An Internet of Things management system for air conditioning equipment

Abstract

The present invention relates to the technical field of Internet of Things management of air-conditioning equipment, and specifically discloses an Internet of Things management system for air-conditioning equipment, which comprises: an indoor information collection module, an optimal setting temperature confirmation module, a refrigeration effect evaluation module, an air-conditioning intelligent control module, an air-conditioning information collection module, a fault type identification feedback module and a database; the present invention sets the optimal setting temperature of the air-conditioning by combining the indoor temperature, indoor humidity and the setting temperature of the air-conditioning, and makes a specific analysis on the setting of the air volume by combining the air quality in the room and the refrigeration effect of the room air-conditioning itself, and finally establishes a fault type diagnosis model corresponding to the central air-conditioning, and outputs the fault type diagnosis result, thereby increasing the comfort of the indoor environment, meeting personalized needs, ensuring the user experience and satisfaction, obtaining the fault type of the central air-conditioning in time, avoiding further expansion of the fault, and reducing the difficulty and cost of maintenance.

Description

Technical Field

[0001] The present invention relates to the technical field of air-conditioning equipment Internet of Things management, and in particular to an air-conditioning equipment Internet of Things management system. Background Art

[0002] With the rapid development of Internet of Things technology, smart home systems are becoming increasingly popular. However, most existing air conditioning equipment management systems have problems such as low intelligence, high energy consumption, and high maintenance costs. The present invention aims to provide an intelligent air conditioning equipment Internet of Things management system with high integration, strong intelligence, and the ability to effectively improve user experience and reduce energy consumption.

[0003] For example, the patent with publication number CN109373506A discloses a control method and system for air conditioners, including: a server pre-builds a device information database, the device information database stores the device information of multiple air conditioners when they leave the factory; the server receives air conditioner data, the air conditioner data comes from the air conditioner and includes the device information of the air conditioner; the server matches the device information database, matches the device information of the air conditioner data with the device information database; the server marks the online information, marks the matched air conditioner device as online in the device information database; the server sends instructions to the air conditioner marked as online. The control method of the air conditioner of the present invention can clearly understand the online status of the air conditioner by marking the online air conditioner, compared with the prior art, the staff can actively issue instructions to the online air conditioner, which increases the accuracy of instruction sending and reduces the possibility of underlying code leakage. In addition, due to the use of the Internet of Things module, the air conditioner can perform more operations and improve the remote management capability.

[0004] The above existing technologies still have the following problems: 1) The current air conditioning temperature setting in the room is not set based on the relationship between the indoor temperature and the indoor humidity under different conditions, which reduces the suitability of the optimal temperature setting of the room air conditioner and causes the indoor environment to be uncomfortable.

[0005] 2) Currently, the air volume setting of the room air conditioner is only matched according to the set temperature of the air conditioner. The air volume setting is not specifically analyzed in combination with the air quality in the room and the cooling effect of the room air conditioner itself. The air volume is not adjusted in time to improve the indoor environment, which may pose a threat to human health. At the same time, it cannot meet personalized needs and cannot guarantee the user experience and satisfaction.

[0006] 3) Currently, the maintenance of central air conditioners is periodic, and there is no real-time monitoring of the operating status of the central air conditioners, so the fault type of the central air conditioners cannot be obtained in time, resulting in further expansion of the fault, increasing the difficulty and cost of maintenance, and causing economic losses to users. Summary of the invention

[0007] In view of this, in order to solve the problems raised in the above background technology, an air-conditioning equipment Internet of Things management system is now proposed.

[0008] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides an air-conditioning equipment Internet of Things management system, including: an indoor information collection module, which is used to collect the indoor temperature, indoor humidity and air-conditioning setting temperature of each room in the target user's home corresponding to each monitoring time period on the current monitoring day.

[0009] The optimal setting temperature confirmation module is used to confirm the optimal setting temperature of the air conditioner in each room of the target user's home corresponding to each monitoring time period on the current monitoring day.

