Heating ventilation air conditioner dynamic control system and method based on multi-sensor data fusion

Abstract

The invention discloses a heating ventilation air conditioner dynamic control system and method based on multi-sensor data fusion. The method comprises the steps that indoor and outdoor environment information and indoor occupancy condition information are collected; according to different privacy requirements, room types are divided into four types including a low privacy requirement, a medium privacy requirement, a high privacy requirement and a large space area; according to privacy requirements, multiple sensor combinations are selected for detection to obtain the number of indoor occupants; and indoor air conditioner cooling and heating loads are calculated, the indoor air conditioner cooling and heating loads are corrected through dynamic factors to adjust air conditioner operation parameters, and the dynamic factors are obtained through weighted summation based on the indoor and outdoor environment change factors, the indoor equipment use change factors and the indoor people number change factors. By integrating various types of sensors, indoor and outdoor environment changes and occupancy conditions can be sensed more comprehensively, and by means of the comprehensive data collecting and processing mode, the air conditioning system can be accurately adjusted according to real-time data, energy utilization is optimized, and unnecessary energy consumption is reduced.

Description

Technical Field

[0001] The present invention belongs to the field of HVAC system control, and in particular, relates to a HVAC dynamic control system and method based on multi-sensor data fusion. Background Art

[0002] In modern building environments, the efficiency and precise control of heating, ventilation and air conditioning (HVAC) systems are crucial for energy conservation, emission reduction and improving indoor environmental quality. Traditional HVAC systems rely on simplified control strategies, such as fixed temperature settings and timed operations, which often cannot adapt to complex and changing indoor and outdoor environments, resulting in energy waste and reduced comfort.

[0003] With the advancement of technology, the application of multi-sensor data fusion technology in HVAC systems has gradually attracted attention. This technology provides more comprehensive and accurate environmental information by comprehensively utilizing data collected by multiple sensors, thereby achieving more efficient system control. However, the existing multi-sensor fusion methods still have shortcomings in the comprehensive processing of indoor and outdoor environmental data, the accurate detection of occupancy conditions, and the real-time calculation of air conditioning cooling and heating loads.

[0004] First, most existing systems cannot fully utilize indoor and outdoor environmental data to optimize the operation of air conditioning systems. Second, existing technologies often rely on a single sensor type, such as infrared or video sensors, for occupancy detection, which limits the accuracy and scope of detection. For example, relying solely on video sensors is difficult to implement in environments with high privacy requirements. Finally, existing air conditioning load calculation models often use static analysis methods and cannot reflect the impact of changing indoor and outdoor environmental conditions on cooling and heating loads in real time.

[0005] These deficiencies lead to reduced HVAC system efficiency and increased energy consumption, which is not conducive to achieving green energy saving and improving indoor environmental quality. Summary of the invention

[0006] The present invention aims to address the deficiencies of the prior art and to propose a HVAC dynamic control system and method based on multi-sensor data fusion.

[0007] The present invention is realized by the following technical solutions: a dynamic control system for HVAC based on multi-sensor data fusion, the system comprising an information acquisition unit, an information processing unit, a load calculation unit, a parameter calculation unit and a terminal control unit;

[0008] The information collection unit collects indoor and outdoor environment information and indoor occupancy information in real time;

[0009] The information processing unit is used to process the indoor occupancy information collected by the information collection unit, and divide the room types into four types according to different privacy requirements: low privacy requirements, medium privacy requirements, high privacy requirements, and large space areas; according to the privacy requirements, multiple sensor combinations are selected to detect the number of indoor occupants;

[0010] The load calculation unit calculates the indoor air conditioning cooling and heating load according to the indoor and outdoor environment information and the number of indoor occupants, and corrects the indoor air conditioning cooling and heating load by a dynamic factor, wherein the dynamic factor is obtained by weighted summation based on the indoor and outdoor environment change factor, the indoor equipment use change factor and the indoor number of people change factor;

[0011] The parameter calculation unit calculates the operating parameters of the HVAC system according to the indoor air conditioning cooling and heating loads;

[0012] The terminal control unit is used to adjust the operating parameters of the air conditioner according to the operating parameters of the HVAC system.

