Fuzzy control method and device suitable for PTC heating system and medium

Abstract

The invention relates to the technical field of control or regulation, in particular to a fuzzy control method and device suitable for a PTC heating system and a medium, and the method comprises the steps: obtaining the historical temperature data of the PTC heating system, and determining each target heating task in the historical temperature data; determining a heat dissipation expected factor and a system medium loss coefficient of the target heating task by using the temperature data sequence in the target heating task; inputting the system medium loss coefficient as a fuzzy rule parameter into an inference engine of a fuzzy controller so as to output real-time heating power corresponding to the real-time temperature data; and performing heating control on the PTC heating system by using the real-time heating power. According to the method, the medium loss coefficient of the heating task is determined by comprehensively considering the heat dissipation path and the medium degradation factor, so that the heating power is accurately controlled, and the heating effect of the PTC heating system is remarkably improved.

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 based on the temperature dependence of PTC materials, thereby 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: The present invention provides a fuzzy control method applicable to a PTC heating system, and 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 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.

[0005] Further, 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.

[0006] Further, decompose the temperature data sequence in the target heating task to obtain the heat dissipation temperature data of the target heating task, including: Determine the soaking stage temperature data in the temperature data sequence; Perform non-negative matrix factorization on the soaking stage temperature data to obtain multiple data decomposition combinations; Determine the periodicity coefficient and kurtosis of the first data component in each data decomposition combination; Use the periodicity coefficient and kurtosis to determine the optimal decomposition coefficient of each data decomposition combination; Determine the maximum value in the optimal decomposition coefficient 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; Among them, the soaking stage represents the stage where the heating energy and the heat dissipation energy are in balance.

[0007] 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: 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.

[0008] Further, use the heat dissipation temperature data to determine the heat dissipation expectation factor of the target heating task, including: Determine the extreme points and the number of extreme points in the heat dissipation temperature data; Taking the maximum extreme 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.

[0009] Further, use the temperature data sequence in the target heating task to determine the system medium loss coefficient of the target heating task, including: Determine the maximum temperature in the temperature data sequence, the preset steady-state temperature, and the absolute value of the difference between the two; Determine the single time required for the target heating task to reach the maximum temperature and the average time 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 by 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 by using the system medium deterioration coefficients of each group of adjacent heating tasks. Wherein, each group of adjacent heating tasks includes two consecutive target heating tasks.

[0010] Further, determining the system medium loss coefficient of the subsequent target heating task by 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 by using the system medium deterioration coefficients and the heat dissipation expected factors of each group of adjacent heating tasks respectively.

[0011] Further, the system medium loss coefficient is input into the inference engine of the fuzzy controller as a fuzzy rule parameter to output the real-time heating power corresponding to the real-time temperature data, including: The preset real-time heating power is adjusted by using the normalized system medium loss coefficient to obtain the corrected real-time heating power corresponding to the real-time temperature data.

[0012] 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. When the fuzzy control program is executed by the processor, the steps of the fuzzy control method applicable to the PTC heating system described in any one of the above are implemented.

[0013] The present invention also provides a computer-readable storage medium. 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 described in any one of the above are implemented.

[0014] The present invention has the following beneficial effects: 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 temperature regulation is optimized by means of fuzzy control. 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 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the description of the embodiments or the prior art will be briefly introduced below. 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.

[0016] 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; 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; 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; 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; Figure 5 It is a schematic structural diagram of the hardware operating environment of a fuzzy control device applicable to a PTC heating system involved in the embodiment solution of the present invention; Figure 6 It is a schematic framework diagram of a fuzzy control device applicable to a PTC heating system involved in the embodiment solution of the present invention; Figure 7 It is a 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 DESCRIPTION OF THE EMBODIMENTS

[0017] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details a fuzzy control method, device, and medium applicable to a PTC heating system proposed according to the present invention, including its specific implementation manner, structure, features, and effects. 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.

[0018] 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 this invention belongs.

