Control system of intelligent total heat exchange fresh air ventilator

Abstract

The invention relates to the technical field of fresh air ventilator control systems, and discloses a control system of an intelligent total heat exchange fresh air ventilator, which comprises a heat exchange unit, a cross flow type total heat exchange core body, a bypass air duct, a sensor group, a temperature sensor, a humidity sensor, a PM2.5 sensor, a CO2 concentration sensor and a VOC sensor, the control module is provided with a fuzzy PID (Proportion Integration Differentiation) algorithm processor and is used for receiving real-time detection data of the sensor group, and the execution mechanism group comprises a speed-adjustable double centrifugal fan, an air valve driver and a bypass air duct switching device. According to the control system of the intelligent total heat exchange fresh air machine, by adopting the cross flow type total heat exchange core body and the bypass air duct, the energy utilization efficiency is improved, the energy consumption is reduced, various sensors are arranged, the indoor and outdoor temperature, humidity, PM2.5, CO2 concentration, VOC and other parameters can be accurately detected in real time, and data support is provided for accurate control.

Description

Technical Field

[0001] The present invention relates to the technical field of fresh air fan control systems, and specifically to a control system for an intelligent total heat exchange fresh air fan. Background Art

[0002] In today's society, people's attention to indoor air quality has been continuously increasing. With the acceleration of the urbanization process and the improvement of people's living standards, people spend more and more time indoors, and indoor air quality has an important impact on people's health and comfort. However, there are some problems in the operation of traditional fresh air fans.

[0003] In terms of intelligent control, the control method of traditional fresh air fans is relatively simple, unable to adjust operation parameters in real time according to changes in indoor and outdoor environments, and unable to meet people's needs for comfort and health. Therefore, there is an urgent need for a control system for an intelligent total heat exchange fresh air fan. Summary of the Invention

[0004] The purpose of the present invention is to provide a control system for an intelligent total heat exchange fresh air fan to solve the problems raised in the above background art.

[0005] To solve the above technical problems, the present invention provides the following technical solution: A control system for an intelligent total heat exchange fresh air fan, including:

[0006] A heat exchange unit, including a cross-flow total heat exchange core and a bypass air duct;

[0007] A sensor group, including temperature sensors, humidity sensors, PM2.5 sensors, CO2 concentration sensors, and VOC sensors arranged on indoor and outdoor air ducts;

[0008] A control module, configured with a fuzzy PID algorithm processor for receiving real-time detection data from the sensor group;

[0009] An actuator group, including a variable-speed double centrifugal fan, a damper driver, and a bypass air duct switching device;

[0010] A power management module, having a function of switching between mains / solar dual-mode power supply.

[0011] Preferably, the sensor group further includes an infrared particle counter and an ultrasonic flowmeter arranged on the air inlet side of the heat exchange core, and the detection accuracy of the infrared particle counter reaches the 0.3μm level.

[0012] Preferably, the control module is configured with a dynamic mode switching logic, including:

[0013] Energy-saving priority mode: Activate total heat exchange when the indoor-outdoor temperature difference exceeds the set threshold ΔT1;

[0014] Health - priority mode: Automatically switch to the bypass mode when the PM2.5 concentration exceeds 75 μg / m 3 or the CO2 concentration exceeds 1000 ppm;

[0015] Silent mode: Automatically reduce the fan speed to 60% of the reference value during the night period.

[0016] Preferably, the dynamic mode - switching logic further includes an emergency ventilation mode. When the VOC concentration is detected to exceed the safety threshold, the following operations are performed:

[0017] a) Fully open the bypass air duct valve;

[0018] b) Increase the exhaust fan speed to 120% of the rated value;

[0019] c) Send an emergency alarm signal to the user terminal.

[0020] Preferably, it further includes a wireless communication module that supports the LoRaWAN and NB - IoT dual - mode communication protocols. The wireless communication module is configured with:

[0021] A remote firmware upgrade interface;

[0022] An energy consumption data statistics unit;

[0023] A filter life prediction algorithm module.

