Low-energy-consumption control method and equipment for large centrifugal fan and storage medium

Abstract

The invention discloses a low-energy-consumption control method and equipment for a large centrifugal fan and a storage medium, and relates to the technical field of environment control and energy management. Environmental parameters in the clean room are monitored in real time through the multi-sensor fusion technology, the air volume prediction model is established through the support vector machine algorithm, the target air volume range is accurately determined, the air conditioning system is controlled to adjust the air volume, the cleanliness requirement of medical operation is met, a draught fan flexibly adapts to environmental changes, and the cleaning efficiency is improved. The problem that a traditional fan cannot adjust the air volume according to the real-time environment is solved, in the speed adjusting process, energy consumption and noise are monitored in real time, when the energy consumption and noise exceed the standard, the rotating speed is adjusted to the minimum value meeting the requirement through a fuzzy control algorithm, intelligent algorithm parameters are optimized, the control precision and the response speed are improved, and energy waste is reduced. The problems that a traditional fan is high in energy consumption and low in efficiency are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental control and energy management, and in particular to a low-energy consumption control method, equipment and storage medium for a large centrifugal fan. Background Art

[0002] During the operation of medical clean rooms, the speed regulation of centrifugal fans is a key technical issue. The internal environmental parameters of clean rooms are complex and changeable, such as temperature, humidity, pressure, cleanliness, etc., and the air volume requirements of different medical operations vary greatly. Traditional fixed-speed centrifugal fans cannot flexibly adapt to these changes, which can easily cause excess or insufficient air volume, affecting the environmental quality and energy efficiency of the clean room. Summary of the invention

[0003] The purpose of the present invention is to provide a low-energy consumption control method, equipment and storage medium for a large centrifugal fan, which monitors the clean room environmental parameters and the fan operating status in real time through multi-sensor fusion technology, combines intelligent algorithms and frequency conversion technology, and realizes accurate, efficient and energy-saving speed control of the centrifugal fan.

[0004] The purpose of the present invention can be achieved through the following technical solutions: The present application provides a low energy consumption control method for a large centrifugal fan, comprising the following steps: Obtain environmental parameter data in the clean room, including temperature, humidity, pressure and cleanliness, collect and transmit them to the data processing module in real time through multi-sensor fusion technology, and then determine the target air volume range under the current working conditions based on the preset cleanliness threshold and the air volume requirements of medical operations combined with the environmental parameter data; used to control the air volume of the air conditioning system; The speed of the centrifugal fan is adjusted by frequency conversion technology. The speed adjustment range is determined by using fuzzy control algorithm by calculating the difference between the target air volume and the current air volume. When the target air volume is higher than the current air volume, the speed is increased; when the target air volume is lower than the current air volume, the speed is reduced. Among them, by obtaining the target air volume and current air volume of the centrifugal fan, calculating the difference between the target air volume and the current air volume, obtaining the air volume difference value, and judging whether the air volume difference value is positive or negative. When the air volume difference value is positive, it means that the target air volume is higher than the current air volume, and the fan speed needs to be increased; When the air volume difference value is negative, it means that the target air volume is lower than the current air volume, and the fan speed needs to be reduced; according to the size of the air volume difference value, the fuzzy control algorithm is used to determine the adjustment range of the fan speed and obtain the speed adjustment value; The speed adjustment value is input into the frequency converter, and the actual speed of the fan is adjusted through frequency conversion technology to gradually approach the target speed; In the process of adjusting the fan speed, the current air volume of the fan is obtained in real time, which is used as feedback and compared with the target air volume to form a closed-loop control; Analyze the changing trend of environmental parameters through intelligent algorithms, predict the air volume demand in the future, and adjust the fan speed in advance to match the predicted demand; During the speed regulation process, energy consumption and noise data are monitored in real time. When the energy consumption exceeds the preset threshold or the noise exceeds the allowable range, the speed is adjusted to the minimum value that meets the cleanliness requirements; then, based on historical data and real-time monitoring results, the parameter settings of the intelligent algorithm are optimized.

[0005] Furthermore, the environmental parameter data in the clean room, including temperature, humidity, pressure and cleanliness, is acquired in real time through multi-sensor fusion technology and transmitted to the data processing module, including: According to the environmental parameter monitoring requirements of the clean room, the temperature, humidity, pressure and cleanliness sensors are reasonably arranged at different locations in the clean room. The environmental parameter data such as temperature, humidity, pressure and cleanliness in the clean room are collected in real time through multiple sensors, and the sampling frequency is set according to actual needs; The environmental parameter data collected by multiple sensors are transmitted to the data processing module via wired or wireless means; In the data processing module, the received multi-sensor data is preprocessed, and the multi-sensor data is fused using the Kalman filter algorithm. By establishing a state space model, the optimal values ​​of environmental parameters are estimated.

