A heating safety control method and system based on a fuzzy control model

Abstract

The application discloses a heating safety control method and system based on a fuzzy control model, and relates to the technical field of heating safety control. The application constructs a test environment combined by different heating body temperatures and fan gear positions; in each test environment, an obstacle is placed at different position points, a combination of parameter values of each kind of wave form feature of a plurality of heating body temperatures-fan gear positions-reflection signal waves corresponding to each numerical interval of a windward temperature is detected and recorded, and a safe fuzzy set and a dangerous fuzzy set are obtained by division; the heating body temperature, the fan gear position and the parameter value of each kind of wave form feature of the reflection signal wave at the current moment are acquired; the membership degrees of the current numerical combination to the safe fuzzy set and the dangerous fuzzy set are calculated, and it is judged whether it is high-temperature dangerous; if yes, the fan gear position is reduced; and if no, the fan is not subjected to a safety intervention operation. The application effectively avoids that a user is scalded by hot air.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of heating safety control, and particularly relates to a heating safety control method and system based on a fuzzy control model. BACKGROUND

[0002] With the popularization and intelligent development of modern building heating systems, the safety and energy efficiency control of the heating system become key problems. The traditional heating system control method mainly depends on preset heating power thresholds, which can meet basic requirements to a certain extent, but has the problem of insufficient control accuracy in dealing with complex working conditions, especially user behavior uncertainty, which may cause safety hazards. SUMMARY

[0003] The purpose of the present application 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 a fuzzy control model, effectively avoiding the user from being scalded by hot air.

[0004] To solve the above technical problems, the present application is realized by the following technical scheme:

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

[0006] Constructing a test environment combined by different heating body temperatures and fan gears;

[0007] In each test environment, place obstacles at different position points, detect the windward temperature of the obstacle surface, record the combination of the parameter values of the several heating body temperatures, fan gears and each type of reflected signal wave waveform characteristics corresponding to each numerical interval of the windward temperature, denoted as joint numerical combination, divide the several joint numerical combinations corresponding to the numerical interval of the windward temperature less than the set safety temperature into a safety fuzzy set, and the rest of the joint numerical combinations into a dangerous fuzzy set;

[0008] Obtain the heating body temperature, fan gear and parameter value of each type of reflected signal wave waveform characteristic at the current time, denoted as current numerical combination;

[0009] Calculate the membership degrees of the current numerical combination to the safety fuzzy set and the dangerous fuzzy set respectively and determine whether it is high-temperature dangerous;

[0010] If yes, reduce the fan gear;

[0011] If not, do not perform safety intervention operation on the fan.

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

[0013] A shell;

[0014] a heating body;

[0015] a fan;

[0016] The heating body is contained in the shell, and the heat accumulated in the heating body is sent out in the form of hot air by the fan for heating;

[0017] The shell surface is further provided with an ultrasonic transducer for emitting a fixed waveform detection signal wave and receiving reflected signal waves in different distance and angle states of different obstacles;

[0018] A fuzzy control model is further provided, and a safe fuzzy set and a dangerous fuzzy set are stored in the fuzzy control model;

[0019] During operation, the heating body temperature, the fan gear and the parameter values of the waveform features of each type of reflected signal wave at the current time are obtained, and the current numerical combination is recorded;

[0020] The membership degrees of the current numerical combination to the safe fuzzy set and the dangerous fuzzy set are calculated, and whether it is high-temperature dangerous is judged;

[0021] If yes, the fan gear is reduced;

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

[0023] The safe fuzzy set and the dangerous fuzzy set are obtained by detecting and collecting data in different test environments, and the parameter values of the heating body temperature, the fan gear and the waveform features of each type of reflected signal wave are continuously collected during the use of the warmer, the membership degrees to the safe fuzzy set and the dangerous fuzzy set are calculated, the surface wind temperature of the obstacle (human body, pet, etc.) is estimated, and the fuzzy control is performed on the air volume regulator, so that the user is effectively prevented from being scalded by the hot air.

