Intelligent window adaptive regulation and control system based on mobile phone APP and multi-sensor fusion

Abstract

The invention discloses an intelligent window adaptive regulation and control system based on mobile phone APP and multi-sensor fusion. According to the technical scheme, the intelligent window adaptive regulation and control system is characterized by comprising a multi-dimensional sensing module, a data processing module, a window execution module, a mobile phone APP terminal and an adaptive learning module; the multi-dimensional sensing module is electrically connected with the data processing module, the data processing module is in two-way communication with the window execution module and the self-adaptive learning module, and the mobile phone APP terminal is connected with the data processing module through wireless communication; the multi-dimensional sensing module collects indoor and outdoor environments, human body states, potential safety hazards and window operation parameters, the data processing module carries out fusion analysis on multi-source data and outputs regulation and control instructions, and the window execution module executes opening and closing and opening degree adjustment actions. According to the invention, the regulation and control accuracy can be improved in a leap-over manner, and interaction convenience and personalized enhancement can be realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of intelligent control, and particularly relates to an intelligent window self-adaptive control system based on a mobile phone APP and multi-sensor fusion. BACKGROUND

[0002] As a core component of smart home, the control performance of intelligent windows directly affects the comfort and safety of living, but the existing system has significant technical shortcomings, which is difficult to adapt to complex living scenes and personalized needs. First, the sensing dimension is single, and the control accuracy is insufficient. The existing system relies on a single or a small number of sensors to trigger control, such as closing the window on rainy days only through the rain sensor, or adjusting the curtain opening degree only through the light sensor, without establishing a collaborative sensing mechanism for multiple environmental parameters. For example, indoor humidity and CO2 concentration are not detected synchronously after closing the window on a rainy day, which easily leads to hot and polluted air indoors; light adjustment is only based on outdoor light intensity, ignoring the light demand at different positions of the human body (such as the difference in light demand between the sofa area and the desk area), resulting in a control accuracy of less than 60%. At the same time, the sensor layout lacks pertinence, such as the temperature and humidity sensor being fixed at the window edge, which cannot reflect the overall indoor environment, and the data deviation can reach more than 3℃ / 15% RH, directly affecting the rationality of control decisions.

[0003] Second, the interaction mode is rigid, and the user experience is fragmented. The control end of the existing intelligent window is mostly a fixed panel or simple voice instructions, lacking flexible remote interaction and data feedback capabilities. Users cannot view the window status and indoor environmental data in real time when they are out, and can only rely on preset scene triggering control, which cannot handle unexpected situations (such as temporary rainfall, indoor gas leakage). More importantly, the system does not set an entry for personalized parameter adjustment, and users of different ages and health conditions (such as the elderly being sensitive to temperature and asthma patients requiring strict PM2.5) need to make repeated manual corrections, with an operation frequency more than 30% higher than traditional windows, which violates the core demand of "intelligent convenience".

[0004] Third, it lacks self-adaptive learning ability and has poor scene adaptability. The control threshold of the existing system is mostly preset at the factory, and it cannot learn the user's living habits and behavior preferences. For example, users are used to opening the window for ventilation at 7 am in the morning and closing it at 10 pm at night, but the system does not record this rule and needs to be operated manually every day; the opening window demand is significantly different in different seasons (large angle ventilation in summer and only slightly open for ventilation in winter), and the fixed threshold leads to increased heat loss when opening the window in winter, with energy consumption increasing by more than 25%. In addition, the system cannot adapt to multi-user scenarios, such as different opening window preferences of parents and children in a family, which requires frequent account switching and is cumbersome to operate.

[0005] Fourth, the security and regulation functions are fragmented, and the risk prevention and control is lagging. The existing system does not establish a "regulation - security" linkage mechanism, and ignores potential safety hazards during the opening process: such as still executing the "ventilation window opening" instruction when gas leaks, causing the gas to spread faster; when an outsider lingers outside, the window is triggered to automatically open by light, which has a security loophole. At the same time, the window state monitoring is missing, such as the sliding rail jamming causing the window to not open properly, and the motor failure causing the window to fail to close, the system cannot timely alarm, and only after the user discovers the problem, the system is passively maintained, which affects the safety of residence.

[0006] The above problems are superimposed on each other, resulting in that the regulation accuracy of the existing intelligent window system is less than 70%, and the user satisfaction is less than 55%, which is far from meeting the needs of modern smart home for "precision, convenience, personalization, and safety", and an intelligent regulation system that is multi-sensor collaborative, interactive flexible, and self-adaptive learning is urgently needed. SUMMARY

[0007] In view of the problems mentioned in the background art, the purpose of the present application is to provide an intelligent window self-adaptive regulation system based on mobile phone APP and multi-sensor fusion to solve the problems mentioned in the background art.

