Fan rotating speed control method and system

Abstract

The invention discloses a fan rotating speed control method and system, and relates to the technical field of fan control, and the fan rotating speed control method comprises the steps that the rated rotating speed, the rated current, the rated voltage, the real-time rotating speed, the real-time current and the real-time voltage of a fan are obtained, and a fan operation deviation coefficient is calculated; the fan output deviation coefficient is calculated according to the real-time output wind speed, the real-time output wind direction and the real-time output wind pressure of the fan, the fan adjustment deviation coefficient is calculated according to the real-time temperature, the real-time air pressure and the real-time humidity of the fan adjustment area, and then the fan control deviation index is calculated according to the fan output deviation coefficient and the fan adjustment deviation coefficient; and obtaining fan operation deviation coefficients and fan control deviation indexes of all the fans, calculating a rank correlation coefficient, and importing the fan operation deviation coefficients, the fan control deviation indexes and the rank correlation coefficient of all the fans into a machine learning model to build a control adjustment model, so that the control adjustment model determines an adjustment value of the operation deviation coefficients. And remote monitoring and intelligent adjustment of the fan can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of fan control, and in particular to a method and system for controlling the rotation speed of a fan. Background Art

[0002] In wind power generation and ventilation and cooling systems, fan speed control is a crucial technology. Accurate control of fan speed can not only improve the operating efficiency of the system, but also effectively reduce energy waste and ensure the safe and stable operation of the equipment. Traditional fan speed control methods mainly rely on mechanical adjustment or simple electrical control. Although these methods can achieve basic speed adjustment functions, they often seem powerless when faced with complex and changeable operating conditions. Traditional control methods are usually unable to achieve accurate control of fan speed, resulting in the system operating efficiency being difficult to achieve the optimal state. When faced with external interference such as sudden changes in wind speed, traditional control methods have a slow response speed and cannot make timely adjustments, thus affecting the stability of the system. Traditional control methods are often unable to dynamically adjust the fan speed according to actual needs, resulting in the fan being in a non-optimal operating state at low or high loads, increasing unnecessary energy consumption.

[0003] In the Chinese invention application with application publication number CN118347130A, an internal fan speed control method, device, equipment and computer storage medium are disclosed, including obtaining the indoor temperature and set temperature of the smart home appliance, and judging whether the smart home appliance is in the pre-heating state; when the smart home appliance is in the pre-heating state, according to the indoor temperature and the set temperature, the temperature drop difference and the target temperature reaching time of the smart home appliance are determined, and then the target heat leakage level of the space where the smart home appliance is located is determined; according to the target heat leakage level, the target frequency reduction speed of the smart home appliance is determined, and the smart home appliance is reduced in speed according to the target frequency reduction speed. This method avoids the energy waste caused by the frequent start and stop of the smart home appliance, helps to extend the service life of the smart home appliance, improves the air quality of the space where the smart home appliance is located, provides users with a suitable indoor environment, and improves the user experience.

[0004] In a Chinese invention application with application publication number CN118257738A, a control method, device, equipment and storage medium for a fan speed are disclosed, including determining a current fan speed based on a laser signal recorded by a laser transceiver, the laser signal indicating that the laser transceiver receives a laser beam reflected by a laser reflector, and the laser reflector is located on the fan blade; when the current fan speed and the target fan speed are different, determining a target operating voltage based on the target fan speed; based on the target operating voltage, controlling the fan speed to adjust from the current fan speed to the target fan speed.

[0005] In the above invention application, invention CN118347130A determines the temperature drop difference and target temperature reaching time of the smart home appliance, and then determines the target heat leakage level of the space where the smart home appliance is located; according to the target heat leakage level, the target frequency reduction speed of the smart home appliance is determined, and the algorithm for determining the target heat leakage level and the target frequency reduction speed needs to be continuously optimized and adjusted according to the actual situation. However, due to differences in usage habits, environmental conditions and other factors of different users, the optimization difficulty of the algorithm is relatively large, and a large amount of manpower and resources may be required. Invention CN118257738A determines the target operating voltage based on the target fan speed; based on the target operating voltage, the fan speed is controlled to be adjusted from the current fan speed to the target fan speed. This method requires accurate measurement of the fan speed and real-time adjustment of the operating voltage according to the measurement results. This requires the system to have high-precision sensors, fast signal processing capabilities and complex control algorithms, thereby increasing the overall complexity of the system.