[0013] Furthermore, the indoor and outdoor environmental information is data collected based on temperature and humidity sensors, solar radiation sensors and smart sockets.

[0014] Furthermore, the indoor occupancy information is occupancy information of different types of rooms collected based on cameras, pressure-sensitive sensor carpets, light interruption sensors, CO2 sensors, PIR sensors or wireless sensors.

[0015] Furthermore, for rooms with low privacy requirements, cameras are used to output the number of occupants in the room in real time. For rooms with medium privacy requirements, a combination of cameras and CO2 sensors is used to calculate the number of occupants in the room through discrete differential equations. For rooms with high privacy requirements, pressure-sensitive sensor carpets and light interruption sensors are used to judge the entry and exit status of people and calculate the number of occupants in the room based on the time difference between the two sensor signals. For large space areas, CO2 sensors, PIR sensors and wireless sensors are used, and the ANN model and multivariate linear regression model are used to fuse the sensor data to calculate the number of occupants in the area.

[0016] Furthermore, the process of the load calculation unit calculating the indoor air conditioning cooling and heating load is as follows:

[0017] 1) Measure the indoor and outdoor temperature and humidity through indoor and outdoor temperature sensors, and calculate the heat loss Q of infiltration and ventilation infiltration ; Combine the building envelope area and the building envelope heat transfer coefficient to calculate the heat gain Q through the building envelope conduction .

[0018] 2) Read the power of lights and equipment through smart sockets, calculate the internal heat gain from lights and equipment based on the ledger information; calculate the internal heat gain from the human body based on the number of indoor occupants measured by the occupancy detection sensor; add the above two heat gains to get the internal heat gain Q internal .

[0019] 3) The solar radiation intensity is measured by the solar radiation sensor, and the solar radiation heat gain Q transmitted and absorbed by the transparent enclosure structure is calculated by combining the transparent enclosure structure area and heat transfer coefficient solar .

[0020] 4) Establish a heat balance model, add all heat gains and heat losses to obtain the total heat load, and calculate it by the following formula:

[0021] Q total =Q infitration +Q conduction +Q internal +Q solar

[0022] 5) Introduce the dynamic factor DF and establish a dynamic heat balance model. This factor captures and quantifies the real-time changes in the building's internal and external environment, such as personnel activities, equipment usage, external climate conditions, etc., and reflects the impact of real-time data changes on heat load. The dynamic factor DF is calculated by the following formula:

[0023] DF=w1·F occupancy +w2·F equipment +w3·F external

[0024] F occupancy =N current ×100

[0025]

[0026] F external =ΔT×C T +ΔH×C H

[0027]

[0028] Among them, F occupancy is the occupancy factor based on the occupancy sensor data, N current is the current number of people in the room; F equipment is the device usage factor based on the device data collected by the smart socket, P t is the power of device i, S t is the state of device i (0 for off, 1 for on), n is the total number of devices in the room; F externalis the external environmental factor based on the data of temperature and humidity sensor and solar radiation sensor, ΔT is the temperature difference between indoor and outdoor, ΔH is the relative humidity difference between indoor and outdoor, C T and C H are the influence coefficients of temperature difference and humidity difference respectively; w1, w2, w3 are weight coefficients, X i is the historical data set of factor i, Y is the i The corresponding data set of air conditioning load response, the weight coefficient is adjusted according to the influence of sensor data on heat load; the dynamic heat load is calculated by the following formula:

[0029] Q total_dynamic =Q total (1+DF)

[0030] Among them, Q total_dynamic is the total heat load after correction by dynamic factor.

[0031] Furthermore, the parameter calculation unit selects the air supply volume and air supply temperature as adjustment parameters of the HVAC dynamic control strategy, and calculates the air supply volume and air supply temperature of the air conditioner using the heat and moisture balance method according to the real-time cooling and heating load and humidity load of the air conditioner.

