Household appliance internet of things management and control system and method based on artificial intelligence

Abstract

The invention discloses a household appliance Internet of Things management and control system and method based on artificial intelligence, and relates to the technical field of household appliance Internet of Things management and control. Setting a household appliance interaction strategy according to the whole house household appliance data and the user portrait data, wherein the household appliance interaction strategy comprises an interaction strategy between household appliances and an interaction strategy between whole house lamplight and the household appliances; the household appliance use condition is monitored according to the household appliance interaction strategy, the household appliance use environment and energy consumption are detected, and household appliance use is intelligently regulated and controlled; and obtaining a fault self-test period of each household appliance based on the household appliance data of the whole house, carrying out regular fault self-test on each household appliance according to the fault self-test period of each household appliance, and triggering an emergency self-test process in combination with the use mark condition of the user. Personalized interaction, intelligent linkage, dynamic energy consumption management and fault self-test maintenance of the household appliances are realized, and the energy-saving performance, the safety and the user experience of the system are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of home appliance Internet of Things management and control, and in particular to a home appliance Internet of Things management and control system and method based on artificial intelligence. Background Art

[0002] With the rapid development of the Internet of Things and artificial intelligence technologies, smart homes have gradually become an important part of modern family life. The demand for intelligent home appliances has increased significantly. Users hope that home appliances can not only complete basic functions, but also provide convenient, intelligent and personalized service experience, which has promoted the research and innovation of smart home appliance systems.

[0003] However, existing home appliance systems usually lack an in-depth understanding of user portraits, resulting in an inability to provide personalized services based on user preferences and living habits. Most home appliances can only perform preset functions and lack automatic learning and adaptation to user needs. Home appliances in existing technologies generally work independently and lack collaborative interaction capabilities, making it difficult to form a unified smart home experience. There is a lack of effective collaboration between home appliances and between home appliances and lights, which affects a smoother home experience. In addition, existing home appliance systems are relatively simple in terms of energy consumption control and usually cannot be dynamically adjusted, which may lead to unnecessary energy waste. In terms of home appliance fault detection, they often rely on users to manually report or repair them only after obvious problems occur, lacking preventive maintenance.

[0004] Therefore, in response to the above problems, there is an urgent need for an artificial intelligence-based home appliance Internet of Things management and control system and method. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides an artificial intelligence-based home appliance Internet of Things management and control system and method, which solves the problems of traditional home appliance systems such as insufficient personalized experience, lack of intelligent interaction between home appliances, imperfect energy consumption management, delayed fault detection and lack of security.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an artificial intelligence-based home appliance Internet of Things management and control system, comprising: a data acquisition module, used to acquire the data of home appliances in the whole house and user portrait data; an interactive strategy setting module, used to set the home appliance interaction strategy according to the data of home appliances in the whole house and user portrait data, the home appliance interaction strategy includes the interaction strategy between home appliances and the interaction strategy between lights in the whole house and home appliances; a real-time control module, used to monitor the use status of home appliances according to the home appliance interaction strategy, and detect the use environment and energy consumption of home appliances, and intelligently control the use of home appliances; a home appliance fault self-test module, used to obtain the fault self-test cycle of each home appliance based on the data of home appliances in the whole house, perform regular fault self-test on each home appliance according to the fault self-test cycle of each home appliance, and trigger the emergency self-test process in combination with the user's usage mark status.

[0007] Furthermore, the whole-house appliance data includes the entered geographic location coordinates of each appliance, the maintenance record of each appliance, the usage frequency coefficient of each appliance, the remaining service life of each appliance, the operating power of each appliance and the average operating time; the user portrait data specifically includes the order in which users use each appliance, the frequency of users using each appliance and the time period in which users use each appliance.

[0008] Furthermore, the specific analysis of setting the home appliance interaction strategy based on the whole-house home appliance data and user portrait data is as follows: data cleaning and organization of the whole-house home appliance data and user portrait data, and standardization of data formats of different home appliances; identifying the furniture in each room based on the entered geographic location coordinates of each home appliance, and identifying the user's home appliance usage behavior pattern based on the user portrait data, and then defining the linkage logic between home appliances in combination with the whole-house home appliance data; generating interaction strategies between home appliances and interaction strategies between whole-house lights and home appliances based on the linkage logic between home appliances; constructing a whole-house home appliance simulation operation environment based on the whole-house home appliance data and user portrait data, verifying the interaction strategies between home appliances and interaction strategies between whole-house lights and home appliances based on the whole-house home appliance simulation operation environment, and determining the final home appliance interaction strategy.

[0009] Furthermore, the interaction strategies between the home appliances specifically include temperature control linkage, security linkage, home appliance energy-saving linkage, entertainment mode linkage and environmental monitoring linkage; the interaction strategies between the whole-house lights and home appliances specifically include scene mode linkage and activity linkage.

