Intelligent door and window rainproof adaptive control system and method

Abstract

The application discloses a kind of intelligent door and window rainproof self-adapting control system and method, including multi-dimensional environment perception module, data preprocessing module, data fusion analysis module, dynamic threshold adjustment module, hierarchical control decision module, execution drive module, control effect feedback module, communication module and power management module.Multi-dimensional environment perception module collects rainfall, wind speed, wind direction, temperature and humidity and illumination intensity and other multi-dimensional environmental parameters, data fusion analysis module generates comprehensive environmental risk index by weighted fusion algorithm, dynamic threshold adjustment module dynamically adjusts control threshold according to season, geographical location and user habit, hierarchical control decision module generates hierarchical control instruction according to the comparison result of risk index and threshold value in combination with wind direction correlation analysis, and control effect feedback module realizes the continuous optimization of control strategy.The application realizes multi-dimensional comprehensive judgment, dynamic self-adapting threshold adjustment, hierarchical fine control and continuous learning optimization.

Description

Technical Field

[0001] This invention relates to the field of smart home technology, and in particular to a smart door and window rainproof adaptive control system and method. Background Technology

[0002] With the rapid development of IoT, sensor, and artificial intelligence technologies, smart home systems have gradually entered people's daily lives. As an important component of smart home systems, smart doors and windows directly affect users' quality of life and residential security through their level of automation and intelligence.

[0003] Among the many functions of smart doors and windows, rain protection control is one of the most basic and crucial. When users are out or resting at night, if it rains suddenly and the doors and windows are open, rainwater may enter the room, causing problems such as soaked floors, damaged furniture, and damp walls, resulting in economic losses and inconvenience. Therefore, developing a smart door and window control system that can automatically sense rainfall and take corresponding rain protection measures is of significant practical importance.

[0004] Currently, several intelligent window and door rainproof control technologies have been proposed. For example, Chinese utility model patent CN202417240U discloses an intelligent rainproof window with a louvered structure that adjusts the glass tilt angle by sensing wind force through a wind vane. Chinese utility model patent CN206769661U discloses a rain-sensing intelligent window and door system that detects rainwater through an infrared transmitter and receiver in a water tank, and a control module controls the drive module to open and close the glass panel. Chinese invention patent application CN112878852A discloses an artificial intelligence window that detects rainfall through a rain sensor and controls a geared motor to drive the movable window frame to automatically close the window. Chinese invention patent application CN120251034A discloses an intelligent window and door control system that includes a data collection module to acquire rainfall and wind force data, and then uses analysis to obtain rainfall and wind force coefficients for control.

[0005] However, the above-mentioned existing technical solutions still have the following shortcomings: First, the control strategy is simplistic and lacks multi-dimensional comprehensive judgment. Existing technical solutions typically rely on only a single type of sensor (such as only a rain sensor or only a wind sensor) for control judgment, failing to comprehensively consider the synergistic effects of multiple environmental factors such as rainfall, wind speed, wind direction, and ambient humidity. This results in insufficient accuracy and reliability of control decisions, and is prone to misjudgment or omission.

[0006] Second, the control threshold is fixed and lacks adaptive adjustment capability. Existing technical solutions use pre-set fixed judgment thresholds, which cannot adaptively adjust according to dynamic factors such as seasonal changes, geographical differences, and user habits. This results in significant differences in control performance under different environmental conditions, making it difficult to meet the needs of diverse scenarios.

[0007] Third, there is a lack of learning and optimization mechanisms. Most existing technical solutions involve setting the control logic only once, and lack the ability to learn from and optimize based on historical control effects, thus failing to continuously improve the accuracy and adaptability of the control strategy.

[0008] Fourth, the impact of wind direction on rainwater intrusion is not fully considered. Although existing technical solutions collect wind direction data in some aspects, they do not correlate wind direction with window orientation and opening angle, thus failing to meet the need for natural ventilation while ensuring basic rain protection.

[0009] Fifth, the control mode is singular and lacks a hierarchical control strategy. Existing technical solutions typically only provide two states, "on" and "off," failing to offer differentiated hierarchical control strategies based on different rainfall scenarios (such as light rain, moderate rain, heavy rain, torrential rain, and accompanying wind directions).

[0010] To address the shortcomings of the existing technologies, this invention provides an intelligent door and window rainproof adaptive control system and method, aiming to solve technical problems such as single control strategy, fixed threshold, lack of learning optimization, and failure to consider the influence of wind direction in the existing technologies. Summary of the Invention

[0011] To address the shortcomings of existing technologies, the purpose of this invention is to provide an intelligent door and window rainproof adaptive control system and method, so as to achieve multi-dimensional environmental perception, dynamic adaptive decision-making, continuous learning optimization and hierarchical intelligent control of door and window rainproof control.

[0012] The above-mentioned objective of this invention is achieved through the following technical solutions: In a first aspect, the present invention provides an intelligent door and window rainproof adaptive control system, comprising: A multi-dimensional environmental sensing module, installed on the exterior of doors and windows, is used to collect environmental parameters of the location of the doors and windows in real time. The multi-dimensional environmental sensing module includes a rainfall sensor, a wind speed sensor, a wind direction sensor, a temperature and humidity sensor, and a light sensor. The rainfall sensor is used to detect real-time rainfall data; the wind speed sensor is used to detect real-time wind speed data; the wind direction sensor is used to detect real-time wind direction data; the temperature and humidity sensor is used to detect real-time ambient temperature and humidity data; and the light sensor is used to detect real-time light intensity data.

[0013] The data preprocessing module is communicatively connected to the multidimensional environment sensing module. It is used to receive various environmental parameters collected by the multidimensional environment sensing module, and to perform filtering and noise reduction processing and outlier removal processing on the various environmental parameters to generate a preprocessed environmental parameter dataset.

[0014] The data fusion analysis module is communicatively connected to the data preprocessing module. It is used to receive the preprocessed environmental parameter dataset and perform weighted fusion analysis on rainfall data, wind speed data, wind direction data, temperature and humidity data, and light intensity data based on a preset multi-source data fusion algorithm to generate a comprehensive environmental risk index. The comprehensive environmental risk index is used to characterize the overall impact of the current environment on the rainproof control of doors and windows.

[0015] The dynamic threshold adjustment module, communicatively connected to the data fusion and analysis module, is used to acquire current time and geographic location information, and dynamically adjust the rain protection control decision threshold based on user historical behavior data. The dynamic threshold adjustment module includes a seasonal factor calculation unit, a geographic location factor calculation unit, and a user habit factor calculation unit. The seasonal factor calculation unit calculates the seasonal influence factor based on the current date; the geographic location factor calculation unit calculates the geographic location influence factor based on the geographic location of doors and windows; and the user habit factor calculation unit calculates the user habit influence factor based on the user's historical manual control records. The dynamic threshold adjustment module performs a weighted correction on the basic rain protection control decision threshold based on the seasonal influence factor, geographic location influence factor, and user habit influence factor to generate a dynamic adaptive rain protection control decision threshold.

[0016] The hierarchical control decision module, communicatively connected to the data fusion analysis module and the dynamic threshold adjustment module, is used to generate hierarchical control commands based on the comparison result between the comprehensive environmental risk index and the dynamic adaptive rainproof control decision threshold, combined with the angle relationship between the current wind direction and the orientation of doors and windows. The hierarchical control commands include a complete closure command, a partial closure command, an adjustment opening angle command, and a maintain status command. The hierarchical control decision module includes a wind direction correlation analysis unit, which is used to calculate the angle between the current wind direction and the orientation of doors and windows, and determine the recommended opening angle of doors and windows based on the angle and the comprehensive environmental risk index.

[0017] The execution drive module is communicatively connected to the hierarchical control decision module and is used to receive the hierarchical control commands and drive the door and window actuators to perform corresponding door and window actions. The execution drive module includes a motor drive unit and a position feedback unit. The motor drive unit is used to drive the opening, closing, or angle adjustment of the doors and windows. The position feedback unit is used to detect the current position status of the doors and windows in real time and feed it back to the hierarchical control decision module.

[0018] The control effect feedback module is communicatively connected to the execution drive module and the dynamic threshold adjustment module. It is used to record the execution results of each control decision, including the indoor environmental state after the door and window actions are executed, user intervention records, and indoor conditions after the rainfall stops. The control effect feedback module generates feedback evaluation indicators based on the recorded control effect data and transmits the feedback evaluation indicators to the dynamic threshold adjustment module to update the user historical behavior data and optimize the dynamic adaptive rainproof control decision threshold.

