time temperature difference, which may lead to unsatisfactory temperature control effect due to the change in the resistance value of the PTC material. Therefore, it is necessary to consider the situation of external temperature change, which can be reflected by the heat dissipation expectation factor.

[0072] The purpose of decomposing the temperature data sequence is to determine and distinguish the heating temperature data and the heat dissipation temperature data therein. The heating temperature data can reflect the heat generation situation, while the heat dissipation temperature data can reflect the heat loss situation during heat dissipation.

[0073] More specifically, please refer to Figure 3 , the step S21 includes:

[0074] Step S211, determine the temperature data in the soaking stage of the temperature data sequence;

[0075] Step S212, perform non-negative matrix factorization on the temperature data in the soaking stage to obtain multiple data decomposition combinations;

[0076] Step S213, determine the periodicity coefficient and kurtosis of the first data component in each data decomposition combination;

[0077] Step S214, use the periodicity coefficient and kurtosis to determine the optimal decomposition coefficient of each data decomposition combination;

[0078] Step S215, determine the maximum value in the optimal decomposition coefficients and its corresponding target data decomposition combination;

[0079] Step S216, determine the second data component in the target data decomposition combination as the heat dissipation temperature data of the target heating task;

[0080] Among them, the soaking stage represents the stage where the heating energy and the heat dissipation energy are in balance.

[0081] Please refer to Figure 7 , Figure 7Schematic diagram of the fitting curve of the temperature data for a single heating task involved in the solution of the embodiment of the present invention.

[0082] After obtaining the temperature data of a single target heating task of the PTC heating system, the least squares method can be used in combination with polynomial fitting to obtain the fitting curve of the temperature data sequence ;

[0083] A complete single target heating task of the PTC system includes a preheating section, a heating section, and an isothermal section, as Figure 7 shown.

[0084] Since in the isothermal stage of the PTC system, the heating current does not need to be adjusted significantly, the main factors affecting the heat dissipation effect are the different heat dissipation paths of the PTC heating system. The PTC heating system contacts different heating objects, and there are different degrees of temperature differences between each heated object and the system, which is likely to cause the greater the temperature difference, and thus more heat dissipation paths are generated;

[0085] Based on this, it is necessary to obtain the heat dissipation prediction factor of each target heating task of each historical heating record;

[0086] NMF (Nonnegative Matrix Factorization) is a conventional data decomposition algorithm, which can decompose the original temperature data signal into different combinations of basis vectors for separating data of different frequencies;

[0087] In the isothermal stage of the PTC heating system, the overall heat generation and heat loss of the system reach equilibrium. Therefore, the temperature data of the isothermal stage of the target heating task (here the data corresponds to the isothermal section of the fitting curve) is set with a decomposition ratio of 1:1;

[0088] Perform data processing on the temperature data of the isothermal stage using NMF to obtain several groups of data decomposition results. Each data decomposition combination includes basis vector data components (the first data component), and the basis vector data component (the second data component); it should be noted that and The data forms of are all data fitting curves for subsequent analysis.

[0089] For the data component in any decomposition result, use the autocorrelation function to obtain its data periodicity coefficient , the larger this value, the greater the probability that the data component has periodicity, and the more likely it is to represent the true heating temperature data of the PTC heating system;

[0090] Calculate the kurtosis of the distribution of the data component ​ , the closer this value is to 3, the more representative the data component has a uniform steady-state distribution, corresponding to the heating temperature data distribution of the PTC heating system under stable operating conditions, which has a strong Gaussian property, that is, the data has a uniform steady-state distribution;

[0091] Using the periodicity coefficient and kurtosis, determine the optimal decomposition coefficient for each data decomposition combination:

[0092]

[0093] In the formula, represents any th data decomposition combination among all data decomposition results, represents the th data decomposition combination, and represents the periodicity coefficient of the data component in the th data decomposition combination, represents the kurtosis of the data component in the th data decomposition combination, represents the optimal decomposition coefficient of the

[0094] th data decomposition combination; in addition, any non-zero anti-zero constant , such as 0.0001, can be set to avoid the denominator being zero. Calculate the optimal decomposition coefficients of all data decomposition results of the target heating task , sort them by size, select the maximum value , obtain

[0095] In one embodiment, after step S216, it further includes:

[0096] Use the ICA algorithm to decompose the heat dissipation temperature data of the target heating task to obtain the heat dissipation temperature data of the target heating task on different heat dissipation paths.