[0010] Furthermore, the usage status of home appliances is monitored according to the home appliance interaction strategy, and the usage environment and energy consumption of home appliances are detected. The specific analysis of intelligent control of home appliance usage is as follows: real-time monitoring of home appliance usage status, and then obtaining home appliance usage environment data and the operating power of each currently running home appliance, and the operating time of each home appliance; the home appliance usage environment data includes the current indoor humidity and the current indoor temperature; the home appliances to be prepared for operation are predicted according to the home appliance interaction strategy, and the predicted operating power and predicted operating time of the home appliances to be prepared are identified by retrieving the home appliance data of the whole house; the current energy consumption data is obtained based on the operating power and operating time of each home appliance, and the pre-operation energy consumption data is obtained based on the predicted operating power and predicted operating time of the home appliances to be prepared; the target indoor temperature and target indoor humidity are obtained, and an objective function is generated based on the target indoor temperature, the target indoor humidity, the current indoor humidity, the current indoor temperature, the current energy consumption data and the pre-operation energy consumption data, wherein the objective function is an energy consumption balance formula, and the usage status of home appliances is regulated based on the processing logic of minimizing the objective function of the energy consumption balance formula.

[0011] Furthermore, the specific analysis of obtaining the fault self-test cycle of each home appliance based on the data of home appliances in the whole house is: obtaining the maintenance interval of each home appliance based on the maintenance record of each home appliance, and then obtaining the default monitoring cycle of each home appliance according to the maintenance interval of each home appliance; adjusting the default monitoring cycle of each home appliance in combination with the usage frequency coefficient of each home appliance to obtain the fault self-test cycle of each home appliance.

[0012] Furthermore, each household appliance is subjected to periodic fault self-test according to its fault self-test cycle, and the specific analysis of triggering the emergency self-test process in combination with the user's usage marking status is as follows: each household appliance is subjected to periodic fault self-test according to the fault self-test cycle, and a self-test fault score of each self-test is obtained, and the self-test fault score is obtained by summing and averaging the voltage deviation value, vibration deviation value and temperature deviation value obtained in each self-test; when it is identified that the user has made an abnormal mark during the use of the household appliance, the emergency self-test process is immediately triggered to obtain a self-test fault score of the emergency self-test; the self-test fault score is compared with the fault threshold, and when the self-test fault score exceeds the fault threshold, the emergency alarm mechanism is triggered to prompt that the household appliance is at risk of failure.

[0013] A method for controlling the Internet of Things of household appliances based on artificial intelligence is applied to the above-mentioned system for controlling the Internet of Things of household appliances based on artificial intelligence, and comprises the following steps: obtaining data of household appliances in the whole house and user portrait data; setting a household appliance interaction strategy according to the data of household appliances in the whole house and user portrait data, wherein the household appliance interaction strategy comprises an interaction strategy between household appliances and an interaction strategy between lights in the whole house and household appliances; monitoring the use status of household appliances according to the interaction strategy of household appliances, detecting the use environment and energy consumption of household appliances, and intelligently regulating the use of household appliances; obtaining the fault self-test cycle of each household appliance based on the data of household appliances in the whole house, performing regular fault self-test on each household appliance according to the fault self-test cycle of each household appliance, and triggering an emergency self-test process in combination with the user's use mark status.

[0014] The present invention has the following beneficial effects:

[0015] The home appliance Internet of Things management and control system and method based on artificial intelligence can automatically set the home appliance interaction strategy according to the user's usage preferences and daily habits by acquiring user portraits and home appliance data of the whole house, realize intelligent linkage between home appliances and between home appliances and lights, and provide users with a more personalized and convenient use experience; after monitoring the use of home appliances and environmental energy consumption, the working state of the home appliances can be intelligently adjusted according to the home appliance interaction strategy, effectively reducing unnecessary energy consumption, achieving the purpose of energy saving, and reducing energy consumption and costs; based on the use frequency and operation status of the home appliances, the fault self-test cycle of each home appliance is dynamically set, and potential faults are discovered in advance through regular self-tests and emergency self-tests, reducing unexpected downtime and maintenance costs, and extending the service life of home appliances; combined with the user's usage mark status and sensor data, the system can timely trigger the emergency self-test process when an abnormality is detected or user feedback is given, ensuring that the home appliances are warned or shut down in time when an abnormal state occurs, reducing safety hazards; through intelligent monitoring and self-test strategies, maintenance can be carried out before a small fault develops into a large fault, reducing the difficulty and cost of maintenance, reducing the undue downtime of equipment, and improving the overall use efficiency and economic benefits of home appliances.