[0019] The following specifically describes the specific solution of a fuzzy control method applicable to a PTC heating system provided by the present invention in conjunction with the accompanying drawings.

[0020] Embodiment 1: Regarding the fuzzy control method applicable to a 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 a PTC heating system provided by an embodiment of the present invention.

[0021] The method includes: 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; In this embodiment, a thermocouple temperature sensor with high accuracy 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 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.

[0022] Based on the fact that the historical temperature data that can be stored in the local storage medium is accumulated by the temperature data sets or sequences corresponding to two or more target heating tasks, the historical temperature data can be divided into temperature data sequences corresponding to different target heating tasks.

[0023] Step S2, 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; 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, such as the temperature data sequence formed according to the chronological order of collection.

[0024] Among them, 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. Both the heat dissipation expectation factor and the system medium loss coefficient can be used as fuzzy rule parameters in the fuzzy control unit.

[0025] 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: The fuzzy controller consists of structures such as a knowledge base, an input / output interface, a fuzzification interface (converting input variables into fuzzy values), a knowledge base, an inference engine, and a defuzzification interface (converting the fuzzy inference result into an accurate control signal).

[0026] The knowledge base of the fuzzy controller consists of a database and a rule base: The database contains all the historical temperature data records of the thermocouple sensor; The rule base contains all the fuzzy rule parameters extracted from the changes in historical temperature data, and the temperature of the PTC heating system can be fuzzy controlled using the fuzzy rule parameters.

[0027] Specifically, in one embodiment, please refer to Figure 2 , step S2 includes: Step S21, decompose the temperature data sequence in the target heating task to obtain the heat dissipation temperature data of the target heating task; The reason for considering the heat dissipation expectation factor here is that 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 the external temperature change, which can be reflected by the heat dissipation expectation factor.

[0028] 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.

[0029] More specifically, please refer to Figure 3 , the step S21 includes: Step S211, determine the temperature data in the isothermal stage in the temperature data sequence; Step S212, perform non - negative matrix factorization on the soaking - stage temperature data to obtain multiple data decomposition combinations; Step S213, determine the periodicity coefficient and kurtosis of the first data component in each data decomposition combination; Step S214, use the periodicity coefficient and kurtosis to determine the optimal decomposition coefficient for each data decomposition combination; Step S215, determine the maximum value in the optimal decomposition coefficients and its corresponding target data decomposition combination; Step S216, determine the second data component in the target data decomposition combination as the heat dissipation temperature data of the target heating task; Among them, the soaking stage represents the stage where the heating energy and the heat dissipation energy are in balance.

[0030] Please refer to Figure 7 , Figure 7 , which is a schematic 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.

[0031] After obtaining the temperature data of a single - target heating task of the PTC heating system, the least - squares method combined with polynomial fitting can be used to obtain the fitting curve of the temperature data sequence ; A complete single - target heating task of the PTC system includes a pre - heating section, a heating section, and a soaking section, as Figure 7 shown.

[0032] Since in the soaking 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 temperature differences between each heated object and the system, which is likely to cause the larger the temperature difference, and thus more heat dissipation paths are generated; Based on this, it is necessary to obtain the heat dissipation prediction factor for each target heating task of each historical heating record; NMF (Nonnegative Matrix Factorization) is a conventional data decomposition algorithm, which can decompose the original temperature data signal into different basis vector combinations for separating data of different frequencies; In the soaking stage of the PTC heating system, the overall heat generation and heat loss of the system reach balance. Therefore, the decomposition ratio of the temperature data in the soaking stage of the target heating task (here the data corresponds to the soaking - section part of the fitting curve) is set to 1:1; Perform data processing on the soaking - stage temperature data 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 components (Second data component); It should be noted that and The data are all in the form of data fitting curves to facilitate subsequent analysis.

