Intelligent control system and method for small and miniature direct-current brushless fan based on Internet of Things

Abstract

The invention discloses an intelligent control system and method for a small and miniature direct-current brushless fan based on the Internet of Things, and relates to the technical field of brushless fan control. The small and miniature direct current brushless fan intelligent control system based on the Internet of Things comprises a fan heat dissipation fault analysis control module, a fan coupling fault identification control module and a fan fault protection analysis control module. According to the method, whether stator winding heat dissipation parameter optimization is carried out or not is judged through stator winding heat dissipation fault analysis, then whether electrical coupling identification optimization is carried out or not is judged through electrical coupling fault analysis, and finally whether safety protection parameter optimization is carried out or not is judged through fan safety protection analysis. The electrical coupling fault recognition reliability of the small and miniature direct current brushless fan in the communication process of the Internet of Things is improved, and the problem that the fan fault protection timeliness of the small and miniature direct current brushless fan is not high due to the fact that the control accuracy of the corresponding fan heat dissipation process is not high in the communication process of the Internet of Things in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of brushless fan control technology, and in particular to a smart control system and method for miniature DC brushless fans based on the Internet of Things. Background Technology

[0002] In the field of industrial automation, miniature DC brushless fans are often used in key aspects such as equipment heat dissipation and air circulation, and are an important component to ensure the stable operation of the production line. Existing technology employs a cluster control scheme based on dynamic switching control algorithms. This involves connecting multiple small brushless DC fans within a region to the same medium-sized programmable logic controller (PLC) or central controller via control lines. In optimizing the stator winding heat dissipation parameters, the design focuses on the core components of the fans: the fan located at the rotor shaft end adopts a biomimetic airfoil structure, with blade curvature and spacing optimized through simulation. During rotation, it generates a directional high-speed airflow that directly acts on the heat dissipation fins on the outside of the stator winding, carrying away heat through forced convection. Simultaneously, sensors for temperature, humidity, and dust concentration are strategically deployed at key locations such as bearing housing temperature rise points, the fan stator winding, and different areas of the workshop. Based on the fan's response speed and environmental inertia, a dynamic switching cycle is set, and the signals collected by the sensors are transmitted to the controller in real time. The controller has a built-in heat dissipation model that incorporates operating parameters (such as voltage, current, and speed) during the fan control process into the dynamic switching cycle algorithm, achieving a rapid and balanced decrease in overall temperature for faster overall temperature reduction.

[0003] For example, Chinese invention patent CN111367220B discloses an IoT device control method and apparatus, which includes: receiving a control request from a target entity device in the IoT; obtaining an instruction set corresponding to the control request, wherein the instruction set contains at least one preset device group corresponding to a business device control instruction, the business device control instruction being any type of control instruction of the business device, wherein the business device corresponds to the control interface of the target entity device; performing a status check on the business devices corresponding to the instruction set, and after all the business devices corresponding to the instruction set have passed the status check, executing the instruction set to control the target entity device.

[0004] For example, Chinese invention patent CN102654765B discloses a control method, device, and IoT for an IoT device, comprising: a control module acquiring initial state information of the subject based on the name and detection data of the sensor, the name and working status of the IoT device, using the sensor and the IoT device as the subject, and sending it to an intelligent module of the software system; the intelligent module includes an inference engine with the subject and execution rules set, sets the initial state of the subject in the inference engine based on the initial state information of the subject, runs the inference engine, obtains device operation instructions, and returns them to the control module; the control module controls a second detector to reset the working status of the IoT device according to the device operation instructions.

[0005] The above-mentioned technology has at least the following technical problems: In existing technologies, the cooling of miniature brushless DC fans requires a process of collecting stator winding temperature data for a sustained period to diagnose electrical coupling faults, followed by overcurrent protection. Specifically, during the collection of stator winding temperature data, a decrease in fan speed leads to a decrease in fan cooling efficiency. However, the viscosity of the fan voltage decreases with increasing temperature. If the fan speed is not dynamically adjusted according to the viscosity, the medium flow rate will decrease, further reducing the convective heat transfer coefficient and thus reducing cooling efficiency. During the identification of electrical coupling faults, the fault identification algorithm does not fully consider the dynamic changes in carrier frequency. When an abnormal signal is detected, the algorithm mistakenly determines that the inverter carrier frequency needs to be increased to enhance control accuracy, causing a surge in inverter carrier frequency and increased switching losses. At this time, the cooling fan speed is not adjusted in real time with the carrier frequency, resulting in increased delay of the current sensor during the fan safety protection process. This makes it impossible to capture rapid changes in current in time, further causing the fan speed to fail to accurately respond to control commands. This leads to a problem of low timeliness in the fan fault protection of miniature brushless DC fans due to the low accuracy of the control of the fan cooling process during IoT communication. Summary of the Invention

[0006] This application provides an intelligent control system and method for miniature brushless DC fans based on the Internet of Things (IoT). This solves the problem of low timeliness of fan fault protection for miniature brushless DC fans caused by the low accuracy of fan heat dissipation process control during IoT communication in the prior art. It also improves the reliability of electrical coupling fault identification of miniature brushless DC fans during IoT communication.

