Building air conditioning system partition control method based on knx technology

Abstract

The application discloses a building air conditioning system partition control method based on KNX technology and relates to the related technical field of building air conditioning systems.The method comprises the following steps: acquiring building structure information and air conditioning system topology information based on a KNX protocol, dividing an air conditioning control partition set, setting a target temperature interval, and performing global optimization of a partition target temperature; initializing partition control parameters of each air conditioning control partition according to real-time data collected by indoor and outdoor temperature sensors and in combination with a reference personnel density and a partition control target; dynamically adjusting air conditioning operation parameters of each air conditioning control partition based on personnel flow data and predicting a personnel flow direction to perform prospective regulation and control. The technical problems that the prior art cannot finely regulate and control according to actual demands of different functional areas in a building, leading to energy waste of an air conditioning system and poor user comfort, are solved, and the technical effects of reducing air conditioning energy consumption and improving user comfort are achieved.

Description

Technical Field

[0001] This application relates to the technical field of building air conditioning systems, and in particular to a zoning control method for building air conditioning systems based on KNX technology. Background Technology

[0002] With the acceleration of urbanization and the continuous expansion of building scale, building energy consumption has become increasingly prominent. Air conditioning systems, as a major component of building energy consumption, account for a significant proportion of total building energy consumption. Modern buildings typically have complex structures and diverse functional zones, such as office areas, meeting rooms, and rest areas. Each functional zone has different requirements for environmental parameters such as temperature and humidity. Traditional air conditioning system control methods often employ centralized control or simple zone control, making it difficult to achieve precise regulation of different functional areas within the building based on building structure, personnel distribution, and environmental changes. This not only leads to energy waste but may also affect user comfort. With the diversification of building functions and the increasing complexity of personnel flow, traditional control methods can no longer meet the needs of modern buildings.

[0003] At present, there are technical problems with the technology, such as difficulty in finely controlling the air conditioning system according to the actual needs of different functional areas in the building, resulting in energy waste and poor user comfort. Summary of the Invention

[0004] This application provides a zonal control method for building air conditioning systems based on KNX technology, which solves the technical problem in the prior art that it is difficult to make precise control according to the actual needs of different functional areas in the building, resulting in energy waste and poor user comfort in the air conditioning system. It achieves the technical effect of reducing air conditioning energy consumption and improving user comfort.

[0005] This application provides a building air conditioning system zoning control method based on KNX technology, comprising: acquiring building structure information and air conditioning system topology information based on the KNX protocol, and dividing the air conditioning control zoning set based on the building structure information and the air conditioning system topology information; setting a target temperature range for each air conditioning control zone, and performing global optimization of the zone target temperature based on the target temperature range to obtain the zone control target; initializing the zoning control parameters of each air conditioning control zone based on real-time data collected by indoor and outdoor temperature sensors, combined with the baseline personnel density and the zoning control target, wherein the zoning control parameters include set temperature, cooling / heating capacity, and air volume parameters; dynamically adjusting the air conditioning operation parameters of each air conditioning control zone based on personnel flow data collected by building entrance and exit sensors, and predicting the personnel flow direction to perform forward-looking regulation.

[0006] In a possible implementation, the building air conditioning system zoning control method based on KNX technology also performs the following processing: obtaining building structure information and air conditioning system topology information based on the KNX protocol, wherein the building structure information includes floor plans and functional zoning information, and the air conditioning system topology information includes the location, connection relationship and coverage of air conditioning equipment.

[0007] In a possible implementation, the building air conditioning system zoning control method based on KNX technology further performs the following processing: defining preliminary control zones based on the functional zoning information and the floor plan; refining the preliminary control zones according to the coverage of the air conditioning equipment, and establishing the association between the air conditioning equipment and each preliminary control zone based on the adjustment results; extracting historical indoor image information, and performing functional object recognition based on the historical indoor image information; and performing scene-based correction on the refined preliminary control zones based on the functional object recognition results to obtain an air conditioning control zone set.