[0032] On the other hand, the present invention also provides a HVAC dynamic control method based on multi-sensor data fusion, the method comprising the following steps:

[0033] (1) Real-time collection of indoor and outdoor environmental information;

[0034] (2) According to different privacy requirements, the room types are divided into four types: low privacy requirements, medium privacy requirements, high privacy requirements, and large space areas; according to the privacy requirements, multiple sensor combinations are selected to detect the number of indoor occupants;

[0035] (3) Calculate the indoor air conditioning cooling and heating load based on the indoor and outdoor environmental information and the number of indoor occupants, and correct the indoor air conditioning cooling and heating load through a dynamic factor. The dynamic factor is obtained by weighted summation of the indoor and outdoor environmental change factor, the indoor equipment usage change factor, and the indoor occupancy change factor;

[0036] (4) Calculate the operating parameters of the HVAC system based on the indoor air conditioning cooling and heating loads;

[0037] (5) Adjust the operating parameters of the air conditioner according to the operating parameters of the HVAC system.

[0038] The beneficial effects of the present invention are as follows: By integrating various types of sensors, the present method can more comprehensively perceive changes in indoor and outdoor environments and occupancy conditions. This comprehensive data collection and processing method enables the air-conditioning system to be accurately adjusted according to real-time data, optimize energy utilization, and reduce unnecessary energy consumption. Especially in terms of occupancy detection, the present invention ensures the accuracy and adaptability of detection by combining detection technologies with different privacy levels, such as the YOLOV8 algorithm and sensor fusion methods. In addition, by establishing a dynamic air-conditioning cooling and heating load calculation model, the present method can accurately predict and respond to the impact of indoor and outdoor environmental changes on air-conditioning demand, thereby providing a more comfortable indoor environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 It is a block diagram of the composition of the HVAC dynamic control system based on multi-sensor data fusion of the present invention;

[0041] Figure 2 The present invention is a flow chart of a HVAC dynamic control method based on multi-sensor data fusion. DETAILED DESCRIPTION

[0042] The present invention will be described in detail below according to the accompanying drawings and preferred embodiments, and the purpose and effect of the present invention will become more clear. The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0043] like Figure 1 As shown, the present invention provides a HVAC dynamic control system based on multi-sensor data fusion, which specifically includes an information acquisition unit, an information processing unit, a load calculation unit, a parameter calculation unit and a terminal control unit.

[0044] The information collection unit is used to collect indoor and outdoor environment information and indoor occupancy information in real time. The information collection unit includes an indoor and outdoor environment information collection module and an indoor occupancy information collection module.

[0045] The indoor and outdoor environment information collection module is used to collect outdoor temperature and humidity, solar radiation intensity, indoor temperature and humidity, operating power of lighting equipment and other equipment, and mainly includes temperature and humidity sensors, solar radiation sensors and smart sockets.

[0046] The indoor occupancy information collection module is used to collect indoor occupancy information by arranging different types of sensors according to the requirements of different types of rooms, mainly including cameras, pressure-sensitive sensor carpets, light interruption sensors, CO2 sensors, PIR sensors and wireless sensors.

[0047] The information processing unit is used to process the indoor occupancy information collected by the information collection unit, and can obtain the number of indoor occupants according to the data of different types of sensors. The information processing unit includes a low privacy requirement processing module, a medium privacy requirement processing module, a high privacy requirement processing module and a large space area processing module.

[0048] The low privacy requirement processing module is used to process the camera data of the low privacy requirement room and output the number of occupants in the room in real time. The low privacy requirement room is a public or semi-public space with low privacy requirements, such as a reception area, an open office area, etc.

[0049] The medium privacy requirement processing module is used to process the camera and CO2 sensor data of the medium privacy requirement room and output the number of occupants in the room in real time. The medium privacy requirement room is a semi-private space that requires a certain degree of privacy protection, such as a conference room, classroom, small office, lounge, etc.

[0050] The high privacy requirement processing module is used to process the pressure-sensitive sensor carpet and light interruption sensor data of the high privacy requirement room, and output the number of occupants in the room in real time. The high privacy requirement room is a private space with very high requirements for privacy protection, such as personal offices, wards, dressing rooms, dormitory rooms, etc.

[0051] The large space area processing module is used to process the data of CO2 sensors, PIR sensors and wireless sensors in the large space area, and output the number of people in the large space area in real time. The large space area is a large open space that can accommodate multiple people, such as a library reading room, a restaurant, an exhibition center, a large conference room, etc.