[0007] This application provides an IoT-based intelligent control system for a miniature brushless DC fan, comprising: a fan heat dissipation fault analysis and control module, a fan coupling fault identification and control module, and a fan fault protection analysis and control module. The fan heat dissipation fault analysis and control module performs stator winding heat dissipation fault analysis based on the duration of the acquired stator winding temperature during the heat dissipation fault analysis period, and simultaneously optimizes stator winding heat dissipation parameters to improve the heat dissipation efficiency of the miniature brushless DC fan. The fan coupling fault identification and control module performs electrical coupling fault analysis based on acquired electrical coupling data during the coupling fault identification period, and simultaneously optimizes electrical coupling identification to determine whether adjusting the fan speed and fan operating load can improve the fault identification efficiency of the miniature brushless DC fan. The fan fault protection analysis and control module performs fan safety protection analysis based on acquired fan safety protection response data during the fan fault protection period, and simultaneously optimizes safety protection parameters to improve the fault protection execution efficiency of the miniature brushless DC fan.

[0008] This application provides an intelligent control method for a miniature brushless DC fan based on the Internet of Things, including the following steps: Step 1, performing stator winding heat dissipation fault analysis based on the acquired stator winding temperature duration, and simultaneously optimizing and determining stator winding heat dissipation parameters; Step 2, performing electrical coupling fault analysis based on the acquired electrical coupling data, and simultaneously optimizing and determining electrical coupling identification; Step 3, performing fan safety protection analysis based on the acquired fan safety protection response data, and simultaneously optimizing and determining safety protection parameters.

[0009] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. By analyzing stator winding heat dissipation faults to determine whether stator winding heat dissipation parameters need optimization, stator winding heat dissipation faults can be accurately identified. Based on the judgment results, parameters can be adjusted reasonably to effectively improve the heat dissipation efficiency of miniature DC brushless fans and avoid performance degradation or damage caused by poor heat dissipation. Then, by analyzing electrical coupling faults to determine whether electrical coupling identification and optimization are needed, electrical coupling faults can be quickly and accurately detected. Through optimization, fault identification efficiency can be continuously improved, potential problems can be detected as early as possible, faults can be prevented from escalating, sudden equipment failures and downtime can be reduced, and the continuous and stable operation of the fan can be ensured. Finally, by analyzing the fan safety protection to determine whether safety protection parameters need to be optimized, the fault protection execution efficiency of miniature DC brushless fans can be improved, and the damage caused by faults to the fan can be reduced.

[0010] 2. Electrical coupling encompasses the interaction and correlation between electricity and magnetism, and between different electrical components. By acquiring electrical coupling data at the end of the coupling fault identification period, and simultaneously retrieving preset electrical coupling data from the database, and processing them separately, an effective value for identifying the degree of electrical coupling fault is obtained. This enables quantitative assessment and graded early warning of coupling faults, providing data support for maintenance strategies. Consequently, early fault identification and dynamic protection during the electrical coupling process are achieved. Through real-time comparison with preset thresholds, the fault handling process is optimized, the risk of mechanical transmission failure is reduced, and the reliability and safety of wind turbine operation are improved.

[0011] 3. By acquiring the wind turbine safety protection response data at the end of the wind turbine fault protection period, and simultaneously retrieving the preset wind turbine safety protection response data from the database, and processing them separately, the effectiveness index of wind turbine safety protection is obtained. Based on the effectiveness index of safety protection, the real-time response capability of overload, short circuit and other protection strategies is evaluated, the accuracy and timeliness of the protection device action are accurately judged, and a quantitative basis for parameter optimization is provided. This realizes the dynamic verification and intelligent calibration of the wind turbine safety protection system. Through comparative analysis with preset standards, the risk of false action or failure to act is reduced, the safety redundancy design of the equipment is strengthened, and the reliable protection and safe operation of the wind turbine under abnormal operating conditions are ensured. Attached Figure Description

[0012] Figure 1 A schematic diagram of the structure of the IoT-based intelligent control system for a miniature brushless DC fan provided in this application embodiment; Figure 2 A flowchart for analyzing and determining stator winding heat dissipation faults provided in this application embodiment; Figure 3 A flowchart for electrical coupling fault analysis and determination provided in the embodiments of this application; Figure 4 A flowchart of wind turbine safety protection analysis and judgment provided in the embodiments of this application; Figure 5 A flowchart illustrating the intelligent control method for a miniature brushless DC fan based on the Internet of Things provided in this application embodiment. Detailed Implementation

[0013] This application provides an intelligent control system and method for miniature brushless DC fans based on the Internet of Things (IoT). It solves the problem of low timeliness in fan fault protection for miniature brushless DC fans due to the low accuracy of fan cooling process control during IoT communication in existing technologies. The system utilizes a fan cooling fault analysis and control module to analyze stator winding cooling faults based on the acquired stator winding temperature duration during the analysis period. It compares the real-time collected stator winding temperature duration with preset stator winding temperature durations in the database and optimizes stator winding cooling parameters. Then, a fan coupling fault identification and control module analyzes electrical coupling faults based on acquired electrical coupling data during the coupling fault identification period and optimizes electrical coupling identification. Finally, a fan fault protection analysis and control module analyzes fan safety protection based on acquired fan safety protection response data during the fan fault protection period and optimizes safety protection parameters. This improves the reliability of electrical coupling fault identification for miniature brushless DC fans during IoT communication.

[0014] The technical solution in this application embodiment is to solve the problem of low timeliness of fan fault protection for miniature DC brushless fans caused by the low accuracy of fan heat dissipation process control during the above-mentioned IoT communication process. The overall idea is as follows: By analyzing stator winding heat dissipation faults to determine whether stator winding heat dissipation parameters need to be optimized, then by analyzing electrical coupling faults to determine whether electrical coupling identification optimization needs to be performed, and finally by analyzing fan safety protection to determine whether safety protection parameters need to be optimized, the reliability of electrical coupling fault identification of miniature DC brushless fans during IoT communication is improved.