[0010] The cooling effect evaluation module is used to extract the cooling information corresponding to each room in each cooling operation of the central air conditioner installed in the target user's home, and analyze the cooling effect evaluation index corresponding to the air outlet of the air conditioner in each room of the target user's home.

[0011] The air conditioning intelligent control module is used to collect the PM2.5 concentration, CO2 concentration, CO concentration and O3 concentration of each room in the target user's home corresponding to each monitoring time period on the current monitoring day, and to intelligently control the air volume of the air conditioning outlet of each room corresponding to each monitoring time period on the current monitoring day.

[0012] The air conditioning information collection module is used to extract the air conditioning operation loudness corresponding to each room in the target user's home on each historical monitoring day, extract the working information corresponding to the central air conditioning installed in the target user's home on each historical monitoring day, and collect the corresponding images of the air conditioning duct of the central air conditioning.

[0013] The fault type identification and feedback module is used to identify the fault type corresponding to the central air conditioner installed in the target user's home and provide feedback on the fault type.

[0014] The database is used to store the optimal comfortable temperature and optimal comfortable humidity for human body under air-conditioning refrigeration conditions, store the required adjustment temperature corresponding to the unit comfortable temperature difference and the unit comfortable humidity difference, store the required compensation outlet air volume corresponding to the unit refrigeration effect difference and the required increase outlet air volume corresponding to the unit air quality difference, and store the outlet volume balance influencing factors corresponding to the dirt degree of each air duct.

[0015] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention sets the optimal setting temperature of the air conditioner by combining the indoor temperature, indoor humidity and air-conditioning setting temperature of each room in the target user's home during each monitoring time period on the current monitoring day, thereby improving the suitability of the optimal temperature setting of the room air conditioner and increasing the comfort of the indoor environment.

[0016] (2) The present invention makes a specific analysis of the air volume setting by combining the air quality in the room and the cooling effect of the room air conditioner itself, thereby improving the timeliness of the air volume adjustment of the room air conditioner, timely improving the indoor environment to avoid threats to human health, and at the same time meeting personalized needs and ensuring the user experience and satisfaction.

[0017] (3) The present invention establishes a fault type diagnosis model corresponding to the central air conditioner installed in the target user's home and outputs the fault type diagnosis result, so as to monitor the operating status of the central air conditioner in real time, thereby timely obtaining the fault type of the central air conditioner, avoiding further expansion of the fault, reducing the difficulty and cost of maintenance, and avoiding economic losses to the user. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0019] Figure 1 It is a schematic diagram of the system module structure connection of the present invention.

[0020] Figure 2 It is a schematic diagram of air supply of the blower of the present invention.

[0021] Description of the accompanying drawings: 1. Air blower, 2. Air duct, 3. Air outlet, 4. Room. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] See also Figure 1 As shown, the present invention provides an air-conditioning equipment IoT management system, including: an indoor information collection module, an optimal setting temperature confirmation module, a cooling effect evaluation module, an air-conditioning intelligent control module, an air-conditioning information collection module, a fault type identification feedback module and a database.

[0024] The indoor information collection module is connected to the optimal setting temperature confirmation module, the optimal setting temperature confirmation module and the cooling effect evaluation module are both connected to the air conditioning intelligent control module, the air conditioning information collection module is connected to the fault type identification feedback module, and the optimal setting temperature confirmation module, the air conditioning intelligent control module and the fault type identification feedback module are all connected to the database.

[0025] The indoor information collection module is used to collect the indoor temperature, indoor humidity and air-conditioning setting temperature of each room in the target user's home corresponding to each monitoring time period on the current monitoring day.

[0026] It should be noted that the indoor temperature and indoor humidity of each room in the target user's home corresponding to each monitoring time period on the current monitoring day are respectively collected by temperature sensors and humidity sensors installed in the room, and the air conditioner setting temperature is directly collected from the air conditioner remote control.

