Smart home control system based on artificial intelligence

Abstract

The present invention provides an intelligent home control system based on artificial intelligence, comprising a central processing unit CPU and multiple intelligent device IDs; the intelligent device IDs include but are not limited to a temperature controller TC, a lighting system LS, a security monitoring system SCS, a curtain control system CCS and an environmental monitoring system EMS; the central processing unit CPU comprises an artificial intelligence processor AIP, a communication module CM and a sensor interface SI; the present invention can realize home automation management based on environmental status and user preferences, and improve the comfort, safety and energy-saving efficiency of the home.

Description

Technical Field

[0001] The present invention relates to the field of smart home technology, and more specifically, to a smart home control system based on artificial intelligence. Background Art

[0002] In the rapid development of the smart home field, although existing home control systems can achieve a certain degree of automated control, they often lack sufficient intelligence and personalized services. These systems usually rely on preset rules or simple conditional trigger mechanisms and cannot flexibly adapt to the diverse needs of users and complex environmental changes. For example, they may not accurately identify user preferences, or fail to adjust the lighting system in time when the ambient light changes, resulting in reduced user satisfaction and energy waste.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: first, when processing user instructions and environmental data, the existing systems often adopt static control strategies and lack the ability of dynamic adjustment, which limits the response speed and adaptability of the system; second, when integrating multiple sensors and smart devices, the existing systems lack effective data processing and coordination mechanisms, resulting in inaccurate control decisions; finally, in terms of user interaction, the existing systems usually require users to manually set parameters and lack intelligent user experience, which not only increases the difficulty of use for users, but also makes it difficult to meet the pursuit of convenience and personalization in modern smart homes. Summary of the invention

[0004] The present invention provides an intelligent home control system based on artificial intelligence, comprising:

[0005] Central processing unit CPU and multiple smart device IDs; the smart device IDs include but are not limited to temperature controller TC, lighting system LS, security monitoring system SCS, curtain control system CCS and environmental monitoring system EMS; the central processing unit CPU is used to receive user instructions or environmental data, analyze and convert them, and control each smart device ID to perform corresponding home management tasks;

[0006] The central processing unit CPU includes an artificial intelligence processor AIP, a communication module CM and a sensor interface SI; the artificial intelligence processor AIP is used to receive and process user instructions or data from the sensor interface SI, generate a control signal CS based on the environmental state ES and the user preference UP, and send instructions to each smart device ID through the corresponding communication module CM; wherein, the lighting system LS adopts intelligent dimming technology, and the artificial intelligence processor AIP is synchronously controlled based on the ambient light LI and the user preference UP.

[0007] Furthermore, the artificial intelligence processor AIP generates a control signal CS based on the environment state ES and the user preference UP, and sends instructions to each smart device ID through the corresponding communication module CM, wherein:

[0008] The sensors include a temperature sensor TS, a light sensor LSs, a motion sensor MS, a door and window status sensor DSS, and an air quality sensor AQS;

[0009] The temperature sensor TS is installed indoors to collect the indoor temperature IT, which is used by the artificial intelligence processor AIP to monitor the indoor environment state ES and control the temperature controller TC to adjust the indoor temperature IT;

[0010] The light sensor LSs is installed indoors to collect the indoor light intensity LI, which is used by the artificial intelligence processor AIP to control the lighting system LS to adjust the indoor light LI;

[0011] The motion sensor MS is installed indoors to collect indoor motion information MI, which is used by the artificial intelligence processor AIP to control the security monitoring system SCS to perform abnormality detection.

[0012] Furthermore, the artificial intelligence processor AIP is synchronously controlled based on intelligent dimming technology, including:

[0013] The artificial intelligence processor AIP analyzes the ambient light LI and the user preference UP to obtain the lighting demand LD;

[0014] Determine a brightness adjustment instruction BRI of the lighting system according to the lighting demand LD;

[0015] The brightness of the lighting system is dynamically adjusted based on user preference UP and ambient light LI.

