Control system for cooling fan

Abstract

The invention discloses a control system for a cooling fan, and relates to the technical field of cooling fan control systems. The temperature acquisition module is used for monitoring the temperature of a key heating part in real time by adopting a high-precision temperature sensor and converting an analog signal into a digital signal through a data acquisition card; the data processing module is used for receiving the data from the temperature acquisition module and analyzing the data by using an embedded processor; a self-adaptive filtering algorithm is built in the module so as to reduce the influence of environmental noise on temperature reading; and the core of the control algorithm module is a PID (Proportion Integration Differentiation) controller, and a hybrid control strategy is formed by combining fuzzy logic control. Through accurate temperature monitoring and an intelligent control algorithm, the system can quickly respond to temperature change, automatically adjust the rotating speed of the fan, realize efficient heat dissipation and prolong the service life of equipment; the system adopts a variable-frequency speed regulation technology, the operation of the fan is adjusted according to actual requirements, unnecessary energy consumption is avoided, and the energy-saving effect is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of heat dissipation fan control systems, and in particular to a control system for a heat dissipation fan. Background Art

[0002] As electronic devices develop towards high performance and miniaturization, heat dissipation becomes a key factor limiting device performance and stability. Traditional heat dissipation systems mostly use fixed-speed fans or simple temperature control strategies, which are difficult to meet the heat dissipation requirements in complex environments and have high energy consumption.

[0003] After searching, the application scheme of Chinese patent application No. 201210363516.3 discloses an automatic control system for a fresh air unit, characterized by comprising a fresh air unit, a DDC controller, a central control computer and an alarm, wherein the fresh air unit is connected to the DDC controller, and the DDC controller is connected to the central control computer and the alarm. The automatic control system in the above patent has the following shortcomings: although it can meet certain control requirements, it cannot respond well to environmental changes, and the energy consumption control needs to be improved. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a control system for a heat dissipation fan.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A control system for a heat dissipation fan, comprising:

[0007] Temperature acquisition module: Use high-precision temperature sensors to monitor the temperature of key heating components in real time, and convert analog signals into digital signals through data acquisition cards;

[0008] Data processing module: receives data from the temperature acquisition module and uses an embedded processor to analyze the data; this module has a built-in adaptive filtering algorithm to reduce the impact of environmental noise on temperature readings;

[0009] Control algorithm module: The core of the control algorithm module is the PID controller, which is combined with fuzzy logic control to form a hybrid control strategy;

[0010] Fan drive module: According to the control signal output by the control algorithm module, the power supply frequency of the fan is adjusted through the inverter, thereby changing the fan speed; the inverter adopts vector control technology to ensure that the motor maintains high efficiency and stability during the speed change process;

[0011] Air duct control module, the control system includes multiple linearly distributed air ducts, each fan is distributed in each air duct, each air duct is independently provided with a control valve for controlling the on-off of the air duct, the air duct control module realizes on-off control of each air duct by controlling the control valve, and the air duct control module opens the corresponding passage based on the control strategy;

[0012] Communication interface module: Provides RS485 and CAN bus communication interfaces, supports data transmission and remote control with other control systems or monitoring centers;

[0013] Human-machine interaction interface: equipped with LCD display and buttons for on-site parameter setting, status display and fault diagnosis;

[0014] Safety protection module: including over-temperature protection, overload protection and short-circuit protection functions, ensuring that the system can respond promptly and take protective measures under abnormal circumstances.

[0015] Preferably: the temperature acquisition module comprises:

[0016] Real-time temperature sensing unit: uses high-precision temperature sensors arranged in an array to capture real-time temperature data of key parts of the equipment;

[0017] Environmental temperature and humidity monitoring unit: integrated temperature and humidity sensors to monitor environmental temperature and humidity changes in real time. The data is used to optimize internal heat dissipation strategies and adjust fan operating status.

[0018] Data acquisition and conversion unit: including analog-to-digital converter, responsible for converting the analog signal of the sensor into digital signal for subsequent digital processing.

