An Internet of Things-based air conditioner outdoor fan fault prediction system

Abstract

This invention belongs to the field of fan fault analysis and relates to data analysis technology. It addresses the problem that existing air conditioning outdoor fan fault monitoring systems cannot predict and analyze fan faults before they occur. Specifically, it is an Internet of Things (IoT)-based air conditioning outdoor fan fault prediction system, including a fault prediction platform. This platform is communicatively connected to an operation monitoring module, an environmental analysis module, a fault prediction module, and a storage module. The operation monitoring module monitors and analyzes the operating status of the air conditioning outdoor fan: generating an analysis period and dividing it into several analysis time periods, marking the air conditioning outdoor fan undergoing operation status monitoring and analysis as the analysis object. This invention can monitor and analyze the operating status of air conditioning outdoor fans, providing feedback on the fan's operating status through an operation coefficient, thereby marking whether the analysis object has potential faults.

Description

Technical Field

[0001] This invention belongs to the field of fan failure analysis and involves data analysis technology. Specifically, it is an Internet of Things-based air conditioning outdoor fan failure prediction system. Background Technology

[0002] The outdoor unit of an air conditioner is a crucial component for heat exchange, and the fan is essential for its proper operation. If the fan in the outdoor unit is not working, the air conditioner cannot function properly, leading to excessively high or low indoor temperatures and impacting the quality of life.

[0003] Existing air conditioner outdoor fan fault monitoring systems can only perform fault analysis based on the fan's operating parameters and determine whether the fan has malfunctioned based on the analysis results. They can only issue alarms after a fault occurs, but they cannot perform fault prediction analysis on the fan before a fault occurs, so as to avoid faults through abnormal handling.

[0004] To address the aforementioned technical problems, this application provides a solution. Summary of the Invention

[0005] The purpose of this invention is to provide an Internet of Things-based air conditioner outdoor fan fault prediction system to solve the problem that existing air conditioner outdoor fan fault monitoring systems cannot perform fault prediction and analysis on the fan before a fault occurs.

[0006] The technical problem to be solved by this invention is: how to provide an Internet of Things-based air conditioning outdoor fan fault prediction system that can perform fault prediction analysis on the fan before a fault occurs.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] An Internet of Things-based air conditioner outdoor fan fault prediction system includes a fault prediction platform, which is communicatively connected to an operation monitoring module, an environmental analysis module, a fault prediction module, and a storage module.

[0009] The operation monitoring module is used to monitor and analyze the operating status of the outdoor air conditioner fan: it generates an analysis period and divides the analysis period into several analysis time periods; it marks the outdoor air conditioner fan undergoing operation status monitoring and analysis as the analysis object; it acquires the vibration data ZD, abnormal noise data YX, and speed data ZS of the analysis object within the analysis time period; it obtains the operating coefficient YX of the analysis object within the analysis time period by numerically calculating the vibration data ZD, abnormal noise data YX, and speed data ZS; and it determines whether there are potential faults in the analysis object within the analysis time period based on the operating coefficient YX.

[0010] The environmental analysis module is used to perform outdoor environmental analysis when the analyzed object has potential faults: acquiring outdoor temperature data WW, outdoor wind data WF, and outdoor rainfall data WY during the analysis period; obtaining the anisotropy coefficient HY of the analyzed object during the analysis period by numerically calculating the outdoor temperature data WW, outdoor wind data WF, and outdoor rainfall data WY; and determining whether the analyzed object has extreme environmental risks based on the anisotropy coefficient HY.

[0011] The fault prediction module is used to perform fault prediction analysis on the outdoor fan of the air conditioner.

[0012] In a preferred embodiment of the present invention, vibration data ZD is the maximum vibration amplitude of the casing of the analyzed object during the analysis period, abnormal noise data YX is the maximum noise decibel value generated by the analyzed object during the analysis period, and speed data ZS is the minimum fan speed of the analyzed object during the analysis period.

[0013] As a preferred embodiment of the present invention, the specific process for determining whether there are potential faults in the analyzed object during the analysis period includes: obtaining the operating thresholds YXmin and YXmax through the storage module, comparing the operating coefficient YX of the analyzed object during the analysis period with the operating thresholds YXmin and YXmax: if YX≤YXmin, it is determined that the operating status of the analyzed object during the analysis period meets the requirements; if YXmin<YX<YXmax, it is determined that there are potential faults in the operating status of the analyzed object during the analysis period, generating an environmental analysis signal and sending the environmental analysis signal to the fault prediction platform, which then sends the environmental analysis signal to the environmental analysis module; if YX≥YXmax, it is determined that the operating status of the analyzed object during the analysis period does not meet the requirements, generating a fault alarm signal and sending it to the fault prediction platform, which then sends the fault alarm signal to the mobile terminal of the management personnel.