[0006] Furthermore, according to the preset cleanliness threshold and the air volume requirements of medical operations, combined with the environmental parameter data, the target air volume range under the current working conditions is determined, including: Obtain the preset cleanliness threshold and air volume requirements for medical operations, and obtain parameter data of the current environment; According to the cleanliness threshold, air volume demand and environmental parameter data, an air volume prediction model is established, and the air volume prediction model is trained using a support vector machine algorithm to obtain a trained air volume prediction model; Input the current environmental parameter data into the trained air volume prediction model to predict the target air volume under the current working condition, and determine whether the predicted target air volume is within the air volume requirement range of the medical operation. If it is, it is determined as the final target air volume. If not, the air volume prediction model is adjusted and re-predicted; The determined target air volume range is outputted to control the air conditioning system to adjust the air volume.

[0007] Furthermore, the changing trend of environmental parameters is analyzed by intelligent algorithms, the air volume demand in the future is predicted, and the fan speed is adjusted in advance to match the predicted demand, including: obtaining historical data of environmental parameters in a period of time, using a time series analysis algorithm to model and predict the changing trend of environmental parameters, and obtaining the predicted value of each parameter in the future; According to the predicted environmental parameter values, combined with the pre-established air volume demand model, the predicted air volume demand value in the future period is calculated, and the predicted air volume demand value is compared with the current fan speed. When the predicted demand is greater than the air volume corresponding to the current speed, the fan speed is increased to a level that meets the predicted demand; when the predicted demand is less than the air volume corresponding to the current speed, the fan speed is reduced to a level that matches the predicted demand; After adjusting the fan speed, the difference between the actual air volume and the predicted demand is continuously monitored. When the difference exceeds the preset threshold, the update of the prediction model and the adjustment of the fan speed are triggered; The historical data of fan speed adjustment is fed back to the prediction model, and the model parameters are continuously optimized using incremental learning algorithms such as online learning or reinforcement learning.

[0008] Furthermore, the energy consumption and noise data are monitored in real time. When the energy consumption exceeds the preset threshold or the noise exceeds the allowable range, the speed is adjusted to the minimum value that meets the cleanliness requirements, including: Obtain the preset energy consumption threshold and noise allowable range data, and during the speed regulation process, collect the energy consumption data and noise data of the equipment in real time, and compare them with the preset threshold and allowable range; When the energy consumption data collected in real time exceeds the preset threshold, it is judged as energy consumption exceeding the standard; when the noise data collected in real time exceeds the allowable range, it is judged as noise exceeding the standard; According to the results of energy consumption and noise, determine whether the speed of the equipment needs to be adjusted. If the energy consumption or noise exceeds the standard, the speed needs to be adjusted; When adjusting the speed, a correlation model between the speed and cleanliness is established through a machine learning algorithm to obtain the corresponding minimum speed value while meeting the cleanliness requirements, and the actual speed of the equipment is adjusted to the minimum speed value obtained according to the machine learning algorithm.

[0009] Furthermore, based on historical data and real-time monitoring results, the parameter settings of the intelligent algorithm are optimized to improve the accuracy and response speed of speed control, including: Obtain historical monitoring data and real-time monitoring data, use them as input data for intelligent algorithms, pre-process the input data, and use machine learning algorithms such as support vector machines or neural networks to establish a speed control model based on the pre-processed data; In the established speed control model, relevant parameters are set, model performance is improved through parameter optimization, the optimized speed control model is deployed to the actual system, and speed control is performed according to real-time monitoring data; During the speed control process, operating data is continuously collected and used as new training data. The model is retrained and optimized regularly. By continuously iterating and optimizing the speed control model, the control accuracy and response speed are continuously improved, and intelligent speed control is performed.

[0010] It also includes: continuously monitoring the environmental parameters of the clean room and the operating status of the fan through multi-sensor fusion technology. When abnormal fluctuations are detected, an alarm is triggered and the fan speed is automatically adjusted. The speed control system is then regularly tested for stability to ensure that the adjustment accuracy and response time of the fan speed under different working conditions meet the preset requirements.

