Internet-based smart home system

Abstract

The invention, which relates to the technical field of the smart home, discloses an Internet-based smart home system comprising an equipment management module, a user interface module, a network communication module, an automatic control module, a data processing and analysis module, a notification and alarm module and a voice assistant integration module. The method is used for solving the problems that in the prior art, the refinement degree of equipment management is low, the operation state of each piece of equipment cannot be systematically tracked and monitored, the intelligent analysis capability is lacked, the home environment is difficult to optimize through data processing and an algorithm, and control and feedback are lagged. According to the invention, the equipment management module classifies and stores the equipment operation data in detail, so that the equipment management is more systematized and refined, and a user can easily grasp and control the use condition of equipment at home; the automatic control module and the notification and alarm module ensure accurate control and timely feedback, and the operation reliability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart home, and in particular to a smart home system based on the Internet. Background Art

[0002] Smart home systems connect various devices in the home through the Internet, allowing users to remotely monitor and operate various devices including lighting, security systems, thermostats, smart appliances, etc., thereby realizing the intelligence and convenience of home life. Users can easily control home devices through smartphones, tablets or voice assistants, and can grasp the real-time status of their homes no matter where they are. At the same time, these systems can also automatically adjust the operating status of devices according to user habits and environmental changes, such as automatically turning off lights at night or automatically adjusting air conditioning when the temperature rises, further improving the comfort of life and the efficiency of energy use;

[0003] However, these systems still have some significant defects in practical applications. First, the management is extensive. The existing technology has a low degree of refinement in equipment management and cannot systematically track and monitor the operating status of each device. Second, the intelligence is insufficient and lacks intelligent analysis capabilities, making it difficult to optimize the home environment through data processing and algorithms. In addition, user behavior analysis is lacking. Existing technologies often cannot effectively analyze and use user behavior data to improve the intelligent response capabilities of equipment. Finally, control and feedback lag. Traditional systems have lags in equipment control and feedback mechanisms and cannot respond to user needs in a timely manner. Summary of the invention

[0004] The purpose of the present invention is to solve the above-mentioned problems and to provide an Internet-based smart home system.

[0005] The object of the present invention can be achieved by the following technical solutions: an Internet-based smart home system, comprising a device management module, an automation control module and a data processing and analysis module;

[0006] The device management module is responsible for integrating various intelligent devices into a unified platform; identifying and connecting different types of devices; configuring and managing devices, including naming, location allocation and group management of devices; and monitoring the operating status data of devices in real time;

[0007] The automation control module automatically identifies the room layout and clearly identifies the function and location of each room; it is also used to comprehensively control the intelligence value, behavior association data and behavior determination data corresponding to the room layout;

[0008] The data processing and analysis module obtains the operating status data of each smart home appliance through the network communication module, and analyzes it to obtain the smart value ZNZ of the home smart home;

[0009] Then analyze the user's behavior data and the operating status data of the smart home appliance; analyze the user's behavior data to extract key features from the behavior data; match the extracted features with multiple preset behavior action templates, and calculate the feature similarity S1 using the similarity calculation formula; if the similarity S ≥ 0.8, it is judged as a valid feature and marked as matching a preset behavior action;

[0010] Then obtain the effective features in the room layout of the home, and perform correlation analysis with the operating status data of the home appliances in the corresponding location area, specifically:

[0011] The extracted effective features are located in the home room layout to determine the specific room where the features occur; according to the room layout, the features are mapped to the corresponding equipment operation status data set; the operation status data is collected from the smart home appliances in the mapped room; the association rules between the features and the equipment status are analyzed using association rule mining technology; starting from a single feature and equipment status, the item sets whose support exceeds the minimum support threshold are found, and the initial frequent item sets are used to generate a larger candidate item set; for each candidate item set, its support and confidence are calculated, and the association rules that meet the threshold are screened out by the automation control module: if the confidence ZXD is greater than the preset corresponding threshold, it is behavior association data; if the confidence ZXD is 100%, it is behavior determination data.

[0012] As a preferred embodiment of the present invention, it also includes a user interface module, a network communication module, a notification and alarm module and a voice assistant integration module;

[0013] The network communication module manages the communication between all devices and servers in the system, supports multiple communication protocols, ensures the safe and fast transmission of data between devices, and supports remote access;

[0014] The user interface module manually inputs the room layout; the user interface module obtains the intelligence value ZNZ through the network communication module and displays the intelligence value corresponding to the room layout; it is also used for alarm and notification display

[0015] The notification and alarm module performs alarm notification;

[0016] The voice assistant integration module receives user voice commands, parses them into behavior determination data, and executes them through the automation control module; converts the voice commands into device control commands; and provides a voice interaction interface.