[0014] In a preferred embodiment of the present invention, the external temperature data WW is the maximum value of the air temperature of the operating environment of the analyzed object during the analysis period; the external wind data WF is the maximum value of the wind force level of the operating environment of the analyzed object during the analysis period; and the external rainfall data WY is the rainfall in the area where the analyzed object is located during the analysis period.

[0015] As a preferred embodiment of the present invention, the specific process for determining whether the analyzed object has extreme environmental hazards includes: obtaining the ring-anomaly threshold HYmax through the storage module, comparing the ring-anomaly coefficient HY of the analyzed object during the analysis period with the ring-anomaly threshold HYmax; if the ring-anomaly coefficient HY is less than the ring-anomaly threshold HYmax, it is determined that the analyzed object does not have extreme environmental hazards, generating a predictive analysis signal and sending the predictive analysis signal to the fault prediction platform, and the fault prediction platform, upon receiving the predictive analysis signal, sends the predictive analysis signal to the fault prediction module; if the ring-anomaly coefficient HY is greater than or equal to the ring-anomaly threshold HYmax, it is determined that the analyzed object has extreme environmental hazards, generating an environmental warning signal and sending the environmental warning signal to the fault prediction platform, and the fault prediction platform, upon receiving the environmental warning signal, sends the environmental warning signal to the mobile terminal of the management personnel.

[0016] In a preferred embodiment of the present invention, the specific process of the fault prediction module for fault prediction analysis of the outdoor fan of the air conditioner includes: arranging the analysis periods within the analysis period in ascending order of the anomaly coefficient HY to obtain the anomaly sequence; arranging the analysis periods within the analysis period in ascending order of execution time to obtain the execution sequence; arranging the analysis periods within the analysis period in ascending order of the operation coefficient YX to obtain the operation sequence; marking the absolute value of the difference between the sequence number of the analysis period in the anomaly sequence and the sequence number in the operation sequence as the anomaly difference value of the analysis period; summing and averaging the anomaly differences of all analysis periods to obtain the anomaly coefficient HC; marking the absolute value of the difference between the sequence number of the analysis period in the execution sequence and the sequence number in the operation sequence as the execution difference value of the analysis period; summing and averaging the execution difference values ​​of all analysis periods to obtain the execution difference coefficient ZC; numerically calculating the anomaly coefficient HC and the execution difference coefficient ZC to obtain the prediction coefficient YC of the analyzed object; obtaining the prediction threshold YCmin through the storage module; comparing the prediction coefficient YC of the analyzed object with the prediction threshold YCmin; and determining whether the analyzed object has potential faults based on the comparison result.

[0017] As a preferred embodiment of the present invention, the specific process of comparing the prediction coefficient YC of the analyzed object with the prediction threshold YCmin includes: if the prediction coefficient YC is less than the prediction threshold YCmin, it is determined that the analyzed object has a potential fault, a fault prediction signal is generated and sent to the mobile terminal of the management personnel; if the prediction coefficient YC is greater than or equal to the prediction threshold YCmin, it is determined that the analyzed object does not have a potential fault, a normal operation signal is generated and sent to the fault prediction platform.

[0018] As a preferred embodiment of the present invention, the working method of the Internet of Things-based air conditioner outdoor fan fault prediction system includes the following steps:

[0019] Step 1: Monitor and analyze the operating status of the outdoor air conditioner fan: Generate an analysis period and divide the analysis period into several analysis time periods. Mark the outdoor air conditioner fan that is being monitored and analyzed as the analysis object. Obtain the vibration data ZD, abnormal noise data YX, and speed data ZS of the analysis object during the analysis time period and perform numerical calculations to obtain the operating coefficient YX. Use the operating coefficient YX to determine whether the operating status of the analysis object meets the requirements.

[0020] Step 2: When the object under analysis has potential faults, conduct outdoor environmental analysis: acquire the external temperature data WW, external wind data WF, and external rainfall data WY during the analysis period and perform numerical calculations to obtain the anisotropy coefficient HY. Use the anisotropy coefficient HY to determine whether the object under analysis has potential extreme environmental faults.