[0011] Furthermore, multi-sensor fusion technology is used to continuously monitor the environmental parameters and fan operating status of the clean room, including: Preprocessing and feature extraction are performed based on the acquired environmental parameters and the fan's operating status data such as speed and current; The extracted feature vector is input into the pre-trained support vector machine model to determine whether the current clean room environmental parameters and fan operating status are within the normal range. When abnormal fluctuations are detected, an alarm is triggered; According to the degree of abnormal fluctuation of environmental parameters and the deviation of fan operation status, the adjustment amount of fan speed is calculated through fuzzy control algorithm, and the adjustment instruction is sent to the inverter to automatically adjust the fan speed; After the fan speed adjustment is completed, the feedback data of the speed control system is filtered through the Kalman filter algorithm to determine whether the speed control system is stable. If the stability does not meet the preset requirements, the speed is fine-tuned until a stable state is reached. Then, the speed control system is regularly tested for stability. By setting step changes in the fan speed under different working conditions, the response data of the speed control system is collected, and the speed control accuracy and response time indicators are calculated to determine whether the preset performance requirements are met. When the stability test result is unqualified, the rule base and membership function of the fuzzy controller are optimized online through the incremental learning algorithm; The optimized fuzzy controller parameters are written into the non-volatile memory of the fan controller for persistent storage of the speed regulation algorithm.

[0012] A low energy consumption control device for a large centrifugal fan comprises a processor, a memory and computer program instructions stored in the memory. When the computer program instructions are executed by the processor, the low energy consumption control method for the large centrifugal fan is implemented.

[0013] A storage medium stores computer program instructions, which, when executed by a processor, implement the low-energy consumption control method for a large centrifugal fan described above.

[0014] The beneficial effects of the present invention are: The temperature, humidity, pressure, cleanliness and other environmental parameter data in the clean room are collected in real time through multi-sensor fusion technology and transmitted to the data processing module. The air volume prediction model is established using the support vector machine algorithm to determine the target air volume range under the current working conditions, so as to accurately control the air conditioning system to adjust the air volume to meet the cleanliness requirements of medical operations, so that the fan can flexibly adapt to the changes in environmental parameters in the clean room, solving the problem that traditional fans cannot adjust the air volume according to real-time environmental changes; During the speed regulation process, the energy consumption and noise data are monitored in real time. When the energy consumption exceeds the preset threshold or the noise exceeds the allowable range, the speed is adjusted to the minimum value that meets the cleanliness requirements through the fuzzy control algorithm. At the same time, the parameter settings of the intelligent algorithm are optimized according to historical data and real-time monitoring results to further improve the accuracy and response speed of speed regulation control, reduce unnecessary energy waste, and effectively solve the problems of high energy consumption and low efficiency of traditional fans. The environmental parameters of the clean room and the operating status of the fan are continuously monitored through multi-sensor fusion technology. When abnormal fluctuations are detected, an alarm is triggered and the fan speed is automatically adjusted to ensure that the fan can operate stably under various working conditions. The speed control system is then regularly tested for stability, and the incremental learning algorithm is used to perform online optimization of the rule base and membership function of the fuzzy controller to improve the robustness and adaptability of the speed control system, avoid the loss of control strategy due to sudden failures, and improve fault tolerance and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] For better understanding and implementation, the technical solution of the present application is described in detail below with reference to the accompanying drawings.

[0016] Figure 1 A schematic flow chart of a low energy consumption control method for a large centrifugal fan provided in this application; Figure 2 A schematic diagram of a flow chart of a low energy consumption control method for a large centrifugal fan provided in the present application for determining a target air volume range under current working conditions; Figure 3 A low energy consumption control method for a large centrifugal fan provided in this application is a flow chart of using a fuzzy control algorithm to determine the speed adjustment range. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects taken by the present invention to achieve the predetermined invention purpose, exemplary embodiments will be described in detail here, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are only examples of methods and systems consistent with some aspects of the present application as detailed in the attached claims.

[0018] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0019] The specific implementation methods, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0020] See also Figure 1-Figure 3 This embodiment provides a low energy consumption control method for a large centrifugal fan, comprising the following steps: S1. Obtain environmental parameter data in the clean room, including temperature, humidity, pressure and cleanliness, collect and transmit them to the data processing module in real time through multi-sensor fusion technology, and then determine the target air volume range under the current working conditions based on the preset cleanliness threshold and the air volume requirements of medical operations combined with the environmental parameter data; use it to control the air volume of the air conditioning system to meet the cleanliness requirements of medical operations.

