Heating safety control method and system based on fuzzy control model

Abstract

The invention discloses a heating safety control method and system based on a fuzzy control model, and relates to the technical field of heating safety control. According to the invention, a test environment combined by different heating body temperatures and fan gears is constructed; in each test environment, obstacles are placed at different position points, the combination of parameter values of each type of waveform characteristics of a plurality of heating body temperature-fan gear-reflection signal waves corresponding to each numerical value interval for recording the windward temperature is detected, and a safety fuzzy set and a danger fuzzy set are obtained through division; obtaining the temperature of the heating body, the gear of the fan and the parameter value of each type of waveform characteristic of the reflected signal wave at the current moment; calculating membership degrees between the current numerical value combination and the safety fuzzy set and between the current numerical value combination and the danger fuzzy set and judging whether high-temperature danger exists or not; if yes, the fan gear is lowered; and if not, safety intervention operation is not carried out on the fan. A user is effectively prevented from getting close and being scalded by hot air.

Description

Technical Field

[0001] The present invention belongs to the technical field of heating safety control, and in particular relates to a heating safety control method and system based on a fuzzy control model. Background Art

[0002] With the widespread adoption and intelligent development of modern building heating systems, safety and energy efficiency control have become critical issues. Traditional heating system control methods rely primarily on preset heating power thresholds. While these methods can meet basic requirements to a certain extent, they lack control precision when dealing with complex operating conditions, especially when user behavior is uncertain, potentially posing safety risks. Summary of the Invention

[0003] The purpose of the present invention is to provide a heating safety control method and system based on a fuzzy control model, which controls the fan gear of the air outlet through the fuzzy control model to effectively prevent users from being burned by hot air when they get close to it.

[0004] To solve the above technical problems, the present invention is achieved through the following technical solutions:

[0005] The present invention provides a heating safety control method based on a fuzzy control model, comprising:

[0006] Construct a test environment with different heating element temperatures and fan gear combinations;

[0007] In each test environment, the obstacle is placed at different locations, and the windward temperature of the obstacle surface is detected. The combinations of parameter values of the heating element temperature, fan gear position, and each type of waveform characteristic of the reflected signal wave corresponding to each numerical interval of the windward temperature are recorded and recorded as joint numerical combinations. The joint numerical combinations corresponding to the numerical interval where the windward temperature is lower than the set safety temperature are classified into the safe fuzzy set, and the remaining joint numerical combinations are classified into the dangerous fuzzy set.

[0008] Obtain the current temperature of the heating element, the fan gear position, and the parameter values of each type of waveform characteristics of the reflected signal wave, and record them as the current value combination;

[0009] Calculate the membership of the current value combination with the safety fuzzy set and the danger fuzzy set respectively and judge whether it is a high temperature danger;

[0010] If so, reduce the fan gear;

[0011] If not, no safety intervention operation will be performed on the fan.

[0012] The present invention also discloses a heating safety control system based on a fuzzy control model, comprising:

[0013] case;

[0014] Fever body;

[0015] Fan;

[0016] The shell contains a heating element, and the heat stored in the heating element is sent out to the outside in the form of hot air through the fan for heating;

[0017] The shell surface is also equipped with an ultrasonic transducer, which emits a fixed waveform detection signal wave and receives the reflected signal waves from different obstacles at different distances and angles.

[0018] A fuzzy control model is also provided, storing a safe fuzzy set and a dangerous fuzzy set;

[0019] During operation, the current temperature of the heating element, the fan gear position, and the parameter values of each type of waveform characteristics of the reflected signal wave are obtained and recorded as the current value combination;

[0020] Calculate the membership of the current value combination with the safety fuzzy set and the danger fuzzy set respectively and judge whether it is a high temperature danger;

[0021] If so, reduce the fan gear;

[0022] If not, no safety intervention operation will be performed on the fan.

[0023] The present invention detects and collects data under different test environments to obtain a safe fuzzy set and a dangerous fuzzy set. During the use of the heater, the parameter values of the heating element temperature, fan gear and each type of waveform characteristics of the reflected signal wave are continuously collected, and the membership with the safe fuzzy set and the dangerous fuzzy set is calculated to realize the estimation of the windward temperature of the surface of the obstacle (human body, pets, etc.), and then the fuzzy control of the air volume regulator is performed to effectively prevent users from being burned by hot air when they are close to it.