[0017] As a preferred implementation of the present invention, the specific process of analyzing and obtaining the smart value ZNZ of the home smart home is:

[0018] The calculation is based on the number of smart homes, coverage and frequency of use of devices, using the established formula: Output intelligent value ZNZ, A i is the number of the i-th device, B j is the device coverage of the jth room, C k is the usage frequency of the kth device, C max,k is the maximum usage frequency of the kth device, D l is the interconnectivity weight of the lth device combination, E o is the response time of the oth device, F s is the energy consumption of the sth device, G u is the complexity of the u-th automation scenario, H w Score the user satisfaction of the w-th device; a1, a2, a3, a4, a5, a6, a7 and a8 are their corresponding preset weight factors.

[0019] As a preferred implementation of the present invention, the specific process of analyzing the user's behavior data to extract key features from the behavior data is as follows:

[0020] Use signal processing and machine learning algorithms to extract key features from the collected behavioral data, using the feature extraction formula: Extraction, F1 i1 is the i 1th feature, x1(t1) is the signal value at time t1, p1 is the feature extraction parameter, and T1 is the time window.

[0021] As a preferred implementation of the present invention, the specific process of calculating the feature similarity S1 by the similarity calculation formula is:

[0022] The similarity calculation formula is: Output feature similarity S1, where A1 i 1 and B1 i 1 are the i 1th values ​​of user behavior features and template features, respectively, and n1 is the number of features.

[0023] As a preferred implementation of the present invention, the automatic control module screens out association rules that meet the threshold, and the specific association rules are:

[0024] The basic form of association rules is: (support, confidence), A2 represents the user behavior characteristics, B2 represents the device operation status; support ZCD represents the probability of feature A2 and status B2 appearing at the same time, The total number of transactions indicates the total number of transactions in all recorded data sets; the number of occurrences of A2 and B2 indicates the number of times user behavior feature A2 and device operation status B2 appear simultaneously in these transactions; the confidence ZXD measures the probability that status B2 also occurs when feature A2 occurs. Confidence ZXD: If the confidence value is high, whenever the user exhibits feature A2, device status B2 will also appear.

[0025] As a preferred implementation of the present invention, the user interface module obtains the intelligence value ZNZ and the intelligence value corresponding to the room layout through the network communication module and displays them, specifically:

[0026] Obtain the functions and locations corresponding to the room layout of the split home; map each room to the corresponding color range based on its intelligence value; display it in multiple dimensions on the user interface in the form of room layout, including functions, locations, and intelligence levels, and provide an integrated visual chart in combination with color and layout information.

[0027] As a preferred embodiment of the present invention, the automation control module is also used to comprehensively control the intelligence value, behavior association data and behavior determination data corresponding to the room layout, specifically:

[0028] When the behavior determination data is received, a behavior determination instruction is generated. If the user issues a clear instruction through the audio assistant, the instruction is immediately parsed into the behavior determination data; and the parsed behavior determination data is directly converted into a device control instruction;

[0029] When the user behavior is determined, the correlation is inferred through the room layout and smart value, and the device is precisely controlled. The behavior is mapped to the corresponding room layout, the behavior is mapped to the living room, and the relationship between the behavior and other devices is analyzed; then the current smart value ZNZ corresponding to each room is obtained 当前 , according to the intelligent value ZNZ 当前 To determine whether the equipment in the room needs to be adjusted, use the set formula: C1 j1 =ZNZ 当前 ×f1(ZXD(i2,j1)) outputs the control decision strength C1 of device j1 j1 , f1 is the mapping function, which converts the confidence ZXD(i 2,j1) into control strength; i2 and j1 are two devices;

[0030] According to the control decision strength of the equipment, appropriate adjustments are made to the relevant equipment in the room. After the control is completed, the equipment status is monitored in real time to ensure that the adjustment meets expectations. If the equipment status does not meet expectations, the notification and alarm module will issue an alarm notification and display it in the user interface module. The automation control module will then make readjustments.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. The present invention integrates various intelligent devices (such as home appliances, sensors, smart cameras, etc.) into a unified platform through the device management module. It supports multiple communication protocols, such as Wi-Fi, Zigbee and Z-Wave, ensuring seamless connection and efficient communication between devices. The device management module can not only identify and connect different types of devices, but also monitor the operating status of the device in real time, such as temperature, switch status and battery power. In addition, the module can also obtain the layout information of the family room, name, position and group the devices, so that users can accurately control and manage each intelligent device in the home and achieve a personalized device management experience.

