Control method for smart window ventilation system

Abstract

The present application relates to the technical field of building ventilation intelligent control, especially to a control method of intelligent window type ventilation system. The control method comprises the following steps: S1: environmental data fusion and situation awareness; S2: dynamic environment partition; S3: multi-mode collaborative decision; S4: natural wind pressure priority control; S5: ventilation path optimization; S6: multi-end system collaborative execution; S7: energy management and optimization. Through the "natural wind pressure priority control" strategy, the present application maximizes the use of free natural energy, and only starts low-power mechanical assistance when necessary, which can significantly reduce energy consumption compared with the traditional continuous power-driven ventilation system. Through "dynamic environment partition" and "ventilation path optimization", the present application realizes the collaborative intelligent control of multiple rooms and multiple ends, can automatically plan the optimal airflow path, quickly remove pollution, balance the indoor environment, and improve the overall ventilation efficiency and air quality.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for building ventilation, and in particular to a control method for an intelligent window ventilation system. Background Technology

[0002] With increasing demands for indoor air quality and building energy efficiency, traditional ventilation control methods face numerous limitations. Existing technologies, such as timed control, simple sensor feedback control (e.g., opening and closing windows based on a single CO2 concentration), or centralized control of central fresh air systems, generally suffer from the following problems: 1) They fail to fully utilize free energy sources such as natural wind pressure and thermal pressure, relying heavily on electric power and resulting in high energy consumption; 2) They lack coordinated control and global optimization for multiple rooms and multiple ventilation terminals (windows, fans), making it difficult to form efficient and reasonable indoor airflow organization, easily leading to ventilation dead zones or cross-contamination; 3) Their control strategies are rigid, unable to adapt to complex dynamic changes in the indoor and outdoor environment (e.g., human activity, pollution source generation, sudden weather changes) and diverse user needs (e.g., entertaining guests, sleeping); 4) Their level of intelligence is insufficient, with low integration with emerging smart home ecosystems (e.g., smart agents), resulting in inconvenient interaction.

[0003] While existing technologies have made improvements in functional integration, they still have shortcomings in energy efficiency, multi-device collaborative optimization algorithms, and deep interaction with the intelligent agent ecosystem. Therefore, there is an urgent need for a high-efficiency control method that can intelligently coordinate natural and mechanical ventilation, optimize ventilation paths in multiple rooms, and flexibly respond to changes in the environment and scenarios. Summary of the Invention

[0004] In order to overcome the shortcomings of the prior art described in the background art, the present invention provides a control method for an intelligent window ventilation system.

[0005] The technical solution adopted by this invention to solve its technical problem is: a control method for an intelligent window ventilation system, the system comprising multiple distributed window ventilation components, at least one exhaust auxiliary system, an environmental sensor network, and a control unit, the method being executed by the control unit and comprising the following steps:

[0006] S1: Environmental data fusion and situational awareness: Periodically collect indoor environmental data, acquire outdoor environmental data, and acquire status data of each execution component within the system;

[0007] S2: Dynamic environmental zoning: Based on the data from step S1, the indoor space is dynamically divided into multiple zones with different ventilation requirements.

[0008] S3: Multi-mode collaborative decision-making: Based on the environmental data, decisions are made and switched among multiple preset operating modes; the operating modes include at least shutdown mode, automatic mode, forced ventilation mode and natural ventilation mode;

[0009] S4: Natural wind pressure priority control: Real-time assessment of the pressure difference ΔP in the building ventilation direction; if ΔP is greater than or equal to the first threshold P1, natural wind pressure is used to drive ventilation first, and the operation of the exhaust auxiliary system is limited; if ΔP is less than P1, the exhaust auxiliary system is activated or enhanced to assist ventilation.

[0010] S5: Ventilation path optimization: Based on the current environmental situation, zoning results and operating mode, an optimization algorithm is used to calculate the optimal control strategy for the opening degree of each window ventilation component and the wind speed of the exhaust auxiliary system;

[0011] S6: Multi-terminal system collaborative execution: Decompose the optimal control strategy obtained in step S5 into control instructions for each execution component and issue them for execution. The control instructions include differentiated air volume control for different areas.

