An adaptive environmental control system

Abstract

This invention relates to the field of intelligent building environment control technology, specifically an adaptive environmental regulation system. It utilizes an AI strategy module with an integrated edge computing platform, housed in the main control cabinet. Based on outdoor weather forecasts, local climate information, and group behavior patterns, the module performs multi-parameter environmental control strategy reasoning to generate adaptive environmental regulation commands. Simultaneously, a master-slave hierarchical control architecture is employed. Based on centralized optimization decisions in the main control cabinet, regional autonomous regulation and user interaction are achieved through various user terminals. The AI ​​strategy module further uses predictive models and optimization algorithms to predict future environmental loads based on historical environmental data and real-time conditions, calculating the optimal control parameters for each environmental regulation device. This system not only significantly improves the accuracy and foresight of environmental regulation through AI predictive control and real-time optimization, but also ensures global strategy coordination and energy efficiency optimization through the master-slave hierarchical architecture, guaranteeing rapid response and system reliability for regional control.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for building environments, specifically an adaptive environmental regulation system. Background Technology

[0002] With the rapid development of smart building and IoT technologies, people's demands for indoor environmental comfort and energy conservation are constantly increasing. However, traditional environmental control often uses fixed thresholds or simple PID control, which cannot dynamically respond to changes in external weather and indoor occupant activities, resulting in either large fluctuations in environmental comfort or serious energy waste. Furthermore, existing systems lack effective hierarchical control mechanisms; either they rely on a single central control system, leading to insufficient flexibility, or they use independent control in different areas, lacking overall coordination and optimization, making it difficult to balance local needs with global energy efficiency. In terms of user interaction, traditional systems typically only provide temperature control panels or ordinary switches, lacking intuitive graphical interfaces and voice interaction methods, making user operation inconvenient.

[0003] While there have been attempts to apply artificial intelligence to environmental control in recent years, most have remained at the level of data monitoring or simple linkage, and have not yet formed a systematic solution for predictive optimization and adaptive regulation. Therefore, there is an urgent need for an adaptive environmental regulation system that can perform predictive analysis based on the external environment and group behavior, combine centralized management with regional autonomy, and provide user-friendly human-computer interaction. Summary of the Invention

[0004] The technical problem solved by this invention is to provide an adaptive environmental control system to address the technical problems of existing environmental control systems being unable to dynamically respond to the external environment for adjustment, lacking overall coordination and optimization in independent control between regions, and inconvenient interactive operation.

[0005] The basic solution provided by this invention is an adaptive environmental regulation system, which adopts a master-slave hierarchical control architecture, including a main control cabinet and at least one user terminal device. The main control cabinet is equipped with an AI strategy module, which integrates an edge computing platform for performing multi-parameter environmental control strategy reasoning based on outdoor weather forecasts, local climate information and group behavior patterns, and generating adaptive environmental regulation instructions.

[0006] The user terminal device is equipped with a control panel LCD screen and an AI language interaction interface; the control panel LCD screen is used for centralized scheduling and overall status monitoring, and the control panel LCD screen of each user terminal device serves as a regional autonomous control and user interaction interface; the AI ​​language interaction interface supports voice command recognition and voice feedback, and users can perform environmental control operations through natural language.

[0007] Furthermore, the AI ​​strategy module includes a prediction model and an optimization algorithm module;

[0008] The prediction model is a time series prediction model, trained using historical environmental data, weather data, and group behavior data. The prediction model predicts future environmental load based on outdoor weather changes and typical group activity patterns. The optimization algorithm module calculates the optimal control parameters for each environmental regulation device based on the prediction results and preset comfort / energy-saving targets.

[0009] Furthermore, the control panel LCD screen is equipped with a temperature sensor, a humidity sensor, and an air quality sensor, which are used to collect environmental data of the corresponding area in real time and send the data to the AI ​​strategy module for analysis and processing.

[0010] Furthermore, the AI ​​voice interaction interface includes a microphone and a speaker, used to receive user voice control commands and provide voice feedback; the voice collected by the microphone is converted into control commands through a voice recognition algorithm and sent to the AI ​​strategy module or the corresponding user terminal device for execution; the speaker is used to broadcast environmental status information and command execution results.