[0016] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a structural diagram of an artificial intelligence-based home appliance Internet of Things management and control system of the present invention.

[0018] Figure 2 This is a flow chart of a method for controlling home appliance Internet of Things based on artificial intelligence in the present invention. DETAILED DESCRIPTION

[0019] The embodiment of the present application realizes personalized interaction, intelligent linkage, dynamic energy consumption management and fault self-test maintenance of home appliances through an artificial intelligence-based home appliance Internet of Things management and control system and method, effectively improving the energy saving, safety and user experience of the system.

[0020] The overall idea of ​​the problem in the embodiment of this application is as follows:

[0021] First, the operating data and user portraits of all home appliances are obtained, covering the usage status, energy consumption, and user habits and preferences of each home appliance. Through data collection and the establishment of user portraits, we can fully understand the usage scenarios and user needs of home appliances, providing a basis for intelligent control and management; analyze the relationship between home appliances and user preferences through artificial intelligence algorithms, and formulate personalized home appliance interaction strategies; according to the home appliance interaction strategy, the usage of home appliances is monitored in real time, and the energy consumption and usage environment of home appliances are detected. During the operation of home appliances, the operating status of each device can be dynamically adjusted to ensure that both the user's comfort needs are met and energy conservation is effective; to ensure the safe and stable operation of home appliances, the self-test cycle of each device is set according to the home appliance data, and the status is checked regularly. At the same time, combined with user feedback or marked abnormal conditions, the emergency self-test process is triggered to evaluate the device status in real time.

[0022] See also Figure 1 The embodiment of the present invention provides a technical solution: an artificial intelligence-based home appliance Internet of Things management and control system, comprising: a data acquisition module, used to acquire the data of home appliances in the whole house and user portrait data; an interactive strategy setting module, used to set the home appliance interactive strategy according to the data of home appliances in the whole house and user portrait data, the home appliance interactive strategy includes the interactive strategy between home appliances and the interactive strategy between lights in the whole house and home appliances; a real-time control module, used to monitor the use status of home appliances according to the home appliance interactive strategy, and detect the use environment and energy consumption of home appliances, and intelligently control the use of home appliances; a home appliance fault self-test module, used to acquire the fault self-test cycle of each home appliance based on the data of home appliances in the whole house, perform regular fault self-test on each home appliance according to the fault self-test cycle of each home appliance, and trigger the emergency self-test process in combination with the user's usage mark status.

[0023] Specifically, the data of home appliances in the whole house include the entered geographic location coordinates of each home appliance, the maintenance record of each home appliance, the usage frequency coefficient of each home appliance, the remaining service life of each home appliance, the operating power and average operating time of each home appliance; the user portrait data specifically includes the order in which users use each home appliance, the frequency of users using each home appliance and the time period when users use each home appliance.

[0024] The specific analysis of setting the home appliance interaction strategy based on the whole-house home appliance data and user portrait data is as follows: clean and organize the whole-house home appliance data and user portrait data, and standardize the data formats of different home appliances; identify the furniture in each room based on the entered geographic location coordinates of each home appliance, and identify the user's home appliance usage behavior pattern based on the user portrait data, and then define the linkage logic between home appliances in combination with the whole-house home appliance data; generate the interaction strategy between home appliances and the interaction strategy between the whole-house lights and home appliances based on the linkage logic between home appliances; build a whole-house home appliance simulation operation environment based on the whole-house home appliance data and user portrait data, verify the interaction strategy between home appliances and the interaction strategy between the whole-house lights and home appliances based on the whole-house home appliance simulation operation environment, and determine the final home appliance interaction strategy.

[0025] In this implementation scheme, the entered geographic location coordinates represent the specific location of each household appliance, which is obtained through initial installation or scanning of the device location; the maintenance record represents the maintenance and repair history of each household appliance, which is obtained from the equipment management system or maintenance service provider; the usage frequency coefficient represents the usage frequency of the household appliance (such as the number of times per day), which is obtained through real-time data collection and historical usage data analysis; the remaining service life is used to estimate the length of time the household appliance can still be used, which is obtained through usage time, power consumption and equipment status evaluation; the operating power represents the power consumption level of the household appliance, which is obtained through the device's own sensors or power detection equipment; the average operating time represents the average operating time of the household appliance after each startup, which is obtained through historical operating data statistics; the user usage order, frequency and time period are used to record the order, frequency and usage time period of the user's use of the household appliances in a day, which is obtained through behavior analysis and historical data collection.

[0026] The linkage logic between home appliances is established based on location, purpose and usage mode. For example, home appliances in the same room are associated with lights, air conditioners and curtains based on geographic location coordinates, such as automatically closing doors and windows when the air conditioner is turned on.