[0019] The communication module is connected to the user terminal equipment to send door and window status information and environmental warning information to the user terminal equipment, and to receive remote control commands from the user terminal equipment.

[0020] The power management module provides a stable power supply to all modules of the intelligent door and window rainproof adaptive control system, and has battery power monitoring and low battery alarm functions.

[0021] Preferably, the rainfall sensor in the multi-dimensional environmental sensing module is a piezoelectric rainfall sensor or an optical rainfall sensor; the wind speed sensor is a three-cup wind speed sensor or an ultrasonic wind speed sensor; the wind direction sensor is a wind vane type wind direction sensor or an ultrasonic wind direction sensor; the temperature and humidity sensor is an integrated digital temperature and humidity sensor; and the light sensor is a photodiode or a phototransistor type light sensor.

[0022] Preferably, the data fusion analysis module uses the following weighted fusion algorithm to calculate the comprehensive environmental risk index R: R = α·P + β·W + γ·D + δ·H + ε·L; Wherein, P is the normalized rainfall data, W is the normalized wind speed data, D is the normalized wind direction risk coefficient, H is the normalized ambient humidity data, and L is the normalized light intensity data; α, β, γ, δ, and ε are the rainfall weight coefficient, wind speed weight coefficient, wind direction weight coefficient, humidity weight coefficient, and light intensity weight coefficient, respectively, and α+β+γ+δ+ε=1; the wind direction risk coefficient D is calculated based on the angle θ between the wind direction and the orientation of the doors and windows. When θ=0°, the wind direction is directly facing the doors and windows, and D takes the maximum value of 1; when θ=180°, the wind direction is away from the doors and windows, and D takes the minimum value of 0. The intermediate angle is calculated by linear interpolation or cosine function.

[0023] Preferably, the dynamic threshold adjustment module calculates the dynamic adaptive rain protection control decision threshold according to the following formula. : ; in, Based on the basic rain protection control decision threshold, Seasonal influencing factors Geographical location is an influencing factor. Factors influencing user habits These are the weighting coefficients for seasonal influence, geographical location influence, and user habit influence, respectively. .

[0024] Preferably, the control logic of the hierarchical control decision module is as follows: When the comprehensive environmental risk index R is less than the first dynamic threshold At that time, a command to maintain the status quo is generated; When the comprehensive environmental risk index R is greater than or equal to the first dynamic threshold And less than the second dynamic threshold At that time, an instruction to adjust the opening angle is generated, and a recommended opening angle is calculated based on the angle between the wind direction and the orientation of the doors and windows; When the comprehensive environmental risk index R is greater than or equal to the second dynamic threshold And less than the third dynamic threshold At that time, an incomplete shutdown instruction is generated; When the comprehensive environmental risk index R is greater than or equal to the third dynamic threshold At that time, a complete shutdown command is generated; in, ,and All are dynamic adaptive rain protection control decision thresholds.

[0025] Secondly, the present invention provides an intelligent door and window rainproof adaptive control method, comprising the following steps: Step S1: Collect environmental parameters of the location of doors and windows in real time through the multi-dimensional environmental perception module. The environmental parameters include rainfall data, wind speed data, wind direction data, temperature and humidity data, and light intensity data. Step S2: Perform filtering and noise reduction processing and outlier removal processing on the collected environmental parameters to generate a preprocessed environmental parameter dataset; Step S3: Perform weighted fusion analysis on the preprocessed environmental parameter dataset based on a preset multi-source data fusion algorithm to generate a comprehensive environmental risk index; Step S4: Obtain current time and location information, and combine them with user historical behavior data to dynamically adjust the rain protection control decision threshold and generate a dynamic adaptive rain protection control decision threshold; Step S5: Based on the comparison result between the comprehensive environmental risk index and the dynamic adaptive rainproof control decision threshold, and combined with the angle relationship between the current wind direction and the orientation of doors and windows, generate graded control instructions; Step S6: Drive the door and window actuator to perform the corresponding door and window actions according to the hierarchical control command; Step S7: Record the execution results of each control decision, generate feedback evaluation indicators, and use them to update user historical behavior data and optimize the dynamic adaptive rainproof control decision threshold.

[0026] Preferably, in step S3, the multi-source data fusion algorithm is a weighted fusion algorithm, and the comprehensive environmental risk index R is calculated according to the following formula: R = α·P + β·W + γ·D + δ·H + ε·L; Where P is the normalized rainfall data, W is the normalized wind speed data, D is the normalized wind direction risk coefficient, H is the normalized ambient humidity data, and L is the normalized light intensity data; α, β, γ, δ, and ε are the rainfall weight coefficient, wind speed weight coefficient, wind direction weight coefficient, humidity weight coefficient, and light intensity weight coefficient, respectively, and α+β+γ+δ+ε=1.

[0027] Preferably, in step S4, the dynamic adaptive rain protection control decision threshold T is calculated according to the following formula: ; in, Based on the basic rain protection control decision threshold, Seasonal influencing factors Geographical location is an influencing factor. Factors influencing user habits These are the weighting coefficients for seasonal influence, geographical location influence, and user habit influence, respectively. .

[0028] Preferably, in step S5, the generation logic of the hierarchical control instruction is as follows: When the comprehensive environmental risk index R is less than the first dynamic threshold At that time, a command to maintain the status quo is generated; When the comprehensive environmental risk index R is greater than or equal to the first dynamic threshold And less than the second dynamic threshold At that time, an instruction to adjust the opening angle is generated, and a recommended opening angle is calculated based on the angle between the wind direction and the orientation of the doors and windows; When the comprehensive environmental risk index R is greater than or equal to the second dynamic threshold And less than the third dynamic threshold At that time, an incomplete shutdown instruction is generated; When the comprehensive environmental risk index R is greater than or equal to the third dynamic threshold At that time, a complete shutdown command is generated; in, ,and All are dynamic adaptive rain protection control decision thresholds.

[0029] Preferably, in step S5, the method for calculating the recommended opening angle is as follows: The wind direction risk coefficient D is calculated based on the angle θ between the wind direction and the orientation of the doors and windows. When θ = 0°, the wind direction is directly opposite the doors and windows, and D takes the maximum value of 1. When θ = 180°, the wind direction is away from the doors and windows, and D takes the minimum value of 0. Based on the ratio of the comprehensive environmental risk index R to the dynamic adaptive rain protection control decision threshold, and combined with the wind direction risk coefficient D, the recommended opening angle A is calculated according to the following formula: ; in, This refers to the current opening angle of the doors and windows. This is the second dynamic threshold.

[0030] Preferably, in step S7, the feedback evaluation indicators include control accuracy, user intervention rate, and rainproof effectiveness; the control accuracy rate is the proportion of the number of times the control decision is correctly executed to the total number of control decisions; the user intervention rate is the proportion of the number of times the user manually intervenes in the control decision to the total number of control decisions; and the rainproof effectiveness rate is the proportion of the number of times the indoor space does not get wet after the rainproof control is executed to the total number of rainproof control operations.

[0031] In summary, compared with the prior art, the present invention has at least one of the following beneficial technical effects: Multi-dimensional comprehensive judgment improves control accuracy: This invention sets up a multi-dimensional environmental perception module including rainfall sensors, wind speed sensors, wind direction sensors, temperature and humidity sensors, and light sensors, and uses a weighted fusion algorithm to comprehensively process multi-source sensor data to generate a comprehensive environmental risk index. This overcomes the limitations of existing technologies that rely on a single sensor for judgment, significantly improves the accuracy and reliability of rainfall judgment, and effectively avoids misjudgment and omission.

[0032] Dynamic adaptive threshold enhances environmental adaptability: This invention introduces a dynamic threshold adjustment module to dynamically correct the control threshold by incorporating seasonal factors, geographical location factors, and user habit factors. This enables the control system to adaptively adjust the control strategy according to different seasonal characteristics, geographical location conditions, and user habits, maintaining excellent control performance under various environmental conditions.

[0033] Learning optimization mechanism to continuously improve control effect: This invention sets up a control effect feedback module to record the actual execution effect of each control decision, generate feedback evaluation indicators and use them to update user historical behavior data and optimize control thresholds, forming a closed-loop control link of "perception-decision-execution-feedback-optimization", so that the performance of the control system can be continuously improved with the increase of usage time.

[0034] Wind direction correlation analysis, balancing rain protection and ventilation: This invention sets up a wind direction correlation analysis unit to calculate the angle between the wind direction and the orientation of doors and windows and determine the wind direction risk coefficient accordingly. When generating graded control commands, it fully considers the impact of wind direction on rainwater intrusion and maximizes the preservation of natural ventilation function while ensuring rain protection effect, thus achieving an optimized balance between rain protection and ventilation.