[0006] To this end, the present invention provides a method and system for controlling the rotation speed of a fan. Summary of the invention

[0007] 1. Technical issues to be resolved

[0008] In view of the deficiencies in the prior art, the present invention provides a method and system for controlling the speed of a wind turbine. The present invention obtains the rated speed Ez, rated current Ed, rated voltage Ey, real-time speed Zs, real-time current Dl and real-time voltage Dy of the wind turbine, calculates the wind turbine operation deviation coefficient Yp, calculates the wind turbine output deviation coefficient Sp according to the real-time output wind speed Fs, real-time output wind direction Fx and real-time output wind pressure Fy of the wind turbine, and calculates the wind turbine adjustment deviation coefficient Tp according to the real-time temperature Wd, real-time air pressure Qy and real-time humidity Sd of the wind turbine adjustment area, and then calculates the wind turbine control deviation index Kpz according to the fan output deviation coefficient Sp and the fan adjustment deviation coefficient Tp, builds a control and adjustment model, and uses the control and adjustment model to determine the adjustment value of the operation deviation coefficient, so as to realize remote monitoring, automatic diagnosis and intelligent adjustment of the wind turbine, which is helpful to reduce operation and maintenance costs, improve operation and maintenance efficiency, and provide strong support for the overall optimization operation of the wind farm, thereby solving the technical problems recorded in the background technology.

[0009] (II) Technical solution

[0010] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for controlling the speed of a fan, comprising the following steps:

[0011] Obtain the rated speed Ez, rated current Ed, rated voltage Ey, real-time speed Zs, real-time current Dl and real-time voltage Dy of the fan, and calculate the fan operation deviation coefficient Yp;

[0012] The fan output deviation coefficient Sp is calculated based on the real-time output wind speed Fs, real-time output wind direction Fx and real-time output wind pressure Fy of the fan, and the fan regulation deviation coefficient Tp is calculated based on the real-time temperature Wd, real-time air pressure Qy and real-time humidity Sd of the fan regulation area, and then the fan control deviation index Kpz is calculated based on the fan output deviation coefficient Sp and the fan regulation deviation coefficient Tp;

[0013] Get the fan operation deviation coefficient Yp of all fans i and fan control deviation index Kpz i , calculate the Spearman rank correlation coefficient ρ, and convert the fan operation deviation coefficient Yp of all fans i , fan control deviation index Kpz i The Spearman rank correlation coefficient ρ is imported into the machine learning model to build a control and adjustment model, and the adjustment value of the operating deviation coefficient is determined by the control and adjustment model.

[0014] Furthermore, the rated speed Ez, rated current Ed and rated voltage Ey of the fan are obtained by consulting the product manual or technical specification sheet, a photoelectric encoder is attached to the fan hub, the real-time speed Zs of the fan is calculated by measuring the frequency of the encoder output signal, and a current sensor is used to measure the real-time current Dl of the fan motor, and a voltage sensor is used to measure the real-time voltage Dy of the fan motor.

[0015] Furthermore, the rated speed Ez, rated current Ed, rated voltage Ey, real-time speed Zs, real-time current Dl and real-time voltage Dy of the fan are obtained, and the fan operation deviation coefficient Yp is calculated:

[0016]

[0017] The corresponding calculation formula for the fan operation deviation coefficient Yp is as above.

[0018] Furthermore, an anemometer is used to detect the real-time output wind speed Fs of the fan, a wind vane is used to detect the real-time output wind direction Fx of the fan, and a differential pressure gauge is used to detect the real-time output wind pressure Fy of the fan, and the fan output deviation coefficient Sp is calculated:

[0019]

[0020] Among them, Es is the output wind speed of the fan under rated operating conditions, Ex is the output wind direction of the fan under rated operating conditions, and Ef is the output wind pressure of the fan under rated operating conditions.

[0021] Furthermore, a temperature sensor is used to detect the real-time temperature Wd of the fan adjustment area, a barometer is used to detect the real-time air pressure Qy of the fan adjustment area, and a humidity sensor is used to detect the real-time humidity Sd of the fan adjustment area, and the fan adjustment deviation coefficient Tp is calculated:

[0022]

[0023] Among them, Bw is the adjustment setting temperature of the fan adjustment area, By is the adjustment setting wind direction of the fan in the fan adjustment area, and Bd is the adjustment setting humidity of the fan in the fan adjustment area.