[0027] The specific steps for constructing the whole-house home appliance simulation operation environment are as follows: clean and standardize the whole-house home appliance data and user portrait data to ensure data consistency and integrity; define the simulation model of each home appliance according to its function, power consumption, service life, etc., including its switching, state changes and response characteristics; define the virtual space of each room according to the geographical location coordinates of the home appliance to provide a spatial reference for the home appliance linkage strategy; define user behavior patterns based on user portrait data, such as daily home appliance usage time, sequence and frequency; establish interaction rules between home appliances based on the home appliance linkage logic, and test the collaborative operation of home appliances in a simulated environment.

[0028] The specific steps for verifying the interaction strategies between home appliances and between the lights in the whole house and home appliances are as follows: set verification conditions according to different scenarios of the simulated operating environment of home appliances in the whole house, covering common usage scenarios (such as at night, before traveling, etc.); execute various home appliance interaction strategies in the simulated environment, and observe the system's response to user behavior, including the actual effects of home appliance startup, linkage and environmental regulation; analyze the energy consumption and environmental effects (such as temperature and brightness) of each home appliance operation to ensure that the user's comfort needs are met while maintaining low energy consumption; compare the verification results with the expected results, optimize the home appliance linkage rules and parameter settings, and adjust the strategies that do not meet expectations; after all verification conditions are met, confirm the final home appliance interaction strategy and linkage rules, and deploy them in the actual environment.

[0029] By analyzing user usage behavior and the geographic location of home appliances, it is possible to accurately identify user habits, realize personalized home appliance linkage, and enhance user experience; through intelligent linkage strategies, it can avoid unnecessary power consumption of home appliances, realize efficient energy management, and save energy; based on maintenance records and monitoring of home appliance service life, it can discover potential problems in advance, reduce failure rates, and extend the life of home appliances; through whole-house home appliance linkage and simulated environment verification, it can make the interaction strategy of home appliances more reliable and create a harmonious and consistent home experience.

[0030] Specifically, the interaction strategies between home appliances include temperature control linkage, security linkage, home appliance energy-saving linkage, entertainment mode linkage and environmental monitoring linkage; the interaction strategies between whole-house lighting and home appliances include scene mode linkage and activity linkage.

[0031] In this implementation scheme, the temperature control linkage specifically includes the interaction between the air-conditioning device and the humidifier device: when the air-conditioning device enters the cooling mode and detects that the air humidity is lower than a preset threshold, the humidifier device is automatically controlled to turn on to adjust the ambient humidity; when the air-conditioning device is turned on, if it is detected that the air pollution index exceeds the preset range, the air purifier device is automatically controlled to turn on to improve the air quality.

[0032] Security linkage specifically includes the interaction between smart door lock devices and camera devices: when the smart door lock device detects that the door lock is open, it automatically controls the camera device to enter real-time monitoring state or start recording to record environmental information after the door lock is opened; when the security system detects an abnormal intrusion, it automatically turns off all lights and non-essential home appliances to enhance concealment and reduce power consumption.

[0033] The energy-saving linkage of home appliances specifically includes the interaction between the refrigerator equipment and the kitchen exhaust fan equipment: when it is detected that the kitchen environment temperature exceeds the preset temperature, the exhaust fan equipment is automatically controlled to turn on to help maintain the normal operating temperature of the refrigerator equipment and reduce the energy consumption of the refrigerator equipment; the interaction between the washing machine equipment and the water heater equipment: before the washing machine equipment is started, the water heater equipment is automatically controlled to preheat to ensure that the washing machine equipment has hot water supply when in use.

[0034] The entertainment mode linkage specifically includes the interaction between TV equipment and audio equipment: when the TV equipment is turned on, the audio equipment is automatically controlled to enter the preset sound effect mode to enhance the user's audio-visual experience; when the TV equipment is turned on and detects that the user is watching for a long time, it is recommended to start the coffee machine to prepare coffee to keep the user focused; the environmental monitoring linkage specifically includes the interaction between air-conditioning equipment and window sensors: when the window sensor detects that the window is open, the air-conditioning equipment is automatically turned off to save energy, and the air-conditioning equipment is restarted after the window is closed; when the air quality sensor detects that the air humidity is high, it automatically reminds the user to stop using the clothes drying rack equipment, or automatically controls the start of the dryer equipment.

[0035] The scene mode linkage specifically includes automatically controlling the light brightness to a preset low brightness or warm light when the user selects the movie mode, and automatically controlling the TV device to enter the playback state; when the user selects the dinner mode, the restaurant light is automatically adjusted to a soft brightness, and the air-conditioning equipment temperature is reduced to a preset value; when the user selects the reading mode, the reading light is automatically adjusted to a bright cool tone, and other non-essential home appliances are turned off.

