Smart home environment adaptive adjustment system and adjustment method thereof

Abstract

The invention discloses a smart home environment adaptive adjustment system and adjustment method, and relates to the technical field of smart home, the smart home environment adaptive adjustment system comprises an environment data acquisition module, a user behavior data analysis module, an intelligent decision module, a control module, a user interaction module, an optimization module and a security module, the method has the advantages that capturing of multi-dimensional parameters of a home environment and description of a user behavior mode are achieved by fusing an environment data collection module and a user behavior data analysis module, a neural network algorithm is adopted, a complex nonlinear relation between environment variables and user behaviors is automatically mined and modeled, and the user behavior is more accurate. Therefore, a customized home equipment control strategy is generated, and a living environment conforming to user preferences is created, so that the intelligent adjustment precision of the home environment is improved, and the satisfaction degree of personalized demands of the user is enhanced.

Description

Technical Field

[0001] The present invention belongs to the technical field of smart home, and in particular relates to a smart home environment self-adaptive adjustment system and an adjustment method thereof. Background Art

[0002] With the rapid development of science and technology, smart home technology, as one of the typical applications of Internet of Things technology, is gradually penetrating into people's daily lives. Traditional smart home control methods mostly rely on fixed parameters and scene modes pre-set by users, and control home appliances to execute predetermined events within a predetermined time period through parameters. Although this method has achieved the automatic control of home appliances to a certain extent, its intelligence level is limited and it is difficult to adapt to the complex and changeable home environment and changes in user behavior habits. For this reason, we propose a smart home environment adaptive adjustment system and method. Summary of the invention

[0003] The purpose of the present invention is to provide a smart home environment self-adaptive adjustment system and an adjustment method thereof to solve the technical problems raised in the above-mentioned background technology.

[0004] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows:

[0005] A smart home environment adaptive adjustment system, including the following modules:

[0006] An environmental data acquisition module, the environmental data acquisition module includes a sensor unit and a marking unit, the sensor unit is used to receive data from various sensors in the environment, and the marking unit is used to mark data from different areas in the environment;

[0007] A user behavior data analysis module, the user behavior data analysis module includes a data collection unit, a cleaning unit, a feature extraction unit and a prediction unit, the data collection unit is used to collect historical data on the time, frequency and preference parameters of users using home appliances, the cleaning unit is used to clean, organize and convert the collected raw data, remove noise and outliers, and standardize the data format, the feature extraction unit is used to extract the usage status of home appliances in different time periods and the user's sound wave feature information, and the prediction unit is used to predict the operation status of home appliances in future time periods;

[0008] An intelligent decision-making module, which is used to receive the data collected and marked by the environmental data collection module and the analysis results of the user behavior data analysis module, and use a neural network algorithm to formulate operating parameters of the home appliance;

[0009] A control module, the control module is used to receive the control instructions generated by the intelligent decision-making module, and control the corresponding home appliances to execute the corresponding commands;

[0010] A user interaction module, the user interaction module includes a visualization unit and an operation unit, the visualization unit is used to display the current environmental data and the operation data of the household equipment, the operation unit is used to assist the input command of the control module to adjust the environmental parameters, and at the same time feedback the adjustment effect of the system;

[0011] An optimization module, the optimization module includes a data receiving unit, a data analysis unit and a strategy optimization unit, the data receiving unit is used to receive user feedback from the user interaction module and various data during the system operation, the data analysis unit is used to analyze the content of user feedback and various data during the system operation, and the strategy optimization unit is used to receive the analysis results of the data analysis unit to improve and adjust the existing adjustment strategy;

[0012] The safety module includes a data storage unit, an alarm unit and an emergency processing unit. The data storage unit is used to store various types of data generated in the optimization module. The alarm unit is used to monitor the operating parameter data of household appliances and generate corresponding types of alarm instructions when the data is abnormal. The emergency processing unit is used to take corresponding measures according to the type of alarm instructions.