[0021] Step 3: Perform fault prediction analysis on the outdoor fan of the air conditioner and obtain the prediction coefficient YC of the analysis object. Use the prediction coefficient YC to determine whether the analysis object has potential faults.

[0022] The present invention has the following beneficial effects:

[0023] 1. The operation monitoring module can monitor and analyze the operation status of the outdoor air conditioner fan. By analyzing the various operating parameters in different time periods, the operation coefficient is obtained through comprehensive analysis and calculation. The operation coefficient is used to provide feedback on the operation status of the fan, thereby marking whether the analyzed object has potential faults.

[0024] 2. The environmental analysis module can perform outdoor environmental analysis when the analyzed object has potential faults. By comprehensively analyzing various parameters of the outdoor environment, the environmental anomaly coefficient is obtained. The environmental anomaly coefficient is used to monitor the degree of environmental anomaly and to provide timely warnings when the analyzed object has potential faults and extreme abnormal environments occur.

[0025] 3. The fault prediction module can perform fault prediction analysis on the outdoor fan of the air conditioner, compare the generated anomaly sequence, execution sequence and operation sequence, and provide feedback on the correlation between the operating coefficient of the outdoor fan of the air conditioner and the environment and the running time through the comparison results, so as to determine the potential fault based on the calculated prediction coefficient. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0028] Figure 2 This is a flowchart of the method in Embodiment 2 of the present invention. Detailed Implementation

[0029] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] Example 1

[0031] like Figure 1 As shown, an Internet of Things-based air conditioner outdoor fan fault prediction system includes a fault prediction platform, which is communicatively connected to an operation monitoring module, an environmental analysis module, a fault prediction module, and a storage module.

[0032] The operation monitoring module is used to monitor and analyze the operating status of the outdoor air conditioning fan: It generates an analysis period and divides it into several analysis time periods. The outdoor air conditioning fan being monitored is marked as the analysis object. The module acquires the vibration data ZD, abnormal noise data YX, and speed data ZS of the analysis object within each analysis time period. Vibration data ZD represents the maximum vibration amplitude of the fan casing during the analysis time period; abnormal noise data YX represents the maximum noise level (in decibels) generated by the analysis object during the analysis time period; and speed data ZS represents the minimum fan speed of the analysis object during the analysis time period. The operating coefficient YX of the analysis object within the analysis time period is obtained using the formula YX = α1*ZD + α2*YX - α3*ZS, where α1, α2, and α3 are proportionality coefficients, and α1 > α2 > α3 > 1. The module also obtains the operating thresholds YXmin and YXmax through storage. Finally, the module compares the operating coefficient YX of the analysis object within the analysis time period with the operating thresholds YXmin and YXmax. The comparison process is as follows: If YX ≤ YXmin, the operating status of the analyzed object during the analysis period is deemed to meet the requirements; if YXmin < YX < YXmax, the operating status of the analyzed object during the analysis period is deemed to have potential faults, an environmental analysis signal is generated and sent to the fault prediction platform, which then sends the signal to the environmental analysis module; if YX ≥ YXmax, the operating status of the analyzed object during the analysis period is deemed to not meet the requirements, a fault alarm signal is generated and sent to the fault prediction platform, which then sends the signal to the mobile terminal of the management personnel. The operating status of the outdoor air conditioning fan is monitored and analyzed. Operating parameters are comprehensively analyzed and calculated during the analysis period using a time-segmented analysis method to obtain operating coefficients. These operating coefficients provide feedback on the fan's operating status, thereby marking whether the analyzed object has potential faults.