[0019] Preferably: the data processing module comprises:

[0020] Data filtering unit: Kalman filtering and moving average filtering technology are used to denoise the temperature data to ensure data accuracy and stability;

[0021] Data fusion unit: uses data fusion algorithm to integrate different sensor data to provide more accurate temperature assessment and provide basis for control decision;

[0022] Abnormal detection and pre-processing unit: Identifies and warns of abnormal temperature data and performs preliminary diagnosis by setting thresholds and pattern recognition technology;

[0023] The data processing module integrates data using a data fusion algorithm, and the specific formula is:

[0024]

[0025] P k =(IK k Hk ) k-1

[0026]

[0027] in:

[0028] is the state estimation vector at time step k;

[0029] K k is the Kalman gain;

[0030] z k is the observation vector at time step k;

[0031] H k is the observation matrix, which converts the state into observation;

[0032] P k is the error covariance matrix at time step k;

[0033] M k is the observation noise covariance matrix.

[0034] Preferably: the control algorithm module comprises:

[0035] PID control unit: Based on PID control logic, the fan speed is adjusted according to the set point and actual temperature difference;

[0036] Fuzzy logic unit: Combined with fuzzy set theory, it can handle the nonlinear relationship between temperature and speed and improve the robustness of the control system;

[0037] Hybrid control strategy unit: combines PID control with fuzzy logic to achieve more precise and flexible fan control;

[0038] The control algorithm module combines PID control with fuzzy logic to control the fan, specifically:

[0039]

[0040] in:

[0041] u(t) is the controller output;

[0042] e(t) is the error signal;

[0043] K p , K i , K d They are proportional, integral and differential gains respectively;

[0044] Fuzzy logic tuning:

[0045] Through the fuzzy logic rule base, the PID gain is adjusted according to the error and the error change rate, as follows:

[0046] If the error is large and the error rate of change is large, increase K p , reduce K i , keep K d .

[0047] Preferably: the fan driving module comprises:

[0048] Frequency conversion speed regulation unit: Use high-efficiency frequency converter to adjust the fan power supply frequency according to the control algorithm output to achieve wide range stepless speed regulation;

[0049] Motor parameter monitoring unit: monitors the current, voltage and power factor of the fan motor for protection and efficiency optimization;

[0050] Energy efficiency management unit: calculates and optimizes the operating energy efficiency of the fan, and guides the speed regulation strategy to reduce energy consumption;

[0051] The heat dissipation fan comprises a fan base and a fan body, the fan body is installed in the fan base, a protective net is arranged on the fan base, a plurality of fan bases are arranged in an array on a base, a lifting drive motor is installed on the base, the output end of the lifting drive motor is connected to the lifting drive screw, a guide rod is installed on the base, a wind shield is slidably connected to the outer side of the guide rod, the wind shield is located on the outer side of the heat dissipation fan, and the lifting drive screw is connected to the inner wall of the wind shield through a thread; a groove adapted to the wind shield is provided on the base;

[0052] The fan drive module also includes an isolation control unit, which is used to control the operation of the lifting drive motor.

[0053] Preferably: a slot is provided on the top of the windshield, a top cover is detachably mounted on the top of the windshield, a plug post matching the slot is provided at the bottom of the top cover, and the top cover is detachably mounted in the slot of the windshield through the plug post;

[0054] A blade is rotatably installed on the inner side of the top of the top cover through an installation shaft, a gear is installed on one end of the installation shaft, a guide seat is fixed on one side of the top cover, a sleeve is slidably connected in the guide seat, an opening and closing drive motor is installed on one side of the top cover through a bracket, the output end of the opening and closing drive motor is transmission-connected with a control screw, the control screw rotates on the bracket, the control screw is connected to the inner wall of the sleeve through a thread, a rack is fixed on the top of the sleeve, and the rack is meshed with each gear.

[0055] Preferably, it also includes:

[0056] Prediction algorithm module: This module uses machine learning algorithms to predict future heat load conditions based on historical temperature data and external environmental variables; helping the control system adjust the fan speed in advance to prevent potential overheating problems;

[0057] Adaptive learning module: Through the reinforcement learning algorithm, the system can self-learn and optimize according to past control effects;

[0058] Intelligent scheduling module: When there are multiple cooling fans, the intelligent scheduling module uses genetic algorithms or particle swarm optimization algorithms to perform optimal scheduling based on the performance, aging degree and current working status of each fan, balance the workload of each fan, extend the overall service life, and improve energy efficiency;

[0059] Fault diagnosis and predictive maintenance module: Combined with deep learning technology, it analyzes the wind turbine operation data to achieve early fault detection and diagnosis; at the same time, through the predictive maintenance algorithm, it evaluates the remaining service life of the wind turbine and makes maintenance recommendations when appropriate;

[0060] Human-computer interaction optimization module: uses natural language processing technology to optimize the human-computer interaction interface, allowing users to interact with the control system through voice or natural language text, improving operational convenience and user experience;

[0061] Energy consumption management module: Integrates evolutionary algorithms to optimize the energy consumption of the cooling system; automatically searches for the best fan operation combination and speed regulation strategy to minimize energy consumption while meeting cooling requirements.