[0097] During the heat dissipation process of the PTC heating system, different heat dissipation paths (such as convection, radiation, and conduction) affect the heat dissipation effect through different heat transfer media. When analyzing the external environment, the different effects of each path on heat dissipation need to be considered;

[0098] Obtain the heat dissipation temperature data of the target heating task for each historical PTC system;

[0099] The ICA (imperialist competitive algorithm) algorithm is used to decompose the heat dissipation temperature data and extract the independent components in the heat dissipation temperature data. Each independent data component represents the heat dissipation temperature data of different paths of the PTC heating system. Here, using the ICA algorithm to decompose the data is an existing technology and will not be elaborated here.

[0100] Step S22: Determine the heat dissipation prediction factor of the target heating task using the heat dissipation temperature data.

[0101] Specifically, step S22 includes:

[0102] Determine the extreme points and the number of extreme points in the heat dissipation temperature data;

[0103] Taking the maximum point as the starting point, determine the window area with a preset step size;

[0104] Determine the occurrence frequency and the slope difference on both sides of each extreme point in the window area in the heat dissipation temperature data;

[0105] Calculate the fluctuation characteristic coefficient of the heat dissipation temperature data using the occurrence frequency and the slope difference on both sides;

[0106] Calculate the heat dissipation prediction factor of the target heating task using the fluctuation characteristic coefficient and the absolute value of the difference between the maximum value and the minimum value in the heat dissipation temperature data.

[0107] The heat dissipation temperature data here can be the heat dissipation temperature data without distinguishing heat dissipation paths in the above embodiments, or preferably the heat dissipation temperature data that distinguishes heat dissipation paths. Here, the heat dissipation temperature data that distinguishes heat dissipation paths is taken as an example for elaboration.

[0108] Obtain the maximum value and the minimum value of the heat dissipation temperature data of different heat dissipation paths respectively, and calculate the absolute value of the difference between the two ;

[0109] For the heat dissipation path with a large temperature difference, due to the small heat conduction resistance, the heat dissipation rate is fast. However, the fast heat dissipation rate will cause rapid heat loss. During the heating process of the PTC system, when overshoot or undershoot occurs in the heating temperature regulation, the heat loss of the fast heat dissipation path is too fast, resulting in disorder and fluctuation of the heat dissipation temperature;

[0110] Obtain the number of extreme points in each heat dissipation temperature data ;

[0111] Divide each heat dissipation temperature data within each target heating task with the data maximum point to obtain the local window area characteristics;

[0112] Specifically, the local window starts from the maximum point, and the step size can be set to 10 data extreme points (adjustable as needed) to construct a window area (when there are insufficient data points, all the remaining data points are regarded as the same area);

[0113] Obtain the occurrence frequencies of all extreme points within the window area in the corresponding heat dissipation temperature data respectively , and the slope difference on both sides of the extreme point , and determine the fluctuation characteristic coefficient of the heat dissipation temperature data:

[0114]

[0115] In the formula, represents the th maximum point in the set, represents the set of window data extreme points corresponding to the th maximum point, represents the th extreme point in the set, represents the absolute value of the slope difference on both sides at the th extreme point, represents the occurrence frequency of the th extreme point in the corresponding heat dissipation temperature data,

[0116] The larger the

[0117] value is, the greater the slope difference on both sides of each extreme point within the local area corresponding to the data extreme point, indicating that the change of the heat dissipation temperature data at the data extreme point is more drastic and rapid. And the extreme points corresponding to these changes appear less frequently in the heat dissipation temperature data, which indicates that the heat dissipation path corresponding to this heat dissipation temperature data experiences more severe overshoot or undershoot phenomena during the heat dissipation process due to system heating. Statistically count the set of the number of all heat dissipation paths corresponding to the target heating task