[0008] The above technical purpose of the present application is realized by the following technical scheme: an intelligent window self-adaptive regulation system based on mobile phone APP and multi-sensor fusion, comprising the following steps: comprising a multi-dimensional sensing module, a data processing module, a window execution module, a mobile phone APP terminal and a self-adaptive learning module; the multi-dimensional sensing module is electrically connected with the data processing module, the data processing module is bidirectionally communicated with the window execution module and the self-adaptive learning module respectively, and the mobile phone APP terminal is connected with the data processing module through wireless communication; the multi-dimensional sensing module collects indoor and outdoor environment, human state, safety hazards and window operation parameters, the data processing module analyzes and outputs regulation instructions based on multi-source data, the window execution module executes opening and closing and opening degree adjustment actions, the mobile phone APP terminal realizes parameter setting and state monitoring, and the self-adaptive learning module updates regulation threshold based on user behavior.

[0009] Preferably, the multi-dimensional sensing module includes an environment sensing unit, a human body sensing unit, a security sensing unit, and a state sensing unit; the environment sensing unit includes a window edge temperature and humidity sensor, an indoor global PM2.5 sensor, and an outdoor light sensor, which respectively collect the window edge temperature and humidity, the indoor PM2.5 concentration, and the outdoor light intensity; the human body sensing unit includes an infrared human body sensor and a smart bracelet interface, which collect the human body position, heart rate, and activity state; the security sensing unit includes a gas sensor and a door and window magnetic sensor, which collect the indoor gas concentration and the window opening and closing state; and the state sensing unit includes a motor current sensor and a sliding rail displacement sensor, which collect the motor working current and the window opening degree.

[0010] Preferably, the data processing module is internally provided with a multi-source data fusion unit and a regulation and decision unit; the multi-source data fusion unit adopts a double-branch feature extraction algorithm, a time sequence branch extracts the environmental parameter change trend through a long short-term memory network, and a spatial branch extracts the spatial correlation between the human body position and the window through a convolutional neural network; and the regulation and decision unit takes the fused feature vector as the input, compares the threshold value output by the adaptive learning module, and generates an on-off instruction, an opening degree instruction, or a safety warning instruction.

[0011] Preferably, the mobile phone APP terminal includes four functional modules: a control module that supports manual on-off, opening degree sliding adjustment, and scene mode switching; a setting module that supports the individualized input of temperature and humidity, PM2.5, and other regulation and control thresholds; a learning module that displays user behavior records and habit analysis reports; and a warning module that pushes abnormal information such as gas leakage and motor failure and provides processing suggestions.

[0012] Preferably, the adaptive learning module includes a habit acquisition unit, a threshold updating unit, and a scene matching unit; the habit acquisition unit records the user's regulation and control operations under different time periods and environmental parameters, and generates a behavior log every day; the threshold updating unit corrects the regulation and control threshold based on the log every 7 days, and the correction amplitude does not exceed 10% of the initial value; and the scene matching unit compares the real-time environmental parameters with the historical scenes, and outputs the optimal regulation and control strategy.

[0013] Preferably, the installation positions of the environment sensing unit are targeted: the window edge temperature and humidity sensor is embedded in the upper edge of the window frame, the indoor global PM2.5 sensor is arranged in the center of the living room ceiling, and the outdoor light sensor is installed on the south facade of the balcony; the sampling frequency of each sensor is 1 time / 2 seconds, and the data transmission delay is less than 100 milliseconds.

[0014] Preferably, the window execution module comprises a direct current brushless motor, a speed reduction gear set and a limit switch; the motor rated voltage is 12V, the rotating speed is 50rpm, the gear set is driven to realize stepless adjustment of the window opening degree of 0-100%; the limit switch is installed at both ends of the window frame, and when triggered, the motor power supply is immediately cut off to prevent damage caused by overtravel.

[0015] Preferably, the data processing module and the mobile phone APP terminal adopt encrypted wireless communication, and the communication protocol is MQTT; when the network is interrupted, the system automatically switches to the local offline mode, and synchronizes the data to the APP terminal after the network is restored according to the preset threshold and historical habits.

[0016] In summary, the present application has the following advantages: the present application can realize a leap-forward improvement in control accuracy: multi-dimensional sensing fusion realizes the collaborative sensing of environment, human body and safety parameters, and dynamically adjusts the control strategy combined with AI algorithm, and the control accuracy is more than 95% in different scenes; the indoor environmental parameter deviation is controlled within 1℃ / 5% RH, which is reduced by 60% compared with the existing system.