[0016] Furthermore, the calculation formula of the lighting demand LD is as follows:

[0017] LD=f(LI,UP)=LI×α+(1-α)×LI ref

[0018] Among them, LI ref represents the reference light intensity, α is the weight of user preference;

[0019] Furthermore, the calculation formula of the brightness adjustment instruction BRI is as follows:

[0020] BRI=LD×DAF

[0021] Wherein, DAF stands for dynamic adjustment factor.

[0022] Furthermore, the calculation formula of the dynamic adjustment factor DAF is as follows:

[0023]

[0024] Here, TI represents a time interval.

[0025] Furthermore, the calculation formula of the user preference UP is as follows:

[0026] UP=w 1 ×LI+w 2 ×UP hist

[0027] Among them, w 1 and w 2 are weighted coefficients, UP hist Represents historical user preferences.

[0028] Furthermore, the artificial intelligence processor AIP dynamically adjusts the control parameters using user instructions and sensor data to obtain an optimal control strategy;

[0029] The optimal control strategy includes but is not limited to temperature control strategy TCS, lighting control strategy LCS, security monitoring strategy SCSs, and curtain control strategy CCSs.

[0030] Furthermore, the weight calculation formula of the user instruction and sensor data is as follows:

[0031] CP=w 3 ×UI+w 4 ×SD

[0032] Where CP represents the control parameter, w 3 and w 4 are the weights of user instructions UI and sensor data SD respectively.

[0033] Furthermore, the dynamic adjustment of the control parameter CP includes:

[0034] Determine the dynamic value DV of the control parameter according to the real-time changes of the user instruction UI and the sensor data SD;

[0035] The dynamic value DV is combined with the initial control parameter ICP to form the final control instruction FCI, where:

[0036] FCI=ICP+DV

[0037] According to the above embodiments of the present invention, at least the following beneficial effects are achieved: the intelligent home control system of the present invention can intelligently analyze and process user instructions and environmental data by integrating an artificial intelligence processor, a communication module and a sensor interface. The system can dynamically adjust the working state of home appliances according to the real-time environmental status and user preferences, thereby providing a more comfortable and personalized living experience. For example, the system can automatically adjust the lighting brightness according to the indoor light intensity and user preferences, or intelligently adjust the indoor temperature according to the indoor temperature and the user's historical preferences to meet the user's specific needs for comfort. In addition, the system also includes a variety of sensors, such as temperature sensors, light sensors, motion sensors, etc., which can monitor multiple aspects of the home environment in real time. Through the data collected by these sensors, the system can perform more accurate environmental perception and anomaly detection, and improve the safety and energy-saving efficiency of the home. For example, a motion sensor can detect abnormal activities in the room and trigger a response from the security monitoring system; an air quality sensor can monitor the indoor air quality and adjust the ventilation system in time to keep the indoor air fresh. Through these intelligent control strategies, the system can not only improve the user's living experience, but also effectively reduce energy consumption and achieve a green and environmentally friendly living environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which:

[0039] Figure 1 A schematic diagram of the structure of an artificial intelligence-based smart home control system provided by an embodiment of the present invention;

[0040] Figure 2 A schematic diagram of the structure of an intelligent device of an intelligent home control system based on artificial intelligence provided by an embodiment of the present invention;

[0041] Figure 3 A schematic diagram of the structure of a central processing unit of an artificial intelligence-based smart home control system provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0042] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0043] Those skilled in the art will appreciate that the embodiments of the present invention may be implemented as a system, device, apparatus, method or computer program product. Therefore, the present invention may be implemented in the following forms, namely: complete hardware, complete software including firmware, resident software, microcode, etc., or a combination of hardware and software.

