Smart home information integration system and device

Abstract

The invention relates to the technical field of smart home systems, and discloses a smart home information integration system, which comprises an information acquisition module used for acquiring multi-source data related to a home environment, including temperature and humidity, air quality, illumination intensity, a human body activity state and a running state of smart home equipment; according to the system, intelligent control is achieved through deep learning and an artificial intelligence algorithm, the equipment state can be automatically adjusted according to user requirements and environment changes, the comfort level and personalized experience of the home environment are optimized, the system can recognize user behaviors and environment changes in real time, intelligent equipment adjustment is provided, and the user experience is improved. And secondly, an optimized alarm mechanism effectively reduces false alarms and redundant alarms, the timeliness and accuracy of alarm information are ensured by accurately calculating the alarm priority, and the system can make the most appropriate response according to different conditions through a multi-threshold comparison and weighting algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of smart home systems, and in particular to a smart home information integration system and device. Background Art

[0002] With the rapid development of the Internet of Things (IoT), artificial intelligence (AI) and big data technology, smart home systems have gradually become an important part of modern families. Smart home systems connect various home appliances, sensors and devices to achieve automated control of the home environment, provide convenience, comfort and energy-saving management. For example, smart air conditioners, smart lighting, smart security and other devices are interconnected through the Internet and sensor networks, allowing users to remotely control and monitor the home environment in real time.

[0003] In practice, the smart home systems currently on the market mainly rely on single devices or functional modules, lacking a highly integrated data collection, analysis and response mechanism. Existing systems lack the ability to collect and analyze data, and are unable to deeply process and intelligently analyze environmental data, resulting in the inability to judge household needs and user behavior in real time. Control and response usually rely on user instructions, lack intelligent decision-making capabilities, and are unable to automatically respond to environmental changes or abnormal situations. The alarm mechanism is too simple, often resulting in false alarms or untimely responses, affecting user experience. At the same time, poor compatibility between devices makes effective collaboration and information sharing impossible, thus limiting system efficiency and stability.

[0004] Therefore, a smart home information integration system and device are proposed to solve the above-mentioned problems. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a smart home information integration system and device, which solves the problems of insufficient intelligence, poor data analysis and collaboration, and delayed response of the existing smart home systems.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A smart home information integration system, comprising:

[0007] The information collection module is used to collect multi-source data related to the home environment, including temperature and humidity, air quality, light intensity, human activity status, and the operating status of smart home devices;

[0008] The central control module is connected to the information acquisition module for fusion processing of multi-source data, analyzing user behavior patterns and environmental requirements through deep learning algorithms, and generating control instructions;

[0009] The intelligent distribution module is connected to the central control module and is used to distribute control instructions to corresponding smart home devices and monitor the execution status of the devices in real time;

[0010] An abnormal response module, connected to the central control module, is used to identify and respond to abnormal conditions in the home environment, including fire, smoke leakage, or abnormal user behavior, and intervene through an alarm device or remote notification to the user;

[0011] The threshold setting module is used to set the safety threshold of key indicators related to the home environment. When the detection value exceeds the preset threshold, the alarm mechanism is triggered;

[0012] The alarm algorithm module is used to calculate the alarm priority based on the set threshold and the collected data, and select the corresponding alarm mode, including sound and light alarm, mobile phone push notification and linkage with third-party services.

[0013] Preferably, the alarm algorithm module comprises the following steps:

[0014] Real-time monitoring: real-time collection and analysis of environmental data to detect whether the threshold is exceeded;

[0015] Alarm decision-making: when the detection value exceeds the preset threshold, the alarm level is evaluated, the alarm type is determined, and the corresponding response measures are triggered;

[0016] Priority scheduling: automatically or manually select alarm processing solutions based on the priorities of different types of alarm events, such as first performing a local sound and light alarm, then pushing a mobile phone notification or linking a third-party response.

[0017] Preferably, the alarm decision step includes:

[0018] Multi-threshold comparison: set multiple levels of thresholds for different alarm requirements. For example, a high-priority alarm is triggered when the temperature exceeds the set high-temperature threshold, and a medium-priority alarm is triggered when the air quality drops above a certain value.

[0019] Condition weighting: When multiple conditions trigger an alarm at the same time, a comprehensive alarm level is calculated through a weighted algorithm, and the alarm response is executed based on the comprehensive level.

[0020] Preferably, the calculation formula for the alarm level is:

[0021]

[0022] Among them, wi is the weight coefficient of each condition, Ai is the alarm level of each condition, and the calculation formula of the alarm level Ai is:

[0023]

[0024] Where Li and Hi are the lower threshold and upper threshold of the i-th condition respectively, and Ci is the actual collected value of the i-th condition.