[0021] Furthermore, the environmental parameter data in the clean room, including temperature, humidity, pressure and cleanliness, is acquired in real time through multi-sensor fusion technology and transmitted to the data processing module, including: According to the environmental parameter monitoring requirements of the clean room, the temperature, humidity, pressure and cleanliness sensors are reasonably arranged at different locations in the clean room. The environmental parameter data such as temperature, humidity, pressure and cleanliness in the clean room are collected in real time through multiple sensors, and the sampling frequency is set according to actual needs; The environmental parameter data collected by multiple sensors are transmitted to the data processing module via wired or wireless means. Data encryption and error checking are used during the transmission process to ensure the security and integrity of the data. In the data processing module, the received multi-sensor data is pre-processed, including data cleaning, denoising, normalization and other operations to improve data quality. The Kalman filter algorithm is used to fuse the multi-sensor data, and the optimal value of the environmental parameters is estimated by establishing a state space model to improve the measurement accuracy and reliability. Furthermore, step S1 also includes steps S11, S12, S13 and S14, wherein the target air volume range under the current working condition is determined according to the preset cleanliness threshold and the air volume requirement of the medical operation in combination with the environmental parameter data, specifically including: S11, obtaining the preset cleanliness threshold and air volume requirement of medical operation, and obtaining parameter data of the current environment, including temperature, humidity, pressure, etc.; S12. Establish an air volume prediction model according to the cleanliness threshold, air volume demand and environmental parameter data, and train the air volume prediction model using a support vector machine algorithm to obtain a trained air volume prediction model; S13, inputting the current environmental parameter data into the trained air volume prediction model, predicting the target air volume under the current working condition, and determining whether the predicted target air volume is within the air volume requirement range of the medical operation, and if so, determining it as the final target air volume, and if not, adjusting the air volume prediction model and re-predicting; S14. Output the determined target air volume range to control the air conditioning system to adjust the air volume to meet the cleanliness requirements of medical operations.

[0022] Specifically, multi-sensor fusion technology is used to collect environmental parameter data such as temperature, humidity, pressure and cleanliness in the clean room in real time, and the data is transmitted to the data processing module for preprocessing and fusion, so as to solve the problem of incomplete and inaccurate data collection; according to the preset cleanliness threshold and the air volume requirements of medical operations, combined with environmental parameter data, the air volume prediction model (trained using support vector machine algorithm) is used to determine the target air volume range under the current working conditions, so as to solve the problem that traditional methods cannot flexibly adjust the air volume according to real-time environmental changes; thereby accurately controlling the air-conditioning system to adjust the air volume, meet the cleanliness requirements of medical operations, provide stable and clean environmental support for medical operations, and improve the safety and reliability of medical operations.

[0023] S2. Use frequency conversion technology to adjust the speed of the centrifugal fan. By calculating the difference between the target air volume and the current air volume, the speed adjustment range is determined using the fuzzy control algorithm. When the target air volume is higher than the current air volume, the speed is increased; when the target air volume is lower than the current air volume, the speed is reduced. Further, step S2 also includes steps S21, S22, S23 and S24, wherein S21, obtaining the target air volume and the current air volume of the centrifugal fan, calculating the difference between the target air volume and the current air volume, obtaining the air volume difference value, judging the positive or negative of the air volume difference value, when the air volume difference value is positive, it means that the target air volume is higher than the current air volume, and the fan speed needs to be increased; S22. When the air volume difference value is negative, it means that the target air volume is lower than the current air volume, and the fan speed needs to be reduced; according to the size of the air volume difference value, a fuzzy control algorithm is used to determine the adjustment range of the fan speed to obtain the speed adjustment value; S23, inputting the speed adjustment value into the frequency converter, and adjusting the actual speed of the fan through the frequency conversion technology so that it gradually approaches the target speed; S24. In the process of adjusting the fan speed, the current air volume of the fan is obtained in real time, and it is used as feedback to compare with the target air volume to form a closed-loop control.

[0024] Specifically, frequency conversion technology is used to adjust the speed of the centrifugal fan. By calculating the difference between the target air volume and the current air volume, the speed adjustment range is determined using a fuzzy control algorithm, and the fan speed is accurately adjusted to gradually approach the target speed. At the same time, the current air volume of the fan is obtained in real time as feedback and compared with the target air volume to form a closed-loop control, thereby achieving precise adjustment of the fan air volume, meeting the air volume requirements of the clean room, and improving the operating efficiency and energy-saving effect of the fan.