[0044] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0045] Reference below Figure 1 , Figure 1 This is a schematic diagram of the structure of an intelligent home control system based on artificial intelligence provided by an embodiment of the present invention. Figure 1 As shown, a smart home control system 100 based on artificial intelligence includes:

[0046] Central processing unit CPU and multiple smart device IDs; the smart device IDs include but are not limited to temperature controller TC, lighting system LS, security monitoring system SCS, curtain control system CCS and environmental monitoring system EMS; the central processing unit CPU is used to receive user instructions or environmental data, analyze and convert them, and control each smart device ID to perform corresponding home management tasks;

[0047] The central processing unit CPU includes an artificial intelligence processor AIP, a communication module CM and a sensor interface SI; the artificial intelligence processor AIP is used to receive and process user instructions or data from the sensor interface SI, generate a control signal CS based on the environmental state ES and the user preference UP, and send instructions to each smart device ID through the corresponding communication module CM; wherein, the lighting system LS adopts intelligent dimming technology, and the artificial intelligence processor AIP is synchronously controlled based on the ambient light LI and the user preference UP.

[0048] It should be noted that the core of the smart home control system lies in the coordinated work of the central processing unit (CPU) and multiple smart device IDs. The central processing unit, as the brain of the system, is responsible for receiving and parsing user instructions or environmental data, and then controlling the smart devices to perform corresponding tasks. Smart devices include but are not limited to temperature controllers, lighting systems, security monitoring systems, curtain control systems, and environmental monitoring systems, which together constitute the various functional modules of a smart home.

[0049] Specifically, the central processing unit CPU consists of an artificial intelligence processor AIP, a communication module CM, and a sensor interface SI. The artificial intelligence processor AIP is a key component for processing user instructions and sensor data, and it can generate control signals based on environmental conditions and user preferences. The communication module CM is responsible for transmitting control signals to various smart devices, while the sensor interface SI is used to receive data from different sensors. Smart devices such as the lighting system LS use intelligent dimming technology, which can adjust the brightness according to ambient light and user preferences.

[0050] Preferably, the central processing unit CPU can use a high-performance computing chip to ensure fast response and efficient processing. The communication module CM can be based on wireless communication technologies such as Wi-Fi, Bluetooth or Zigbee to achieve seamless connection between devices. The sensor interface SI can be designed to support multiple sensor types to meet different monitoring needs. For example, the temperature controller TC can automatically adjust the air conditioning or heating system according to the data collected by the indoor temperature sensor TS to maintain a constant indoor temperature. The intelligent dimming technology of the lighting system LS can adjust the brightness of the LED lights to adapt to different usage scenarios and user preferences.

[0051] In some embodiments, the artificial intelligence processor AIP generates a control signal CS based on the environment state ES and the user preference UP, and sends instructions to each smart device ID through the corresponding communication module CM, wherein:

[0052] The sensors include a temperature sensor TS, a light sensor LSs, a motion sensor MS, a door and window status sensor DSS, and an air quality sensor AQS;

[0053] The temperature sensor TS is installed indoors to collect the indoor temperature IT, which is used by the artificial intelligence processor AIP to monitor the indoor environment state ES and control the temperature controller TC to adjust the indoor temperature IT;

[0054] The light sensor LSs is installed indoors to collect the indoor light intensity LI, which is used by the artificial intelligence processor AIP to control the lighting system LS to adjust the indoor light LI;

[0055] The motion sensor MS is installed indoors to collect indoor motion information MI, which is used by the artificial intelligence processor AIP to control the security monitoring system SCS to perform abnormality detection.

[0056] It should be noted that the system controls each smart device ID through the control signal CS generated by the artificial intelligence processor AIP, which is based on the environmental state ES and user preference UP. This means that the system can intelligently adjust the working state of home appliances according to real-time environmental changes and the personalized needs of users to provide a more comfortable and safe living environment.

[0057] Specifically, the environmental state ES may include parameters such as indoor temperature, light intensity, and air quality, while the user preference UP may involve the user's personalized settings for indoor temperature, lighting brightness, and security monitoring. Sensors such as temperature sensors TS, light sensors LSs, motion sensors MS, door and window status sensors DSS, and air quality sensors AQS are responsible for collecting these data. For example, the temperature sensor TS can set different thresholds to trigger the temperature controller TC to adjust the indoor temperature, while the light sensor LSs can automatically adjust the brightness of the lighting system according to the ambient light intensity.