[0052] The load calculation unit is used to calculate the indoor air conditioning cooling and heating load, and calculates the indoor air conditioning cooling and heating load according to the indoor and outdoor environmental information collected by the information collection unit and the number of indoor occupants output by the information processing unit.

[0053] The parameter calculation unit is used to calculate the operating parameters of the HVAC system according to the indoor air conditioning cooling and heating loads obtained by the load calculation unit.

[0054] The terminal control unit is used to adjust the operating parameters of the air conditioner according to the received data.

[0055] like Figure 2 As shown, the present invention also provides a HVAC dynamic control method based on multi-sensor data fusion, based on the system, including:

[0056] S1. The information collection unit collects indoor and outdoor environment information and indoor occupancy information in real time;

[0057] S2. The information processing unit calculates the number of indoor occupants based on different types of rooms and different types of occupancy sensor data;

[0058] S3. The load calculation unit establishes an air conditioning cooling and heating load calculation model based on the dynamic heat balance method, reads the data of each sensor and imports it into the air conditioning cooling and heating load calculation model based on the dynamic heat balance method, and realizes real-time calculation of indoor air conditioning cooling and heating loads;

[0059] S4. The parameter calculation unit selects the air supply volume and air supply temperature as the adjustment parameters of the HVAC dynamic control strategy. According to the real-time cooling and heating load and humidity load of the air conditioner, the air supply volume and air supply temperature of the air conditioner are calculated using the heat and humidity balance method.

[0060] S5. The terminal control unit sends the received air supply volume and air supply temperature adjustment commands to the air conditioning terminal to realize dynamic control of the HVAC system.

[0061] Furthermore, the step of calculating the number of indoor occupants according to different types of rooms and different types of occupancy sensor data in S2 is:

[0062] S21. For rooms with low privacy requirements, train the YOLOV8 algorithm to identify the number of occupants from the RGB image and load it to the camera installed indoors to achieve real-time detection of indoor occupancy;

[0063] S22. For rooms with medium privacy requirements, the YOLOV8 algorithm is trained to identify the number of occupants from the RGB image and load it to the cameras installed at the entrance and exit of the room. In order to reduce the errors caused by light and camera angle, CO2 sensors are deployed to correct the image recognition results and calculate the number of room occupants through discrete differential equations.

[0064] S23. For rooms with high privacy requirements, pressure-sensitive sensor carpets and light interruption sensors are placed at the entrance and exit of the room to detect the number of people entering and leaving. Based on the time difference between the two sensor signals, the entry and exit status of the person is determined and the number of people occupying the room is calculated.

[0065] S24. For large space areas, the method of fusing CO2 sensors, PIR sensors and Wi-Fi is adopted, and the sensor data are fused using ANN model and multivariate linear regression model to calculate the number of occupants in the area.

[0066] Furthermore, the step of calculating indoor air conditioning cooling and heating loads in real time in S3 is:

[0067] S31. Measure the indoor and outdoor temperature and humidity through indoor and outdoor temperature sensors, and calculate the heat loss Q of infiltration and ventilation infiltration ; Combine the building envelope area and the building envelope heat transfer coefficient to calculate the heat gain Q through the building envelope conduction .

[0068] S32. Read the power of lights and equipment through smart sockets, calculate the internal heat gain from lights and equipment based on the ledger information; calculate the internal heat gain from the human body based on the number of indoor occupants measured by the occupancy detection sensor; add the above two heat gains to get the internal heat gain Q internal .

[0069] S33. The solar radiation intensity is measured by the solar radiation sensor, and the solar radiation heat gain Q transmitted and absorbed by the transparent enclosure structure is calculated by combining the transparent enclosure structure area and heat transfer coefficient. solar .