[0062] Preferably: the prediction algorithm module comprises:

[0063] Heat load forecasting unit: Use time series analysis or machine learning models to predict short-term and long-term heat load changes;

[0064] Environmental change prediction unit: analyzes external environmental data and predicts its impact on heat dissipation requirements;

[0065] Prediction result integration unit: Integrate the prediction results of heat load and environmental changes to provide a scientific basis for fan control;

[0066] The prediction algorithm module uses a long short-term memory network to predict short-term and long-term heat load changes. The specific formula is:

[0067]

[0068] in, is the predicted value of heat load at time t, LSTM(·) is the long short-term memory network model, x t-n ,…,x t-2 ,x t-1is the temperature and environmental variable data at the past n time points;

[0069] Among them, the gating mechanism of the LSTM unit includes:

[0070] Input Gate:

[0071]

[0072] Forget Gate:

[0073]

[0074] Unit status update:

[0075]

[0076] Output Gate:

[0077]

[0078] Unit output:

[0079]

[0080] Where W represents the weight matrix, b represents the bias vector, σ is the sigmoid activation function, represents the Haddam product.

[0081] Preferably: the adaptive learning module comprises:

[0082] Performance feedback unit: collects heat dissipation and energy consumption data to provide feedback for the learning algorithm;

[0083] Strategy Optimization Unit: Apply reinforcement learning to continuously optimize heat dissipation strategies;

[0084] Knowledge base updating unit: stores and updates the knowledge and rules accumulated during the learning process for rapid response in new situations.

[0085] Preferably: the intelligent scheduling module includes:

[0086] Fan Health Assessment Unit: monitors the health of each fan, assessing efficiency and life expectancy;

[0087] Load balancing unit: realizes dynamic distribution of heat dissipation load among fans, prolongs fan life and improves overall system efficiency;

[0088] Maintenance scheduling unit: intelligently arranges maintenance plans based on fan status and predictive maintenance information to reduce system downtime.

[0089] Preferably: the fault diagnosis and predictive maintenance module comprises:

[0090] Fault feature extraction unit: extract fault feature signals through vibration analysis and acoustic monitoring methods;

[0091] Fault pattern recognition unit: Apply deep learning to identify fault types;

[0092] Predictive maintenance decision-making unit: Combines fault diagnosis results and equipment operation data to assess maintenance urgency and develop maintenance plans.

[0093] The beneficial effects of the present invention are:

[0094] 1. Through precise temperature monitoring and intelligent control algorithms, the system of the present invention can quickly respond to temperature changes, automatically adjust the fan speed, achieve efficient heat dissipation, and extend the service life of the equipment; the system adopts variable frequency speed regulation technology to adjust the fan operation according to actual needs, avoid unnecessary energy consumption, and achieve energy-saving effects.

[0095] 2. The present invention introduces a prediction algorithm and an adaptive learning mechanism, which enables the system to predict future heat loads and adaptively optimize control strategies, thereby improving the autonomy and intelligence of the system; the integrated fault diagnosis and predictive maintenance module can monitor the equipment status in real time, predict potential faults, perform maintenance in advance, and reduce unexpected downtime.

[0096] 3. By setting up structures such as a wind shield and a lifting drive motor, the present invention can control the wind shield to cover the outside of the heat dissipation fan based on the operation of the lifting drive motor when the corresponding heat dissipation fan is not working, so as to isolate the airflow generated by other working heat dissipation fans. This avoids the phenomenon that when the corresponding heat dissipation fan is switched from a non-working state to a working state, the fan body is disturbed by the airflow, resulting in excessive energy consumption or even damage in the initial startup, thereby improving reliability.