[0024] Furthermore, the fan output deviation coefficient Sp and the fan adjustment deviation coefficient Tp are obtained, and the fan control deviation index Kpz is calculated:

[0025]

[0026] Among them, max(Sp) is the maximum value of all fan output deviation coefficients in history, min(Sp) is the minimum value of all fan output deviation coefficients in history, max(Tp) is the maximum value of all fan regulation deviation coefficients in history, and min(Tp) is the minimum value of all fan regulation deviation coefficients in history.

[0027] Furthermore, the fan operation deviation coefficient Yp of all fans is obtained i and fan control deviation index Kpz i , calculate the Spearman rank correlation coefficient ρ, and convert the fan operation deviation coefficient Yp of all fans i , fan control deviation index Kpz i The Spearman rank correlation coefficient ρ is imported into the machine learning model to build a control and adjustment model, and the adjustment value of the operating deviation coefficient is determined by the control and adjustment model.

[0028] Get the fan operation deviation coefficient Yp of all fans i and fan control deviation index Kpz i , calculate the Spearman rank correlation coefficient ρ:

[0029]

[0030] Where i represents the time sequence number of the sample data, i = 1, 2, 3, 4, ..., n, n is a positive integer, and n represents the total number of samples; Yp i and Kpz i Represents sample data of different influencing variables or regional electricity consumption, and Represents the corresponding sample data mean.

[0031] Spearman's Rank Correlation Coefficient, also often referred to as Spearman rank correlation or Spearman correlation coefficient, is a non-parametric correlation coefficient used to measure the monotonic relationship between two variables. This relationship can be linear or non-linear, as long as an increase (or decrease) in one variable is accompanied by an increase (or decrease) in the other variable.

[0032] A control system for fan speed, comprising:

[0033] The operation deviation analysis module obtains the rated speed Ez, rated current Ed, rated voltage Ey, real-time speed Zs, real-time current Dl and real-time voltage Dy of the fan, and calculates the fan operation deviation coefficient Yp;

[0034] The control deviation analysis module calculates the fan output deviation coefficient Sp according to the real-time output wind speed Fs, real-time output wind direction Fx and real-time output wind pressure Fy of the fan, and calculates the fan regulation deviation coefficient Tp according to the real-time temperature Wd, real-time air pressure Qy and real-time humidity Sd of the fan regulation area, and then calculates the fan control deviation index Kpz according to the fan output deviation coefficient Sp and the fan regulation deviation coefficient Tp;

[0035] Control adjustment module, obtain the fan operation deviation coefficient Yp of all fans i and fan control deviation index Kpz i , calculate the Spearman rank correlation coefficient ρ, and convert the fan operation deviation coefficient Yp of all fans i , fan control deviation index Kpz i The Spearman rank correlation coefficient ρ is imported into the machine learning model to build a control and adjustment model, and the adjustment value of the operating deviation coefficient is determined by the control and adjustment model.

[0036] (III) Beneficial effects

[0037] The present invention provides a method and system for controlling the speed of a fan, which have the following beneficial effects:

[0038] 1. Obtain the rated speed Ez, rated current Ed, rated voltage Ey, real-time speed Zs, real-time current Dl and real-time voltage Dy of the fan, and calculate the fan operation deviation coefficient Yp. This can quickly understand the operating status of the fan, determine whether it is in the best working state, discover potential problems in time, avoid the expansion of faults, and reduce downtime and maintenance costs.

[0039] 2. Calculate the fan output deviation coefficient Sp based on the fan's real-time output wind speed Fs, real-time output wind direction Fx and real-time output wind pressure Fy, and calculate the fan adjustment deviation coefficient Tp based on the real-time temperature Wd, real-time air pressure Qy and real-time humidity Sd in the fan adjustment area. Then calculate the fan control deviation index Kpz based on the fan output deviation coefficient Sp and the fan adjustment deviation coefficient Tp, which can more comprehensively evaluate the deviation between the actual output performance of the fan and the expected target, thereby providing a more accurate performance evaluation.