[0024] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 This is a schematic diagram of functional units and information flows of a heating safety control system based on a fuzzy control model according to an embodiment of the present invention;

[0027] Figure 2A schematic flow chart of the steps of the fuzzy control model in test data collection and fuzzy control process in one embodiment of the present invention;

[0028] Figure 3 This is a schematic diagram of the process flow of step S4 in one embodiment of the present invention;

[0029] Figure 4 This is a schematic diagram of the process flow of step S44 in one embodiment of the present invention;

[0030] Figure 5 This is a schematic diagram of the process flow of step S441 in one embodiment of the present invention;

[0031] Figure 6 This is a schematic diagram of the process flow of step S442 in one embodiment of the present invention;

[0032] In the accompanying drawings, the components represented by the reference numerals are as follows:

[0033] 1-heating element, 2-fan, 3-ultrasonic transducer, 4-fuzzy control model, 5-air volume regulator. DETAILED DESCRIPTION

[0034] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0035] It should be noted that the terms "first," "second," and the like in this application are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Instead, they are merely examples of devices and methods consistent with certain aspects of the present application as detailed in the appended claims.

[0036] See also Figures 1 to 2 As shown, the present invention provides a heating safety control system based on a fuzzy control model. In terms of mechanical structure, the heater in this system has a shell that is resistant to high temperatures and has sufficient mechanical strength. The shell includes a heating element. The heating element can be heated by an external heat source or by a heating tube in the shell that is powered on during off-peak electricity pricing. The shell is also provided with a fan, which blows the heat accumulated in the heating element to the outside through convection. Since the temperature of the heating element may be as high as 600°C or above, the temperature of the fan outlet is very high, which can easily cause risks such as burns.

[0037] In order to detect obstacles near the air outlet of the heater, an ultrasonic transducer is also provided on the surface of the shell to emit a detection signal wave with a fixed waveform and receive reflected signal waves from different obstacles at different distances and angles.

[0038] See also Figure 2 As shown, the heater has a built-in control chip, and the memory of the control chip stores safety fuzzy sets and dangerous fuzzy sets, which are the basis for fuzzy control during the use of the heater. These data can be obtained from previous tests. During the test process, step S1 can be first executed to construct a test environment composed of different heating element temperatures and fan gear combinations. For example, a series of heating element temperatures and fan gears are set gradiently within the operating temperature range of the heater's heating degree and the operating wind speed range of the fan, respectively forming a plurality of heating element temperature-fan gear combinations and serving as the heating element temperature and fan gear of the test environment.

[0039] In each specific test environment, step S2 can be performed to place obstacles at different locations. In this solution, obstacles can be any movable objects, such as people, pets, and furniture. The windward temperature of the obstacle surface is detected, which is typically the location with the highest surface temperature. This temperature can be measured by placing a temperature sensor on the obstacle surface or by reading the obstacle surface temperature data using infrared thermal imaging.

[0040] Record the combinations of several parameters corresponding to each numerical interval of the headwind temperature: heating element temperature, fan gear position, and each type of waveform characteristic of the reflected signal wave. These combinations are recorded as joint numerical combinations. The joint numerical combinations corresponding to the numerical interval where the headwind temperature is less than the set safety temperature are classified into a safe fuzzy set, and the remaining joint numerical combinations are classified into a dangerous fuzzy set.

[0041] The waveform characteristics of the reflected signal wave can include any of the following: reflection duration, phase difference, cross-correlation peak position, Doppler shift, echo entropy, reflection coefficient, attenuation coefficient, echo waveform distortion, and scattered signal distribution. Reflection duration, phase difference, and cross-correlation peak position typically reflect the overall spatial position and shape of the obstacle, while Doppler shift typically reflects the obstacle's mobility. Echo entropy, reflection coefficient, and attenuation coefficient typically reflect the obstacle's material and surface roughness.