[0013] As a further solution of the present invention: the sensor unit will receive data from the temperature and humidity sensor, light sensor, air quality sensor and sound sensor in the environment, and the marking unit will mark the data received by the sensor unit according to the preset area, and the preset area is divided according to the working space of the home appliance.

[0014] As a further solution of the present invention: the data collection unit will classify the collected data according to the type of household appliances and the area to which they belong, and organize the data on the usage time into a time series format; the feature extraction unit will extract the daily usage time and usage parameters of the household appliances, and organize them into a table format; the prediction unit will extract the daily start time of the household appliances according to the division of working days and rest days based on the data extracted by the feature extraction unit, and set the data with an error of more than two hours as abnormal data and not substitute it into the calculation; for the usage time of the equipment, substitute it into the formula to predict the future usage of the household appliances, and the specific formula is as follows:

[0015]

[0016] Among them, T n+1 Indicates the device usage time on the n+1th day, n indicates the number of days the device is used, S i Indicates the device usage time on the i-th day.

[0017] As a further solution of the present invention: the feature extraction unit extracts the user's sound wave feature information, and establishes a regional voice recognition database based on the user's sound wave information. The standard for storing the sound wave information in the regional voice recognition database is that the sound wave information appears in the region for three consecutive days. The data analysis module also includes a semantic recognition unit, which is used to recognize the actual meaning in the user's voice command and generate a corresponding regional control command to send to the control module. When a voice command with actual meaning appears in the region, the feature extraction unit extracts the sound wave information of the user who issued the voice command, and determines the user to whom the sound wave information belongs through a formula. The specific formula is as follows:

[0018] B i = {b1, B2…B i}

[0019] K n =f(B0,B n )

[0020] Among them, B0 is the waveform shape of this acquisition, B n is the nth waveform stored in the regional speech recognition database, f represents the cosine similarity, K n Indicates the similarity between the collected waveform and the nth waveform stored in the regional speech recognition data. When K n When >0.9, it means that the waveform collected this time is the waveform marked n in the regional speech recognition database. After determining the user to whom the waveform belongs, the control module will execute the user's voice command according to the user's environmental preference.

[0021] As a further solution of the present invention: the intelligent decision-making module will receive and integrate the temperature, humidity values, light intensity levels, air quality indicators and sound level data of different areas collected and marked by the environmental data acquisition module, extract the current home environment data, and according to the prediction results of the user behavior data analysis module, use the neural network algorithm to automatically learn and identify the relationship and rules between the environment and user behavior, formulate the temperature setting of the thermostat, the brightness and color temperature configuration of the lighting equipment, the operating intensity and time interval of the air purifier, and the sound level of the area, generate corresponding equipment operation parameter plans, and send control instructions to the control module.

[0022] As a further solution of the present invention: after receiving the instruction, the control module will immediately start the execution mechanism and establish connections with various corresponding household appliances. The control module is equipped with a learning unit, which can automatically identify the communication characteristics and operating requirements of the device when connecting to a new device for the first time, and record this information. As the number of uses increases, the control strategy and instruction conversion method of the device are continuously optimized.

[0023] As a further solution of the present invention: the visualization unit is provided with three display modes: simple display, complete display and professional display. The simple display mode only displays the current indoor temperature interval, humidity range and a simple evaluation of the air quality. For the operation data of household appliances, only the on status of the appliance and the current operation mode are displayed. The complete display mode will present the environmental data in detail, including temperature, humidity, light intensity, various specific indicators of air quality accurate to specific values, and environmental change trend charts. For the operation data of household appliances, the specific operation parameters, energy consumption, operation time and maintenance reminders of the equipment will be displayed. The professional display mode will present detailed data analysis results and professional environmental assessment reports.