[0036] The activity linkage strategy specifically includes automatically dimming or turning off all the lights in the house when the system detects that the user has entered sleep mode, adjusting the temperature of the air-conditioning equipment to the preset temperature for night, and keeping the air purification equipment running at low noise; when user activity is detected at night, the dim aisle lights and bathroom lights are automatically turned on, and other home appliances such as air-conditioning equipment remain in silent mode.

[0037] Among them: the humidity threshold is used to determine whether to turn on the humidifier to ensure that the air humidity is within a comfortable range, and is set in combination with industry standards for ambient humidity, seasonal changes, and user personalized preferences; the air pollution threshold is used to determine the air quality and trigger the start of the air purifier, referring to local air quality standards (such as the appropriate range of PM2.5 and PM10), and is set according to the user's sensitivity preferences; the ambient temperature threshold is used to detect that the kitchen temperature is too high and decide whether to turn on the exhaust fan to assist the refrigerator in dissipating heat. The threshold is set based on the analysis of the refrigerator's optimal operating temperature and ambient temperature data and the temperature range recommended by the device to ensure energy-saving operation of the refrigerator; the light brightness preset value is used to set the light brightness in different scene modes, based on the optimal brightness recommendation for different scenes and combined with the user's lighting preferences; the air conditioning temperature preset value is used to set the temperature value of the air conditioner in different modes (such as sleep mode, reading mode, etc.), and the temperature is recommended based on the user's sleep and activity habits, and is determined in combination with the temperature range of the air conditioning energy-saving mode.

[0038] Specifically, the use of home appliances is monitored according to the home appliance interaction strategy, and the use environment and energy consumption of home appliances are detected. The specific analysis of intelligent control of home appliance use is as follows: real-time monitoring of home appliance use, and then obtaining home appliance use environment data and the operating power of each currently running home appliance, and the operating time of each home appliance; home appliance use environment data includes current indoor humidity and current indoor temperature; predict the ready-to-run home appliances according to the home appliance interaction strategy, and identify the predicted operating power and predicted operating time of the ready-to-run home appliances by retrieving the home appliance data of the whole house; obtain the current energy consumption data based on the operating power and operating time of each home appliance, and obtain the ready energy consumption data based on the predicted operating power and predicted operating time of the ready-to-run home appliances; obtain the target indoor temperature and target indoor humidity, and generate the objective function based on the target indoor temperature, target indoor humidity, current indoor humidity, current indoor temperature, current energy consumption data and ready energy consumption data. The objective function is the energy consumption balance formula. The specific energy consumption balance formula is:

[0039] f(W,H,E)=|WW 0 |+|HH 0 |+E+E Y , where f(W,H,E) is the objective function, W is the current indoor temperature, W 0 is the target indoor temperature, H is the current indoor humidity, H 0 is the target indoor humidity, E represents the current energy consumption data, and E Y It represents the preparation of energy consumption data, and regulates the use of household appliances according to the processing logic of minimizing the objective function of the energy consumption balance formula.

[0040] In this implementation scheme, the usage status of household appliances indicates the switch status, power and operating time of the currently running household appliances. The working status and power sensor data of each device are monitored in real time through the household appliance Internet of Things system, or power information is obtained through smart sockets; household appliance usage environment data* is obtained through indoor temperature and humidity sensors, or real-time data is obtained from the integrated environmental monitoring system; the operating power of the currently running household appliances is read in real time from the household appliance power sensor or smart meter, or obtained from the system's power consumption record data; the operating time of the currently running household appliances is calculated by the real-time cumulative operating time of each household appliance through the household appliance time recording system; the predicted operating power of the ready-to-run household appliances retrieves the power parameters of the equipment from the whole house household appliance data, and estimates the average operating power of the household appliances in combination with the historical operating data; the predicted operating time is combined with user portrait data and time period and user habits, and predictions are made through statistical models or machine learning models; the current energy consumption data represents the total power consumption of all current home appliances. The system summarizes the power of currently running home appliances in real time, and calculates the real-time cumulative energy consumption by duration; the reserve energy consumption data represents the expected power consumption of the home appliances that are about to run. The predicted operating power is multiplied by the predicted operating duration to obtain the expected energy consumption of the home appliances to be started; the target indoor temperature is preset by the system or customized by the user. The user can set the temperature through the APP, or it can be automatically set based on AI learning of user temperature preferences; the target indoor humidity is preset by the system or customized by the user. The user can set the humidity through the APP, or the system can automatically adjust it based on the user's historical preferences; the current indoor temperature and humidity are obtained through temperature and humidity sensors installed in each room, and the data are summarized in the home appliance management and control system.

