Range hood outlet wind pressure prediction method, system, storage medium and processing terminal

Abstract

The present invention belongs to the technical field of intelligent control of range hoods, and discloses a range hood outlet wind pressure prediction method, system, storage medium and processing terminal. The method uses an experimental method to obtain different reference speeds and corresponding reference currents, wind pressures and proportional factors of the range hood under standard working conditions; collects data of the current period through a sensor or a control panel, calculates the speed relative deviation and current relative deviation; substitutes them into a fuzzy calculation model to obtain the wind pressure deviation rate; calculates the public flue wind pressure value according to the wind pressure deviation rate, proportional factor and reference wind pressure value; substitutes the calculated public flue wind pressure into the control panel, and the control panel controls the motor speed in real time according to the public flue pressure value. The present invention uses the predicted public flue wind pressure value as a parameter to adjust the speed of the fan in real time, reduces the useless power and noise of the range hood in use while maintaining the air volume, and improves the energy utilization efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent control of range hoods, and in particular relates to a method, system, storage medium and processing terminal for predicting outlet wind pressure of a range hood. Background Art

[0002] At present, the structure of the public flue system of modern high-rise residential buildings has been finalized after construction, but the range hoods used by residents on each floor are different, resulting in different exhaust flow and flow rate for each floor. Therefore, the pressure value in the public flue varies with the different usage of each user. When the wind pressure provided by the range hood used by the resident is not enough to counter the external duct pressure, the problem of oil fume backflow will occur. When the wind pressure provided by the range hood used by the resident far exceeds the external duct pressure, it will not only affect other residents, but also cause an increase in useless power during use.

[0003] Through the above analysis, the problems and defects of the prior art are as follows:

[0004] (1) The prior art does not use the public flue wind pressure value as the control panel feedback value to perform intelligent speed control, and cannot solve the problem of public flue smoke filling and reduce useless power.

[0005] (2) The existing technology cannot reduce the useless power noise of the range hood during use, and the energy utilization efficiency is low. Summary of the invention

[0006] In view of the problems existing in the prior art, the present invention provides a range hood outlet wind pressure prediction method, system, storage medium and processing terminal, and specifically relates to a range hood fan intelligent prediction method based on fuzzy theory.

[0007] The purpose of the present invention is to provide a method for predicting the common flue wind pressure value through current and speed parameters according to fuzzy theory, and realizing the prediction of the range hood outlet wind pressure by taking the common flue wind pressure value as the control quantity.

[0008] The present invention is implemented as follows: a method for predicting the outlet wind pressure of a range hood includes: predicting the common flue wind pressure value through current and speed parameters according to fuzzy theory, and performing intelligent control of the range hood fan based on the common flue wind pressure value as a control quantity.

[0009] Furthermore, the range hood outlet wind pressure prediction method specifically comprises the following steps:

[0010] The experimental method is used to obtain different reference speeds of the range hood under standard working conditions and the corresponding reference current and wind pressure, and the proportional factor K u ;

[0011] Collect data of the current period through sensors or control panels, and calculate the relative deviation of speed and current;

[0012] Substitute into the fuzzy calculation model to obtain the wind pressure deviation rate u p ;

[0013] Based on wind pressure deviation rate u p , scaling factor K u The wind pressure value of the public flue is calculated by the reference wind pressure value;

[0014] The calculated public flue air pressure is substituted into the control panel, and the motor speed is controlled by the control panel in real time according to the public flue pressure value.

[0015] Furthermore, the experimental method first measures the speed, current and wind pressure values ​​of the fan multiple times under standard operating conditions, removes the maximum and minimum values, and then performs average processing on the speed, current and wind pressure values ​​to obtain the reference speed and corresponding reference current and reference wind pressure values ​​under the operating conditions.

[0016] Furthermore, the speed relative deviation and current relative deviation are calculated as follows: the data collected by the sensor or FOC control board are averaged to obtain the average speed and average current, then the speed relative deviation = (average speed - reference speed) / reference speed, and the current relative deviation = (average current - reference current) / reference current.

