A constant air volume control method for range hood

Abstract

The present invention relates to a constant air volume control method for a range hood, which includes setting a fuzzy calculator and converting e k , Δe k , P is the input of the fuzzy calculator, e k =y r -y k ;Δe k =e k -e k‑1 ; where y r is the target air volume value, y k is the actual air volume value of the range hood at the current moment k, e k Indicates the air volume error value; e k‑1 is the air volume error value calculated at the sampling moment before the current moment k, Δe k Indicates the change in air volume error; P indicates the floor number of the range hood where the air volume is to be adjusted; k , Δe k After fuzzy calculations are performed on P and P, a fuzzy variable for adjusting the air volume is output. After defuzzification of the fuzzy variable, an air volume adjustment value u is obtained. The range hood air volume is then adjusted based on this air volume adjustment value u. This range hood constant air volume control method can shorten adjustment time and quickly achieve stable constant air volume control.

Description

Technical Field

[0001] The invention relates to a constant air volume control method for a range hood. Background Art

[0002] The range hood system has complex and changeable load conditions. Especially for high-rise residential buildings, the exhaust of the user's range hood is easily affected by the public flue and causes congestion. Existing indoor range hoods basically use constant gear control, that is, the user clicks the fan gear on the control panel, and the fan in the range hood operates according to the performance curve corresponding to the gear. This mode of operation will cause the air volume to change with the change of the rear-end resistance. For example, at a time of high startup rate, when users on the lower floors of a high-rise residential building use the range hood, it is very likely that the exhaust will be difficult due to the excessive resistance of the public flue. Therefore, even if a range hood with large air volume and high suction power on the market is used, its actual fume extraction effect may not achieve the expected effect when used in the homes of users on the lower floors.

[0003] Constant air volume range hoods can essentially solve the above pain points. Regardless of the current startup rate, the user's floor location, or how the usage conditions change, they can ensure that the user's home air volume remains constant, ensuring the best range hood extraction effect. For example, in the Chinese invention patent application with publication number CN106895467A (application number 201710321317.9), a constant air volume control method is used to control the constant air volume of a range hood. The specific working method includes the following steps: Step 1: The range hood is turned on; Step 2: The controller records the real-time air volume value detected by the pressure sensor; Step 3: The controller compares the current air volume value with the constant air volume threshold. If the current air volume value is less than the constant air volume threshold, the controller jumps to Step 4; if the current air volume value is greater than the constant air volume threshold, the controller jumps to Step 5; Step 4: The drive motor current is increased, thereby increasing the drive motor speed and increasing the air volume output from the range hood main body outlet; Step 5: The controller determines whether the drive motor current is greater than a preset value. If so, the controller jumps to Step 6; if not, the controller does not perform subsequent control operations; Step 6: The drive motor current is reduced, thereby reducing the drive motor speed and reducing the air volume output from the range hood main body outlet. The core of this constant air volume control process lies in the calculation of the air volume. The premise of air volume adjustment must be based on the stability of the speed. That is, it takes time for the speed to stabilize during the air volume calculation process, the air volume calculation takes time, and the adjustment process takes time. Therefore, constant air volume will inevitably lead to the problem of slow response speed. In addition, for building users, the adjustment step time is inconsistent on different floors where range hoods are located. The number of adjustments on lower floors is more, and the number of adjustments on higher floors is less. This will further lead to slow system response speed, poor oil fume extraction effect, and poor user experience. In addition, if external sensors are used to achieve constant air volume by collecting wind pressure or air volume information in real time, it will increase costs. And because of the oil circuit environment in which the range hoods are located, the sensor life is generally short and the maintenance rate is high. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a constant air volume control method for a range hood, which can shorten the adjustment time and quickly achieve stable control of the constant air volume, in response to the above-mentioned prior art.