[0025] Preferably, the threshold setting module dynamically adjusts the threshold according to historical data and user preferences, and adjusts the alarm thresholds of parameters such as temperature and humidity through the following formula:

[0026] L new =L0×(1-α·ΔT),H new =H0×(1+β·ΔT)

[0027] Where L0 and H0 are the initial thresholds, ΔT is the rate of change of environmental parameters, and α and β are adjustment coefficients used to control the sensitivity of the thresholds.

[0028] Preferably, the alarm algorithm module also includes a time series threshold adjustment mechanism, which dynamically adjusts the alarm threshold according to time changes or seasonal factors, and is adjusted by the following formula:

[0029] L adjusted =L0·f(t,S),H adjusted =H0·g(t,S)

[0030] Among them, f(t, S) and g(t, S) are functions of time t and season S, and the change of threshold is adjusted to adapt to different seasons, day and night temperature difference and external factors.

[0031] Preferably, the alarm algorithm module adopts a machine learning model and is optimized according to the patterns of historical alarm data and environmental data, so as to improve the accuracy and response speed of the alarm through training algorithms, reduce false alarms and improve the warning effect.

[0032] Preferably, the alarm algorithm module uses a multiple redundant alarm mechanism, and the system will alarm through multiple channels simultaneously to ensure that users can obtain alarm information in a timely manner.

[0033] Preferably, the abnormal response module also includes an intelligent adjustment module, which can dynamically adjust the alarm threshold according to real-time changes in the environment. The system can automatically lower the temperature alarm threshold through the intelligent adjustment function.

[0034] A smart home information integration device comprises an information integration device body, wherein the information integration device body is internally provided with an information acquisition module, a central control module, an intelligent distribution module, an abnormal response module, a threshold setting module and an alarm algorithm module, and the information integration device body interacts with other smart home devices via wireless communication.

[0035] Beneficial Effects

[0036] The present invention provides a smart home information integration system. Compared with the prior art, it has the following beneficial effects:

[0037] In the present invention, the system realizes intelligent control through deep learning and artificial intelligence algorithms, and can automatically adjust the device status according to user needs and environmental changes, optimize the comfort and personalized experience of the home environment, and the system can identify user behavior and environmental changes in real time, provide intelligent device adjustment, and improve the convenience and accuracy of the home experience. Secondly, the optimized alarm mechanism effectively reduces false alarms and redundant alarms, and ensures the timeliness and accuracy of alarm information by accurately calculating the alarm priority. Through multi-threshold comparison and weighted algorithm, the system can make the most appropriate response according to different situations, reduce user interference, and improve the stability and response efficiency of the system. The system has good environmental adaptability and can dynamically adjust the control strategy according to external factors such as season and time, so as to ensure that the system can make the most appropriate response under different environmental conditions. Through the timing threshold adjustment mechanism, the system flexibly adjusts the alarm threshold according to time changes, thereby improving the alarm mechanism. The system has high adaptability and accuracy. Multiple redundant alarm mechanisms ensure that alarm information is delivered to users in a timely manner through multiple channels such as sound and light alarms, mobile phone push notifications, and third-party linkage, avoiding missing critical alarms. This redundant design improves the reliability of alarm information and ensures that users can respond quickly in emergency situations. In addition, the system breaks the island effect between manufacturers and platforms through efficient equipment collaboration, achieves seamless interconnection between devices, and improves the interoperability and stability of devices. Through wireless communication and unified control, it ensures the collaborative work between different devices and improves the compatibility and stability of the overall system. Finally, the system reduces the need for manual intervention through intelligent adjustment and abnormal response mechanisms, improves user operation convenience, and through real-time monitoring, the system can also automatically adjust the operating status of home appliances to achieve energy-saving effects and optimize the use of household resources. Overall, this system provides efficient and intelligent solutions for improving family comfort, safety and energy saving. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A schematic diagram of the system flow of a smart home information integration system and device proposed by the present invention;

[0039] Figure 2 This is a front structural entity diagram of a smart home information integration system and device proposed by the present invention;

[0040] Figure 3 This is a schematic diagram of the internal structure of a smart home information integration system and device proposed by the present invention.