[0008] In a possible implementation, the KNX-based building air conditioning system zoning control method further performs the following processing: based on an expert system, traverses the air conditioning control zoning set, and matches and obtains the target temperature range and temperature sensitivity; combining the air conditioning control zoning set with the building structure information, calculates and obtains the overcurrent coefficient between adjacent zones; using the overcurrent coefficient as the first weight and the temperature sensitivity as the second weight, and combining the temperature gradient operator and the temperature offset operator, defines an optimization objective function; performs optimization initialization with the target temperature range as a constraint, and obtains the center value of the target temperature range as the initial condition, combines the optimization objective function to optimize the target temperature, and redefines the optimal core temperature of the target temperature range; using the optimal core temperature as the zoning target temperature, the output is the zoning control target.

[0009] In a possible implementation, the KNX-based building air conditioning system zoning control method further performs the following processing: acquiring indoor and outdoor temperature sensor data via the KNX protocol; acquiring historical pedestrian traffic data of the target building and statistically analyzing it to determine a baseline pedestrian density, wherein the baseline pedestrian density is a weighted combination of fixed pedestrian density and mobile pedestrian density; inputting the temperature sensor data, the baseline pedestrian density, and the zoning control target into a preset heat load model to calculate the cooling / heating capacity and airflow parameters of each air conditioning control zone; defining the zoning control parameters based on the zoning control target as the set temperature, combined with the cooling / heating capacity and airflow parameters.

[0010] In a possible implementation, the building air conditioning system zoning control method based on KNX technology also performs the following processing: generating an inflow zone probability distribution based on the population migration probability matrix of the target building and the population flow data; and performing forward-looking regulation of the population inflow zone according to the generated inflow zone probability distribution.

[0011] In a possible implementation, the building air conditioning system zoning control method based on KNX technology further performs the following processing: acquiring personnel behavior images of each air conditioning control zone through the KNX protocol and in conjunction with a preset acquisition period; performing action feature recognition based on the personnel behavior images, and evaluating personnel comfort based on the action features; if the personnel comfort deviates from a preset comfort range, dynamically adjusting the zoning control target of the corresponding air conditioning control zone according to the direction of deviation of the personnel comfort.

[0012] In a possible implementation, the building air conditioning system zoning control method based on KNX technology further performs the following processing: if the occupant comfort level is greater than the upper limit of the comfort range, then the upper limit of the target temperature range of the corresponding air conditioning control zone is reduced; if the occupant comfort level is less than the lower limit of the comfort range, then the lower limit of the target temperature range of the corresponding air conditioning control zone is increased.

[0013] In a possible implementation, the building air conditioning system zoning control method based on KNX technology further performs the following processing: based on the KNX protocol, real-time monitoring of the operating status parameters of each air conditioning unit; if the operating status parameters exceed a preset threshold range or KNX protocol communication is interrupted, the unit is determined to be faulty; based on the air conditioning control zoning set, identifying the neighboring air conditioning control zoning set of the faulty unit; using the air conditioning units corresponding to the neighboring air conditioning control zoning set as redundant units to compensate the faulty unit.

[0014] In a possible implementation, the building air conditioning system zoning control method based on KNX technology also performs the following processing: the forward-looking regulation includes at least starting backup equipment, adjusting cooling and heating capacity and air volume.