[0005] The technical solution adopted by the present invention to solve the above problems is: a constant air volume control method for a range hood, characterized in that: a fuzzy calculator is set to k , Δe k , P is the input of the fuzzy calculator, e k =y r -y k ;Δe k =e k -e k-1 ; where y r is the target air volume value, y kis the actual air volume value of the range hood at the current moment k, e k Indicates the air volume error value; e k-1 The air volume error value calculated at the sampling moment before the current moment k, Δe k Indicates the change in air volume error; P indicates the floor number where the range hood to be adjusted is located;

[0006] For e k , Δe k After fuzzy calculation is performed on P, the fuzzy variables used to adjust the air volume are output. After defuzzification calculation is performed on the fuzzy variables, the air volume adjustment value u is obtained, and the air volume of the range hood is adjusted according to the air volume adjustment value u.

[0007] As an improvement, the floors are divided into at least two height intervals. For each height interval, the following work is performed:

[0008] Set e separately k , Δe k , the discretized data interval of u and the domain of the rank quantity after discretization, and then determine e k , Δe k , the discretization parameters corresponding to u and the quantization factors Ke, Kec, Ku of the discretization data into the data in the domain of hierarchical quantity;

[0009] For each floor of the height interval, preset e k The discretized data interval is [-A, A], and Δe is preset k The discretized data interval of u is [-B, B], and the discretized data interval of u is [-C, C]; A, B, C are the actual data e collected and calculated. k , Δe k , u is the data interval threshold after discretization;

[0010] Set up k The domain of the rank quantity after discretization is {-n,-(n-1),-(n-2),……,-1,0,1,2,……,n};

[0011] Setting Δe k The domain of the rank quantity after discretization is {-m,-(m-1),-(m-2),……,-1,0,1,2,……,m};

[0012] Set the domain of the discretized rank quantity u to {-j,-(j-1),-(j-2),……,-1,0,1,2,……,j};

[0013] For each floor interval, according to e k The discretized data interval and e kThe discretized domain of the level quantity is calculated to determine e k The quantization factor Ke for quantization calculation of the corresponding discretized data;

[0014] For each floor interval, according to Δe k The discretized data interval and Δe k The discretized domain of the rank quantity is used to determine Δe k The quantization factor Kec for quantizing the corresponding discretized data;

[0015] For each floor interval, the quantization factor Ku for quantizing the discretized data corresponding to u is determined based on the discretized data interval of u and the level domain of u after discretization.

[0016] Set up k , Δe k 、u's fuzzy variable word set, set e k , Δe k , fuzzy rules for the fuzzy relationship between u;

[0017] Set up k , Δe k The fuzzy variable word set is {NB, NM, NS, ZE, PS, PM, PB}, where NB represents a large negative value; NM represents a medium negative value; NS represents a small negative value; ZE represents zero; PS represents a small positive value; PM represents a medium positive value; PB represents a large positive value;

[0018] Set the fuzzy variable word set of u to {NB, NS, ZE, PS, PB};

[0019] The constant air volume control method of the range hood comprises the following steps:

[0020] S1, determine the corresponding discretization parameters according to the input quantity P, and calculate the e in real time when the range hood is working. k , Δe k Discretize and obtain Xe k 、XΔe k ;

[0021] S2, determine the corresponding Ke, Kec, Ku according to the input quantity P; then calculate Xe according to Ke and Kec. k 、XΔe k Perform quantitative calculations to obtain Xe k Quantized values ​​D and XΔe in the corresponding domain of the level quantity k The quantized value E in the corresponding domain of the rank quantity;

[0022] S3, use the membership function to perform fuzzy calculation on D and E, and then obtain ek , Δe k The corresponding fuzzy subset;

[0023] S4. Determine the fuzzy relationship between the fuzzy subset and the air volume adjustment variable u according to the set fuzzy rules;

[0024] S5, performing defuzzification calculation on the fuzzy relationship to obtain the quantitative data u0 corresponding to u in the corresponding level quantity domain;

[0025] S6. Calculate the discretized air volume adjustment value U=u0 / Ku based on u0 and Ku, perform de-discretization processing on U to obtain the corresponding air volume adjustment value u, and then adjust the air volume of the range hood based on u.

[0026] Preferably, a triangle membership function is used to perform fuzzy calculations on D and E.

[0027] Preferably, the centroid method is used for defuzzification.