[0017] Furthermore, the fuzzy calculation model is a two-dimensional fuzzy calculation model with the speed relative deviation and the current relative deviation as inputs and the wind pressure deviation rate as output;

[0018] The fuzzy calculation model includes: the relative deviation of the rotation speed e n , current relative deviation e i And wind pressure deviation rate u p The selected basic domain is set to [-1, 1], the fuzzy variable domain of the input variable is [-3, 3], and the fuzzy variable domain of the output variable is [-5, 5]; the quantization factor Ke n and Ke i are all 3, and the initial scale factor K u is set to 0.2; the fuzzy subsets corresponding to the fuzzy domains of input variables and output variables are {NB, NM, NS, ZR, PS, PM, PB}, where NB represents large negative, NM represents medium negative, NS represents small negative, ZR represents zero, PS represents small positive, PM represents medium positive, and PB represents large positive; and triangular membership functions are used for all of them; the fuzzy model conditions are in the form of if input1 and input2 then output; the Mamdani fuzzy algorithm is used, and the fuzzy control rules are formulated and the fuzzy control rule table is established based on the Delphi method; the centroid method is used for defuzzification operations.

[0019] Further, the method for determining the proportional factor is: obtaining two sets of reference speeds and corresponding reference currents and reference wind pressures, calculating the relative deviations of the speed, current and wind pressure, inputting the relative deviations of the speed and current into the fuzzy model, performing fuzzy calculations, obtaining the initial wind pressure deviation rate, calculating the error rate between the wind pressure deviation rate and the above-mentioned wind pressure relative deviation, and judging whether the error rate is less than the allowable value; if so, the proportional factor does not need to be changed; if not, introducing the adjustment coefficient K t , determine the new scaling factor K u '=K t *K u ;

[0020] The error rate is calculated as follows: error rate = (proportional factor*wind pressure deviation rate-wind pressure relative deviation) / wind pressure relative deviation.

[0021] The adjustment factor K t The calculation method is: adjustment coefficient = relative deviation of wind pressure / (wind pressure deviation rate*proportional factor).

[0022] Further, according to the output variable wind pressure deviation rate u p , scaling factor K u The public flue wind pressure value is determined together with the built-in reference wind pressure. The calculation method is: output wind pressure = (1 + K u *u p )*Reference wind pressure value.

[0023] Another object of the present invention is to provide a range hood outlet wind pressure prediction system comprising:

[0024] The reference current, wind pressure and proportional factor acquisition module is used to obtain different reference speeds of the range hood under standard working conditions and the corresponding reference current, wind pressure and proportional factor K by experimental method. u ;

[0025] The speed and current relative deviation acquisition module is used to collect the current period data through the sensor or FOC control board, and calculate the speed relative deviation and current relative deviation;

[0026] The wind pressure deviation rate acquisition module is used to substitute the fuzzy calculation model, and obtain the wind pressure deviation rate u after fuzzification, fuzzy reasoning and defuzzification. p ;

[0027] The public flue wind pressure value acquisition module is used to obtain the wind pressure deviation rate u p , scaling factor K u The public flue wind pressure value can be calculated based on the reference wind pressure value;

[0028] The control panel is used to substitute the calculated public flue wind pressure and control the motor speed in real time according to the public flue pressure value.

[0029] Another object of the present invention is to provide a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the following steps: predicting the common flue wind pressure value through current and speed parameters according to fuzzy theory, and performing intelligent control of the range hood fan based on the common flue wind pressure value as the control quantity.

[0030] Another object of the present invention is to provide an information data processing terminal, which includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the following steps: predicting the common flue wind pressure value through current and speed parameters according to fuzzy theory, and performing intelligent control of the range hood fan based on the common flue wind pressure value as the control quantity.

[0031] In combination with the above technical solutions and the technical problems solved, please analyze the advantages and positive effects of the technical solutions to be protected by the present invention from the following aspects:

[0032] First, in view of the technical problems existing in the above-mentioned prior art and the difficulty of solving the problems, the technical solutions to be protected by the present invention and the results and data during the research and development process are closely combined to analyze in detail and deeply how the technical solutions of the present invention solve the technical problems, and some creative technical effects brought about after solving the problems. The specific description is as follows:

[0033] The present invention proposes a method of predicting the wind pressure value of a public flue according to fuzzy theory, and using it as the feedback of a control panel to realize intelligent speed control to solve the problem of public flue filling with smoke and reducing useless power.