[0024] Of course, any product implementing the present application does not necessarily need to achieve all the advantages described above. BRIEF DESCRIPTION OF DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0026] Fig. 1 The functional units and information flow direction of the heating safety control system based on the fuzzy control model according to an embodiment of the present application are shown in the schematic diagram.

[0027] Fig. 2A step flow diagram of the step S4 in an embodiment of the present application is shown in FIG. 4.

[0028] Fig. 3 A step flow diagram of the step S4 in an embodiment of the present application is shown in FIG. 4.

[0029] Fig. 4 A step flow diagram of the step S44 in an embodiment of the present application is shown in FIG. 44.

[0030] Fig. 5 A step flow diagram of the step S441 in an embodiment of the present application is shown in FIG. 441.

[0031] Fig. 6 A step flow diagram of the step S442 in an embodiment of the present application is shown in FIG. 442.

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

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

[0034] In order to make the purpose, technical solutions and advantages of the present application clearer, the embodiments of the present application will be described in further detail below with reference to the drawings.

[0035] It should be noted that the terms "first", "second", and the like in the present application are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, 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. Rather, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0036] Referring to Figs. 1-2 As shown in the drawings, the present application provides a heating safety control system based on a fuzzy control model. In terms of mechanical structure, the heater in the system has a shell with high temperature resistance and sufficient mechanical strength, which includes a heating body. The heating body can be heated by an external heat source or by a heating pipe inside the shell during off-peak electricity pricing. The shell is also provided with a fan that blows the heat accumulated by the heating body in the form of convection. Since the temperature of the heating body can be as high as 600°C or higher, the temperature of the fan inlet is very high, which can easily cause burns and other risks.

[0037] In order to detect the obstacles near the air outlet of the heater, the surface of the shell is also provided with an ultrasonic transducer, which emits a fixed waveform detection signal wave, and receives the reflected signal wave under different distances and different angle states of different obstacles.

[0038] Referring to Fig. 2 As shown in the figure, the heater is built-in with a control chip, and the memory of the control chip stores a safe fuzzy set and a dangerous fuzzy set, which is the basis for fuzzy control during the use of the heater. These data can be obtained by prior testing. During the testing process, step S1 can be performed first to construct a test environment composed of different heating body temperatures and fan gear positions. For example, a series of heating body temperatures and fan gears are set in the working temperature range of the heating body and the working wind speed range of the fan, respectively, to form a plurality of heating body temperature-fan gear position combinations and serve as the heating body temperature and fan gear of the test environment.

[0039] Under each test environment, step S2 can be performed to place obstacles at different positions. The obstacles in this scheme can be any movable objects, such as human bodies, pets, furniture, etc. The windward temperature of the surface of the obstacle is detected, which is usually the highest temperature position on the surface of the obstacle. The detection of the windward temperature can usually be to obtain the temperature value by arranging temperature sensors on the surface of the obstacle, or to read the temperature data of the surface of the obstacle by infrared thermal imaging.

[0040] Record the combination of the parameter values of the several heating body temperature-fan gear-reflected signal wave of each kind of waveform feature corresponding to each numerical interval of the windward temperature, denoted as joint numerical combination. The several joint numerical combinations corresponding to the numerical interval of the windward temperature less than the set safe temperature are classified into the safe fuzzy set, and the remaining joint numerical combinations are classified into the dangerous fuzzy set.

[0041] The waveform feature types of the reflected signal wave can be any of the reflected time length, phase difference, cross-correlation peak position, Doppler frequency shift, echo entropy value, reflection coefficient, attenuation coefficient, echo waveform distortion, and scattering signal distribution. The reflected time length, phase difference, and cross-correlation peak position usually reflect the overall spatial position and shape of the obstacle, and the Doppler frequency shift usually reflects the moving state of the obstacle. The echo entropy value, reflection coefficient, and attenuation coefficient usually reflect the material and surface roughness state of the obstacle.

