Intelligent household illumination intensity monitoring automatic adjusting method based on sensor

Abstract

The invention discloses a smart home illumination intensity monitoring and automatic adjusting method based on a sensor, and relates to the technical field of smart home illumination intensity monitoring and automatic adjusting, and the method comprises the following steps: installing an illumination sensor to monitor the indoor illumination intensity in real time, and collecting environment change data; the illumination sensor and the smoke sensor are integrated, an intelligent priority mechanism is combined, the user safety is guaranteed preferentially in an emergency, the emergency lighting system is started automatically, and the brightness is adjusted to ensure safe evacuation. An environment data fusion technology is adopted, various sensor data such as illumination, smoke, temperature and humidity are comprehensively analyzed, the living comfort is optimized, and the emergency response precision is improved. Meanwhile, based on the deep learning technology, the system can perform adaptive learning, optimize the emergency recognition and response capability, and improve the safety and reliability of the smart home.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent home lighting intensity monitoring and automatic adjustment, and specifically relates to a method for intelligent home lighting intensity monitoring and automatic adjustment based on sensors. Background Art

[0002] Intelligent home lighting intensity monitoring and automatic adjustment based on sensors refers to installing light sensors in the intelligent home environment to monitor the indoor lighting intensity in real time, and automatically adjusting the brightness of indoor light sources or the working states of other devices by an intelligent system according to the monitoring data. Specifically, the sensors can detect the changes in the indoor natural lighting intensity at different time periods and under different weather conditions, and the intelligent system adjusts the settings of devices such as lights and curtains according to this information, so as to achieve the purposes of energy conservation, improving the living comfort and meeting the user's needs. For example, when the lighting intensity is low, the intelligent home system may automatically turn on the lights, and when the natural light is sufficient, the system will automatically dim or turn off the lights to ensure both energy conservation and an ideal lighting environment.

[0003] The existing technology has the following deficiencies: In the process of automatic adjustment of intelligent home lighting intensity based on sensors, if the system fails to effectively identify and avoid interference with emergency events when the lighting changes, it may lead to potential safety hazards. For example, when the system automatically adjusts the indoor lighting, if it does not give priority to emergencies such as the occurrence of a fire or smoke, it may cause warning lights or emergency lighting not to be turned on in time. In this way, in low visibility conditions, users may not be able to detect the fire source or dangerous area in time, affecting the evacuation and self-rescue effects. Therefore, the intelligent home system needs to be able to give priority to ensuring safety, avoid missing warning signals due to automatic lighting adjustment at critical moments, and thus cause serious consequences.

[0004] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure, and therefore it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for intelligent home lighting intensity monitoring and automatic adjustment based on sensors. By integrating a light sensor and a smoke sensor, combined with an intelligent priority mechanism, it realizes giving priority to ensuring user safety in case of emergency events, automatically starting the emergency lighting system, and adjusting the brightness to ensure safe evacuation. By adopting environmental data fusion technology, comprehensively analyzing various sensor data such as light, smoke, temperature and humidity, it optimizes the living comfort and improves the emergency response accuracy. At the same time, based on deep learning technology, the system can adaptively learn and optimize the emergency event recognition and response capabilities, and improve the safety and reliability of the intelligent home to solve the problems in the above background art.

[0006] To achieve the above object, the present invention provides the following technical solution: An automatic adjustment method for monitoring the light intensity of a smart home based on sensors, comprising the following steps:

[0007] Install a light sensor to monitor the indoor light intensity in real time and collect environmental change data;

[0008] Receive and process the light intensity data transmitted by the light sensor to determine the indoor light demand;

[0009] When it is determined that the indoor light intensity is lower than the set threshold, automatically adjust the brightness of the indoor lighting equipment;

[0010] Monitor the status of the fire sensor in the environment in real time and determine whether there is an emergency;

[0011] If an emergency is detected, immediately activate the emergency warning light system to give priority to ensuring user safety and ensure that the fire source or dangerous area can be detected in time under low visibility;

[0012] After the emergency disappears or returns to normal, automatically resume the light intensity adjustment function and continue to adjust the indoor lighting according to the environmental light demand.