[0041] Legend:

[0042] 1. Information integration device body; 2. Information collection module; 3. Central control module; 4. Intelligent distribution module; 5. Abnormal response module; 6. Threshold setting module; 7. Alarm algorithm module. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] See also Figure 1 - Figure 3 The present invention provides a technical solution, which specifically includes the following embodiments:

[0045] Example:

[0046] A smart home information integration system, comprising:

[0047] The information acquisition module 2 is used to collect multi-source data related to the home environment, including temperature and humidity, air quality, light intensity, human activity status and the operating status of smart home devices; the information acquisition module 2 can monitor the concentration of harmful substances in the air, such as PM2.5, CO2, etc., through the air quality sensor; the information acquisition module 2 can use the light sensor to detect the changes in light intensity in the room, thereby providing a basis for automatic adjustment of lighting equipment; the information acquisition module 2 can use the human activity sensor to identify the activity status of family members and provide accurate feedback on home environment needs. In addition, the information acquisition module 2 is also responsible for monitoring the operating status of each smart home device to ensure the normal operation of the equipment, and feed back the data to the central control module 3 to ensure real-time and accuracy.

[0048] The central control module 3 is connected to the information acquisition module 2 for fusion processing of multi-source data, and analyzes user behavior patterns and environmental requirements through deep learning algorithms to generate control instructions. As the core unit of the system, the central control module 3 is responsible for data interaction with the information acquisition module 2, obtaining multi-source data and fusion processing. This module uses deep learning algorithms to analyze user behavior patterns, environmental requirements and device status in order to intelligently judge changes in demand in the home environment. For example, by analyzing the user's daily behavior (such as activity time, commonly used equipment, etc.), the central control module 3 can predict the behavior of family members and generate corresponding control instructions based on these predictions. The central control module 3 also analyzes environmental data through artificial intelligence algorithms and automatically adjusts the working mode of home appliances to optimize energy use, improve comfort, and ensure intelligent management of the home environment.

[0049] The intelligent distribution module 4 is connected to the central control module 3 and is used to distribute control instructions to corresponding smart home devices and monitor the execution of the devices in real time; the intelligent distribution module 4 is responsible for effectively distributing the control instructions generated by the central control module 3 to various smart home devices (such as air conditioners, humidifiers, curtains, lighting, etc.). The intelligent distribution module 4 transmits the instructions to the corresponding devices through wireless communication or wired connection to ensure that the devices can operate in a predetermined manner. In addition, the intelligent distribution module 4 also monitors the execution of the devices in real time to ensure that the devices work according to the instructions, and can detect device abnormalities (such as failures, abnormal operation, etc.) in a timely manner, and transmits feedback information to the central control module 3 so as to make corresponding adjustments or alarms.

[0050] The abnormal response module 5 is connected to the central control module 3 and is used to identify and respond to abnormal conditions in the home environment, including fire, smoke leakage, or abnormal user behavior, and to intervene through an alarm device or remote notification to the user; the abnormal response module 5 is used to identify abnormal conditions that may exist in the home environment, including fire, smoke leakage, gas leakage, or abnormal user behavior. Once these abnormalities are detected, the module will immediately start the response mechanism, such as activating the alarm system, controlling related equipment (such as closing the gas valve, starting the exhaust fan, etc.), and notifying the user through sound and light alarms, mobile phone push notifications, etc. The abnormal response module 5 can also dynamically adjust the alarm threshold according to environmental changes. For example, when the indoor temperature rises sharply, the abnormal response module 5 can flexibly adjust the threshold to improve the sensitivity of the response, reduce false alarms, and improve the accuracy of emergency handling.

[0051] The threshold setting module 6 is used to set the safety threshold of key indicators related to the home environment. When the detection value exceeds the preset threshold, the alarm mechanism is triggered; the threshold setting module 6 allows the user to set the safety threshold of key indicators (such as temperature, humidity, air quality, etc.) according to the actual needs of the home environment. These thresholds can be adjusted according to the health status of family members, seasonal changes or other personalized needs. For example, the user can set the air conditioner to start automatically when the indoor temperature exceeds 28°C, or enable the air purifier when the PM2.5 concentration exceeds a certain level. The threshold setting module 6 can dynamically adjust these thresholds in different scenarios to ensure that the alarm mechanism can respond to environmental changes in a timely manner and avoid unnecessary alarm triggering.

[0052] The alarm algorithm module 7 is used to calculate the alarm priority according to the set threshold and the collected data, and select the corresponding alarm mode, including sound and light alarm, mobile phone push notification and linkage with third-party services; the alarm algorithm module 7 compares the collected environmental data with the set threshold by calculation, and evaluates the priority of the alarm event in real time. When the detection value exceeds the preset threshold, the module determines the alarm mode according to the priority rule, including sound and light alarm, mobile phone push notification and linkage with third-party services (such as fire department, housekeeping service, etc.). The alarm algorithm module 7 adopts multi-threshold comparison technology. When the environmental changes are large or multiple alarm conditions are triggered at the same time, weighted calculation is performed, and the weights of various types of data are comprehensively considered to ensure the accurate response of the alarm system under multiple conditions. In addition, the module also supports priority scheduling. When multiple alarm events occur at the same time, the alarm processing order can be adjusted according to the urgency of the event, so that the system can first deal with the most critical safety hazards. Through such algorithm optimization, the alarm system can effectively reduce false alarms and redundant alarms, and improve the stability of the system and user experience.