[0034] Second, considering the technical solution as a whole or from the perspective of the product, the technical effects and advantages of the technical solution to be protected by the present invention are described in detail as follows:

[0035] The method of the present invention can predict the public flue wind pressure value according to the rotation speed and current value, without adding a sensor at the fan outlet, thereby reducing investment costs. The predicted public flue wind pressure value is used as a parameter to adjust the fan speed in real time, thereby reducing the useless power and noise of the range hood during use while maintaining the air volume, thereby improving energy utilization efficiency.

[0036] Third, as auxiliary evidence of the inventiveness of the claims of the present invention, it is also reflected in the following important aspects:

[0037] The expected benefits and commercial value of the technical solution of the present invention after transformation are as follows: as a new function of the range hood, the fan output air pressure is adjusted in real time while the air volume remains unchanged. When the fan output air pressure is insufficient, the fan speed is increased to improve the oil fume removal performance. When the fan output air pressure is too high, since such a high air pressure and speed are not required during use, the equipment power is reduced by reducing the fan speed to reduce energy consumption.

[0038] The technical solution of the present invention fills the technical gap in the industry at home and abroad: the prior art "Automatic duct pressurization control method for range hoods" places a sensor in the air duct to detect the air pressure value of the air duct. When the air pressure value reaches a preset value, the boost cruise module is started and the air volume is increased through the range hood control module.

[0039] The prior art "A range hood cruising boosting method and range hood" is a method of boosting the range hood by detecting the range hood current and range hood gear position to determine the congestion of the flue.

[0040] There is no technical solution that uses the predicted wind pressure value obtained after fuzzy calculation of the speed and current as the feedback of the control board to adjust the fan speed to adjust the output wind pressure. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a flow chart of a method for predicting the air pressure at the outlet of a range hood provided by an embodiment of the present invention;

[0042] Figure 2 is a schematic diagram of a range hood outlet wind pressure prediction system provided by an embodiment of the present invention;

[0043] Figure 3 is a schematic diagram of a range hood outlet wind pressure prediction method provided by an embodiment of the present invention;

[0044] Figure 4 is a flow chart of adjusting the scale factor provided by an embodiment of the present invention;

[0045] In the figure: 1. Reference current, wind pressure and proportional factor acquisition module; 2. Speed ​​and current relative deviation acquisition module; 3. Wind pressure deviation rate acquisition module; 4. Public flue wind pressure value acquisition module; 5. Control panel. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0047] 1. Explanatory Examples In order to enable those skilled in the art to fully understand how to implement the present invention, this section provides an illustrative example that expands and describes the technical solution of the claims.

[0048] like Figure 1 As shown, the present invention provides a method for predicting the air pressure at the outlet of a range hood, comprising the following steps:

[0049] S101, using the experimental method to obtain different reference speeds of the range hood under standard working conditions and the corresponding reference current and wind pressure, and proportional factor K u The experimental method first measures the speed, current and wind pressure of the fan multiple times under standard working conditions, removes the maximum and minimum values, and performs average processing on the speed, current and wind pressure values ​​to obtain the reference speed and the corresponding reference current and reference wind pressure values ​​under the working conditions.

[0050] S102, during the user's use, the current time period data is collected through sensors or FOC control boards, and the speed relative deviation and current relative deviation are calculated.

[0051] S103, substitute the fuzzy calculation model, and obtain the wind pressure deviation rate u after fuzzification, fuzzy reasoning and defuzzification. p .

[0052] S104, based on the wind pressure deviation rate u p , scaling factor K u The public flue wind pressure value can be calculated based on the reference wind pressure value.

[0053] S105, the calculated common flue air pressure is substituted into the FOC control board, and the FOC control board controls the motor speed in real time to achieve the purpose of increasing or decreasing the motor speed according to the common flue pressure value.

[0054] In a preferred embodiment of the present invention, the two sets of reference speeds and the corresponding reference currents and reference wind pressures and the ultimately determined proportional factors in step S101 are two different sets of data measured under the same operating conditions for the same model of range hood, and are preset in the control panel after the range hood is debugged.