[0024] Preferably, the control module integrates an adaptive learning unit, which includes:

[0025] A user behavior pattern analysis sub - module that records and learns the historical data of the user's manually adjusted parameters;

[0026] An environmental mode recognition sub - module that establishes a building thermal inertia prediction model based on the LSTM neural network;

[0027] An energy - saving optimization algorithm sub - module that automatically optimizes the operation strategy according to the time - of - use electricity price data and weather forecasts.

[0028] Preferably, the adaptive learning unit further includes:

[0029] An abnormal condition diagnosis module configured with a fault prediction model based on the random forest algorithm, which can identify the following abnormal states:

[0030] Risk warning of dew condensation on the heat - exchange core;

[0031] Recognition of the wear characteristics of the fan bearing;

[0032] Sensor data drift compensation.

[0033] Preferably, the adjustable-speed double centrifugal fan in the actuator group is driven by a BLDC motor without a Hall sensor, and its rotational speed control accuracy reaches ±1 rpm, and is equipped with:

[0034] A starting current soft start control unit;

[0035] A rotor position observer;

[0036] A three-phase current real-time monitoring module.

[0037] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0038] First, in the present invention, by adopting a cross-flow total heat exchange core and a bypass air duct, the energy utilization efficiency is improved, the energy consumption is reduced, and a variety of sensors are equipped to be able to detect parameters such as indoor and outdoor temperature, humidity, PM2.5, CO2 concentration, and VOC in real time and accurately, providing data support for precise control. By configuring a fuzzy PID algorithm processor in the control module, more precise control is achieved, the stability and reliability of the system are improved. By devices such as the adjustable-speed double centrifugal fan in the actuator group, the operating efficiency and flexibility of the system are improved. By the power management module having a function of switching between mains / solar dual-mode power supply, the diversity and sustainability of energy utilization are improved. By configuring a dynamic mode switching logic, the operating mode can be automatically switched according to different environments and requirements to achieve various goals such as energy saving, health, and quietness.

[0039] Second, in the present invention, by the wireless communication module supporting LoRaWAN and NB-IoT dual-mode communication protocols, remote monitoring and management are facilitated, and at the same time, functions such as remote firmware upgrade, energy consumption data statistics, and filter life prediction are provided, improving the intelligence level and maintenance convenience of the system. By the adaptive learning unit integrated in the control module, it can analyze user behavior patterns and environmental patterns, optimize the operation strategy, improve the adaptability and energy-saving effect of the system. At the same time, the abnormal working condition diagnosis module can timely detect and warn of potential faults, improving the reliability and safety of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a block diagram of the control system of the present invention;

[0041] Figure 2 It is a block diagram of the heat exchange unit of the present invention;

[0042] Figure 3 It is a block diagram of the heat exchange unit of the present invention;

[0043] Figure 4 It is a block diagram of the control module of the present invention;

[0044] Figure 5 It is a block diagram of the actuator group of the present invention. Specific embodiments

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0046] The present invention provides the following technical solutions:

[0047] Embodiment

[0048] Please refer to Figure 1 、 Figure 2 、 Figure 3 、 Figure 4 and Figure 5 , a control system for an intelligent total heat exchange fresh air machine, comprising:

[0049] A heat exchange unit, including a cross-flow total heat exchange core and a bypass air duct;

[0050] A sensor group, including temperature sensors, humidity sensors, PM2.5 sensors, CO2 concentration sensors and VOC sensors arranged on the indoor and outdoor air ducts;

[0051] A control module, configured with a fuzzy PID algorithm processor for receiving real-time detection data from the sensor group;

[0052] An actuator group, including a variable-speed double centrifugal fan, a damper driver and a bypass air duct switching device;

[0053] A power management module, having a function of switching between mains / solar dual-mode power supply.

