PID-based feedback control method and system for ptc electric heating device

Abstract

The present application relates to the technical field of temperature control, and in particular to a PTC electric heating device feedback control method and system based on PID. Time series data of temperatures at multiple monitoring positions is obtained, temperature fluctuations caused by external factors are considered, fluctuations and changes in the time series data of temperatures are analyzed, and temperature-sensitive and non-sensitive positions are distinguished. For temperature-sensitive positions, temperature value change differences are analyzed, and the degree of disturbance is obtained in combination with position distribution. In view of the working characteristics of the PTC warmer, the number of temperature-sensitive positions, the positional relationship and the temperature difference of the temperature-sensitive positions are also analyzed, the heat diffusion situation is quantified, and a temperature uniformity index is obtained. The temperature weight is obtained by comprehensively considering the degree of disturbance and the temperature uniformity index, which reflects the importance in PID control. Finally, based on the temperature values and temperature weights of all monitoring positions at the current moment, PID control is performed on the PTC electric heating device at the next moment to ensure the uniformity and rationality of temperature distribution.

Description

Technical Field

[0001] This invention relates to the field of temperature control technology, and specifically to a feedback control method and system for PTC electric heating equipment based on PID. Background Technology

[0002] In modern home heating applications, PTC (positive temperature coefficient) electric heating equipment is widely used due to its self-limiting temperature characteristics, high efficiency, and safety. Its core component is the PTC thermistor ceramic element, whose resistance increases with temperature. When the temperature reaches a certain level, the increase in resistance limits the current flow. This characteristic enables PTC electric heating equipment to maintain a stable heating effect during the heating process.

[0003] In actual use, PTC electric heating equipment is subject to various environmental interferences. In such cases, the PID controller can dynamically adjust the temperature of the PTC electric heating equipment based on the deviation between the set temperature and the actual temperature. Traditional feedback control systems often adjust based on the overall temperature of the heating environment. However, because the heating effect of PTC electric heating equipment is affected by various factors, such as the layout of the heating environment, airflow conditions, and temperature fluctuations, some temperature values ​​collected by sensors may be inaccurate or unreliable. This results in poor PID feedback control of the PTC electric heating equipment, easily causing localized overheating or undercooling, which not only affects the heating effect but may also damage the equipment. Summary of the Invention

[0004] To address the issue that traditional feedback control systems often adjust based on the overall temperature of the heating environment, but the heating effect of PTC electric heating equipment is affected by various factors such as the layout of the heating environment, airflow conditions, and temperature fluctuations, which can lead to errors and unreliability in the temperature values ​​collected by some sensors, resulting in poor PID feedback control performance for PTC electric heating equipment, this invention aims to provide a PID-based feedback control method and system for PTC electric heating equipment. The specific technical solution adopted is as follows:

[0005] Acquire time-series temperature data at various monitoring locations within the heating environment where the PTC electric heating equipment is located;

[0006] Analyze the temperature fluctuations and patterns in the time-series data of each monitoring location to determine the temperature fluctuation index for each monitoring location; based on the temperature fluctuation index, classify all monitoring locations into temperature-sensitive and non-temperature-sensitive locations.

[0007] In the time-series temperature data of each temperature-sensitive location, the differences in temperature values ​​are analyzed, and the degree of disturbance of each temperature-sensitive location is quantified by combining the temperature fluctuation index at the temperature-sensitive location and the location distribution of the temperature-sensitive location in the heating environment. At the current moment, the quantitative characteristics of temperature-sensitive locations, the positional relationship between temperature-sensitive locations, and the differences in temperature values ​​are analyzed to obtain the temperature uniformity index at each temperature-sensitive location.

[0008] By combining the disturbance level and temperature uniformity index at each temperature-sensitive location, the temperature weight of each temperature-sensitive location is obtained; the temperature weight of non-temperature-sensitive locations is set to a preset value; and the PTC electric heating equipment is subjected to PID control for the next moment based on the temperature values ​​and temperature weights of all monitored locations at the current moment.

[0009] Furthermore, the method for obtaining the temperature fluctuation index includes:

[0010] For any monitoring location, the temperature time series data at that monitoring location is curve-fitted using the least squares method to obtain the temperature fitting curve.

[0011] In the temperature fitting curve, all extreme points are obtained, and the absolute value of the temperature difference between any two adjacent extreme points is used as the temperature fluctuation factor, and the absolute value of the time difference between any two adjacent extreme points is used as the time interval factor.

[0012] The normalized value of the ratio of the temperature fluctuation factor to the time interval factor between any two adjacent extreme points is used as the temperature change value.

[0013] In the temperature fitting curve corresponding to the monitoring location, the normalized value of the product of the maximum temperature change value and the number of extreme points is used as the temperature fluctuation index at the monitoring location.

[0014] Furthermore, the method of classifying all monitoring locations into temperature-sensitive and non-temperature-sensitive locations based on temperature fluctuation indicators includes:

[0015] When the temperature fluctuation index at a certain monitoring location is greater than or equal to the preset fluctuation threshold, the monitoring location is determined to be a temperature-sensitive location.

[0016] When the temperature fluctuation index at a certain monitoring location is less than the preset fluctuation threshold, the monitoring location is determined to be a non-temperature-sensitive location.

[0017] Furthermore, the method for obtaining the interference level value includes:

[0018] The distance between each monitoring location and the PTC electric heating device is used as a distance factor;

[0019] The temperature time series data at each temperature-sensitive location is segmented based on the adaptive piecewise constant approximation method, thereby obtaining multiple temperature data segments;

[0020] The multiple temperature data segments corresponding to each temperature-sensitive location are combined in pairs to obtain all unique combinations of data segments.