[0015] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0016] like Figure 1The diagram shown is a structural schematic of the IoT-based intelligent control system for a miniature brushless DC fan provided in this embodiment of the application. The IoT-based intelligent control system for a miniature brushless DC fan includes: a fan heat dissipation fault analysis and control module, a fan coupling fault identification and control module, and a fan fault protection analysis and control module. The fan heat dissipation fault analysis and control module is used to perform stator winding heat dissipation fault analysis based on the duration of the acquired stator winding temperature during the heat dissipation fault analysis period. Simultaneously, it performs stator winding heat dissipation parameter optimization and determination, which determines whether adjusting the fan speed and fan voltage can improve the performance of the miniature brushless DC fan. Heat dissipation efficiency; The fan coupling fault identification control module is used to analyze electrical coupling faults based on the acquired electrical coupling data during the coupling fault identification period, and simultaneously perform electrical coupling identification optimization judgment. The electrical coupling identification optimization is used to determine whether to improve the fault identification efficiency of the miniature DC brushless fan by adjusting the fan speed and fan operating load; The fan fault protection analysis control module is used to analyze fan safety protection based on the acquired fan safety protection response data during the fan fault protection period, and simultaneously perform safety protection parameter optimization judgment. The safety protection parameter optimization is used to determine whether to improve the fault protection execution efficiency of the miniature DC brushless fan by correcting the current step delay and power factor.

[0017] In this embodiment, the system integrates three major modules: fan heat dissipation fault analysis and control, fan coupling fault identification and control, and fan fault protection analysis and control, achieving comprehensive intelligent management and control of the fan's operating status. Regarding heat dissipation, the fan heat dissipation fault analysis and control module accurately analyzes heat dissipation faults based on the duration of stator winding temperature fluctuations, and optimizes the judgment to rationally adjust the fan speed and voltage, effectively improving heat dissipation efficiency and preventing performance degradation and damage caused by overheating. For coupling fault identification, the fan coupling fault identification and control module quickly analyzes faults using electrical coupling data, optimizes the judgment, and adjusts the fan speed and operating load, significantly improving fault identification efficiency, identifying potential problems in advance, and ensuring stable fan operation. In the fault protection phase, the fan fault protection analysis and control module analyzes safety protection response data, optimizes protection parameters by correcting current step delay and power factor, significantly improving fault protection execution efficiency, reducing fault losses, and comprehensively enhancing the reliability and safety of fan operation.

[0018] like Figure 2The diagram shows the stator winding heat dissipation fault analysis and judgment flowchart. The specific design logic is as follows: First, obtain the duration of stator winding temperature fluctuation and determine whether it exceeds the threshold. If it does not exceed the threshold, proceed to fault analysis. If it exceeds the threshold, adjust the fan speed. Then, determine whether the temperature drop meets the standard. If it does, complete the speed adjustment. If it does not, adjust the fan voltage. Then, determine whether the temperature meets the standard. If it does, complete the optimization. If it does not, send a temperature alarm. By adjusting the heat dissipation methods in stages, the abnormal heat dissipation of the stator winding temperature can be handled and fault warnings can be provided.

[0019] Furthermore, the stator winding heat dissipation parameter optimization judgment includes the following steps: obtaining the duration of temperature of the stator winding of the miniature DC brushless fan during the heat dissipation fault analysis period and recording it as the stator winding temperature duration; at the same time, determining whether the obtained stator winding temperature duration is greater than the preset stator winding temperature duration in the database; if so, it is determined to be a stator winding heat dissipation fault and the stator winding heat dissipation parameters are optimized; otherwise, it is determined to be a qualified stator winding heat dissipation and electrical coupling fault analysis is performed.

[0020] The optimization of stator winding heat dissipation parameters includes the following steps: mapping the obtained stator winding temperature duration deviation to the database to obtain a fan speed correction value, so as to reduce the impact of fan speed on fan heat dissipation efficiency. The stator winding temperature duration deviation represents the difference between the obtained stator winding temperature duration and the preset stator winding temperature duration. After one fan speed correction, if the reduction in the obtained stator winding temperature duration deviation is greater than the preset reduction in the stator winding temperature duration deviation in the database, the fan speed correction is completed and electrical coupling fault analysis is performed; otherwise, fan voltage optimization is performed. The reduction in the stator winding temperature duration deviation represents the difference between the stator winding temperature duration deviation obtained before the fan speed correction and the stator winding temperature duration deviation obtained after the first fan speed correction.

[0021] Specifically, the wind turbine voltage optimization involves the following steps: Based on the summation and averaging of the stator winding temperature duration deviation score and the wind turbine voltage deviation score obtained after the initial wind turbine speed correction, a wind turbine voltage correction value is obtained to improve the dynamic balance between wind turbine voltage and wind turbine energy efficiency. The wind turbine voltage deviation score represents the ratio of the obtained wind turbine voltage deviation to the preset wind turbine voltage deviation in the database. The wind turbine voltage deviation represents the difference between the preset wind turbine voltage in the database and the actually obtained wind turbine voltage after the initial wind turbine speed correction. After the wind turbine voltage adjustment, if the obtained stator winding temperature duration is not greater than the preset stator winding temperature duration in the database, the wind turbine voltage optimization is completed and electrical coupling fault analysis is performed; otherwise, a wind turbine temperature alarm command is sent.