[0033] For any data component in the decomposition result , using the autocorrelation function to obtain the data periodicity coefficient The larger the value, the more data components The greater the probability of periodicity, the more likely it is to represent the actual heating temperature data of the PTC heating system; Calculate data components Kurtosis of the distribution The closer the value is to 3, the more data components The more uniform the distribution is, the more stable it is. This corresponds to the fact that under stable working conditions, the heating temperature data distribution of the PTC heating system has a strong Gaussian property, that is, the data is uniformly stable. Using the periodic coefficient and kurtosis, determine the optimal decomposition coefficient for each data decomposition combination: In the formula, Represents any of the decomposition results of all data Data decomposition combination, Representative Data components in the data decomposition combination The periodic coefficient of Representative Data components in the data decomposition combination The kurtosis, Representative The optimal decomposition coefficient of the data decomposition combination; in addition, any non-zero anti-zero constant can be set , such as 0.0001, in order to avoid the denominator being zero.

[0034] Calculate the optimal decomposition coefficient of all data decomposition results of the target heating task , sort by size and select the maximum value , get The corresponding target data decomposition combination is regarded as the optimal decomposition result of the target heating task, and the data components in the data decomposition combination are , that is, the second data component is regarded as the heat dissipation temperature data of the target heating task.

[0035] In one embodiment, step S216 further includes: The ICA algorithm is used to decompose the heat dissipation temperature data of the target heating task, and the heat dissipation temperature data of the target heating task in different heat dissipation paths are obtained.

[0036] During the heat dissipation process of the PTC heating system, different heat dissipation paths (such as convection, radiation, and heat 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; Obtain the heat dissipation temperature data of each historical PTC system's target heating task; Adopt the ICA (imperialist competitive algorithm) algorithm 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 too much here.

[0037] Step S22: Use the heat dissipation temperature data to determine the heat dissipation expectation factor of the target heating task.

[0038] Specifically, step S22 includes: Determine the extreme points and the number of extreme points in the heat dissipation temperature data; Taking the maximum point as the starting point, determine the window area with a preset step size; Determine the occurrence frequency and the difference in slopes on both sides of each extreme point in the window area in the heat dissipation temperature data; Use the occurrence frequency and the difference in slopes 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 and minimum values in the heat dissipation temperature data to calculate the heat dissipation expectation factor of the target heating task.

[0039] The heat dissipation temperature data here can be the heat dissipation temperature data that does not distinguish 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 expansion and explanation.

[0040] 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 ; For heat dissipation paths with a large temperature difference, due to the small heat conduction resistance, the heat dissipation rate is fast. However, a 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; Obtain the number of extreme points in each heat dissipation temperature data ; Each heat dissipation temperature data in each target heating task is divided into windows based on the data maximum point to obtain the local window area features; Specifically, the local window starts with the maximum point, and the step size can be set to 10 data extreme points (specifically adjustable) to construct the window area (when there are insufficient data points, all remaining data points are regarded as the same area); Get the frequency of occurrence of all extreme points in the window area in the corresponding heat dissipation temperature data respectively , and the difference in slopes on both sides of the extreme point , and determine the fluctuation characteristic coefficient of the heat dissipation temperature data: In the formula, Represents The collection A maximum point, Representative The maximum value points correspond to the set of extreme value points of the window data. represent The first in the collection An extreme point, Representative The absolute value of the difference between the slopes on both sides of the extreme point, Representative The frequency of occurrence of extreme points in the corresponding heat dissipation temperature data, Represents the fluctuation characteristic coefficient of heat dissipation temperature data.

[0041] The larger the value, the greater the slope difference on both sides of each extreme point in the local area data corresponding to the data extreme point, which means that the heat dissipation temperature data changes more drastically and rapidly at the data extreme point. In addition, the extreme points corresponding to these changes rarely appear in the heat dissipation temperature data. This indicates that the heat dissipation path corresponding to the heat dissipation temperature data has more serious over-adjustment or under-adjustment caused by system heating during the heat dissipation process.