[0021] In each data segment combination, the DTW distance between two temperature data segments is calculated as the difference factor, and the absolute value of the length difference between two temperature data segments is negatively correlated and mapped as the environmental impact weight.

[0022] By using the environmental impact weights of data segment combinations to weight and fuse the difference factors, the interference factor at each temperature-sensitive location is obtained.

[0023] The normalized value of the product of the interference factor, distance factor, and temperature fluctuation index at each temperature-sensitive location is used as the interference level value at each temperature-sensitive location.

[0024] Furthermore, the method for obtaining the temperature uniformity index includes:

[0025] Choose any temperature-sensitive location as the location to be measured;

[0026] Starting from the PTC electric heating device and ending at the location to be measured, a ray is obtained, and all temperature-sensitive locations on the ray are used as reference locations for the location to be measured.

[0027] On the ray, the absolute value of the difference between the temperature values ​​of each two adjacent reference positions at the current time is calculated, and the ratio of this difference to the Euclidean distance between each two adjacent reference positions is used as the temperature deviation factor. The variance of all temperature deviation factors is negatively correlated and mapped to the value of the temperature consistency index of the position to be measured at the current time.

[0028] Calculate the angle between the unit vector corresponding to the ray and the unit vector corresponding to the air outlet direction of the PTC electric heating device at the current moment, and use it as the angle deviation factor;

[0029] At the current moment, the product of the temperature uniformity index, the angle deviation factor, and the number of reference positions corresponding to the measured position is normalized and used as the first temperature uniformity factor at the measured position at the current moment.

[0030] Temperature-sensitive locations with the same distance factor as the location to be measured are used as the comparison locations corresponding to the location to be measured.

[0031] At the current moment, the quantitative characteristics, positional relationships, and temperature differences of all comparison locations corresponding to the location to be measured are analyzed to obtain the second temperature uniformity factor of the location to be measured.

[0032] At the current moment, the average of the first temperature uniformity factor and the second temperature uniformity factor at the location to be measured is used as the temperature uniformity index of the location to be measured.

[0033] Furthermore, the method for obtaining the second temperature uniformity factor includes:

[0034] Among all the comparison locations of the location to be measured, the ratio of the absolute value of the difference between the temperature values ​​of any two comparison locations at the current time to the Euclidean distance between the two comparison locations is used as the temperature change factor, and the variance of all temperature change factors is used as the temperature disorder index of the location to be measured at the current time.

[0035] At the current moment, the value of the temperature disorder index at the test location after negative correlation mapping and normalization, and the value after normalization of the product of the number of comparison locations, are used as the second temperature uniformity factor at the test location at the current moment.

[0036] Furthermore, the method for obtaining the temperature weight includes:

[0037] The value of the disturbance level at each temperature-sensitive location is negatively correlated and then multiplied by the temperature uniformity index at each temperature-sensitive location. The normalized product is then used as the temperature weight at each temperature-sensitive location.

[0038] Furthermore, the preset value is 1.

[0039] Furthermore, the step of performing PID control on the PTC electric heating equipment for the next moment based on the temperature values ​​and temperature weights at all monitoring locations at the current moment includes:

[0040] At the current moment, the proportion of the temperature weight at each monitoring location to the sum of temperature weights at all monitoring locations is used as the reference weight at each monitoring location.

[0041] The temperature values ​​at the monitoring locations at the current time are weighted and averaged using reference weights at the monitoring locations to obtain the comprehensive temperature of the heating environment at the current time.

[0042] The overall temperature of the heating environment at the current moment is used as the input of the PID controller, so as to perform PID control on the temperature of the PTC electric heating equipment at the next moment.

[0043] A PID-based feedback control system for PTC electric heating equipment includes a processor and a memory. The memory stores at least one instruction, at least one program, code set, or instruction set. When the processor loads and executes the at least one instruction, at least one program, code set, or instruction set, it implements the steps of the PID-based feedback control method for PTC electric heating equipment.

[0044] The present invention has the following beneficial effects:

[0045] By acquiring and analyzing time-series temperature data from multiple monitoring locations, this invention can more accurately reflect the overall temperature status of the heating environment, thereby significantly improving the accuracy of temperature control. Since heating effects are affected by various external factors, the temperature values ​​collected by the temperature sensors will also fluctuate. Therefore, by analyzing the fluctuations and changes in temperature values ​​in the time-series temperature data, the temperature fluctuation index at each monitoring location can be determined, thus distinguishing between temperature-sensitive locations where temperature changes are easily induced and non-temperature-sensitive locations where temperature changes are less likely. When external factors interfere, such as when people move around, temperature changes may fluctuate intermittently, causing intermittent changes in the temperature values ​​collected by the temperature sensors. Therefore, for the time-series temperature data at temperature-sensitive locations, the differences in temperature values ​​can be further analyzed, and combined with the temperature fluctuation index and the location distribution of the temperature-sensitive locations in the heating environment, the degree of interference at each temperature-sensitive location can be obtained. Given that the warm air vents of PTC electric heating equipment oscillate and heat dissipates during operation, to more accurately obtain the overall temperature of the heating environment, the quantity characteristics, positional relationships, and temperature differences of temperature-sensitive locations are analyzed at the current moment. This quantifies heat diffusion and yields a temperature uniformity index for each temperature-sensitive location. Furthermore, by combining the disturbance level at each temperature-sensitive location with the temperature uniformity index, a temperature weight is derived. This temperature weight reflects its importance in the final PID control of the PTC electric heating equipment at the next moment. Finally, based on the temperature values ​​and temperature weights at all monitoring locations at the current moment, PID control of the PTC electric heating equipment is performed for the next moment. In summary, because this invention can intelligently adjust the temperature weights of different monitoring locations, it can ultimately perform PID control of the PTC electric heating equipment based on a more accurate and reliable actual temperature distribution, thereby achieving uniform and reasonable temperature distribution. Attached Figure Description

[0046] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 A flowchart illustrating a PID-based feedback control method for a PTC electric heating device, as provided in one embodiment of the present invention.