[0033] 2. The present invention uses the data processing and analysis module to obtain the operating status data and user behavior data of each smart home appliance through the network communication module, and conducts in-depth analysis. It uses advanced algorithms to calculate the overall intelligence value of the family. This value is based on a comprehensive evaluation of multiple dimensions such as the number of devices, coverage, and frequency of use, reflecting the overall intelligence level of the family's smart home. At the same time, the module can also calculate the intelligence value for each room separately to show the difference in the degree of intelligence of each room. Through the user interface module, these intelligence values ​​are presented in an intuitive graphical way, using a combination of color and layout to show the intelligence level of different rooms, allowing users to quickly understand and manage smart devices in the home and optimize the family's smart experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0035] Figure 1 It is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0036] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] It should be understood that the terms “include” and “comprising” used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0038] It should also be understood that the terms used in this disclosure are only for the purpose of describing specific embodiments and are not intended to limit the disclosure. As used in this disclosure and claims, the singular forms of "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations.

[0039] See also Figure 1 As shown, an Internet-based smart home system includes: a device management module, a user interface module, a network communication module, an automation control module, a data processing and analysis module, a notification and alarm module, and a voice assistant integration module;

[0040] The network communication module manages the communication between all devices and servers in the system, supports multiple communication protocols (such as Wi-Fi, Zigbee, Z-Wave), ensures the secure and fast transmission of data between devices, and supports remote access.

[0041] The device management module is responsible for integrating various smart devices into a unified platform; it can identify and connect different types of devices, such as smart home appliances (refrigerators, washing machines, air conditioners), sensors (temperature and humidity sensors, motion sensors), smart cameras, door locks, etc.; the device management module is also responsible for the configuration and management of devices, including device naming, location allocation, group management, etc.; for example, TVs are named according to the layout of family rooms, including: living room TV, master bedroom TV, second bedroom TV, etc.; air conditioners are named: living room air conditioner, master bedroom air conditioner, south second bedroom air conditioner, north second bedroom air conditioner, etc.; the device management module is also used to monitor the operating status data of the device in real time, such as temperature, switch status, battery power, etc.; it is also used to obtain the layout of the family room, which is divided into living room operating data, bathroom operating data, kitchen operating data, balcony operating data, bedroom operating data, etc.; specifically:

[0042] The user interface module or the automation control module obtains the room layout through manual input or automatic recognition (such as blueprint scanning, smart device location perception); clearly identifies the function and location of each room, such as living room, bedroom, etc.; and associates the equipment in each room with the corresponding room according to its location and function. For example, the smart lights, TV, and audio equipment in the living room will be assigned to the living room operation data; the smart mirrors, bathroom heaters, etc. in the bathroom will be assigned to the bathroom operation data; and stored by room classification; such as: Living room operation data: recording the use time of lights, audio volume, TV viewing time, etc.; Bathroom operation data: recording water flow usage, frequency of use of smart toilets, bathroom temperature changes, etc.; Kitchen operation data: recording the use time of electrical appliances (such as microwave ovens, ovens), range hood operation status, etc.; Balcony operation data: recording light intensity, use of plant maintenance equipment, etc.; Bedroom operation data: recording sleep quality data, indoor temperature and humidity changes, etc.;

[0043] The data processing and analysis module obtains the operating status data of each smart home appliance through the network communication module and performs analysis:

[0044] First, calculate the intelligence value of the home smart home, specifically:

[0045] The calculation is based on the number of smart homes, coverage and frequency of use of devices, using the established formula: Output intelligent value ZNZ, where A i is the number of the i-th device, B j is the device coverage of the jth room, C k is the usage frequency of the kth device, C max,k is the maximum usage frequency of the kth device, D l is the interconnectivity weight of the lth device combination, E o is the response time of the oth device, F s is the energy consumption of the sth device, G u is the complexity of the u-th automation scenario, H w Score the user satisfaction of the wth device; a1, a2, a3, a4, a5, a6, a7 and a8 are the preset weight factors corresponding to each other; and 1 = a1 + a2 + a3 + a4 + a5 + a6 + a7 + a8; Similarly, the smart value corresponding to the room layout is output through the established formula, including the smart value of the living room, the smart value of the bathroom and the smart value of the kitchen;