[0042] The control chip of the warmer stores a safe fuzzy set and a dangerous fuzzy set. In the running process, the fuzzy control model of the warmer can first acquire the parameter values of the heating body temperature, the fan gear and the waveform characteristics of each kind of reflected signal wave at the current time, denoted as the current numerical combination, in step S3. Then, it can determine whether it is high-temperature dangerous in step S4. If it is high-temperature dangerous, it can reduce the gear of the fan 2 through the air volume regulator 5 in step S5. If it is not dangerous, it can not perform the safety intervention operation on the fan in step S6. Of course, it also needs to perform the continuous safety monitoring in the process of performing step S6.

[0043] Please refer to Fig. 3 In the process of determining whether the current obstacle is high-temperature dangerous, it can first determine whether the current numerical combination falls within the range of the safe fuzzy set or the dangerous fuzzy set in step S41. If it falls within the range of the safe fuzzy set, it can then determine that the membership degree of the current numerical combination to the safe fuzzy set is 1 and the membership degree to the dangerous fuzzy set is 0, and determine that it is low-temperature safe in step S42. If it falls within the range of the dangerous fuzzy set, it can then determine that the membership degree of the current numerical combination to the safe fuzzy set is 0 and the membership degree to the dangerous fuzzy set is 1, and determine that it is high-temperature dangerous in step S43.

[0044] Please refer to Fig. 3 and 4 Since the number of tests in the test environment is limited in advance, the warmer is more likely to encounter the following situation in real work, that is, neither falls within the safe fuzzy set nor falls within the dangerous fuzzy set. Then, it can calculate the association degrees of the current numerical combination to the safe fuzzy set and the dangerous fuzzy set as the membership degrees in step S44. Specifically, it can first calculate the representative joint numerical combinations in the safe fuzzy set and the dangerous fuzzy set, denoted as the representative safe joint numerical combination and the representative dangerous joint numerical combination, in step S441.

[0045] Please refer to Fig. 5 Since there can be multiple representative joint numerical combinations, in order to select the one that matches the current numerical combination and has the representative safe joint numerical combination and the representative dangerous joint numerical combination, it can first select multiple joint numerical combinations in the safe fuzzy set and the dangerous fuzzy set as the representative safe joint numerical combination and the representative dangerous joint numerical combination in step S4411. Then, it can verify whether the selected representative safe joint numerical combination and the representative dangerous joint numerical combination are representative enough.

[0046] In the verification process, first, step S4412 can be performed to calculate the cumulative value after subtraction of each joint value combination from the representative safe joint value combination and the representative dangerous joint value combination in all joint value combinations contained in the safe fuzzy set and the dangerous fuzzy set. Next, step S4413 can be performed to divide each joint value combination other than the representative safe joint value combination and the representative dangerous joint value combination into the same fuzzy subset after the cumulative value after subtraction of each joint value combination from the representative safe joint value combination and the representative dangerous joint value combination is minimum. Next, step S4414 can be performed to determine whether the joint value combinations from the safe fuzzy set and the dangerous fuzzy set are mixed in the same fuzzy subset. If yes, it is determined that there is no sufficient representation, and if no, it is determined that there is sufficient representation.

[0047] In the verification process, if it is determined that there is no sufficient representation, the representative safe joint value combination and the representative dangerous joint value combination are reselected. First, step S4416 can be performed to calculate the average joint value combination of each fuzzy subset by calculating the average of all joint value combinations contained in the fuzzy subset. Next, step S4417 can be performed to select the joint value combination with the minimum cumulative value after subtraction of the average joint value combination in each fuzzy subset as the reselected joint value combination. If the reselected joint value combination is from the safe fuzzy set, it is the representative safe joint value combination. If the reselected joint value combination is from the dangerous fuzzy set, it is the representative dangerous joint value combination.

[0048] In the verification process, if it is determined that there is sufficient representation, one representative safe joint value combination and one representative dangerous joint value combination are selected and matched with the current joint value combination. Step S4415 can be performed to retain one representative safe joint value combination and one representative dangerous joint value combination with the minimum cumulative value after subtraction of the current joint value combination from the plurality of representative safe joint value combinations and the representative dangerous joint value combinations, respectively.