[0027] The optimal setting temperature confirmation module is used to confirm the optimal setting temperature of the air conditioner in each room of the target user's home corresponding to each monitoring time period on the current monitoring day.

[0028] In a specific embodiment of the present invention, the specific process of confirming the optimal air conditioning setting temperature corresponding to each room in the target user's home in each monitoring time period on the current monitoring day is as follows: A1, extracting the optimal comfortable temperature and optimal comfortable humidity for the human body under the air conditioning cooling condition from the database, and recording them as W respectively. 舒 and β 舒 .

[0029] A2. The indoor temperature, indoor humidity and air conditioning setting temperature of each room in the target user's home corresponding to each monitoring period on the current monitoring day are recorded as W ij , β ij and Wherein, i represents the room number, i=1,2,...,n, and j represents the monitoring time period number, j=1,2,...,m.

[0030] A3. Extract the required adjustment temperatures corresponding to the unit comfortable temperature difference and the unit comfortable humidity difference from the database, and record them as W0 and β0 respectively.

[0031] A4. Set the optimal air conditioning temperature for each room in the target user's home during each monitoring period on the current monitoring day.

[0032] The embodiment of the present invention sets the optimal setting temperature of the air conditioner by combining the indoor temperature, indoor humidity and air-conditioning setting temperature of each room in the target user's home during each monitoring time period on the current monitoring day, thereby improving the suitability of the optimal temperature setting of the room air conditioner and increasing the comfort of the indoor environment.

[0033] See also Figure 2 As shown, the cooling effect evaluation module is used to extract the cooling information corresponding to each room in each cooling operation of the central air-conditioning installed in the target user's home, and analyze the cooling effect evaluation index corresponding to the air outlet of the air-conditioning in each room of the target user's home.

[0034] In a specific embodiment of the present invention, the refrigeration information includes the air supply volume of the air outlet and the return air volume of the return fan.

[0035] It should be noted that the air supply volume of the air outlet is collected by a flow meter installed at a wind measuring hole at the air outlet, and the return air volume of the return fan is collected by the same flow meter installed at the inlet of the return fan.

[0036] In a specific embodiment of the present invention, the specific process of analyzing the cooling effect evaluation index corresponding to the air outlet of each room in the target user's home is as follows: B1, extracting the air outlet supply volume and the return air volume of the return fan from the cooling information corresponding to each room in each cooling operation of the central air conditioner installed in the target user's home, and recording them as and Wherein, g represents the number of each refrigeration operation, g = 1, 2, ..., r.

[0037] B2. Calculate the cooling effect evaluation index χ corresponding to the air outlet of the air conditioner in each room of the target user's home i , Among them, Δε represents the return air volume deviation of the set reference, and r represents the number of cooling operations.

[0038] The air conditioning intelligent control module is used to collect the PM2.5 concentration, CO2 concentration, CO concentration and O3 concentration of each room in the target user's home corresponding to each monitoring time period on the current monitoring day, and to intelligently control the air volume of the air conditioning outlet of each room corresponding to each monitoring time period on the current monitoring day.

[0039] It should be noted that the PM2.5 concentration, CO2 concentration, CO concentration and O3 concentration corresponding to each room in the target user's home during each monitoring time period on the current monitoring day are all collected by air quality monitors placed in the rooms.

[0040] In a specific embodiment of the present invention, the specific process of intelligently controlling the air volume of the air-conditioning outlet of each room corresponding to each monitoring time period on the current monitoring day is as follows: C1, based on the PM2.5 concentration, CO2 concentration, CO concentration and O3 concentration of each room in the target user's home corresponding to each monitoring time period on the current monitoring day, calculate the air quality evaluation index δ of each room corresponding to each monitoring time period on the current monitoring day ij .