[0118] If different heat dissipation paths are not distinguished, there is no need to perform statistics here;

[0119] Combined with the mean value of the product of the absolute value of the difference and the fluctuation characteristic coefficient of all heat dissipation temperature data corresponding to the target heating task: , taking values , regarded as the heat dissipation expectation factor of the target heating task; here represents the set of the number of heat dissipation paths the absolute value of the difference in the heat dissipation temperature data corresponding to any heat dissipation path, and similarly represents the set of the number of heat dissipation paths the fluctuation characteristic coefficient of the heat dissipation temperature data corresponding to any heat dissipation path.

[0120] Here, if different heat dissipation paths are not distinguished, then only the absolute value of the difference and the fluctuation characteristic coefficient of the product can be used as the heat dissipation expectation factor of the target heating task.

[0121] The heat dissipation expectation factor is one of the fuzzy rule parameters, representing the expectation of the heat dissipation efficiency that may be generated during the heating process due to the characteristics of each target heating task itself.

[0122] In another embodiment, please refer to Figure 4 , step S2, includes:

[0123] Step S201, determining the maximum temperature and the preset steady-state temperature in the temperature data sequence and the absolute value of the difference between the two;

[0124] Step S202, determining the single required duration for the target heating task to reach the maximum temperature and the average value of the required durations for all heating tasks with the same preset steady-state temperature in the historical temperature data to reach the maximum temperature;

[0125] Step S203, using the absolute value of the difference, the single required duration, and the average value of the required durations to calculate the system medium deterioration coefficient of the target heating task;

[0126] In a PTC heating system, the energy lost due to heat dissipation can indirectly affect the heating efficiency of the system and the stability of the medium. When the heat dissipation is too much or too little, it may cause the system to operate unstably, affect the medium temperature, and then cause different degrees of loss of the medium;

[0127] The loss of the system heating medium will have a certain impact on the temperature coefficient of the system, causing a part of the heat to be dissipated into the environment or transferred to other components. This means that the PTC heating element does not transfer all the heat to the medium, resulting in the system being difficult to reach the preset final steady-state temperature, or taking a longer time to approach this temperature;

[0128] Obtain the heating task data records with the same preset steady-state temperature in the historical heating task records (historical temperature data);

[0129] Obtain the maximum temperature of each target heating task in the historical heating task records , and a preset steady-state temperature ;

[0130] Calculate the actual maximum temperature of each target heating task and the corresponding preset steady-state temperature The absolute value of the difference between them: , taking the value ;

[0131] Record the duration required for each target heating task to reach the maximum temperature once ;

[0132] Calculate the average value of the durations required for all heating tasks that reach the preset steady-state temperature in the historical data record , and then calculate the difference between the duration required for each target heating task to reach the maximum temperature once and : , taking the value ; ;

[0133] Combine the and data of each target heating task: , taking the value , regarded as the system medium degradation coefficient of each target heating task.

[0134] Step S204, use the system medium degradation coefficients of each group of adjacent heating tasks to determine the system medium loss coefficient of the subsequent target heating task;

[0135] Specifically, step S204 includes:

[0136] Use the system medium degradation coefficients and heat dissipation expectation factors of each group of adjacent heating tasks to calculate the system medium loss coefficient of the subsequent target heating task.

[0137] Among them, each group of adjacent heating tasks includes two consecutive target heating tasks.