[0035] Hierarchical control strategy for refined rain protection management: This invention adopts a multi-level threshold judgment and hierarchical control strategy, which generates different levels of control commands such as completely closed, partially closed, adjusted opening angle, and maintained status according to different environmental risk levels. This avoids the crude "open or close" control mode in the prior art and realizes refined management and intelligent control of rain protection for doors and windows. Attached Figure Description

[0036] Figure 1 This is a block diagram of the overall structure of an intelligent door and window rainproof adaptive control system according to the present invention.

[0037] Figure 2 This is a schematic diagram of the structure of the multi-dimensional environment perception module of the present invention.

[0038] Figure 3 This is a structural block diagram of the dynamic threshold adjustment module of the present invention.

[0039] Figure 4 This is a structural block diagram of the hierarchical control decision module of the present invention.

[0040] Figure 5 This is a flowchart illustrating an intelligent door and window rainproof adaptive control method according to the present invention.

[0041] Figure 6 This is a schematic diagram illustrating the angle between the wind direction and the orientation of doors and windows in this invention.

[0042] Figure 7 This is a schematic diagram of the threshold judgment logic for hierarchical control decision-making in this invention.

[0043] Figure 8 This is a schematic diagram of the workflow of the control effect feedback module of the present invention.

[0044] Figure 9 This is a schematic diagram of the structure of the driver module of the present invention.

[0045] Figure 10 This is a schematic diagram showing the connection between the communication module of the present invention and the user terminal device.

[0046] Figure reference numerals: 100, Multi-dimensional environmental perception module; 110, Rainfall sensor; 120, Wind speed sensor; 130, Wind direction sensor; 140, Temperature and humidity sensor; 150, Light sensor; 200, Data preprocessing module; 300, Data fusion and analysis module; 400, Dynamic threshold adjustment module; 410, Seasonal factor calculation unit; 420, Geographical location factor calculation unit; 430, User habit factor calculation unit; 440, Threshold synthesis unit; 500, Hierarchical control decision-making module; 510, Threshold. Comparison Unit; 520, Wind Direction Correlation Analysis Unit; 530, Command Generation Unit; 540, Status Monitoring Unit; 600, Execution Drive Module; 610, Motor Drive Unit; 620, Position Feedback Unit; 630, Transmission Mechanism; 700, Control Effect Feedback Module; 710, Data Recording Unit; 720, Effect Evaluation Unit; 730, Feedback Transmission Unit; 800, Communication Module; 810, Wireless Communication Unit; 820, Local Communication Unit; 900, Power Management Module; 1000, User Terminal Equipment. Detailed Implementation

[0047] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0048] Example 1: Overall System Structure like Figure 1 As shown, the present invention provides an intelligent door and window rainproof adaptive control system, including a multi-dimensional environmental perception module 100, a data preprocessing module 200, a data fusion analysis module 300, a dynamic threshold adjustment module 400, a hierarchical control decision module 500, an execution drive module 600, a control effect feedback module 700, a communication module 800, and a power management module 900.

[0049] The multi-dimensional environmental sensing module 100 is installed on the outside of the doors and windows to collect environmental parameters at the location of the doors and windows in real time. The multi-dimensional environmental sensing module 100 is connected to the data preprocessing module 200 via wired or wireless communication to transmit the collected raw environmental parameter data to the data preprocessing module 200.

[0050] The data preprocessing module 200 receives various environmental parameters collected by the multi-dimensional environmental perception module 100, performs filtering and noise reduction processing and outlier removal on these parameters, and generates a preprocessed environmental parameter dataset. The data preprocessing module 200 communicates with the data fusion and analysis module 300 via a communication interface, transmitting the preprocessed environmental parameter dataset to the data fusion and analysis module 300.

[0051] The data fusion analysis module 300 receives the preprocessed environmental parameter dataset and performs weighted fusion analysis on rainfall, wind speed, wind direction, temperature and humidity, and light intensity data based on a preset multi-source data fusion algorithm to generate a comprehensive environmental risk index. The data fusion analysis module 300 is communicatively connected to the dynamic threshold adjustment module 400 and the hierarchical control decision module 500, respectively, and transmits the comprehensive environmental risk index to the hierarchical control decision module 500.

[0052] The dynamic threshold adjustment module 400 acquires current time and location information, and combines this with historical user behavior data to dynamically adjust the rain protection control decision threshold, generating a dynamic adaptive rain protection control decision threshold. The dynamic threshold adjustment module 400 is communicatively connected to the hierarchical control decision module 500, transmitting the dynamic adaptive rain protection control decision threshold to the hierarchical control decision module 500.

[0053] The hierarchical control decision module 500 generates hierarchical control commands based on the comparison results of the comprehensive environmental risk index and the dynamic adaptive rainproof control decision threshold, combined with the angle relationship between the current wind direction and the orientation of doors and windows. The hierarchical control decision module 500 is communicatively connected to the execution drive module 600, transmitting the hierarchical control commands to the execution drive module 600.

[0054] The execution drive module 600 receives hierarchical control commands and drives the door and window actuators to perform corresponding door and window actions, including opening, closing, or angle adjustment. The execution drive module 600 detects the current position status of the doors and windows in real time through the position feedback unit and feeds it back to the hierarchical control decision module 500.

[0055] The control effect feedback module 700 is communicatively connected to the execution drive module 600 and the dynamic threshold adjustment module 400. It records the execution results of each control decision, generates feedback evaluation indicators, and transmits the feedback evaluation indicators to the dynamic threshold adjustment module 400 for updating user historical behavior data and optimizing the dynamic adaptive rainproof control decision threshold.

[0056] The communication module 800 is connected to the user terminal equipment to send door and window status information and environmental warning information to the user terminal equipment, and to receive remote control commands from the user terminal equipment.

[0057] The power management module 900 provides a stable power supply for all the above modules and has battery power monitoring and low battery alarm functions.

[0058] like Figure 2 As shown, the multi-dimensional environmental perception module 100 includes a rainfall sensor 110, a wind speed sensor 120, a wind direction sensor 130, a temperature and humidity sensor 140, and a light sensor 150.

[0059] A rainfall sensor 110 is positioned above the exterior of doors and windows to detect real-time rainfall data. In this embodiment, the rainfall sensor 110 is a piezoelectric rain sensor. Its working principle is as follows: when a raindrop impacts the sensor's sensitive surface, a voltage signal proportional to the raindrop's momentum is generated. By counting and analyzing the amplitude of the voltage signal, the real-time rainfall can be calculated. Piezoelectric rain sensors have advantages such as fast response speed, high measurement accuracy, and small size. In other embodiments, the rainfall sensor 110 can also be an optical rain sensor, which measures rainfall by detecting the change in light intensity caused by raindrops passing through a light beam.

[0060] The wind speed sensor 120 is positioned on the side of the exterior of doors and windows to detect real-time wind speed data. In this embodiment, the wind speed sensor 120 is a three-cup anemometer, which works by rotating three cups under wind force. The rotation speed is detected by a photoelectric encoder or Hall effect sensor, and the real-time wind speed is calculated. The three-cup anemometer has advantages such as simple structure, reliable measurement, and low cost. In other embodiments, the wind speed sensor 120 can also be an ultrasonic anemometer, which calculates wind speed by measuring the time difference of ultrasonic waves propagating in the air.

[0061] A wind direction sensor 130 is positioned at the top of the exterior of doors and windows to detect real-time wind direction data. In this embodiment, the wind direction sensor 130 is a vane-type wind direction sensor. Its working principle is as follows: the vane points in the direction of the wind under the influence of wind force, and the rotation angle of the vane is detected by a potentiometer or magnetic encoder to determine the real-time wind direction. Vane-type wind direction sensors have advantages such as intuitive structure and accurate measurement. In other embodiments, the wind direction sensor 130 can also be an ultrasonic wind direction sensor.

[0062] The temperature and humidity sensor 140 is installed in a concealed location on the exterior of doors and windows to detect real-time ambient temperature and humidity data. In this embodiment, the temperature and humidity sensor 140 is an integrated digital temperature and humidity sensor (such as the SHT30 or DHT22 type sensor). This type of sensor integrates the temperature-sensitive element and the humidity-sensitive element into the same chip, and outputs digital temperature and humidity data through I²C or a single bus interface. It has the advantages of high accuracy, good stability, and simple interface.