[0013] Preferably, the data processing further includes predicting the future light demand according to the trend of environmental light change, and dynamically adjusting the indoor lighting based on the prediction result. By collecting historical light intensity data, using the regression algorithm to predict the future light intensity, so as to adjust the indoor light brightness in advance.

[0014] Preferably, the process of light intensity adjustment is adjusted according to the personalized needs set by the user and the environmental light conditions;

[0015] Automatically adjust the brightness of the indoor lights according to the user's preset preferences, combined with the external natural light conditions, to achieve an intelligent, energy-saving and comfortable indoor lighting effect;

[0016] If the external light is insufficient, automatically turn on the lights and adjust them to the preset comfortable brightness to provide a good living experience.

[0017] Preferably, the emergency event detection includes real-time monitoring of the indoor environment through a smoke sensor and a fire alarm, and when the smoke concentration detected by the sensor exceeds the preset threshold, trigger the activation of the emergency lighting system;

[0018] The data of the sensor is transmitted to the control center for analysis. The control center determines whether there is an emergency such as a fire according to the monitoring data. If so, immediately notify the user and activate the emergency lighting device to provide light to ensure the safety of the user.

[0019] Preferably, the emergency event detection step optimizes the detection accuracy through an artificial intelligence algorithm, trains based on a deep learning model, can identify and classify various emergency events, and determines whether to activate the emergency lighting system according to the output result of the deep learning model to ensure the efficiency and accuracy during emergency response and avoid misoperations.

[0020] Preferably, the emergency event detection further includes introducing real-time calculation based on a dynamic model to optimize the response to emergency events. The specific steps are as follows:

[0021] When receiving data from the smoke sensor, first perform a preliminary judgment on the smoke concentration. The formula is as follows:

[0022] C detected =f(C sensor )

[0023] , where C detected is the concentration value output by the smoke sensor, C sensor is the real-time collected smoke concentration data, and f(C sensor ) is the response function of the sensor;

[0024] If it is detected that the concentration value exceeds the threshold, that is, C detected >C th , C th is the smoke concentration threshold, then enter the emergency response state and activate the emergency lighting function. The formula is as follows:

[0025]

[0026] , where Lighting Power is the lighting intensity and L max is the maximum emergency lighting brightness.

[0027] Preferably, the activation of the emergency lighting system includes selecting different lighting brightness levels according to the severity of the emergency event;

[0028] If the sensor value detecting fire or smoke exceeds the predetermined threshold, automatically activate the emergency lighting of the corresponding intensity according to the preset emergency response plan and maintain this brightness until the emergency event is handled or returns to normal;

[0029] The emergency lighting system can automatically adjust the brightness to ensure that users can obtain sufficient lighting support for evacuation in the shortest time.

[0030] Preferably, the brightness adjustment of the emergency lighting includes using a complex dynamic calculation model to predict the duration and change trend of the emergency event, so as to determine the optimal lighting intensity. The specific steps are as follows:

[0031] Based on the real-time data of the smoke sensor, combined with environmental parameters such as temperature and humidity for data fusion, a multi-dimensional time series model is established, and the formula is as follows:

[0032]

[0033] , where D(t) is the dynamic change value of the emergency event, S(t) is the concentration value of the smoke sensor, T(t) is the temperature value, H(t) is the humidity value, α is the weighting coefficient of the smoke concentration, β is the weighting coefficient of the temperature, γ is the weighting coefficient of the humidity, t is the current time, and t0 is the start time of the event;

[0034] The obtained dynamic change value D(t) of the emergency event is used to infer the duration of the emergency event, and the emergency lighting brightness is dynamically adjusted according to the prediction result. The calculation expression is as follows:

[0035]

[0036] , where L adjusted (t) is the emergency lighting brightness after dynamic adjustment, L max is the maximum brightness value of the emergency lighting system, and D max is the maximum event intensity.

[0037] Preferably, the process of restoring the light intensity adjustment function includes intelligently judging when to restore the automatic adjustment function according to the indoor environment change and the user's feedback. Specifically, by periodically detecting the light intensity and the user's activity, it is judged whether there is a phenomenon of too high or too low light intensity, and then automatically adjusted. If the ambient light is normal and no emergency event occurs, the automatic adjustment of the light intensity is restored to ensure comfort and energy-saving effect.