[0033] The environmental analysis module is used to perform outdoor environmental analysis when the analyzed object has potential faults: It acquires outdoor temperature data (WW), outdoor wind data (WF), and outdoor rainfall data (WY) for the analysis period. Outdoor temperature data (WW) is the maximum air temperature value of the operating environment of the analyzed object during the analysis period; outdoor wind data (WF) is the maximum wind force level of the operating environment of the analyzed object during the analysis period; outdoor rainfall data (WY) is the rainfall in the region where the analyzed object is located during the analysis period. The anomaly coefficient (HY) of the analyzed object during the analysis period is obtained using the formula HY = β1*WW + β2*WF + β3*WY, where β1, β2, and β3 are proportionality coefficients, and β1 > β2 > β3 > 1. The anomaly threshold (HYmax) is obtained through the storage module, and the anomaly coefficient (HY) of the analyzed object during the analysis period is compared with the anomaly threshold (HYmax). If the anomaly coefficient (HY) is higher than the threshold (HYmax), then... If the coefficient of environmental anomaly is less than the ring anomaly threshold HYmax, the analyzed object is determined not to have extreme environmental hazards. A predictive analysis signal is generated and sent to the fault prediction platform, which then sends it to the fault prediction module. If the ring anomaly coefficient HY is greater than or equal to the ring anomaly threshold HYmax, the analyzed object is determined to have extreme environmental hazards. An environmental warning signal is generated and sent to the fault prediction platform, which then sends it to the mobile terminal of the management personnel. When the analyzed object has potential fault hazards, outdoor environmental analysis is performed. The ring anomaly coefficient is obtained by comprehensively analyzing various parameters of the outdoor environment. The degree of environmental anomaly is monitored through the ring anomaly coefficient, and a timely warning is issued when the analyzed object has potential fault hazards and extreme abnormal environment occurs.

[0034] The fault prediction module is used to perform fault prediction analysis on the outdoor fan of the air conditioner. The analysis periods within the analysis period are arranged in ascending order of the anomaly coefficient HY to obtain the anomaly sequence; the analysis periods within the analysis period are arranged in ascending order of execution time to obtain the execution sequence; and the analysis periods within the analysis period are arranged in ascending order of the operation coefficient YX to obtain the operation sequence. The absolute value of the difference between the sequence number of the analysis period in the anomaly sequence and the sequence number in the operation sequence is marked as the anomaly difference value of the analysis period. The anomaly difference coefficient HC is obtained by summing and averaging all the anomaly difference values ​​of the analysis periods. The absolute value of the difference between the sequence number of the analysis period in the execution sequence and the sequence number in the operation sequence is marked as the execution difference value of the analysis period. The execution difference coefficient ZC is obtained by summing and averaging all the execution difference values ​​of the analysis periods. The prediction coefficient of the analyzed object is obtained through the formula YC = γ1*HY / HC + γ2*SC / ZC. The system calculates YC, where γ1 and γ2 are proportionality coefficients, with γ1 > γ2 > 1, and SC is the continuous runtime of the analyzed object. A prediction threshold YCmin is obtained through the storage module. The predicted coefficient YC of the analyzed object is compared with the prediction threshold YCmin. If the predicted coefficient YC is less than the prediction threshold YCmin, the analyzed object is determined to have a potential fault, a fault prediction signal is generated, and the signal is sent to the administrator's mobile terminal. If the predicted coefficient YC is greater than or equal to the prediction threshold YCmin, the analyzed object is determined not to have a potential fault, a normal operation signal is generated, and the signal is sent to the fault prediction platform. Fault prediction analysis is performed on the outdoor air conditioning fan. The generated anomaly sequence, execution sequence, and running sequence are compared. The comparison results provide feedback on the correlation between the outdoor air conditioning fan's operating coefficient and the environment and runtime, thereby determining potential faults based on the calculated predicted coefficients.

[0035] Example 2

[0036] like Figure 2 As shown, an IoT-based method for predicting the failure of an outdoor air conditioner fan includes the following steps:

[0037] Step 1: Monitor and analyze the operating status of the outdoor air conditioner fan: Generate an analysis period and divide the analysis period into several analysis time periods. Mark the outdoor air conditioner fan that is being monitored and analyzed as the analysis object. Obtain the vibration data ZD, abnormal noise data YX, and speed data ZS of the analysis object during the analysis time period and perform numerical calculations to obtain the operating coefficient YX. Use the operating coefficient YX to determine whether the operating status of the analysis object meets the requirements.

[0038] Step 2: When the object under analysis has potential faults, conduct outdoor environmental analysis: acquire the external temperature data WW, external wind data WF, and external rainfall data WY during the analysis period and perform numerical calculations to obtain the anisotropy coefficient HY. Use the anisotropy coefficient HY to determine whether the object under analysis has potential extreme environmental faults.

[0039] Step 3: Perform fault prediction analysis on the outdoor fan of the air conditioner and obtain the prediction coefficient YC of the analysis object. Use the prediction coefficient YC to determine whether the analysis object has potential faults.