[0046] The user interface module then obtains the intelligence value ZNZ through the network communication module and displays the intelligence value corresponding to the room layout; specifically:

[0047] Obtain the functions and locations of the room layout of the split home, such as the living room, bedroom, kitchen, etc.; map each room to a corresponding color range based on its intelligence value (the higher the intelligence value, the darker the color; the lower the intelligence value, the lighter the color, enhancing the gradient effect of color display so that users can intuitively feel the difference in the intelligence level of each room); display the room layout in multiple dimensions on the user interface, including functions, locations and intelligence levels, and provide an integrated visual chart in combination with color and layout information so that users can quickly grasp the intelligence status of each area of ​​the home;

[0048] Color mapping example: Dark blue: rooms with higher intelligence values, showing extremely high intelligence levels; light blue: rooms with medium intelligence values, at a medium intelligence state; light gray: rooms with lower intelligence values, with lower intelligence levels, which need to be optimized or improved.

[0049] Then, by analyzing the user's behavior data (behavior data is the user's approximate body movement data, collected through corresponding sensors) and the operating status data of smart home appliances, specifically:

[0050] Analyze user behavior data:

[0051] Perform feature extraction on the user's behavior data, and use signal processing and machine learning algorithms to extract key features from the collected behavior data, such as speed, acceleration, angle change, etc., through the feature extraction formula: Extraction is performed, where F1 i1 is the i 1th feature, x1(t1) is the signal value at time t1, p1 is the feature extraction parameter, and T1 is the time window;

[0052] The extracted features are then matched with a variety of preset action templates (the preset action template library contains common household actions, such as turning on and off lights, adjusting temperature, starting electrical appliances, etc., and the flexibility of matching is enhanced through the dynamic time warping (DTW) algorithm). The similarity calculation formula is used to measure the degree of matching. The similarity calculation formula is: Output feature similarity S1, where A1i 1 and B1 i 1 are the i 1th values ​​of the user behavior feature and template feature respectively, and n1 is the number of features; if the similarity S1 ≥ 0.8 (i.e. 80%), it is judged as a valid feature and marked as matching a preset behavior action;

[0053] Then obtain the effective features in the room layout of the home, and perform correlation analysis with the operating status data of the home appliances in the corresponding location area, specifically:

[0054] The extracted effective features are located in the layout of the family rooms to determine the specific room where the features occur; according to the room layout, the features are mapped to the corresponding equipment operation status data set; the operation status data is collected from the smart home appliances in the mapped room, such as the switching frequency, usage time, energy consumption, etc. of the equipment; the association rules between the features and the equipment status are analyzed using association rule mining technology (such as April algorithm); starting from a single feature and equipment status, the item sets whose support exceeds the minimum support threshold are found, and the initial frequent item sets are used to generate a larger candidate item set; for each candidate item set, its support and confidence are calculated, and the association rules that meet the threshold are screened out by the automation control module. The process is: if the confidence ZXD is greater than the preset corresponding threshold, it is behavior association data; if the confidence ZXD is 100%, it is behavior determination data;

[0055] The basic form of association rules is: (support, confidence), where A2 represents the user behavior feature and B2 represents the device operation status; support ZCD represents the probability of feature A2 and status B2 appearing at the same time, The total number of transactions indicates the total number of transactions in all recorded data sets; for example, in a family's smart home system, the total number of transactions may be the total number of records of all device operations and user behaviors in a day; the number of occurrences of A2 and B2 indicates the number of times user behavior feature A2 and device operation state B2 appear at the same time in these transactions; the confidence ZXD measures the probability that state B2 also occurs when feature A2 occurs. Confidence ZXD: If the confidence value is high, it means that whenever the user exhibits feature A2, device state B2 is likely to also appear, which reflects a strong association;

[0056] Example: Assume that the user has a feature A2 in the living room: adjusting the volume of the stereo, and the device state B2 is turning on the TV; the total number of transactions is 1000, A2 and B2 occur at the same time 150 times, and feature A2 occurs 200 times; support calculation: Indicates that in all transactions, feature A2 and state B2 occur simultaneously in 15% of cases; confidence calculation: This means that whenever feature A2 occurs, there is a 75% chance that state B2 will also occur.