[0097] 4. The present invention provides a detachable top cover, which can shield the fan body when the wind shield moves downward, avoiding disturbance, and the top cover can be flexibly installed and removed, which is highly practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] Figure 1 A control flow chart of a control system for a heat dissipation fan proposed by the present invention;

[0099] Figure 2 A schematic diagram of the structure of multiple heat dissipation fans of a control system for heat dissipation fans proposed by the present invention;

[0100] Figure 3 A schematic structural diagram of a heat dissipation fan and a wind shield for a control system of a heat dissipation fan proposed by the present invention;

[0101] Figure 4A schematic diagram of the structure of a control system for a heat dissipation fan proposed by the present invention with a wind shield removed from the outside of the heat dissipation fan;

[0102] Figure 5 A schematic diagram of the structure of a control system for a heat dissipation fan with a wind shield removed from the outside of the heat dissipation fan proposed in Embodiment 2 of the present invention;

[0103] Figure 6 This is a schematic diagram of the structure of the gear and rack coordination of a control system for a heat dissipation fan proposed in Example 2 of the present invention.

[0104] In the figure:

[0105] 1-base; 2-lifting drive screw; 3-wind shield; 4-protective net; 5-guide rod; 6-fan seat; 7-groove; 8-lifting drive motor; 9-fan body; 10-top cover; 11-insertion column; 12-slot; 13-opening and closing drive motor; 14-rack; 15-gear; 16-blade; 17-installation shaft; 18-control screw; 19-guide seat, 20-sleeve. DETAILED DESCRIPTION

[0106] The technical solution of the present invention is further described in detail below in conjunction with specific implementation methods.

[0107] Embodiment 1:

[0108] A control system for a heat dissipation fan, comprising:

[0109] Temperature acquisition module: Use high-precision temperature sensors to monitor the temperature of key heating components in real time, and convert analog signals into digital signals through data acquisition cards;

[0110] Data processing module: receives data from the temperature acquisition module and uses an embedded processor to analyze the data; this module has a built-in adaptive filtering algorithm to reduce the impact of environmental noise on temperature readings;

[0111] Control algorithm module: The core of the control algorithm module is the PID controller, which is combined with fuzzy logic control to form a hybrid control strategy;

[0112] Fan drive module: According to the control signal output by the control algorithm module, the power supply frequency of the fan is adjusted through the inverter, thereby changing the fan speed; the inverter adopts vector control technology to ensure that the motor maintains high efficiency and stability during the speed change process;

[0113] Air duct control module, the control system includes multiple linearly distributed air ducts, each fan is distributed in each air duct, each air duct is independently provided with a control valve for controlling the on-off of the air duct, the air duct control module realizes on-off control of each air duct by controlling the control valve, and the air duct control module opens the corresponding passage based on the control strategy;

[0114] Communication interface module: provides RS485, CAN bus and other communication interfaces, supports data transmission and remote control with other control systems or monitoring centers;

[0115] Human-machine interaction interface: equipped with LCD display and buttons for on-site parameter setting, status display and fault diagnosis;

[0116] Safety protection module: including over-temperature protection, overload protection and short-circuit protection functions, ensuring that the system can respond promptly and take protective measures under abnormal circumstances.

[0117] Wherein, the temperature acquisition module includes:

[0118] Real-time temperature sensing unit: uses high-precision temperature sensors arranged in an array to capture real-time temperature data of key parts of the equipment;

[0119] Environmental temperature and humidity monitoring unit: integrated temperature and humidity sensors to monitor environmental temperature and humidity changes in real time. The data is used to optimize internal heat dissipation strategies and adjust fan operating status.

[0120] Data acquisition and conversion unit: including analog-to-digital converter (ADC), responsible for converting the analog signal of the sensor into a digital signal for subsequent digital processing.

[0121] Wherein, the data processing module includes:

[0122] Data filtering unit: Kalman filtering and moving average filtering technology are used to denoise the temperature data to ensure data accuracy and stability;

[0123] Data fusion unit: uses data fusion algorithm to integrate different sensor data to provide more accurate temperature assessment and provide basis for control decision;

[0124] Abnormal detection and preprocessing unit: By setting thresholds and pattern recognition technology, it can identify and warn abnormal temperature data and perform preliminary diagnosis.

[0125] The data processing module integrates data using a data fusion algorithm, and the specific formula is:

[0126]

[0127] P k =(IKk H k ) k-1

[0128]

[0129] in:

[0130] is the state estimation vector of time step k (temperature value after fusion);

[0131] K k is the Kalman gain;

[0132] z k is the observation vector (sensor data) at time step k;

[0133] H k is the observation matrix, which converts the state into observation;

[0134] P k is the error covariance matrix at time step k;

[0135] M k is the observation noise covariance matrix.