[0054] Through the above technical solutions, the core component is the cross-flow total heat exchange core, which can achieve heat and moisture exchange between indoor and outdoor air, thereby improving energy utilization efficiency. The bypass air duct, as an auxiliary path, can be activated under specific conditions (such as when the indoor-outdoor temperature difference is small or the air quality is poor) to achieve more efficient air circulation. Temperature sensors (accuracy ±0.5°C), humidity sensors (±3%RH), PM2.5 sensors (detection range 0 - 500 μg / m 3) CO2 sensor (±50 ppm) and VOC sensor (detecting benzene, formaldehyde, etc., resolution 0.01 ppm). The temperature sensor is used to monitor the indoor and outdoor temperatures in real time, providing temperature data for the control system. The humidity sensor monitors the air humidity to ensure that the indoor humidity is maintained within a comfortable range. The PM2.5 sensor is used to detect the concentration of fine particulate matter in the air to ensure that the air quality meets the standards. The CO2 concentration sensor monitors the indoor carbon dioxide concentration to prevent discomfort caused by excessive concentration. The VOC sensor detects volatile organic compounds, which may be harmful to human health. The control module is equipped with a fuzzy PID algorithm processor, which can receive the real-time detection data of the sensor group and precisely control the system based on this data. The fuzzy PID algorithm combines the advantages of fuzzy logic and traditional PID control, enabling more stable and accurate control in complex and changing environments. The actuator group uses a variable-speed double centrifugal fan, a damper actuator, and a bypass duct switching device. The variable-speed double centrifugal fan is responsible for driving the air flow, and its speed can be adjusted according to the instructions of the control module. The damper actuator is used to control the opening and closing degree of the damper, thereby adjusting the air flow. The bypass duct switching device can switch between the total heat exchange mode and the bypass mode to adapt to different environmental requirements. The power management module has a function of switching between mains / solar dual-mode power supply, which can select the most suitable power supply method according to the current situation. When the mains power supply is insufficient or unstable, it can automatically switch to the solar power supply mode to ensure the stable operation of the system. The system integrates a heat exchange unit, a sensor group, a control module, an actuator group, and a power management module, which can monitor the indoor and outdoor environmental conditions in real time and precisely control the system based on these conditions, thereby achieving an efficient, energy-saving, and comfortable indoor environment.

[0055] The sensor group also includes an infrared particle counter and an ultrasonic flowmeter arranged on the air inlet side of the heat exchange core. The detection accuracy of the infrared particle counter reaches the 0.3 μm level.

[0056] Through the above technical solution, an infrared particle counter and an ultrasonic flowmeter are added on the air inlet side of the heat exchange core. The infrared particle counter is used to accurately count the tiny particulate matter in the air, which can reach the 0.3 μm level and can detect very fine particulate matter, enabling more effective evaluation and control of indoor air quality. The ultrasonic flowmeter is used to measure the air flow. By monitoring the air flow in real time, the ultrasonic flowmeter can help the control system more accurately adjust the speed of the fan and the opening and closing degree of the damper to ensure the maximization of air circulation efficiency and avoid energy waste.

[0057] The control module is configured with a dynamic mode switching logic, including:

[0058] Energy-saving priority mode: Activate the total heat exchange when the indoor-outdoor temperature difference exceeds the set threshold ΔT1;

[0059] Health Priority Mode: Automatically switch to the bypass mode when the PM2.5 concentration exceeds 75 μg / m 3 or the CO2 concentration exceeds 1000 ppm;

[0060] Silent Mode: Automatically reduce the fan speed to 60% of the reference value during the night period.

[0061] Through the above technical solutions, the control module is configured with dynamic mode switching logic. These logic modes are designed to automatically adjust the working state of the system according to different environmental conditions and usage requirements to achieve optimal energy efficiency, health comfort, and noise control. Among them, in the energy-saving priority mode, when the indoor-outdoor temperature difference exceeds the preset threshold ΔT1, the total heat exchange function is activated. By utilizing the temperature difference between indoor and outdoor air for heat and moisture exchange, energy consumption can be minimized while maintaining the stability of the indoor temperature. In the health priority mode, when the indoor PM2.5 concentration exceeds 75 μg / m 3 or the CO2 concentration exceeds 1000 ppm, it automatically switches to the bypass mode. In case of poor air quality, to avoid bringing pollutants into the indoor environment, the system will close the total heat exchange core and open the bypass air duct to ensure fresh and healthy indoor air. In the silent mode, during the preset night period (such as from 10 pm to 6 am), the fan speed is automatically reduced to 60% of its reference value. By reducing the fan speed, noise pollution can be significantly reduced, providing a quieter sleeping environment for users. At the same time, this mode also helps to save energy. Through intelligent judgment, the system can automatically adapt to different environmental conditions and usage requirements, thus providing a more comfortable, healthy, and energy-saving indoor environment. Users can also customize the triggering conditions and parameters of these modes according to actual needs through the control panel or remote control system to meet personalized usage requirements.