[0057] The automation control module is also used to comprehensively control the intelligence value, behavior association data and behavior determination data corresponding to the room layout, specifically:

[0058] It should be noted that the voice commands collected by the audio assistant integration module are marked as behavior determination data;

[0059] After receiving the behavior determination data, a behavior determination instruction is generated. If the user issues a clear instruction (such as "turn on the TV") through the audio assistant, the system will immediately parse the instruction into behavior determination data; then directly convert the parsed behavior determination data into a device control instruction; for example, if it is parsed that the user wants to turn on the TV, the system will immediately send a turn-on signal to the TV; after the device executes, it will monitor the device status to confirm whether the instruction is successfully executed, and feedback to the user in the audio assistant or application;

[0060] When the user behavior is determined, the room layout and smart value are used to infer the correlation and accurately control the device. The process is as follows:

[0061] First, map the behavior to the corresponding room layout. For example, if a user turns on the "living room lights" through the voice assistant command, map the behavior to the living room and analyze the association between the behavior and other devices. For example, turning on the living room lights may require adjusting the air conditioner or closing the curtains at the same time. After analyzing the user's behavior data, the associated behaviors are matched and other device operations that are most related to the behavior are found.

[0062] Then get the current intelligence value ZNZ corresponding to each room 当前 (Reflects the comprehensive intelligent status of the equipment in the room), according to the intelligent value ZNZ 当前 Determine whether the equipment in the room needs to be adjusted to ensure that the operation does not affect the overall environment of the room; the process is: through the set formula: C1 j1 =ZNZ 当前 ×f1(ZXD(i2,j1)) outputs the control decision strength C1 of device j1 j1 , f1 is the mapping function, which converts the confidence ZXD(i2,j1) into control strength; i2 and j1 are two devices;

[0063] According to the control decision strength of the equipment, appropriate adjustments are made to the relevant equipment in the room. For example, after the user turns on the light, the air-conditioning temperature may be lowered at the same time to improve comfort. After the control is completed, the equipment status is monitored in real time to ensure that the adjustment is in line with expectations. If the equipment status does not meet expectations, the notification and alarm module will issue an alarm notification and display it in the user interface module. The automation control module will then make readjustments.

[0064] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An Internet-based smart home system, comprising a device management module, an automation control module and a data processing and analysis module; characterized in that: The device management module is responsible for integrating various intelligent devices into a unified platform; identifying and connecting different types of devices; configuring and managing devices, including naming, location allocation and group management of devices; and monitoring the operating status data of devices in real time; The automation control module automatically identifies the room layout and clearly identifies the function and location of each room; it is also used to comprehensively control the intelligence value, behavior association data and behavior determination data corresponding to the room layout; The data processing and analysis module obtains the operating status data of each smart home appliance through the network communication module, and analyzes it to obtain the smart value ZNZ of the home smart home; Then analyze the user's behavior data and the operating status data of smart home appliances; Analyze the user's behavior data to extract key features from the behavior data; The extracted features are matched with a plurality of preset behavior action templates, and the feature similarity S1 is calculated by the similarity calculation formula; If the similarity S ≥ 0.8, it is judged as a valid feature and marked as matching a preset behavior action; Then obtain the effective features in the room layout of the home, and perform correlation analysis with the operating status data of the home appliances in the corresponding location area, specifically: The extracted effective features are located in the home room layout to determine the specific room where the features occur; according to the room layout, the features are mapped to the corresponding equipment operation status data set; the operation status data is collected from the smart home appliances in the mapped room; the association rules between the features and the equipment status are analyzed using association rule mining technology; starting from a single feature and equipment status, the item sets whose support exceeds the minimum support threshold are found, and the initial frequent item sets are used to generate a larger candidate item set; for each candidate item set, its support and confidence are calculated, and the association rules that meet the threshold are screened out by the automation control module: if the confidence ZXD is greater than the preset corresponding threshold, it is behavior association data; if the confidence ZXD is 100%, it is behavior determination data.