[0011] Furthermore, the main control cabinet is also equipped with a communication module; the user terminal device is also equipped with a network interface.

[0012] The communication module is used to establish a data communication connection between the main control cabinet and each user terminal device. The network interface is connected to the communication module via wired or wireless means, thereby sending the control strategy generated by the AI ​​strategy module to the user terminal device and uploading the environmental data and user instructions collected by the user terminal device to the AI ​​strategy module.

[0013] Furthermore, the main control cabinet is also equipped with an AI control LCD screen, which displays environmental parameters, equipment operating status and energy consumption information of each area of ​​the entire system, and provides an operation interface for adjusting strategy parameters and switching modes, so as to realize the configuration and debugging functions of the AI ​​strategy module.

[0014] Furthermore, the main control cabinet is also equipped with a Modbus gateway, which is connected to multiple environmental control devices and sensors to receive control commands issued by the AI ​​strategy module through the Modbus communication protocol and control the environmental control devices, and to feed back the operating status of each device and sensor data to the AI ​​strategy module.

[0015] The environmental control equipment includes temperature control equipment, ventilation equipment, humidification and dehumidification equipment, and air purification equipment.

[0016] Furthermore, the main control cabinet is also equipped with a UPS power module, which is used to supply power to the main control cabinet and its internal key modules when the mains power is interrupted, so as to ensure the continuous operation of modules including the AI ​​strategy module and the communication module.

[0017] The principles and advantages of this invention are as follows: This invention integrates an AI strategy module with an edge computing platform within the main control cabinet. Based on outdoor weather forecasts, local climate information, and group behavior patterns, it performs multi-parameter environmental control strategy reasoning to generate adaptive environmental regulation commands. Simultaneously, it employs a master-slave hierarchical control architecture. Based on centralized optimization decisions in the main control cabinet, regional autonomous regulation and user interaction are achieved through various user terminals. The AI ​​strategy module further uses predictive models and optimization algorithms to predict future environmental loads based on historical environmental data and real-time status, and calculates the optimal control parameters for each environmental regulation device. This not only significantly improves the accuracy and foresight of environmental regulation through AI predictive control and real-time optimization, but also ensures global strategy coordination and energy efficiency optimization through the master-slave hierarchical architecture, guaranteeing rapid response and system reliability in regional control. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the overall structure of an embodiment of an adaptive environment control system according to the present invention.

[0019] Figure 2 This is a schematic diagram of the internal structure of the main control cabinet in an embodiment of the adaptive environmental control system of the present invention.

[0020] Figure 3 This is a side view of the main control cabinet structure of an embodiment of the adaptive environmental control system of the present invention.

[0021] Figure 4 This is a schematic diagram of the user terminal device structure according to an embodiment of the adaptive environment control system of the present invention. Detailed Implementation

[0022] The following detailed description illustrates the specific implementation methods:

[0023] The markings in the accompanying drawings include: control cabinet 1, UPS power module 2, AI strategy module 3, communication module 4, Modbus gateway 5, terminal block 6, AI control LCD screen 7, cabinet door 8, DIN rail 9, control panel LCD screen 10, network interface 11, AI voice interaction interface 12, and power socket 13.

[0024] The specific implementation process is as follows:

[0025] Example 1

[0026] Example 1 is attached. Figure 1 As shown, an adaptive environmental control system adopts a master-slave hierarchical control architecture, including a master control cabinet 1 and several user terminal devices distributed in various areas of the building.

[0027] The main control cabinet 1 is installed in the building's control room, power distribution room, or fire control room, such as... Figure 2 , Figure 3 As shown, multiple functional modules are fixedly installed inside via DIN rails 9, including a UPS power module 2, an AI strategy module 3, a communication module 4, and a Modbus gateway 5. These modules are interconnected via terminal blocks 6 and necessary wiring to achieve signal communication and power distribution. The main control cabinet 1 has a door 8 on its front, with an AI control LCD screen 7 embedded in it, allowing maintenance personnel to view the system's operating status and perform settings. The UPS power module 2 connects to the mains power grid and the internal circuitry of the main control cabinet. When the mains power is normal, it supplies power to all system modules and charges its own energy storage unit. When the mains power fails, it immediately switches to UPS power supply mode, continuously providing uninterrupted power support to key units such as the AI ​​strategy module 3 and the communication module 4, ensuring continuous system operation. This avoids environmental control interruptions and equipment damage caused by sudden power outages, improving system reliability.