[0136] Wherein, the control algorithm module includes:

[0137] PID control unit: Based on PID control logic, the fan speed is adjusted according to the set point and actual temperature difference;

[0138] Fuzzy logic unit: Combined with fuzzy set theory, it can handle the nonlinear relationship between temperature and speed and improve the robustness of the control system;

[0139] Hybrid control strategy unit: combines PID control with fuzzy logic to achieve more precise and flexible fan control.

[0140] The control algorithm module combines PID control with fuzzy logic to achieve more accurate and flexible fan control, specifically:

[0141]

[0142] in:

[0143] u(t) is the controller output (fan speed adjustment signal);

[0144] e(t) is the error signal (the difference between the set temperature and the actual temperature);

[0145] K p , K i , K d They are proportional, integral and differential gains respectively;

[0146] Fuzzy logic tuning:

[0147] Through the fuzzy logic rule base, the PID gain is adjusted according to the error and the error change rate, as follows:

[0148] If the error is large and the error rate of change is large, increase K p , reduce K i , keep K d .

[0149] Wherein, the fan driving module comprises:

[0150] Frequency conversion speed regulation unit: Use high-efficiency frequency converter to adjust the fan power supply frequency according to the control algorithm output to achieve wide range stepless speed regulation;

[0151] Motor parameter monitoring unit: monitors the fan motor's current, voltage, power factor and other parameters for protection and efficiency optimization;

[0152] Energy efficiency management unit: calculates and optimizes the operating energy efficiency of the fan, and guides the speed regulation strategy to reduce energy consumption.

[0153] The heat dissipation fan comprises a fan seat 6 and a fan body 9, the fan body 9 is installed in the fan seat 6, a protective net 4 is arranged on the fan seat 6, a plurality of fan seats 6 are arranged in an array on a base 1, a lifting drive motor 8 is installed on the base 1, the output end of the lifting drive motor 8 is connected to the lifting drive screw 2, a guide rod 5 is installed on the base 1, a windshield 3 is slidably connected to the outer side of the guide rod 5, the windshield 3 is located on the outer side of the heat dissipation fan, and the lifting drive screw 2 is connected to the inner wall of the windshield 3 by a thread; a groove 7 adapted to the windshield 3 is opened on the base 1;

[0154] The fan drive module also includes an isolation control unit, which is used to control the operation of the lifting drive motor 8;

[0155] By setting up structures such as the wind shield 3 and the lifting drive motor 8, when the corresponding cooling fan is not working, the wind shield 3 can be controlled to cover the outside of the cooling fan based on the operation of the lifting drive motor 8, so as to isolate the airflow generated by other working cooling fans. This avoids the phenomenon that when the corresponding cooling fan is switched from a non-working state to a working state, the fan body 9 is disturbed by the airflow, resulting in excessive energy consumption or even damage in the initial startup, thereby improving reliability.

[0156] Embodiment 2:

[0157] A control system for a heat dissipation fan. Based on the embodiment 1, in order to better avoid mutual interference between fans, a slot 12 is provided on the top of the wind shield 3, a top cover 10 is detachably installed on the top of the wind shield 3, and a plug post 11 adapted to the slot 12 is provided at the bottom of the top cover 10. The top cover 10 is detachably installed in the slot 12 of the wind shield 3 through the plug post 11;

[0158] A blade 16 is rotatably mounted on the inner side of the top of the top cover 10 through a mounting shaft 17, a gear 15 is mounted on one end of the mounting shaft 17, a guide seat 19 is fixed on one side of the top cover 10, a sleeve 20 is slidably connected in the guide seat 19, an opening and closing drive motor 13 is mounted on one side of the top cover 10 through a bracket, a control screw 18 is transmission-connected to the output end of the opening and closing drive motor 13, the control screw 18 rotates on the bracket, the control screw 18 is threadedly connected to the inner wall of the sleeve 20, a rack 14 is fixed on the top of the sleeve 20, and the rack 14 is meshed with each gear 15;

[0159] By providing a detachable top cover 10, when the wind shield 3 moves downward, the rack 14 can be driven to move by the opening and closing drive motor 13, thereby controlling the closing of each blade 16 to achieve shielding of the fan body 9 and avoid disturbance. The top cover 10 can be flexibly loaded and unloaded, and is highly practical.