[0012] S7: Energy Management and Optimization: Prioritize the use of the system's own renewable energy sources during the control process, and optimize ventilation strategies based on ambient temperature forecasts to reduce overall building energy consumption.

[0013] According to another embodiment of the present invention, before step S1, step S0 is further included: receiving a call instruction from a home smart agent, the smart agent communicating with the control unit via a local area network to start or adjust ventilation control.

[0014] According to another embodiment of the present invention, step S2 further includes, at least, a dynamic zone with frequent human activity, a static zone with no human presence, and a polluted zone with a pollution source.

[0015] According to another embodiment of the present invention, step S3 further includes the following: the triggering conditions for the forced ventilation mode include indoor CO2 concentration exceeding a first concentration threshold and outdoor particulate matter concentration being lower than a first pollution threshold; the triggering conditions for the natural ventilation mode include outdoor temperature being within a comfortable temperature range and outdoor wind speed exceeding a first wind speed threshold.

[0016] According to another embodiment of the present invention, step S4 further includes estimating the pressure difference ΔP using the formula ΔP = 0.5 × ρ × v² × (C_p1 - C_p2), where ρ is the air density, v is the wind speed, and C_p1 and C_p2 are the wind pressure coefficients of different orientation parts of the building.

[0017] According to another embodiment of the present invention, step S5 further includes an optimization algorithm that is a deep reinforcement learning algorithm; the state space of the algorithm includes environmental parameters of each region and the state of each execution component, the action space includes the target opening degree of each window ventilation component and the target rotation speed of the exhaust ventilation auxiliary system, and the reward function is a weighted sum of indoor comfort, system energy consumption and air quality index.

[0018] According to another embodiment of the present invention, step S6 further includes the differential air volume control comprising: controlling the exhaust volume of the polluted area to be greater than its supply volume, so as to create negative pressure in the area.

[0019] According to another embodiment of the present invention, step S6 further includes time-zone control, including a silent mode that reduces airflow and fan speed during nighttime hours, and an ultra-quiet mode that relies mainly on natural ventilation during sleep hours.

[0020] According to another embodiment of the present invention, in step S6, the control command further includes scene linkage control, including: automatically increasing the exhaust volume of the kitchen area and adjusting the air supply of adjacent areas in response to detecting cooking activities in the kitchen; and automatically increasing the ventilation volume of the living room area in response to detecting people gathering in the living room.

[0021] According to another embodiment of the present invention, step S7 further includes the renewable energy source being solar energy; the energy management and optimization further includes an intelligent battery management strategy that prioritizes charging when the battery power is below a first power threshold and reduces the charging current when the battery power is above a second power threshold.

[0022] The beneficial effect of this invention is that it maximizes the use of free natural energy through the "natural wind pressure priority control" strategy, and only activates low-power mechanical assistance when necessary, which can significantly reduce energy consumption compared to traditional continuously electrically driven ventilation systems.

[0023] Through "dynamic environmental zoning" and "ventilation path optimization", collaborative intelligent control of multiple rooms and multiple terminals is achieved. It can automatically plan the optimal airflow path, quickly remove pollution, balance the indoor environment, and improve overall ventilation efficiency and air quality.

[0024] Through "multi-mode collaborative decision-making" and "multi-terminal system collaborative execution", the system can automatically identify and adapt to various indoor and outdoor environmental changes and user life scenarios (such as sleeping, gathering, cooking), providing comfortable, quiet, and efficient personalized ventilation services.

[0025] By using the "intelligent agent interaction call" step, this method can be seamlessly integrated into the modern smart home ecosystem. Users can control ventilation by interacting with the intelligent agent through natural language, which greatly improves the ease of use and intelligent experience of the system.

[0026] The control method is based on a distributed, wirelessly connected hardware system, combined with modular, solar-powered window ventilation components. It is particularly suitable for the renovation of existing buildings, and is easy to install without complicated wiring. Attached Figure Description

[0027] The invention will be further described below with reference to the accompanying drawings and embodiments.

[0028] Figure 1 This is a system flowchart of the present invention; Detailed Implementation

[0029] Figure 1 This is the system flowchart of the present invention.