[0041] In the energy balance control system, the optimal regulation of the use of home appliances is achieved by minimizing the objective function. The processing logic of minimizing the objective function includes the following steps: collecting real-time environmental data (current room temperature and humidity), obtaining the power consumption data and usage status of the current home appliances, obtaining the predicted power demand and running time of the home appliances to be started, constructing the objective function to balance energy consumption and environmental comfort, solving the objective function by selecting a suitable optimization algorithm (such as gradient descent, particle swarm optimization, etc.), setting optimization constraints, such as maximum power limit, temperature and humidity control range, determining the start, stop or adjustment operation of the home appliance according to the result of the optimal solution, and adjusting the power output and state of the home appliance in real time according to the optimal solution. Taking the gradient descent method as an example, the basic steps of minimizing the objective function are: starting from the current power consumption status and environmental data of the home appliance, setting the initial value, calculating the gradient of the objective function at the current point, determining the impact of changes in energy consumption, temperature and humidity on the objective function, adjusting the power consumption output of the home appliance along the gradient direction, updating the control strategy, repeating the above steps until the objective function converges to the minimum value or meets the threshold condition, and executing the regulation operation of the home appliance according to the final optimal parameter combination.

[0042] By optimizing the energy balance formula, intelligent control of home appliance power consumption is achieved, overall energy consumption is reduced, and energy is saved; real-time regulation of indoor temperature and humidity makes the linkage strategy of home appliances not only energy-saving but also more in line with the user's comfort needs; unnecessary home appliance loads are avoided, high-frequency startup and long-term operation of home appliances are reduced, and the service life of equipment is increased; by predicting the energy consumption of home appliances in preparation for operation, advance optimization and adjustment in home appliance linkage is achieved to avoid peak power consumption caused by too many devices running at the same time; through intelligent regulation and user portrait data analysis, the system can realize personalized and automated control of home appliances, improving user convenience and intelligent experience.

[0043] Specifically, the specific analysis of obtaining the fault self-test cycle of each home appliance based on the data of the whole house home appliances is as follows: based on the maintenance records of each home appliance, the maintenance interval of each home appliance is obtained, and then the default monitoring cycle of each home appliance is obtained according to the maintenance interval of each home appliance; the default monitoring cycle of each home appliance is adjusted in combination with the usage frequency coefficient of each home appliance to obtain the fault self-test cycle of each home appliance. The specific formula for obtaining the fault self-test cycle is: Where T Z Indicates the fault self-test period, T M represents the default monitoring period, and σ represents the usage frequency coefficient.

[0044] In this implementation scheme, by combining the maintenance records and usage frequency of home appliances, the possible failure time points of home appliances can be predicted more accurately, effectively avoiding sudden failures; the monitoring cycle can be adjusted according to the usage frequency and historical maintenance data of each home appliance to reduce unnecessary self-test frequency and extend the service life of the equipment; self-test and maintenance can be arranged before potential failures occur, shortening the fault detection time and improving the user experience; while reducing the number of unnecessary repairs, the performance of home appliances is guaranteed and maintenance costs are reduced.

[0045] The maintenance interval represents the time difference in the maintenance history records of home appliances and is used to analyze the frequency of home appliance failures. The default monitoring period is represented by a preliminary self-test period based on historical maintenance interval settings. The usage frequency coefficient indicates the usage intensity of the home appliance and is usually calculated from the user's usage data. The fault self-test period is the final self-test period after combining the default monitoring period and usage frequency, and is used as the time interval for regular self-tests.

[0046] Specifically, each household appliance is subjected to periodic fault self-test according to the fault self-test cycle of each household appliance, and the specific analysis of triggering the emergency self-test process in combination with the user's usage mark status is as follows: each household appliance is subjected to periodic fault self-test according to the fault self-test cycle, and a self-test fault score of each self-test is obtained. The self-test fault score is obtained by summing and averaging the voltage deviation value, vibration deviation value, and temperature deviation value obtained from each self-test. The specific formula for obtaining the self-test fault score is: Where ZP represents the self-test fault score, ΔV represents the voltage deviation value, ΔD represents the vibration deviation value, and ΔW represents the temperature deviation value. When it is recognized that the user has made an abnormal mark during the use of the home appliance, the emergency self-test process is immediately triggered to obtain the self-test fault score of the emergency self-test. The self-test fault score is compared with the fault threshold. When the self-test fault score exceeds the fault threshold, the emergency alarm mechanism is triggered to prompt that the home appliance has a fault risk.