[0160] Embodiment 3:

[0161] A control system for a heat dissipation fan. This embodiment, based on the first embodiment, further includes:

[0162] Prediction algorithm module: This module uses machine learning algorithms, such as support vector machines (SVM) or neural networks (NN), to predict future heat load conditions based on historical temperature data and external environmental variables; this can help the control system adjust the fan speed in advance to prevent potential overheating problems;

[0163] Adaptive learning module: Through the reinforcement learning algorithm, the system can self-learn and optimize according to past control effects;

[0164] Intelligent scheduling module: In the case of multiple cooling fans, the intelligent scheduling module can use genetic algorithms or particle swarm optimization algorithms to perform optimal scheduling based on the performance, aging degree and current working status of each fan, balance the workload of each fan, extend the overall service life, and improve energy efficiency;

[0165] Fault diagnosis and predictive maintenance module: Combined with deep learning technology, it analyzes the wind turbine operation data to achieve early fault detection and diagnosis. At the same time, through predictive maintenance algorithms, such as the prediction model based on recurrent neural network (RNN), it evaluates the remaining service life of the wind turbine and makes maintenance recommendations when appropriate.

[0166] Human-computer interaction optimization module: uses natural language processing (NLP) technology to optimize the human-computer interaction interface, allowing users to interact with the control system through voice or natural language text, improving operational convenience and user experience;

[0167] Energy consumption management module: Integrate evolutionary algorithms, such as genetic algorithms, to optimize the energy consumption of the cooling system; automatically search for the best fan operation combination and speed regulation strategy to minimize energy consumption while meeting the cooling requirements.

[0168] Wherein, the prediction algorithm module includes:

[0169] Heat load forecasting unit: Use time series analysis or machine learning models, such as long short-term memory networks (LSTM), to predict short-term and long-term heat load changes;

[0170] Environmental change prediction unit: Analyzes external environmental data, such as the operating status of other equipment in the data center, and predicts its impact on cooling requirements;

[0171] Prediction result integration unit: Integrate the prediction results of heat load and environmental changes to provide a scientific basis for fan control.

[0172] The prediction algorithm module uses a long short-term memory network to predict short-term and long-term heat load changes. The specific formula is:

[0173]

[0174] in, is the predicted value of heat load at time t, LSTM(·) is the long short-term memory network model, x t-n ,…,x t-2 ,x t-1 is the temperature and environmental variable data at the past n time points;

[0175] Among them, the gating mechanism of the LSTM unit includes:

[0176] Input Gate:

[0177]

[0178] Forget Gate:

[0179]

[0180] Unit status update:

[0181]

[0182] Output Gate:

[0183]

[0184] Unit output:

[0185]

[0186] Where W represents the weight matrix, b represents the bias vector, σ is the sigmoid activation function, represents the Haddam product.

[0187] Wherein, the adaptive learning module includes:

[0188] Performance feedback unit: collects heat dissipation and energy consumption data to provide feedback for the learning algorithm;

[0189] Policy Optimization Unit: Apply reinforcement learning, such as Deep Q Network (DQN), to continuously optimize cooling strategies;

[0190] Knowledge base updating unit: stores and updates the knowledge and rules accumulated during the learning process for rapid response in new situations.

[0191] Wherein, the intelligent scheduling module includes:

[0192] Fan Health Assessment Unit: monitors the health of each fan and assesses its efficiency and life expectancy;

[0193] Load balancing unit: realizes dynamic distribution of heat dissipation load among fans, prolongs fan life and improves overall system efficiency;

[0194] Maintenance scheduling unit: intelligently arranges maintenance plans based on fan status and predictive maintenance information to reduce system downtime.

[0195] Wherein, the fault diagnosis and predictive maintenance module includes:

[0196] Fault feature extraction unit: extract fault feature signals through vibration analysis, acoustic monitoring and other methods;

[0197] Fault pattern recognition unit: Apply deep learning, such as convolutional neural network (CNN), to identify fault types;

[0198] Predictive maintenance decision-making unit: Combines fault diagnosis results and equipment operation data to assess maintenance urgency and develop maintenance plans.