[0063] A light sensor 150 is positioned above the exterior of a door or window to detect real-time light intensity data. In this embodiment, the light sensor 150 is a photodiode-type light sensor. Its working principle is as follows: the photodiode generates a photocurrent under illumination, and the magnitude of the photocurrent is proportional to the light intensity. The light intensity can be calculated by detecting the magnitude of the photocurrent. Photodiode-type light sensors have advantages such as fast response speed and good linearity. In other embodiments, the light sensor 150 can also be a phototransistor-type light sensor.

[0064] All sensors in the multi-dimensional environmental sensing module 100 are housed in a waterproof and dustproof enclosure to ensure long-term stable operation under harsh outdoor conditions. Each sensor has a sampling frequency of 1Hz (i.e., data is collected once per second) to meet the requirements of real-time control.

[0065] like Figure 3 As shown, the dynamic threshold adjustment module 400 includes a seasonal factor calculation unit 410, a geographic location factor calculation unit 420, a user habit factor calculation unit 430, and a threshold synthesis unit 440.

[0066] The seasonal factor calculation unit 410 is used to calculate the seasonal influence factor S based on the current date. In this embodiment, the seasonal factor calculation unit 410 first obtains the current date, and then determines the current season based on the date. Specifically, March, April, and May are classified as spring; June, July, and August as summer; September, October, and November as autumn; and December, January, and February as winter. The seasonal influence factor S values ​​differ for different seasons: spring is rainy, so S is 0.2; summer is rainy and often accompanied by strong winds, so S is 0.3; autumn is relatively dry, so S is 0.1; and winter has less rainfall, so S is 0.05. The larger the value of the seasonal influence factor S, the higher the demand for rain control in the current season, and the control threshold is correspondingly lowered to make the control system more sensitive to rainfall. It should be noted that the specific values ​​of the seasonal influence factor mentioned above are only examples, and in actual applications, they can be adaptively adjusted according to the climate characteristics of different regions.

[0067] The geographic location factor calculation unit 420 is used to calculate the geographic location influence factor G based on the geographic location of the doors and windows. In this embodiment, the geographic location factor calculation unit 420 obtains the latitude and longitude coordinates of the location of the doors and windows through a GPS module or network IP positioning, and then determines the climate type of the region based on the latitude and longitude coordinates. Specifically, the region is divided into rainy areas (such as the southern coastal areas), dry areas (such as the northwestern inland areas), and general areas. The geographic location influence factor G is 0.3 for rainy areas, 0.15 for general areas, and 0.05 for dry areas. The larger the value of the geographic location influence factor G, the higher the rainfall frequency in the region, and the control threshold is lowered accordingly.

[0068] The user habit factor calculation unit 430 calculates the user habit influence factor U based on the user's historical manual control records. In this embodiment, the user habit factor calculation unit 430 obtains the user's historical manual control records from the control effect feedback module 700, including environmental parameters when the user manually closes the window, environmental parameters when the user manually opens the window, and the frequency of user intervention in automatic control decisions. Through statistical analysis of this historical data, the user habit influence factor U is calculated. For example, if the user tends to close doors and windows at the beginning of rainfall, the value of U is positive (e.g., 0.2), lowering the control threshold and causing the control system to take rain protection measures earlier; if the user tends to close doors and windows only when the rainfall is heavy, the value of U is negative (e.g., -0.1), raising the control threshold and causing the control system to take rain protection measures later. The initial value of the user habit influence factor U is 0, and the U value is continuously updated and optimized based on the user's actual behavior as usage time increases.

[0069] The threshold synthesis unit 440 is communicatively connected to the seasonal factor calculation unit 410, the geographical location factor calculation unit 420, and the user habit factor calculation unit 430. It receives the seasonal influence factor S, the geographical location influence factor G, and the user habit influence factor U, and calculates the dynamic adaptive rain protection control decision threshold T according to the following formula: ; in, Based on the basic rain protection control decision threshold, Seasonal influencing factors Geographical location is an influencing factor. Factors influencing user habits These are the weighting coefficients for seasonal influence, geographical location influence, and user habit influence, respectively. .

[0070] In this embodiment, =0.4, =0.35, =0.25. Basic rain protection control decision threshold. The value is set to 0.5 (the comprehensive environmental risk index ranges from 0 to 1). It should be noted that the specific values ​​of the above weighting coefficients and basic thresholds are merely examples and can be adjusted adaptively according to specific needs in actual applications.

[0071] The data fusion analysis module 300 receives the preprocessed environmental parameter dataset transmitted by the data preprocessing module 200, and performs weighted fusion analysis on rainfall data, wind speed data, wind direction data, temperature and humidity data, and light intensity data based on a preset multi-source data fusion algorithm to generate a comprehensive environmental risk index R.

[0072] Specifically, the data fusion analysis module 300 first normalizes various environmental parameters, mapping the values ​​of all parameters to a range of 0 to 1. The specific normalization method is as follows: for rainfall data P_raw, it is divided by the maximum value of the sensor's range, P_max, to obtain normalized rainfall data P = P_raw / P_max; for wind speed data W_raw, it is divided by the maximum value of the sensor's range, W_max, to obtain normalized wind speed data W = W_raw / W_max; for ambient humidity data H_raw, it is divided by 100% (the maximum range of the humidity sensor), to obtain normalized ambient humidity data H = H_raw / 100%; for light intensity data L_raw, it is divided by the maximum value of the sensor's range, L_max, to obtain normalized light intensity data L = L_raw / L_max.

[0073] For wind direction data, the data fusion analysis module 300 first calculates the angle θ between the wind direction and the orientation of doors and windows based on the real-time wind direction detected by the wind direction sensor 130 and the orientation of doors and windows. For example... Figure 6 As shown, the angle θ between the wind direction and the orientation of doors and windows is defined as the angle between the wind direction and the normal direction of the doors and windows. When θ = 0°, it means the wind is directly facing the doors and windows, and rainwater is most likely to be blown into the room; when θ = 180°, it means the wind is away from the doors and windows, and rainwater is least likely to be blown into the room. The wind risk coefficient D is calculated based on the angle θ. When θ = 0°, D takes the maximum value of 1, and when θ = 180°, D takes the minimum value of 0. The intermediate angle is calculated using the cosine function: D = (1 + cosθ) / 2.

[0074] After normalization, the data fusion analysis module 300 calculates the comprehensive environmental risk index R according to the following weighted fusion algorithm: R = α·P + β·W + γ·D + δ·H + ε·L; Where P is the normalized rainfall data; W is the normalized wind speed data; D is the normalized wind direction risk coefficient; H is the normalized ambient humidity data; L is the normalized light intensity data; α, β, γ, δ, and ε are the rainfall weight coefficient, wind speed weight coefficient, wind direction weight coefficient, humidity weight coefficient, and light intensity weight coefficient, respectively, and α+β+γ+δ+ε=1.

[0075] In this embodiment, the weighting coefficients are set as follows: α=0.35, β=0.20, γ=0.20, δ=0.15, ε=0.10. Rainfall data has the largest weight (35%) in the comprehensive environmental risk index because it is the most direct indicator for determining whether rain protection control is needed. Wind speed and wind direction each have a weight of 20%, as they jointly determine the likelihood of rainwater being blown indoors. Humidity has a weight of 15%, serving as an auxiliary indicator. Sunlight has a weight of 10%, used to distinguish between day and night, and can appropriately increase the sensitivity of rain protection control at night.

[0076] It should be noted that the specific values ​​of the weighting coefficients mentioned above are only examples, and can be adaptively adjusted according to the climate characteristics of different regions and user needs in actual applications. For example, in coastal areas with strong winds, the weighting coefficients for wind speed and wind direction can be appropriately increased; in southern regions with high humidity, the weighting coefficient for humidity can be appropriately increased.

[0077] like Figure 4 As shown, the hierarchical control decision module 500 includes a threshold comparison unit 510, a wind direction correlation analysis unit 520, an instruction generation unit 530, and a status monitoring unit 540.

[0078] The threshold comparison unit 510 is communicatively connected to the data fusion analysis module 300 and the dynamic threshold adjustment module 400, and receives the comprehensive environmental risk index R and the dynamic adaptive rain protection control decision threshold (including the first dynamic threshold). Second dynamic threshold and the third dynamic threshold The comprehensive environmental risk index R is compared with various dynamic thresholds to determine the current level of environmental risk.