[0055] In a preferred embodiment of the present invention, the fuzzy calculation model of step S103 is as follows: the relative deviation of the rotation speed e n , current relative deviation e i And wind pressure deviation rate u p The selected basic domain is set to [-1, 1], the fuzzy variable domain of the input variable is [-3, 3], and the fuzzy variable domain of the output variable is [-5, 5]; the quantization factor Ke n and Ke i are all 3, and the initial scale factor K uSet to 0.2; the fuzzy subsets corresponding to the fuzzy domains of input variables and output variables are {NB, NM, NS, ZR, PS, PM, PB}, where NB means negative large, NM means negative medium, NS means negative small, ZR means zero, PS means positive small, PM means positive medium, and PB means positive large; and all use triangular membership functions. The fuzzy model conditions are in the form of if input1 and input2 then output. The Mamdani fuzzy algorithm is used, and the fuzzy control rules are formulated and the fuzzy control rule table is established based on the Delphi method; the centroid method is used for defuzzification operations.

[0056] According to the output variable wind pressure deviation rate u p , scaling factor K u The public flue wind pressure value is determined together with the built-in reference wind pressure. The calculation method is: output wind pressure = (1 + K u *u p )*Reference wind pressure value.

[0057] In a preferred embodiment of the present invention, in the fuzzy calculation model described in step S103: the proportional factor in the fuzzy calculation needs to be corrected during the experimental stage. The method for determining the proportional factor is: obtain two sets of reference speeds and corresponding reference currents and reference wind pressures, calculate the relative deviations of the speed, current and wind pressure, input the relative deviations of the speed and current into the fuzzy model, perform fuzzy calculations, obtain the initial wind pressure deviation rate, calculate the error rate between the wind pressure deviation rate and the above-mentioned wind pressure relative deviation, and determine whether the error rate is less than the allowable value; if "yes", the proportional factor does not need to be changed; if "no", introduce the adjustment coefficient K t , determine the new scaling factor K u '=K t *K u .

[0058] The error rate is calculated as follows: error rate = (proportional factor*wind pressure deviation rate-wind pressure relative deviation) / wind pressure relative deviation.

[0059] The adjustment factor K t The calculation method is: adjustment coefficient = relative deviation of wind pressure / (wind pressure deviation rate*proportional factor).

[0060] like Figure 2 As shown, the present invention also provides a range hood outlet wind pressure prediction system comprising:

[0061] Reference current and wind pressure, proportional factor acquisition module 1, used to obtain different reference speeds of the range hood under standard working conditions and the corresponding reference current and wind pressure, proportional factor K by experimental method u .

[0062] The speed and current relative deviation acquisition module 2 is used to collect data of the current period through sensors or FOC control boards, and calculate the speed relative deviation and current relative deviation.

[0063] The wind pressure deviation rate acquisition module 3 is used to substitute the fuzzy calculation model, and obtain the wind pressure deviation rate u after fuzzification, fuzzy reasoning and defuzzification. p .

[0064] The public flue wind pressure value acquisition module 4 is used to obtain the wind pressure deviation rate u p , scaling factor K u The public flue wind pressure value can be calculated based on the reference wind pressure value.

[0065] The control panel 5 is used to substitute the calculated public flue wind pressure and control the motor speed in real time, so as to increase or decrease the motor speed according to the public flue pressure value.

[0066] The technical solution of the present invention is further described below by taking a fan outlet air pressure control method based on fuzzy theory as an example.

[0067] like Figure 3 As shown, the present invention provides a method for controlling the fan outlet air pressure based on fuzzy theory, the steps are:

[0068] Step 1: Use the experimental method to obtain 10 different reference speeds and corresponding reference currents, wind pressures, and proportional factors K of the range hood under standard working conditions u ; For each set of data, first measure the speed, current and wind pressure of the fan multiple times under standard working conditions, remove the maximum and minimum values ​​and perform average processing on the speed, current and wind pressure values. In this way, all reference speeds and corresponding reference current and reference wind pressure values ​​under the working conditions are obtained; the reference values ​​are preset in the range hood before the range hood leaves the factory; for range hoods of different models and different air outlet structures, the reference values ​​are also different;