[0028] Aiming at the larger wind volume fluctuation error in the height interval of the lower floors, the wind volume fuzzy adjustment is performed with a larger adjustment step, which can better reflect the constant wind volume adjustment characteristics of the height interval of the lower floors. For each height interval, the preset e k The discretized data interval is [-A, A], and Δe is preset k The discretized data interval of u is [-B, B], and the discretized data interval of u is [-C, C]; A, B, C are the actual data e collected and calculated. k , Δe k , u is the data interval threshold after discretization;

[0029] Among them, the Δe corresponding to the height interval of the high floor k The discretized data interval threshold B is greater than the Δe corresponding to the height interval of the lower floor k The discretized data interval threshold B;

[0030] The discretized data interval threshold C of u corresponding to the height interval segment of the high floor is smaller than the discretized data interval threshold C of u corresponding to the height interval segment of the low floor.

[0031] Compared with existing technologies, the present invention offers advantages: The range hood constant air volume control method employs a fuzzy control algorithm for air volume regulation, significantly shortening system stabilization time. Furthermore, the method incorporates the influence of the range hood's floor location on air volume regulation, taking into account the varying smoke exhaust characteristics of different floors and the corresponding required air volume regulation strategies. This results in more accurate air volume regulation results and improved constant air volume regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 2 is a control framework diagram of a constant air volume control method for a range hood in an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The present invention will be described in further detail below with reference to the accompanying drawings and embodiments.

[0034] In the smoke exhaust control of a building, under the condition of the same pressure in the public flue, the difficulty of exhausting smoke on the lower floors is greater than that on the higher floors. In order to ensure constant air volume for smoke exhaust on the lower and higher floors, the fan speed required on the lower floors is higher, and the corresponding fan current required is also larger. In order to achieve air volume regulation within the constant air volume area, first, the adjustment ranges of the fan current control on the lower and higher floors are different. The fan current on the lower floors is adjusted within a relatively high range, and the fan current on the higher floors is adjusted within a relatively low range. In the actual adjustment process, in order to determine whether the set air volume has been reached, it is necessary to perform a test after the fan current is adjusted and the air volume is stable, that is, it is necessary to go through a cyclic adjustment-stabilization process. If the adjustment is performed according to the set step size, the adjustment speed is slow, and the adjustment process takes too long, then it is easy to encounter the problem that the working condition has disappeared, and the adjusted current may no longer be applicable to the current working condition. The constant air volume control method of the range hood in this embodiment solves the above problems.

[0035] like Figure 1 As shown, the constant air volume control method of the range hood in this embodiment is: set a fuzzy calculator, and set e k , Δe k , P is the input of the fuzzy calculator, e k =y r -y k ;Δe k =e k -e k-1 ; where y r is the target air volume value, y k is the actual air volume value of the range hood at the current moment k, e k Indicates the air volume error value; e k-1 is the air volume error value calculated at the sampling moment before the current moment k, Δe k Indicates the change in air volume error; P indicates the floor number where the range hood whose air volume is to be adjusted is located.

[0036] For e k , Δe kAfter fuzzy calculations are performed on P and P, the fuzzy variables used to adjust the air volume are output. After defuzzification of the fuzzy variables, the air volume adjustment value u is obtained. In the control system, a proportional relationship is established between the air volume adjustment value and the fan current, and the corresponding adjustment current is calculated to ensure that the adjusted air volume approaches the target constant air volume value.

[0037] The constant air volume control method of the range hood is specifically as follows.

[0038] The floors are divided into at least two height intervals, and the number of height intervals is determined according to the height of the building. The higher the total height of the building, the more height intervals there are. In this embodiment, two floor height intervals are used as an example for explanation, that is, the floors are divided into upper and lower height intervals.

[0039] For each height interval, perform the following tasks: set e k , Δe k , the discretized data interval of u and the domain of the rank quantity after discretization, and then determine e k , Δe k , the discretization parameters corresponding to u and the quantization factors Ke, Kec, Ku of the discretized data into the data in the domain of the level quantity; set e k , Δe k 、u's fuzzy variable word set, set e k , Δe k , and fuzzy rules for the fuzzy relationship between u.