[0042] The heater's control chip stores a safety fuzzy set and a danger fuzzy set. During operation, the heater's fuzzy control model first executes step S3 to obtain the current heating element temperature, fan gear, and parameter values for each type of waveform characteristic of the reflected signal wave, recorded as the current value combination. Step S4 can then be executed to determine whether high temperature is dangerous. If so, step S5 is executed to lower the gear of fan 2 via air volume regulator 5. If not, step S6 is executed to avoid safety intervention on the fan. Of course, continuous safety monitoring is required during the execution of step S6.

[0043] See also Figure 3 As shown, when determining whether the obstacle at the current moment poses a high temperature hazard, step S41 can be first executed to determine whether the current value combination falls within the range of the safe fuzzy set or the dangerous fuzzy set. If it falls within the range of the safe fuzzy set, step S42 can be executed to determine that the current value combination has a membership of 1 with the safe fuzzy set and a membership of 0 with the dangerous fuzzy set, and the low temperature is determined to be safe. If it falls within the range of the dangerous fuzzy set, step S43 can be executed to determine that the current value combination has a membership of 0 with the safe fuzzy set and a membership of 1 with the dangerous fuzzy set, and the high temperature is determined to be dangerous.

[0044] See also Figure 3 and 4 As shown, since the number of tests in the pre-test environment is limited, the heater is more likely to encounter the following situation in real-world operation: that is, falling into neither the safe fuzzy set nor the dangerous fuzzy set. Then, step S44 can be executed to calculate the degree of association between the current value combination and the safe fuzzy set and the dangerous fuzzy set, respectively, as the membership degree. Specifically, step S441 can first be executed to calculate representative joint value combinations in the safe fuzzy set and the dangerous fuzzy set, denoted as the representative safe joint value combination and the representative dangerous joint value combination, respectively.

[0045] See also Figure 5 As shown, since there may be multiple representative joint value combinations, in order to select a representative safety joint value combination and a representative risk joint value combination that matches the current value combination and is representative, step S4411 can first be performed to arbitrarily select multiple joint value combinations from the safety fuzzy set and the risk fuzzy set, respectively, as the representative safety joint value combination and the representative risk joint value combination. Verification can then be performed to determine whether the selected representative safety joint value combination and representative risk joint value combination are sufficiently representative.

[0046] During the verification process, step S4412 can be first executed to calculate the cumulative value of each representative safety joint numerical combination and representative danger joint numerical combination in all joint numerical combinations included in the safety fuzzy set and the danger fuzzy set after item-by-item subtraction from each joint numerical combination other than them. Next, step S4413 can be executed to divide each joint numerical combination other than the representative safety joint numerical combination and the representative danger joint numerical combination into the same fuzzy subset with the safety joint numerical combination or the representative danger joint numerical combination having the smallest cumulative value after item-by-item subtraction. Next, step S4414 can be executed to determine whether the joint numerical combinations from the safety fuzzy set and the danger fuzzy set appear mixed in the same fuzzy subset. If so, it is determined that it is not sufficiently representative; otherwise, it is determined that it is sufficiently representative.

[0047] If it is determined during the verification process that it is not sufficiently representative, the representative safety joint numerical combination and the representative risk joint numerical combination will be reselected. First, step S4416 can be executed to calculate the item-by-item mean of all the joint numerical combinations contained in each fuzzy subset as the average joint numerical combination of the fuzzy subset. Next, step S4417 can be executed to take the joint numerical combination with the smallest cumulative value after item-by-item subtraction from the corresponding average joint numerical combination in each fuzzy subset as the reselected joint numerical combination. If the reselected joint numerical combination comes from the safety fuzzy set, it will be used as the representative safety joint numerical combination. If the reselected joint numerical combination comes from the risk fuzzy set, it will be used as the representative risk joint numerical combination.

[0048] If the verification process determines that the combination is sufficiently representative, a representative safety joint numerical combination and a representative risk joint numerical combination are selected and matched with the current numerical combination. Step S4415 can be executed to retain the representative safety joint numerical combination and the representative risk joint numerical combination that have the smallest cumulative value after item-by-item subtraction from the current numerical combination, respectively, from among the multiple representative safety joint numerical combinations and the representative risk joint numerical combination.