[0047] In this implementation scheme, the voltage deviation value is obtained by comparing the difference between the actual voltage and the standard voltage through the voltage monitoring module built into the home appliance to monitor the voltage data of the equipment in real time; the vibration deviation value is collected by the vibration sensor built into the home appliance to detect whether there is abnormal vibration of the equipment during operation, and is obtained by calculating the deviation between the actual vibration and the normal vibration; the temperature deviation value is obtained by the temperature sensor built into the home appliance, and is obtained by comparing the difference between the temperature when the home appliance is running and the standard temperature; the fault threshold is set according to the equipment type and previous historical data experience, and is used to determine whether the self-test fault score reaches the alarm condition.

[0048] Through regular fault self-tests and user-marked emergency self-tests, potential faults can be identified in a timely manner, reducing the risk of sudden shutdown or failure of home appliances. After a fault warning, users can perform inspections or replacements according to the prompts, reducing the maintenance costs and inconvenience caused by sudden failures. By monitoring indicators such as voltage, vibration, and temperature, abnormal conditions of equipment can be discovered in a timely manner to avoid long-term accumulation of faults that shorten the service life of home appliances. The fault self-test cycle can be adjusted according to actual usage data to reduce unnecessary self-test operations and enhance the intelligence and energy-saving effects of home appliance management.

[0049] A method for controlling the Internet of Things of home appliances based on artificial intelligence, using the above-mentioned system for controlling the Internet of Things of home appliances based on artificial intelligence, comprises the following steps: obtaining data on home appliances in the whole house and user portrait data; setting a home appliance interaction strategy according to the data on home appliances in the whole house and user portrait data, wherein the home appliance interaction strategy includes an interaction strategy between home appliances and an interaction strategy between lights in the whole house and home appliances; monitoring the use of home appliances according to the home appliance interaction strategy, detecting the use environment and energy consumption of home appliances, and intelligently regulating the use of home appliances; obtaining the fault self-test cycle of each home appliance based on the data on home appliances in the whole house, performing regular fault self-test on each home appliance according to the fault self-test cycle of each home appliance, and triggering an emergency self-test process in combination with the user's usage mark status.

[0050] In summary, this application has at least the following effects:

[0051] By real-time monitoring of the usage and energy consumption of home appliances, the working mode of home appliances can be automatically optimized to reduce unnecessary energy consumption, thereby effectively reducing electricity bills; based on user portrait data and home appliance usage habits, the interaction strategy between home appliances and lights can be adjusted in a personalized manner to improve user comfort and satisfaction; the design of regular fault self-test and emergency self-test processes allows users to promptly discover and handle potential home appliance failures, reducing the inconvenience and maintenance costs caused by sudden failures; the interaction strategy between home appliances can achieve linked control of home devices and improve the level of home intelligence; by collecting and analyzing home appliance data throughout the house, usage reports and prompts can be generated to help users understand the usage habits and energy consumption of home appliances, allowing users to make more informed decisions; the home appliance interaction strategy is continuously adjusted according to the user's actual usage, making home appliance management more flexible and adaptable to changes in user needs; monitoring and analyzing the usage of home appliances can promptly detect abnormal situations and automatically take measures to improve home safety.

[0052] It will be appreciated by those skilled in the art that the embodiments of the present invention may be provided as methods and systems. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0053] The present invention is described with reference to the flowcharts and structure diagrams of the methods and systems according to the embodiments of the present invention. It should be understood that each process and combination of modules in the flowcharts and structure diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts. Figure 1 process or processes and structures Figure 1 A device that specifies functionality in a module or modules.

[0054] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate 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 process or processes and structures Figure 1 A function specified in a module or multiple modules.

[0055] 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 instructions for implementing the process. Figure 1 process or processes and structures Figure 1 Steps to specify functionality in a module or multiple modules.

[0056] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0057] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A home appliance Internet of Things management and control system based on artificial intelligence, characterized in that: include: Data acquisition module, used to obtain data of household appliances and user portraits; An interactive strategy setting module, used to set an interactive strategy for home appliances based on the home appliance data and user portrait data, wherein the interactive strategy for home appliances includes an interactive strategy between home appliances and an interactive strategy between lights in the whole house and home appliances; Real-time control module, which is used to monitor the use of home appliances according to the home appliance interaction strategy, detect the use environment and energy consumption of home appliances, and intelligently control the use of home appliances; The home appliance fault self-test module is used to obtain the fault self-test cycle of each home appliance based on the data of the entire home appliance, perform regular fault self-tests on each home appliance according to the fault self-test cycle of each home appliance, and trigger the emergency self-test process in combination with the user's usage marking status.

2. According to the artificial intelligence-based home appliance Internet of Things management and control system according to claim 1, it is characterized in that: The whole-house appliance data includes the recorded geographic location coordinates of each appliance, the maintenance record of each appliance, the usage frequency coefficient of each appliance, the remaining service life of each appliance, and the operating power and average operating time of each appliance; The user portrait data specifically includes the order in which the user uses each home appliance, the frequency with which the user uses each home appliance, and the time period in which the user uses each home appliance.