[0025] S3, analyze the changing trend of environmental parameters through intelligent algorithms, predict the air volume demand in the future, and adjust the fan speed in advance to match the predicted demand; Furthermore, step S3 also includes: by acquiring historical data of environmental parameters within a period of time, including temperature, humidity, air pressure, etc., using a time series analysis algorithm, such as an ARIMA model or an LSTM neural network, to model and predict the change trend of the environmental parameters, and obtain the predicted value of each parameter within a period of time in the future; According to the predicted environmental parameter values, combined with the pre-established air volume demand model, the predicted air volume demand value in the future period is calculated, and the predicted air volume demand value is compared with the current fan speed. When the predicted demand is greater than the air volume corresponding to the current speed, the fan speed is increased to a level that meets the predicted demand; when the predicted demand is less than the air volume corresponding to the current speed, the fan speed is reduced to a level that matches the predicted demand; After adjusting the fan speed, the difference between the actual air volume and the predicted demand is continuously monitored. When the difference exceeds the preset threshold, the update of the prediction model and the adjustment of the fan speed are triggered; The historical data of fan speed adjustment is fed back to the prediction model, and incremental learning algorithms such as online learning or reinforcement learning are used to continuously optimize model parameters to improve prediction accuracy and control effects.

[0026] Specifically, the historical data of environmental parameters is analyzed through intelligent algorithms, and time series analysis algorithms (such as ARIMA model or LSTM neural network) are used to predict the air volume demand in the future. The fan speed is adjusted in advance to match the predicted demand, so as to achieve active and precise adjustment of the fan, adapt to environmental changes in advance, reduce energy waste and environmental instability caused by lagging adjustment, and improve the operating efficiency of the fan and the environmental stability of the clean room.

[0027] S4. During the speed regulation process, the energy consumption and noise data are monitored in real time. When the energy consumption exceeds the preset threshold or the noise exceeds the allowable range, the speed is adjusted to the minimum value that meets the cleanliness requirements; then, based on historical data and real-time monitoring results, the parameter settings of the intelligent algorithm are optimized to improve the accuracy and response speed of speed regulation control; Furthermore, in step S4, the energy consumption and noise data are monitored in real time. When the energy consumption exceeds a preset threshold or the noise exceeds an allowable range, the rotation speed is adjusted to the minimum value that meets the cleanliness requirements, specifically including: Obtain the preset energy consumption threshold and noise allowable range data, and during the speed regulation process, collect the energy consumption data and noise data of the equipment in real time, and compare them with the preset threshold and allowable range; When the energy consumption data collected in real time exceeds the preset threshold, it is judged as energy consumption exceeding the standard; when the noise data collected in real time exceeds the allowable range, it is judged as noise exceeding the standard; According to the results of energy consumption and noise, determine whether the speed of the equipment needs to be adjusted. If the energy consumption or noise exceeds the standard, the speed needs to be adjusted; When adjusting the speed, a correlation model between the speed and cleanliness is established through machine learning algorithms, such as decision trees or support vector machines, to obtain the corresponding minimum speed value while meeting the cleanliness requirements. The actual speed of the equipment is adjusted to the minimum speed value obtained according to the machine learning algorithm to reduce energy consumption and noise while ensuring that the cleanliness requirements are met.

[0028] Specifically, during the speed regulation process, by real-time monitoring of energy consumption and noise data, when energy consumption exceeds the preset threshold or noise exceeds the allowable range, a machine learning algorithm is used to establish a correlation model between speed and cleanliness, and the speed is adjusted to the minimum value that meets the cleanliness requirements, effectively reducing energy consumption and noise. At the same time, the parameter settings of the intelligent algorithm are optimized based on historical data and real-time monitoring results, improving the accuracy and response speed of speed regulation control, and ensuring that the fan achieves efficient, energy-saving, and low-noise operation while meeting the cleanliness requirements.

[0029] Furthermore, in step S4, according to historical data and real-time monitoring results, the parameter settings of the intelligent algorithm are optimized to improve the accuracy and response speed of the speed control, specifically including: Obtain historical monitoring data and real-time monitoring data, use them as input data for intelligent algorithms, pre-process the input data, and use machine learning algorithms such as support vector machines or neural networks to establish a speed control model based on the pre-processed data; In the established speed control model, relevant parameters such as learning rate and regularization coefficient are set to improve model performance through parameter optimization. The optimized speed control model is deployed to the actual system to perform speed control according to real-time monitoring data. During the speed control process, operating data is continuously collected and used as new training data. The model is retrained and optimized regularly. By continuously iterating and optimizing the speed control model, the control accuracy and response speed are continuously improved to achieve intelligent speed control.

[0030] Specifically, historical monitoring data and real-time monitoring data are obtained and used as input data for the intelligent algorithm. After preprocessing, a speed control model is established using a machine learning algorithm. Relevant parameters are set in the model for optimization and deployed to the actual system for speed control. At the same time, operation data is continuously collected as new training data, and the model is retrained and optimized regularly. This solves the problems of low precision and slow response speed of traditional speed control methods, realizes intelligent speed control, and continuously improves the precision and response speed of speed control, so that the fan can adapt to environmental changes and air volume requirements more accurately and quickly, thereby improving the operating efficiency and energy-saving effect of the fan and ensuring the environmental stability of the clean room and the safety of medical operations.