[0040] 3. Obtain the fan operation deviation coefficient Yp of all fans i and fan control deviation index Kpz i , calculate the Spearman rank correlation coefficient ρ, and convert the fan operation deviation coefficient Yp of all fans i , fan control deviation index Kpz i The Spearman rank correlation coefficient ρ is imported into the machine learning model to build a control and regulation model, and the adjustment value of the operation deviation coefficient is determined by the control and regulation model. This can realize remote monitoring, automatic diagnosis and intelligent regulation of wind turbines, help reduce operation and maintenance costs, improve operation and maintenance efficiency, and provide strong support for the overall optimization operation of wind farms. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A schematic flow chart of a method for controlling the speed of a fan according to the present invention;

[0042] Figure 2 The present invention is a schematic structural diagram of a fan speed control system. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] See also Figure 1 The present invention provides a method for controlling the speed of a fan, comprising the following steps:

[0045] Step 1: Obtain the rated speed Ez, rated current Ed, rated voltage Ey, real-time speed Zs, real-time current Dl and real-time voltage Dy of the fan, and calculate the fan operation deviation coefficient Yp.

[0046] The step 1 includes the following contents:

[0047] Step 101, refer to the product manual or technical specification sheet to obtain the rated speed Ez, rated current Ed and rated voltage Ey of the fan, paste a photoelectric encoder on the fan hub, calculate the real-time speed Zs of the fan by measuring the frequency of the encoder output signal, and use a current sensor to measure the real-time current Dl of the fan motor, and a voltage sensor to measure the real-time voltage Dy of the fan motor.

[0048] Step 102: Obtain the rated speed Ez, rated current Ed, rated voltage Ey, real-time speed Zs, real-time current Dl and real-time voltage Dy of the fan, and calculate the fan operation deviation coefficient Yp:

[0049]

[0050] The corresponding calculation formula for the fan operation deviation coefficient Yp is as above.

[0051] When using, combine the contents in steps 101 and 102:

[0052] By obtaining the rated speed Ez, rated current Ed, rated voltage Ey, real-time speed Zs, real-time current Dl and real-time voltage Dy of the fan and calculating the fan operation deviation coefficient Yp, we can quickly understand the operating status of the fan, determine whether it is in the best working state, discover potential problems in time, avoid the expansion of faults, and reduce downtime and maintenance costs.

[0053] Step 2: Calculate the fan output deviation coefficient Sp based on the fan's real-time output wind speed Fs, real-time output wind direction Fx and real-time output wind pressure Fy, and calculate the fan adjustment deviation coefficient Tp based on the fan adjustment area's real-time temperature Wd, real-time air pressure Qy and real-time humidity Sd, and then calculate the fan control deviation index Kpz based on the fan output deviation coefficient Sp and the fan adjustment deviation coefficient Tp.

[0054] The step 2 includes the following contents:

[0055] Step 201: Use an anemometer to detect the real-time output wind speed Fs of the fan, a wind vane to detect the real-time output wind direction Fx of the fan, and a differential pressure gauge to detect the real-time output wind pressure Fy of the fan, and calculate the fan output deviation coefficient Sp:

[0056]

[0057] Among them, Es is the output wind speed of the fan under rated operating conditions, Ex is the output wind direction of the fan under rated operating conditions, and Ef is the output wind pressure of the fan under rated operating conditions.

[0058] Step 202: Use a temperature sensor to detect the real-time temperature Wd of the fan adjustment area, a barometer to detect the real-time air pressure Qy of the fan adjustment area, and a humidity sensor to detect the real-time humidity Sd of the fan adjustment area, and calculate the fan adjustment deviation coefficient Tp:

[0059]

[0060] Among them, Bw is the adjustment setting temperature of the fan adjustment area, By is the adjustment setting wind direction of the fan in the fan adjustment area, and Bd is the adjustment setting humidity of the fan in the fan adjustment area.

[0061] Step 203, obtain the fan output deviation coefficient Sp and the fan adjustment deviation coefficient Tp, and calculate the fan control deviation index Kpz:

[0062]

[0063] Among them, max(Sp) is the maximum value of all fan output deviation coefficients in history, min(Sp) is the minimum value of all fan output deviation coefficients in history, max(Tp) is the maximum value of all fan regulation deviation coefficients in history, and min(Tp) is the minimum value of all fan regulation deviation coefficients in history.

[0064] When using, combine the contents in steps 201 to 203:

[0065] The fan output deviation coefficient Sp is calculated based on the real-time output wind speed Fs, real-time output wind direction Fx and real-time output wind pressure Fy of the fan, and the fan regulation deviation coefficient Tp is calculated based on the real-time temperature Wd, real-time air pressure Qy and real-time humidity Sd of the fan regulation area. The fan control deviation index Kpz is then calculated based on the fan output deviation coefficient Sp and the fan regulation deviation coefficient Tp. This can more comprehensively evaluate the deviation between the actual output performance of the fan and the expected target, thereby providing a more accurate performance evaluation.