[0138] The historical heating task record contains the temperature data sequences of all the occurred target heating tasks. Consider the adjacent target heating tasks in the sequence as a group. In each group of adjacent heating tasks, the previous heating task is called process, and the subsequent heating task is called process;

[0139] Obtain the heat dissipation expectation factors of process A and process B in each target heating task combination respectively ;

[0140] Based on the historical heating task records, calculate the expected errors of Process A and Process B in each combination, and regard them as the heat dissipation fuzzy transfer amount of Process B:

[0141]

[0142] In the formula, represents the th heating task combination in the historical heating task records, represents the system medium deterioration coefficient of Process A in the th heating task combination, represents the system medium deterioration coefficient of Process B in the th heating task combination, represents the heat dissipation expected factor of Process B in the th heating task combination, represents the system medium loss coefficient of Process B; in addition, a non-zero anti-zero constant , such as 0.0001, can be set to avoid the situation where the denominator is zero during the calculation process.

[0143] The medium loss coefficient of the PTC system is one of the fuzzy rule parameters, representing the degree of transfer of heat dissipation energy to medium loss in the historical heating records. The larger the medium loss coefficient, the more it means that part of the dissipated heat loss energy is converted into excess medium loss.

[0144] Furthermore, the optimal control parameters can be obtained according to all the real-time temperature data of the current heating task to be processed and the fuzzy relationship factors (fuzzy rule parameters).

[0145] Step S3, input the system medium loss coefficient as a fuzzy rule parameter into the inference engine of the fuzzy controller to output the real-time heating power corresponding to the real-time temperature data;

[0146] Specifically, Step S3 includes:

[0147] Adjust the preset real-time heating power by using the normalized system medium loss coefficient to obtain the corrected real-time heating power corresponding to the real-time temperature data.

[0148] Input all the fuzzy rule parameters in the rule base from the input interface into the fuzzification interface of the fuzzy controller, and then map them into the inference engine;

[0149] Use the data integration unit to obtain the integrated data of the object to be heated. This integrated data includes historical and real-time temperature data and the corresponding fuzzy rule parameters, and input them into the inference engine of the fuzzy controller from the input interface;

[0150] According to the medium loss coefficient of the PTC heating system at the current moment, adopt a real-time power adjustment scheme based on the fuzzy control algorithm;

[0151] Specifically, by real-time monitoring the medium loss situation, the heater power is dynamically adjusted to avoid the system outputting too high power to maintain the preset temperature when the medium loss is large, resulting in excessive heat accumulation inside the system. This heat accumulation will cause temperature fluctuations, which will in turn lead to overshoot (overheating) or undershoot (insufficient temperature) phenomena during the temperature control process. Through the fuzzy control algorithm, the system can accurately adjust the power output under changing working conditions, ensuring a stable and efficient heating process and optimizing the temperature control accuracy.

[0152] Obtain the preset heater power at any moment ; at the same time, normalize the medium loss coefficient at this moment for subsequent data calculation;

[0153]

[0154] In the formula, represents the normalized medium loss coefficient at the moment, represents the preset heater power of the PTC system at the moment, that is, it can represent the preset real-time heating power, represents the output function of the corrected heating motor power of the PTC system at the moment, that is, it can represent the corrected real-time heating power.

[0155] Step S4, use the real-time heating power to control the PTC heating system;

[0156] Finally, after defuzzification, the obtained real-time heating power is used as the actual control output to directly control the heating power of the PTC heater. This control quantity can further adjust the current or voltage of the heater, thereby affecting the heating capacity of the heater and achieving a stable and accurate temperature regulation effect.

[0157] In view of the non-linear change of the resistance of the PTC material with temperature caused by the change of the working environment in the existing PTC heating system, the present invention optimizes the temperature regulation through a fuzzy control method. First, based on the heat dissipation and heating energy balance characteristics in the soaking stage, the fuzzy rule parameters in the historical temperature data are extracted to quantify the influence of different heat dissipation paths on temperature control. At the same time, combined with the deterioration degree of the system medium, according to different temperature change curves, the influence of medium deterioration on the heating process is quantified. By comprehensively considering the heat dissipation path and the medium deterioration factors, the medium loss coefficient at the current moment and each moment of the heating task is determined, and then the heating power is accurately controlled. Thus, by adjusting the current or voltage of the heater, accurate temperature regulation and stable heating performance can be achieved, avoiding the overshoot or undershoot phenomena of temperature control that are prone to occur due to the non-linear change of the medium resistance caused by external factors in the traditional temperature control process, and significantly improving the heating effect.