[0069] Step 2: Collect speed and current data through sensors or FOC control board and perform average processing to output the average speed and average current of the period;

[0070] Step 3: Calculate the deviation between the average speed and the reference speed, and divide the deviation by the reference speed to obtain the speed relative deviation en. Similarly, the current relative deviation e can be obtained. i ;

[0071] Step 4: Execute fuzzy calculation. The fuzzy calculation process is as follows: relative speed deviation e n and the current relative deviation e i As the input of the fuzzy model, after fuzzification, fuzzy reasoning and defuzzification, the wind pressure deviation rate u is output. p ; The relative speed deviation en , current relative deviation e i And wind pressure deviation rate u p The selected basic domain is set to [-1, 1], the fuzzy variable domain of the input variable is [-3, 3], and the fuzzy variable domain of the output variable is [-5, 5]; the quantization factor Ke n and Ke i are all 3, and the initial scale factor K u Set to 0.2; the fuzzy subsets corresponding to the fuzzy domains of input variables and output variables are {NB, NM, NS, ZR, PS, PM, PB}, where NB means negative large, NM means negative medium, NS means negative small, ZR means zero, PS means positive small, PM means positive medium, and PB means positive large; and all use triangular membership functions. The fuzzy model conditions are in the form of if input1 and input2then output. The Mamdani fuzzy algorithm is used, and the fuzzy control rules are formulated according to the Delphi method and the fuzzy control rule table is established, as shown in Table 1; the centroid method is used for defuzzification.

[0072] Table 1 Fuzzy control rules table

[0073]

[0074]

[0075] Step 5: According to the output variable wind pressure deviation rate u p The wind pressure value of the public duct is determined together with the built-in reference wind pressure. The calculation method is: output wind pressure = (1 + K u *u p )*Reference wind pressure value.

[0076] Step 6: Substitute the public air duct air pressure value into the control board, and the control board will make corresponding decisions on the speed parameters.

[0077] In a preferred embodiment of the present invention, Figure 3 As shown, it also includes:

[0078] 1) Experimental method to determine the reference value, adjust and determine the fuzzy model proportional factor K u , these three values ​​are preset in the control panel after the experiment is completed

[0079] 2) Sample the speed and current data within a certain period of time, and perform mean processing to obtain the average speed and average current respectively.

[0080] 3) Calculate the relative deviation: speed relative deviation = (average speed - reference value where the value is less than the average speed) / reference value. The same is true for current relative deviation.

[0081] 4) Take the two relative deviations as the two inputs of the fuzzy model n and e i , multiplied by the quantization factor respectively, and fuzzy calculation is performed to obtain the wind pressure deviation rate u.

[0082] 5) Calculate the predicted wind pressure value = (1 + u*K u ) / reference wind pressure mentioned above.

[0083] 6) Input the predicted value into the control board as a feedback quantity of the control board, and the adjusted speed is used as the output of the control board.

[0084] Because the usage scenarios of each range hood are different, a fixed proportional factor is meaningless and needs to be adjusted. The process of determining the proportional factor by experimental method is basically the same as above, except that two sets of reference values ​​are used for calculation input. At this time, an expected wind pressure change rate can be obtained, which is called the relative deviation of wind pressure (corresponding to the actual situation). After substituting the relative deviation calculated from the two sets of reference values ​​into the model calculation, a wind pressure change rate can also be obtained (corresponding to the predicted situation). There are errors between reality and prediction, so the error must be calculated (and the industrial industry generally requires an allowable range for this error, as long as the error is not too large, it can be recognized). If the error is too large, a coefficient is introduced for adjustment.

[0085] like Figure 4 As shown in the figure, the proportional factor in the fuzzy calculation is corrected in the experimental method. The method for determining the proportional factor is: obtain two sets of reference speeds and corresponding reference currents and reference wind pressures, calculate the relative deviations of speed, current and wind pressure, input the relative deviations of speed and current into the fuzzy model, perform fuzzy calculations, and obtain the initial wind pressure deviation rate u p , calculate the wind pressure deviation rate u p Relative deviation from the above wind pressure u f The error rate is determined to determine whether the error rate is less than the allowable value; if "yes", the proportional factor does not need to be changed; if "no", the adjustment coefficient K is introduced t = relative wind pressure deviation / (wind pressure deviation rate*proportional factor), determine the new proportional factor K u '=K t *K u .