[0079] The wind direction correlation analysis unit 520 is communicatively connected to the wind direction sensor 130 in the multi-dimensional environmental perception module 100 to acquire real-time wind direction data and calculate the angle θ between the current wind direction and the orientation of doors and windows. The wind direction correlation analysis unit 520 calculates the wind direction risk coefficient D based on the angle θ and transmits the calculation result to the instruction generation unit 530.

[0080] The instruction generation unit 530 is communicatively connected to the threshold comparison unit 510 and the wind direction correlation analysis unit 520, and generates graded control instructions based on the threshold comparison results and the wind direction risk coefficient.

[0081] like Figure 7 As shown, the control logic of the hierarchical control decision module 500 is as follows: When the comprehensive environmental risk index R is less than the first dynamic threshold When the current environmental risk is low and no rain protection measures are required, the instruction generation unit 530 generates a "maintain status quo" instruction.

[0082] When the comprehensive environmental risk index R is greater than or equal to the first dynamic threshold And less than the second dynamic threshold When the signal indicates a certain risk of rainfall, but not a high level of risk, the instruction generation unit 530 generates an instruction to adjust the opening angle. At this time, the wind direction correlation analysis unit 520 calculates a recommended opening angle based on the angle between the wind direction and the orientation of the doors and windows. Specifically, the recommended opening angle A is calculated according to the following formula: ; in, This refers to the current opening angle of the doors and windows. Here, D represents the second dynamic threshold, and D is the wind direction risk coefficient. The formula shows that the higher the comprehensive environmental risk index R, the smaller the recommended opening angle; conversely, the higher the wind direction risk coefficient D (i.e., the more directly the wind is blowing into the doors and windows), the smaller the recommended opening angle. This ensures adequate ventilation while minimizing the risk of rainwater intrusion.

[0083] When the comprehensive environmental risk index R is greater than or equal to the second dynamic threshold And less than the third dynamic threshold When the signal indicates a high risk of rainfall, the instruction generation unit 530 generates an incomplete closure instruction, closing the doors and windows to only a small gap, in order to maintain minimum ventilation while ensuring basic rain protection.

[0084] When the comprehensive environmental risk index R is greater than or equal to the third dynamic threshold When the signal indicates an extremely high risk of rainfall, the instruction generation unit 530 generates a complete closure instruction to completely close the doors and windows.

[0085] in, ,and All are dynamic adaptive rain protection control decision thresholds. In this embodiment, =0.3·T, =0.6·T, =0.8·T, where T is the dynamic adaptive rainproof control decision threshold calculated by the dynamic threshold adjustment module 400.

[0086] The status monitoring unit 540 is communicatively connected to the execution drive module 600 to monitor the current position status of the doors and windows in real time and feed back the status information to the instruction generation unit 530 to ensure that the generated instructions match the actual status of the doors and windows and avoid duplicate or conflicting instructions.

[0087] like Figure 9 As shown, the execution drive module 600 includes a motor drive unit 610, a position feedback unit 620, and a transmission mechanism 630.

[0088] The motor drive unit 610 is communicatively connected to the hierarchical control decision module 500, receives hierarchical control commands, and drives the corresponding motor action according to the command type. In this embodiment, the motor drive unit 610 includes a window opening motor and an angle adjustment motor. The window opening motor is used to drive the opening and closing of doors and windows, and the angle adjustment motor is used to adjust the opening angle of doors and windows. The motor drive unit 610 adopts a DC brushless motor, which has the advantages of high efficiency, low noise, and long life. The motor drive unit 610 also has a built-in motor driver for converting control signals into motor drive current.

[0089] The position feedback unit 620 is used to detect the current position status of the doors and windows in real time and feed it back to the hierarchical control decision module 500. In this embodiment, the position feedback unit 620 uses a Hall sensor or photoelectric encoder to calculate the position and opening angle of the doors and windows by detecting the number of rotations and the direction of rotation of the motor. The detection accuracy of the position feedback unit 620 reaches ±1°, which can meet the requirements of precise control.

[0090] The transmission mechanism 630 connects the motor drive unit 610 to the door / window body, converting the rotational motion of the motor into linear or rotational motion of the door / window. In this embodiment, the transmission mechanism 630 adopts a gear and rack transmission mechanism or a screw and nut transmission mechanism, which has advantages such as high transmission efficiency, accurate positioning, and good self-locking performance.

[0091] like Figure 8 As shown, the control effect feedback module 700 includes a data recording unit 710, an effect evaluation unit 720, and a feedback transmission unit 730.

[0092] The data recording unit 710 is communicatively connected to the execution drive module 600 to record the execution results of each control decision. Specifically, the information recorded by the data recording unit 710 includes: the time of the control decision, the type of control decision (maintain status quo, adjust angle, incomplete closure, complete closure), environmental parameters during execution (rainfall, wind speed, wind direction, temperature and humidity, light intensity), the status of doors and windows after execution, whether the user intervened manually, and whether water entered the room after the rainfall stopped.

[0093] The effect evaluation unit 720 is communicatively connected to the data recording unit 710, and generates feedback evaluation indicators based on the recorded control effect data. In this embodiment, the feedback evaluation indicators include control accuracy, user intervention rate, and rain protection effectiveness.

[0094] Control accuracy is the proportion of correctly executed control decisions out of the total number of control decisions. The criteria for correctly executing a control decision are: no water enters the room after implementing rain protection control, and indoor air quality improves after implementing ventilation control. A higher control accuracy indicates more accurate decision-making by the control system.

[0095] The user intervention rate is the proportion of times a user manually intervenes in a control decision out of the total number of control decisions. User intervention includes manually closing a window (when the system does not close the window automatically) and manually opening a window (when the user believes that closing the window is unnecessary after the system has closed it automatically). The lower the user intervention rate, the more the control system's decisions align with the user's expectations.

[0096] The rainproof effectiveness rate is the percentage of times the indoor area did not experience water ingress after rainproof control measures were implemented, out of the total number of rainproof control measures implemented. A higher rainproof effectiveness rate indicates better rainproof control.

[0097] The feedback transmission unit 730 is communicatively connected to the effect evaluation unit 720 and the dynamic threshold adjustment module 400, and transmits the feedback evaluation index to the user habit factor calculation unit 430 of the dynamic threshold adjustment module 400 for updating user historical behavior data and optimizing the dynamic adaptive rainproof control decision threshold.

[0098] Specifically, when the user intervention rate is high, the user habit factor calculation unit 430 will adjust the user habit influence factor U according to the environmental parameters at the time of user intervention, so that subsequent control decisions are more in line with the user's actual preferences. When the rain protection effectiveness is low, the system will appropriately lower the control threshold, so that the control system can take rain protection measures earlier.

[0099] like Figure 10 As shown, the communication module 800 is connected to the user terminal device 1000 to realize information interaction between the system and the user.

[0100] The communication module 800 includes a wireless communication unit 810 and a local communication unit 820. The wireless communication unit 810 employs a Wi-Fi module or a 4G / 5G cellular communication module for remote communication with user terminal devices 1000, such as smartphones, tablets, or personal computers, via the internet. The local communication unit 820 employs a Bluetooth module or a ZigBee module for short-range communication with the user terminal device 1000.

[0101] The information sent by the communication module 800 to the user terminal device 1000 includes: current status information of doors and windows (open / closed / opening angle), environmental parameter information (current rainfall, wind speed, wind direction, temperature and humidity, light intensity), environmental early warning information (early rain warning, strong wind warning, etc.), and control decision information (control actions that the system is about to execute and the reasons).

[0102] The information received by the communication module 800 from the user terminal device 1000 includes: remote control commands (manually opening and closing windows, adjusting the opening angle, switching working modes, etc.) and user preference settings (rainproof sensitivity settings, ventilation preference settings, etc.).

[0103] In this embodiment, the system provides three working modes for the user to choose from: Ventilation mode: The system prioritizes ventilation and will only close doors and windows when the risk of rainfall is extremely high. Otherwise, it will keep them open.

[0104] Rainproof mode: The system prioritizes rain protection and takes rainproof measures as soon as any risk of rainfall is detected, ensuring that the indoor environment is protected from rain.

[0105] Intelligent Mode: The system automatically switches between ventilation and rain protection based on the comprehensive environmental risk index, maximizing natural ventilation while ensuring basic rain protection. Intelligent mode is the system's default operating mode.

[0106] After the system is installed for the first time or reinstalled, initialization and calibration are required to ensure that the system can work properly.

[0107] Initialization operations include: setting basic parameters for doors and windows (door / window type, door / window size, maximum opening angle, minimum opening angle, etc.), setting sensor parameters (sensor range, sampling frequency, etc.), and setting basic rainproof control decision thresholds. Set the weighting coefficients (α, β, γ, δ, ε, ... wait).