[0158] Embodiment 2:

[0159] The embodiment of the present invention also proposes a fuzzy control device applicable to a PTC heating system. The fuzzy control device applicable to the PTC heating system can be a data calculation and processing device such as a computer, a server, or a combination of multiple devices.

[0160] As Figure 5 shown, Figure 5 is a schematic structural diagram of the hardware operating environment of the fuzzy control device applicable to the PTC heating system according to the embodiment of the present invention.

[0161] As Figure 5 shown, the fuzzy control device applicable to the PTC heating system may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display (Display), an input unit such as a control panel. Optionally, the user interface 1003 may further include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WIFI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001. The memory 1005, as a computer storage medium, may include a fuzzy control program applicable to the PTC heating system.

[0162] Those skilled in the art can understand, Figure 5The hardware structure shown does not constitute a limitation on the device, and it may include more or fewer components than shown, or combine certain components, or have a different component arrangement.

[0163] Continuing to refer to Figure 5 , Figure 5 In [reference], the memory 1005 as a computer-readable storage medium may include an operating system, a user interface module, a network communication module, and a fuzzy control program applicable to the PTC heating system.

[0164] In Figure 5 , the network communication module is mainly used to connect to the server and can communicate with the server for data; while the processor 1001 can call the fuzzy control program stored in the memory 1005 applicable to the PTC heating system and execute the steps in each of the above embodiments.

[0165] Based on the above hardware structure of the fuzzy control device applicable to the PTC heating system, each embodiment for implementing the fuzzy control method of the present invention applicable to the PTC heating system is realized.

[0166] In addition, the present invention also provides a fuzzy control device applicable to the PTC heating system. Please refer to Figure 6 , the fuzzy control device applicable to the PTC heating system includes:

[0167] A temperature acquisition module A10, configured to obtain historical temperature data of the PTC heating system and determine the target heating task each time in the historical temperature data;

[0168] A rule extraction module A20, configured to use the temperature data sequence in the target heating task to determine the heat dissipation expectation factor and the system medium loss coefficient of the target heating task;

[0169] A heating adjustment module A30, configured to input the system medium loss coefficient as a fuzzy rule parameter into the inference engine of the fuzzy controller to output the real-time heating power corresponding to the real-time temperature data; and perform heating control on the PTC heating system using the real-time heating power;

[0170] Further, the rule extraction module A20 is further configured to:

[0171] Decompose the temperature data sequence in the target heating task to obtain the heat dissipation temperature data of the target heating task;

[0172] Use the heat dissipation temperature data to determine the heat dissipation expectation factor of the target heating task.

[0173] Further, the rule extraction module A20 is further configured to:

[0174] Determine the temperature data during the soaking stage in the temperature data sequence;

[0175] Perform non - negative matrix factorization on the soaking stage temperature data to obtain multiple data decomposition combinations;

[0176] Determine the periodicity coefficient and kurtosis of the first data component in each data decomposition combination;

[0177] Use the periodicity coefficient and kurtosis to determine the optimal decomposition coefficient for each data decomposition combination;

[0178] Determine the maximum value in the optimal decomposition coefficients and its corresponding target data decomposition combination;

[0179] Determine the second data component in the target data decomposition combination as the heat dissipation temperature data of the target heating task;

[0180] Among them, the soaking stage represents the stage where the heating energy and the heat dissipation energy are in balance.

[0181] Furthermore, the rule extraction module A20 is also used for:

[0182] Use the ICA algorithm to decompose the heat dissipation temperature data of the target heating task to obtain the heat dissipation temperature data of the target heating task on different heat dissipation paths.