[0022] In this embodiment, the duration of stator winding temperature is obtained through the timing function built into the fan drive controller; the fan speed is monitored by a photoelectric encoder or Hall sensor; and the fan voltage is monitored by an electromagnetic meter or turbine meter. The preset duration of stator winding temperature is represented by the sum and average of the historical stator winding temperature durations corresponding to the end of the heat dissipation fault analysis period for historical fans in the database. The preset deviation of stator winding temperature duration is represented by the sum and average of the historical stator winding temperature duration deviations corresponding to the end of the heat dissipation fault analysis period for historical fans in the database. The preset fan speed deviation is represented by the sum and average of the historical fan speed deviations corresponding to the end of the heat dissipation fault analysis period for historical fans in the database. The preset fan speed is represented by the sum and average of the historical fan speeds corresponding to the end of the heat dissipation fault analysis period for historical fans in the database. The preset fan voltage deviation is represented by the sum and average of the historical fan voltage deviations corresponding to the end of the heat dissipation fault analysis period for historical fans in the database. The preset fan voltage is represented by the sum and average of the historical fan voltages corresponding to the end of the heat dissipation fault analysis period for historical fans in the database.

[0023] The fan speed correction value is a dynamic adjustment parameter value in the adaptive control algorithm of the fan drive controller. Based on the acquired fan speed correction value, the adaptive control algorithm prompts the fan drive controller to adjust the fan speed in real time, increasing the corresponding impeller speed to adapt to the real-time heat dissipation requirements of the fan. The fan voltage correction value is a proportional error value of the adaptive control algorithm. Based on the acquired fan voltage correction value, the adaptive control algorithm maps it to an adjustment amount through a proportional coefficient to increase the correction of the fan voltage. The fan drive controller increases the fan voltage accordingly to achieve precise adjustment of heat dissipation efficiency.

[0024] This example generates a fan speed correction value by weighted averaging the two-dimensional deviations between stator winding temperature and fan speed, thereby improving the accuracy of heat dissipation efficiency assessment. The fan drive controller dynamically adjusts the speed accordingly, reducing the fan temperature fluctuation range and improving the response speed. Furthermore, when the speed adjustment does not meet expectations, it automatically triggers secondary optimization of the fan voltage, comprehensively enhancing the heat dissipation stability and fault response capability of the miniature DC brushless fan under complex operating conditions.

[0025] Furthermore, electrical coupling fault analysis is performed based on the acquired electrical coupling data. Specific steps include: acquiring electrical coupling data at the end of the coupling fault identification period; simultaneously acquiring preset electrical coupling data from the database; performing relative difference calculations to obtain relative difference calculation results; simultaneously compensating for each relative difference calculation result by combining the electrical coupling data compensation values ​​from the database; and coupling the compensation results to obtain an effective value for identifying the degree of electrical coupling fault. The electrical coupling data includes stator winding resistance, fan air gap length, and fan input power. The stator winding resistance and fan air gap length are monitored by vibration displacement sensors, and the fan input power is monitored by high-precision current sensors. The preset electrical coupling data includes preset stator winding resistance, fan air gap length, and fan input power. The proportional calculation results include the first proportional calculation result, the second proportional calculation result, and the third proportional calculation result. The electrical coupling data compensation values ​​include stator winding resistance compensation values, fan air gap length compensation values, and fan input power compensation values. The effective value for identifying the degree of electrical coupling fault represents the quantitative data on the influence of the electrical coupling data on the degree of electrical coupling fault.

[0026] The specific constraint expression for the first proportional calculation result D1 is as follows: In the formula, D1 represents the first proportional calculation result of the fan in the micro DC brushless fan at the end of the coupling fault identification period, w1 represents the stator winding resistance compensation value, Q1 represents the stator winding resistance of the fan at the end of the electrical coupling fault identification period, and X1 represents the preset stator winding resistance, which is represented by the sum and average of the historical stator winding resistances corresponding to the end of the historical fan electrical coupling fault identification period in the database.

[0027] The specific constraint expression for the second proportional calculation result D2 is: In the formula, D2 represents the second proportional calculation result of the fan in the micro DC brushless fan at the end of the coupling fault identification period, w2 represents the fan air gap length compensation value, Q2 represents the fan air gap length of the micro DC brushless fan at the end of the coupling fault identification period, and X2 represents the preset fan air gap length, which is represented by the sum and average of the historical fan air gap lengths of historical fans at the end of the coupling fault identification period in the database.

[0028] The specific constraint expression for the third proportional calculation result D3 is: In the formula, D3 represents the third proportional calculation result of the fan in the micro-sized brushless DC fan at the end of the coupling fault identification period, w3 represents the fan input power compensation value, Q3 represents the fan input power of the fan in the micro-sized brushless DC fan at the end of the coupling fault identification period, and X3 represents the preset fan input power, which is represented by the sum and average of the historical fan input power of historical fans at the end of the coupling fault identification period in the database.

[0029] The specific limiting expression for the effective value D of electrical coupling fault severity identification is: In the formula, D represents the effective value of electrical coupling fault identification at the end of the coupling fault identification period in the micro DC brushless fan.

[0030] The database contains pre-stored values ​​for stator winding resistance compensation, fan air gap length compensation, and fan input power compensation. These values ​​correspond to the degree of influence of each value in the calculation of the effective value for identifying the degree of electrical coupling fault. Specifically, the database stores preset compensation values ​​corresponding to stator winding resistance, fan air gap length, and fan input power. These values ​​have a specific pre-defined mapping relationship with stator winding resistance, fan air gap length, and current harmonics. This mapping relationship can be one-to-one or many-to-one. In practical applications, the real-time collected data on stator winding resistance, fan air gap length, and current harmonics can be substituted into this mapping relationship to quickly obtain the corresponding compensation values.

[0031] In this example, the values ​​for stator winding resistance compensation, fan air gap length compensation, and fan input power compensation are all limited to the range of 0 to 1, and the algebraic sum of the three is always 1.