[0108] The calibration process includes: zero-point calibration and range calibration of each sensor to ensure the accuracy of the sensor output data; stroke calibration of the door and window actuator to ensure that the motor drive unit can accurately control the door and window to the target position; and zero-position calibration of the position feedback unit to ensure the accuracy of the position feedback data.

[0109] After system initialization and calibration, users can set personal preferences through user terminal device 1000, including: rain protection sensitivity (high / medium / low), ventilation preference (priority ventilation / balanced / priority rain protection), and operating mode (ventilation mode / rain protection mode / smart mode). User preference settings will serve as the initial input for user habit factor calculation unit 430.

[0110] The system of this invention also has self-diagnosis and fault handling functions to improve the reliability and security of the system.

[0111] The system's self-diagnostic functions include: sensor self-diagnosis (detecting whether each sensor is working properly, whether there is signal loss or abnormality), actuator self-diagnosis (detecting whether the motor drive unit is working properly, whether there is jamming or abnormality in the transmission mechanism), communication self-diagnosis (detecting whether the communication module is working properly, whether the connection with the user terminal equipment is normal), and power supply self-diagnosis (detecting whether the power management module is working properly, whether the battery power is sufficient).

[0112] When the system detects a fault, the following fault handling measures are taken: For sensor failures: The system records the fault information and sends the fault alarm information to the user terminal device 1000 through the communication module 800. At the same time, it suspends the use of data from the sensor and uses data from other sensors for alternative judgment to ensure that the system can still work basically even if some sensors fail.

[0113] In case of actuator failure: The system records the fault information and sends a fault alarm, while keeping the doors and windows in their current positions to prevent them from going out of control due to actuator failure.

[0114] In case of communication failure: The system records the failure information and attempts to reconnect automatically. Before communication is restored, the system continues to work independently according to the current dynamic threshold and hierarchical control logic, without relying on remote control.

[0115] In case of power failure: When the power management module 900 detects that the battery level is lower than the preset threshold, the system sends a low battery alarm message and automatically closes the doors and windows to a safe position to ensure that the doors and windows are in a rainproof state in the event of a power outage.

[0116] Example 2: Control Method Flow like Figure 5 As shown, the present invention provides an intelligent door and window rainproof adaptive control method, comprising the following steps: Step S1: Environmental Parameter Collection The multi-dimensional environmental sensing module 100 collects environmental parameters in real time at the location of doors and windows. The multi-dimensional environmental sensing module 100 includes a rainfall sensor 110, a wind speed sensor 120, a wind direction sensor 130, a temperature and humidity sensor 140, and a light intensity sensor 150, which collect rainfall data, wind speed data, wind direction data, temperature and humidity data, and light intensity data, respectively. The sampling frequency of each sensor is 1Hz, and the collected data is transmitted to the data preprocessing module 200 via wired or wireless means.

[0117] Step S2: Data Preprocessing The data preprocessing module 200 receives various environmental parameters collected by the multi-dimensional environmental perception module 100, performs filtering and noise reduction processing and outlier removal processing on the various environmental parameters, and generates a preprocessed environmental parameter dataset.

[0118] The filtering and denoising process employs a median filtering algorithm, which effectively removes impulse noise and random noise from sensor data while preserving the edge features of the data relatively well. Specifically, for each sensor's time-series data, N data points (N=5) before and after the current time (a total of 2N+1 data points) are taken, sorted, and the median value is used as the filtered output value for the current time.

[0119] Outlier removal adopts the statistical 3σ principle, which means that when the difference between a data point and the mean of its data sequence exceeds three times the standard deviation, the data point is identified as an outlier and removed, and replaced by linear interpolation of the data before and after it.

[0120] Step S3: Data Fusion Analysis The data fusion analysis module 300 receives the preprocessed environmental parameter dataset and performs weighted fusion analysis on rainfall data, wind speed data, wind direction data, temperature and humidity data, and light intensity data based on a preset multi-source data fusion algorithm to generate a comprehensive environmental risk index R.

[0121] Specifically, firstly, all environmental parameters are normalized, mapping their values ​​to a range of 0 to 1. Then, the comprehensive environmental risk index R is calculated using the following formula: R = α·P + β·W + γ·D + δ·H + ε·L Where P is the normalized rainfall data; W is the normalized wind speed data; D is the normalized wind direction risk coefficient; H is the normalized ambient humidity data; L is the normalized light intensity data; α, β, γ, δ, and ε are the rainfall weight coefficient, wind speed weight coefficient, wind direction weight coefficient, humidity weight coefficient, and light intensity weight coefficient, respectively, and α+β+γ+δ+ε=1.

[0122] The wind direction risk factor D is calculated based on the angle θ between the wind direction and the orientation of doors and windows. The angle θ is defined as the angle between the wind direction and the normal direction of the doors and windows. When θ = 0°, the wind direction is directly opposite the doors and windows, and D takes its maximum value of 1; when θ = 180°, the wind direction is away from the doors and windows, and D takes its minimum value of 0; the intermediate angle is calculated using the cosine function: D = (1 + cosθ) / 2.

[0123] Step S4: Dynamic threshold adjustment The dynamic threshold adjustment module 400 acquires current time and location information, and combines it with user historical behavior data to dynamically adjust the rain protection control decision threshold, generating a dynamic adaptive rain protection control decision threshold T.

[0124] Specifically, the seasonal factor calculation unit 410 of the dynamic threshold adjustment module 400 calculates the seasonal influence factor S based on the current date; the geographical location factor calculation unit 420 calculates the geographical location influence factor G based on the geographical location of the doors and windows; and the user habit factor calculation unit 430 calculates the user habit influence factor U based on the user's historical manual control records. The threshold synthesis unit 440 calculates the dynamic adaptive rainproof control decision threshold according to the following formula. : ; in, Based on the basic rain protection control decision threshold, Seasonal influencing factors Geographical location is an influencing factor. Factors influencing user habits These are the weighting coefficients for seasonal influence, geographical location influence, and user habit influence, respectively. .

[0125] In this embodiment, the dynamic threshold adjustment module 400 also generates three graded thresholds: a first dynamic threshold. =0.3·T, the second dynamic threshold =0.6·T, the third dynamic threshold =0.8·T.

[0126] Step S5: Hierarchical Control Decision The hierarchical control decision module 500 generates hierarchical control instructions based on the comparison results between the comprehensive environmental risk index R and the dynamic adaptive rain protection control decision threshold, combined with the angle relationship between the current wind direction and the orientation of doors and windows.

[0127] Specifically, the threshold comparison unit 510 of the hierarchical control decision module 500 compares the comprehensive environmental risk index R with the first dynamic threshold. Second dynamic threshold and the third dynamic threshold Comparison: When R < At that time, a command to maintain the status quo is generated; when ≤ R< At that time, an instruction to adjust the opening angle is generated, and the wind direction correlation analysis unit 520 calculates and recommends the opening angle based on the angle between the wind direction and the orientation of the doors and windows. ,in D represents the current opening angle of the doors and windows, and D represents the wind direction risk coefficient. when ≤ R< At that time, an incomplete shutdown instruction is generated; When R ≥ At that time, a complete shutdown command is generated.

[0128] Step S6: Execute the driver The execution drive module 600 receives hierarchical control commands and drives the door and window actuators to perform corresponding door and window actions. Specifically, the motor drive unit 610 drives the corresponding motor according to the command type, and the transmission mechanism 630 converts the rotational motion of the motor into linear or rotational motion of the door and window, realizing the opening, closing, or angle adjustment of the door and window. The position feedback unit 620 detects the current position status of the door and window in real time and feeds it back to the hierarchical control decision module 500 to form a closed-loop control.

[0129] Step S7: Feedback Optimization The control effect feedback module 700 records the execution results of each control decision, generates feedback evaluation indicators, and is used to update user historical behavior data and optimize the dynamic adaptive rainproof control decision threshold.

[0130] Specifically, the data recording unit 710 records the time, type, environmental parameters during execution, post-execution status of doors and windows, whether the user intervened manually, and whether water entered the room after the rainfall stopped, among other information, for each control decision. The effect evaluation unit 720 calculates feedback evaluation indicators such as control accuracy, user intervention rate, and rainproof effectiveness based on the recorded data. The feedback transmission unit 730 transmits the feedback evaluation indicators to the user habit factor calculation unit 430 of the dynamic threshold adjustment module 400 to update the user habit influence factor U, making the control system's decisions increasingly aligned with the user's actual preferences and usage habits.