[0024] As a further solution of the present invention: the data receiving unit receives user feedback from the user interaction module and various types of data during the system operation, and extracts valuable information to transmit to the data analysis unit. The data analysis unit will analyze and interpret the content of the user feedback and various types of complex data during the system operation, and use text mining technology and descriptive statistical analysis methods to mine the needs and problems in the user feedback. The strategy optimization unit will generate a problem handling solution based on the mined information and issue corresponding control instructions to the control module.

[0025] As a further solution of the present invention: the data storage unit will classify the stored data according to time and type, and encrypt the classified sensitive data, the sensitive data is the user's personal privacy information, the alarm unit will obtain the power, current, voltage, temperature and humidity data of the home equipment in real time, once fault data appears for 3 consecutive seconds, it will connect to the server of the equipment manufacturer to determine the fault type according to the type of abnormal data, and display the source device of the fault data and the information of the area where the device is located in the visualization unit and notify the user by text message, the fault data is the operating data that is not within the normal working range of the equipment, the emergency processing unit will receive the fault type judgment result from the alarm unit in real time, and after receiving the fault information, it will classify the fault into mild fault, moderate fault and serious fault according to the severity of the fault. The emergency processing unit can be divided into three levels: minor fault and serious fault. For a minor fault, the emergency processing unit adjusts the operating parameters of the equipment through the control module and continuously monitors the equipment status. For a moderate fault, the emergency processing unit will send a stop operation instruction to the equipment to prevent the fault from further expanding. For a serious fault, the emergency processing unit will send an emergency notification to the user, reminding the user to take necessary safety measures, and provide the user with a reference maintenance plan from the maintenance plan library. The reference maintenance plan is a detailed solution to the historical occurrence of the fault in the equipment. The maintenance plan library is the fault types and solutions established by the manufacturer of the equipment. The data storage unit will count the usage time of each household appliance in the environment, and the alarm unit will extract the statistical data and alarm the household appliances whose usage time is less than one month from the equipment installation cycle.

[0026] In addition, the present invention also provides a method for self-adapting a smart home environment, comprising the following steps:

[0027] Step 1: Environmental data collection and labeling: using the sensor unit to receive data from the temperature and humidity sensor, light sensor, air quality sensor and sound sensor, and using the labeling unit to divide and label the received data;

[0028] Step 2: User behavior data analysis: The data collection unit collects the historical usage data and preferences of home appliances. The cleaning unit removes and processes these data and unifies the format. The feature extraction unit extracts the device usage features. Finally, the prediction unit uses algorithms to predict the future operation of the device.

[0029] Step 3: Intelligent decision-making: The intelligent decision-making module integrates environmental and user behavior data, and uses neural network algorithms to predict and set the operating parameters of home appliances;

[0030] Step 4: The control module receives the command and accurately controls the operation of the home appliances. The learning unit automatically identifies the characteristics of the new appliances and continuously optimizes the control strategy.

[0031] Step 5: User interaction and feedback: The user interaction module visualizes environmental data and device status, supports users to adjust parameters and provide feedback on adjustment effects;

[0032] Step 6: System optimization: The optimization module uses data analysis and strategy adjustment to provide corresponding optimization strategies using user feedback and system data to improve system performance.

[0033] Step 7: Security assurance: data storage classifies data security, the alarm unit monitors the device status in real time, and the emergency processing unit responds quickly to ensure home safety.

[0034] The intelligent home environment adaptive adjustment system and adjustment method of the present invention have the following advantages:

[0035] 1. The present invention realizes the capture of multi-dimensional parameters of home environment and the characterization of user behavior patterns by integrating two modules: environmental data collection and user behavior data analysis. It adopts neural network algorithm to automatically mine and model the complex nonlinear relationship between environmental variables and user behavior, and then generates customized home equipment control strategies, dynamically adjusts the operating parameters of home equipment, thereby realizing the intelligent adjustment accuracy of home environment and improving the living comfort and satisfaction of users;

[0036] 2. The present invention continuously monitors the operating status of household appliances by setting an alarm and emergency processing unit in the safety module. When the alarm unit detects that a device has fault data, it will display the fault information in the visualization unit and notify the user by SMS, so that the user can learn about the device fault at the first time. The emergency processing unit will automatically classify the fault according to the severity and take corresponding emergency processing measures to speed up the processing efficiency of the fault. At the same time, the alarm unit will alarm the device that is about to reach the end of its service life, avoiding safety hazards caused by aging of the equipment.