[0030] As attached Figure 1 As shown, a control method for an intelligent window ventilation system is disclosed. The system includes multiple distributed window ventilation components, at least one exhaust auxiliary system, an environmental sensor network, and a control unit. The method is executed by the control unit and includes the following steps:

[0031] S0: Receive a call command from a home smart agent, which communicates with the control unit via a local area network to start or adjust ventilation control.

[0032] S1: Environmental data fusion and situational awareness: Periodically collect indoor environmental data, acquire outdoor environmental data, and acquire status data of each execution component within the system;

[0033] S2: Dynamic environmental zoning: Based on the data from step S1, the indoor space is dynamically divided into multiple zones with different ventilation requirements.

[0034] The multiple areas include at least a dynamic area with frequent human activity, a static area with no human presence, and a polluted area where pollution sources exist.

[0035] S3: Multi-mode collaborative decision-making: Based on the environmental data, decisions are made and switched among multiple preset operating modes; the operating modes include at least shutdown mode, automatic mode, forced ventilation mode and natural ventilation mode;

[0036] The triggering conditions for the forced ventilation mode include indoor CO2 concentration exceeding a first concentration threshold and outdoor particulate matter concentration being lower than a first pollution threshold; the triggering conditions for the natural ventilation mode include outdoor temperature being within a comfortable temperature range and outdoor wind speed exceeding a first wind speed threshold.

[0037] S4: Natural wind pressure priority control: Real-time assessment of the pressure difference ΔP in the building ventilation direction; if ΔP is greater than or equal to the first threshold P1, natural wind pressure is used to drive ventilation first, and the operation of the exhaust auxiliary system is limited; if ΔP is less than P1, the exhaust auxiliary system is activated or enhanced to assist ventilation.

[0038] The pressure difference ΔP is estimated using the formula ΔP = 0.5 × ρ × v² × (C_p1 - C_p2), where ρ is the air density, v is the wind speed, and C_p1 and C_p2 are the wind pressure coefficients at different orientations of the building.

[0039] S5: Ventilation path optimization: Based on the current environmental situation, zoning results and operating mode, an optimization algorithm is used to calculate the optimal control strategy for the opening degree of each window ventilation component and the wind speed of the exhaust auxiliary system;

[0040] The window ventilation assembly includes a damper, a drive mechanism, a CO2 sensor, and a differential pressure sensor.

[0041] The optimization algorithm is a deep reinforcement learning algorithm; the state space of the algorithm includes the environmental parameters of each region and the state of each execution component, the action space includes the target opening degree of each window ventilation component and the target rotation speed of the exhaust auxiliary system, and the reward function is the weighted sum of indoor comfort, system energy consumption and air quality index.

[0042] S6: Multi-terminal system collaborative execution: Decompose the optimal control strategy obtained in step S5 into control instructions for each execution component and issue them for execution. The control instructions include differentiated air volume control for different areas.

[0043] The differentiated air volume control includes controlling the exhaust volume of the polluted area to be greater than its supply volume, so as to create negative pressure in the area.

[0044] The control commands also include time-zone control, including a silent mode that reduces airflow and fan speed during nighttime hours, and an ultra-quiet mode that relies mainly on natural ventilation during sleep hours.

[0045] The control commands also include scene linkage control, including: automatically increasing the exhaust volume in the kitchen area and adjusting the air supply in adjacent areas in response to detecting cooking activities in the kitchen; and automatically increasing the ventilation volume in the living room area in response to detecting people gathering in the living room.

[0046] S7: Energy Management and Optimization: Prioritize the use of the system's own renewable energy sources during the control process, and optimize ventilation strategies based on ambient temperature forecasts to reduce overall building energy consumption.

[0047] The renewable energy source is solar energy; the energy management and optimization also includes an intelligent battery management strategy, which prioritizes charging when the battery level is below a first power threshold and reduces the charging current when the battery level is above a second power threshold.