[0053] Preferably, the alarm algorithm module 7 comprises the following steps:

[0054] Real-time monitoring: real-time collection and analysis of environmental data to detect whether the threshold is exceeded;

[0055] Alarm decision-making: when the detection value exceeds the preset threshold, the alarm level is evaluated, the alarm type is determined, and the corresponding response measures are triggered;

[0056] Priority scheduling: automatically or manually select alarm processing solutions based on the priorities of different types of alarm events, such as first performing a local sound and light alarm, then pushing a mobile phone notification or linking a third-party response.

[0057] Real-time monitoring: In the initial stage of the alarm algorithm module 7, the system will continue to collect and analyze environmental data in real time, and monitor key environmental indicators such as temperature and humidity, air quality, gas concentration, smoke, etc. through sensors in real time to ensure that the system can respond quickly. After data collection, the system will automatically compare it with the preset safety threshold to check whether there are any changes beyond the safety range. Once an indicator exceeds the set threshold, the system will immediately trigger the alarm decision process.

[0058] Alarm decision: When the system detects that environmental data exceeds the set threshold, the alarm decision module will evaluate the severity of the alarm event, calculate the alarm priority, and determine the alarm type. The key to alarm decision is to accurately identify the nature of the problem, such as fire, smoke leakage, excessive temperature, gas leakage, etc. The module will determine whether to issue a high-priority alarm or perform regular alarm processing based on the data. In addition, the system will also comprehensively consider the user's preferences and historical behavior patterns to make the most appropriate response when selecting the alarm type, such as through sound and light alarms, mobile phone push notifications, or directly linking other equipment (such as activating the fire protection system, turning on the exhaust equipment, etc.) for emergency processing.

[0059] Priority scheduling: In order to deal with different types of alarm events, the system will schedule alarm responses according to the priority of the event. Priority scheduling includes automatic or manual selection of appropriate alarm processing solutions. For high-priority emergencies (such as fire or gas leaks), the system will first trigger local sound and light alarms or directly link emergency services to ensure the fastest response time. For low-priority alarms (such as deteriorating air quality), the system can first issue a minor local warning, then remind the user through push notifications, and further process through third-party services (such as smart home platforms) if necessary. This mechanism not only improves the timeliness of the alarm, but also reduces user interference with non-emergency alarms.

[0060] Preferably, the alarm decision step includes:

[0061] Multi-threshold comparison: set multiple levels of thresholds for different alarm requirements. For example, a high-priority alarm is triggered when the temperature exceeds the set high-temperature threshold, and a medium-priority alarm is triggered when the air quality drops above a certain value.

[0062] Condition weighting: When multiple conditions trigger an alarm at the same time, a comprehensive alarm level is calculated through a weighted algorithm, and the alarm response is executed based on the comprehensive level.

[0063] Among them, multi-threshold comparison, in order to make accurate responses to different types of abnormal situations, the alarm decision module adopts a multi-threshold comparison mechanism. The system sets multiple levels of thresholds to adapt to different alarm needs. For example, when the temperature exceeds the set high temperature threshold, the system will trigger a high-priority alarm and quickly take emergency measures, such as starting air conditioning or other cooling equipment to avoid equipment damage or fire risks. Similarly, if the air quality drops to a certain value, the system will trigger a medium-priority alarm to remind the user to take necessary measures, such as turning on an air purifier or ventilation system. This multi-threshold mechanism can be flexibly adjusted according to changes in different environmental factors, enabling the alarm system to adapt to changes in a variety of complex environments and ensure that each alarm event can receive an appropriate response according to its severity.

[0064] Condition weighting: When multiple conditions trigger an alarm at the same time, the alarm decision module will calculate a comprehensive alarm level through a weighted algorithm. This weighted algorithm assigns weights according to the influence and urgency of each condition, and then calculates a comprehensive alarm level. If multiple sensors detect different anomalies (for example, high temperature and reduced air quality), the system will evaluate the weights of each data to ensure that high-priority conditions (such as fire or gas leaks) are handled first, while lower-priority alarms (such as slight changes in air quality) may be delayed. Through this weighted mechanism, the system can make more intelligent decisions on complex multi-condition events, avoid false alarms under a single condition, and ensure that users can get the most urgent and important alarm information in a timely manner.