[0049] To supplement the implementation process of steps S4411 to S4417 described above, the source code of part of the function module is provided, and the corresponding explanation is given in the comment part. In order to avoid the leakage of data related to trade secrets, the data that does not affect the implementation of the scheme is desensitized, and the same applies here.

[0050]

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[0059] The code implements a complete fuzzy set representative combination selection function module. In the running process, a multi-stage processing flow is first performed to initially randomly select a representative combination. Then, fuzzy sub-sets are divided based on distance measurement, and the set purity is checked to determine the representation, and iteration optimization is performed until a qualified representative is obtained.

[0060] The above application uses a weighted distance measurement (temperature 40%, fan 30%, and all ultrasonic features 30%).

[0061] The above function module can effectively process the fuzzy set representative selection problem in heating safety control, and through the automatic iteration optimization process, it ensures that the obtained representative combination has sufficient discrimination and representation, providing a reliable basis for subsequent safety control decisions.

[0062] Please refer to Fig. 4 and 6 After selecting the representative safe joint numerical combination and the representative dangerous joint numerical combination, in order to calculate the membership of the current numerical combination and the closest fuzzy sub-set of danger and safety, the representative numerical combination of the fuzzy sub-set (representative safe joint numerical combination and representative dangerous joint numerical combination) and the closest numerical combination (adjacent safe joint numerical combination and adjacent dangerous joint numerical combination) need to be considered at the same time, which can better measure the association state of the current numerical combination and the fuzzy sub-set. Therefore, the next step S442 can be performed to calculate and obtain the joint numerical combination closest to the current numerical combination in the safe fuzzy set and the dangerous fuzzy set, respectively as the adjacent safe joint numerical combination and the adjacent dangerous joint numerical combination. Specifically, first, step S4421 can be performed to calculate the cumulative value of each joint numerical combination in the safe fuzzy set after subtracting the current numerical combination item by item, and the joint numerical combination corresponding to the minimum cumulative value is taken as the joint numerical combination closest to the current numerical combination in the safe fuzzy set. Then, step S4422 can be performed to calculate the cumulative value of each joint numerical combination in the dangerous fuzzy set after subtracting the current numerical combination item by item, and the joint numerical combination corresponding to the minimum cumulative value is taken as the joint numerical combination closest to the current numerical combination in the dangerous fuzzy set.

[0063] Please continue to refer to Fig. 4 As shown, in order to quantitatively calculate the association state of the current numerical combination and the fuzzy sub-set, next step S443 can be performed to take the accumulated value after item-by-item subtraction between the representative safe joint numerical combination and the adjacent safe joint numerical combination as the safe range scale of the safe fuzzy set. Next step S444 can be performed to take the accumulated value after item-by-item subtraction between the representative dangerous joint numerical combination and the adjacent dangerous joint numerical combination as the dangerous range scale of the dangerous fuzzy set. Next step S445 can be performed to take the ratio between the current numerical combination and the accumulated value after item-by-item subtraction between the representative safe joint numerical combination as the membership degree of the current numerical combination and the safe fuzzy set. Finally, step S446 can be performed to take the ratio between the current numerical combination and the accumulated value after item-by-item subtraction between the representative dangerous joint numerical combination as the membership degree of the current numerical combination and the dangerous fuzzy set.

[0064] Please continue to refer to Fig. 3 As shown, in the membership degrees of the current numerical combination and the safe fuzzy set and the dangerous fuzzy set respectively, if the larger value of the membership degree corresponds to the safe fuzzy set, next step S45 can be performed to judge the low-temperature safety. In the membership degrees of the current numerical combination and the safe fuzzy set and the dangerous fuzzy set respectively, if the larger value of the membership degree corresponds to the dangerous fuzzy set, next step S46 can be performed to judge the high-temperature danger.

[0065] The flowcharts and block diagrams in the drawings show the possible implementation architecture, function and operation of the apparatus, system, method and computer program product according to the embodiments of the present application. In this regard, each block in the flowchart or block diagram can represent a module, program segment or part of an instruction, which contains one or more executable instructions for implementing the specified logic function. In some alternative implementations, the functions noted in the blocks can also occur in different order from that noted in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and they can also be executed in reverse order, depending on the functions involved.