[0183] Furthermore, the rule extraction module A20 is also used for:

[0184] Determine the extreme points and the number of extreme points in the heat dissipation temperature data;

[0185] Taking the maximum extreme point as the starting point, determine the window area with a preset step size;

[0186] Determine the occurrence frequency and the slope difference on both sides of each extreme point in the window area in the heat dissipation temperature data;

[0187] Use the occurrence frequency and the slope difference on both sides to calculate the fluctuation characteristic coefficient of the heat dissipation temperature data;

[0188] Use the fluctuation characteristic coefficient and the absolute value of the difference between the maximum value and the minimum value in the heat dissipation temperature data to calculate the heat dissipation expectation factor of the target heating task.

[0189] Furthermore, the rule extraction module A20 is also used for:

[0190] Determine the maximum temperature and the preset steady - state temperature in the temperature data sequence and the absolute value of the difference between the two;

[0191] Determine the time required for the target heating task to reach the maximum temperature once and the average value of the time required for all heating tasks with the same preset steady - state temperature in the historical temperature data to reach the maximum temperature;

[0192] The system medium deterioration coefficient of the target heating task is calculated using the absolute value of the difference, the duration required for a single time, and the average value of the required duration.

[0193] Using the system medium deterioration coefficients of each group of adjacent heating tasks, the system medium loss coefficient of the subsequent target heating task is determined.

[0194] Wherein, each group of adjacent heating tasks includes two consecutive target heating tasks.

[0195] Furthermore, the rule extraction module A20 is further configured to:

[0196] Using the system medium deterioration coefficient and the heat dissipation expectation factor of each group of adjacent heating tasks respectively, the system medium loss coefficient of the subsequent target heating task is calculated.

[0197] Furthermore, the heating adjustment module A30 is further configured to:

[0198] Using the normalized system medium loss coefficient to adjust the preset real-time heating power, and obtaining the corrected real-time heating power corresponding to the real-time temperature data.

[0199] The specific implementation manner of the fuzzy control device applicable to the PTC heating system in the present invention is basically the same as each embodiment of the above-mentioned fuzzy control method applicable to the PTC heating system, and will not be elaborated here.

[0200] In addition, the present invention also provides a computer-readable storage medium. A fuzzy control program applicable to the PTC heating system is stored on the computer-readable storage medium of the present invention. Wherein, when the fuzzy control program applicable to the PTC heating system is executed by a processor, the steps of the fuzzy control method applicable to the PTC heating system as described above are implemented.

[0201] Wherein, the method implemented when the fuzzy control program applicable to the PTC heating system is executed can refer to each embodiment of the fuzzy control method applicable to the PTC heating system of the present invention, and will not be elaborated here.

[0202] It should be noted that: the above sequence of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0203] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key point of each embodiment is to illustrate the differences from other embodiments.

[0204] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0205] The above are only the preferred embodiments of the present invention, and do not limit the protection scope of the present invention. Any equivalent structure / method transformation made using the content of the specification and drawings of the present invention under the inventive concept of the present invention, or any direct / indirect application in other related technical fields is included in the protection scope of the present invention.

Claims

1. A fuzzy control method applicable to a PTC heating system, characterized in that, Applied to a PTC heating system, the method includes: Obtain historical temperature data of the PTC heating system and determine the target heating task each time in the historical temperature data; Use the temperature data sequence in the target heating task to determine the heat dissipation expectation factor and the system medium loss coefficient of the target heating task; Input the system medium loss coefficient as a fuzzy rule parameter into the inference engine of the fuzzy controller to output the real-time heating power corresponding to the real-time temperature data; Use the real-time heating power to control the heating of the PTC heating system; Wherein, the heat dissipation expectation factor characterizes the possible expectation of the characteristics of the target heating task itself for the heat dissipation efficiency generated during the heating process; the system medium loss coefficient characterizes the loss degree of the heating medium.

2. The fuzzy control method applicable to the PTC heating system according to claim 1, wherein Using the temperature data sequence in the target heating task to determine the heat dissipation expectation factor of the target heating task includes: Perform data decomposition on the temperature data sequence in the target heating task to obtain the heat dissipation temperature data of the target heating task; Use the heat dissipation temperature data to determine the heat dissipation expectation factor of the target heating task.