[0199] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A control system for a heat dissipation fan, characterized in that: include: Temperature acquisition module: Use high-precision temperature sensors to monitor the temperature of key heating components in real time, and convert analog signals into digital signals through data acquisition cards; Data processing module: receives data from the temperature acquisition module and uses an embedded processor to analyze the data; this module has a built-in adaptive filtering algorithm to reduce the impact of environmental noise on temperature readings; Control algorithm module: The core of the control algorithm module is the PID controller, which is combined with fuzzy logic control to form a hybrid control strategy; Fan drive module: According to the control signal output by the control algorithm module, the power supply frequency of the fan is adjusted through the inverter, thereby changing the fan speed; the inverter adopts vector control technology to ensure that the motor maintains high efficiency and stability during the speed change process; Air duct control module, the control system includes multiple linearly distributed air ducts, each fan is distributed in each air duct, each air duct is independently provided with a control valve for controlling the on-off of the air duct, the air duct control module realizes on-off control of each air duct by controlling the control valve, and the air duct control module opens the corresponding passage based on the control strategy; Communication interface module: Provides RS485 and CAN bus communication interfaces, supports data transmission and remote control with other control systems or monitoring centers; Human-machine interaction interface: equipped with LCD display and buttons for on-site parameter setting, status display and fault diagnosis; Safety protection module: including over-temperature protection, overload protection and short-circuit protection functions, ensuring that the system can respond promptly and take protective measures under abnormal circumstances.

2. A control system for a heat dissipation fan according to claim 1, characterized in that: The temperature acquisition module comprises: Real-time temperature sensing unit: uses high-precision temperature sensors arranged in an array to capture real-time temperature data of key parts of the equipment; Environmental temperature and humidity monitoring unit: integrated temperature and humidity sensors to monitor environmental temperature and humidity changes in real time. The data is used to optimize internal heat dissipation strategies and adjust fan operating status. Data acquisition and conversion unit: including analog-to-digital converter, responsible for converting the analog signal of the sensor into digital signal for subsequent digital processing.

3. A control system for a heat dissipation fan according to claim 2, characterized in that: The data processing module comprises: Data filtering unit: Kalman filtering and moving average filtering technology are used to denoise the temperature data to ensure data accuracy and stability; Data fusion unit: uses data fusion algorithm to integrate different sensor data to provide more accurate temperature assessment and provide basis for control decision; Abnormal detection and pre-processing unit: Identifies and warns of abnormal temperature data and performs preliminary diagnosis by setting thresholds and pattern recognition technology; The data processing module integrates data using a data fusion algorithm, and the specific formula is: P k =(I-K k H k )P k-1 in: is the state estimation vector at time step k; K k is the Kalman gain; z k is the observation vector at time step k; H k is the observation matrix, which converts the state into observation; P k is the error covariance matrix at time step k; M k is the observation noise covariance matrix.

4. A control system for a heat dissipation fan according to claim 3, characterized in that: The control algorithm module comprises: PID control unit: Based on PID control logic, the fan speed is adjusted according to the set point and actual temperature difference; Fuzzy logic unit: Combined with fuzzy set theory, it can handle the nonlinear relationship between temperature and speed and improve the robustness of the control system; Hybrid control strategy unit: combines PID control with fuzzy logic to achieve more precise and flexible fan control; The control algorithm module combines PID control with fuzzy logic to control the fan, specifically: in: u(t) is the controller output; e(t) is the error signal; K p , K i , K d They are proportional, integral and differential gains respectively; Fuzzy logic tuning: Through the fuzzy logic rule base, the PID gain is adjusted according to the error and the error change rate, as follows: If the error is large and the error rate of change is large, increase K p , reduce K i , keep K d .

5. A control system for a heat dissipation fan according to claim 1, characterized in that: The fan driving module comprises: Frequency conversion speed regulation unit: Use high-efficiency frequency converter to adjust the fan power supply frequency according to the control algorithm output to achieve wide range stepless speed regulation; Motor parameter monitoring unit: monitors the current, voltage and power factor of the fan motor for protection and efficiency optimization; Energy efficiency management unit: calculates and optimizes the operating energy efficiency of the fan, and guides the speed regulation strategy to reduce energy consumption; The heat dissipation fan comprises a fan base (6) and a fan body (9), wherein the fan body (9) is installed in the fan base (6), a protective net (4) is arranged on the fan base (6), a plurality of fan bases (6) are arranged in an array on a base (1), a lifting drive motor (8) is installed on the base (1), an output end of the lifting drive motor (8) is drivingly connected to a lifting drive screw (2), a guide rod (5) is installed on the base (1), a windshield (3) is slidably connected to the outer side of the guide rod (5), the windshield (3) is located on the outer side of the heat dissipation fan, and the lifting drive screw (2) is connected to the inner wall of the windshield (3) by means of a thread; a groove (7) adapted to the windshield (3) is provided on the base (1); The fan drive module also includes an isolation control unit, which is used to control the operation of the lifting drive motor (8).