[0031] S5. Continuously monitor the environmental parameters of the clean room and the operating status of the fan through multi-sensor fusion technology. When abnormal fluctuations are detected, an alarm is triggered and the fan speed is automatically adjusted. The speed control system is then regularly tested for stability to ensure that the adjustment accuracy and response time of the fan speed under different working conditions meet the preset requirements.

[0032] Furthermore, step S5 also includes: performing preprocessing and feature extraction based on the acquired environmental parameters and the operating status data such as the speed and current of the fan; The extracted feature vector is input into the pre-trained support vector machine model to determine whether the current clean room environmental parameters and fan operating status are within the normal range. When abnormal fluctuations are detected, an alarm is triggered; According to the degree of abnormal fluctuation of environmental parameters and the deviation of fan operation status, the adjustment amount of fan speed is calculated through fuzzy control algorithm, and the adjustment instruction is sent to the inverter to realize automatic adjustment of fan speed; After the fan speed adjustment is completed, the feedback data of the speed control system is filtered through the Kalman filter algorithm to determine whether the speed control system is stable. When the stability does not meet the preset requirements, the speed is fine-tuned until a stable state is reached.

[0033] Regularly test the stability of the speed control system by setting step changes in the fan speed under different working conditions, collecting the response data of the speed control system, calculating indicators such as speed control accuracy and response time, and judging whether the preset performance requirements are met; When the stability test result is unqualified, the rule base and membership function of the fuzzy controller are optimized online through the incremental learning algorithm to improve the robustness and adaptability of the speed control system and ensure long-term stable operation; The optimized fuzzy controller parameters are written into the non-volatile memory of the fan controller to achieve persistent storage of the speed regulation algorithm, avoid loss of control strategy due to sudden failures, and improve the fault tolerance and reliability of the system.

[0034] Specifically, the multi-sensor fusion technology continuously monitors the environmental parameters of the clean room (such as temperature, humidity, pressure difference, etc.) and the operating status of the fan (such as speed, current, etc.), solving the problem that traditional monitoring methods cannot fully and accurately obtain the clean room environment and fan status information. When abnormal fluctuations are detected, the pre-trained support vector machine model is used to determine whether the current state is normal, trigger an alarm, and promptly remind the operator to avoid potential risks. The fuzzy control algorithm automatically calculates the adjustment amount of the fan speed according to the degree of abnormal fluctuations and the deviation of the fan state, and sends adjustment instructions to the inverter to achieve fast and accurate adjustment of the fan speed, solving the problem of slow response and low precision of manual adjustment, so that the fan can adapt to environmental changes in a timely manner and maintain a stable environment in the clean room. The speed control system is regularly tested for stability. By setting a step change in the fan speed, collecting response data, calculating indicators such as speed control accuracy and response time, and judging whether the system performance meets the standard, the problem of performance degradation that may occur in the long-term operation of the speed control system is solved. When the test results are unqualified, the incremental learning algorithm is used to optimize the rule base and membership function of the fuzzy controller online to improve the robustness and adaptability of the speed control system and ensure the long-term stable operation of the system. The optimized fuzzy controller parameters are written into the non-volatile memory of the fan controller to achieve persistent storage of the speed control algorithm, avoid loss of control strategy due to sudden failures, improve the fault tolerance and reliability of the system, ensure the continuous stability of the clean room environment and fan operation, and provide a solid guarantee for medical operations.

[0035] This embodiment uses multi-sensor fusion technology to monitor the clean room environmental parameters and fan operating status in real time, and combines intelligent algorithms and frequency conversion technology to achieve accurate, efficient, and energy-saving speed control of the centrifugal fan. It can dynamically adjust the fan speed according to the actual air volume demand and environmental change trends in the clean room to meet the cleanliness requirements of medical operations, while reducing energy consumption and noise, ensuring the stability of the clean room environment, and improving the safety and reliability of medical operations. Through continuous optimization and stability testing, the long-term stable operation and control accuracy of the speed control system are ensured, solving the problems of traditional fan control methods that cannot flexibly adapt to changes in clean room operating conditions and are prone to energy waste and environmental instability.

[0036] A low energy consumption control device for a large centrifugal fan comprises a processor, a memory and computer program instructions stored in the memory. When the computer program instructions are executed by the processor, the low energy consumption control method for the large centrifugal fan is implemented.