[0028] In this embodiment, the AI ​​strategy module 3 is the brain of the system, consisting of a high-speed computing processor and an AI acceleration unit. The AI ​​strategy module pre-stores various environmental control models and algorithms, including weather forecasting models, group behavior analysis models, comfort evaluation models, and energy optimization algorithms. When the system is running, the AI ​​strategy module 3 continuously receives data from sensors in various areas (such as temperature, humidity, and carbon dioxide concentration, uploaded via user terminal sensors or independent sensors), and obtains outdoor weather forecast information and typical climate parameters for the current period from the internet or local weather stations through the communication module 4.

[0029] Simultaneously, the system analyzes historical data from infrared sensors, access control systems, or attendance systems to extract patterns in group behavior, including changes in the number of people, activity types, and work / rest schedules. It then uses machine learning methods such as cluster analysis to quantitatively model these behavioral patterns, generating typical behavioral curves that can be used for prediction. Based on this, the AI ​​strategy module performs multi-parameter fusion analysis on outdoor weather parameters, local climate conditions, group behavioral characteristics, and real-time indoor environmental data. Through a complete strategy generation process, including data preprocessing, feature extraction, and multivariate model inference, it achieves adaptive inference of environmental control strategies.

[0030] To improve the prediction and control accuracy, the AI strategy module in this embodiment also integrates a time series prediction model, which uses LSTM to predict the future environmental load based on historical environmental data, meteorological information, and behavior pattern data; and uses optimization algorithms including reinforcement learning and genetic algorithms to calculate the optimal control parameters of each environmental regulation device with the comfort index and energy consumption as the dual optimization objectives. In summary, the AI strategy module 3 uses edge computing capabilities to predict the environmental load and user needs for a period of time in the future locally and generate an optimal control strategy. For example, before the morning work peak, the temperature of the office area is lowered in advance to offset the heat load brought by the entry of personnel; when it is predicted that the outdoor temperature will rise in the afternoon in summer, the start-stop threshold of the refrigeration system is appropriately increased to make full use of the precooling amount during the low-temperature period; or according to the meeting reservation schedule, the fresh air system of the meeting room is turned on in advance. The strategies output by the AI strategy module include parameters such as the adjustment of the temperature set point, fresh air ventilation frequency, and humidification / dehumidification mode switching in each area, as well as the start-stop or power adjustment instructions for each execution device.

[0031] The communication module 4 is responsible for the data communication tasks inside and outside the system. In this embodiment, the communication module 4 uses an Ethernet switch or a wireless communication device to interconnect the main control cabinet 1, each user terminal device, and a possible remote monitoring platform into a network. On the one hand, the communication module 4 receives the control instructions generated by the AI strategy module 3 and distributes the instructions to each user terminal through a high-speed network; on the other hand, the communication module 4 aggregates the real-time environmental data and user instruction feedback from the user terminals and transmits them to the AI strategy module 3 for decision-making reference. In addition, the communication module 4 can also access the public weather server through a secure network interface to obtain weather forecasts, or access the building management system to coordinate other subsystems (such as security and lighting systems) to achieve interlocking control. Considering some special application scenarios, the communication module 4 preferably supports dual communication redundancy of wired and wireless. When the wired network fails, it automatically switches to the wireless network to improve the reliability of system communication.