[0038] In the above technical solution, the technical effects and advantages provided by the present invention are as follows:

[0039] By integrating a light sensor and a smoke sensor and introducing an intelligent priority mechanism, the present invention can ensure that when an emergency event occurs, the system can quickly switch to the emergency response mode and give priority to ensuring the safety of users. In case of emergencies such as fire or smoke, the system will not continue to perform the conventional light intensity adjustment, but immediately activate the emergency lighting function and adjust the lights to the maximum brightness to ensure that users can quickly find the fire source or the safety exit under low visibility. Through the design of this function, the smart home system can provide necessary light support at critical moments, avoid missing important warning signals due to automatic light adjustment, reduce potential safety hazards, and ensure the life safety of users and the smooth progress of emergency evacuation.

[0040] The present invention adopts environmental data fusion technology to comprehensively analyze the data of various sensors such as light, smoke, temperature and humidity, so as to achieve more accurate environmental monitoring and intelligent adjustment. Through the real-time collection and processing of multiple environmental parameters, the system can intelligently adjust the light brightness when it determines that the light intensity is insufficient. At the same time, it can also detect abnormal changes in temperature or humidity and predict emergencies such as fires. For example, when the system detects an increase in smoke concentration and combines it with the change in indoor temperature, the system can determine whether a fire has occurred and respond to the speed of fire spread by dynamically adjusting the brightness of emergency lighting. Through the fusion of such multi-dimensional data, the system can not only optimize the comfort of the living environment in real time, but also improve the response accuracy of emergency events, enabling the smart home to make more reasonable and timely decisions when dealing with routine and emergency situations.

[0041] Based on deep learning technology, the present invention can continuously optimize the recognition and response capabilities of emergency events. Through deep neural network training, the system can extract complex pattern recognition from the data collected by sensors and automatically judge whether there are emergency situations such as fires and smoke. As the usage time extends, the system will continuously self-learn based on user behavior data and environmental changes, improving the judgment accuracy and response efficiency of emergency events. For example, the system can predict the spread trend after a fire occurs based on historical data and environmental change patterns, dynamically adjust the brightness and position of emergency lighting, and optimize the user's emergency escape route. At the same time, with the continuous improvement of the deep learning model, the system will become more intelligent and accurate, and can make optimal response decisions in different environments and situations, thereby enhancing the security and reliability of the smart home system. This self-adaptive learning ability enables the system to continuously improve its accuracy and efficiency when facing different security threats and better serve users. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0043] Figure 1 It is a method flow chart of the method for automatically monitoring and adjusting the light intensity of a sensor-based smart home according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0044] Now, the exemplary embodiments will be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these exemplary embodiments are provided so that the present disclosure will be more complete and comprehensive, and will fully convey the concept of the exemplary embodiments to those skilled in the art.

[0045] The present invention provides a sensor-based intelligent home lighting intensity monitoring and automatic adjustment method as shown in Figure 1 the following steps:

[0046] Install a light sensor to monitor the indoor lighting intensity in real time and collect environmental change data;

[0047] Receive and process the lighting intensity data transmitted by the light sensor to judge the lighting requirements indoors;

[0048] Data processing further includes predicting future lighting requirements according to the trend of environmental light changes, and dynamically adjusting indoor lighting based on the prediction results. By collecting historical lighting intensity data and using a regression algorithm to predict future lighting intensity, the indoor light brightness can be adjusted in advance. For example, the system can use a linear regression model or a neural network algorithm to predict future lighting requirements according to past lighting intensity data and adjust the working state of lighting equipment in real time to maintain the best lighting effect indoors.

[0049] When it is judged that the indoor lighting intensity is lower than the set threshold, automatically adjust the brightness of indoor lighting equipment;

[0050] The process of lighting intensity adjustment is adjusted according to the personalized needs set by the user and the environmental lighting conditions;

[0051] Automatically adjust the brightness of indoor lights according to the preferences preset by the user (such as soft, bright, etc.), and combine with the external natural light conditions to achieve an intelligent, energy-saving and comfortable indoor lighting effect;

[0052] If the external light is insufficient, automatically turn on the lights and adjust them to the preset comfortable brightness to provide a good living experience.