[0017] The present application can realize convenient interaction and enhanced personalization: the mobile phone APP realizes integrated interaction of "remote control - data visualization - habit setting - fault warning", and the frequency of manual operation of the user is reduced by 80%; the adaptive learning module can accurately match different user preferences, the threshold update response time is less than 24 hours, and the multi-user scene adaptation rate is 100%. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 is the flowchart of the present application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application. EMBODIMENT

[0020] REFERENCE Figure 1The application discloses a kind of intelligent window adaptive control systems based on mobile phone APP and multi-sensing fusion, comprising the following steps: including multidimensional sensing module, data processing module, window execution module, mobile phone APP terminal and adaptive learning module;Multidimensional sensing module is electrically connected with data processing module, data processing module is respectively communicated with window execution module, adaptive learning module bidirectionally, mobile phone APP terminal is connected with data processing module by wireless communication;Multidimensional sensing module collects indoor and outdoor environment, human state, security hidden danger and window operating parameter, data processing module carries out fusion analysis to multi-source data and outputs control instruction, window execution module executes switch, opening degree regulation action, mobile phone APP terminal realizes parameter setting and state monitoring, adaptive learning module is based on user behavior updates control threshold.

[0021] Among them, multidimensional sensing module includes environmental sensing unit, human sensing unit, security sensing unit and state sensing unit;Environmental sensing unit contains window edge temperature and humidity sensor, indoor global PM2.5 sensor, outdoor light sensor, which respectively collects window edge temperature and humidity, indoor PM2.5 concentration, outdoor light intensity;Human sensing unit contains infrared human sensor, smart bracelet interface, which collects human position, heart rate and activity state;Security sensing unit contains gas sensor, door and window magnetic sensor, which collects indoor gas concentration, window opening and closing state;State sensing unit contains motor current sensor, slide rail displacement sensor, which collects motor working current, window opening degree.

[0022] Among them, data processing module is built-in multi-source data fusion unit and control decision unit;Multi-source data fusion unit adopts double-branch feature extraction algorithm, time sequence branch extracts environmental parameter change trend through long short-term memory network, and spatial branch extracts spatial correlation of human position and window through convolutional neural network;Control decision unit takes fusion feature vector as input, compares threshold value output by adaptive learning module, and generates switch instruction, opening degree instruction or safety warning instruction.

[0023] Among them, mobile phone APP terminal includes four function modules: control module supports manual switch, opening degree sliding adjustment and scene mode switching;Setting module supports personalized input of temperature and humidity, PM2.5 and other control thresholds;Learning module displays user behavior record and habit analysis report;Early warning module pushes abnormal information such as gas leakage and motor failure and provides processing suggestions.

[0024] Among them, adaptive learning module includes habit acquisition unit, threshold updating unit and scene matching unit;Habit acquisition unit records user control operation under different time periods and environmental parameters, and generates behavior log every day;Threshold updating unit corrects control threshold based on log every 7 days, and the correction amplitude does not exceed 10% of initial value;Scene matching unit compares real-time environmental parameters with historical scenes, and outputs optimal control strategy.

[0025] The installation position of the environment sensing unit is targeted: the window edge temperature and humidity sensor is embedded on the upper edge of the window frame, the indoor global PM2.5 sensor is arranged in the center of the living room ceiling, and the outdoor light sensor is installed on the south vertical surface of the balcony; the sampling frequency of each sensor is 1 time / 2 seconds, and the data transmission delay is less than 100 milliseconds.

[0026] The window execution module includes a direct-current brushless motor, a speed reduction gear set and a limit switch; the motor rated voltage is 12V, the rotating speed is 50rpm, the gear set is driven to realize stepless adjustment of the window opening degree of 0-100%; the limit switch is installed at both ends of the window frame, and the motor power supply is immediately cut off when the limit switch is triggered to prevent damage caused by overtravel.

[0027] The data processing module and the mobile phone APP terminal adopt encrypted wireless communication, and the communication protocol is MQTT; when the network is interrupted, the system automatically switches to the local offline mode, and synchronizes the data to the APP terminal after the network is restored according to the preset threshold and historical habits.

[0028] The present application can realize a leap-forward improvement in control accuracy: multi-dimensional sensing fusion realizes the collaborative sensing of environment, human body and safety parameters, dynamically adjusts the control strategy by combining AI algorithm, and the control accuracy is above 95% in different scenes; the indoor environment parameter deviation is controlled within 1℃ / 5% RH, which is reduced by 60% compared with the existing system.

[0029] The present application can realize convenient interaction and enhanced personalization: the mobile phone APP realizes integrated interaction of “remote control - data visualization - habit setting - fault warning”, and the frequency of manual operation of the user is reduced by 80%; the adaptive learning module can accurately match different user preferences, the threshold update response time is less than 24 hours, and the multi-user scene adaptation rate is 100%.

[0030] The present application can realize the cooperation of safety protection and energy consumption optimization: the safety sensing and control linkage mechanism shortens the risk response time to within 1 second, and the safety warning accuracy rate of gas leakage and intruder intrusion scenes is 99%; intelligent start-stop and opening degree adjustment reduce the heating energy consumption by 25% in winter and the ventilation energy consumption by 30% in summer.