6. A control system for a heat dissipation fan according to claim 5, characterized in that: The top of the windshield (3) is provided with a slot (12); a top cover (10) is detachably mounted on the top of the windshield (3); a plug post (11) adapted to the slot (12) is disposed at the bottom of the top cover (10); the top cover (10) is detachably mounted in the slot (12) of the windshield (3) via the plug post (11); A blade (16) is rotatably mounted on the inner side of the top of the top cover (10) via a mounting shaft (17); a gear (15) is mounted on one end of the mounting shaft (17); a guide seat (19) is fixed on one side of the top cover (10); a sleeve (20) is slidably connected in the guide seat (19); an opening and closing drive motor (13) is mounted on one side of the top cover (10) via a bracket; a control screw (18) is transmission-connected to the output end of the opening and closing drive motor (13); the control screw (18) rotates on the bracket; the control screw (18) is threadedly connected to the inner wall of the sleeve (20); a rack (14) is fixed on the top of the sleeve (20); the rack (14) is meshed with each gear (15).

7. A control system for a heat dissipation fan according to claim 1, characterized in that: Also includes: Prediction algorithm module: This module uses machine learning algorithms to predict future heat load conditions based on historical temperature data and external environmental variables; helping the control system adjust the fan speed in advance to prevent potential overheating problems; Adaptive learning module: Through the reinforcement learning algorithm, the system can self-learn and optimize according to past control effects; Intelligent scheduling module: When there are multiple cooling fans, the intelligent scheduling module uses genetic algorithms or particle swarm optimization algorithms to perform optimal scheduling based on the performance, aging degree and current working status of each fan, balance the workload of each fan, extend the overall service life, and improve energy efficiency; Fault diagnosis and predictive maintenance module: Combined with deep learning technology, it analyzes the wind turbine operation data to achieve early fault detection and diagnosis; at the same time, through the predictive maintenance algorithm, it evaluates the remaining service life of the wind turbine and makes maintenance recommendations when appropriate; Human-computer interaction optimization module: uses natural language processing technology to optimize the human-computer interaction interface, allowing users to interact with the control system through voice or natural language text, improving operational convenience and user experience; Energy consumption management module: Integrates evolutionary algorithms to optimize the energy consumption of the cooling system; automatically searches for the best fan operation combination and speed regulation strategy to minimize energy consumption while meeting cooling requirements.

8. A control system for a heat dissipation fan according to claim 6, characterized in that: The prediction algorithm module comprises: Heat load forecasting unit: Use time series analysis or machine learning models to predict short-term and long-term heat load changes; Environmental change prediction unit: analyzes external environmental data and predicts its impact on heat dissipation requirements; Prediction result integration unit: Integrate the prediction results of heat load and environmental changes to provide a scientific basis for fan control; The prediction algorithm module uses a long short-term memory network to predict short-term and long-term heat load changes. The specific formula is: in, is the predicted value of heat load at time t, LSTM(·) is the long short-term memory network model, x t-n ,…,x t-2 ,x t-1 is the temperature and environmental variable data at the past n time points; Among them, the gating mechanism of the LSTM unit includes: Input Gate: Forget Gate: Unit status update: Output Gate: Unit output: Among them, W represents the weight matrix, b represents the bias vector, σ is the sigmoid activation function, and ° represents the Haddam product.

9. A control system for a heat dissipation fan according to claim 6, characterized in that: The adaptive learning module comprises: Performance feedback unit: collects heat dissipation and energy consumption data to provide feedback for the learning algorithm; Strategy Optimization Unit: Apply reinforcement learning to continuously optimize heat dissipation strategies; Knowledge base update unit: stores and updates the knowledge and rules accumulated during the learning process for quick response in new situations; The intelligent scheduling module includes: Fan Health Assessment Unit: monitors the health of each fan, assessing efficiency and life expectancy; Load balancing unit: realizes dynamic distribution of heat dissipation load among fans, prolongs fan life and improves overall system efficiency; Maintenance scheduling unit: intelligently arranges maintenance plans based on fan status and predictive maintenance information to reduce system downtime.

10. A control system for a heat dissipation fan according to claim 6, characterized in that: The fault diagnosis and predictive maintenance module includes: Fault feature extraction unit: extract fault feature signals through vibration analysis and acoustic monitoring methods; Fault pattern recognition unit: Apply deep learning to identify fault types; Predictive maintenance decision-making unit: Combines fault diagnosis results and equipment operation data to assess maintenance urgency and develop maintenance plans.

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