[0032] In this embodiment, the effective value for identifying the degree of electrical coupling fault varies with the stator winding resistance deviation (i.e., ), fan air gap length deviation (i.e. ) and fan input power deviation (i.e. The effective value increases with the increase of the stator winding resistance deviation, which alters the current distribution, affects the magnetic field strength, and thus interferes with electrical coupling, leading to an increase in the effective value. Deviations in the air gap length of the fan disrupt the uniformity of the magnetic field, causing changes in magnetic circuit reluctance and exacerbating electrical coupling faults, thus increasing the effective value. Deviations in the fan input power alter the motor's operating state, affecting electromagnetic relationships and making electrical coupling faults more pronounced, further increasing the effective value. Moreover, these three factors are interconnected; a change in one deviation can trigger changes in others, collectively driving up the effective value.

[0033] By considering the interrelationships between stator winding resistance deviation, fan air gap length deviation, and fan input power deviation, the operating parameters of miniature brushless DC fans are comprehensively optimized and adjusted. Under complex operating conditions, changes in the internal electrical coupling state of the fan can be more accurately detected, and the heat dissipation strategy can be adjusted in real time, making the heat dissipation process more precisely controlled. This avoids abnormal fan temperature caused by inaccurate heat dissipation control, thereby improving the response speed and accuracy of fault protection, significantly enhancing fan operating efficiency, and effectively solving the problem of low timeliness of fan fault protection due to poor heat dissipation control in IoT communication.

[0034] like Figure 3 The diagram shown is a flowchart for electrical coupling fault analysis and judgment. The specific design logic is as follows: First, obtain the valid value for fault severity identification and determine whether it is below the threshold. If so, determine that there is no coupled fault and enter the safety protection analysis; otherwise, perform fan speed optimization. After optimization, check whether the effect meets the standard. If it meets the standard, complete the speed correction; if it does not meet the standard, enter the fan operation load optimization. After this optimization, determine whether the fault value exceeds the threshold. If it exceeds the threshold, trigger the coupling warning; if it does not exceed the threshold, complete the electrical coupling identification optimization. Through hierarchical judgment and optimization measures, effective fault identification, handling and risk warning are achieved.

[0035] Furthermore, the electrical coupling identification and optimization judgment includes the following steps: determining whether the obtained effective value of electrical coupling fault degree identification is greater than the preset effective value of electrical coupling fault degree identification in the database. If so, it is determined to be an electrical coupling fault and electrical coupling identification optimization is performed; otherwise, it is determined to be an electrical coupling fault-free condition and wind turbine safety protection analysis is performed. The specific steps of electrical coupling identification optimization are as follows: based on the deviation of the obtained effective value of electrical coupling fault degree identification, a wind turbine speed correction value is obtained by mapping it in the database to reduce the interference of high-frequency noise signal components on the coupling fault identification. The deviation of the effective value of electrical coupling fault degree identification represents the difference between the preset effective value of electrical coupling fault degree identification in the database and the obtained effective value of electrical coupling fault degree identification. After wind turbine speed correction, if the reduction of the obtained effective value of electrical coupling fault degree identification is greater than the reduction of the preset effective value of electrical coupling fault degree identification in the database, wind turbine speed correction is completed and wind turbine safety protection analysis is performed; otherwise, wind turbine operating load optimization is performed. The reduction of the effective value of electrical coupling fault degree identification represents the difference between the effective value of electrical coupling fault degree identification obtained before wind turbine speed correction and the effective value of electrical coupling fault degree identification obtained after wind turbine speed correction.

[0036] In this embodiment, the wind turbine speed is monitored by an acceleration sensor or a vibration velocity sensor. The preset effective value for identifying the degree of electrical coupling fault is represented by the sum and average of the historical effective values ​​for identifying the degree of electrical coupling fault corresponding to the wind turbine at the end of the coupling fault identification period in the database. The wind turbine speed correction value is a dynamic adjustment parameter value in the adaptive control algorithm in the wind turbine drive controller, which realizes error compensation for the wind turbine vibration signal and improves the accuracy of fault diagnosis. Through real-time dynamic adjustment of the wind turbine speed, the impact of vibration interference on fault feature extraction is effectively reduced, further uncovering potential fault features and avoiding misjudgment or missed judgment due to vibration noise.

[0037] Furthermore, the electrical coupling identification optimization also includes wind turbine operating load optimization. The specific steps include: mapping the deviation of the effective value of electrical coupling fault identification re-acquired after wind turbine speed correction to the database to obtain the wind turbine operating load correction value, so as to reduce the interference of the additional load generated by wind turbine operation on the degree of coupling fault; after wind turbine operating load correction, if the effective value of electrical coupling fault identification is greater than the preset effective value of electrical coupling fault identification, an electrical coupling warning is issued; otherwise, the wind turbine operating load optimization is completed and wind turbine safety protection analysis is performed.

[0038] In this embodiment, the wind turbine operating load is obtained through a torque sensor; the wind turbine operating load correction value is the dynamic compensation value in the adaptive filtering algorithm. Based on the obtained wind turbine operating load correction value, the adaptive filtering algorithm enhances the correction of the wind turbine operating load. Based on this value, the wind turbine drive controller reduces the operating current of the wind turbine, thereby correcting the wind turbine operating load, effectively improving the stability and energy efficiency of wind turbine operation, and ensuring the reliable operation of the wind turbine under complex working conditions.