[0041] In a specific embodiment of the present invention, the specific process of calculating the air quality evaluation index corresponding to each room in each monitoring time period on the current monitoring day is as follows: D1, the PM2.5 concentration, CO2 concentration, CO concentration and O3 concentration corresponding to each monitoring time period in each room in the target user's home on the current monitoring day are recorded as

[0042] D2. Calculate the air quality assessment index δ for each room in each monitoring period on the current monitoring day. ij , Among them, τ PM , τ co and They respectively represent the PM2.5 concentration, CO2 concentration, CO concentration and O3 concentration corresponding to the case where the air quality in the reference room is good.

[0043] C2. Extract the required outlet air volume compensation corresponding to the unit cooling effect difference and the required outlet air volume increase corresponding to the unit air quality difference from the database, and record them as μ 补 and μ 增 .

[0044] C3. Compare the optimal air conditioning setting temperature of each room in the target user's home during each monitoring period on the current monitoring day with the air outlet air volume corresponding to each optimal air conditioning setting temperature stored in the database, and obtain the air outlet air volume corresponding to each monitoring period on the current monitoring day for each room, and record it as μ ij .

[0045] C4. Set the air volume μ′ of the air conditioner outlet corresponding to each room in each monitoring time period during the current monitoring day. ij ,

[0046] Wherein, χ′ and δ′ represent the reference cooling effect evaluation index and air quality evaluation index, respectively.

[0047] The embodiment of the present invention makes a specific analysis of the air volume setting in combination with the air quality in the room and the cooling effect of the room air conditioner itself, thereby improving the timeliness of adjusting the air volume of the room air conditioner, timely improving the indoor environment to avoid threats to human health, and at the same time meeting personalized needs and ensuring user experience and satisfaction.

[0048] The air conditioning information collection module is used to extract the air conditioning operation loudness corresponding to each room in the target user's home on each historical monitoring day, extract the working information corresponding to the central air conditioning installed in the target user's home on each historical monitoring day, and collect the images corresponding to the air conditioning ducts of the central air conditioning.

[0049] It should be noted that the air conditioner operation loudness corresponding to each room in the target user's home on each historical monitoring day is collected by installing a sound monitor.

[0050] In a specific embodiment of the present invention, the working information includes the total air supply volume of the air supply fan, the total air outlet volume of the air conditioner in each room, and the total return air volume of the return fan.

[0051] It should be noted that the total air supply volume of the supply fan, the total air outlet volume of the air conditioner in each room, and the total return air volume of the return fan are respectively collected by flow meters installed at the air supply outlet of the supply fan, flow meters installed at wind measuring holes at the air outlet of the room air conditioner, and flow meters installed at the inlet of the return fan. The image corresponding to the air-conditioning duct of the central air-conditioner is collected by a high-definition camera installed in the air-conditioning duct.

[0052] The fault type identification and feedback module is used to identify the fault type corresponding to the central air conditioner installed in the target user's home and to provide feedback on the fault type.

[0053] In a specific embodiment of the present invention, the specific process of identifying the fault type corresponding to the central air conditioner installed in the target user's home is: E1. Based on the image corresponding to the air duct of the central air conditioner, calculate the air duct dirtiness ψ corresponding to the central air conditioner.

[0054] It should be noted that the specific process of calculating the dirt degree of the air duct corresponding to the central air conditioner is as follows: the number of dirt locations and the thickness corresponding to each dirt location are located from the image corresponding to the air duct of the central air conditioner, and are recorded as σ and H respectively. f , where f represents the number of the dirt location, f = 1, 2, ..., p.

[0055] Calculate the air duct dirtiness ψ corresponding to the central air conditioner, Wherein, σ′ and H′ represent the number of fouling sites and fouling thickness of the reference, respectively.