[0049] To supplement the implementation of steps S4411 to S4417, we provide the source code for some functional modules, with cross-references and explanations provided in the comments. To prevent the leakage of data involving commercial secrets, data that does not affect the implementation of the solution has been desensitized. The same applies below.

[0050]

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[0059] This code implements a complete module for selecting representative combinations of fuzzy sets. During operation, it first performs a multi-stage process, initially randomly selecting a representative combination. It then partitions the fuzzy sets based on a distance metric, checks the set purity to determine representativeness, and iterates until a qualified representative combination is obtained.

[0060] A weighted distance metric (temperature 40%, fan 30%, total ultrasonic signature 30%) is used in the above application.

[0061] The above functional modules can effectively handle the fuzzy set representative selection problem in heating safety control. Through the automated iterative optimization process, it ensures that the obtained representative combination has sufficient discrimination and representativeness, providing a reliable basis for subsequent safety control decisions.

[0062] See also Figure 4 and 6 As shown, after selecting the representative joint numerical combinations representing safety and the joint numerical combinations representing danger, in order to calculate the membership of the current numerical combination with the closest dangerous and safe fuzzy subsets, it is necessary to consider the representative numerical combinations (representative joint numerical combinations for safety and representative joint numerical combinations for danger) and the closest numerical combinations (adjacent joint numerical combinations for safety and adjacent joint numerical combinations for danger) of the fuzzy subset at the same time, which can better measure the association status of the current numerical combination with the fuzzy subset. Therefore, step S442 can be executed next to calculate and obtain the joint numerical combinations in the safety fuzzy set and the dangerous fuzzy set that are closest to the current numerical combination, as the adjacent safety joint numerical combination and the adjacent dangerous joint numerical combination, respectively. Specifically, step S4421 can be executed first to calculate the cumulative value of each joint numerical combination in the safety fuzzy set and the current numerical combination after subtracting each item one by one, and the joint numerical combination corresponding to the smallest cumulative value is used as the joint numerical combination in the safety fuzzy set that is closest to the current numerical combination. Then, step S4422 can be executed to calculate the cumulative value of each joint numerical combination in the dangerous fuzzy set and the current numerical combination after subtracting each item one by one, and the joint numerical combination corresponding to the smallest cumulative value is taken as the joint numerical combination in the dangerous fuzzy set that is closest to the current numerical combination.

[0063] Please continue reading Figure 4 As shown, in order to quantitatively calculate the association state of the current numerical combination and the fuzzy subset, step S443 can be executed next to use the cumulative value of the item-by-item subtraction between the representative safety joint numerical combination and the adjacent safety joint numerical combination as the safety range scale of the safety fuzzy set. Step S444 can be executed next to use the cumulative value of the item-by-item subtraction between the representative danger joint numerical combination and the adjacent danger joint numerical combination as the danger range scale of the danger fuzzy set. Step S445 can be executed next to use the cumulative value of the item-by-item subtraction between the current numerical combination and the representative safety joint numerical combination and the ratio of the safe range scale as the membership of the current numerical combination to the safety fuzzy set. Finally, step S446 can be executed next to use the cumulative value of the item-by-item subtraction between the current numerical combination and the representative danger joint numerical combination and the ratio of the dangerous range scale as the membership of the current numerical combination to the danger fuzzy set.

[0064] Please continue reading Figure 3 As shown, if the larger membership value of the current numerical combination with the safe fuzzy set and the dangerous fuzzy set corresponds to the safe fuzzy set, then step S45 can be executed to determine low-temperature safety. If the larger membership value of the current numerical combination with the safe fuzzy set and the dangerous fuzzy set corresponds to the dangerous fuzzy set, then step S46 can be executed to determine high-temperature danger.

[0065] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, systems, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a part for a module, program segment or instruction, and the part for the module, program segment or instruction comprises one or more executable instructions for realizing the logical function of the specification. In some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous boxes can actually be performed substantially in parallel, and they can sometimes also be performed in the opposite order, depending on the function involved.