[0065] Preferably, the calculation formula for the alarm level is:

[0066]

[0067] Among them, wi is the weight coefficient of each condition, Ai is the alarm level of each condition, and the calculation formula of the alarm level Ai is:

[0068]

[0069] Where Li and Hi are the lower threshold and upper threshold of the i-th condition respectively, and Ci is the actual collected value of the i-th condition.

[0070] Preferably, the threshold setting module 6 dynamically adjusts the threshold according to historical data and user preferences, and adjusts the alarm thresholds of parameters such as temperature and humidity through the following formula:

[0071] L new =L0×(1-α·ΔT),H new =H0×(1+β·ΔT)

[0072] Where L0 and H0 are the initial thresholds, ΔT is the rate of change of environmental parameters, and α and β are adjustment coefficients used to control the sensitivity of the thresholds.

[0073] Preferably, the alarm algorithm module also includes a time series threshold adjustment mechanism, which dynamically adjusts the alarm threshold according to time changes or seasonal factors, and is adjusted by the following formula:

[0074] L adjusted =L0·f(t,S),H adjusted =H0·g(t,S)

[0075] Among them, f(t, S) and g(t, S) are functions of time t and season S, and the change of threshold is adjusted to adapt to different seasons, day and night temperature difference and external factors.

[0076] Preferably, the alarm algorithm module 7 adopts a machine learning model and is optimized according to the patterns of historical alarm data and environmental data, so as to improve the accuracy and response speed of the alarm through training algorithms, reduce false alarms and improve the warning effect.

[0077] Among them, the alarm algorithm module 7 integrates machine learning models and optimizes according to the patterns of historical alarm data and environmental data, gradually improving the accuracy and response speed of alarms, significantly reducing false alarms, and enhancing the warning effect. Its specific working principles include the following aspects:

[0078] Historical data learning and pattern recognition:

[0079] The system trains machine learning models by collecting and analyzing past alarm records, environmental data, and user behavior patterns. By identifying the correlation between environmental data and alarm events, the model can identify potential alarm patterns. For example, a certain temperature fluctuation may trigger a fire alarm under certain conditions, or a specific decrease in air quality may be a precursor to equipment failure. Through the accumulation of historical data, the machine learning model can continuously adjust its recognition criteria and improve its sensitivity and recognition capabilities to environmental changes.

[0080] Optimize alarm decisions:

[0081] Machine learning models learn how to distinguish between normal and abnormal situations through training, thereby optimizing the accuracy of alarm decisions. For example, if the system detects a change in an environmental parameter (such as temperature or humidity), the model will comprehensively analyze similar situations in historical data to determine whether the change is a potential danger signal. If the model determines that the change is a normal fluctuation, it will not trigger an alarm; if it is determined to be a possible danger, an alarm will be processed. In this way, by learning historical data, the system can reduce false alarms for normal fluctuations and improve the stability and reliability of the alarm system.

[0082] Improve responsiveness:

[0083] Through continuous optimization and training, machine learning algorithms can speed up the response time to abnormal situations. For example, when abnormal temperature and humidity or deterioration of air quality is detected, the system can use the learned patterns to quickly determine whether it is an emergency and immediately initiate the corresponding alarm response measures (such as push notifications or automatic activation of emergency equipment). This rapid response mechanism can greatly improve the system's emergency response efficiency and reduce the threat of potential danger to users.

[0084] Reduce false alarms and improve early warning effectiveness:

[0085] Machine learning models can continuously learn from real-time data, improve judgments on various environmental changes, and reduce false alarms. For example, if the system has had false alarms multiple times in a certain environment, the machine learning model can learn the characteristics of these false alarms and adjust the thresholds or alarm decision rules to reduce the false alarm rate. At the same time, the system's early warning capabilities are improved, not only being able to respond quickly when an anomaly occurs, but also identifying potential danger signals in advance and taking preventive measures in advance.

[0086] Preferably, the alarm algorithm module 7 uses a multiple redundant alarm mechanism, and the system will alarm through multiple channels simultaneously to ensure that the user can obtain the alarm information in time.