[0086] In a preferred embodiment of the present invention, there are two sets of reference data: n1 = 600 r / min, i1 = 1A, P1 = 500 Pa, n2 = 800 r / min, i2 = 1.2A, P2 = 650 Pa, then e can be calculated. nf =0.33, e if =0.2,u f =0.3, e nf and eif Multiply them with the quantization factor respectively and substitute them into the calculation model to output u p =1.36, calculated error rate = (1.36*0.2-0.3) / 0.3 = 9.33%, which is less than or equal to the allowable value of 10%, meeting the requirement, and the proportional factor is 0.2. During the user's use, a set of data n = 700r / min, i = 1.1A is collected, and the relative deviation e is calculated based on the reference data n1 and i1 n =0.17, e i = 0.1, e n and e i Multiply them with the quantization factor respectively and substitute them into the calculation model to output u p =0.893, then the output wind pressure value = (1+0.2*0.893)*600 = 707.2 Pa. Substitute it into the control board, which compares the built-in list data and makes decisions on the increase or decrease of the fan speed.

[0087] 2. Evidence of the effects of the embodiments The embodiments of the present invention have achieved some positive effects during the development or use process, and indeed have great advantages over the prior art. The following content is described in conjunction with the data, charts, etc. of the test process.

[0088] The data is obtained from: fuzzy calculation code based on matlab. The code is input into matlab software and run. The pop-up window includes: fuzzy calculation model diagram, speed relative deviation membership function diagram, current relative deviation membership function diagram, wind pressure deviation rate membership function diagram, fuzzy calculation window, etc.

[0089] The experimental method includes: first check whether the input, output, calculation method and number of rules in the model diagram are correct, and then check whether the membership function of the input and output is the same as expected. If it is wrong, return to modify the corresponding module content in the code. If it is correct, you can enter the parameter value in the fuzzy calculation window. (Although the type of membership function is specified, the coordinates of the inflection point of the function graph can still be changed, and the formulated control rule table can also be changed).

[0090] According to the embodiment, e is calculated nf =0.33, e if =0.2, multiply by the quantization factor, and get e nf *3=0.99, e if *3=0.6, enter 0.99 and 0.6 in the corresponding positions in the input, and calculate. You can see that the u value = 1.36 in the upper position. The u value is the output result.

[0091] Calculate the error rate and determine whether the proportional factor needs to be adjusted. According to the embodiment, the proportional factor does not need to be adjusted. At this time, the current and speed values ​​obtained by the detection can be substituted to obtain their respective relative deviation values. According to the embodiment, e n =0.17, e i =0.1 multiplied by the proportional factor and input, we can get u=0.893, and calculate the wind pressure value.

[0092] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0093] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A method for predicting the air pressure at the outlet of a range hood, characterized in that: The range hood outlet wind pressure prediction method comprises: predicting the common flue wind pressure value through current and speed parameters according to fuzzy theory, and performing intelligent control of the range hood fan according to the common flue wind pressure value as a control quantity; The range hood outlet wind pressure prediction method specifically comprises the following steps: The experimental method is used to obtain different reference speeds of the range hood under standard working conditions and the corresponding reference current and wind pressure, and the proportional factor K u ; Collect data of the current period through sensors or control panels, and calculate the relative deviation of speed and current; Substitute into the fuzzy calculation model to obtain the wind pressure deviation rate u p ; Based on wind pressure deviation rate u p , scaling factor K u The wind pressure value of the public flue is calculated by the reference wind pressure value; Substitute the calculated public flue wind pressure into the control panel, and the control panel will control the motor speed in real time according to the public flue pressure value; The speed relative deviation and current relative deviation are calculated as follows: the data collected by the sensor or FOC control board are averaged to obtain the average speed and average current, then the speed relative deviation = (average speed - reference speed) / reference speed, and the current relative deviation = (average current - reference current) / reference current.

2. The method for predicting the air pressure at the range hood outlet according to claim 1, characterized in that: Firstly, the speed, current and wind pressure of the fan are measured multiple times under standard working conditions. After eliminating the maximum and minimum values, the speed, current and wind pressure values ​​are averaged to obtain the reference speed and the corresponding reference current and reference wind pressure values ​​under the working conditions.