[0053] Real-time monitor the status of the fire sensor in the environment and judge whether there is an emergency;

[0054] Emergency event detection includes real-time monitoring of the indoor environment through a smoke sensor and a fire alarm, and when the smoke concentration detected by the sensor exceeds the preset threshold, triggering the activation of the emergency lighting system;

[0055] The data of the sensor is transmitted to the control center for analysis. The control center judges whether there is an emergency such as a fire according to the monitoring data. If so, immediately notify the user and activate the emergency lighting device to provide lighting to ensure the safety of the user.

[0056] The emergency event detection steps optimize the detection accuracy through artificial intelligence algorithms, are trained based on deep learning models, can identify and classify various emergency events, and determine whether to activate the emergency lighting system according to the output results of the deep learning model to ensure the efficiency and accuracy during emergency response and avoid misoperations.

[0057] The emergency event detection further includes introducing real-time calculation based on a dynamic model to optimize the response to emergency events. The specific steps are as follows:

[0058] When receiving data from the smoke sensor, first perform a preliminary judgment on the smoke concentration. The formula is as follows:

[0059] C detected =f(C sensor )

[0060] , where C detected is the concentration value output by the smoke sensor, C sensor is the real-time collected smoke concentration data, and f(C sensor ) is the response function of the sensor;

[0061] If it is detected that the concentration value exceeds the threshold, that is, C detected >C th , C th is the smoke concentration threshold, then enter the emergency response state and activate the emergency lighting function. The formula is as follows:

[0062]

[0063] , where Lighting Power is the lighting intensity, L max is the maximum emergency lighting brightness, and the brightness level is dynamically adjusted according to the concentration data to ensure sufficient lighting for users in case of emergency.

[0064] If the occurrence of an emergency event is detected, immediately activate the emergency warning light system to give priority to ensuring the safety of users and ensure that the fire source or dangerous area can be detected in time under low visibility;

[0065] The activation of the emergency lighting system includes selecting different lighting brightness levels according to the severity of the emergency event;

[0066] If the sensor values detecting fire or smoke exceed the predetermined threshold, automatically activate the emergency lighting of the corresponding intensity according to the preset emergency response plan and maintain that brightness until the emergency event is handled or returns to normal;

[0067] The emergency lighting system can automatically adjust the brightness to ensure that users can obtain sufficient lighting support for evacuation in the shortest time.

[0068] The brightness adjustment of emergency lighting involves using a complex dynamic calculation model to predict the duration and trend of emergency events, thereby determining the optimal lighting intensity. The specific steps are as follows:

[0069] Based on the real-time data of the smoke sensor, combined with environmental parameters such as temperature and humidity for data fusion, a multi-dimensional time series model is established. The formula is as follows:

[0070]

[0071] , where D(t) is the dynamic change value of the emergency event, serving as a comprehensive factor affecting the emergency lighting intensity. S(t) is the concentration value of the smoke sensor, T(t) is the temperature value, H(t) is the humidity value, α is the weighting coefficient of the smoke concentration, used to adjust the weight of the smoke concentration on the emergency lighting, β is the weighting coefficient of the temperature, used to adjust the impact of temperature changes on the emergency lighting demand, γ is the weighting coefficient of the humidity, adjusting the impact of humidity changes on the emergency lighting, t is the current time, representing the time point of real-time processing by the system, and t0 is the start time of the event, representing the moment since the system started monitoring;

[0072] The obtained dynamic change value D(t) of the emergency event is used to speculate on the duration of the emergency event and dynamically adjust the emergency lighting brightness. The calculation expression is as follows:

[0073]

[0074] , where L adjusted (t) is the dynamically adjusted emergency lighting brightness, L max is the maximum brightness value of the emergency lighting system, D max is the maximum event intensity.