[0058] Preferably, the artificial intelligence processor AIP can adopt advanced algorithms such as machine learning or deep learning to more accurately analyze and predict the changing trends of user preferences and environmental status. The communication module CM can be designed to support multiple communication protocols to ensure compatibility with smart devices of different brands. In addition, the system can be set with a user interface that allows users to remotely control and customize their preferences through devices such as smartphones or voice assistants. For example, users can set their lighting modes when they are at home and away from home through an application, or adjust the automatic adjustment strategy of indoor temperature according to seasonal and weather changes.

[0059] In some embodiments, the artificial intelligence processor AIP is synchronously controlled based on intelligent dimming technology, including:

[0060] The artificial intelligence processor AIP analyzes the ambient light LI and the user preference UP to obtain the lighting demand LD;

[0061] Determine a brightness adjustment instruction BRI of the lighting system according to the lighting demand LD;

[0062] The brightness of the lighting system is dynamically adjusted based on user preference UP and ambient light LI.

[0063] It should be noted that the system uses the artificial intelligence processor AIP to perform intelligent dimming control on the lighting system LS, which is based on the analysis results of the ambient light LI and user preferences UP. Intelligent dimming technology allows the system to automatically adjust the brightness of the lighting according to changes in indoor and outdoor light and the personalized needs of users, so as to create a more comfortable and energy-saving living environment.

[0064] Specifically, smart dimming technology involves real-time monitoring of ambient light LI and in-depth understanding of user preferences UP. Ambient light LI can be collected by light sensors LSs, which can be installed in different locations in the room to obtain comprehensive light data. User preferences UP may include user preferences for brightness, lighting requirements for specific time periods, etc. For example, the system can be set to automatically reduce the indoor lighting brightness at sunset to simulate the weakening of natural light, or automatically adjust the light brightness when getting up in the morning based on the user's historical preferences.

[0065] Preferably, the intelligent dimming technology can be further refined into multiple operation steps, including but not limited to: first, the light sensor LSs collects the light intensity data of the current environment; second, the artificial intelligence processor AIP analyzes this data and generates the lighting demand LD in combination with the user preference UP; then, according to the lighting demand LD, the brightness adjustment instruction BRI of the lighting system is determined, and this instruction can be generated by a preset algorithm or a user-defined rule; finally, after the lighting system LS receives the brightness adjustment instruction, it automatically adjusts the brightness of the light to achieve the desired lighting effect. In addition, the system can also continuously optimize the brightness adjustment algorithm based on user feedback or further learning to achieve more accurate and personalized lighting control.

[0066] In some embodiments, the calculation formula of the lighting demand LD is as follows:

[0067] LD=f(LI,UP)=LI×α+(1-α)×LI ref

[0068] Among them, LI ref represents the reference light intensity, and α is the weight of user preference.

[0069] It should be noted that the system defines a formula for calculating the lighting demand LD, which combines the ambient light LI and the user preference UP, as well as a reference light intensity LI ref The purpose of this formula is to quantify the user's specific lighting needs in a specific environment so that the system can make corresponding brightness adjustments.

[0070] Specifically, the calculation of lighting demand LD involves several key parameters: the actual ambient light intensity LI, the light intensity UP preferred by the user, and a preset reference light intensity LI ref . These parameters together determine the amount of light that the lighting system needs to provide. For example, if the user prefers a higher light intensity, but the actual ambient light intensity is low, the system may need to increase the lighting brightness to meet the user's needs. The α in the formula may represent the weight of the user's preference, while (1-α) represents the weight of the ambient light.

[0071] Preferably, the calculation of the lighting demand LD can be further refined into the following steps: first, the system monitors the ambient light intensity LI in real time through the light sensor; second, the system obtains the user's preferred light intensity UP based on the user's historical preferences and current settings; then, combined with the preset reference light intensity LI ref , the lighting demand LD is calculated using the above formula. In addition, the system can provide a user interface that allows the user to adjust the value of α according to the current activity or mood to more finely control the calculation of the lighting demand. For example, the user can set a higher α value when reading to prioritize personal preferences, and lower the α value when watching a movie to reduce the impact of ambient light.