[0087] This mechanism uses multiple alarm channels to sound alarms simultaneously, enhancing the reliability of the system and the timeliness of response. The specific implementation includes: first, the system uses local sound and light alarm devices (such as loud alarm sounds and flashing lights) to attract the attention of family members; second, the alarm information is delivered to users remotely through mobile phone application push notifications, text messages, and emails to ensure that users can receive alarms no matter where they are. For users who cannot receive push notifications, the system provides backup reminders through text messages and emails. The alarm module also supports linkage with third-party services, such as contacting property or emergency rescue teams to initiate further emergency response. In addition, the system has dynamic redundancy function. Once an alarm channel fails, it automatically switches to the backup channel and notifies the user, avoiding the omission of alarm information. Through multi-channel redundant design, the system greatly improves the efficiency and reliability of alarm information transmission, ensuring that users can obtain alarm information in time under any circumstances.

[0088] Preferably, the abnormal response module 5 also includes an intelligent adjustment module, which can dynamically adjust the alarm threshold according to real-time changes in the environment. The system can automatically lower the temperature alarm threshold through the intelligent adjustment function.

[0089] The intelligent adjustment module monitors the temperature, humidity, air quality and other data of the environment through intelligent algorithms, and automatically adjusts the alarm threshold based on historical data and user preferences to adapt to different environmental conditions. For example, when the system detects a large temperature change or reaches a certain sensitive value, the intelligent adjustment module will automatically lower the temperature alarm threshold to ensure timely response to potential dangers or abnormal situations. This function not only improves the system's adaptability to environmental changes, but also effectively reduces false alarms, while ensuring that the alarm can be triggered in an emergency. The introduction of the intelligent adjustment module enables the system to respond more flexibly to changes in the home environment and provide more personalized and accurate alarm services.

[0090] A smart home information integration device includes an information integration device body 1, wherein the information integration device body 1 is internally provided with an information collection module 2, a central control module 3, an intelligent distribution module 4, an abnormal response module 5, a threshold setting module 6 and an alarm algorithm module 7, and the information integration device body 1 interacts with other smart home devices via wireless communication.

[0091] Among them, wireless communication technologies such as Wi-Fi, Bluetooth and other technologies are connected to smart devices in the home and exchange data to achieve seamless collaboration between various modules. The information collection module 2 is responsible for collecting various data related to the home environment, such as temperature and humidity, air quality, light intensity, human activity status, etc. These data are transmitted to the central control module 3 wirelessly. The central control module 3 uses deep learning and artificial intelligence algorithms to process and analyze the collected data, identify user behavior patterns and generate corresponding control instructions. After receiving the instructions from the central control module 3, the intelligent distribution module 4 accurately distributes them to smart home devices in the home, such as air conditioners, lights, curtains, etc., and monitors the execution of the equipment in real time. When the system detects an abnormal situation (such as fire, smoke leakage, etc.), the abnormal response module 5 responds in time and activates the alarm mechanism to ensure family safety. The threshold setting module 6 enables users to set the alarm threshold of environmental parameters according to their own needs, and automatically triggers the alarm mechanism when the detection value exceeds the preset value.

[0092] According to different module functions and their measurement parameters, the following is a demonstration experiment table for the key indicators and experimental data of each module in the smart home information integration system:

[0093] Experimental table:

[0094]

[0095]

[0096] This experimental table covers the key functions and test data of each module in the smart home information integration system, and tests the system's responsiveness, control accuracy, alarm mechanism and comprehensive performance in different scenarios. Through these experiments, the stability, accuracy and reliability of the system in a real environment can be effectively evaluated, ensuring that the system can provide efficient services and guarantees in smart home applications.

[0097] Based on the test data of the smart home information integration system provided in the experimental table, the following are several actual cases that show how the system can provide intelligent services and emergency responses in different environments and situations:

[0098] Case 1: Temperature and humidity monitoring and intelligent air conditioning adjustment

[0099] Background: The ambient temperature of homes often rises to above 28°C in summer, causing the air conditioner to be turned on frequently, consuming a lot of energy. The system needs to intelligently adjust the air conditioner settings based on environmental data (such as temperature, humidity, etc.) to improve comfort and save energy.

[0100] Test results: When the temperature and humidity sensor detects that the indoor temperature reaches 30°C, the system automatically turns on the air conditioner and sets a suitable temperature (for example, 25°C), while adjusting the wind speed according to the humidity level. The system presets the air conditioner start time in advance based on the user's behavior prediction algorithm (such as past data showing that users usually turn on the air conditioner at 3 pm) to reduce the delay in turning the air conditioner on and off.

[0101] Case 2: Abnormal response and emergency alarm

[0102] Background: There are elderly people and children in the family, and family members are concerned about whether the smart home system can respond in time in emergencies (such as fire or smoke leaks) to ensure the safety of the family.

[0103] Test results: The system detected fire smoke in the kitchen (PM2.5 concentration rose sharply) and immediately triggered the alarm system through smoke detectors and air quality sensors. The system not only triggered the audible alarm in the home, but also pushed notifications to family members' phones through mobile applications, providing emergency evacuation instructions.