[0131] Example 3: Typical Application Scenarios The following examples illustrate the practical application effects of the technical solution of this invention through several typical application scenarios.

[0132] Scene 1: Light rain in spring In a southern city, on a cloudy spring day, the outdoor temperature was 18°C ​​and the relative humidity was 75%. Before leaving for work, the resident opened the window to about 60° for ventilation. Light rain began at 10:00 AM, with a rainfall of 2 mm / h, a wind speed of 3 m / s, and a southeast wind direction. The window faced south.

[0133] The multi-dimensional environmental perception module 100 detected rainfall data P=0.1 (normalized), wind speed data W=0.15 (normalized), the angle between wind direction and window orientation θ=30° (wind direction risk coefficient D=0.93), humidity data H=0.75 (normalized), and light intensity data L=0.4 (normalized). The data fusion analysis module 300 calculated the comprehensive environmental risk index R=0.35×0.1+0.20×0.15+0.20×0.93+0.15×0.75+0.10×0.4=0.035+0.03+0.186+0.1125+0.04=0.4035.

[0134] The dynamic threshold adjustment module 400 calculates the dynamic adaptive rain protection control decision threshold T=0.5×(1+0.4×0.2+0.35×0.3+0.25×0.15)=0.5×(1+0.08+0.105+0.0375)=0.5×1.2225=0.61125 based on the current date (spring, S=0.2), geographical location (southern city, G=0.3), and user habits (users tend to close windows earlier, U=0.15). The three tiered thresholds are as follows: =0.1834、 =0.3668、 =0.489.

[0135] Since R = 0.4035, it satisfies... ≤R< (0.3668≤0.4035<0.489), the hierarchical control decision module 500 generates a partial closing command, closing the window to only a tiny gap (approximately a 5° opening angle), maintaining minimum ventilation while ensuring basic rain protection. The execution drive module 600 executes this command, and the window is closed to approximately a 5° opening angle. The light rain stopped after about 1 hour, with no water entering the room and good ventilation.

[0136] Scenario 2: Summer rainstorm In a coastal city, on a sunny summer day, the outdoor temperature was 32℃ and the relative humidity was 65%. The resident was at home with the window open about 45° for ventilation. At 2:30 PM, a sudden severe convective weather event occurred, with rainfall rapidly increasing to 25 mm / h and wind speeds reaching 12 m / s from the south. The window faced south.

[0137] The multi-dimensional environmental perception module 100 detected rainfall data P=1.0 (after normalization, reaching the sensor's upper limit), wind speed data W=0.8 (after normalization), the angle between wind direction and window orientation θ=0° (wind direction risk coefficient D=1.0), humidity data H=0.9 (after normalization), and light intensity data L=0.2 (after normalization, due to a sudden drop in light intensity caused by dense clouds). The data fusion analysis module 300 calculated the comprehensive environmental risk index R=0.35×1.0+0.20×0.8+0.20×1.0+0.15×0.9+0.10×0.2=0.35+0.16+0.20+0.135+0.02=0.865.

[0138] The dynamic threshold adjustment module 400 calculates the dynamic adaptive rain protection control decision threshold T=0.5×(1+0.4×0.3+0.35×0.3+0.25×0.1)=0.5×(1+0.12+0.105+0.025)=0.5×1.25=0.625 based on the current date (summer, S=0.3), geographical location (coastal city, G=0.3), and user habits (U=0.1). The three tiered thresholds are as follows: =0.1875、 =0.375、 =0.5.

[0139] Since R = 0.865, R ≥ (0.865≥0.5), the hierarchical control decision module 500 generates a complete closing command. The execution drive module 600 completely closes the window within approximately 3 seconds. Simultaneously, the communication module 800 sends a warning message to the user's smartphone: "Heavy rain detected, windows have automatically closed, please be careful." The heavy rain subsided into light rain after approximately 30 minutes, preventing water from entering the room and avoiding damage to furniture and floors.

[0140] Scenario 3: Autumn windy weather without rain In a northern city, on a sunny and dry autumn day, the outdoor temperature was 15°C and the relative humidity was 30%. Before going out, residents opened their windows about 30° for ventilation. At 9:00 AM, the wind picked up, reaching a speed of 8 m / s, from the north, with the windows facing south (i.e., the wind was blowing away from the windows).

[0141] The multi-dimensional environmental perception module 100 detected rainfall data P=0 (no rainfall), wind speed data W=0.5 (normalized), the angle between wind direction and window orientation θ=180° (wind direction risk coefficient D=0), humidity data H=0.3 (normalized), and light intensity data L=0.6 (normalized). The data fusion analysis module 300 calculated the comprehensive environmental risk index R=0.35×0+0.20×0.5+0.20×0+0.15×0.3+0.10×0.6=0+0.1+0+0.045+0.06=0.205.

[0142] The dynamic threshold adjustment module 400 calculates the dynamic adaptive rain protection control decision threshold T=0.5×(1+0.4×0.1+0.35×0.1+0.25×0.05)=0.5×(1+0.04+0.035+0.0125)=0.5×1.0875=0.54375 based on the current date (autumn, S=0.1), geographical location (northern city, G=0.1), and user habits (U=0.05). The three tiered thresholds are as follows: =0.1631、 =0.3263、 =0.435.

[0143] Since R = 0.205, it satisfies... ≤R< (0.1631≤0.205<0.3263), the hierarchical control decision module 500 generates an instruction to adjust the opening angle. The wind direction correlation analysis unit 520 calculates the recommended opening angle A=30°×[1-(0.205 / 0.3263)×(1-0 / 2)]=30°×[1-0.628×1]=30°×0.372=11.16°. The execution drive module 600 adjusts the window opening angle from 30° to approximately 11°. In this way, although the wind speed is relatively high, because the wind direction is away from the window, the system only appropriately reduces the opening angle instead of completely closing the window, thus maximizing ventilation function while ensuring rain protection (no rainfall, no risk of rainwater intrusion).

[0144] The embodiments described herein are preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape, and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. An intelligent rainproof adaptive control system for doors and windows, characterized in that, include: A multi-dimensional environmental perception module (100) is installed on the outside of the doors and windows to collect environmental parameters of the location of the doors and windows in real time. The multi-dimensional environmental perception module (100) includes a rainfall sensor (110), a wind speed sensor (120), a wind direction sensor (130), a temperature and humidity sensor (140), and a light sensor (150); the rainfall sensor (110) is used to detect real-time rainfall data; the wind speed sensor (120) is used to detect real-time wind speed data; the wind direction sensor (130) is used to detect real-time wind direction data; the temperature and humidity sensor (140) is used to detect real-time ambient temperature and humidity data; and the light sensor (150) is used to detect real-time light intensity data. The data preprocessing module (200) is communicatively connected to the multidimensional environment perception module (100) and is used to receive various environmental parameters collected by the multidimensional environment perception module (100), and to perform filtering and noise reduction processing and outlier removal processing on the various environmental parameters to generate a preprocessed environmental parameter dataset. The data fusion analysis module (300) is communicatively connected to the data preprocessing module (200) and is used to receive the preprocessed environmental parameter dataset and perform weighted fusion analysis on rainfall data, wind speed data, wind direction data, temperature and humidity data and light intensity data based on a preset multi-source data fusion algorithm to generate a comprehensive environmental risk index. The comprehensive environmental risk index is used to characterize the overall impact of the current environment on the rainproof control of doors and windows; The dynamic threshold adjustment module (400) is communicatively connected to the data fusion analysis module (300) and is used to obtain current time information and geographical location information, and dynamically adjust the rain protection control decision threshold in combination with user historical behavior data; The dynamic threshold adjustment module (400) includes a seasonal factor calculation unit (410), a geographic location factor calculation unit (420), and a user habit factor calculation unit (430). The seasonal factor calculation unit (410) is used to calculate the seasonal influence factor based on the current date. The geographic location factor calculation unit (420) is used to calculate the geographic location influence factor based on the geographic location of the doors and windows. The user habit factor calculation unit (430) is used to calculate the user habit influence factor based on the user's historical manual control records. The dynamic threshold adjustment module (400) performs weighted correction on the basic rainproof control decision threshold based on the seasonal influence factor, geographic location influence factor, and user habit influence factor to generate a dynamic adaptive rainproof control decision threshold. A hierarchical control decision module (500), communicatively connected to the data fusion analysis module (300) and the dynamic threshold adjustment module (400), is used to generate hierarchical control instructions based on the comparison result between the comprehensive environmental risk index and the dynamic adaptive rainproof control decision threshold, combined with the angle relationship between the current wind direction and the orientation of the doors and windows; the hierarchical control instructions include a complete closure instruction, a partial closure instruction, an adjustment opening angle instruction, and a maintain status instruction; the hierarchical control decision module (500) includes a wind direction correlation analysis unit (520), which is used to calculate the angle between the current wind direction and the orientation of the doors and windows, and determine the recommended opening angle of the doors and windows based on the angle and the comprehensive environmental risk index; An execution drive module (600) is communicatively connected to the hierarchical control decision module (500) and is used to receive the hierarchical control commands and drive the door and window actuators to perform corresponding door and window actions. The execution drive module (600) includes a motor drive unit (610) and a position feedback unit (620). The motor drive unit (610) is used to drive the opening, closing, or angle adjustment of the doors and windows. The position feedback unit (620) is used to detect the current position status of the doors and windows in real time and feed it back to the hierarchical control decision module (500). The control effect feedback module (700) is communicatively connected to the execution drive module (600) and the dynamic threshold adjustment module (400), and is used to record the execution results of each control decision, including the indoor environmental state after the door and window actions are executed, user intervention records, and indoor conditions after the rainfall stops; the control effect feedback module (700) generates feedback evaluation indicators based on the recorded control effect data, and transmits the feedback evaluation indicators to the dynamic threshold adjustment module (400) to update the user historical behavior data and optimize the dynamic adaptive rainproof control decision threshold; The communication module (800) is connected to the user terminal device (1000) for sending door and window status information and environmental warning information to the user terminal device (1000) and receiving remote control commands from the user terminal device (1000). The power management module (900) is used to provide a stable power supply for each module of the intelligent door and window rainproof adaptive control system, and has battery power monitoring and low battery alarm functions.