3. The fuzzy control method applicable to the PTC heating system according to claim 2, characterized in that, Performing data decomposition on the temperature data sequence in the target heating task to obtain the heat dissipation temperature data of the target heating task includes: Determine the temperature data during the isothermal stage in the temperature data sequence; Perform non-negative matrix factorization on the temperature data during the isothermal stage to obtain multiple data decomposition combinations; Determine the periodic coefficient and kurtosis of the first data component in each data decomposition combination; Use the periodic coefficient and kurtosis to determine the optimal decomposition coefficient of each data decomposition combination; Determine the maximum value in the optimal decomposition coefficients and its corresponding target data decomposition combination; Determine the second data component in the target data decomposition combination as the heat dissipation temperature data of the target heating task; Wherein, the isothermal stage represents the stage where the heating energy and the heat dissipation energy are in balance.

4. The fuzzy control method applicable to the PTC heating system according to claim 3, characterized in that After determining the second data component in the target data decomposition combination as the heat dissipation temperature data of the target heating task, it further includes: Use the ICA algorithm to decompose the heat dissipation temperature data of the target heating task to obtain the heat dissipation temperature data of the target heating task on different heat dissipation paths.

5. The fuzzy control method applicable to the PTC heating system according to claim 3 or 4, characterized in that, Using the heat dissipation temperature data to determine the heat dissipation expectation factor of the target heating task includes: Determine the extreme points and the number of extreme points in the heat dissipation temperature data; Taking the maximum value point as the starting point, determine the window area with a preset step size; Determine the occurrence frequency and the slope difference on both sides of each extreme point in the window area in the heat dissipation temperature data; Use the occurrence frequency and the slope difference on both sides to calculate the fluctuation characteristic coefficient of the heat dissipation temperature data; Use the fluctuation characteristic coefficient and the absolute value of the difference between the maximum value and the minimum value in the heat dissipation temperature data to calculate the heat dissipation expectation factor of the target heating task.

6. The fuzzy control method applicable to the PTC heating system according to claim 1, characterized in that Using the temperature data sequence in the target heating task to determine the system medium loss coefficient of the target heating task includes: Determine the maximum temperature and the preset steady-state temperature in the temperature data sequence and the absolute value of the difference between the two; Determine the single-time duration required for the target heating task to reach the maximum temperature and the average duration required for all heating tasks with the same preset steady-state temperature in the historical temperature data to reach the maximum temperature; The system medium deterioration coefficient of the target heating task is calculated using the absolute value of the difference, the duration required for a single time, and the average value of the required duration. The system medium loss coefficient of the subsequent target heating task is determined using the system medium deterioration coefficients of each group of adjacent heating tasks. Each group of adjacent heating tasks includes two consecutive target heating tasks, namely the previous and the subsequent ones.

7. The fuzzy control method applicable to the PTC heating system according to claim 6, characterized in that Determining the system medium loss coefficient of the subsequent target heating task using the system medium deterioration coefficients of each group of adjacent heating tasks includes: The system medium loss coefficient of the subsequent target heating task is calculated using the system medium deterioration coefficients and the heat dissipation prediction factors of each group of adjacent heating tasks respectively.

8. The fuzzy control method applicable to the PTC heating system according to claim 1, wherein Taking the system medium loss coefficient as a fuzzy rule parameter and inputting it into the inference engine of the fuzzy controller to output the real-time heating power corresponding to the real-time temperature data, including: Adjusting the preset real-time heating power using the normalized system medium loss coefficient to obtain the corrected real-time heating power corresponding to the real-time temperature data.

9. A fuzzy control device applicable to a PTC heating system, characterized in that, The fuzzy control device includes a processor, a storage unit, and a fuzzy control program stored on the storage unit and executable by the processor. When the fuzzy control program is executed by the processor, the steps of the fuzzy control method applicable to the PTC heating system as described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium, characterized in that, A fuzzy control program is stored on the computer-readable storage medium. When the fuzzy control program is executed by a processor, the steps of the fuzzy control method applicable to the PTC heating system as described in any one of claims 1 to 8 are implemented.

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