[0066] Step 3: Obtain the fan operation deviation coefficient Yp of all fans i and fan control deviation index Kpz i , calculate the Spearman rank correlation coefficient ρ, and convert the fan operation deviation coefficient Yp of all fans i , fan control deviation index Kpz i The Spearman rank correlation coefficient ρ is imported into the machine learning model to build a control and adjustment model, and the adjustment value of the operating deviation coefficient is determined by the control and adjustment model.

[0067] Step 301: Obtain the fan operation deviation coefficient Yp of all fans i and fan control deviation index Kpz i , calculate the Spearman rank correlation coefficient ρ:

[0068]

[0069] Where i represents the time sequence number of the sample data, i = 1, 2, 3, 4, ..., n, n is a positive integer, and n represents the total number of samples; Yp i and Kpz i Represents sample data of different influencing variables or regional electricity consumption, and Represents the corresponding sample data mean.

[0070] Spearman's Rank Correlation Coefficient, also often referred to as Spearman rank correlation or Spearman correlation coefficient, is a non-parametric correlation coefficient used to measure the monotonic relationship between two variables. This relationship can be linear or non-linear, as long as an increase (or decrease) in one variable is accompanied by an increase (or decrease) in the other variable.

[0071] Step 302: Set the fan operation deviation coefficient Yp of all fans i , fan control deviation index Kpz i The Spearman rank correlation coefficient ρ is imported into the machine learning model to build a control adjustment model. After sample data training and testing, the control adjustment model is output to determine the adjustment value of the operating deviation coefficient.

[0072] When used, combine the contents in steps 301 and 302:

[0073] Get the fan operation deviation coefficient Yp of all fans i and fan control deviation index Kpz i , calculate the Spearman rank correlation coefficient ρ, and convert the fan operation deviation coefficient Yp of all fans i , fan control deviation index Kpz i The Spearman rank correlation coefficient ρ is imported into the machine learning model to build a control and regulation model, and the adjustment value of the operation deviation coefficient is determined by the control and regulation model. This can realize remote monitoring, automatic diagnosis and intelligent regulation of wind turbines, help reduce operation and maintenance costs, improve operation and maintenance efficiency, and provide strong support for the overall optimization operation of wind farms.

[0074] See also Figure 2 The present invention provides a control system for fan speed, comprising:

[0075] The operation deviation analysis module obtains the rated speed Ez, rated current Ed, rated voltage Ey, real-time speed Zs, real-time current Dl and real-time voltage Dy of the fan, and calculates the fan operation deviation coefficient Yp.

[0076] The control deviation analysis module calculates the fan output deviation coefficient Sp based on the fan's real-time output wind speed Fs, real-time output wind direction Fx and real-time output wind pressure Fy, and calculates the fan regulation deviation coefficient Tp based on the real-time temperature Wd, real-time air pressure Qy and real-time humidity Sd of the fan regulation area, and then calculates the fan control deviation index Kpz based on the fan output deviation coefficient Sp and the fan regulation deviation coefficient Tp.

[0077] Control adjustment module, obtain the fan operation deviation coefficient Yp of all fans i and fan control deviation index Kpz i , calculate the Spearman rank correlation coefficient ρ, and convert the fan operation deviation coefficient Yp of all fans i , fan control deviation index Kpz i The Spearman rank correlation coefficient ρ is imported into the machine learning model to build a control and adjustment model, and the adjustment value of the operating deviation coefficient is determined by the control and adjustment model.

[0078] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0079] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0080] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for controlling the speed of a fan, characterized in that: The steps include: Obtain the rated speed Ez, rated current Ed, rated voltage Ey, real-time speed Zs, real-time current Dl and real-time voltage Dy of the fan, and calculate the fan operation deviation coefficient Yp; The fan output deviation coefficient Sp is calculated based on the real-time output wind speed Fs, real-time output wind direction Fx and real-time output wind pressure Fy of the fan, and the fan regulation deviation coefficient Tp is calculated based on the real-time temperature Wd, real-time air pressure Qy and real-time humidity Sd of the fan regulation area, and then the fan control deviation index Kpz is calculated based on the fan output deviation coefficient Sp and the fan regulation deviation coefficient Tp; Get the fan operation deviation coefficient Yp of all fans i and fan control deviation index Kpz i , calculate the Spearman rank correlation coefficient ρ, and convert the fan operation deviation coefficient Yp of all fans i , fan control deviation index Kpz i The Spearman rank correlation coefficient ρ is imported into the machine learning model to build a control and adjustment model, and the adjustment value of the operating deviation coefficient is determined by the control and adjustment model.