[0042] Count the number of all heat dissipation paths corresponding to the target heating task , if different heat dissipation paths are not distinguished, no statistics are needed here; When the heat dissipation temperature data in the target heating task has large heat loss and temperature fluctuation, it indicates that the heating task has significant over-adjustment or under-adjustment during the process, and the expected heat dissipation impact is greater; Combined with the target heating task, the absolute value of the difference between all heat dissipation temperature data And the fluctuation characteristic coefficient The mean of the products of : , value , regarded as the heat dissipation expectation factor of the target heating task; herein 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. 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.

[0043] 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.

[0044] 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 for the characteristics of each target heating task itself.

[0045] In another embodiment, please refer to Figure 4 , step S2 includes: 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; 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; 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; 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; 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; Obtaining the heating task data records with the same preset steady-state temperature in the historical heating task records (historical temperature data); Obtaining the maximum temperature of each target heating task in the historical heating task records , and the preset steady-state temperature ; Calculating the actual maximum temperature of each target heating task and the corresponding preset steady-state temperature Absolute value of the difference between: , take values ; Record the duration required for each target heating task to reach the maximum temperature once ; Calculate the mean value of the duration required for all heating tasks that reach the preset steady-state temperature in the historical data record , and then calculate the duration required for each target heating task to reach the maximum temperature once and the difference: ; take values ; Combine the and data of each target heating task: , take values , regarded as the system medium deterioration coefficient of each target heating task.

[0046] Step S204, use the system medium deterioration coefficient of each group of adjacent heating tasks to determine the system medium loss coefficient of the subsequent target heating task; Specifically, step S204 includes: Use the system medium deterioration coefficient and heat dissipation expectation factor of each group of adjacent heating tasks to calculate the system medium loss coefficient of the subsequent target heating task.

[0047] Among them, each group of adjacent heating tasks includes two consecutive target heating tasks.

[0048] The historical heating task record contains the temperature data sequence of all occurred target heating tasks. Consider the adjacent target heating tasks in the sequence as a group. The previous heating task in each group of adjacent heating tasks is called the process, and the subsequent heating task is called the process; Obtain the heat dissipation expectation factors of the A process and the B process in each target heating task combination respectively ; Based on the historical heating task record, calculate the expected error of the A process and the B process in each combination, and regard it as the heat dissipation fuzzy transfer amount of the B process: In the formula, represents the th heating task combination in the historical heating task record, represents the th system medium deterioration coefficient of the A process in the th heating task combination, ​The system medium deterioration coefficient of process B in a heating task combination represents the heat dissipation expectation factor of process B in the th heating task combination; in addition, a non-zero anti-zero constant , such as 0.0001, can be set to avoid the situation of a zero denominator in the calculation process.

[0049] 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 record. The larger the medium loss coefficient, the more heat dissipation loss energy is converted into excess medium loss.

[0050] Furthermore, the optimal control parameters can be obtained based on all the real-time temperature data of the current heating task to be performed and the fuzzy relation factors (fuzzy rule parameters).

[0051] 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; Specifically, step S3 includes: 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.

[0052] Input all the fuzzy rule parameters in the rule base into the fuzzy interface of the fuzzy controller from the input interface, and then map them to the inference engine; Use the data integration unit to obtain the integrated data of the object to be heated, which 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; 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; Specifically, by real-time monitoring the medium loss situation, dynamically adjust the heater power 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 (temperature insufficiency) phenomena in 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.

[0053] Obtain the preset heater power at any moment ; at the same time, normalize the medium loss coefficient at this moment for subsequent data calculation; In the formula, represents the normalized medium loss coefficient at the th moment, represents the preset heater power of the PTC system at the th 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 th moment, that is, it can represent the corrected real-time heating power.

[0054] Step S4, using the real-time heating power to perform heating control on the PTC heating system; Finally, after defuzzification processing, the obtained real-time heating power is used as the actual control output to directly control the heating power supply 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 precise temperature regulation effect.

[0055] In view of the non-linear change of the PTC material resistance 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 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 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.