3. The method for predicting the air pressure at the range hood outlet according to claim 1, characterized in that: The fuzzy calculation model is a two-dimensional fuzzy calculation model with the relative deviation of rotation speed and the relative deviation of current as input and the wind pressure deviation rate as output; The fuzzy calculation model includes: the relative deviation of the rotation speed e n , current relative deviation e i And wind pressure deviation rate u p The selected basic domain is set to [-1, 1], the fuzzy variable domain of the input variable is [-3, 3], and the fuzzy variable domain of the output variable is [-5, 5]; the quantization factor Ke n and Ke i are all 3, and the initial scale factor K u is set to 0.2; the fuzzy subsets corresponding to the fuzzy domains of input variables and output variables are {NB, NM, NS, ZR, PS, PM, PB}, where NB represents large negative, NM represents medium negative, NS represents small negative, ZR represents zero, PS represents small positive, PM represents medium positive, and PB represents large positive; and triangular membership functions are used for all of them; the fuzzy model conditions are in the form of if input1 and input2 then output; the Mamdani fuzzy algorithm is used, and the fuzzy control rules are formulated and the fuzzy control rule table is established based on the Delphi method; the centroid method is used for defuzzification operations.

4. The method for predicting the air pressure at the range hood outlet according to claim 3, characterized in that: The method for determining the proportional factor is as follows: obtaining two sets of reference speeds and corresponding reference currents and reference wind pressures, calculating the relative deviations of the speed, current and wind pressure, inputting the relative deviations of the speed and current into the fuzzy model, performing fuzzy calculations, obtaining the initial wind pressure deviation rate, calculating the error rate between the wind pressure deviation rate and the above-mentioned wind pressure relative deviation, and judging whether the error rate is less than the allowable value; if so, the proportional factor does not need to be changed; if not, introducing the adjustment coefficient K t , determine the new scaling factor K u '=K t *K u ; The error rate is calculated as follows: error rate = (proportional factor * wind pressure deviation rate - wind pressure relative deviation) / wind pressure relative deviation; The adjustment factor K t The calculation method is: adjustment coefficient = relative deviation of wind pressure / (wind pressure deviation rate*proportional factor).

5. The method for predicting the air pressure at the range hood outlet according to claim 1, characterized in that: According to the output variable wind pressure deviation rate u p , scaling factor K u The public flue wind pressure value is determined together with the built-in reference wind pressure. The calculation method is: output wind pressure = (1 + K u *u p )*Reference wind pressure value.

6. A system for predicting the outlet wind pressure of a range hood implementing the method for predicting the outlet wind pressure of a range hood according to any one of claims 1 to 5, characterized in that: The range hood outlet wind pressure prediction system comprises: The reference current, wind pressure and proportional factor acquisition module is used to obtain different reference speeds of the range hood under standard working conditions and the corresponding reference current, wind pressure and proportional factor K by experimental method. u ; The speed and current relative deviation acquisition module is used to collect the current period data through the sensor or FOC control board, and calculate the speed relative deviation and current relative deviation; The wind pressure deviation rate acquisition module is used to substitute the fuzzy calculation model, and obtain the wind pressure deviation rate u after fuzzification, fuzzy reasoning and defuzzification. p ; The public flue wind pressure value acquisition module is used to obtain the wind pressure deviation rate u p , scaling factor K u The public flue wind pressure value can be calculated based on the reference wind pressure value; The control panel is used to substitute the calculated public flue wind pressure and control the motor speed in real time according to the public flue pressure value.

7. A computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the following steps: predicting the common flue wind pressure value through current and speed parameters according to fuzzy theory, and performing intelligent control of the range hood fan based on the common flue wind pressure value as the control quantity.

8. An information data processing terminal, characterized in that: The information data processing terminal includes a memory and a processor, wherein the memory stores a computer program. When the computer program is executed by the processor, the processor executes the following steps: predicting the common flue wind pressure value through current and speed parameters according to fuzzy theory, and performing intelligent control of the range hood fan based on the common flue wind pressure value as the control quantity.

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