2. A method for controlling fan speed according to claim 1, characterized in that: Refer to the product manual or technical specification sheet to obtain the rated speed Ez, rated current Ed and rated voltage Ey of the fan, paste a photoelectric encoder on the fan hub, calculate the real-time speed Zs of the fan by measuring the frequency of the encoder output signal, and use a current sensor to measure the real-time current Dl of the fan motor, and a voltage sensor to measure the real-time voltage Dy of the fan motor.

3. A method for controlling fan speed according to claim 1, characterized in that: Get the rated speed Ez, rated current Ed, rated voltage Ey, real-time speed Zs, real-time current Dl and real-time voltage Dy of the fan, and calculate the fan operation deviation coefficient Yp: The corresponding calculation formula for the fan operation deviation coefficient Yp is as above.

4. A method for controlling fan speed according to claim 1, characterized in that: Use an anemometer to detect the fan's real-time output wind speed Fs, a wind vane to detect the fan's real-time output wind direction Fx, and a differential pressure gauge to detect the fan's real-time output wind pressure Fy, and calculate the fan output deviation coefficient Sp: Among them, Es is the output wind speed of the fan under rated operating conditions, Ex is the output wind direction of the fan under rated operating conditions, and Ef is the output wind pressure of the fan under rated operating conditions.

5. A method for controlling fan speed according to claim 1, characterized in that: Use the temperature sensor to detect the real-time temperature Wd of the fan adjustment area, the barometer to detect the real-time air pressure Qy of the fan adjustment area, and the humidity sensor to detect the real-time humidity Sd of the fan adjustment area, and calculate the fan adjustment deviation coefficient Tp: Among them, Bw is the adjustment setting temperature of the fan adjustment area, By is the adjustment setting wind direction of the fan in the fan adjustment area, and Bd is the adjustment setting humidity of the fan in the fan adjustment area.

6. A method for controlling fan speed according to claim 1, characterized in that: Obtain the fan output deviation coefficient Sp and the fan regulation deviation coefficient Tp, and calculate the fan control deviation index Kpz: Among them, max(Sp) is the maximum value of all fan output deviation coefficients in history, min(Sp) is the minimum value of all fan output deviation coefficients in history, max(Tp) is the maximum value of all fan regulation deviation coefficients in history, and min(Tp) is the minimum value of all fan regulation deviation coefficients in history.

7. A method for controlling fan speed according to claim 1, characterized in that: Get the fan operation deviation coefficient Yp of all fans i and fan control deviation index Kpz i , calculate the Spearman rank correlation coefficient ρ: Where i represents the time sequence number of the sample data, i = 1, 2, 3, 4, ..., n, n is a positive integer, and n represents the total number of samples; Yp i and Kpz i Represents sample data of different influencing variables or regional electricity consumption, and Represents the corresponding sample data mean.

8. A fan speed control system, used to implement the method according to any one of claims 1 to 7, characterized in that: include: The operation deviation analysis module obtains the rated speed Ez, rated current Ed, rated voltage Ey, real-time speed Zs, real-time current Dl and real-time voltage Dy of the fan, and calculates the fan operation deviation coefficient Yp; The control deviation analysis module calculates the fan output deviation coefficient Sp according to the real-time output wind speed Fs, real-time output wind direction Fx and real-time output wind pressure Fy of the fan, and calculates the fan regulation deviation coefficient Tp according to the real-time temperature Wd, real-time air pressure Qy and real-time humidity Sd of the fan regulation area, and then calculates the fan control deviation index Kpz according to the fan output deviation coefficient Sp and the fan regulation deviation coefficient Tp; Control adjustment module, obtain the fan operation deviation coefficient Yp of all fans i and fan control deviation index Kpz i , calculate the Spearman rank correlation coefficient ρ, and convert the fan operation deviation coefficient Yp of all fans i , fan control deviation index Kpz i The Spearman rank correlation coefficient ρ is imported into the machine learning model to build a control and adjustment model, and the adjustment value of the operating deviation coefficient is determined by the control and adjustment model.

Images