[0066] It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented by hardware that performs the corresponding function or action, such as a circuit or ASIC (Application Specific Integrated Circuit), or can be implemented by a combination of hardware and software, such as firmware.

[0067] Although the present invention is described herein in conjunction with various embodiments, in the process of implementing the claimed invention, those skilled in the art can understand and implement other variations of the disclosed embodiments by reviewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple situations. A single processor or other unit can implement several functions listed in the claims. Certain measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0068] The embodiments of the present application have been described above. The above description is illustrative and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons skilled in the art to understand the embodiments disclosed herein.

Claims

1. A heating safety control method based on a fuzzy control model, characterized in that: include, Construct a test environment with different heating element temperatures and fan gear combinations; In each test environment, the obstacle is placed at different locations, and the windward temperature of the obstacle surface is detected. The combinations of parameter values of the heating element temperature, fan gear position, and each type of waveform characteristic of the reflected signal wave corresponding to each numerical interval of the windward temperature are recorded and recorded as joint numerical combinations. The joint numerical combinations corresponding to the numerical interval where the windward temperature is lower than the set safety temperature are classified into the safe fuzzy set, and the remaining joint numerical combinations are classified into the dangerous fuzzy set. Obtain the current temperature of the heating element, the fan gear position, and the parameter values of each type of waveform characteristics of the reflected signal wave, and record them as the current value combination; Calculate the membership of the current value combination with the safety fuzzy set and the danger fuzzy set respectively and judge whether it is a high temperature danger; If so, reduce the fan gear; If not, no safety intervention operation will be performed on the fan.

2. The method according to claim 1, characterized in that Types of waveform features include reflection duration, phase difference, cross-correlation peak position, Doppler shift, echo entropy value, reflection coefficient, attenuation coefficient, echo waveform distortion and / or scattered signal distribution.

3. The method according to claim 1, characterized in that The step of calculating the membership of the current value combination with the safety fuzzy set and the danger fuzzy set and judging whether it is a high temperature danger includes: Determine whether the current value combination falls within the range of the safe fuzzy set or the dangerous fuzzy set; If it falls within the range of the safety fuzzy set, the membership degree of the current value combination with the safety fuzzy set is determined to be 1, and the membership degree with the dangerous fuzzy set is 0, and it is judged to be low temperature safe; If it falls within the range of the dangerous fuzzy set, the membership degree of the current value combination with the safe fuzzy set is determined to be 0, and the membership degree with the dangerous fuzzy set is 1, and high temperature danger is determined; If it falls neither into the safe fuzzy set nor into the dangerous fuzzy set, then the degree of association between the current value combination and the safe fuzzy set and the dangerous fuzzy set is calculated as the membership degree; If the larger membership value corresponds to the safety fuzzy set, then it is judged to be low temperature safe; If the larger membership value corresponds to the dangerous fuzzy set, it is judged to be a high temperature danger.

4. The method according to claim 3, characterized in that The step of calculating and obtaining the degree of association between the current value combination and the safety fuzzy set and the dangerous fuzzy set as the degree of membership includes: Calculate and obtain representative joint numerical combinations in the safety fuzzy set and the dangerous fuzzy set, which are respectively recorded as representative safety joint numerical combinations and representative dangerous joint numerical combinations; Calculate and obtain the joint numerical combination in the safety fuzzy set and the danger fuzzy set that is closest to the current numerical combination, and use them as the adjacent safety joint numerical combination and the adjacent danger joint numerical combination respectively; The cumulative value of the item-by-item subtraction between the security joint numerical combination and the adjacent security joint numerical combination is used as the security range scale of the security fuzzy set; The cumulative value of the item-by-item subtraction between the risk joint numerical combination and the adjacent risk joint numerical combination is used as the risk range scale of the risk fuzzy set; The ratio of the accumulated value after subtracting the current value combination from the representative safety joint value combination item by item and the safety range scale is used as the membership degree of the current value combination to the safety fuzzy set; The ratio of the accumulated value after subtracting the current numerical combination from the representative risk joint numerical combination item by item and the risk range scale is used as the membership degree of the current numerical combination to the risk fuzzy set.