[0037] A storage medium stores computer program instructions, which, when executed by a processor, implement the low-energy consumption control method for a large centrifugal fan described above.

[0038] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A low energy consumption control method for a large centrifugal fan, characterized in that: The steps include: Obtain environmental parameter data in the clean room, including temperature, humidity, pressure and cleanliness, collect and transmit them to the data processing module in real time through multi-sensor fusion technology, and then determine the target air volume range under the current working conditions based on the preset cleanliness threshold and the air volume requirements of medical operations combined with the environmental parameter data; used to control the air volume of the air conditioning system; The speed of the centrifugal fan is adjusted by frequency conversion technology. The speed adjustment range is determined by using fuzzy control algorithm by calculating the difference between the target air volume and the current air volume. When the target air volume is higher than the current air volume, the speed is increased; when the target air volume is lower than the current air volume, the speed is reduced. Among them, by obtaining the target air volume and current air volume of the centrifugal fan, calculating the difference between the target air volume and the current air volume, obtaining the air volume difference value, and judging whether the air volume difference value is positive or negative. When the air volume difference value is positive, it means that the target air volume is higher than the current air volume, and the fan speed needs to be increased; When the air volume difference value is negative, it means that the target air volume is lower than the current air volume, and the fan speed needs to be reduced; according to the size of the air volume difference value, the fuzzy control algorithm is used to determine the adjustment range of the fan speed and obtain the speed adjustment value; The speed adjustment value is input into the frequency converter, and the actual speed of the fan is adjusted through frequency conversion technology to gradually approach the target speed; In the process of adjusting the fan speed, the current air volume of the fan is obtained in real time, which is used as feedback and compared with the target air volume to form a closed-loop control; Analyze the changing trend of environmental parameters through intelligent algorithms, predict the air volume demand in the future, and adjust the fan speed in advance to match the predicted demand; During the speed regulation process, energy consumption and noise data are monitored in real time. When the energy consumption exceeds the preset threshold or the noise exceeds the allowable range, the speed is adjusted to the minimum value that meets the cleanliness requirements; then, based on historical data and real-time monitoring results, the parameter settings of the intelligent algorithm are optimized.

2. A low energy consumption control method for a large centrifugal fan according to claim 1, characterized in that: Obtain environmental parameter data in the clean room, including temperature, humidity, pressure and cleanliness, collect them in real time through multi-sensor fusion technology and transmit them to the data processing module, including: According to the environmental parameter monitoring requirements of the clean room, temperature, humidity, pressure and cleanliness sensors are reasonably arranged at different locations in the clean room. The environmental parameter data in the clean room is collected in real time through multiple sensors, and the sampling frequency is set according to actual needs; The environmental parameter data collected by multiple sensors are transmitted to the data processing module via wired or wireless means; In the data processing module, the received multi-sensor data is preprocessed, and the multi-sensor data is fused using the Kalman filter algorithm. By establishing a state space model, the optimal values ​​of environmental parameters are estimated.

3. The low energy consumption control method for a large centrifugal fan according to claim 1, characterized in that: According to the preset cleanliness threshold and the air volume requirements of medical operations, combined with environmental parameter data, the target air volume range under the current working conditions is determined, including: Obtain the preset cleanliness threshold and air volume requirements for medical operations, and obtain parameter data of the current environment; According to the cleanliness threshold, air volume demand and environmental parameter data, an air volume prediction model is established, and the air volume prediction model is trained using a support vector machine algorithm to obtain a trained air volume prediction model; Input the current environmental parameter data into the trained air volume prediction model to predict the target air volume under the current working condition, and determine whether the predicted target air volume is within the air volume requirement range of the medical operation. If it is, it is determined as the final target air volume. If not, the air volume prediction model is adjusted and re-predicted; The determined target air volume range is outputted to control the air conditioning system to adjust the air volume.

4. The low energy consumption control method for a large centrifugal fan according to claim 1, characterized in that: Analyze the changing trend of environmental parameters through intelligent algorithms, predict the air volume demand in the future, and adjust the fan speed in advance to match the predicted demand, including: obtaining historical data of environmental parameters in a period of time, using time series analysis algorithms to model and predict the changing trend of environmental parameters, and obtain the predicted values ​​of each parameter in the future; According to the predicted environmental parameter values, combined with the pre-established air volume demand model, the predicted air volume demand value in the future period is calculated, and the predicted air volume demand value is compared with the current fan speed. When the predicted demand is greater than the air volume corresponding to the current speed, the fan speed is increased to a level that meets the predicted demand; when the predicted demand is less than the air volume corresponding to the current speed, the fan speed is reduced to a level that matches the predicted demand; After adjusting the fan speed, the difference between the actual air volume and the predicted demand is continuously monitored. When the difference exceeds the preset threshold, the update of the prediction model and the adjustment of the fan speed are triggered; The historical data of fan speed adjustment is fed back to the prediction model, and the model parameters are continuously optimized using incremental learning algorithms such as online learning or reinforcement learning.