[0040] In this embodiment, specifically, for each floor interval, the preset e k The discretized data interval [-A, A], preset Δe k The discretized data interval of u is [-B, B], and the discretized data interval of u is [-C, C]. A, B, C are the actual data e collected and calculated. k , Δe k , u is the data interval threshold after discretization. Among them, the Δe corresponding to the height interval of the high floor k The discretized data interval threshold B is greater than the Δe corresponding to the height interval of the lower floor k The discretization data interval threshold B of u is smaller than the discretization data interval threshold C of u corresponding to the height interval of the high floor; the discretization data interval threshold C of u corresponding to the height interval of the low floor.

[0041] Set up k The domain of rank quantity after discretization is {-n,-(n-1),-(n-2),……,-1,0,1,2,……,n}.

[0042] Setting Δe kThe domain of rank quantity after discretization is {-m,-(m-1),-(m-2),……,-1,0,1,2,……,m}.

[0043] Set the domain of the rank quantity after discretization of u to {-j,-(j-1),-(j-2),……,-1,0,1,2,……,j}.

[0044] For each floor interval, according to e k The discretized data interval and e k The discretized domain of the level quantity is calculated to determine e k The quantization factor Ke for quantizing the corresponding discretized data.

[0045] For each floor interval, according to Δe k The discretized data interval and Δe k The discretized domain of the rank quantity is used to determine Δe k The quantization factor Kec for quantizing the corresponding discretized data.

[0046] For each floor interval, the quantization factor Ku for quantizing the discretized data corresponding to u is determined based on the discretized data interval of u and the level quantity domain after discretization of u.

[0047] In this embodiment, for the upper half-height floor interval, that is, the floors with the number of floors P≥N / 2, N is the total number of floors in the building. k The discretized data interval is [-15,15], and Δe is preset k The discretized data interval of is [-3,3], and the discretized data interval of u is preset to be [-3,3]. k , Δe k The discretized data interval of u is determined based on the air volume adjustment characteristics of different height floor intervals. k , Δe k After the discretization data interval of u is determined, the corresponding discretization parameters for discretization calculation can be determined based on the previous experimental data. These discretization parameters can be stored in the system for easy call.

[0048] For the upper half height floor section, set e k The domain of the discretized rank quantity is {-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6}; set Δe k The domain of the rank quantity after discretization is {-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6}; the domain of the rank quantity after discretization of u is set to {-3,-2,-1,0,1,2,3}.

[0049] Therefore, for the upper half-height floor interval, Ke=2*6 / (15-(-15))=0.4, Kec=2*6 / (3-(-3))=2, and Ku=2*3 / (3-(-3))=1 are calculated.

[0050] For the lower half height floor interval, that is, the floor number P<N / 2, the preset e k The discretized data interval is [-15,15], and Δe is preset k The discretized data interval of is [-2,2], and the discretized data interval of u is preset to be [-5,5].

[0051] In this embodiment, for the lower half height floor interval, set e k The domain of the discretized rank quantity is {-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6}; set Δe k The domain of the discretized level quantity is {-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6}; the domain of the discretized level quantity u is {-3,-2,-1,0,1,2,3}. That is, the e in the upper and lower half height intervals k , Δe k , the domain of the level quantity after discretization corresponding to u is the same, but the discretization data interval is different.

[0052] For the lower half height floor interval, calculate e k , Δe k , the discretization quantization factors corresponding to u are: Ke=2*6 / (15-(-15))=0.4, Kec=2*6 / (2-(-2))=1.5, Ku=2*3 / (5-(-5))=0.6.

[0053] For the same domain, set Δe for different height floor intervals. k The discretization data interval of and the discretization data interval of u are different because the error fluctuation is relatively large when the constant air volume is adjusted in the lower half of the height floor. Therefore, the Δe k The discretized data interval [-2,2] is set to be Δe in the interval segment of the upper half height floor kThe discretized data interval [-3,3] for u in the lower half-height floor segment is smaller than the discretized data interval [-5,5] for u in the upper half-height floor segment, which means that the C corresponding to the upper half-height floor segment is smaller than the C corresponding to the lower half-height floor segment. This allows fuzzy adjustment of larger air volume fluctuation errors in the lower half-height floor segment, while allowing for a larger adjustment step for air volume adjustment, better reflecting the constant air volume adjustment characteristics of the lower half-height floor segment.