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

[0067] Although the application has been described in connection with various embodiments, it will be understood that the application is capable of further modifications. These and other changes, along with the apparent alternatives and equivalents, fall within the scope of the claimed application. The description herein is intended to be illustrative only and is presented to enable any person skilled in the art to make and use the application. Numerous modifications and adaptations will be apparent to those skilled in the art without departing from the scope of the described application. The scope of the described application is not to be limited by the specific illustrative embodiments contained herein but only by the scope of the appended claims, which follow this disclosure.

[0068] Various embodiments of the application have been described in connection with the embodiments described above. The description is intended to be illustrative only and not limiting of the application. Many modifications and variations of the described embodiments are possible in light of the above teachings. The terms used in the following claims should not be construed to limit the application to the precise formulations and implementations described herein and so that changes in form and detail inherent in the art can be made without departing from the scope of the claims. The scope of the claims should be construed to include all possible embodiments and their equivalents.

Claims

1. A heating safety control method based on a fuzzy control model, characterized by, comprising, constructing test environments combined by different heater temperatures and fan gears; placing obstacles at different position points under each test environment, detecting the windward temperature of the obstacle surface, recording the parameter values of each kind of wave form feature of the heater temperature-fan gear-reflective signal wave corresponding to each numerical interval of the windward temperature, denoted as joint numerical combination, dividing the joint numerical combinations corresponding to the numerical interval of the windward temperature less than the set safety temperature into the safety fuzzy set, and the rest into the danger fuzzy set; obtaining the heater temperature, fan gear and parameter values of each kind of wave form feature of the reflective signal wave at the current time, denoted as current numerical combination; judging whether the current numerical combination falls within the range of the safety fuzzy set or the danger fuzzy set; if falling within the range of the safety fuzzy set, then recognizing that the membership degree of the current numerical combination to the safety fuzzy set is 1 and to the danger fuzzy set is 0, and judging low temperature safety; if falling within the range of the danger fuzzy set, then recognizing that the membership degree of the current numerical combination to the safety fuzzy set is 0 and to the danger fuzzy set is 1, and judging high temperature danger; if neither falling within the safety fuzzy set nor the danger fuzzy set, then calculating and obtaining the representative joint numerical combination in the safety fuzzy set and the danger fuzzy set, respectively denoted as representative safety joint numerical combination and representative danger joint numerical combination; calculating and obtaining the joint numerical combination in the safety fuzzy set and the danger fuzzy set closest to the current numerical combination, respectively as adjacent safety joint numerical combination and adjacent danger joint numerical combination; taking the accumulated value of the subtraction of the representative safety joint numerical combination from the adjacent safety joint numerical combination as the safety range scale of the safety fuzzy set; taking the accumulated value of the subtraction of the representative danger joint numerical combination from the adjacent danger joint numerical combination as the danger range scale of the danger fuzzy set; taking the ratio of the accumulated value of the subtraction of the current numerical combination from the representative safety joint numerical combination to the safety range scale as the membership degree of the current numerical combination to the safety fuzzy set; taking the ratio of the accumulated value of the subtraction of the current numerical combination from the representative danger joint numerical combination to the danger range scale as the membership degree of the current numerical combination to the danger fuzzy set; if the larger membership degree corresponds to the safety fuzzy set, then judging low temperature safety; if the larger membership degree corresponds to the danger fuzzy set, then judging high temperature danger; if yes, then reducing the fan gear; if no, then not performing safety intervention operation on the fan.

2. The method of claim 1, wherein, The types of wave form features include reflective duration, phase difference, cross-correlation peak position, Doppler frequency shift, echo entropy value, reflection coefficient, attenuation coefficient, echo wave form distortion and / or scattering signal distribution.