[0039] Furthermore, wind turbine safety protection analysis is performed based on the acquired wind turbine safety protection response data. Specific steps include: acquiring wind turbine safety protection response data at the end of the wind turbine fault protection period; simultaneously retrieving preset wind turbine safety protection response data from the database; performing proportional calculations on each data to obtain proportional calculation results; and then performing inverse proportional processing on each proportional calculation result before summing and averaging to obtain the wind turbine safety protection effectiveness index. The wind turbine safety protection response data includes the operating current response time and the emergency shutdown response time, monitored by a high-precision timer and signal triggering device. The proportional calculation results include the first proportional operation... The calculation results are: the first ratio calculation result represents the ratio of the operating current response time of the fan in the micro-sized brushless DC fan at the end of the fault protection period to the preset operating current response time; the second ratio calculation result represents the ratio of the emergency shutdown response time of the fan in the micro-sized brushless DC fan at the end of the fault protection period to the preset emergency shutdown response time. The preset fan safety protection response data includes the preset operating current response time and the emergency shutdown response time. The fan safety protection effectiveness index represents the quantitative data on the degree of influence of the fan safety protection response data on the efficiency of fan fault execution protection.

[0040] In this embodiment, if the operating current response time surges, the current peak may have already caused thermal damage to the winding (e.g., temperature rise exceeding 15°C), resulting in the fan entering an irreversible fault state by the time of emergency shutdown response, thus forcing the emergency shutdown response time to be extended (e.g., from 100ms to 200ms). Through the above-mentioned mutual influence, the protection response time can be shortened, thereby improving the effectiveness of safety protection. This not only reduces the direct damage to the fan caused by overload and short circuit, but also reduces secondary problems caused by untimely fault handling (e.g., bearing overheating, rotor eccentricity), effectively ensuring the stable operation of the miniature DC brushless fan.

[0041] like Figure 4 The diagram shows the wind turbine safety protection analysis and judgment flowchart. The specific design logic is as follows: First, obtain the wind turbine safety protection effectiveness index and determine whether it meets the standard. If it meets the standard, continue monitoring; otherwise, perform parameter optimization. After optimization, evaluate the effect. If the optimization effect is significant, complete the correction; if it is not significant, start IoT edge computing. Then, judge the edge computing effect. If it meets the standard, complete the optimization; otherwise, trigger an early warning. The reliable wind turbine safety protection is ensured through layered processing.

[0042] Furthermore, the safety protection parameter optimization judgment includes the following steps: determining whether the acquired wind turbine safety protection effectiveness index is greater than the preset wind turbine safety protection effectiveness index in the database. If so, the wind turbine safety protection is deemed qualified and the safety protection process continues to be monitored during the next wind turbine fault protection period; otherwise, the wind turbine safety protection is deemed unqualified and safety protection parameters are optimized. Based on the acquired electrical coupling fault degree identification effective value deviation, a current step delay correction value is mapped in the database to reduce the impact of current step delay on the wind turbine fault protection execution efficiency. The wind turbine safety protection effectiveness index deviation represents the difference between the preset wind turbine safety protection effectiveness index in the database and the acquired wind turbine safety protection effectiveness index. The preset wind turbine safety protection effectiveness index is represented by the summation and averaging of the historical wind turbine safety protection effectiveness indices corresponding to the historical wind turbines at the end of the fault protection period in the database. After the current step delay correction, if the reduction in the wind turbine safety protection effectiveness index deviation is less than the preset reduction in the wind turbine safety protection effectiveness index deviation in the database, then power factor optimization is performed; otherwise, the current step delay correction is completed and the safety protection process of the next wind turbine fault protection period continues to be monitored. The reduction in the wind turbine safety protection effectiveness index deviation represents the difference between the wind turbine safety protection effectiveness index deviation obtained before the first current step delay correction and the wind turbine safety protection effectiveness index deviation obtained after the first current step delay correction.

[0043] In this embodiment, the current step delay correction value is the state estimation correction value in the Kalman filter algorithm. Based on the obtained state estimation correction value, the Kalman filter algorithm constructs an error feedback mechanism to reduce the current step delay. The wind turbine drive controller dynamically calibrates the response delay of the current sensor based on the correction value, thereby realizing real-time compensation for the sensor time delay error and improving the accuracy of fault protection signal acquisition.

[0044] In terms of judgment logic, this example directly compares the effectiveness index of the wind turbine safety protection with the preset value, which can quickly identify the wind turbine safety protection status, detect anomalies in a timely manner, and trigger parameter optimization to improve the safety of wind turbine operation. At the same time, based on the reduction of the deviation of the wind turbine safety protection effectiveness index after current step delay correction, it intelligently decides whether to perform power factor optimization, avoids ineffective adjustments, achieves efficient resource utilization, ensures the continuous and efficient operation of the safety protection process during the wind turbine fault protection period, reduces the risk of wind turbine failure, and ensures system stability.

[0045] Furthermore, the optimization of safety protection parameters also includes power factor optimization. Specific steps include: mapping the effective value deviation of the electrical coupling fault degree identification obtained after primary current step delay correction to the database to obtain a power factor correction value, thereby reducing the impact of power factor on wind turbine energy consumption; after primary power factor optimization, if the reduction in the wind turbine safety protection effectiveness index deviation is less than the preset reduction in the wind turbine safety protection effectiveness index deviation, a wind turbine safety warning is issued; otherwise, the power factor optimization is completed and the safety protection process for the next wind turbine fault protection period continues to be monitored. The reduction in the wind turbine safety protection effectiveness index deviation represents the difference between the wind turbine safety protection effectiveness index deviation obtained before primary power factor optimization and the wind turbine safety protection effectiveness index deviation obtained after primary power factor optimization.