[0054] Set up k , Δe k 、u's fuzzy variable word set, set e k , Δe k , u. Specifically, set e k , Δe k The fuzzy variable word set of u is {NB, NM, NS, ZE, PS, PM, PB}, where NB represents a large negative value; NM represents a medium negative value; NS represents a small negative value; ZE represents zero; PS represents a small positive value; PM represents a medium positive value; and PB represents a large positive value. Set the fuzzy variable word set of u to {NB, NS, ZE, PS, PB}.

[0055] In this embodiment, e k , Δe k The fuzzy rules of the fuzzy relationship between ,u are shown in Table 1.

[0056] Table 1

[0057]

[0058] The rule table uses the "if-then" statement format for conversion, for example: IF e k is ZE and Δe k is PB, then the output u is PS.

[0059] The basic idea of ​​the fuzzy control rule table is: when the error is large or relatively large, the control quantity is selected to eliminate the error as quickly as possible; when the error is small, the control quantity is selected to prevent overshoot, with system stability as the main starting point.

[0060] First consider the case where the error is negative. k When the error is large and negative, it means that the air volume is higher than the target value. kIt is also negative. At this time, the error tends to increase. In order to eliminate the existing large negative error as soon as possible and suppress the error from becoming larger, the change in the control quantity takes a large negative value, that is, the control quantity increases, which means that the current adjustment gear is reduced and decreases according to the maximum step length, thereby reducing the air volume.

[0061] When the error e k The error change rate Δe is negative k When the error is positive, the system itself has a tendency to reduce the error, so in order to eliminate the error as quickly as possible, k Without overshoot, a smaller control amount should be used.

[0062] When the error e k When the error is negative, the change of the control quantity should eliminate the error as soon as possible. Based on this principle, the change of the control quantity is selected to be consistent with the error e k The same when it is negative.

[0063] When the error Δe k When the error rate of change is negative, the system is close to steady state. k When the control amount is small, the change is selected as negative to suppress the error Δe k Changes in the positive direction; if the error change rate Δe k When it is positive, the system itself has a tendency to eliminate small negative errors, and the control quantity change is selected to be small positive.

[0064] For the above dual-input single-output system, the Mamdani reasoning method is used to determine the calculation rule of the fuzzy relationship R=min(e k , Δe k ).

[0065] According to the above table, 49 rules such as Rule 1 and Rule 2 can be formulated as follows:

[0066] Rule 1: IFe k is NB, and Δe k is NB, THEN u is NB;

[0067] Rule 2: IFe k is NM, and Δe k is NB, THEN u is NB.

[0068] Based on the above settings, the constant air volume control method of the range hood includes the following steps:

[0069] S1, according to the input quantity P, determine the corresponding height floor interval, and then determine the discretization parameters in the discretization calculation corresponding to the height floor interval, and calculate the e in real time when the range hood is working. k , Δe k Discretize and obtain Xek 、XΔe k .

[0070] S2, determine the corresponding Ke, Kec, Ku according to the input quantity P; then calculate Xe according to Ke and Kec. k 、XΔe k Perform quantitative calculations to obtain Xe k Quantized values ​​D and XΔe in the corresponding domain of the level quantity k The quantized value E in the corresponding level domain.

[0071] In this embodiment, for P located in the upper half of the floor interval, D = Ke*Xe k =0.4*Xe k , E=Kec*XΔe k =2*XΔe k For P located in the lower half of the floor interval, the corresponding D = Ke*e k =0.4*Xe k , E=Kec*XΔe k =1.5*XΔe k .

[0072] S3, use the membership function to perform fuzzy calculation on D and E, and then obtain e k , Δe k The corresponding fuzzy subset. According to the needs, various existing membership functions can be used to perform fuzzy calculations on D and E. In this embodiment, the triangle membership function is used to discretize e k , Δe k Perform fuzzy calculations.