[0070] S34. Establish a heat balance model, add up all heat gains and heat losses, and get the total heat load, which is calculated by the following formula:

[0071] Q total =Q infitration +Q conduction +Q internal +Q solar

[0072] S35. Introduce the dynamic factor DF and establish a dynamic heat balance model. This factor captures and quantifies the real-time changes in the building's internal and external environment, such as personnel activities, equipment usage, external climate conditions, etc., and reflects the impact of real-time data changes on heat load. The dynamic factor DF is calculated by the following formula:

[0073] DF=w1·F occupancy +w2·F equipment +w3·F external

[0074] F occupancy =N current ×100

[0075]

[0076] F external =ΔT×C T +ΔH×C H

[0077]

[0078] Among them, F occupancy is the occupancy factor based on the occupancy sensor data, N current is the current number of people in the room; F equipment is the device usage factor based on the device data collected by the smart socket, P t is the power of device i, S t is the state of device i (0 for off, 1 for on), n is the total number of devices in the room; F external is the external environmental factor based on the data of temperature and humidity sensor and solar radiation sensor, ΔT is the temperature difference between indoor and outdoor, ΔH is the relative humidity difference between indoor and outdoor, C T and C H are the influence coefficients of temperature difference and humidity difference respectively; w1, w2, w3 are weight coefficients, X i is the historical data set of factor i, Y is the i The corresponding data set of air conditioning load response, the weight coefficient can be dynamically adjusted based on historical data and machine learning algorithms to optimize the response of the air conditioning system. The dynamic heat load is calculated by the following formula:

[0079] Q total_dynamic =Q total (1+DF)

[0080] Among them, Q total_dynamic is the total heat load after correction by dynamic factor.

[0081] Furthermore, the step of calculating the air supply volume and air supply temperature of the air conditioner by using the heat and moisture balance method in S4 is as follows:

[0082] S41. Calculate the indoor humidity load using the indoor humidity measured by the humidity sensor.

[0083] S42. Establish heat balance equation and moisture balance equation, and get heat-moisture ratio after sorting. Use the process line of heat-moisture ratio on the enthalpy-humidity diagram to represent the state change process of the incoming air.

[0084] S43. Select the supply air temperature difference and ventilation frequency, determine the indoor state point on the enthalpy-humidity diagram, and make a heat-humidity ratio line passing through the indoor state point based on the heat-humidity ratio.

[0085] S44. According to the selected supply air temperature difference, determine the supply air state point on the heat-humidity ratio line, calculate the required supply air volume according to the equation obtained from the heat balance, and check the ventilation times.

[0086] The above embodiments are used to illustrate the present invention rather than to limit the present invention. Any modification and change made to the present invention within the spirit of the present invention and the protection scope of the claims shall fall within the protection scope of the present invention.

Claims

1. A HVAC dynamic control system based on multi-sensor data fusion, characterized in that: The system includes an information collection unit, an information processing unit, a load calculation unit, a parameter calculation unit and a terminal control unit; The information collection unit collects indoor and outdoor environment information and indoor occupancy information in real time; The information processing unit is used to process the indoor occupancy information collected by the information collection unit, and classify the room types into four types: low privacy requirement, medium privacy requirement, high privacy requirement and large space area according to different privacy requirements; According to privacy requirements, select a combination of multiple sensors to detect the number of indoor occupants; The load calculation unit calculates the indoor air conditioning cooling and heating load according to the indoor and outdoor environment information and the number of indoor occupants, and corrects the indoor air conditioning cooling and heating load by a dynamic factor, wherein the dynamic factor is obtained by weighted summation based on the indoor and outdoor environment change factor, the indoor equipment use change factor and the indoor number of people change factor; The parameter calculation unit calculates the operating parameters of the HVAC system according to the indoor air conditioning cooling and heating loads; The terminal control unit is used to adjust the operating parameters of the air conditioner according to the operating parameters of the HVAC system.

2. A HVAC dynamic control system based on multi-sensor data fusion according to claim 1, characterized in that: Indoor and outdoor environmental information is data collected based on temperature and humidity sensors, solar radiation sensors, and smart sockets.

3. A HVAC dynamic control system based on multi-sensor data fusion according to claim 1, characterized in that: Indoor occupancy information is the occupancy information of different types of rooms collected based on cameras, pressure-sensitive sensor carpets, light interruption sensors, CO2 sensors, PIR sensors or wireless sensors.