[0046] In this embodiment, the power factor is monitored by a high-precision timer, and the power factor correction value is the dynamic delay compensation value in the moving average filtering algorithm. The moving average filtering algorithm corrects the power factor by reducing the power factor value obtained after correcting the current step delay. Based on this value, the wind turbine drive controller reduces the corresponding wind turbine voltage, which can effectively optimize the power efficiency of the wind turbine, reduce reactive power loss, and make the wind turbine more energy-efficient and stable during operation.

[0047] like Figure 5 The diagram shows a flowchart of an IoT-based intelligent control method for a miniature brushless DC fan provided in this application embodiment. The IoT-based intelligent control method for a miniature brushless DC fan provided in this application embodiment includes the following steps: Step 1, performing stator winding heat dissipation fault analysis based on the acquired stator winding temperature duration, and simultaneously optimizing and determining stator winding heat dissipation parameters; Step 2, performing electrical coupling fault analysis based on the acquired electrical coupling data, and simultaneously optimizing and determining electrical coupling identification; Step 3, performing fan safety protection analysis based on the acquired fan safety protection response data, and simultaneously optimizing and determining safety protection parameters.

[0048] In this embodiment, overheating risks are detected in advance and the failure rate is reduced by monitoring the stator winding temperature and analyzing heat dissipation faults; rotor deviation is determined based on electrical coupling data, and vibration compensation parameters are dynamically optimized to reduce fan operating noise; protection thresholds are automatically optimized based on real-time safety protection response data to avoid accidental shutdowns, while shortening the response time for extreme conditions such as overcurrent and overvoltage, ensuring timely protection when the fan malfunctions.

[0049] In summary, this application embodiment analyzes stator winding heat dissipation faults to determine whether stator winding heat dissipation parameters need optimization. This accurately identifies stator winding heat dissipation faults and allows for reasonable parameter adjustment based on the determination results, effectively improving the heat dissipation efficiency of the miniature DC brushless fan and preventing performance degradation or damage due to poor heat dissipation. Then, it analyzes electrical coupling faults to determine whether electrical coupling identification and optimization are needed, which helps to quickly and accurately identify electrical coupling faults. Optimization continuously improves fault identification efficiency, allowing for early detection of potential problems, preventing fault escalation, reducing sudden equipment failures and downtime, and ensuring continuous and stable fan operation. Finally, it analyzes fan safety protection to determine whether safety protection parameters need optimization, thereby improving the fault protection execution efficiency of the miniature DC brushless fan and reducing damage caused by faults.

[0050] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0051] This invention is described with reference to flowchart illustrations and / or block diagrams of systems, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0052] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0053] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0054] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0055] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A smart control system for a miniature brushless DC fan based on the Internet of Things, characterized in that, include: Fan heat dissipation fault analysis and control module, fan coupling fault identification and control module, and fan fault protection analysis and control module; The fan heat dissipation fault analysis and control module is used to perform stator winding heat dissipation fault analysis based on the duration of the stator winding temperature during the heat dissipation fault analysis period, and at the same time to perform stator winding heat dissipation parameter optimization judgment. The stator winding heat dissipation parameter optimization judgment is used to determine whether to improve the heat dissipation efficiency of the miniature DC brushless fan by adjusting the fan speed and fan voltage. The wind turbine coupling fault identification and control module is used to perform electrical coupling fault analysis based on the acquired electrical coupling data during the coupling fault identification period, and at the same time to perform electrical coupling identification optimization judgment. The electrical coupling identification optimization is used to improve the fault identification efficiency of micro DC brushless wind turbines. The wind turbine fault protection analysis and control module is used to perform wind turbine safety protection analysis based on the acquired wind turbine safety protection response data during the wind turbine fault protection period, and at the same time to optimize and determine the safety protection parameters. The optimization of the safety protection parameters is used to improve the fault protection execution efficiency of the micro-sized DC brushless wind turbine.

2. The intelligent control system for a miniature brushless DC fan based on the Internet of Things as described in claim 1, characterized in that, The specific steps for optimizing and determining the heat dissipation parameters of the stator winding include: The duration of the stator winding temperature of a miniature DC brushless fan during the heat dissipation fault analysis period is obtained and recorded as the stator winding temperature duration. At the same time, it is determined whether the obtained stator winding temperature duration is greater than the preset stator winding temperature duration in the database. If so, it is determined to be a stator winding heat dissipation fault and the stator winding heat dissipation parameters are optimized. Otherwise, it is determined to be a stator winding heat dissipation qualified and electrical coupling fault analysis is performed. The stator winding temperature duration is used to reflect the heat accumulation of the stator winding during the heat dissipation fault analysis period. The optimization of the stator winding heat dissipation parameters includes the following steps: obtaining a fan speed correction value based on the summation and average of the stator winding temperature duration deviation fraction and the fan speed deviation fraction, so as to reduce the impact of fan speed on fan heat dissipation efficiency. After a fan speed correction, if the decrease in the duration deviation of the stator winding temperature is greater than the preset decrease in the duration deviation of the stator winding temperature in the database, the fan speed correction is completed and electrical coupling fault analysis is performed; otherwise, the fan voltage is optimized.

3. The intelligent control system for a miniature brushless DC fan based on the Internet of Things as described in claim 2, characterized in that, The specific steps for optimizing the wind turbine voltage include: Based on the summation and average of the stator winding temperature duration deviation fraction and the fan voltage deviation fraction obtained after the first fan speed correction, the fan voltage correction value is obtained to improve the dynamic balance between fan voltage and fan energy efficiency. After the wind turbine voltage is adjusted, if the duration of the obtained stator winding temperature is not greater than the preset duration of the stator winding temperature in the database, the wind turbine voltage optimization is completed and electrical coupling fault analysis is performed; otherwise, a wind turbine temperature alarm command is sent.