[0056] Embodiment 2: 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.

[0057] 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.

[0058] As Figure 5As shown in the figure, 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) and an input unit such as a control panel. Optionally, the user interface 1003 may also 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.

[0059] Those skilled in the art can understand that Figure 5 the hardware structure shown in the figure does not constitute a limitation on the device, and it may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0060] Continuing to refer to Figure 5 , Figure 5 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.

[0061] 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 and execute the steps in each of the above embodiments.

[0062] 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 applicable to the PTC heating system of the present invention is realized.

[0063] 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: 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; 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; The heating adjustment module A30 is configured to input the system medium loss coefficient as a fuzzy rule parameter into the inference engine of the fuzzy controller, so as to output the real-time heating power corresponding to the real-time temperature data; and use the real-time heating power to perform heating control on the PTC heating system. Further, the rule extraction module A20 is further configured to: Decompose 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.

[0064] Further, the rule extraction module A20 is further configured to: Determine the soaking stage temperature data in the temperature data sequence. Perform non-negative matrix factorization on the soaking stage temperature data to obtain multiple data decomposition combinations. Determine the periodicity coefficient and kurtosis of the first data component in each data decomposition combination. Use the periodicity 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 soaking stage represents the stage where the heating energy and the heat dissipation energy are in balance.

[0065] Further, the rule extraction module A20 is further configured to: 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.

[0066] Further, the rule extraction module A20 is further configured to: Determine the extreme points and the number of extreme points in the heat dissipation temperature data. Taking the maximum extreme point as the starting point, determine the window area with a preset step size. Determine the occurrence frequency and the difference in slopes on both sides of each extreme point in the window area in the heat dissipation temperature data. Use the occurrence frequency and the difference in slopes 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.

[0067] Further, the rule extraction module A20 is further configured to: Determine the maximum temperature in the temperature data sequence, the preset steady-state temperature, and the absolute value of the difference between the two; Determine the duration required for a single time when the target heating task reaches 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; Use the absolute value of the difference, the duration required for a single time, and the average duration required to calculate the system medium deterioration coefficient of the target heating task; Use the system medium deterioration coefficients of each group of adjacent heating tasks to determine the system medium loss coefficient of the subsequent target heating task; Wherein, each group of adjacent heating tasks includes two consecutive target heating tasks.

[0068] Furthermore, the rule extraction module A20 is further configured to: Use the system medium deterioration 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.

[0069] Furthermore, the heating adjustment module A30 is further configured to: Use the normalized system medium loss coefficient to adjust the preset real-time heating power to obtain the corrected real-time heating power corresponding to the real-time temperature data.

[0070] The specific implementation manners of the fuzzy control device applicable to the PTC heating system of the present invention are basically the same as those of the above-mentioned embodiments of the fuzzy control method applicable to the PTC heating system, and will not be elaborated herein.

[0071] 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. 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.

[0072] Among them, the method implemented when the fuzzy control program applicable to the PTC heating system is executed can refer to the various embodiments of the fuzzy control method applicable to the PTC heating system of the present invention, and will not be elaborated herein.

[0073] It should be noted that: the above-mentioned sequence of 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 results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0074] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the key point of each embodiment is to illustrate the differences from other embodiments.

[0075] 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 a complete hardware embodiment, a complete 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.

[0076] The above are only the preferred embodiments of the present invention, and do not limit the protection scope of the present invention accordingly. Any equivalent structure / method transformation made by using the specification and drawings of the present invention under the inventive concept of the present invention, or direct / indirect application in other related technical fields, is included in the protection scope of the present invention.