[0056] E2. Extract the total air supply volume of the air supply fan, the total air outlet volume of the air conditioner in each room, and the total return air volume of the return fan from the working information of the central air conditioner installed in the target user's home on each historical monitoring day, and calculate the cooling state abnormality index corresponding to the central air conditioner

[0057] In a specific embodiment of the present invention, the specific process of calculating the abnormal refrigeration state index corresponding to the central air conditioner is as follows: F1, the total air supply volume of the air supply fan corresponding to each historical monitoring day and the total air outlet volume of the air conditioner in each room and the total return air volume of the return fan are recorded as as well as and Wherein, q represents the number of the historical monitoring day, q=1,2,...,z.

[0058] F2. Calculate the return air volume balance index θ corresponding to the central air conditioner 回 , Among them, Δμ 回 It represents the return air volume deviation of the set reference, z represents the number of historical monitoring days, and e represents the natural constant.

[0059] F3. Calculate the air volume balance index θ corresponding to the central air conditioner 出 .

[0060] In a specific embodiment of the present invention, the specific process of calculating the air volume balance index corresponding to the central air conditioner is: G1. Match and compare the air duct dirtiness corresponding to the central air conditioner with the air volume balance influencing factors corresponding to each air duct dirtiness stored in the database to obtain the air volume balance influencing factor corresponding to the air duct dirtiness corresponding to the central air conditioner, and record it as λ.

[0061] G2. Calculate the air volume balance index θ corresponding to the central air conditioner 出 , Among them, Δμ 出 Indicates the deviation of the air volume from the set reference.

[0062] F4. Calculate the abnormal refrigeration status index of the central air conditioner Wherein, a1 and a2 represent the weights of the abnormal cooling state assessment corresponding to the set return air volume balance index and outlet air volume balance index, respectively, and a1+a2=1.

[0063] In the specific embodiment of the present invention, the setting value of a1 is 0.5, and the setting value of a2 is 0.5. In practical applications, the return air volume balance index and the outlet air volume balance index are both important considerations, which are interrelated and affect each other. Generally speaking, both need to maintain a certain balance state to ensure the normal operation and cooling effect of the air conditioning system.

[0064] E3. Calculate the operating sound abnormality index ω corresponding to the central air conditioner based on the corresponding air conditioner operating loudness of each room in the target user's home on each historical monitoring day.

[0065] It should be noted that the specific process of calculating the abnormal operation sound index corresponding to the central air conditioner is as follows: the corresponding air conditioner operation loudness of each room in the target user's home on each historical monitoring day is recorded as ζ iq .

[0066] Calculate the abnormal sound index ω corresponding to the air conditioner in each room of the target user's home i , Wherein, ζ′ represents the operating loudness of the air conditioner set as a reference.

[0067] The maximum value is extracted from the abnormal operation sound index corresponding to the air conditioner in each room of the target user's home, and the maximum value is used as the abnormal operation sound index ω corresponding to the central air conditioner.

[0068] E4. Establish a fault type diagnosis model corresponding to the central air conditioner installed in the target user's home, and output the fault type diagnosis result, where the fault type diagnosis model is expressed as: Among them, P1, P2 and P3 represent the evaluation conditions of the diagnosis results of each fault type, P1 represents ψ≥ψ′, P2 represents P3 means ω≥ω′, where ψ′, and ω′ represent the reference air duct dirtiness, refrigeration state abnormality index and operation sound abnormality index respectively.

[0069] The embodiment of the present invention establishes a fault type diagnosis model corresponding to the central air conditioner installed in the target user's home and outputs the fault type diagnosis result, so as to monitor the operating status of the central air conditioner in real time, thereby timely obtaining the fault type of the central air conditioner, avoiding further expansion of the fault, reducing the difficulty and cost of maintenance, and avoiding economic losses to the user.