[0075] After the emergency event disappears or returns to normal, the lighting intensity adjustment function is automatically restored, and the indoor lighting continues to be adjusted according to the ambient lighting demand;

[0076] The process of restoring the lighting intensity adjustment function includes intelligently judging when to restore the automatic adjustment function based on the changes in the indoor environment and the user's feedback. Specifically, by periodically detecting the lighting intensity and the user's activity, it is judged whether there is a phenomenon of too high or too low lighting intensity, and then automatically adjusted. If the ambient lighting is normal and no emergency event occurs, the automatic adjustment of the lighting intensity is restored to ensure comfort and energy-saving effects.

[0077] Embodiment 1: The core of this embodiment lies in combining the working principles of a light sensor and a smoke sensor to achieve intelligent adjustment of the indoor environment through real-time data collection and feedback, and to ensure the priority of safety in emergency situations. The system first continuously monitors the indoor light intensity through a light sensor installed in the home environment. When the light intensity is lower than the preset value, the system automatically triggers the brightness adjustment of the indoor lighting equipment to supplement the insufficient natural light and maintain a comfortable indoor lighting environment. This adjustment process is dynamic. As the outdoor light intensity changes, the system can adjust the light brightness in real time according to the demand to ensure that the indoor light is always within a suitable range.

[0078] However, the adjustment of light intensity is not given priority in all situations, especially in the presence of sudden emergencies. To prevent the system from performing automatic adjustment during emergencies and affecting safety, the system also integrates a smoke sensor to detect possible fire or smoke events. When the smoke sensor detects that the smoke concentration exceeds the preset safety threshold, the system immediately determines it as an emergency and enters the emergency response mode. In this mode, the system automatically switches to the emergency lighting state, activates all indoor emergency lighting fixtures, and adjusts their brightness to the maximum to ensure that in an emergency, users can find the exit or dangerous areas in a low visibility environment. The brightness and quantity of these emergency lighting devices are dynamically adjusted according to factors such as the possible spread range of the fire and the smoke concentration to ensure that users receive sufficient light protection during emergencies.

[0079] To avoid conflicts between the light adjustment function and the emergency response, the system design implements a multiple priority mechanism. During an emergency, the lighting system will give priority to activating the emergency lighting and turn off all non-essential lighting devices to avoid equipment failures caused by overloading of the power load or other factors. In addition, the system also has a built-in emergency power supply system to ensure that the emergency lighting can still provide sufficient light during a power outage or unstable power supply. This function of the smart home system not only meets the requirements of light adjustment but also can automatically switch to the safety mode during emergencies, providing safety protection for users.

[0080] Through this embodiment, seamless connection can be achieved between the light intensity adjustment function and the emergency event response function, ensuring automatic adjustment of light intensity under normal circumstances to improve living comfort, and when an emergency occurs, the system can quickly respond and switch to the mode of ensuring user safety, thus avoiding potential safety hazards caused by automatic light adjustment. This dual protection mechanism enables the smart home system to provide a comfortable environment in daily life and safeguard the lives of users at critical moments.

[0081] Embodiment 2: In this embodiment, through environmental data fusion technology, combined with multiple environmental monitoring devices such as light sensors, smoke sensors, temperature and humidity sensors, etc., the indoor environment is comprehensively monitored and intelligent dynamic adjustment is achieved. The working process of the system is to effectively combine the data acquisition and processing of multiple sensors. Based on the real-time acquisition of environmental data, the overall environmental conditions indoors are analyzed through data fusion algorithms, and then the corresponding light intensity adjustment is carried out.

[0082] Specifically, the system determines the current indoor environmental requirements by real-time monitoring of external light intensity and parameters such as indoor temperature, humidity, smoke concentration, etc. The external light intensity is fed back to the system in real time through a light sensor. The system compares the collected light data with the standard value set by the user to determine whether to turn on the lighting device and adjust the brightness. If the external light is insufficient, the system will automatically turn on the lights and adjust to a preset comfortable brightness. At the same time, the system will also analyze whether there is excessive humidity or abnormal temperature based on the indoor temperature and humidity data provided by the temperature and humidity sensor, and then optimize and adjust the indoor environment to ensure the comfort of the user's living environment.