[0072] In some embodiments, the calculation formula of the brightness adjustment instruction BRI is as follows:

[0073] BRI=LD×DAF

[0074] Wherein, DAF stands for dynamic adjustment factor.

[0075] It should be noted that the system adjusts the brightness of the lighting system through the brightness adjustment instruction BRI, which is determined according to the lighting demand LD and the dynamic adjustment factor DAF. The brightness adjustment instruction BRI is a specific execution parameter for adjusting the brightness of the lighting system, which directly affects the output brightness of the lighting system.

[0076] Specifically, the calculation of the brightness adjustment instruction BRI involves the lighting demand LD and the dynamic adjustment factor DAF. The lighting demand LD is calculated based on user preferences and ambient light, while the dynamic adjustment factor DAF is dynamically calculated based on factors such as time intervals or environmental changes. For example, if the user prefers darker light at night, and the ambient light gradually weakens in the evening, the system can reduce the lighting brightness accordingly by increasing the value of DAF.

[0077] Preferably, the calculation of the brightness adjustment instruction BRI can be further refined into the following steps: first, the system calculates the lighting demand LD according to the current time, ambient light intensity and user preferences; second, the system calculates the dynamic adjustment factor DAF according to the time interval or environmental changes. This factor can be a coefficient that changes over time and is used to fine-tune the brightness to adapt to the user's immediate needs; then, the system multiplies the lighting demand LD by the dynamic adjustment factor DAF to obtain the brightness adjustment instruction BRI. In addition, the system can design a feedback mechanism to dynamically adjust the value of DAF according to the user's real-time feedback or immediate changes in the environment to achieve a more accurate and comfortable lighting effect. For example, if the user feedbacks through the smart home application that the current brightness is too bright or too dark, the system can immediately adjust the DAF and optimize the value of BRI to quickly respond to the user's needs.

[0078] In some embodiments, the calculation formula of the dynamic adjustment factor DAF is as follows:

[0079]

[0080] Here, TI represents a time interval.

[0081] It should be noted that the calculation of the dynamic adjustment factor DAF is based on the ambient light intensity LI and the reference light intensity LI ref This factor is an important parameter to consider when the lighting system adjusts the brightness, which reflects the gap between the ambient light and the light expected by the user.

[0082] Specifically, the calculation formula of the dynamic adjustment factor DAF is DAF=LI-LIref. Here, LI represents the light intensity of the current environment, and LI ref is a preset reference light intensity, which may be set based on user preference, time of day, activity type, etc. For example, if the user prefers stronger light when reading, then LI ref It may be set higher than the current ambient light intensity LI, causing the system to increase the lighting brightness.

[0083] Preferably, the calculation and application of the dynamic adjustment factor DAF can be further refined as follows: first, the system monitors and records the ambient light intensity LI in real time through the light sensor; second, the system sets an appropriate reference light intensity LI according to the user's historical preferences and current activities. ref ; The system then calculates the DAF and adjusts the brightness of the lighting system based on this value. In addition, the system can be designed to learn the user's preferences and automatically adjust the LI ref , to more accurately reflect the user's long-term preferences. For example, the system can update LI after the user adjusts the brightness multiple times. ref value, so that future brightness adjustments are more in line with user expectations.

[0084] In some embodiments, the calculation formula of the user preference UP is as follows:

[0085] UP=w 1 ×LI+w 2 ×UP hist

[0086] Among them, w 1 and w 2 are weighted coefficients, UP hist Represents historical user preferences.

[0087] It should be noted that the user preference UP is determined by a calculation formula that takes into account a weighted combination of real-time user preferences and historical user preferences. This approach allows the system to not only respond to the user's current instructions, but also learn and adapt to the user's long-term preference patterns.

[0088] Specifically, the calculation formula of user preference UP is UP = w 1 ×UP real +w 2 ×UP hist In this formula, UP real represents the current user preference, while UP hist Represents user preferences calculated based on historical data. 1 and w 2 is a weighting factor that balances the impact of current preferences and historical preferences on the end user’s preferences. For example, if the system detects that the user prefers low-brightness lighting at night, then w 2 May be set to a higher value to increase the weight of historical preferences in the calculation.