[0040] An IoT-based air conditioner outdoor fan fault prediction system, during operation, generates an analysis cycle and divides it into several analysis periods. The air conditioner outdoor fan undergoing operational status monitoring and analysis is marked as the analysis object. Vibration data (ZD), abnormal noise data (YX), and rotational speed data (ZS) of the analysis object within the analysis period are acquired and numerically calculated to obtain an operating coefficient (YX). The operating coefficient (YX) is used to determine whether the operating status of the analysis object meets the requirements. External temperature data (WW), external wind data (WF), and external rainfall data (WY) within the analysis period are acquired and numerically calculated to obtain an environmental anomaly coefficient (HY). The environmental anomaly coefficient (HY) is used to determine whether the analysis object has extreme environmental hazards. Finally, fault prediction analysis is performed on the air conditioner outdoor fan, and a prediction coefficient (YC) is acquired for the analysis object. The prediction coefficient (YC) is used to determine whether the analysis object has potential fault hazards.

[0041] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0042] The above formulas are all derived from software simulation using a large amount of collected data, and are selected to be close to the true values. The coefficients in the formulas are set by those skilled in the art based on the actual situation; for example, the formula YX=α1*ZD+α2*YX-α3*ZS; those skilled in the art collect multiple sets of sample data and set corresponding operating coefficients for each set of sample data; substitute the set operating coefficients and the collected sample data into the formulas, any three formulas form a system of three linear equations, filter the calculated coefficients and take the average, and obtain the values ​​of α1, α2 and α3 as 3.52, 2.85 and 2.26 respectively;

[0043] The magnitude of the coefficient is a specific value obtained by quantifying each parameter to facilitate subsequent comparison. The magnitude of the coefficient depends on the amount of sample data and the corresponding operating coefficient initially set by those skilled in the art for each set of sample data. As long as it does not affect the proportional relationship between the parameter and the quantified value, such as the operating coefficient being proportional to the value of the vibration data.

[0044] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0045] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An Internet of Things-based air conditioner outdoor fan fault prediction system, characterized in that, It includes a fault prediction platform, which is communicatively connected to an operation monitoring module, an environmental analysis module, a fault prediction module, and a storage module; The operation monitoring module is used to monitor and analyze the operating status of the outdoor air conditioner fan: it generates an analysis period and divides the analysis period into several analysis time periods, marks the outdoor air conditioner fan being monitored and analyzed as the analysis object, and obtains the vibration data ZD, abnormal noise data YX, and speed data ZS of the analysis object during the analysis time period; it then calculates the operating coefficient YX of the analysis object during the analysis time period by performing numerical calculations on the vibration data ZD, abnormal noise data YX, and speed data ZS. The operating coefficient YX is used to determine whether there are potential faults in the analyzed object during the analysis period; The environmental analysis module is used to perform outdoor environmental analysis when the analyzed object has potential faults: acquiring outdoor temperature data WW, outdoor wind data WF, and outdoor rainfall data WY during the analysis period; obtaining the anisotropy coefficient HY of the analyzed object during the analysis period by numerically calculating the outdoor temperature data WW, outdoor wind data WF, and outdoor rainfall data WY; and determining whether the analyzed object has extreme environmental risks based on the anisotropy coefficient HY. The fault prediction module is used to perform fault prediction analysis on the outdoor fan of the air conditioner. The specific process of fault prediction analysis for air conditioner outdoor fans by the fault prediction module includes: arranging the analysis periods within the analysis period in ascending order of the anomaly coefficient HY to obtain the anomaly sequence; arranging the analysis periods within the analysis period in ascending order of execution time to obtain the execution sequence; arranging the analysis periods within the analysis period in ascending order of the operation coefficient YX to obtain the operation sequence; marking the absolute value of the difference between the sequence number of the analysis period in the anomaly sequence and the sequence number in the operation sequence as the anomaly difference value of the analysis period; summing and averaging the anomaly differences of all analysis periods to obtain the anomaly coefficient HC; marking the absolute value of the difference between the sequence number of the analysis period in the execution sequence and the sequence number in the operation sequence as the execution difference value of the analysis period; summing and averaging the execution difference values ​​of all analysis periods to obtain the execution difference coefficient ZC; calculating the prediction coefficient YC of the analyzed object by numerically calculating the anomaly coefficient HC and the execution difference coefficient ZC; obtaining the prediction threshold YCmin through the storage module; comparing the prediction coefficient YC of the analyzed object with the prediction threshold YCmin; and determining whether the analyzed object has potential faults based on the comparison result. The specific process of comparing the predicted coefficient YC of the analyzed object with the predicted threshold YCmin includes: if the predicted coefficient YC is less than the predicted threshold YCmin, it is determined that the analyzed object has a potential fault, a fault prediction signal is generated and sent to the mobile terminal of the management personnel; if the predicted coefficient YC is greater than or equal to the predicted threshold YCmin, it is determined that the analyzed object does not have a potential fault, a normal operation signal is generated and sent to the fault prediction platform.