[0032] The Modbus gateway 5 in the main control cabinet 1 connects to various environmental control devices and sensors within the building via bus interfaces such as RS485. Specifically, air conditioning units, fan coil units, supply fans, fresh air valves, air purifiers, and independent environmental detectors (such as PM2.5 sensors and carbon dioxide concentration sensors) are all connected to the Modbus bus and managed by the Modbus gateway 5. The AI ​​strategy module 3, based on optimization strategies, issues control commands to relevant devices through the Modbus gateway 5, such as adjusting the opening of fan coil unit valves to control room temperature, adjusting the angle of fresh air unit valves to maintain air quality, and starting / stopping humidifiers / dehumidifiers to adjust humidity. Simultaneously, the Modbus gateway 5 periodically collects status data and sensor readings from each device and feeds them back to the AI ​​strategy module 3 for closed-loop correction of control decisions. Through this integrated bus control, the system is compatible with devices from different manufacturers, easily expands system scale, and significantly reduces the wiring complexity of traditional point-to-point control.

[0033] In this embodiment, each area is equipped with such Figure 4 The user terminal device shown has a control panel LCD screen 10 at its core. The control panel LCD screen 10 is an embedded touch-screen color display, installed at a suitable height on the room wall, displaying real-time environmental information such as temperature, humidity, and air quality index, as well as the current operating mode (e.g., comfort mode, energy-saving mode). Users can directly touch the controls on the screen interface to adjust settings such as temperature, fan speed, and lighting brightness (if linked to other lighting). In this embodiment, the size and interface design of the control panel LCD screen 10 fully consider ergonomics, displaying clear and concise content, and providing multilingual support to accommodate different users.

[0034] The user terminal device in this embodiment also includes an AI voice interaction interface 12, whose microphone array can accurately pick up user voice within a certain range. With the help of artificial intelligence voice recognition technology, the system can recognize the user's verbal commands. For example, when a user says "Raise the living room temperature to 24 degrees Celsius," the user terminal in this area will parse the command parameters. If it needs to be reported to the main control cabinet AI strategy module 3, it will be transmitted through the network interface 11, and the adjustment will be executed after the AI ​​strategy module calculates and confirms it. For some localized commands (such as "Open the curtains," if the area integrates a smart curtain system), the user terminal can also execute them directly through the local controller. The speaker of the voice interaction interface 12 is used to provide feedback, such as broadcasting "Okay, the living room temperature has been adjusted to 24 degrees Celsius" after the temperature adjustment. For query-type voice commands (such as "What is the indoor air quality now?"), the system will obtain the relevant data and inform the user via voice. This voice-assisted method allows users to control the environment and obtain information without manual operation, making it particularly convenient for the elderly, children, or those with mobility impairments.

[0035] Each user terminal device connects to the communication network via network interface 11 to interact with the main control cabinet 1. When the network is normal, the user terminal mainly adjusts according to the parameters issued by the main control cabinet's AI strategy module 3 and local sensor data, and uploads user requests via touch or voice for the AI ​​strategy module to optimize. When there is an occasional communication interruption or the main control cabinet is under maintenance, each user terminal can temporarily operate independently based on the most recent settings and built-in simple rules to maintain the basic stability of the regional environment (e.g., maintain the previous temperature setpoint for control), and then adjust synchronously after communication is restored to improve system fault tolerance.

[0036] In implementing this invention, the AI ​​control LCD screen 7 of the main control cabinet 1 is primarily for use by system administrators. Through the main control interface, administrators can view real-time overviews of temperature and humidity, air quality, operating status, and power consumption data for all areas of the building. The interface also provides strategy management functions, such as setting target temperature curves for different floors or rooms on weekdays / weekends, configuring holiday modes, or adjusting certain parameters of the AI ​​strategy module 3 (such as weighting factors for comfort priority or energy-saving priority). When it is necessary to debug and optimize the AI ​​strategy, administrators can switch to manual mode and directly send control commands or modify local parameters through the AI ​​control LCD screen 7. The AI ​​strategy module 3 will learn and record these manual adjustments to continuously improve its algorithm model.