5. The low energy consumption control method for a large centrifugal fan according to claim 1, characterized in that: Real-time monitoring of energy consumption and noise data. When energy consumption exceeds the preset threshold or noise exceeds the allowable range, the speed is adjusted to the minimum value that meets the cleanliness requirements, including: Obtain the preset energy consumption threshold and noise allowable range data, and during the speed regulation process, collect the energy consumption data and noise data of the equipment in real time and compare them with the preset threshold and allowable range; When the energy consumption data collected in real time exceeds the preset threshold, it is judged as energy consumption exceeding the standard; when the noise data collected in real time exceeds the allowable range, it is judged as noise exceeding the standard; According to the results of energy consumption and noise, determine whether the equipment speed needs to be adjusted. If the energy consumption or noise exceeds the standard, the speed needs to be adjusted; When adjusting the speed, a correlation model between the speed and cleanliness is established through a machine learning algorithm to obtain the corresponding minimum speed value while meeting the cleanliness requirements, and the actual speed of the equipment is adjusted to the minimum speed value obtained according to the machine learning algorithm.

6. The low energy consumption control method for a large centrifugal fan according to claim 1, characterized in that: According to historical data and real-time monitoring results, optimize the parameter settings of the intelligent algorithm to improve the accuracy and response speed of speed control, including: Obtain historical monitoring data and real-time monitoring data, use them as input data for intelligent algorithms, pre-process the input data, and use machine learning algorithms such as support vector machines or neural networks to establish a speed control model based on the pre-processed data; In the established speed control model, relevant parameters are set, model performance is improved through parameter optimization, the optimized speed control model is deployed to the actual system, and speed control is performed according to real-time monitoring data; During the speed control process, operating data is continuously collected and used as new training data. The model is retrained and optimized regularly. By continuously iterating and optimizing the speed control model, the control accuracy and response speed are continuously improved, and intelligent speed control is performed.

7. A low energy consumption control method for a large centrifugal fan according to claim 1, characterized in that: Also includes: The environmental parameters of the clean room and the operating status of the fan are continuously monitored through multi-sensor fusion technology. When abnormal fluctuations are detected, an alarm is triggered and the fan speed is automatically adjusted. The speed control system is then regularly tested for stability to ensure that the adjustment accuracy and response time of the fan speed under different working conditions meet the preset requirements.

8. A low energy consumption control method for a large centrifugal fan according to claim 7, characterized in that: Continuously monitor the clean room's environmental parameters and fan operating status through multi-sensor fusion technology, including: Preprocessing and feature extraction are performed based on the acquired environmental parameters and the speed and current operating status data of the fan; The extracted feature vector is input into the pre-trained support vector machine model to determine whether the current clean room environmental parameters and fan operating status are within the normal range. When abnormal fluctuations are detected, an alarm is triggered; According to the degree of abnormal fluctuation of environmental parameters and the deviation of fan operation status, the adjustment amount of fan speed is calculated through fuzzy control algorithm, and the adjustment instruction is sent to the inverter to automatically adjust the fan speed; After the fan speed adjustment is completed, the feedback data of the speed control system is filtered through the Kalman filter algorithm to determine whether the speed control system is stable. If the stability does not meet the preset requirements, the speed is fine-tuned until a stable state is reached. Then, the speed control system is regularly tested for stability. By setting step changes in the fan speed under different working conditions, the response data of the speed control system is collected, and the speed control accuracy and response time indicators are calculated to determine whether the preset performance requirements are met. When the stability test result is unqualified, the rule base and membership function of the fuzzy controller are optimized online through the incremental learning algorithm; The optimized fuzzy controller parameters are written into the non-volatile memory of the fan controller for persistent storage of the speed regulation algorithm.

9. A low energy consumption control device for a large centrifugal fan, characterized in that: The invention comprises a processor, a memory and computer program instructions stored in the memory. When the computer program instructions are executed by the processor, a low energy consumption control method for a large centrifugal fan as claimed in any one of claims 1 to 8 is implemented.

10. A storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by the processor, a low energy consumption control method for a large centrifugal fan as described in any one of claims 1 to 8 is implemented.

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