[0062] The dynamic mode switching logic also includes an emergency ventilation mode. When the detected VOC concentration exceeds the safety threshold, the following operations are performed:

[0063] a) Fully open the bypass air duct damper;

[0064] b) Increase the exhaust fan speed to 120% of the rated value;

[0065] c) Send an emergency alarm signal to the user terminal.

[0066] Through the above technical solution, the dynamic mode switching logic further includes an emergency ventilation mode. When the sensor group detects that the indoor VOC concentration exceeds the preset safety threshold, the system will immediately fully open the air valves of the bypass ventilation ducts. This operation will maximize the air circulation volume and accelerate the indoor air update speed. At the same time, the rotational speed of the exhaust fan will be increased to 120% of its rated value. By increasing the exhaust air volume, the system can remove indoor pollutants faster and reduce the VOC concentration. In addition to the physical emergency measures, the system will immediately send an emergency alarm signal to the user terminal (such as a mobile phone APP, a smart home control center, etc.), including information about the VOC concentration exceeding the standard and suggesting temporary measures for the user to take (such as closing doors and windows, avoiding staying indoors for a long time, etc.).

[0067] It also includes a wireless communication module that supports the LoRaWAN and NB-IoT dual-mode communication protocols. The wireless communication module is configured with:

[0068] A remote firmware upgrade interface;

[0069] An energy consumption data statistics unit;

[0070] A filter life prediction algorithm module.

[0071] Through the above technical solutions, the introduction of the wireless communication module provides great convenience for the remote management, energy consumption monitoring, and filter maintenance of the system. The wireless communication module supports the LoRaWAN and NB-IoT dual-mode communication protocols. LoRaWAN is a low-power wide-area network communication protocol suitable for the communication of Internet of Things devices with long distances and low power consumption. NB-IoT, on the other hand, is a communication protocol for narrowband Internet of Things, with advantages such as wide coverage, stable connections, and low costs. The support of the dual-mode communication protocol enables the system to select the most suitable communication method according to actual needs and environmental conditions, ensuring the stability and reliability of data transmission. Through the remote firmware upgrade interface, users can perform remote firmware upgrades on the control system of the fresh air fan. When a new version of the system is released or known problems are fixed, users can download and install updates via the wireless network without having to be present in person. This function greatly improves the maintainability and flexibility of the system. The energy consumption data statistics unit is responsible for collecting and statistics the energy consumption data of the fresh air fan, including key information such as running time and power consumption. These data can be transmitted to the cloud server or user terminal via the wireless communication module for users to analyze and manage. Through the energy consumption data statistics, users can more intuitively understand the energy consumption situation of the fresh air fan, thereby formulating more effective energy-saving strategies. The filter life prediction algorithm module uses advanced algorithms to predict the filter life of the fresh air fan. By analyzing the usage time of the filter, environmental conditions (such as PM2.5 concentration, humidity, etc.), and the actual state of the filter (such as clogging degree, filtration efficiency, etc.), it estimates the remaining life of the filter. When the filter is approaching the end of its life, the system will send a warning message to the user to remind the user to replace the filter in a timely manner, which helps to ensure the continuous and efficient operation of the fresh air fan and extend the service life of the filter.

[0072] The control module is integrated with an adaptive learning unit, which includes:

[0073] The user behavior pattern analysis sub-module records and learns the historical data of the parameters manually adjusted by the user;

[0074] The environmental mode recognition sub-module establishes a building thermal inertia prediction model based on the LSTM neural network;

[0075] The energy-saving optimization algorithm sub-module automatically optimizes the operation strategy according to the time-of-use electricity price data and weather forecasts.