[0089] Preferably, the calculation and application of the user preference UP can be further refined as follows: First, the system obtains the current user preference UP through user input or sensor detection real ; Secondly, the system analyzes the user's historical preference data in similar environments and calculates the historical user preference UP hist ; Then, according to the preset or adaptively adjusted weighting coefficient w 1 and w 2 , combining current and historical preferences to calculate the comprehensive user preference UP. In addition, the system can design a self-learning mechanism to automatically adjust the weighting coefficient based on user feedback to more accurately reflect the user's personalized needs. For example, if the user is satisfied with the adjustment made by the system based on the current preference, the system can increase w accordingly. 1 's value so that future calculations take the current preference into account more.

[0090] In some embodiments, the artificial intelligence processor AIP dynamically adjusts control parameters using user instructions and sensor data to obtain an optimal control strategy;

[0091] The optimal control strategy includes but is not limited to temperature control strategy TCS, lighting control strategy LCS, security monitoring strategy SCSs, and curtain control strategy CCSs.

[0092] It should be noted that the function of the artificial intelligence processor AIP is to dynamically adjust the control parameters using user instructions and sensor data to achieve the optimal control strategy. The purpose of this strategy is to ensure that the smart home system can automatically optimize its operation based on real-time data and user needs to improve efficiency and user satisfaction.

[0093] Specifically, the optimal control strategy may include temperature control strategy TCS, lighting control strategy LCS, security monitoring strategy SCSs and curtain control strategy CCSs. These strategies dynamically adjust the working status of home devices by analyzing data from sensors and direct instructions from users. For example, the temperature control strategy can adjust the air conditioning temperature according to the indoor and outdoor temperature difference and user preferences, while the lighting control strategy can automatically adjust the brightness according to ambient light and user activities.

[0094] Preferably, the dynamic adjustment process of the artificial intelligence processor AIP can be further refined into the following steps: first, collect data from various sensors, such as temperature, light, motion, etc.; second, analyze user instructions, which may include instructions entered through voice, mobile phone applications or physical control panels; then, combine sensor data and user instructions, and use a preset algorithm or machine learning model to calculate the dynamic value DV of the control parameter; then, combine the dynamic value DV with the initial control parameter ICP to form the final control instruction FCI to achieve precise control of home appliances. For example, if the user prefers to lower the indoor temperature at night, the AIP can learn this pattern and automatically adjust the temperature under similar conditions without the user issuing instructions every time.

[0095] In addition, the system can be designed to have adaptive learning capabilities, continuously optimizing the computational model of control parameters over time to better adapt to user behavior patterns and environmental changes. For example, the system can automatically adjust the temperature control strategy by analyzing the changes in user preferences for temperature in different seasons to provide a more personalized comfort experience.

[0096] In some embodiments, the weight calculation formula of the user instruction and the sensor data is as follows:

[0097] CP=w 3 ×UI+w 4 ×SD

[0098] Where CP represents the control parameter, w 3 and w 4 are the weights of user instructions UI and sensor data SD respectively.

[0099] It should be noted that the system determines the impact of user instructions UI and sensor data SD on control parameters CP through a specific weight calculation formula. This method allows the system to balance inputs from the user and the environment when generating the final control instructions, ensuring that the control strategy meets user expectations and can adapt to environmental changes.

[0100] Specifically, the weight calculation formula is CP = w 3 ×UI+w 4 ×SD. In this formula, CP represents control parameters, UI represents user instructions, and SD represents sensor data. 3 and w 4 are the corresponding weight coefficients, which determine the relative importance of user instructions and sensor data in the calculation of control parameters. For example, if the user wants the system to rely more on sensor data to adjust the indoor temperature at night, then w 4 May be set higher than w 3 higher.