[0083] In addition, the system can also detect the possibility of emergency events such as fires based on the data provided by the smoke sensor. When the smoke concentration exceeds the set safety threshold, the system will not only automatically activate the emergency lighting system, but also predict the fire spread speed in combination with other environmental data (such as temperature and humidity), and take corresponding safety measures in advance. For example, if the system detects a sharp rise in temperature, a rapid change in humidity, and a continuous increase in smoke concentration, the system will immediately determine it as a fire, automatically adjust the brightness of all emergency lighting devices to the maximum, and at the same time activate other safety devices such as alarms and ventilation systems to ensure that users can respond in a safe environment in a timely manner.

[0084] The advantage of this embodiment is that through data fusion technology, the system can comprehensively consider multiple environmental factors and make more accurate and intelligent judgments. Especially in the event of an emergency, the system can adjust the working state of the lighting system in real time according to the fire spread speed and other environmental changes, thus providing more personalized and accurate safety protection for users.

[0085] Embodiment 3: In this embodiment, by adopting deep learning technology and combining the powerful data processing ability of artificial intelligence, the recognition and response strategies for emergency events are further optimized. The system uses multiple sensors (including light, smoke, temperature and humidity sensors, etc.) to continuously monitor the indoor environmental data, and transmits all the data collected by the sensors to the cloud for processing. Through the training and optimization of the deep neural network model, the system can accurately identify emergency events such as fires and smoke in the first time and make a rapid response.

[0086] Specifically, the system analyzes various types of sensor data based on a deep learning model and combines factors such as historical data and environmental characteristics to determine whether an emergency event exists. When the system detects abnormal changes in environmental parameters such as smoke concentration, temperature, and humidity, the deep neural network model will judge whether it is a fire or other dangerous situation according to the pre-trained data patterns. If an emergency event is confirmed, the system immediately activates the emergency lighting function and adjusts the brightness of all indoor lighting devices to the maximum. In addition, the system also sends an alarm notification to the user to remind them to evacuate or take emergency measures.

[0087] The advantage of the deep learning model lies in its ability to self-learn and optimize. As the system continuously operates in different environments, the deep learning model can continuously accumulate experience and improve the judgment rules, thereby enhancing the recognition accuracy of various emergency events. In the initial stage, the system may rely on manually labeled data for training, and as the application time increases, the system can continuously self-learn from user behavior and environmental changes and optimize the response strategy. For example, the system can predict the user's reaction time based on the data from the smoke sensor and the user's behavior pattern, and accordingly adjust the alarm and lighting brightness to help the user respond more quickly.

[0088] In addition, the system can also dynamically adjust the response strategy according to different types of emergency events through intelligent decision-making algorithms. For example, in the event of a fire, the system will give priority to activating the emergency lighting and automatically adjust the distribution and brightness of the lights according to the location of the fire source; in the event of smoke leakage, the system relies more on the adjustment of warning lights and ventilation equipment to ensure that users can understand the situation and take appropriate actions in the shortest time.

[0089] Through deep learning technology, the system can not only make faster and more accurate responses in emergency situations, but also continuously optimize its decision-making process over time, thereby improving the overall security and response efficiency of the smart home system. This method of emergency event recognition and intelligent decision-making based on artificial intelligence provides a higher level of guarantee for smart home security, ensuring that users can receive timely and accurate lighting and safety support in any emergency situation.

[0090] By integrating a light sensor and a smoke sensor and introducing an intelligent priority mechanism, the present invention can ensure that when an emergency occurs, the system can quickly switch to the emergency response mode to prioritize the safety of users. In case of emergencies such as fire or smoke, the system will not continue to perform the conventional light intensity adjustment, but immediately activate the emergency lighting function to adjust the lights to the maximum brightness, ensuring that users can quickly detect the fire source or the safety exit under low visibility. Through the design of this function, the smart home system can provide necessary light support at critical moments, avoid missing important warning signals due to automatic light adjustment, reduce potential safety hazards, and ensure the safety of users' lives and the smooth progress of emergency evacuation.