[0048] Figure 2 A schematic diagram of the swing area of ​​the warm air vent of a PTC electric heating device provided in an embodiment of the present invention;

[0049] Figure 3 This is a schematic diagram of a scenario provided in one embodiment of the present invention;

[0050] Figure 4 This is a schematic diagram of a temperature fitting curve provided in one embodiment of the present invention;

[0051] Figure 5 A schematic diagram illustrating a reference position provided in one embodiment of the present invention;

[0052] Figure 6 This is a schematic diagram of a comparison position provided in one embodiment of the present invention;

[0053] Figure 7 This is a schematic diagram of the system structure of a PID-based feedback control system for a PTC electric heating device, provided as an embodiment of the present invention. Detailed Implementation

[0054] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a PID-based feedback control method and system for PTC electric heating equipment proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0056] The following description, in conjunction with the accompanying drawings, details a specific scheme for a PID-based feedback control method and system for PTC electric heating equipment provided by this invention.

[0057] Please see Figure 1 The diagram illustrates a method flowchart of a PID-based feedback control method for a PTC electric heating device according to an embodiment of the present invention. The method includes the following steps:

[0058] Step S1: Obtain temperature time-series data at various monitoring locations within the heating environment where the PTC electric heating device is located.

[0059] PTC effect is short for Positive Temperature Coefficient Thermistor. The core component of PTC electric heating equipment is the PTC thermistor ceramic element, whose resistance increases with temperature. In actual use, PTC electric heating equipment is subject to various environmental interferences. To accurately control the heating effect of PTC electric heating equipment in a heating environment, it is essential to first comprehensively and accurately understand the temperature distribution within the heating environment. The key to this step lies in utilizing a series of temperature sensors, carefully positioned at various critical locations within the heating environment. The selection of these locations is based on the layout of the heating environment, airflow conditions, and potential areas sensitive to temperature fluctuations.

[0060] Specifically, because the warm air vents of PTC electric heating equipment typically swing left and right, forming a fan-shaped area, such as... Figure 2 As shown, this diagram illustrates the swing area of ​​the warm air vent of the PTC electric heating device in an embodiment of the present invention. Therefore, several temperature sensors can be evenly arranged within this fan-shaped area (the angle and distance can be adjusted according to the implementation scenario and are not limited here). Please refer to [link / reference]. Figure 3 The diagram illustrates a scenario from an embodiment of the present invention.

[0061] During the operation of the PTC electric heating equipment, the temperature sensors at each monitoring location simultaneously acquire the temperature time-series data at each monitoring location within the heating area. The acquisition frequency can be set to once per second, and the length of the temperature time-series data can be set to the 5 minutes preceding the current moment in the time sequence.

[0062] It should be noted that the location settings of the temperature sensor (the setting of the monitoring location), the length of the temperature time series data, and the acquisition frequency of the temperature sensor can all be adjusted according to the implementation scenario, and are not limited here.

[0063] Step S2: Analyze the temperature fluctuation and change pattern in the temperature time series data at each monitoring location, and determine the temperature fluctuation index at each monitoring location; based on the temperature fluctuation index, classify all monitoring locations into temperature-sensitive locations and non-temperature-sensitive locations.

[0064] If, within a short period, the temperature time-series data at a certain location experiences a sudden and significant jump, while the actual ambient temperature could not possibly change so rapidly, this indicates a malfunction in the temperature sensor itself or interference from the external environment. Therefore, the temperature time-series data at each monitoring location can be analyzed. Based on the fluctuations and patterns of temperature values, the degree of temperature fluctuation can be quantified to obtain a temperature fluctuation index for each monitoring location. Furthermore, based on this temperature fluctuation index, monitoring locations can be categorized into temperature-sensitive and non-temperature-sensitive locations. Temperature-sensitive locations are those with significant temperature variations and are easily affected by external interference, while non-temperature-sensitive locations are those with relatively stable temperatures.

[0065] Preferably, in one embodiment of the present invention, the method for obtaining the temperature fluctuation index includes:

[0066] For any monitoring location, a coordinate system is established with time on the horizontal axis and temperature on the vertical axis. The temperature time-series data at that monitoring location is then fitted using the least squares method to obtain a temperature fitting curve. This temperature fitting curve more clearly reflects the temperature change over time. For example... Figure 4 As shown, it illustrates a schematic diagram of a temperature fitting curve in one embodiment of the present invention.

[0067] It should be noted that curve fitting based on the least squares method is a well-known technique, and the specific process will not be elaborated here.

[0068] Since extreme points represent local maximum or minimum values ​​in the data, and can reflect drastic changes in the data, all extreme points were obtained in the temperature fitting curve.

[0069] Then, the absolute value of the temperature difference between any two adjacent extreme points is used as the temperature fluctuation factor, and the absolute value of the time difference between any two adjacent extreme points is used as the time interval factor. The temperature fluctuation factor can quantify the temperature change amplitude between two adjacent drastically changing data points, and the larger the value, the greater the fluctuation. The time interval factor can quantify the change rate between two adjacent drastically changing data points, and the smaller the value, the more frequent the change, that is, the faster the change rate.