[0037] 3. The present invention provides three display modes, namely concise, complete and professional, through the user interaction module, which meets the needs of different users for viewing environmental data and equipment operation data. At the same time, the data receiving unit and the data analysis unit deeply mine valuable information in user feedback and system operation data through text mining technology and descriptive statistical analysis methods, and provide data support for the strategy optimization unit, so that the system can respond to user needs in a timely manner, improve user experience, and improve the accuracy of environmental adjustment by continuously optimizing the adjustment strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A schematic diagram of a system flow in an embodiment of the present invention;

[0039] Figure 2 Schematic diagram of the method steps in an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The specific embodiments of the present invention will be further described below in conjunction with the accompanying drawings. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0041] In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0042] Embodiment 1: Smart Home Environment Adaptive Adjustment System and Method In a typical three-bedroom, two-living-room house, different family members have their own requirements for environmental conditions, such as temperature, humidity, light, etc. Traditional home environment control mostly relies on manual adjustment, which is not only inefficient but also difficult to meet personalized needs. In addition, the environment changes quickly and complexly, requiring the system to be able to perceive in real time and make corresponding adjustments;

[0043] Therefore, in order to effectively solve the above problems, this application proposes a smart home environment adaptive adjustment system and method, please refer to the attached Figure 1 -Attached Figure 2 , using sensor technology, intelligent decision-making algorithms, wireless communication technology and user interaction interface, it can automatically adjust the operating status of home appliances according to changes in indoor and outdoor environments and user needs without relying on human intervention, creating a comfortable, energy-saving and safe living environment;

[0044] Specific workflow: Install temperature and humidity sensors, light sensors, air quality sensors and sound sensors in key areas such as the living room, bedroom and kitchen of a three-bedroom, two-living-room house. Use the sensor unit to collect environmental data in real time and transmit it to the system server through a wireless network. The marking unit automatically marks the received data according to the preset area division to ensure the accuracy and traceability of the data. The area division is based on functional spaces such as living rooms, bedrooms and kitchens. The area marking is based on the functional areas of home appliances. For example, if a device is installed in the living room and its functional range is the kitchen, then the device will be marked as the kitchen.

[0045] The data collection unit regularly collects usage time and preference parameter data from smart home devices, such as air conditioners, lighting systems, curtain controllers, etc., and user mobile devices. The cleaning unit performs denoising, outlier removal and format standardization on the data to ensure data quality. The feature extraction unit extracts the daily usage time and usage parameters of home devices, such as the working hours of thermostats and the brightness settings of lighting systems, and organizes them into a time series format. The prediction unit uses neural networks to predict the future operation of home devices based on historical data, including device startup time, usage time, etc.

[0046] The intelligent decision-making module receives environmental data and user behavior data, and uses neural network algorithms to model the complex relationship between environmental variables and user behavior. Based on the model prediction results, the intelligent decision-making module formulates personalized home equipment control strategies, such as automatically adjusting the air-conditioning temperature according to the outdoor temperature and indoor humidity, and adjusting the curtain opening and closing degree and lighting brightness according to the indoor light intensity. The control module receives the control instructions of the intelligent decision-making module and sends control signals to the corresponding home equipment through wireless communication technology to achieve precise control of the equipment;

[0047] The user can view the current environmental data and equipment operation status in real time through the visualization unit of the user interaction module, including temperature, humidity, light intensity, air quality, etc. The user can adjust the environmental parameters through the operation unit according to personal needs, such as manually adjusting the air conditioning temperature and lighting brightness. The system provides a user feedback function, and the user can evaluate the system adjustment effect and put forward improvement suggestions;