[0073] D corresponds to the membership degree μ of each fuzzy variable k The specific calculation formula is shown in formula (1).

[0074] E corresponds to the membership degree μ of each fuzzy variable kc The specific calculation formula is shown in formula (2).

[0075]

[0076]

[0077] The corresponding e of formula (1) and formula (2) k , Δe k The variable assignment table between the level quantity domain data and its fuzzy variable word set after discretization is shown in Table 2 below.

[0078] Table 2

[0079]

[0080] For example, a user's floor data P belongs to the upper half of the floor interval. If the user calculates e at a certain moment, k Corresponding calculated Xe k =4.5, calculated Δe k Corresponding to the calculated XΔek=1.3, the calculated D=Ke*Xe k =0.4*4.5=1.8, E=Kec*Xe k =1.5*1.3=1.95.

[0081] Use the triangle membership function in formula (1) and formula (2) to perform fuzzy calculation on D and E, and then obtain e k , Δe k The corresponding fuzzy subset is: μ kPS =1.8 / 2=0.9, μ kZE =-1 / 2*(1.8-2)=0.1; that is, e k The corresponding membership degree on the fuzzy variable word PS is 0.9, e k The corresponding membership degree on the fuzzy variable word ZE is 0.1.

[0082] μ kcZE =-1 / 2*(1.95-2)=0.025, μ kcPS = -1 / 2*1.95=0.975. That is Δe k The corresponding membership degree on the fuzzy variable word ZE is 0.025, Δe k The corresponding membership degree on the fuzzy variable word PS is 0.975.

[0083] S4. Determine the fuzzy relationship between the fuzzy subset and the air volume adjustment variable u according to the set fuzzy rules.

[0084] Continuing with the example in S3, assume that μ kPS =1.8 / 2=0.9, μ kZE =-1 / 2*(1.8-2)=0.1, the rest of the subsets are 0; μ kcZE =-1 / 2*(1.95-2)=0.025, μ kcPS =-1 / 2*1.95=0.975, and the rest of the subsets are 0.

[0085] According to Table 1, we can obtain the following four rules:

[0086] Rule i: IF e k is ZE,andΔe k is ZE, THEN u is ZE;

[0087] Rule ii: IF ek is ZE,andΔe k is PS, THEN u is PS;

[0088] Rule iii: IF e k is PS,andΔe k is ZE, THEN u is PS;

[0089] Rule iv: IF e k is PS,andΔe k is PS, THEN u is PS.

[0090] S5, performing defuzzification calculation on the fuzzy relationship to obtain the quantitative data u0 corresponding to u in the corresponding level quantity domain;

[0091] Continuing with the example in S4:

[0092] For the conditional part of rule i: u0 = min(0.1, 0.025) = 0.025, that is, the membership degree of u on the fuzzy variable word ZE is 0.025;

[0093] For the conditional part of rule ii: u0=min(0.1,0.975)=0.1, that is, the membership degree of u corresponding to the fuzzy variable word PS is 0.1;

[0094] For the conditional part of rule iii: u0 = min (0.9, 0.025) = 0.025, that is, the membership degree of u on the fuzzy variable word PS is 0.025;

[0095] For the conditional part of rule iv: u0 = min(0.9, 0.975) = 0.9, that is, the membership degree of u corresponding to the fuzzy variable word PS is 0.9.

[0096] In this way, it is determined that the membership degree of u corresponding to the fuzzy variable word PS is 0.025, and the membership degree of u corresponding to the fuzzy variable word PS is 0.9.

[0097] In this embodiment, the centroid method is used for defuzzification calculation. Based on the existing centroid method defuzzification calculation formula, combined with the content of Table 2, u0 is calculated and obtained.

[0098]

[0099] S6. Calculate the discretized air volume adjustment value U=u0 / Ku based on u0 and Ku, perform de-discretization on U to obtain the corresponding air volume adjustment value u, and then adjust the air volume of the range hood according to the fan current adjustment value corresponding to u.