[0070] Therefore, the normalized value of the ratio of the temperature fluctuation factor to the time interval factor between any two adjacent (temporally adjacent) extreme points is taken as the temperature change degree value. This temperature change degree value reflects the degree or rate of temperature change per unit time; the larger the value, the faster the temperature change rate. Normalization is a technique well-known to those skilled in the art, and the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0071] Extreme points are key points in temperature change and are crucial for assessing the intensity and frequency of temperature fluctuations. A higher number of extreme points indicates stronger temperature fluctuations. Therefore, in the temperature fitting curve corresponding to the monitoring location, the product of the maximum temperature change and the number of extreme points is normalized and used as the temperature fluctuation index for that monitoring location. A larger temperature fluctuation index at that location indicates a high likelihood of sudden and significant jumps in the temperature time series data, requiring closer attention in subsequent processes. Normalization is a well-known technique in the field, and the normalization function can be linear or standard normalization, etc. Specific normalization methods are not limited here.

[0072] Based on the aforementioned process, the temperature fluctuation index at each monitoring location can be obtained. Then, the monitoring locations are distinguished according to the temperature fluctuation index, and they are divided into temperature-sensitive locations with more obvious temperature changes and non-temperature-sensitive locations with more stable temperature changes.

[0073] Preferably, in one embodiment of the present invention, all monitoring locations are classified into temperature-sensitive locations and non-temperature-sensitive locations based on a temperature fluctuation index, including:

[0074] When the temperature fluctuation index is larger, it is considered that the temperature time series data at the monitoring location is very likely to have a sudden and large jump. Therefore, when the temperature fluctuation index at a certain monitoring location is greater than or equal to the preset fluctuation threshold, the monitoring location is judged to be a temperature-sensitive location; conversely, when the temperature fluctuation index at a certain monitoring location is less than the preset fluctuation threshold, the monitoring location is judged to be a non-temperature-sensitive location.

[0075] It should be noted that in this embodiment of the present invention, the preset fluctuation threshold is 0.6, and the specific value can be adjusted according to the implementation scenario, and is not limited here.

[0076] Step S3: In the temperature time series data of each temperature-sensitive location, analyze the differences in temperature values, and combine the temperature fluctuation index at the temperature-sensitive location with the location distribution of the temperature-sensitive location in the heating environment to quantify the degree of disturbance of each temperature-sensitive location; at the current moment, analyze the quantitative characteristics of temperature-sensitive locations, the positional relationship between temperature-sensitive locations, and the differences in temperature values ​​to obtain the temperature uniformity index at each temperature-sensitive location.

[0077] When the environment changes, uneven temperatures can occur in the heating environment. For example, when people walk, they move a certain amount of air along with them. At this time, the human body acts as a moving obstacle, pushing the air in front of them forward and creating a low-pressure area behind them, which allows surrounding air to flow in. This airflow pattern is basically similar each time a person walks because human body size, ventilation, walking speed, and path are relatively fixed in daily activities. Therefore, the intensity, direction, and range of airflow caused by each movement are roughly the same. This means that in the temperature time series data of temperature-sensitive locations, temperature changes may show intermittent fluctuations. Therefore, for the temperature time series data of each temperature-sensitive location, the differences in temperature value changes were analyzed. Combined with the temperature fluctuation index at the temperature-sensitive location and the location distribution of the temperature-sensitive location in the heating environment, the degree of disturbance of each temperature-sensitive location was quantified. Since the heating vents of PTC electric heating devices oscillate left and right during operation, the temperature at the temperature-sensitive locations is also affected by the hot air blown out of the vents at any given moment. Furthermore, heat also diffuses. Therefore, in order to more accurately reflect the overall temperature of the heating environment at any given moment and thus make the PID control process more precise, this embodiment of the invention also analyzes the quantitative characteristics, positional relationships, and temperature differences between temperature-sensitive locations at any given moment, obtaining a temperature uniformity index for each temperature-sensitive location to reflect the effects of heat diffusion, etc.

[0078] First, the degree of interference at each temperature-sensitive location is calculated. Preferably, in one embodiment of the present invention, the method for obtaining the degree of interference includes:

[0079] The distance between each monitoring location and the PTC electric heating device is used as a distance factor. The larger the distance factor, the more likely it is to be affected by external environmental factors.

[0080] It should be noted that when calculating the distance factor, the PTC electric heating device and each monitoring position can be regarded as a data point in three-dimensional space, and the Euclidean distance between them can be calculated as the distance factor.

[0081] To analyze intermittent fluctuations in temperature time series data, continuous temperature changes can be decomposed into different time periods, and the temperature changes within each time period can be made relatively stable, thereby analyzing the variation patterns of temperature time series data.

[0082] Therefore, the temperature time series data at each temperature-sensitive location is segmented based on the adaptive piecewise constant approximation method, resulting in multiple temperature data segments. At this time, the temperature change within each temperature data segment is relatively stable.

[0083] It should be noted that the adaptive piecewise constant approximation method is a well-known technique, and the specific process will not be elaborated here.

[0084] Then, the multiple temperature data segments corresponding to each temperature-sensitive location are combined in pairs to obtain all non-repeating data segment combinations. For example, if the temperature time series data at a certain temperature-sensitive location is divided into 4 temperature data segments, denoted as temperature data segment 1, temperature data segment 2, temperature data segment 3, and temperature data segment 4, then the final data segment combinations are (temperature data segment 1, temperature data segment 2), (temperature data segment 1, temperature data segment 3), (temperature data segment 1, temperature data segment 4), (temperature data segment 2, temperature data segment 3), (temperature data segment 2, temperature data segment 4), (temperature data segment 3, temperature data segment 4).