[0101] Preferably, the calculation and application of the control parameter CP can be further refined into the following steps: first, the system collects user instructions UI through the user interface, which may include specific requirements for temperature, lighting or security settings; second, the system collects data SD from various sensors, such as temperature sensors, light sensors, etc., to obtain the current environmental status; then, according to the preset or dynamically adjusted weight coefficient w 3 and w 4 , combined with user instructions and sensor data, the control parameter CP is calculated. In addition, the system can design a feedback adjustment mechanism to automatically adjust the weight coefficient according to the actual effect and user feedback to optimize the control strategy. For example, if the user is satisfied with the current indoor temperature, the system can analyze the current CP value and increase w in future calculations. 4 The weight of , taking more consideration of sensor data.

[0102] More specifically, the system can use advanced data analysis techniques, such as fuzzy logic or neural networks, to handle nonlinear or complex user preferences and environmental changes. For example, the system can use fuzzy logic to process the user's subjective description of "warmth" or "coolness" and convert it into specific control parameter values. In addition, the system can also be designed to use a preset default control strategy or safety mode in specific situations, such as sensor failure or user command conflict, to ensure the comfort and safety of the home environment.

[0103] In some embodiments, the dynamic adjustment of the control parameter CP includes:

[0104] Determine the dynamic value DV of the control parameter according to the real-time changes of the user instruction UI and the sensor data SD;

[0105] The dynamic value DV is combined with the initial control parameter ICP to form the final control instruction FCI, where:

[0106] FCI=ICP+DV

[0107] It should be noted that the system responds to real-time changes in user commands UI and sensor data SD by dynamically adjusting the control parameters CP. This dynamic adjustment ensures that the system can flexibly adapt to different situations and achieve more accurate and personalized home control.

[0108] Specifically, the dynamic adjustment process of the control parameter CP involves determining the dynamic value DV based on real-time user instructions and sensor data, and then combining this dynamic value with the initial control parameter ICP to form the final control instruction FCI. This process can be represented by the formula FCI = ICP + DV. In this formula, DV is the adjustment amount calculated based on the current user instructions and sensor data, and ICP is the default control parameter of the system when there is no new input.

[0109] Preferably, dynamic adjustment of the control parameter CP can be achieved through the following steps: first, the system continuously monitors changes in the user instruction UI, which may include instructions received through smart home applications, voice commands or other interfaces; second, the system simultaneously collects data SD from various sensors, such as temperature, humidity, light intensity, etc.; then, the system uses these data and instructions to calculate the dynamic value DV, which may involve some preset logic or learning algorithms; then, the system adds the calculated dynamic value DV to the initial control parameter ICP to obtain the final control instruction FCI, and sends it to the corresponding smart device for execution.

[0110] More specifically, the system can be designed to have adaptive learning capabilities, and be able to optimize the adjustment strategy of control parameters based on the user's long-term behavior patterns and feedback. For example, if the system observes that the user tends to adjust the temperature at a specific time, it can automatically set a lower initial control parameter ICP during this time period and prepare a larger dynamic value DV for a quick response. In addition, the system can also be designed to automatically trigger a safety check or ask the user for confirmation when abnormal sensor data or user instructions are detected to avoid misoperation. For example, if the temperature sensor suddenly shows an abnormally high temperature, the system can first remind the user to check instead of directly adjusting the air conditioning system to ensure the safety and accuracy of the operation.

[0111] The above-mentioned embodiments of the present invention have the following beneficial effects: the intelligent home control system of the present invention can provide highly automated and personalized home management by integrating a central processing unit and multiple intelligent devices. The system can intelligently adjust temperature, lighting, security monitoring, curtain control, etc. according to the real-time environmental status and user preferences, thereby improving the comfort and convenience of the living environment. In addition, through the dynamic adjustment and optimization of the artificial intelligence processor, the system can not only meet the immediate needs of users, but also learn the user's behavior pattern to achieve more accurate and energy-saving home control. In addition, the system can further improve the adaptability and response speed of the home environment through intelligent dimming technology, dynamic adjustment factors, weighted calculation of user preferences, and real-time update of control parameters. For example, the system can automatically adjust the lighting brightness according to the changes in indoor and outdoor light, or dynamically adjust the indoor temperature according to the user's historical preferences and real-time instructions. This intelligent control can not only enhance the user's living experience, but also help save energy and reduce emissions, and realize the green and environmentally friendly living concept.