[0076] Through the above technical solution, the adaptive learning unit makes the system more intelligent in adapting to user needs and environmental conditions by deeply learning user behaviors, predicting environmental changes, and optimizing energy usage strategies, thereby achieving more efficient and personalized operation. Among them, the user behavior pattern analysis sub-module is used to record and learn the historical data of the user's manual adjustment of the fresh air fan, such as temperature setting, humidity adjustment, wind speed selection, etc. Through data analysis algorithms, it identifies the user's preferences and habits, such as common settings within a specific time period, differences between weekends and weekdays, etc. The environmental pattern recognition sub-module processes time series data through an LSTM neural network. By learning the historical changes of environmental factors inside and outside the building (such as temperature, humidity, solar radiation, etc.), it predicts the thermal inertia changes within a certain period in the future, and then improves the system's response speed to outdoor environmental changes, adjusts the operation strategy of the fresh air fan in advance to adapt to the upcoming environmental changes, and thus maintains the stability of the indoor environment. The energy-saving optimization algorithm sub-module can automatically optimize the operation strategy of the fresh air fan according to the time-of-use electricity price data and weather forecasts. Combining the real-time electricity price information and weather forecasts for the next few days (such as temperature, humidity, wind speed, etc.), it calculates the optimal operation plan through intelligent algorithms, such as increasing the operation time of the fresh air fan during the low electricity price period, or starting the cooling mode in advance in the upcoming hot weather, achieving the maximization of the cost-effectiveness of energy usage while reducing the impact on the environment, in line with the development trend of green energy conservation.

[0077] The adaptive learning unit further includes:

[0078] An abnormal condition diagnosis module, configured with a fault prediction model based on a random forest algorithm, capable of identifying the following abnormal states:

[0079] Early warning of the risk of dew condensation on the heat exchange core;

[0080] Identification of the wear characteristics of the fan bearing;

[0081] Sensor data drift compensation.

[0082] Through the above technical solution, the abnormal condition diagnosis module is configured with a fault prediction model based on the random forest algorithm, which can accurately identify various abnormal states, thus ensuring the stable operation and efficient maintenance of the system. Among them, by monitoring the operating data of the fresh air unit and external environmental conditions (such as temperature, humidity, etc.), the random forest algorithm is used to predict the risk of dew condensation on the heat exchange core. Once the dew condensation risk is predicted, the system will immediately issue a warning to prompt the user to take corresponding measures, such as adjusting the operating parameters of the fresh air unit, strengthening the heat preservation measures, etc., to avoid equipment damage and performance degradation caused by dew condensation. Among them, by analyzing the vibration signal, temperature data, etc. of the fan bearing, combined with the random forest algorithm to identify the characteristics of bearing wear. When the signs of bearing wear are identified, the system will issue a warning in time to remind the user to replace the bearing or perform maintenance to prevent equipment shutdown or failure caused by bearing damage. Among them, during the long-term operation of the sensor, data drift may occur due to factors such as aging and pollution, affecting the accuracy and stability of the system. The abnormal condition diagnosis module uses the random forest algorithm to correct and compensate the sensor data to ensure that the system can obtain accurate environmental parameters and operating data, so as to make correct decisions and controls. The abnormal condition diagnosis module of the adaptive learning unit can accurately identify abnormal states such as the risk of dew condensation on the heat exchange core, the characteristics of fan bearing wear, and sensor data drift by configuring a fault prediction model based on the random forest algorithm, providing a strong guarantee for the stable operation and efficient maintenance of the intelligent total heat exchange fresh air unit.

[0083] The adjustable-speed double centrifugal fan in the actuator group is driven by a BLDC motor without a Hall sensor, and its speed control accuracy reaches ±1 rpm, and is configured with:

[0084] A starting current soft start control unit;

[0085] A rotor position observer;

[0086] A three-phase current real-time monitoring module.