2. The Internet-based smart home system according to claim 1, characterized in that: It also includes a user interface module, a network communication module, a notification and alarm module, and a voice assistant integration module; The network communication module manages the communication between all devices and servers in the system, supports multiple communication protocols, ensures the safe and fast transmission of data between devices, and supports remote access; The user interface module manually inputs the room layout; the user interface module obtains the intelligence value ZNZ through the network communication module and displays the intelligence value corresponding to the room layout; it is also used for alarm and notification display The notification and alarm module performs alarm notification; The voice assistant integration module receives user voice commands, parses them into behavior determination data, and executes them through the automation control module; converts the voice commands into device control commands; and provides a voice interaction interface.

3. The Internet-based smart home system according to claim 1, characterized in that: The specific process of analyzing and obtaining the smart value ZNZ of the home smart home is as follows: The calculation is based on the number of smart homes, coverage and frequency of use of devices, using the established formula: Output intelligent value ZNZ, A i is the number of the i-th device, B j is the equipment coverage of the jth room, C k is the usage frequency of the kth device, C max,k is the maximum usage frequency of the kth device, D l is the interconnectivity weight of the lth device combination, E o is the response time of the oth device, F s is the energy consumption of the sth device, G u is the complexity of the u-th automation scenario, H w Score the user satisfaction of the w-th device; a1, a2, a3, a4, a5, a6, a7 and a8 are their corresponding preset weight factors.

4. The Internet-based smart home system according to claim 3, characterized in that: The specific process of analyzing the user's behavior data to extract key features from the behavior data is as follows: Use signal processing and machine learning algorithms to extract key features from the collected behavioral data, using the feature extraction formula: Extraction, F1 i1 is the i 1th feature, x1(t1) is the signal value at time t1, p1 is the feature extraction parameter, and T1 is the time window.

5. The Internet-based smart home system according to claim 1, characterized in that: The specific process of calculating the feature similarity S1 by the similarity calculation formula is as follows: The similarity calculation formula is: Output feature similarity S1, where A1i 1 and B1 i 1 are the i 1th values ​​of user behavior features and template features, respectively, and n1 is the number of features.

6. The Internet-based smart home system according to claim 1, characterized in that: The automation control module selects association rules that meet the threshold, and the specific association rules are: The basic form of association rules is: (support, confidence), A2 represents the user behavior characteristics, B2 represents the device operation status; support ZCD represents the probability of feature A2 and status B2 appearing at the same time, The total number of transactions indicates the total number of transactions in all recorded data sets; the number of occurrences of A2 and B2 indicates the number of times user behavior feature A2 and device operation status B2 appear simultaneously in these transactions; the confidence ZXD measures the probability that status B2 also occurs when feature A2 occurs. Confidence ZXD: If the confidence value is high, whenever the user exhibits feature A2, device status B2 will also appear.

7. The Internet-based smart home system according to claim 1, characterized in that: The user interface module obtains the intelligence value ZNZ and the intelligence value corresponding to the room layout through the network communication module and displays them, specifically: Obtain the functions and locations corresponding to the room layout of the split home; map each room to the corresponding color range based on its intelligence value; display it in multiple dimensions on the user interface in the form of room layout, including functions, locations, and intelligence levels, and provide an integrated visual chart in combination with color and layout information.

8. The Internet-based smart home system according to claim 1, characterized in that: The automation control module is also used to comprehensively control the intelligence value, behavior association data and behavior determination data corresponding to the room layout, specifically: When the behavior determination data is received, a behavior determination instruction is generated. If the user issues a clear instruction through the audio assistant, the instruction is immediately parsed into the behavior determination data; and the parsed behavior determination data is directly converted into a device control instruction; When the user behavior is determined, the correlation is inferred through the room layout and smart value, and the device is precisely controlled. The behavior is mapped to the corresponding room layout, the behavior is mapped to the living room, and the relationship between the behavior and other devices is analyzed; then the current smart value ZNZ corresponding to each room is obtained 当前 , according to the intelligent value ZNZ 当前 To determine whether the equipment in the room needs to be adjusted, use the set formula: C1 j1 =ZNZ 当前 ×f1(ZXD(i2,j1)) outputs the control decision strength C1 of device j1 j1 , f1 is the mapping function, which converts the confidence ZXD(i 2,j1) into control strength; i2 and j1 are two devices; According to the control decision strength of the equipment, appropriate adjustments are made to the relevant equipment in the room. After the control is completed, the equipment status is monitored in real time to ensure that the adjustment meets expectations. If the equipment status does not meet expectations, the notification and alarm module will issue an alarm notification and display it in the user interface module. The automation control module will then make readjustments.

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