[0048] Each family member has his or her own unique living habits and home environment preferences. In order to better meet the personalized needs of each family member, the family installed a smart home environment adaptive adjustment system with sound wave feature recognition function. The feature extraction unit of the smart home system first starts to collect the sound wave feature information of each family member, captures the voice communication sound of family members in daily life through the microphone array, and extracts the shape characteristics of the sound wave waveform. When a certain sound wave information appears in the area for three consecutive days and is stably recognized by the system, the sound wave information is added to the regional voice recognition database as the unique sound wave identifier of the user. Family members can issue control instructions to the system through voice, such as "lower the air conditioning temperature in the living room". The semantic recognition unit of the system is responsible for identifying the actual meaning of these voice instructions. Then the feature extraction unit will extract the sound wave information of the user who issued the voice instruction again. Subsequently, the cosine similarity between the sound wave information and each waveform stored in the regional voice recognition database is calculated using the above formula. If a similarity value is greater than 0.9, the system determines that the waveform collected this time belongs to the user marked as n in the database and generates the corresponding regional control instruction. For example, when the system recognizes the instruction of "lowering the air conditioning temperature in the living room", it will generate a control instruction for lowering the air conditioning temperature in the living room for the user.

[0049] Embodiment 2: The maintenance and management of the home environment faces many challenges, such as equipment failures are difficult to detect in time, safety hazards are difficult to prevent effectively, etc. These problems not only affect the comfort of home life, but also may threaten the safety of family members;

[0050] Please see attached Figure 1 -Attached Figure 2, the data storage unit stores the operation data and user behavior data of household appliances in real time to ensure the integrity and traceability of the data. The alarm unit continuously monitors the operation status of household appliances. Once abnormal data is found, such as a sudden increase in equipment power, abnormal current, etc., the fault warning mechanism is immediately triggered, and the fault type is intelligently identified through the emergency processing unit. For example, when the smoke sensor in the kitchen detects abnormal smoke concentration, the alarm unit immediately determines it as a fire risk and starts the emergency response process. The emergency processing unit first shuts down the gas valve and power supply in the kitchen through the control module to prevent the fire from spreading. Subsequently, the fault information is displayed through the visualization unit of the user interaction module, and the user is notified by SMS. At the same time, the emergency processing unit provides users with emergency maintenance guidance from the maintenance solution library based on the fault severity assessment results to ensure that users can quickly take safety measures and contact professional maintenance personnel;

[0051] The alarm unit will also sound an alarm on the use of home appliances. For example, one day, the smart door lock malfunctioned due to insufficient battery power. The alarm unit immediately detected the abnormality and displayed the fault information on the visualization unit. At the same time, it notified the elderly’s relatives via text message. After receiving the notification, the relatives quickly contacted the maintenance personnel and solved the problem in time. In addition, the alarm unit also regularly checks the service life of other home appliances. For example, when the air conditioner is approaching its recommended replacement cycle, it will also send a reminder text message to the elderly in advance to avoid safety hazards caused by aging equipment. This timely fault warning and emergency handling mechanism provides a strong safety guarantee for the elderly’s home life.

[0052] Example 3: In a typical three-bedroom, two-living-room house, each family member has different needs and preferences for the comfort of the home environment, such as temperature, humidity, light, etc. Traditional home environment control lacks intelligence and personalization, and it is difficult to meet these diverse needs. At the same time, with the increase in smart home devices, how to effectively integrate and manage these devices to improve the convenience and comfort of home life has become an urgent problem to be solved;