[0104] Case 3: Air quality monitoring and environmental improvement

[0105] Background: Users are very sensitive to indoor air quality, especially when PM2.5 and CO2 concentrations are high, which can easily cause breathing discomfort. The system needs to monitor air quality and take intelligent adjustment measures when air pollution is too high, such as turning on an air purifier.

[0106] Test results: When the air quality sensor detects that the indoor PM2.5 concentration exceeds the preset threshold (such as 35μg / m 3 ), the system immediately starts the air purifier to keep the air quality within a healthy range. When the CO2 concentration is high, the system will also remind the user to open windows for ventilation and maintain air circulation.

[0107] Case 4: Smart home appliance control

[0108] Background: Users want their home appliances (such as air conditioners, televisions, and stereos) to remain energy-efficient when they are not at home during work hours, but they also want them to be turned on in time when they get home.

[0109] Test results: The system uses a deep learning algorithm to predict the time when the user will return home (for example, through outgoing records and time period behavior analysis), and automatically turns on the air conditioner, TV and lights one hour before the user is expected to arrive home. The system can adjust the on and off status of electrical appliances according to the real-time activity status of family members.

[0110] Summary: These cases demonstrate the various applications of smart home information integration systems in actual use. From environmental monitoring, intelligent adjustment to abnormal response, the system plays an important role in improving home comfort, safety and energy efficiency. By combining real-time data and intelligent algorithms, the system can accurately adjust the home environment to provide a smarter and more convenient life experience.

[0111] When working, this smart home information integration system is committed to achieving comprehensive perception, intelligent decision-making and automatic response of the home environment through the collaborative work of multiple modules. The system includes information collection module 2, central control module 3, intelligent distribution module 4, abnormal response module 5, threshold setting module 6, alarm algorithm module 7 and core components such as timing threshold adjustment and intelligent learning mechanism to ensure comprehensive monitoring and efficient management of the home environment. The information collection module 2 collects key data such as temperature, humidity, air quality, light intensity, etc. in the home environment in real time through various sensors, and transmits the data to the central control module 3. The central control module 3 uses deep learning and artificial intelligence algorithms to analyze these multi-source data and automatically identify the user's living habits and behaviors. Mode and family environment requirements, and then generate corresponding control instructions to ensure the coordination of device status and environmental requirements. The intelligent distribution module 4 transmits control signals to various smart home devices, such as air conditioning, lighting, curtains, etc., according to the instructions of the central control module 3, and interacts with the devices in real time through wireless communication to ensure the accuracy and efficiency of device operation. When abnormal conditions occur in the home environment, such as fire, smoke leakage, abnormal user behavior, etc., the abnormal response module 5 will quickly identify and respond. This module can quickly trigger sound and light alarms, mobile phone push notifications or direct remote notifications to users by linking with the alarm device, thereby guiding users to respond in time. In addition, the abnormal response module 5 can automatically adjust the alarm threshold according to real-time changes in the environment, further enhancing The alarm algorithm module 7 is responsible for calculating the alarm priority according to the set threshold after receiving abnormal data. Through multi-threshold comparison, weighted algorithm and priority scheduling, the system can accurately evaluate the priority of the alarm and select the most appropriate alarm method, such as local sound and light alarm, mobile phone push notification or linkage with third-party services, so as to ensure the timeliness and accuracy of the alarm. The introduction of timing threshold adjustment and intelligent learning mechanism enables the alarm system to dynamically adjust the threshold according to external factors such as season, time and weather, further improving the flexibility and accuracy of alarm decision-making. The smart home information integration system has many significant beneficial effects. First of all, deep learning and artificial intelligence algorithms give the system powerful intelligent control capabilities. It can automatically adjust the device status according to user needs and environmental changes, optimize the home environment, and improve living comfort and personalized experience. Secondly, the optimized alarm mechanism can reduce false alarms and redundant alarms by accurately calculating the alarm priority, thereby improving the system response efficiency. At the same time, it also avoids user interference caused by frequent alarms, significantly improving the system stability and user experience. In addition, the system has strong environmental adaptability and can dynamically adjust the control strategy according to seasonal changes, time changes, and fluctuations of various variables in the home environment to ensure the most appropriate response under different environmental conditions. The design of multiple redundant alarm mechanisms ensures that alarm information is delivered to users in a timely manner through multiple channels such as sound and light alarms, mobile phone push notifications, and third-party linkage.This avoids the omission of alarm information and ensures that users can respond immediately and take timely countermeasures.