[0085] In each data segment combination, the DTW distance between two temperature data segments is calculated as a difference factor. The difference factor reflects the degree of difference in temperature changes within different temperature data segments, and the larger the value, the greater the difference in change; conversely, the smaller the difference factor, the smaller the difference in change, and the more similar the fluctuations between the two temperature data segments. Furthermore, the absolute value of the length difference between two temperature data segments is calculated. The smaller the absolute value of this difference, the closer the two temperature data segments are in terms of time length, which is considered to be possibly affected by similar environmental factors. Therefore, a negative correlation mapping is performed on the absolute value of this difference to obtain the environmental influence weight. The larger the environmental influence weight, the higher the possibility of being affected by external environmental interference.

[0086] Next, the environmental impact weights of the data segment combinations are used to weight and fuse the difference factors. That is, the environmental impact weight of each data segment combination is multiplied by the difference factor, and the sum of the products of all data segment combinations is used as the interference factor at each temperature-sensitive location. Based on the above analysis, it can be seen that the larger the environmental impact weight, the higher the possibility of being disturbed by the external environment; the larger the difference factor, the more dissimilar the temperature fluctuations between the two temperature data segments, indicating a greater external environmental interference. Therefore, for a certain temperature-sensitive location, the larger the interference factor obtained by weighting and fusing the difference factors using the environmental impact weights, the greater the degree of external environmental interference at that temperature-sensitive location.

[0087] Given that a larger distance factor corresponds to a temperature-sensitive location, it is more likely to be affected by external environmental factors, and a larger interference factor indicates a greater degree of external interference, and a larger temperature fluctuation index indicates a high probability of sudden and significant jumps in the temperature time series data, the normalized product of the interference factor, distance factor, and temperature fluctuation index at each temperature-sensitive location is used as the interference level value for each temperature-sensitive location. This interference level value incorporates multiple factors, and a larger value indicates a greater impact of external environmental interference on the temperature-sensitive location. Normalization is a technique well-known to those skilled in the art, and the normalization function can be linear normalization or standard normalization, etc. Specific normalization methods are not limited here.

[0088] The above process can obtain the degree of external disturbance at each temperature-sensitive location. Furthermore, since temperature uniformity is an important indicator for measuring the uniformity of temperature distribution in a heating environment, it can be used to evaluate the overall effect of PTC electric heating equipment on heating the ambient temperature, thereby helping to use PID control process to provide feedback adjustment for the PTC's operation at the next moment. Temperature uniformity can be quantified by comparing the positional relationship between different temperature-sensitive locations and the differences between temperature values, combined with the quantitative characteristics of temperature-sensitive locations. Thus, the temperature uniformity index at each temperature-sensitive location is obtained.

[0089] Preferably, in one embodiment of the present invention, the method for obtaining the temperature uniformity index includes:

[0090] For ease of explanation, a temperature-sensitive location is selected as the measurement location, and the specific calculation process is explained by obtaining the temperature uniformity index of the measurement location.

[0091] First, a X-ray is acquired starting from the PTC electric heating device and ending at the location to be measured. All temperature-sensitive locations along the X-ray are then used as reference locations for the location to be measured. (See [link to relevant documentation]). Figure 5 It shows a schematic diagram of a reference position, specifically, in Figure 5 In the diagram, if the position to be measured is g, then q and q+1 on the dashed line represent the reference positions of the position to be measured g.

[0092] On the ray, the absolute value of the difference between the temperature values ​​of any two adjacent reference positions at the current moment is calculated, and the ratio of this absolute value to the Euclidean distance between any two adjacent reference positions is used as the temperature deviation factor. The larger the absolute value of this difference, the greater the deviation in temperature values ​​between the two adjacent reference positions at the same moment. The smaller the Euclidean distance, the closer the two adjacent reference positions are. In this case, the closer the distance, the greater the deviation in temperature values, and the larger the temperature deviation factor, reflecting the temperature change between the two adjacent reference positions.

[0093] Under normal circumstances, the temperature change between adjacent reference positions should be relatively uniform, meaning the values ​​of the temperature deviation factors should be similar. Therefore, the variance of all temperature deviation factors was calculated. A larger variance indicates a greater temperature variation deviation between the measured position and the temperature-sensitive location along the radiation beam of the PTC electric heating device, indicating lower temperature uniformity. Therefore, this variance was negatively correlated to correct the logical relationship, thus obtaining the temperature consistency index of the measured position at the current moment. A larger temperature consistency index indicates a more uniform temperature change. This negative correlation mapping can be achieved using the formula... ,in, Let x represent an exponential function with the natural constant e as the base, and let x represent the independent variable.

[0094] Since the direction of the warm air blowing from the PTC electric heating device can affect the temperature data collected at different monitoring locations, the angle between the unit vector of the ray corresponding to the location to be measured and the unit vector corresponding to the air outlet direction of the PTC electric heating device at the current moment is calculated as an angle deviation factor. The smaller the angle deviation factor, the greater the influence of the blowing on the temperature values ​​of different reference positions on the ray corresponding to the location to be measured at the current moment, and the lower the reliability of the calculated temperature consistency index.