2. The intelligent door and window rainproof adaptive control system according to claim 1, characterized in that, The rainfall sensor (110) in the multi-dimensional environmental perception module (100) is a piezoelectric rainfall sensor or an optical rainfall sensor; the wind speed sensor (120) is a three-cup wind speed sensor or an ultrasonic wind speed sensor; the wind direction sensor (130) is a wind vane type wind direction sensor or an ultrasonic wind direction sensor; the temperature and humidity sensor (140) is an integrated digital temperature and humidity sensor; and the light sensor (150) is a photodiode or a phototransistor type light sensor.

3. The intelligent door and window rainproof adaptive control system according to claim 1, characterized in that, The data fusion analysis module (300) uses the following weighted fusion algorithm to calculate the comprehensive environmental risk index R: R = α·P + β·W + γ·D + δ·H + ε·L; Wherein, P is the normalized rainfall data, W is the normalized wind speed data, D is the normalized wind direction risk coefficient, H is the normalized ambient humidity data, and L is the normalized light intensity data; α, β, γ, δ, and ε are the rainfall weight coefficient, wind speed weight coefficient, wind direction weight coefficient, humidity weight coefficient, and light intensity weight coefficient, respectively, and α+β+γ+δ+ε=1; the wind direction risk coefficient D is calculated based on the angle θ between the wind direction and the orientation of the doors and windows. When θ=0°, the wind direction is directly facing the doors and windows, and D takes the maximum value of 1; when θ=180°, the wind direction is away from the doors and windows, and D takes the minimum value of 0. The intermediate angle is calculated by linear interpolation or cosine function.

4. The intelligent door and window rainproof adaptive control system according to claim 1, characterized in that, The dynamic threshold adjustment module (400) calculates the dynamic adaptive rain protection control decision threshold according to the following formula. : ； in, Based on the basic rain protection control decision threshold, Seasonal influencing factors Geographical location is an influencing factor. Factors influencing user habits These are the weighting coefficients for seasonal influence, geographical location influence, and user habit influence, respectively. .

5. The intelligent door and window rainproof adaptive control system according to claim 1, characterized in that, The control logic of the hierarchical control decision module (500) is as follows: When the comprehensive environmental risk index R is less than the first dynamic threshold At that time, a command to maintain the status quo is generated; When the comprehensive environmental risk index R is greater than or equal to the first dynamic threshold And less than the second dynamic threshold At that time, an instruction to adjust the opening angle is generated, and a recommended opening angle is calculated based on the angle between the wind direction and the orientation of the doors and windows; When the comprehensive environmental risk index R is greater than or equal to the second dynamic threshold And less than the third dynamic threshold At that time, an incomplete shutdown instruction is generated; When the comprehensive environmental risk index R is greater than or equal to the third dynamic threshold At that time, a complete shutdown command is generated; in, ,and All are dynamic adaptive rain protection control decision thresholds.

6. A smart door and window rainproof adaptive control method, characterized in that, Includes the following steps: Step S1: The environmental parameters of the door and window location are collected in real time by the multi-dimensional environmental perception module (100). The environmental parameters include rainfall data, wind speed data, wind direction data, temperature and humidity data and light intensity data. Step S2: Perform filtering and noise reduction processing and outlier removal processing on the collected environmental parameters to generate a preprocessed environmental parameter dataset; Step S3: Perform weighted fusion analysis on the preprocessed environmental parameter dataset based on a preset multi-source data fusion algorithm to generate a comprehensive environmental risk index; Step S4: Obtain current time and location information, and combine them with user historical behavior data to dynamically adjust the rain protection control decision threshold and generate a dynamic adaptive rain protection control decision threshold; Step S5: Based on the comparison result between the comprehensive environmental risk index and the dynamic adaptive rainproof control decision threshold, and combined with the angle relationship between the current wind direction and the orientation of doors and windows, generate graded control instructions; Step S6: Drive the door and window actuator to perform the corresponding door and window actions according to the hierarchical control command; Step S7: Record the execution results of each control decision, generate feedback evaluation indicators, and use them to update user historical behavior data and optimize the dynamic adaptive rainproof control decision threshold.

7. The intelligent door and window rainproof adaptive control method according to claim 6, characterized in that, In step S3, the multi-source data fusion algorithm is a weighted fusion algorithm, and the comprehensive environmental risk index R is calculated according to the following formula: R = α·P + β·W + γ·D + δ·H + ε·L; Where P is the normalized rainfall data, W is the normalized wind speed data, D is the normalized wind direction risk coefficient, H is the normalized ambient humidity data, and L is the normalized light intensity data; α, β, γ, δ, and ε are the rainfall weight coefficient, wind speed weight coefficient, wind direction weight coefficient, humidity weight coefficient, and light intensity weight coefficient, respectively, and α+β+γ+δ+ε=1.

8. The intelligent door and window rainproof adaptive control method according to claim 6, characterized in that, In step S4, the dynamic adaptive rain protection control decision threshold T is calculated according to the following formula: ； in, Based on the basic rain protection control decision threshold, Seasonal influencing factors Geographical location is an influencing factor. Factors influencing user habits These are the weighting coefficients for seasonal influence, geographical location influence, and user habit influence, respectively. .

9. The intelligent door and window rainproof adaptive control method according to claim 6, characterized in that, In step S5, the generation logic of the hierarchical control instruction is as follows: When the comprehensive environmental risk index R is less than the first dynamic threshold At that time, a command to maintain the status quo is generated; When the comprehensive environmental risk index R is greater than or equal to the first dynamic threshold And less than the second dynamic threshold At that time, an instruction to adjust the opening angle is generated, and a recommended opening angle is calculated based on the angle between the wind direction and the orientation of the doors and windows; When the comprehensive environmental risk index R is greater than or equal to the second dynamic threshold And less than the third dynamic threshold At that time, an incomplete shutdown instruction is generated; When the comprehensive environmental risk index R is greater than or equal to the third dynamic threshold At that time, a complete shutdown command is generated; in, ,and All are dynamic adaptive rain protection control decision thresholds.

10. The intelligent door and window rainproof adaptive control method according to claim 9, characterized in that, In step S5, the method for calculating the recommended opening angle is as follows: The wind direction risk coefficient D is calculated based on the angle θ between the wind direction and the orientation of the doors and windows. When θ = 0°, the wind direction is directly opposite the doors and windows, and D takes the maximum value of 1. When θ = 180°, the wind direction is away from the doors and windows, and D takes the minimum value of 0. Based on the ratio of the comprehensive environmental risk index R to the dynamic adaptive rain protection control decision threshold, and combined with the wind direction risk coefficient D, the recommended opening angle A is calculated according to the following formula: ； in, This refers to the current opening angle of the doors and windows. This is the second dynamic threshold.

Images