[0100] Continuing with the example in S5, for the upper half-height floor interval, Ku = 1. Therefore, U = u0 / Ku = 2 / 1 = 1. If the calculated U is a non-integer, it can be rounded up, and then the corresponding air volume adjustment value u can be calculated based on the discretized parameter corresponding to u. Constant air volume control is then achieved using the fan adjustment current corresponding to the air volume adjustment value u.

[0101] It can be seen that by utilizing the constant air volume control method of the range hood, the size of the air volume adjustment step can be intelligently changed according to the specific air volume error situation, without the need to adjust according to a fixed step, thus shortening the adjustment time and enabling the range hood to achieve constant air volume adjustment faster.

[0102] The range hood constant air volume control method of this invention employs a fuzzy control algorithm for air volume regulation, significantly shortening system stabilization time. Furthermore, based on the varying exhaust characteristics of different floors and the corresponding required air volume adjustment strategies, the influence of the range hood's floor location on air volume regulation is also considered. This results in more accurate air volume adjustment results and improved constant air volume regulation.

Claims

1. A constant air volume control method for a range hood, characterized by: Set the fuzzy calculator and set e k , Δe k , P is the input of the fuzzy calculator, e k =y r -y k ;Δe k = e k -e k-1 ; where y r is the target air volume value, y k is the actual air volume value of the range hood at the current moment k, e k Indicates the air volume error value; e k-1 is the air volume error value calculated at the sampling moment before the current moment k, Δe k Indicates the change in air volume error; P indicates the floor number where the range hood to be adjusted is located; For e k , Δe k After P performs fuzzy calculation, it outputs the fuzzy variables used to adjust the air volume. After defuzzification calculation of the fuzzy variables, it obtains the air volume adjustment value u, and adjusts the air volume of the range hood according to the air volume adjustment value u; In the public flue smoke exhaust control of the building, the floors are divided into at least two height intervals, and for the floors in each height interval, Set e separately k , Δe k , the discretized data interval of u and the domain of the rank quantity after discretization, respectively determine e k , Δe k , the discretization parameters corresponding to u and the quantization factors Ke, Kec, Ku of the discretized data into the data in the domain of the level quantity; set e k , Δe k 、u's fuzzy variable word set, set e k , Δe k , fuzzy rules for the fuzzy relationship between u; For each floor of the height interval, preset e k The discretized data interval is [-A, A], and Δe is preset k The discretized data interval of u is [-B, B], and the discretized data interval of u is [-C, C]; A, B, C are the actual data e collected and calculated. k , Δe k , u is the data interval threshold after discretization; The B corresponding to the height interval of the high floor is greater than the B corresponding to the height interval of the low floor; The C corresponding to the height interval of the high floor is smaller than the C corresponding to the height interval of the low floor.

2. The constant air volume control method for a range hood according to claim 1, characterized in that: The following steps are involved: S1, determine the corresponding discretization parameters according to the input quantity P, and calculate the e in real time when the range hood is working. k , Δe k Discretize and obtain Xe k 、XΔe k ; S2, determine the corresponding Ke, Kec, Ku according to the input quantity P; then calculate Xe according to Ke and Kec. k 、XΔe k Perform quantitative calculations to obtain Xe k Quantized values ​​D and XΔe in the corresponding domain of the level quantity k The quantized value E in the corresponding domain of the rank quantity; S3, use the membership function to perform fuzzy calculation on D and E, and then obtain e k , Δe k The corresponding fuzzy subset; S4. Determine the fuzzy relationship between the fuzzy subset and the air volume adjustment variable u according to the set fuzzy rules; S5, performing defuzzification calculation on the fuzzy relationship to obtain the quantitative data u0 corresponding to u in the corresponding level quantity domain; S6. Calculate the discretized air volume adjustment value U = u0 / Ku based on u0 and Ku, perform de-discretization on U to obtain the corresponding air volume adjustment value u, and then adjust the air volume of the range hood based on u.

3. The constant air volume control method for a range hood according to claim 2, characterized in that: The triangle membership function is used to perform fuzzy calculation on D and E.

4. The constant air volume control method for a range hood according to claim 2, characterized in that: The centroid method is used for defuzzification.

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