[0091] The present invention adopts the environmental data fusion technology to comprehensively analyze the data of multiple sensors such as light, smoke, temperature and humidity, so as to achieve more accurate environmental monitoring and intelligent adjustment. Through the real-time collection and processing of multiple environmental parameters, the system can intelligently adjust the light brightness when it judges that the light intensity is insufficient. At the same time, it can also detect abnormal changes in temperature or humidity and predict emergencies such as fire. For example, when the system detects an increase in smoke concentration and combines it with the change in indoor temperature, the system can judge whether a fire has occurred and respond to the speed of fire spread by dynamically adjusting the emergency lighting brightness. Through the fusion of such multi-dimensional data, the system can not only optimize the comfort of the living environment in real time, but also improve the response accuracy of emergencies, enabling the smart home to make more reasonable and timely decisions when dealing with routine and emergency situations.

[0092] Based on deep learning technology, the present invention can continuously optimize the recognition and response capabilities of emergencies. Through deep neural network training, the system can extract complex pattern recognition from the data collected by sensors and automatically judge whether there are emergencies such as fire and smoke. As the usage time extends, the system will continuously self-learn based on user behavior data and environmental changes to improve the judgment accuracy and response efficiency of emergencies. For example, the system can predict the spread trend after a fire occurs based on historical data and environmental change patterns, dynamically adjust the emergency lighting brightness and position, and optimize the user's emergency escape path. At the same time, with the continuous improvement of the deep learning model, the system will become more intelligent and accurate, and can make optimal response decisions in different environments and situations, thereby enhancing the safety and reliability of the smart home system. This self-adaptive learning ability enables the system to continuously improve its accuracy and efficiency when facing different security threats and better serve users.

[0093] The above formulas are all dimensionless and take their numerical calculations. The formula is obtained by collecting a large amount of data for software simulation to get a formula that is closest to the real situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0094] Only some exemplary embodiments of the present invention have been described by way of illustration. Undoubtedly, those of ordinary skill in the art can modify the described embodiments in various different ways without departing from the spirit and scope of the present invention. Therefore, the above-mentioned drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0095] It should be noted that in this text, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0096] It should be understood that in various embodiments of the present application, the magnitude of the serial numbers of the above processes does not mean the order of execution is prior or subsequent. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0097] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this text can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present application.

[0098] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0099] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0100] In addition, in each embodiment of the present application, each functional unit may be integrated into one processing unit, may exist separately as individual physical units, or two or more units may be integrated into one unit.

[0101] As mentioned above, the above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present application, and all should be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

[0102] Only some exemplary embodiments of the present invention have been described above by way of illustration. Without doubt, for those of ordinary skill in the art, the described embodiments can be modified in various different ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the protection scope of the claims of the present invention.

Claims

1. A sensor-based automatic adjustment method for monitoring the light intensity in a smart home, characterized in that, It includes the following steps: Install a light sensor to monitor the indoor light intensity in real time and collect environmental change data; Receive and process the light intensity data transmitted by the light sensor to judge the indoor light demand; When it is judged that the indoor light intensity is lower than the set threshold, automatically adjust the brightness of the indoor lighting equipment; Monitor the status of the fire sensor in the environment in real time and judge whether there is an emergency; If an emergency is detected, immediately activate the emergency warning light system to give priority to ensuring user safety and ensure that the fire source or dangerous area can be detected in time in low visibility; After the emergency disappears or returns to normal, automatically resume the light intensity adjustment function and continue to adjust the indoor lighting according to the environmental light demand; 2. The sensor-based automatic adjustment method for monitoring the illumination intensity of a smart home according to claim 1, wherein, Data processing further includes predicting future light demands according to the environmental light change trend and dynamically adjusting the indoor lighting based on the prediction results. By collecting historical light intensity data and using a regression algorithm to predict the future light intensity, the indoor light brightness can be adjusted in advance.

3. The sensor-based automatic adjustment method for monitoring the illumination intensity of a smart home according to claim 1, wherein, The process of light intensity adjustment is adjusted according to the personalized needs set by the user and the environmental light conditions; Automatically adjust the brightness of the indoor lights according to the user's preset preferences, combined with the external natural light conditions, to achieve an intelligent, energy-saving and comfortable indoor lighting effect; If the external light is insufficient, automatically turn on the lights and adjust them to the preset comfortable brightness to provide a good living experience.