[0112] The above are only preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the application should be included in the protection scope of the present application.

Claims

1. A smart home information integration system, characterized in that: include: An information collection module (2) is used to collect multi-source data related to the home environment, including temperature and humidity, air quality, light intensity, human activity status, and operating status of smart home devices; The central control module (3) is connected to the information acquisition module (2) for fusing and processing multi-source data, analyzing user behavior patterns and environmental requirements through a deep learning algorithm, and generating control instructions; An intelligent distribution module (4) is connected to the central control module (3) and is used to distribute control instructions to corresponding smart home devices and monitor the execution status of the devices in real time; an abnormal response module (5), connected to the central control module (3), for identifying and responding to abnormal conditions in the home environment, including fire, smoke leakage, or abnormal user behavior, and intervening through an alarm device or remotely notifying the user; A threshold setting module (6) is used to set a safety threshold of key indicators related to the home environment, and when the detection value exceeds the preset threshold, an alarm mechanism is triggered; The alarm algorithm module (7) is used to calculate the alarm priority according to the set threshold value and the collected data, and select the corresponding alarm mode, including sound and light alarm, mobile phone push notification and linkage with third-party services.

2. The smart home information integration system according to claim 1, characterized in that: The alarm algorithm module (7) comprises the following steps: Real-time monitoring: real-time collection and analysis of environmental data to detect whether the threshold is exceeded; Alarm decision-making: when the detection value exceeds the preset threshold, the alarm level is evaluated, the alarm type is determined, and the corresponding response measures are triggered; Priority scheduling: automatically or manually select alarm processing solutions based on the priorities of different types of alarm events, such as first performing a local sound and light alarm, then pushing a mobile phone notification or linking a third-party response.

3. The smart home information integration system according to claim 2, characterized in that: The alarm decision step includes: Multi-threshold comparison: set multiple levels of thresholds for different alarm requirements. For example, a high-priority alarm is triggered when the temperature exceeds the set high-temperature threshold, and a medium-priority alarm is triggered when the air quality drops above a certain value. Condition weighting: When multiple conditions trigger an alarm at the same time, a comprehensive alarm level is calculated through a weighted algorithm, and the alarm response is executed based on the comprehensive level.

4. The smart home information integration system according to claim 1, characterized in that: The calculation formula for the alarm level is: Among them, wi is the weight coefficient of each condition, Ai is the alarm level of each condition, and the calculation formula of the alarm level Ai is: Where Li and Hi are the lower threshold and upper threshold of the i-th condition respectively, and Ci is the actual collected value of the i-th condition.

5. The smart home information integration system according to claim 1, characterized in that: The threshold setting module (6) dynamically adjusts the threshold according to historical data and user preferences, and adjusts the alarm thresholds of parameters such as temperature and humidity through the following formula: L new =L0×(1-α·△T), H new =H0×(1+β·△T) Where L0 and H0 are the initial thresholds, △T is the rate of change of environmental parameters, and α and β are adjustment coefficients used to control the sensitivity of the thresholds.

6. The smart home information integration system according to claim 1, characterized in that: The alarm algorithm module (7) also includes a time series threshold adjustment mechanism, which dynamically adjusts the alarm threshold according to time changes or seasonal factors, and is adjusted by the following formula: L adjusted =L0·f(t,S),H adjusted =H0·g(t,S) Among them, f(t,S) and ɡ(t,S) are functions of time t and season S, and the changes in the threshold are adjusted to adapt to different seasons, day-night temperature differences and external factors.

7. The smart home information integration system according to claim 1, characterized in that: The alarm algorithm module adopts a machine learning model and is optimized according to the patterns of historical alarm data and environmental data. The accuracy and response speed of the alarm are improved through training algorithms, thereby reducing false alarms and improving the warning effect.

8. The smart home information integration system according to claim 1, characterized in that: The alarm algorithm module (7) uses a multiple redundant alarm mechanism, and the system will alarm through multiple channels simultaneously to ensure that users can obtain alarm information in a timely manner.

9. The smart home information integration system according to claim 1, characterized in that: The abnormal response module (5) also includes an intelligent adjustment module, which can dynamically adjust the alarm threshold according to real-time changes in the environment. The system can automatically lower the temperature alarm threshold through the intelligent adjustment function.

10. A smart home information integration device, characterized in that: The invention comprises an information integration device body (1), wherein an information collection module (2), a central control module (3), an intelligent distribution module (4), an abnormal response module (5), a threshold setting module (6) and an alarm algorithm module (7) are arranged inside the information integration device body, and the information integration device body (1) interacts with other smart home devices via wireless communication.

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