[0095] Based on the foregoing analysis, at the current moment, a larger temperature uniformity index corresponding to the measured location indicates higher temperature uniformity; a smaller angle deviation factor indicates lower reliability of the temperature uniformity index. Therefore, the temperature uniformity index is multiplied by the angle deviation factor, and the resulting product is used as the temperature uniformity adjustment index. A larger temperature uniformity adjustment index indicates better uniformity of temperature values ​​between reference locations corresponding to the measured location, and also higher reliability. Similarly, a larger number of reference locations corresponding to the measured location also indicates higher reliability of the calculation results. Therefore, the product of the temperature uniformity adjustment index and the number of reference locations on the ray corresponding to the measured location is normalized to obtain the first temperature uniformity factor at the measured location at the current moment. A larger first temperature uniformity factor indicates better temperature uniformity on the ray between the measured location and the PTC electric heating device. Normalization is a technique well-known to those skilled in the art. The normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0096] In a heating environment, heat will diffuse. That is, at the same time, the temperature data of monitoring positions that are the same distance from the PTC electric heating device but at different angles may be affected by the heat diffusion between them. Therefore, in this embodiment of the invention, the influence of heat diffusion is analyzed to obtain the second temperature uniformity factor of the position to be measured at the current time.

[0097] Temperature-sensitive locations with the same distance factor as the location to be measured are used as comparison locations. Please refer to [link / reference]. Figure 6 The diagram illustrates a comparison position in one embodiment of the present invention, wherein if the position to be tested is g, then p and p+1 represent the comparison positions of the position to be tested g.

[0098] Among all the comparison locations of the location to be measured, the ratio of the absolute value of the difference between the temperature values ​​of any two comparison locations at the current time to the Euclidean distance between the two comparison locations is used as the temperature change factor. The larger the absolute value of the difference, the more obvious the temperature change, and the larger the temperature change factor.

[0099] The variance of all temperature change factors is used as the temperature disorder index of the test location at the current time. The larger the temperature disorder index, the worse the temperature uniformity between the comparison locations and the more chaotic the temperature changes.

[0100] At the current moment, the temperature disorder index at the test location is negatively correlated and normalized to correct the logical relationship. A larger corrected value indicates better temperature uniformity among all comparison locations corresponding to the test location. The more comparison locations there are, the higher the reliability of the calculation results. Therefore, the corrected temperature disorder index value is multiplied by the number of comparison locations, and the normalized product is used as the second temperature uniformity factor at the test location at the current moment. A larger second temperature uniformity factor indicates more consistent temperature changes at other temperature-sensitive locations equidistant from the test location and the PTC electric heating device, indicating better temperature uniformity. Normalization is a well-known technique, and the normalization function can be linear or standard normalization, etc. Specific normalization methods are not limited here.

[0101] Finally, based on the aforementioned process, at the current moment, the larger the first temperature uniformity factor at the test location, the better the temperature uniformity along the ray between the test location and the PTC electric heating device; the larger the second temperature uniformity factor at the test location, the more consistent the temperature changes at other temperature-sensitive locations at the same distance from the test location and the PTC electric heating device, which means the temperature uniformity is better. Therefore, the average of the first and second temperature uniformity factors at the test location is taken as the temperature uniformity index of the test location. At this time, the larger the temperature uniformity index, the stronger the temperature uniformity is considered.

[0102] Thus, in this step, we can obtain the disturbance level value at each temperature-sensitive location and the temperature uniformity index at each temperature-sensitive location at the current moment.

[0103] Step S4: Combine the disturbance level value and temperature uniformity index at each temperature-sensitive location to obtain the temperature weight at each temperature-sensitive location; set the temperature weight at non-temperature-sensitive locations to a preset value; perform PID control on the PTC electric heating equipment at the next moment based on the temperature values ​​and temperature weights at all monitoring locations at the current moment.

[0104] Considering temperature uniformity indices helps reduce temperature fluctuations and gradients during control, thereby improving the temperature uniformity of the entire heating environment. This is particularly important for applications requiring uniform temperature distribution. The degree of disturbance at temperature-sensitive locations reflects their importance in measuring the overall temperature of the heating environment. Therefore, by comprehensively evaluating the disturbance level and temperature uniformity index of each temperature-sensitive location, an appropriate temperature weight can be assigned to each location. This weight reflects the importance of the temperature-sensitive location. Finally, based on the temperature values ​​and temperature weights of all monitored locations at the current moment, the PID controller can dynamically adjust the output of the PTC electric heating equipment to quickly respond to temperature changes and achieve more stable and accurate temperature control.

[0105] Preferably, in one embodiment of the present invention, the method for obtaining the temperature weight includes:

[0106] The greater the disturbance level at a temperature-sensitive location, the lower the reliability of the temperature value at that location, meaning its importance decreases. Conversely, a higher temperature uniformity index at a temperature-sensitive location indicates better temperature uniformity compared to other locations, thus increasing its reliability in assessing overall temperature changes in the heating environment. Therefore, the disturbance level value at each temperature-sensitive location is negatively correlated, multiplied by the temperature uniformity index at that location, and the normalized product is used as the temperature weight for each location. A higher temperature weight for a particular location indicates that its temperature data better reflects the overall temperature trend of the heating environment, resulting in higher reliability. This negative correlation mapping can be achieved using the formula... ,in, Let x represent an exponential function with the natural constant e as the base, and let x represent the independent variable. Normalization is a technique well known to those skilled in the art. The choice of normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0107] Based on the analysis and calculation in step S2, it is known that the temperature value at temperature-sensitive locations will fluctuate to some extent, while the temperature value at non-temperature-sensitive locations is relatively stable. Therefore, when performing PID control on the PTC electric heating device at the next moment based on the temperature value and temperature weight at the current monitoring location, the temperature value at the non-temperature-sensitive location is more reliable. Therefore, in this embodiment of the invention, the temperature weight of the non-temperature-sensitive location is set to a preset value. Since the temperature weight of the temperature-sensitive location is a normalized value, ranging from 0 to 1, in this embodiment of the invention, the preset value is set to 1, which aims to increase the proportion of the temperature value at the non-temperature-sensitive location, thereby more accurately reflecting the overall temperature of the heating environment and improving the accuracy of PID control.