4. The intelligent control system for a miniature brushless DC fan based on the Internet of Things as described in claim 1, characterized in that, The steps for performing electrical coupling fault analysis based on the acquired electrical coupling data include: Electrical coupling data at the end of the coupling fault identification period is acquired. Preset electrical coupling data is simultaneously retrieved from the database, and relative difference calculations are performed on each data point to obtain the relative difference calculation results. At the same time, the relative difference calculation results are compensated by combining the compensation values ​​of electrical coupling data in the database. The compensation results are then coupled to obtain the effective value for identifying the degree of electrical coupling fault. The electrical coupling data includes stator winding resistance, fan air gap length, and fan input power. The effective value for identifying the degree of electrical coupling fault represents the quantitative data on the influence of electrical coupling data on the degree of electrical coupling fault.

5. The intelligent control system for a miniature brushless DC fan based on the Internet of Things as described in claim 4, characterized in that, The electrical coupling identification and optimization determination process includes the following specific steps: Determine whether the obtained valid value for electrical coupling fault identification is greater than the preset valid value for electrical coupling fault identification in the database. If so, it is determined to be an electrical coupling fault and electrical coupling identification optimization is performed. Otherwise, it is determined to be an electrical coupling fault-free and wind turbine safety protection analysis is performed. The specific steps of the electrical coupling identification optimization are as follows: based on the obtained effective value deviation of the electrical coupling fault degree identification, the fan speed correction value is mapped in the database to reduce the interference of high-frequency noise signal components on the coupling fault identification; After the wind turbine speed is corrected, if the reduction in the effective value deviation of the electrical coupling fault degree identification is greater than the preset reduction in the effective value deviation of the electrical coupling fault degree identification in the database, the wind turbine speed correction is completed and the wind turbine safety protection analysis is performed; otherwise, the wind turbine operating load optimization is performed.

6. The intelligent control system for a miniature brushless DC fan based on the Internet of Things as described in claim 5, characterized in that, The wind turbine operating load optimization includes the following specific steps: Based on the deviation of the effective value of electrical coupling fault identification re-acquired after wind turbine speed correction, the wind turbine operating load correction value is mapped in the database to reduce the interference of the additional load generated by wind turbine operation on the degree of coupling fault. After the wind turbine operating load is corrected, if the effective value of the electrical coupling fault level identification is greater than the preset effective value of the electrical coupling fault level identification, an electrical coupling warning will be issued; otherwise, the wind turbine operating load optimization will be completed and the wind turbine safety protection analysis will be performed.

7. The intelligent control system for a miniature brushless DC fan based on the Internet of Things as described in claim 1, characterized in that, The specific steps for performing wind turbine safety protection analysis based on the acquired wind turbine safety protection response data include: The wind turbine safety protection response data at the end of the wind turbine fault protection period is obtained. Simultaneously, the preset wind turbine safety protection response data is obtained from the database, and proportional calculations are performed on each data to obtain the proportional calculation results. At the same time, each proportional calculation result is processed inversely and then summed and averaged to obtain the wind turbine safety protection effectiveness index. The wind turbine safety protection response data includes the operating current response time and the emergency shutdown response time. The wind turbine safety protection effectiveness index represents the quantitative data on the impact of the wind turbine safety protection response data on the efficiency of wind turbine fault execution protection.

8. The intelligent control system for a miniature brushless DC fan based on the Internet of Things as described in claim 7, characterized in that, The optimization and determination of the security protection parameters includes the following specific steps: Determine whether the obtained wind turbine safety protection effectiveness index is greater than the preset wind turbine safety protection effectiveness index in the database. If so, the wind turbine safety protection is deemed qualified and the safety protection process for the next wind turbine fault protection period is monitored. Otherwise, the wind turbine safety protection is deemed unqualified and the safety protection parameters are optimized. Based on the deviation of the effective value of the electrical coupling fault degree identification obtained, the current step delay correction value is mapped in the database to reduce the impact of the current step delay on the execution efficiency of the wind turbine fault protection. After the current step delay correction, if the reduction in the deviation of the wind turbine safety protection effectiveness index is less than the preset reduction in the deviation of the wind turbine safety protection effectiveness index in the database, then power factor optimization is performed; otherwise, the current step delay correction is completed and the safety protection process for the next wind turbine fault protection period is monitored.

9. The intelligent control system for a miniature brushless DC fan based on the Internet of Things as described in claim 8, characterized in that, The power factor optimization specifically includes the following steps: The deviation of the wind turbine safety protection effectiveness index obtained after the primary current step delay correction is mapped into the database to obtain the power factor correction value, so as to reduce the impact of the power factor on the wind turbine energy consumption. After a power factor optimization, if the reduction in the deviation of the wind turbine safety protection effectiveness index is less than the preset reduction in the deviation of the wind turbine safety protection effectiveness index, a wind turbine safety warning will be issued; otherwise, the power factor optimization will be completed and the safety protection process for the next wind turbine fault protection period will continue to be monitored.

10. A method applied to the intelligent control system for a miniature brushless DC fan based on the Internet of Things as described in any one of claims 1-9, characterized in that, The following steps are involved: Step 1: Analyze the stator winding heat dissipation fault based on the duration of the obtained stator winding temperature, and simultaneously determine the optimization of stator winding heat dissipation parameters. Step 2: Perform electrical coupling fault analysis based on the acquired electrical coupling data, and simultaneously perform electrical coupling identification and optimization judgment. Step 3: Analyze the wind turbine safety protection based on the acquired wind turbine safety protection response data, and simultaneously optimize and determine the safety protection parameters.

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