3. The home appliance Internet of Things management and control system based on artificial intelligence according to claim 2 is characterized in that: The specific analysis of setting the home appliance interaction strategy based on the whole-house home appliance data and user portrait data is as follows: Clean and organize the data of all household appliances and user portraits, and standardize the data formats of different household appliances; Identify the furniture in each room based on the recorded geographic coordinates of each appliance, and identify the user's appliance usage behavior pattern based on the user portrait data, and then define the linkage logic between appliances in combination with the appliance data of the whole house; Generate interaction strategies between home appliances and between lights and home appliances based on the linkage logic between home appliances; A simulated operating environment for home appliances is built based on the data of home appliances in the whole house and the user portrait data. The interaction strategies between home appliances and the interaction strategies between lights in the whole house and home appliances are verified based on the simulated operating environment for home appliances in the whole house to determine the final interaction strategy for home appliances.

4. According to the artificial intelligence-based home appliance Internet of Things management and control system of claim 3, it is characterized in that: The interaction strategies between the home appliances specifically include temperature control linkage, security linkage, home appliance energy saving linkage, entertainment mode linkage and environment monitoring linkage; The interaction strategy between the whole-house lighting and home appliances specifically includes scene mode linkage and activity linkage.

5. According to the artificial intelligence-based home appliance Internet of Things management and control system of claim 3, it is characterized in that: According to the interactive strategy of home appliances, the use of home appliances is monitored, and the use environment and energy consumption of home appliances are tested. The specific analysis of intelligent control of home appliance use is as follows: Real-time monitoring of home appliance usage, and obtaining home appliance usage environment data, the operating power of each currently running home appliance, and the operating time of each home appliance; Home appliance usage environment data includes current indoor humidity and current indoor temperature; Predict the appliances that are ready to run based on the appliance interaction strategy, and identify the predicted operating power and predicted operating time of the appliances that are ready to run by searching the appliance data of the whole house; The current energy consumption data is obtained according to the operating power and operating time of each household appliance, and the reserve energy consumption data is obtained according to the predicted operating power and predicted operating time of the household appliances to be operated; Obtain the target indoor temperature and the target indoor humidity, generate an objective function based on the target indoor temperature, the target indoor humidity, the current indoor humidity, the current indoor temperature, the current energy consumption data and the prepared energy consumption data, wherein the objective function is an energy consumption balance formula, and adjust the use of household appliances based on the processing logic of minimizing the objective function of the energy consumption balance formula.

6. The home appliance Internet of Things management and control system based on artificial intelligence according to claim 2 is characterized in that: The specific analysis of obtaining the fault self-test cycle of each household appliance based on the household appliance data of the whole house is as follows: Based on the maintenance records of each home appliance, the maintenance interval of each home appliance is obtained, and then the default monitoring period of each home appliance is obtained according to the maintenance interval of each home appliance; The default monitoring period of each household appliance is adjusted in combination with the usage frequency coefficient of each household appliance to obtain the fault self-test period of each household appliance.

7. The home appliance Internet of Things management and control system based on artificial intelligence according to claim 6 is characterized in that: The specific analysis of performing regular fault self-test on each appliance according to the fault self-test cycle of each appliance and triggering the emergency self-test process in combination with the user's usage mark status is as follows: Performing regular fault self-tests on each household appliance according to a fault self-test cycle to obtain a self-test fault score for each self-test, wherein the self-test fault score is obtained by summing and averaging the voltage deviation value, the vibration deviation value, and the temperature deviation value obtained from each self-test; When it is identified that the user has made an abnormal mark during the use of the home appliance, the emergency self-test process is immediately triggered to obtain the self-test fault score of the emergency self-test; The self-test fault score is compared with the fault threshold. When the self-test fault score exceeds the fault threshold, the emergency alarm mechanism is triggered to indicate that the home appliance is at risk of failure.

8. A method for controlling home appliance Internet of Things based on artificial intelligence, using a home appliance Internet of Things control system based on artificial intelligence as described in any one of claims 1 to 7, characterized in that: The following steps are involved: Obtain data on home appliances and user portraits; Setting a home appliance interaction strategy based on the home appliance data and user portrait data, wherein the home appliance interaction strategy includes an interaction strategy between home appliances and an interaction strategy between the lights in the whole house and the home appliances; Monitor the use of home appliances according to the home appliance interaction strategy, detect the use environment and energy consumption of home appliances, and intelligently regulate the use of home appliances; Based on the data of all household appliances in the house, the fault self-test cycle of each household appliance is obtained, and each household appliance is regularly self-tested according to the fault self-test cycle of each household appliance, and the emergency self-test process is triggered in combination with the user's usage marking status.

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