[0053] Please see attached Figure 1 -Attached Figure 2 , through a highly configurable user interface and intuitive and easy-to-use visualization technology, it provides users with a flexible and diverse interactive experience. Users can select simple display, complete display or professional display mode through the visualization unit of the user interaction module to view the current environmental data and equipment operation status according to personal needs. At the same time, the system continuously receives user feedback and system operation data through the optimization module to conduct in-depth analysis and strategy optimization. For example, on a busy weekday night, they can choose the simple mode to quickly understand the basic conditions of the home such as temperature and humidity, while on the weekend, they can choose the complete or professional mode to gain an in-depth understanding of various data and equipment status;

[0054] In addition, the system collects user feedback and system operation data through the data receiving unit and the data analysis unit, and uses text mining technology and descriptive statistical analysis methods to mine valuable information to provide data support for the strategy optimization unit. For example, the system finds that family members often complain about the high temperature in the bedroom around 10 o'clock in the evening, so it automatically adjusts the control strategy of the air-conditioning temperature at night to make the bedroom cooler and more comfortable at night. By continuously optimizing the adjustment strategy, the system not only improves the accuracy of environmental adjustment, but also improves the user experience and user feedback.

[0055] The above-mentioned front, back, left, right, top and bottom are all based on the figures in the specification. Figure 1 As a benchmark, according to the person's observation perspective, the side of the device facing the observer is defined as the front, the left side of the observer is defined as the left, and so on.

[0056] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the scope of protection of the present invention.

[0057] It should be noted that the device structure and the drawings of the present invention mainly describe the principle of the present invention. In terms of the technology of the design principle, the settings of the power mechanism, power supply system and control system of the device are not fully described. On the premise that the technical personnel in this field understand the principle of the above invention, the details of the power mechanism, power supply system and control system can be clearly known. The control method of the application document is automatic control through a controller, and the control circuit of the controller can be realized by simple programming by the technical personnel in this field.

[0058] The standard parts used therein can all be purchased from the market and can be customized according to the instructions and drawings. The specific connection methods of each part adopt conventional means such as mature bolts, rivets, welding, etc. in the prior art. The machinery, parts and equipment all adopt conventional models in the prior art, and the structures and principles of the components known to technical personnel in this field can be known by these technical personnel through technical manuals or through conventional experimental methods.

[0059] The embodiments of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the described embodiments. For those skilled in the art, without departing from the principles and spirit of the present invention, various changes, modifications, substitutions and variations are made to these embodiments, which still fall within the scope of protection of the present invention. It can be understood that the present invention is described by some embodiments, and it is known to those skilled in the art that various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments can be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application belong to the scope protected by the present invention.

Claims

1. A smart home environment adaptive adjustment system, characterized in that: include: An environmental data acquisition module, the environmental data acquisition module includes a sensor unit and a marking unit, the sensor unit is used to receive data from various sensors in the environment, and the marking unit is used to mark data from different areas in the environment; A user behavior data analysis module, the user behavior data analysis module includes a data collection unit, a cleaning unit, a feature extraction unit and a prediction unit, the data collection unit is used to collect historical data on the time, frequency and preference parameters of users using home appliances, the cleaning unit is used to clean, organize and convert the collected raw data, remove noise and outliers, and standardize the data format, the feature extraction unit is used to extract the usage status of home appliances in different time periods and the user's sound wave feature information, and the prediction unit is used to predict the operation status of home appliances in future time periods; An intelligent decision-making module, which is used to receive the data collected and marked by the environmental data collection module and the analysis results of the user behavior data analysis module, and use a neural network algorithm to formulate operating parameters of the home appliance; A control module, the control module is used to receive the control instructions generated by the intelligent decision-making module, and control the corresponding home appliances to execute the corresponding commands; A user interaction module, the user interaction module includes a visualization unit and an operation unit, the visualization unit is used to display the current environmental data and the operation data of the household equipment, the operation unit is used to assist the input command of the control module to adjust the environmental parameters, and at the same time feedback the adjustment effect of the system; An optimization module, the optimization module includes a data receiving unit, a data analysis unit and a strategy optimization unit, the data receiving unit is used to receive user feedback from the user interaction module and various data during the system operation, the data analysis unit is used to analyze the content of user feedback and various data during the system operation, and the strategy optimization unit is used to receive the analysis results of the data analysis unit to improve and adjust the existing adjustment strategy; The safety module includes a data storage unit, an alarm unit and an emergency processing unit. The data storage unit is used to store various types of data generated in the optimization module. The alarm unit is used to monitor the operating parameter data of household appliances and generate corresponding types of alarm instructions when the data is abnormal. The emergency processing unit is used to take corresponding measures according to the type of alarm instructions.