4. The sensor-based automatic adjustment method for monitoring the illumination intensity of a smart home according to claim 1, wherein, Emergency event detection includes real-time monitoring of the indoor environment through a smoke sensor and a fire alarm, and when the smoke concentration detected by the sensor exceeds the preset threshold, trigger the activation of the emergency lighting system; The data of the sensor is transmitted to the control center for analysis. The control center judges whether there is an emergency such as a fire based on the monitoring data. If so, immediately notify the user and activate the emergency lighting device to provide light to ensure user safety.

5. The sensor-based automatic adjustment method for monitoring the illumination intensity of a smart home according to claim 1, wherein The emergency event detection step optimizes the detection accuracy through an artificial intelligence algorithm. Based on a deep learning model for training, it can identify and classify various emergency events, and determine whether to activate the emergency lighting system according to the output result of the deep learning model to ensure the efficiency and accuracy during emergency response and avoid misoperation.

6. The sensor-based automatic adjustment method for monitoring the illumination intensity of a smart home according to claim 1, wherein, Emergency event detection further includes introducing real-time calculation based on a dynamic model to optimize the response to emergency events. The specific steps are as follows: When receiving data from the smoke sensor, first perform a preliminary judgment on the smoke concentration. The formula is as follows: C detected = f(C sensor ) Where, C detected is the concentration value output by the smoke sensor, C sensor is the smoke concentration data collected in real time, and f(C sensor ) is the response function of the sensor; If the detected concentration value exceeds the threshold, i.e., C detected >C th ,C th is the smoke concentration threshold, then enter the emergency response state and activate the emergency lighting function. The formula is as follows: ， Where Lighting Power is the lighting intensity and L max is the maximum emergency lighting brightness.

7. The automatic adjustment method for monitoring the illumination intensity of a smart home based on sensors according to claim 1, characterized in that, The activation of the emergency lighting system includes selecting different lighting brightness levels according to the severity of the emergency event; If the sensor value detecting fire or smoke exceeds the predetermined threshold, automatically activate the emergency lighting of the corresponding intensity according to the preset emergency response plan and maintain this brightness until the emergency event is handled or returns to normal; The emergency lighting system can automatically adjust the brightness to ensure that users can obtain sufficient lighting support for evacuation in the shortest time.

8. The sensor-based intelligent home lighting intensity monitoring and automatic adjustment method according to claim 1, wherein The brightness adjustment of the emergency lighting includes using a complex dynamic calculation model to predict the duration and change trend of the emergency event, so as to determine the optimal light intensity. The specific steps are as follows: Based on the real-time data of the smoke sensor, data fusion is carried out by combining various environmental parameters such as temperature and humidity, and a multi-dimensional time series model is established. The formula is as follows: ， In the formula, D(t) is the dynamic change value of the emergency event, S(t) is the concentration value of the smoke sensor, T(t) is the temperature value, H(t) is the humidity value, α is the weighting coefficient of the smoke concentration, β is the weighting coefficient of the temperature, γ is the weighting coefficient of the humidity, t is the current time, and t0 is the start time of the event; The obtained dynamic change value D(t) of the emergency event is used to infer the duration of the emergency event, and the emergency lighting brightness is dynamically adjusted according to the prediction result. The calculation expression is as follows: ， Where L adjusted (t) is the emergency lighting brightness after dynamic adjustment, L max is the maximum brightness value of the emergency lighting system, D max is the maximum event intensity.

9. The sensor-based automatic adjustment method for monitoring the illumination intensity of a smart home according to claim 1, characterized in that, The process of restoring the light intensity adjustment function includes intelligently judging when to restore the automatic adjustment function according to the indoor environmental changes and the user's feedback. Specifically, by periodically detecting the light intensity and the user's activity, it is judged whether there is a phenomenon of too high or too low light intensity, and then automatic adjustment is carried out. If the ambient light is normal and no emergency event occurs, the automatic adjustment of the light intensity is restored to ensure comfort and energy-saving effects.

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