4. A HVAC dynamic control system based on multi-sensor data fusion according to claim 1, characterized in that: For rooms with low privacy requirements, cameras are used to output the number of occupants in the room in real time. For rooms with medium privacy requirements, a combination of cameras and CO2 sensors is used to calculate the number of occupants in the room through discrete differential equations. For rooms with high privacy requirements, pressure-sensitive sensor carpets and light interruption sensors are used. According to the time difference between the two sensor signals, the entry and exit status of people is judged and the number of occupants in the room is calculated. For large space areas, CO2 sensors, PIR sensors and wireless sensors are used, and the ANN model and multivariate linear regression model are used to fuse the sensor data to calculate the number of occupants in the area.

5. A HVAC dynamic control system based on multi-sensor data fusion according to claim 1, characterized in that: The process of the load calculation unit calculating the indoor air conditioning cooling and heating load is as follows: 1) Measure the indoor and outdoor temperature and humidity through indoor and outdoor temperature sensors, and calculate the heat loss Q of infiltration and ventilation infiltration ; Combine the building envelope area and the building envelope heat transfer coefficient to calculate the heat gain Q through the building envelope conduction . 2) Read the power of lights and equipment through smart sockets, and calculate the internal heat gain from lights and equipment based on the ledger information; Calculate internal heat gain from human bodies based on the number of indoor occupants measured by occupancy detection sensors; Add the above two heat gains to get the internal heat gain Q internal . 3) The solar radiation intensity is measured by the solar radiation sensor, and the solar radiation heat gain Q transmitted and absorbed by the transparent enclosure structure is calculated by combining the transparent enclosure structure area and heat transfer coefficient. solar . 4) Establish a heat balance model, add all heat gains and heat losses to obtain the total heat load, and calculate it by the following formula: Q total =Q infitration +Q conduction +Q internal +Q solar 5) Introduce the dynamic factor DF and establish a dynamic heat balance model. This factor captures and quantifies the real-time changes in the building's internal and external environment, such as personnel activities, equipment usage, external climate conditions, etc., and reflects the impact of real-time data changes on heat load. The dynamic factor DF is calculated by the following formula: DF=w1·F occupancy +w2·F equipment +w3·F external F occupancy =N current ×100 F external =ΔT×C T +ΔH×C H Among them, F occupancy is the occupancy factor based on the occupancy sensor data, N current is the current number of people in the room; F equipment is the device usage factor based on the device data collected by the smart socket, P t is the power of device i, S t is the state of device i (0 for off, 1 for on), n is the total number of devices in the room; F external is the external environmental factor based on the data of temperature and humidity sensor and solar radiation sensor, ΔT is the temperature difference between indoor and outdoor, ΔH is the relative humidity difference between indoor and outdoor, C T and C H are the influence coefficients of temperature difference and humidity difference respectively; w1, w2, w3 are weight coefficients, X i is the historical data set of factor i, Y is the i The corresponding data set of air conditioning load response, the weight coefficient is adjusted according to the influence of sensor data on heat load; the dynamic heat load is calculated by the following formula: Q total_dynamic =Q total (1+DF) Among them, Q total_dynamic is the total heat load after correction by dynamic factor.

6. A HVAC dynamic control system based on multi-sensor data fusion according to claim 1, characterized in that: The parameter calculation unit selects the supply air volume and the supply air temperature as adjustment parameters of the HVAC dynamic control strategy. According to the real-time cooling, heating and humidity loads of the air conditioner, the air supply volume and air supply temperature are calculated using the heat and humidity balance method.

7. A HVAC dynamic control method based on multi-sensor data fusion based on the system according to any one of claims 1 to 6, characterized in that: The method comprises the following steps: (1) Real-time collection of indoor and outdoor environmental information; (2) According to different privacy requirements, the room types are divided into four types: low privacy requirements, medium privacy requirements, high privacy requirements, and large space areas; according to the privacy requirements, multiple sensor combinations are selected to detect the number of indoor occupants; (3) Calculate the indoor air conditioning cooling and heating load based on the indoor and outdoor environmental information and the number of indoor occupants, and correct the indoor air conditioning cooling and heating load through a dynamic factor. The dynamic factor is obtained by weighted summation of the indoor and outdoor environmental change factor, the indoor equipment usage change factor, and the indoor occupancy change factor; (4) Calculate the operating parameters of the HVAC system based on the indoor air conditioning cooling and heating loads; (5) Adjust the operating parameters of the air conditioner according to the operating parameters of the HVAC system.

Images