[0108] Preferably, in one embodiment of the present invention, PID control of the PTC electric heating device at the next moment is performed based on the temperature values ​​and temperature weights at all monitoring locations at the current moment, including:

[0109] At the current moment, the proportion of the temperature weight at each monitoring location to the total temperature weights and values ​​at all monitoring locations is used as the reference weight for each monitoring location. This reference weight ensures that the importance of the temperature data at each monitoring location is reasonably allocated. The monitoring location with a larger temperature weight will have a larger reference weight and be more important.

[0110] Then, the reference weights at the monitoring locations are used to calculate the weighted average of the temperature values ​​at the current monitoring locations to obtain the comprehensive temperature of the heating environment at the current time. That is, at the current time, the reference weight at each monitoring location is multiplied by the temperature value to obtain the weighted temperature value at each monitoring location. The average of the weighted temperature values ​​at all monitoring locations is taken as the comprehensive temperature of the heating environment at the current time. This comprehensive temperature can more accurately reflect the overall temperature of the heating environment.

[0111] Finally, the overall temperature of the heating environment at the current moment is used as the input of the PID controller. The PID controller can then output control commands based on the deviation between the overall temperature and the preset temperature to perform PID control on the temperature of the PTC electric heating equipment at the next moment, making the temperature in the heating environment more uniform and closer to the preset temperature.

[0112] It should be noted that the preset temperature value needs to be adjusted according to the specific implementation scenario, and there is no limit here. For example, if the implementation scenario is a living room, the preset temperature value can be set to 25 degrees Celsius.

[0113] In summary, by acquiring and analyzing temperature time-series data from multiple monitoring locations, this invention can more accurately reflect the overall temperature status of the heating environment, thereby significantly improving the accuracy of temperature control. Since heating effects are affected by various external factors, the temperature values ​​collected by the temperature sensors will also fluctuate. Therefore, by analyzing the fluctuations and changes in temperature values ​​in the temperature time-series data, the temperature fluctuation index at each monitoring location can be determined, thus distinguishing between temperature-sensitive locations where temperature changes are easily induced and non-temperature-sensitive locations where temperature changes are not easily induced. When external factors interfere, such as when people move around, temperature changes may fluctuate intermittently, causing intermittent changes in the temperature values ​​collected by the temperature sensors. Therefore, for the temperature time-series data at temperature-sensitive locations, the differences in temperature values ​​can be further analyzed, and combined with the temperature fluctuation index and the location distribution of the temperature-sensitive locations in the heating environment, the degree of interference at each temperature-sensitive location can be obtained. Given that the warm air vents of PTC electric heating equipment oscillate and heat dissipates during operation, to more accurately obtain the overall temperature of the heating environment, the quantity characteristics, positional relationships, and temperature differences of temperature-sensitive locations are analyzed at the current moment. This quantifies heat diffusion and yields a temperature uniformity index for each temperature-sensitive location. Furthermore, by combining the disturbance level at each temperature-sensitive location with the temperature uniformity index, a temperature weight is derived. This temperature weight reflects its importance in the final PID control of the PTC electric heating equipment at the next moment. Finally, based on the temperature values ​​and temperature weights at all monitoring locations at the current moment, PID control of the PTC electric heating equipment is performed for the next moment. In summary, because this embodiment of the invention can intelligently adjust the temperature weights of different monitoring locations, it can ultimately perform PID control of the PTC electric heating equipment based on a more accurate and reliable actual temperature distribution, thereby achieving uniformity and rationality in temperature distribution.

[0114] This invention also proposes a feedback control system for PTC electric heating equipment based on PID control. Please refer to [link to relevant documentation]. Figure 7This document illustrates a schematic diagram of a feedback control system for a PTC electric heating device based on PID control, according to an embodiment of the present invention. The system includes a processor 700, a memory 701, a bus 702, and a communication interface 703. The processor 700, communication interface 703, and memory 701 are connected via the bus 702. The memory 701 may contain a high-speed random access memory, and the bus 702 may be an ISA bus, PCI bus, or EISA bus, etc. The processor 700 may be an integrated circuit chip with signal processing capabilities. The memory 701 stores at least one instruction, at least one program, code set, or instruction set. When the processor loads and executes the at least one instruction, at least one program, code set, or instruction set, it implements the steps in a stroke cognitive impairment risk auxiliary assessment method.

[0115] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0116] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A feedback control method for PTC electric heating equipment based on PID, characterized in that, The method includes: Acquire time-series temperature data at various monitoring locations within the heating environment where the PTC electric heating equipment is located; Analyze the temperature fluctuations and patterns in the time-series data of each monitoring location to determine the temperature fluctuation index for each monitoring location; based on the temperature fluctuation index, classify all monitoring locations into temperature-sensitive and non-temperature-sensitive locations. In the time-series temperature data of each temperature-sensitive location, the differences in temperature values ​​are analyzed, and the degree of disturbance of each temperature-sensitive location is quantified by combining the temperature fluctuation index at the temperature-sensitive location and the location distribution of the temperature-sensitive location in the heating environment. At the current moment, the quantitative characteristics of temperature-sensitive locations, the positional relationship between temperature-sensitive locations, and the differences in temperature values ​​are analyzed to obtain the temperature uniformity index at each temperature-sensitive location. By combining the disturbance level and temperature uniformity index at each temperature-sensitive location, the temperature weight at each temperature-sensitive location is obtained; the temperature weight at non-temperature-sensitive locations is set to a preset value; and PID control of the PTC electric heating equipment is performed at the next moment based on the temperature values ​​and temperature weights at all monitored locations at the current moment. The method for obtaining the interference level value includes: The distance between each monitoring location and the PTC electric heating device is used as a distance factor; The temperature time series data at each temperature-sensitive location is segmented based on the adaptive piecewise constant approximation method, thereby obtaining multiple temperature data segments; The multiple temperature data segments corresponding to each temperature-sensitive location are combined in pairs to obtain all unique combinations of data segments. In each data segment combination, the DTW distance between two temperature data segments is calculated as the difference factor, and the absolute value of the length difference between two temperature data segments is negatively correlated and mapped as the environmental impact weight. By using the environmental impact weights of data segment combinations to weight and fuse the difference factors, the interference factor at each temperature-sensitive location is obtained. The normalized value of the product of the interference factor, distance factor, and temperature fluctuation index at each temperature-sensitive location is used as the interference level value at each temperature-sensitive location.