[0070] The database is used to store the optimal comfortable temperature and optimal comfortable humidity for human body under air-conditioning refrigeration conditions, store the required adjustment temperature corresponding to the unit comfortable temperature difference and the unit comfortable humidity difference, store the required compensation outlet air volume corresponding to the unit refrigeration effect difference and the required increase outlet air volume corresponding to the unit air quality difference, and store the outlet volume balance influencing factors corresponding to the dirtiness of each air duct.

[0071] Table 1 Data sources in the database of this embodiment

[0072]

[0073] The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. An air conditioning equipment IoT management system, characterized in that: include: Indoor information collection module, used to collect indoor temperature, indoor humidity and air conditioning setting temperature of each room in the target user's home corresponding to each monitoring time period on the current monitoring day; The optimal setting temperature confirmation module is used to confirm the optimal setting temperature of the air conditioner in each room of the target user's home corresponding to each monitoring time period on the current monitoring day; The cooling effect evaluation module is used to extract the cooling information corresponding to each room in each cooling operation of the central air conditioner installed in the target user's home, and analyze the cooling effect evaluation index corresponding to the air outlet of the air conditioner in each room of the target user's home; The air conditioning intelligent control module is used to collect the corresponding data of each room in the target user’s home during each monitoring period on the current monitoring day. concentration, concentration, Concentration and concentration, and intelligently control the air volume of the air-conditioning outlets in each room during each monitoring period on the current monitoring day; The air conditioning information collection module is used to extract the air conditioning operation loudness of each room in the target user's home on each historical monitoring day, extract the working information of the central air conditioning installed in the target user's home on each historical monitoring day, and collect the image corresponding to the air conditioning duct of the central air conditioning; The fault type identification and feedback module is used to identify the fault type corresponding to the central air conditioner installed in the target user's home and provide feedback on the fault type; A database is used to store the optimal comfortable temperature and optimal comfortable humidity for human body under air conditioning refrigeration conditions, store the required adjustment temperature corresponding to the unit comfortable temperature difference and the unit comfortable humidity difference, store the required compensation outlet air volume corresponding to the unit refrigeration effect difference and the required increase outlet air volume corresponding to the unit air quality difference, and store the outlet air volume balance influencing factors corresponding to the dirt degree of each air duct; The specific process of analyzing the cooling effect evaluation index corresponding to the air outlet of the air conditioner in each room of the target user's home is as follows: B1. Extract the outlet air volume and return air volume from the cooling information of each room in each cooling operation of the central air conditioner installed in the target user's home, and record them as and ,in, Indicates the number of each refrigeration operation. ; B2. Calculate the cooling effect evaluation index corresponding to the air outlet of the air conditioner in each room of the target user's home , ,in, Indicates the return air volume deviation of the set reference. Indicates the number of refrigeration operations; The specific process of intelligently controlling the air volume of the air-conditioning outlets of each room corresponding to each monitoring time period on the current monitoring day is as follows: C1, based on the corresponding time periods of each room in the target user's home during the current monitoring day concentration, concentration, Concentration and concentration, calculate the air quality assessment index of each room in each monitoring time period on the current monitoring day ; C2. Extract the required outlet air volume compensation corresponding to the unit cooling effect difference and the required outlet air volume increase corresponding to the unit air quality difference from the database, and record them as and ; C3. Compare the optimal air conditioning setting temperature of each room in the target user's home corresponding to each monitoring time period on the current monitoring day with the air outlet air volume corresponding to each optimal air conditioning setting temperature stored in the database, and obtain the air outlet air volume corresponding to each monitoring time period on the current monitoring day for each room, and record it as ; C4. Set the air volume of the air conditioner outlets in each room corresponding to each monitoring time period on the current monitoring day. , ,in, and They respectively represent the cooling effect evaluation index and air quality evaluation index of the set reference; The specific process of calculating the air quality evaluation index of each room corresponding to each monitoring time period on the current monitoring day is as follows: D1. The corresponding time periods of each room in the target user's home during the current monitoring day concentration, concentration, Concentration and The concentrations were recorded as , , and ; D2. Calculate the air quality assessment index of each room in each monitoring time period on the current monitoring day , ,in, , , and They represent the corresponding concentration, concentration, Concentration and concentration.