2. The air conditioner outdoor fan fault prediction system based on the Internet of Things according to claim 1, characterized in that, Vibration data ZD represents the maximum vibration amplitude of the casing of the analyzed object during the analysis period; abnormal noise data YX represents the maximum noise decibel value generated by the analyzed object during the analysis period; and speed data ZS represents the minimum fan speed of the analyzed object during the analysis period.

3. The air conditioner outdoor fan fault prediction system based on the Internet of Things according to claim 2, characterized in that, The specific process for determining whether there are potential faults in the analyzed object during the analysis period includes: obtaining the operating thresholds YXmin and YXmax through the storage module; comparing the operating coefficient YX of the analyzed object during the analysis period with the operating thresholds YXmin and YXmax; if YX≤YXmin, the operating status of the analyzed object during the analysis period is determined to meet the requirements; if YXmin<YX<YXmax, the operating status of the analyzed object during the analysis period is determined to have potential faults, an environmental analysis signal is generated and sent to the fault prediction platform, and the fault prediction platform sends the environmental analysis signal to the environmental analysis module after receiving it; if YX≥YXmax, the operating status of the analyzed object during the analysis period is determined to not meet the requirements, a fault alarm signal is generated and sent to the fault prediction platform, and the fault prediction platform sends the fault alarm signal to the mobile terminal of the management personnel after receiving it.

4. The air conditioner outdoor fan fault prediction system based on the Internet of Things according to claim 3, characterized in that, The external temperature data WW represents the maximum air temperature value of the operating environment of the analyzed object during the analysis period; the external wind data WF represents the maximum wind force level of the operating environment of the analyzed object during the analysis period; and the external rainfall data WY represents the rainfall in the area where the analyzed object is located during the analysis period.

5. The air conditioner outdoor fan fault prediction system based on the Internet of Things according to claim 4, characterized in that, The specific process for determining whether an analyzed object poses a risk of extreme environmental hazards includes: obtaining the ring-anomaly threshold HYmax through the storage module; comparing the ring-anomaly coefficient HY of the analyzed object during the analysis period with the ring-anomaly threshold HYmax; if the ring-anomaly coefficient HY is less than the ring-anomaly threshold HYmax, the analyzed object is determined not to have a risk of extreme environmental hazards, a predictive analysis signal is generated and sent to the fault prediction platform, and the fault prediction platform, upon receiving the predictive analysis signal, sends it to the fault prediction module; if the ring-anomaly coefficient HY is greater than or equal to the ring-anomaly threshold HYmax, the analyzed object is determined to have a risk of extreme environmental hazards, an environmental warning signal is generated and sent to the fault prediction platform, and the fault prediction platform, upon receiving the environmental warning signal, sends it to the mobile terminal of the management personnel.

6. A fault prediction system for outdoor air conditioning fans based on the Internet of Things according to any one of claims 1-5, characterized in that, The working method of this IoT-based air conditioner outdoor fan fault prediction system includes the following steps: Step 1: Monitor and analyze the operating status of the outdoor air conditioner fan: Generate an analysis period and divide the analysis period into several analysis time periods. Mark the outdoor air conditioner fan that is being monitored and analyzed as the analysis object. Obtain the vibration data ZD, abnormal noise data YX, and speed data ZS of the analysis object during the analysis time period and perform numerical calculations to obtain the operating coefficient YX. Use the operating coefficient YX to determine whether the operating status of the analysis object meets the requirements. Step 2: When the object under analysis has potential faults, conduct outdoor environmental analysis: acquire the external temperature data WW, external wind data WF, and external rainfall data WY during the analysis period and perform numerical calculations to obtain the anisotropy coefficient HY. Use the anisotropy coefficient HY to determine whether the object under analysis has potential extreme environmental faults. Step 3: Perform fault prediction analysis on the outdoor fan of the air conditioner and obtain the prediction coefficient YC of the analysis object. Use the prediction coefficient YC to determine whether the analysis object has potential faults.

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