5. The method according to claim 4, characterized in that The step of calculating and obtaining a representative joint numerical combination of the safety fuzzy set and the dangerous fuzzy set, include, A plurality of joint numerical value combinations are arbitrarily selected from the safety fuzzy set and the danger fuzzy set as representative safety joint numerical value combinations and representative danger joint numerical value combinations respectively; Verify whether the selected representative safety joint value combination and the representative risk joint value combination are sufficiently representative; If not, reselect the combination of joint values representing safety and the combination of joint values representing danger; If so, a combination of joint values representing safety and a combination of joint values representing danger are selected.

6. The method according to claim 5, characterized in that The step of verifying whether the selected representative safety joint numerical combination and the representative risk joint numerical combination are sufficiently representative includes: Among all the joint value combinations included in the safety fuzzy set and the danger fuzzy set, calculate the cumulative value of each joint value combination representing safety and each joint value combination representing danger, after subtracting each joint value combination other than them item by item; Each joint numerical combination other than the joint numerical combination representing safety and the joint numerical combination representing danger is divided into the same fuzzy subset along with the joint numerical combination representing safety or the joint numerical combination representing danger with the smallest cumulative value after item-by-item subtraction; Determine whether the joint numerical combinations from the safe fuzzy set and the dangerous fuzzy set appear mixedly in the same fuzzy subset; If so, it is deemed not to be sufficiently representative; If not, it is deemed to be sufficiently representative.

7. The method according to claim 5, characterized in that The step of reselecting a safety-representing joint numerical value combination and a risk-representing joint numerical value combination, include, In each fuzzy subset, the item-by-item mean of all joint value combinations contained therein is calculated and obtained as the average joint value combination of the fuzzy subset; The joint numerical value combination with the smallest cumulative value after item-by-item subtraction from the corresponding average joint numerical value combination in each fuzzy subset is used as the joint numerical value combination to be reselected; If the reselected joint numerical combination comes from the secure fuzzy set, it is taken as the representative secure joint numerical combination; If the reselected joint numerical combination comes from the dangerous fuzzy set, it is taken as the representative dangerous joint numerical combination.

8. The method according to claim 5, characterized in that The step of selecting a combination of numerical values representing safety and a combination of numerical values representing danger includes: Among the multiple representative safety joint numerical value combinations and representative danger joint numerical value combinations, the one representing safety joint numerical value combination and the one representing danger joint numerical value combination having the smallest cumulative value after item-by-item subtraction from the current numerical value combination are retained respectively.

9. The method according to claim 4, characterized in that The step of calculating and obtaining the joint numerical combination closest to the current numerical combination in the safe fuzzy set and the dangerous fuzzy set includes: Calculate the cumulative value of each joint numerical combination in the safe fuzzy set and the current numerical combination after subtracting them one by one, and take the joint numerical combination with the smallest cumulative value as the joint numerical combination in the safe fuzzy set that is closest to the current numerical combination; The cumulative value of each joint numerical combination in the dangerous fuzzy set and the current numerical combination after subtraction is calculated respectively, and the joint numerical combination corresponding to the smallest cumulative value is taken as the joint numerical combination in the dangerous fuzzy set that is closest to the current numerical combination.

10. A heating safety control system based on a fuzzy control model, characterized in that: include, case; Fever body; Fan; The shell contains a heating element, and the heat stored in the heating element is sent out to the outside in the form of hot air through the fan for heating; The shell surface is also equipped with an ultrasonic transducer, which emits a fixed waveform detection signal wave and receives the reflected signal waves from different obstacles at different distances and angles. A fuzzy control model is also provided, storing the safety fuzzy set and the danger fuzzy set in the heating safety control method based on the fuzzy control model according to any one of claims 1 to 9; During operation, the current temperature of the heating element, the fan gear position, and the parameter values of each type of waveform characteristics of the reflected signal wave are obtained and recorded as the current value combination; Determine whether there is a high temperature danger according to the steps in the heating safety control method based on the fuzzy control model according to any one of claims 1 to 9; If so, reduce the fan gear; If not, no safety intervention operation will be performed on the fan.

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