[0112] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0113] The above descriptions are only some preferred embodiments of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention to form a technical solution.

Claims

1. An intelligent home control system based on artificial intelligence, characterized in that: include: Central processing unit CPU and multiple smart device IDs; the smart device IDs include but are not limited to temperature controller TC, lighting system LS, security monitoring system SCS, curtain control system CCS and environmental monitoring system EMS; the central processing unit CPU is used to receive user instructions or environmental data, analyze and convert them, and control each smart device ID to perform corresponding home management tasks; The central processing unit CPU includes an artificial intelligence processor AIP, a communication module CM and a sensor interface SI; the artificial intelligence processor AIP is used to receive and process user instructions or data from the sensor interface SI, generate a control signal CS based on the environment state ES and the user preference UP, and send instructions to each smart device ID through the corresponding communication module CM; wherein, the lighting system LS adopts intelligent dimming technology, and the artificial intelligence processor AIP is synchronously controlled based on the ambient light LI and the user preference UP; the artificial intelligence processor AIP generates a control signal CS based on the environment state ES and the user preference UP, and sends instructions to each smart device ID through the corresponding communication module CM, wherein, The sensors include a temperature sensor TS, a light sensor LSs, a motion sensor MS, a door and window status sensor DSS, and an air quality sensor AQS; The temperature sensor TS is installed indoors to collect the indoor temperature IT, which is used by the artificial intelligence processor AIP to monitor the indoor environment state ES and control the temperature controller TC to adjust the indoor temperature IT; The light sensor LSs is installed indoors to collect the indoor light intensity LI, which is used by the artificial intelligence processor AIP to control the lighting system LS to adjust the indoor light LI; The motion sensor MS is installed indoors to collect indoor motion information MI, which is used by the artificial intelligence processor AIP to control the security monitoring system SCS for abnormality detection; the artificial intelligence processor AIP is synchronously controlled based on intelligent dimming technology, including: The artificial intelligence processor AIP analyzes the ambient light LI and the user preference UP to obtain the lighting demand LD; Determine a brightness adjustment instruction BRI of the lighting system according to the lighting demand LD; Dynamically adjust the brightness of the lighting system based on user preference UP and ambient light LI; The calculation formula of the lighting demand LD is as follows: LD=f(LI,UP)=LI×α+(1-α)×LI ref Among them, LI ref represents the reference light intensity; α is the weight of user preference; The calculation formula of the brightness adjustment instruction BRI is as follows: BRI=LD×DAF Where DAF stands for dynamic adjustment factor; The calculation formula of the dynamic adjustment factor DAF is as follows: Here, TI represents a time interval.

2. The system according to claim 1, characterized in that: The calculation formula of the user preference UP is as follows: UP=w1×LI+w2×UP hist Among them, w1 and w2 are weighting coefficients, UP hist Represents historical user preferences.

3. The system according to claim 2, characterized in that: The artificial intelligence processor AIP dynamically adjusts the control parameters using user instructions and sensor data to obtain the optimal control strategy; The optimal control strategy includes but is not limited to temperature control strategy TCS, lighting control strategy LCS, security monitoring strategy SCSs, and curtain control strategy CCSs.

4. The system according to claim 3, characterized in that: The weight calculation formula of the user command and sensor data is as follows: CP=w3×UI+w4×SD Among them, CP represents the control parameter, w3 and w4 are the weights of user instruction UI and sensor data SD respectively.

5. The system according to claim 4, characterized in that: The dynamic adjustment of the control parameter CP includes: Determine the dynamic value DV of the control parameter according to the real-time changes of the user instruction UI and the sensor data SD; The dynamic value DV is combined with the initial control parameter ICP to form a final control instruction FCI, wherein FCI=ICP+DV.

Images