2. The air conditioning equipment IoT management system according to claim 1, characterized in that: The specific process of confirming the optimal air conditioning setting temperature for each room in the target user's home corresponding to each monitoring time period on the current monitoring day is as follows: A1. Extract the optimal comfortable temperature and optimal comfortable humidity for human body under air conditioning conditions from the database and record them as and ; A2. Record the indoor temperature, indoor humidity and air conditioning setting temperature of each room in the target user's home during each monitoring period on the current monitoring day as , and ,in, Indicates the room number. , Indicates the monitoring time period number. ; A3. Extract the required adjustment temperatures corresponding to the unit comfortable temperature difference and the unit comfortable humidity difference from the database and record them as and ; A4. Set the optimal air conditioning temperature for each room in the target user's home during each monitoring period on the current monitoring day. , .

3. The air conditioning equipment IoT management system according to claim 2, characterized in that: The refrigeration information includes the air supply volume of the air outlet and the return air volume of the return fan.

4. The air conditioning equipment IoT management system according to claim 2, characterized in that: The working information includes the total air supply volume of the supply fan, the total air outlet volume of the air conditioner in each room, and the total return air volume of the return fan.

5. The air conditioning equipment IoT management system according to claim 4, characterized in that: The specific process of identifying the fault type corresponding to the central air conditioner installed in the target user's home is as follows: E1. Based on the image corresponding to the air duct of the central air conditioner, calculate the dirtiness of the air duct corresponding to the central air conditioner ; E2. Extract the total air supply volume of the air supply fan, the total air outlet volume of the air conditioner in each room, and the total return air volume of the return fan from the working information of the central air conditioner installed in the target user's home on each historical monitoring day, and calculate the cooling state abnormality index corresponding to the central air conditioner ; E3. Calculate the abnormal sound index of the central air conditioner based on the air conditioner operation loudness of each room in the target user's home on each historical monitoring day. ; E4. Establish a fault type diagnosis model corresponding to the central air conditioner installed in the target user's home, and output the fault type diagnosis result, where the fault type diagnosis model is expressed as: ,in, , and Respectively represent the evaluation conditions of the diagnosis results of each fault type, express , express , express ,in, , and They respectively represent the set reference air duct dirtiness, refrigeration state abnormality index and operation sound abnormality index.

6. The air conditioning equipment IoT management system according to claim 5, characterized in that: The specific process of calculating the abnormal refrigeration state index corresponding to the central air conditioner is as follows: F1. The total air supply volume of the central air conditioner installed in the target user's home on each historical monitoring day, the total air outlet volume of the air conditioner in each room, and the total return air volume of the return air fan are recorded as as well as and ,in, Indicates the number of the historical monitoring day, ; F2. Calculate the return air volume balance index corresponding to the central air conditioner , ,in, Indicates the total return air volume deviation of the set reference. Indicates the number of historical monitoring days. represents a natural constant; F3. Calculate the air volume balance index corresponding to the central air conditioner ; F4. Calculate the abnormal refrigeration status index of the central air conditioner , ,in, and They represent the weights of the abnormal cooling status assessment corresponding to the set return air volume balance index and outlet air volume balance index. .

7. The air conditioning equipment IoT management system according to claim 6, characterized in that: The specific process of calculating the air volume balance index corresponding to the central air conditioner is as follows: G1. Match and compare the air duct dirtiness corresponding to the central air conditioner with the air volume balance influencing factors corresponding to each air duct dirtiness stored in the database to obtain the air volume balance influencing factor corresponding to the air duct dirtiness corresponding to the central air conditioner, and record it as ; G2. Calculate the air volume balance index corresponding to the central air conditioner , ,in, Indicates the total air volume deviation from the set reference.

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