2. The feedback control method for PTC electric heating equipment based on PID according to claim 1, characterized in that, The method for obtaining the temperature fluctuation index includes: For any monitoring location, the temperature time series data at that monitoring location is curve-fitted using the least squares method to obtain the temperature fitting curve. In the temperature fitting curve, all extreme points are obtained, and the absolute value of the temperature difference between any two adjacent extreme points is used as the temperature fluctuation factor, and the absolute value of the time difference between any two adjacent extreme points is used as the time interval factor. The normalized value of the ratio of the temperature fluctuation factor to the time interval factor between any two adjacent extreme points is used as the temperature change value. In the temperature fitting curve corresponding to the monitoring location, the normalized value of the product of the maximum temperature change value and the number of extreme points is used as the temperature fluctuation index at the monitoring location.

3. The feedback control method for PTC electric heating equipment based on PID according to claim 1, characterized in that, The method of classifying all monitoring locations into temperature-sensitive and non-temperature-sensitive locations based on temperature fluctuation indicators includes: When the temperature fluctuation index at a certain monitoring location is greater than or equal to the preset fluctuation threshold, the monitoring location is determined to be a temperature-sensitive location. When the temperature fluctuation index at a certain monitoring location is less than the preset fluctuation threshold, the monitoring location is determined to be a non-temperature-sensitive location.

4. The feedback control method for PTC electric heating equipment based on PID according to claim 1, characterized in that, The method for obtaining the temperature uniformity index includes: Choose any temperature-sensitive location as the location to be measured; Starting from the PTC electric heating device and ending at the location to be measured, a ray is obtained, and all temperature-sensitive locations on the ray are used as reference locations for the location to be measured. On the ray, the absolute value of the difference between the temperature values ​​of each two adjacent reference positions at the current time is calculated, and the ratio of this difference to the Euclidean distance between each two adjacent reference positions is used as the temperature deviation factor. The variance of all temperature deviation factors is negatively correlated and mapped to the value of the temperature consistency index of the position to be measured at the current time. Calculate the angle between the unit vector corresponding to the ray and the unit vector corresponding to the air outlet direction of the PTC electric heating device at the current moment, and use it as the angle deviation factor; At the current moment, the product of the temperature uniformity index, the angle deviation factor, and the number of reference positions corresponding to the measured position is normalized and used as the first temperature uniformity factor at the measured position at the current moment. Temperature-sensitive locations with the same distance factor as the location to be measured are used as the comparison locations corresponding to the location to be measured. At the current moment, the quantitative characteristics, positional relationships, and temperature differences of all comparison locations corresponding to the location to be measured are analyzed to obtain the second temperature uniformity factor of the location to be measured. At the current moment, the average of the first temperature uniformity factor and the second temperature uniformity factor at the location to be measured is used as the temperature uniformity index of the location to be measured.

5. The feedback control method for PTC electric heating equipment based on PID according to claim 4, characterized in that, The method for obtaining the second temperature uniformity factor includes: Among all the comparison locations of the location to be measured, the ratio of the absolute value of the difference between the temperature values ​​of any two comparison locations at the current time to the Euclidean distance between the two comparison locations is used as the temperature change factor, and the variance of all temperature change factors is used as the temperature disorder index of the location to be measured at the current time. At the current moment, the value of the temperature disorder index at the test location after negative correlation mapping and normalization, and the value after normalization of the product of the number of comparison locations, are used as the second temperature uniformity factor at the test location at the current moment.

6. The feedback control method for PTC electric heating equipment based on PID according to claim 1, characterized in that, The method for obtaining the temperature weight includes: The value of the disturbance level at each temperature-sensitive location is negatively correlated and then multiplied by the temperature uniformity index at each temperature-sensitive location. The normalized product is then used as the temperature weight at each temperature-sensitive location.

7. The feedback control method for PTC electric heating equipment based on PID according to claim 6, characterized in that, The preset value is 1.

8. The feedback control method for PTC electric heating equipment based on PID according to claim 1, characterized in that, The step of performing PID control on the PTC electric heating equipment for the next moment based on the temperature values ​​and temperature weights at all monitoring locations at the current moment includes: At the current moment, the proportion of the temperature weight at each monitoring location to the sum of temperature weights at all monitoring locations is used as the reference weight at each monitoring location. The temperature values ​​at the monitoring locations at the current time are weighted and averaged using reference weights at the monitoring locations to obtain the comprehensive temperature of the heating environment at the current time. The overall temperature of the heating environment at the current moment is used as the input of the PID controller, so as to perform PID control on the temperature of the PTC electric heating equipment at the next moment.

9. A feedback control system for PTC electric heating equipment based on PID, characterized in that, The method includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, code set, or instruction set, and the at least one instruction, at least one program, code set, or instruction set is loaded and executed by the processor to implement the steps of the PID-based PTC electric heating equipment feedback control method as described in any one of claims 1-8.

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