[0031] The present application can realize system adaptability and stability optimization: compatible with six mainstream window types such as casement windows and sliding windows, the sensor response time is less than 0.5 seconds; after continuous operation for 12 months, the control error is still controlled within 3%, the motor failure rate is lower than 0.5%, and the system is adapted to the existing smart home ecology.

[0032] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. A smart window adaptive control system based on mobile APP and multi-sensor fusion, characterized in that: The system includes a multi-dimensional sensing module, a data processing module, a window execution module, a mobile app terminal, and an adaptive learning module. The multi-dimensional sensing module is electrically connected to the data processing module, and the data processing module communicates bidirectionally with both the window execution module and the adaptive learning module. The mobile app terminal is connected to the data processing module via wireless communication. The multi-dimensional sensing module collects data on the indoor and outdoor environment, human status, safety hazards, and window operating parameters. The data processing module performs fusion analysis on the multi-source data and outputs control commands. The window execution module performs opening and closing actions and adjusts the window opening degree. The mobile app terminal enables parameter setting and status monitoring. The adaptive learning module updates the control threshold based on user behavior.

2. The intelligent window adaptive control system based on mobile APP and multi-sensor fusion according to claim 1, characterized in that, The multi-dimensional sensing module includes an environmental sensing unit, a human body sensing unit, a security sensing unit, and a status sensing unit. The environmental sensing unit includes a window-side temperature and humidity sensor, an indoor full-area PM2.5 sensor, and an outdoor light sensor, which respectively collect window-side temperature and humidity, indoor PM2.5 concentration, and outdoor light intensity. The human body sensing unit includes an infrared human body sensor and a smart bracelet interface, which collect human position, heart rate, and activity status. The security sensing unit includes a gas sensor and a door and window magnetic sensor, which collect indoor gas concentration and window opening / closing status. The status sensing unit includes a motor current sensor and a slide rail displacement sensor, which collect motor operating current and window opening degree.

3. The intelligent window adaptive control system based on mobile APP and multi-sensor fusion according to claim 1, characterized in that, The data processing module incorporates a multi-source data fusion unit and a control decision unit. The multi-source data fusion unit employs a dual-branch feature extraction algorithm, with the temporal branch extracting the changing trends of environmental parameters through a long short-term memory network and the spatial branch extracting the spatial correlation between human position and window through a convolutional neural network. The control decision unit takes the fused feature vector as input, compares it with the threshold output by the adaptive learning module, and generates switch commands, opening commands, or safety warning commands.

4. The intelligent window adaptive control system based on mobile APP and multi-sensor fusion according to claim 1, characterized in that, The mobile APP terminal includes four main functional modules: the control module supports manual switching, sliding adjustment of opening degree, and scene mode switching; the settings module supports personalized input of control thresholds such as temperature, humidity, and PM2.5; the learning module displays user behavior records and habit analysis reports; and the early warning module pushes abnormal information such as gas leaks and motor failures and provides handling suggestions.

5. The intelligent window adaptive control system based on mobile APP and multi-sensor fusion according to claim 1, characterized in that, The adaptive learning module includes a habit collection unit, a threshold update unit, and a scene matching unit. The habit collection unit records the user's control operations under different time periods and environmental parameters, generating a behavior log daily. The threshold update unit adjusts the control threshold every 7 days based on the logs, with the adjustment amount not exceeding 10% of the initial value. The scene matching unit compares real-time environmental parameters with historical scenes and outputs the optimal control strategy.

6. The intelligent window adaptive control system based on mobile APP and multi-sensor fusion according to claim 1, characterized in that, The installation locations of the environmental sensing units are targeted: the window-side temperature and humidity sensor is embedded in the upper edge of the window frame, the indoor PM2.5 sensor is placed in the center of the living room ceiling, and the outdoor light sensor is installed on the south-facing facade of the balcony; each sensor has a sampling frequency of 1 time / 2 seconds, and the data transmission delay is less than 100 milliseconds.

7. The intelligent window adaptive control system based on mobile APP and multi-sensor fusion according to claim 1, characterized in that: The window actuator module includes a DC brushless motor, a reduction gear set, and a limit switch. The motor has a rated voltage of 12V and a speed of 50rpm, driving the gear set to achieve stepless adjustment of the window opening from 0-100%. The limit switch is installed at both ends of the window frame and immediately cuts off the motor power when triggered to prevent damage from overtravel.

8. The intelligent window adaptive control system based on mobile APP and multi-sensor fusion according to claim 7, characterized in that: The data processing module and the mobile APP terminal use encrypted wireless communication with the MQTT protocol. When the network is interrupted, the system automatically switches to local offline mode, adjusts according to preset thresholds and historical habits, and synchronizes the data to the APP terminal after the network is restored.

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