[0048] A specific implementation flow of the control method of the present invention is as follows:

[0049] S0: Intelligent Agent Interaction Invocation: The user says to the home intelligent agent (such as a smart speaker): "Turn on the ventilation in the living room" or "The indoor air quality is not good." After recognizing the intent, the intelligent agent calls the control unit of the ventilation system through the local network API, triggering the system to start and enter AUTO (automatic) mode.

[0050] S1: Environmental Data Fusion and Situational Awareness: The control unit collects indoor temperature, humidity, CO2, and PM2.5 data uploaded by the built-in sensors of each window ventilation component via Zigbee / Bluetooth network at 2-5 second intervals. Simultaneously, it obtains local real-time wind speed, wind direction, temperature, humidity, AQI, and future weather forecasts via internet API. Furthermore, it reads the current opening degree of all guide vanes, fan speed, solar panel power generation, and battery charge.

[0051] S2: Dynamic Environmental Zoning: Based on sensor data, the control unit determines whether there is activity in the living room (CO2 increase), no one in the bedroom, and a slight increase in VOC concentration in the kitchen. The system automatically marks the living room as the "active zone," the bedroom as the "quiet zone," and the kitchen as the "contaminated zone."

[0052] S3: Multi-mode collaborative decision-making: Control unit assessment data: Current indoor CO2 is 850 ppm, outdoor PM2.5 is 50 μg / m³, meeting the condition of "CO2 > 800 ppm and PM2.5 < 75". The system decides to switch from the current mode to the VENTILATION (forced ventilation) mode to quickly reduce the CO2 concentration.

[0053] S4: Natural Wind Pressure Priority Control: The control unit obtains the current wind speed v=1m / s through the meteorological API and calculates the pressure difference ΔP between the north and south windows based on the building model parameters. The calculated ΔP=0.8Pa, which is less than the preset threshold P1 (e.g., 2Pa). The system determines this as a "weak wind pressure situation" and decides to activate the exhaust auxiliary system.

[0054] S5: Ventilation Path Optimization: The control unit runs a pre-trained deep reinforcement learning model. The model's input includes current environmental parameters for each zone, window status, fan status, and mode commands. After calculation, the model outputs the following actions: opening the south-facing window deflector blades in the living room to 85% and the north-facing window to 70%; opening the north-facing window deflector blades in the kitchen (near the exhaust fan) to 90%; and maintaining the bedroom window opening at 40%. Simultaneously, the output sets the auxiliary exhaust fan speed to 65%.

[0055] S6: Multi-terminal system collaborative execution: The control unit will issue optimization instructions:

[0056] Differentiated control: The living room (active zone) receives a large air volume (approximately 75 m³ / h), the kitchen (polluted zone) has an exhaust volume greater than the supply volume to create a slight negative pressure, and the bedroom (quiet zone) maintains a low air volume (approximately 35 m³ / h) for quiet ventilation.

[0057] Scene linkage: Because the kitchen is identified as a contaminated area, the system strengthens its exhaust ventilation while appropriately reducing the air supply to the adjacent restaurant to prevent the spread of cooking fumes.

[0058] The ESP32-S3 controller within each window ventilation unit receives the command and drives the stepper motor to adjust the guide vanes to the specified angle. The exhaust auxiliary fan receives the speed command and begins operating at 65% power.

[0059] S7: Energy Management and Optimization: Throughout the process, the system prioritizes using the electricity currently generated by the solar panels to drive the motor and fan. With the battery level above 80%, the system is in float charging mode. The control unit notices that the weather forecast indicates the outdoor temperature will drop to a comfortable range in the evening and pre-stores a strategy: after 18:00, if conditions are met, it will attempt to switch to ENERGY_SAVING mode to further reduce mechanical energy consumption.

[0060] A few minutes later, the CO2 concentration dropped below 600ppm, and the natural wind increased, causing ΔP to exceed P1. The system automatically switched back to ENERGY_SAVING mode according to the rules, gradually reducing the exhaust fan speed until it was turned off, relying entirely on natural wind pressure to maintain ventilation.