[0037] This invention has a wide range of applications. For large public buildings such as office buildings, shopping malls, and hospitals, this system can automatically adjust the air conditioning and fresh air supply according to pedestrian flow patterns and business hours to achieve high efficiency and energy saving. For residential communities and smart homes, this system can provide personalized environmental control through voice assistants, allowing residents to have a more comfortable and convenient living experience. In a typical application scenario, after people leave the office at night, the system detects that no one is in each area and the indoor air quality is good. The AI ​​strategy module 3 then reduces the power of the HVAC system and enters energy-saving mode. Before work in the morning, based on weather forecasts and historical data, the system turns on the air conditioning in advance to gradually adjust the temperature to a comfortable range. When employees enter the office, if the increase in the number of people in a local area causes the temperature to rise or the carbon dioxide concentration to increase, the sensor data of the area terminal will trigger the system to strengthen the air supply and fresh air volume in that area to maintain comfort in a timely manner. Throughout the process, users hardly need to manually intervene, but can still make local fine adjustments at any time through voice or touch. If someone feels stuffy in a meeting, they only need to say "turn up the meeting room air conditioning" and the system will quickly respond to meet the demand.

[0038] The above are merely embodiments of the present invention. Commonly known structures and characteristics are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. An adaptive environmental conditioning system employing a master-slave hierarchical control architecture comprising a master control cabinet and at least one user terminal device, characterized in that: The main control cabinet is provided with an AI strategy module, which integrates an edge computing platform, is used for multi-parameter environment control strategy reasoning based on outdoor weather forecast, local climate information and group behavior rules, and generates adaptive environment regulation instructions; The user terminal device is provided with a control panel liquid crystal screen and an AI language interaction interface; the control panel liquid crystal screen is used for centralized scheduling and overall state monitoring, and the control panel liquid crystal screen of each user terminal device serves as a regional autonomous control and user interaction interface; the AI language interaction interface supports voice instruction recognition and voice feedback, and the user can perform environment control operation through natural language.

2. The self-adapting environmental conditioning system of claim 1, wherein: The AI strategy module comprises a prediction model and an optimization algorithm module; The prediction model is a time series prediction model, which is trained using historical environment data, weather data and group behavior data; the prediction model predicts future environment load according to outdoor weather changes and typical group activity rules; The optimization algorithm module calculates the optimal control parameters of each environment regulation device based on the prediction results and preset comfort / energy saving targets.

3. An adaptive environmental conditioning system according to claim 2, wherein: The control panel liquid crystal screen is provided with a temperature sensor, a humidity sensor and an air quality sensor, which are used for collecting environment data of the corresponding region in real time and sending the data to the AI strategy module for analysis and processing.

4. The self-adapting environmental conditioning system of claim 3, wherein: The AI voice interaction interface comprises a microphone and a loudspeaker, which are used for receiving voice control instructions of the user and providing voice feedback; the voice collected by the microphone is converted into a control command through a voice recognition algorithm and sent to the AI strategy module or the corresponding user terminal device for execution; the loudspeaker is used for broadcasting environment state information and instruction execution results.

5. An adaptive environmental conditioning system according to claim 4, wherein: The main control cabinet is also provided with a communication module; the user terminal device is also provided with a network interface; The communication module is used for establishing data communication connection between the main control cabinet and each user terminal device, and the network interface is connected to the communication module through a wired or wireless manner, so as to download the control strategy generated by the AI strategy module to the user terminal device and upload the environment data and user instructions collected by the user terminal device to the AI strategy module.

6. An adaptive environmental conditioning system according to claim 5, wherein: The main control cabinet is also provided with an AI control liquid crystal screen, which is used for displaying environment parameters, device running states and energy consumption information of each region of the whole system, and providing an operation interface for strategy parameter adjustment and mode switching, so as to realize configuration and debugging functions of the AI strategy module.

7. An adaptive environmental conditioning system according to claim 6, wherein: The main control cabinet is also provided with a Modbus gateway, which is connected to a plurality of environment regulation devices and sensors, so as to receive control instructions issued by the AI strategy module through a Modbus communication protocol and control the environment regulation devices, and feed back running states of the devices and sensor data to the AI strategy module; The environment regulation devices comprise temperature regulation devices, ventilation devices, humidification and dehumidification devices and air purification devices.

8. An adaptive environmental conditioning system according to claim 7, wherein: The main control cabinet is also provided with a UPS power module, which is used for supplying power to the main control cabinet and its internal key modules when commercial power is interrupted, so as to ensure continuous operation of the modules including the AI strategy module and the communication module.

Images