3. The method of claim 1, wherein, The step of calculating and obtaining the representative joint numerical combination in the safety fuzzy set and the danger fuzzy set, comprising, respectively selecting multiple joint numerical combinations in the safety fuzzy set and the danger fuzzy set as the representative safety joint numerical combination and the representative danger joint numerical combination; verifying whether the selected representative safe joint numerical combination and the representative dangerous joint numerical combination have sufficient representativeness; if not, reselecting the representative safe joint numerical combination and the representative dangerous joint numerical combination; if yes, selecting one representative safe joint numerical combination and one representative dangerous joint numerical combination.

4. The method of claim 3, wherein, The step of verifying whether the selected representative safe joint numerical combination and the representative dangerous joint numerical combination have sufficient representativeness comprises: calculating the cumulative value after subtraction of each representative safe joint numerical combination and representative dangerous joint numerical combination from each joint numerical combination outside the safe fuzzy set and the dangerous fuzzy set; dividing each joint numerical combination outside the representative safe joint numerical combination and the representative dangerous joint numerical combination into the same fuzzy sub-set according to the minimum cumulative value after subtraction of each joint numerical combination from the representative safe joint numerical combination or the representative dangerous joint numerical combination; judging whether the joint numerical combinations from the safe fuzzy set and the dangerous fuzzy set are mixed in the same fuzzy sub-set; if yes, it is determined that the representative safe joint numerical combination and the representative dangerous joint numerical combination do not have sufficient representativeness; if not, it is determined that the representative safe joint numerical combination and the representative dangerous joint numerical combination have sufficient representativeness.

5. The method of claim 3, wherein, The step of reselecting the representative safe joint numerical combination and the representative dangerous joint numerical combination comprises: calculating the average joint numerical combination of each fuzzy sub-set by calculating the average value of all joint numerical combinations contained in the fuzzy sub-set; selecting the joint numerical combination with the minimum cumulative value after subtraction of the average joint numerical combination in each fuzzy sub-set as the reselected joint numerical combination; if the reselected joint numerical combination is from the safe fuzzy set, it is the representative safe joint numerical combination; if the reselected joint numerical combination is from the dangerous fuzzy set, it is the representative dangerous joint numerical combination. The step of selecting one representative safe joint numerical combination and one representative dangerous joint numerical combination comprises:

6. The method of claim 3, wherein, respectively retaining one representative safe joint numerical combination and one representative dangerous joint numerical combination with the minimum cumulative value after subtraction of the current numerical combination in the plurality of representative safe joint numerical combinations and representative dangerous joint numerical combinations. The step of calculating the joint numerical combination closest to the current numerical combination in the safe fuzzy set and the dangerous fuzzy set comprises:

7. The method of claim 1, wherein, respectively calculating the cumulative value after subtraction of the current numerical combination from each joint numerical combination in the safe fuzzy set, and selecting the joint numerical combination with the minimum cumulative value as the joint numerical combination closest to the current numerical combination in the safe fuzzy set; respectively calculating the cumulative value after subtraction of the current numerical combination from each joint numerical combination in the dangerous fuzzy set, and selecting the joint numerical combination with the minimum cumulative value as the joint numerical combination closest to the current numerical combination in the dangerous fuzzy set. comprise:

8. A heating safety control system based on a fuzzy control model, characterized in that, a shell; a heating body; a fan; the heating body is contained in the shell, and the heat accumulated in the heating body is sent out in the form of hot air by the fan; an ultrasonic transducer is further arranged on the surface of the shell to emit a fixed waveform detection signal wave and receive reflected signal waves in different distance and angle states of different obstacles; ​ The fuzzy control model is also provided, and the safe fuzzy set and the dangerous fuzzy set in the heating safety control method based on the fuzzy control model according to any one of claims 1 to 7 are stored; During operation, the current values of the heating body temperature, the fan gear and the parameter values of each type of waveform feature of the reflected signal wave are acquired, which are recorded as a current numerical combination; The step of judging whether it is high-temperature dangerous or not in the heating safety control method based on the fuzzy control model according to any one of claims 1 to 7 is performed; If yes, the fan gear is reduced; If no, the safety intervention operation is not performed on the fan.

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