[0087] Through the above technical solution, the adjustable-speed dual centrifugal fan adopts a BLDC (brushless DC) motor drive without a Hall sensor, which not only improves the rotational speed control accuracy of the fan, but also enhances the stability and reliability of the system. The BLDC motor drive system without a Hall sensor does not rely on a physical position sensor, reducing the system cost while improving the reliability and stability of the system. The rotational speed control accuracy reaches ±1 rpm, ensuring the stability and accuracy of the fan operation. By adjusting the starting current and voltage of the motor, smooth startup of the fan is achieved, avoiding shocks and vibrations during startup. Estimating the rotor position and speed by using variables in the motor winding (such as stator current and stator voltage) reduces the system cost, improves the reliability and stability of the system, and achieves high-precision field-oriented control. The three-phase current data during the operation of the motor is collected and monitored in real time to timely detect current anomalies and prevent motor failures, providing data support for the optimal control and protection of the motor.

[0088] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principle and spirit. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A control system for an intelligent total heat exchange fresh air machine, characterized in that, Including: A heat exchange unit, comprising a cross-flow total heat exchange core and a bypass air duct; A sensor group, including temperature sensors, humidity sensors, PM2.5 sensors, CO2 concentration sensors and VOC sensors arranged on the indoor and outdoor air ducts; A control module, configured with a fuzzy PID algorithm processor for receiving real-time detection data from the sensor group; An actuator group, including a variable-speed double centrifugal fan, a damper driver and a bypass air duct switching device; A power management module, with the function of switching between mains / solar dual-mode power supply.

2. The control system of an intelligent total heat exchange fresh air machine according to claim 1, characterized in that, The sensor group further includes an infrared particle counter and an ultrasonic flowmeter arranged on the air inlet side of the heat exchange core, and the detection accuracy of the infrared particle counter reaches the 0.3μm level.

3. The control system of an intelligent total heat exchange fresh air machine according to claim 1, characterized in that, The control module is configured with a dynamic mode switching logic, including: Energy-saving priority mode: Activate total heat exchange when the indoor-outdoor temperature difference exceeds the set threshold ΔT1; Health Priority Mode: Automatically switch to the bypass mode when the PM2.5 concentration exceeds 75 μg / m 3 or the CO2 concentration exceeds 1000 ppm; Silent mode: Automatically reduce the fan speed to 60% of the reference value during the night period.

4. The control system of an intelligent total heat exchange fresh air machine according to claim 3, characterized in that, The dynamic mode switching logic further includes an emergency ventilation mode. When the detected VOC concentration exceeds the safety threshold, the following operations are performed: a) Fully open the bypass air duct damper; b) Increase the exhaust fan speed to 120% of the rated value; c) Send an emergency alarm signal to the user terminal.

5. The control system of an intelligent total heat exchange fresh air fan according to claim 1, characterized in that: It also includes a wireless communication module, supporting LoRaWAN and NB-IoT dual-mode communication protocols, and the wireless communication module is configured with: A remote firmware upgrade interface; An energy consumption data statistics unit; A filter life prediction algorithm module.

6. The control system of an intelligent total heat exchange fresh air fan according to claim 1, characterized in that: The control module integrates an adaptive learning unit, which includes: A user behavior pattern analysis sub-module, recording and learning historical data of user manual adjustment parameters; An environmental mode recognition sub-module, establishing a building thermal inertia prediction model based on the LSTM neural network; An energy-saving optimization algorithm sub-module, automatically optimizing the operation strategy according to time-of-use electricity price data and weather forecasts.

7. The control system of an intelligent total heat exchange fresh air machine according to claim 6, characterized in that: The adaptive learning unit further includes: An abnormal condition diagnosis module, configured with a fault prediction model based on the random forest algorithm, capable of identifying the following abnormal states: Early warning of dew condensation risk in the heat exchange core; Identification of fan bearing wear characteristics; Sensor data drift compensation.

8. The control system of an intelligent total heat exchange fresh air machine according to claim 1, characterized in that: The variable-speed double centrifugal fan in the actuator group is driven by a BLDC motor without a Hall sensor, and its speed control accuracy reaches ±1rpm, and is configured with: A starting current soft start control unit; A rotor position observer; A three-phase current real-time monitoring module.

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