2. The smart home environment adaptive adjustment system according to claim 1, characterized in that: The sensor unit will receive data from the temperature and humidity sensor, light sensor, air quality sensor and sound sensor in the environment, and the marking unit will mark the data received by the sensor unit according to the preset area, and the preset area is divided according to the working space of the home appliance.

3. The smart home environment adaptive adjustment system according to claim 1, characterized in that: The data collection unit will classify the collected data according to the type of household appliances and the area they belong to, and organize the data on the usage time into a time series format. The feature extraction unit will extract the daily usage time and usage parameters of the household appliances and organize them into a table. The prediction unit will extract the daily start time of the household appliances according to the division of working days and rest days based on the data extracted by the feature extraction unit, and set the data with an error of more than two hours as abnormal data and not substitute it into the calculation. For the usage time of the equipment, substitute it into the formula to predict the future usage of the household appliances. The specific formula is as follows: Among them, T n+1 Indicates the device usage time on the n+1th day, n indicates the number of days the device is used, S i Indicates the device usage time on the i-th day.

4. The smart home environment adaptive adjustment system according to claim 1, characterized in that: The feature extraction unit extracts the user's sound wave feature information and establishes a regional voice recognition database based on the user's sound wave information. The standard for storing the sound wave information in the regional voice recognition database is that the sound wave information appears in the region for three consecutive days. The data analysis module also includes a semantic recognition unit, which is used to recognize the actual meaning in the user's voice command and generate a corresponding regional control command to send to the control module. When a voice command with actual meaning appears in the region, the feature extraction unit extracts the sound wave information of the user who issued the voice command, and determines the user to whom the sound wave information belongs through a formula. The specific formula is as follows: B i ={B1,b2…B i } K n =f(B0,B n ) Among them, B0 is the waveform shape of this acquisition, B n is the nth waveform stored in the regional speech recognition database, f represents the cosine similarity, K n Indicates the similarity between the collected waveform and the nth waveform stored in the regional speech recognition data. When K n When >0.9, it means that the waveform collected this time is the waveform marked n in the regional speech recognition database. After determining the user to whom the waveform belongs, the control module will execute the user's voice command according to the user's environmental preference.

5. The smart home environment adaptive adjustment system according to claim 1, characterized in that: The intelligent decision-making module will receive and integrate the temperature, humidity values, light intensity levels, air quality indicators and sound level data of different areas collected and marked by the environmental data acquisition module, extract the current home environment data, and use the neural network algorithm based on the prediction results of the user behavior data analysis module to automatically learn and identify the relationship and rules between the environment and user behavior, formulate the temperature setting of the thermostat, the brightness and color temperature configuration of the lighting equipment, the operating intensity and time interval of the air purifier, and the sound level of the area, generate the corresponding equipment operation parameter plan, and send control instructions to the control module.

6. The smart home environment adaptive adjustment system according to claim 1, characterized in that: After receiving the instruction, the control module will immediately start the execution mechanism and establish connections with various corresponding home appliances. The control module is equipped with a learning unit, which can automatically identify the communication characteristics and operation requirements of the device when connecting to a new device for the first time, and record this information. As the number of uses increases, the control strategy and instruction conversion method of the device are continuously optimized.