[0061] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A control method for an intelligent window ventilation system, characterized in that, The system includes multiple distributed window ventilation components, at least one exhaust auxiliary system, an environmental sensor network, and a control unit. The method is executed by the control unit and includes the following steps: S1: Environmental data fusion and situational awareness: Periodically collect indoor environmental data, acquire outdoor environmental data, and acquire status data of each execution component within the system; S2: Dynamic environmental zoning: Based on the data from step S1, the indoor space is dynamically divided into multiple zones with different ventilation requirements. S3: Multi-mode collaborative decision-making: Based on the environmental data, decisions are made and switched among multiple preset operating modes; the operating modes include at least shutdown mode, automatic mode, forced ventilation mode and natural ventilation mode; S4: Natural wind pressure priority control: Real-time assessment of the pressure difference ΔP in the building ventilation direction; if ΔP is greater than or equal to the first threshold P1, natural wind pressure is used to drive ventilation first, and the operation of the exhaust auxiliary system is limited; if ΔP is less than P1, the exhaust auxiliary system is activated or enhanced to assist ventilation. S5: Ventilation path optimization: Based on the current environmental situation, zoning results and operating mode, an optimization algorithm is used to calculate the optimal control strategy for the opening degree of each window ventilation component and the wind speed of the exhaust auxiliary system; S6: Multi-terminal system collaborative execution: Decompose the optimal control strategy obtained in step S5 into control instructions for each execution component and issue them for execution. The control instructions include differentiated air volume control for different areas. S7: Energy Management and Optimization: Prioritize the use of the system's own renewable energy sources during the control process, and optimize ventilation strategies based on ambient temperature forecasts to reduce overall building energy consumption.

2. The control method for the intelligent window ventilation system according to claim 1, characterized in that, Before step S1, there is also step S0: receiving a call command from a home smart agent, which communicates with the control unit via a local area network to start or adjust ventilation control.

3. The control method for the intelligent window ventilation system according to claim 1, characterized in that, In step S2, the multiple areas include at least a dynamic area with frequent human activity, a static area with no human activity, and a polluted area where pollution sources exist.

4. The control method for the intelligent window ventilation system according to claim 1, characterized in that, In step S3, the triggering conditions for the forced ventilation mode include indoor CO2 concentration exceeding a first concentration threshold and outdoor particulate matter concentration being lower than a first pollution threshold; the triggering conditions for the natural ventilation mode include outdoor temperature being within a comfortable temperature range and outdoor wind speed exceeding a first wind speed threshold.

5. The control method for the intelligent window ventilation system according to claim 1, characterized in that, In step S4, the pressure difference ΔP is estimated using the formula ΔP = 0.5 × ρ × v² × (C_p1 - C_p2), where ρ is the air density, v is the wind speed, and C_p1 and C_p2 are the wind pressure coefficients of different orientation parts of the building.

6. The control method for the intelligent window ventilation system according to claim 1, characterized in that, In step S5, the optimization algorithm is a deep reinforcement learning algorithm; the state space of the algorithm includes the environmental parameters of each region and the state of each execution component, the action space includes the target opening degree of each window ventilation component and the target rotation speed of the exhaust auxiliary system, and the reward function is the weighted sum of indoor comfort, system energy consumption and air quality index.

7. The control method for the intelligent window ventilation system according to claim 1, characterized in that, In step S6, the differentiated air volume control includes controlling the exhaust volume of the polluted area to be greater than its supply volume, so as to create negative pressure in the area.

8. The control method for the intelligent window ventilation system according to claim 1, characterized in that, In step S6, the control command also includes time-zone control, including a silent mode that reduces air volume and fan speed during nighttime hours, and an ultra-quiet mode that relies mainly on natural ventilation during sleep hours.

9. The control method for the intelligent window ventilation system according to claim 1, characterized in that, In step S6, the control command also includes scene linkage control, including: automatically increasing the exhaust volume of the kitchen area and adjusting the air supply of adjacent areas in response to detecting cooking activities in the kitchen; and automatically increasing the ventilation volume of the living room area in response to detecting people gathering in the living room.

10. The control method for the intelligent window ventilation system according to claim 1, characterized in that, In step S7, the renewable energy source is solar energy; the energy management and optimization also includes an intelligent battery management strategy, which prioritizes charging when the battery power is below a first power threshold and reduces the charging current when the battery power is above a second power threshold.

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