Claims

1. A fuzzy control method suitable for a PTC heating system, characterized in that: Applied to a PTC heating system, the method comprises: Obtain historical temperature data of the PTC heating system and determine the target heating task for each time in the historical temperature data; Using the temperature data sequence in the target heating task, the heat dissipation expectation factor and system medium loss coefficient of the target heating task are determined; The system medium loss coefficient is input into the inference engine of the fuzzy controller as a fuzzy rule parameter to output the real-time heating power corresponding to the real-time temperature data; Use real-time heating power to control the heating of the PTC heating system; Among them, the heat dissipation expectation factor represents the possible expectation of the heat dissipation efficiency generated during the heating process due to the characteristics of the target heating task itself; the system medium loss coefficient represents the degree of loss of the heating medium.

2. The fuzzy control method for a PTC heating system according to claim 1, characterized in that: Using the temperature data sequence in the target heating task, the heat dissipation expectation factor of the target heating task is determined, including: Decomposing the temperature data sequence in the target heating task to obtain the heat dissipation temperature data of the target heating task; Using the heat dissipation temperature data, determine the heat dissipation expectation factor for the target heating task.

3. The fuzzy control method for a PTC heating system according to claim 2, characterized in that: The temperature data sequence in the target heating task is decomposed to obtain the heat dissipation temperature data of the target heating task, including: Determine the temperature data of the soaking stage in the temperature data sequence; Perform non-negative matrix decomposition on the temperature data of the soaking stage to obtain multiple data decomposition combinations; Determine the periodic coefficient and kurtosis of the first data component in each data decomposition combination; Using periodic coefficient and kurtosis, the optimal decomposition coefficient of each data decomposition combination is determined; Determine the maximum value among the optimal decomposition coefficients and the 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; Among them, the soaking stage represents the stage where the heating energy and the heat dissipation energy are in balance.

4. The fuzzy control method for a PTC heating system according to claim 3 is characterized in that: Determine the second data component in the target data decomposition combination as the heat dissipation temperature data of the target heating task, and then further include: The ICA algorithm is used to decompose the heat dissipation temperature data of the target heating task, and the heat dissipation temperature data of the target heating task in different heat dissipation paths are obtained.

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, determine the heat dissipation expected factors for the target heating task, including: Determine the extreme points and the number of extreme points in the heat dissipation temperature data; Taking the maximum point as the starting point, determine the window area with a preset step size; Determine the frequency of occurrence of each extreme point in the window area in the heat dissipation temperature data and the difference in slopes on both sides; The fluctuation characteristic coefficient of the heat dissipation temperature data is calculated using the occurrence frequency and the difference in slopes on both sides; The heat dissipation expectation factor of the target heating task is calculated using the fluctuation characteristic coefficient and the absolute value of the difference between the maximum and minimum values ​​in the heat dissipation temperature data.

6. The fuzzy control method for a PTC heating system according to claim 1, characterized in that: Using the temperature data sequence in the target heating task, determine the system medium loss coefficient of the target heating task, including: 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 required for the target heating task to reach the maximum temperature and the average time 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 degradation coefficient of the target heating task is calculated using the absolute value of the difference, the time required for a single time, and the average time required; Using the system medium degradation coefficient of each group of adjacent heating tasks, determine the system medium loss coefficient of the next target heating task; Each group of adjacent heating tasks includes two target heating tasks.

7. The fuzzy control method for a PTC heating system according to claim 6, characterized in that: The system medium deterioration coefficient of each group of adjacent heating tasks is used to determine the system medium loss coefficient of the next target heating task, including: The system medium loss coefficient of the next target heating task is calculated by using the system medium degradation coefficient and heat dissipation expectation factor of each group of adjacent heating tasks.

8. The fuzzy control method applicable to a PTC heating system according to claim 1, characterized in that: The system medium loss coefficient is input into the inference engine of the fuzzy controller as a fuzzy rule parameter to output the real-time heating power corresponding to the real-time temperature data, including: The preset real-time heating power is adjusted 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 suitable for a PTC heating system, characterized in that: The fuzzy control device includes a processor, a storage unit, and a fuzzy control program stored in 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 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, wherein when the fuzzy control program is executed by a processor, the steps of the fuzzy control method applicable to a PTC heating system as claimed in any one of claims 1 to 8 are implemented.

Images