7. The smart home environment adaptive adjustment system according to claim 1, characterized in that: The visualization unit is provided with three display modes: simple display, complete display and professional display. The simple display mode only displays the current indoor temperature interval, humidity range and a simple evaluation of the air quality. For the operation data of household appliances, only the on status of the appliance and the current operation mode are displayed. The complete display mode will present the environmental data in detail, including temperature, humidity, light intensity, various specific indicators of air quality accurate to specific values, and environmental change trend charts. For the operation data of household appliances, the specific operation parameters, energy consumption, operation time and maintenance reminders of the equipment will be displayed. The professional display mode will present detailed data analysis results and professional environmental assessment reports.

8. The smart home environment adaptive adjustment system according to claim 1, characterized in that: The data receiving unit receives user feedback from the user interaction module and various data during the system operation, and extracts valuable information to transmit to the data analysis unit. The data analysis unit will analyze and interpret the content of the user feedback and various complex data during the system operation, and use text mining technology and descriptive statistical analysis methods to mine the needs and problems in the user feedback. The strategy optimization unit will generate a problem handling plan based on the mined information and issue corresponding control instructions to the control module.

9. The smart home environment adaptive adjustment system according to claim 1, characterized in that: The data storage unit will classify the stored data according to time and type, and encrypt the classified sensitive data. The sensitive data is the user's personal privacy information. The alarm unit will obtain the power, current, voltage, temperature and humidity data of the home equipment in real time. Once fault data appears for 3 consecutive seconds, it will connect to the equipment manufacturer's server to determine the fault type based on the type of abnormal data, and display the source device of the fault data and the information of the area where the device is located in the visualization unit and notify the user via text message. The fault data is operating data that is not within the normal working range of the equipment. The emergency processing unit will receive the fault type judgment result from the alarm unit in real time. After receiving the fault information, it will classify the fault into mild fault, moderate fault and severe fault according to the severity of the fault. There are three levels. For minor faults, the emergency processing unit adjusts the operating parameters of the equipment through the control module and continuously monitors the equipment status. For moderate faults, the emergency processing unit will send a stop operation instruction to the equipment to prevent the fault from further expanding. For serious faults, the emergency processing unit will send an emergency notification to the user, reminding the user to take necessary safety measures, and provide the user with a reference maintenance plan from the maintenance plan library. The reference maintenance plan is a detailed solution to the historical failure of the device. The maintenance plan library is the fault types and solutions established by the manufacturer of the device. The data storage unit will count the usage time of each household appliance in the environment, and the alarm unit will extract the statistical data and alarm the household appliances whose usage time is less than one month from the equipment installation period.

10. A method for self-adapting an intelligent home environment applicable to the self-adapting intelligent home environment system of any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Environmental data collection and labeling: using the sensor unit to receive data from the temperature and humidity sensor, light sensor, air quality sensor and sound sensor, and using the labeling unit to divide and label the received data; Step 2: User behavior data analysis: The data collection unit collects the historical usage data and preferences of home appliances. The cleaning unit removes and processes these data and unifies the format. The feature extraction unit extracts the device usage features. Finally, the prediction unit uses algorithms to predict the future operation of the device. Step 3: Intelligent decision-making: The intelligent decision-making module integrates environmental and user behavior data, and uses neural network algorithms to predict and set the operating parameters of home appliances; Step 4: The control module receives the command and accurately controls the operation of the home appliances. The learning unit automatically identifies the characteristics of the new appliances and continuously optimizes the control strategy. Step 5: User interaction and feedback: The user interaction module visualizes environmental data and device status, supports users to adjust parameters and provide feedback on adjustment effects; Step 6: System optimization: The optimization module uses data analysis and strategy adjustment, user feedback and system data to provide corresponding optimization strategies to improve system performance; Step 7: Security assurance: data storage classifies data security, the alarm unit monitors the device status in real time, and the emergency processing unit responds quickly to ensure home safety.

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