[0015] This application proposes a KNX-based zoning control method for building air conditioning systems. This method acquires building structure information and air conditioning system topology information using the KNX protocol, and divides the system into air conditioning control zones. It sets target temperature ranges and performs global optimization of the target temperatures for each zone to obtain the zone control target. Based on real-time data collected by indoor and outdoor temperature sensors, combined with baseline personnel density and the zone control target, it initializes the zone control parameters for each air conditioning control zone. Based on personnel flow data, it dynamically adjusts the air conditioning operating parameters of each control zone and predicts personnel flow direction to perform proactive control. This method solves the technical problem in existing technologies where it is difficult to perform precise control based on the actual needs of different functional areas within a building, leading to energy waste and poor user comfort. It achieves the technical effect of reducing air conditioning energy consumption and improving user comfort. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0017] Figure 1 A flowchart illustrating the zoning control method for a building air conditioning system based on KNX technology provided in this application embodiment;

[0018] Figure 2 This is a schematic diagram illustrating the process of dividing the air conditioning control zone set in the KNX-based building air conditioning system zoning control method provided in the embodiments of this application. Detailed Implementation

[0019] The above description is merely an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below.

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application will be provided in conjunction with the accompanying drawings. The described embodiments should not be considered as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" can be the same or different subsets of all possible embodiments and can be combined with each other without conflict. The terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, product, or server that includes a series of steps is not necessarily limited to those steps explicitly listed, but may include other steps not explicitly listed or inherent to such processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.

[0022] This application provides a method for zoning control of building air conditioning systems based on KNX technology, such as... Figure 1 As shown, the method includes:

[0023] Step S100: Obtain building structure information and air conditioning system topology information based on the KNX protocol, and divide the air conditioning control zone set based on the building structure information and the air conditioning system topology information.

[0024] Step S100 further includes obtaining building structure information and air conditioning system topology information based on the KNX protocol, wherein the building structure information includes floor plans and functional zoning information, and the air conditioning system topology information includes the location, connection relationship and coverage of air conditioning equipment.

[0025] Preferably, KNX technology is an international, open building automation standard specifically designed for equipment control and automated management in smart buildings. It is widely used in lighting control, HVAC, security systems, energy management, curtain control, and many other fields. Supporting various communication media (such as twisted pair, power line, radio frequency, and IP networks), it aims to achieve interconnectivity and intelligent control between different devices through a unified communication protocol. The main goal of KNX technology is to improve building energy efficiency, user comfort, and system flexibility and scalability. Through the KNX protocol, the system can acquire real-time building structure information, air conditioning system topology information, and data from various sensors, and perform intelligent zone control based on this information.

[0026] Preferably, the KNX protocol is used to achieve data interaction and integration between the building automation system and the building structure and air conditioning system equipment. Based on the building's functional zoning and the layout of the air conditioning equipment, the building is divided into multiple independent air conditioning control zones (i.e., air conditioning control zone sets) to achieve refined and intelligent air conditioning control. The KNX building automation communication protocol connects various devices within the building (such as sensors, controllers, actuators, etc.) to a unified network. Specifically, sensors (such as temperature and humidity sensors) in the KNX network acquire environmental data from each room; controllers in the KNX network acquire functional zoning information; and the building management system in the KNX network acquires building structure information, including floor plans and functional zoning. Floor plans are layout diagrams of each floor, showing the spatial layout (such as the location and dimensions of rooms, stairs, elevators, etc., floor distribution, and corridor layout), wall materials, and door and window locations. Functional zoning information refers to the functional division of different areas within the building, such as office areas, meeting rooms, rest areas, and corridors.

[0027] Preferably, the KNX communication protocol is used to obtain the air conditioning system topology information, that is, to obtain the operating status, control parameters, and topological relationships between various devices in the air conditioning system, including the layout of air conditioning equipment (such as chiller units, fan coil units, cooling towers, air ducts, etc.), the connection relationships between devices, and the control logic of the devices. The air conditioning system topology information refers to data describing the layout and connection relationships of air conditioning equipment. For example, obtaining the location of chiller units and the areas they control, the distribution of fan coil units and the rooms they serve, the layout of air ducts and the fan coil units they connect to, and the status and operating parameters of auxiliary equipment such as cooling towers and water pumps. Then, based on the functional zoning of the building and the topology of the air conditioning system, the building is divided into multiple independent control areas. Each area can independently control parameters such as temperature, humidity, and airflow. Specifically, the areas are divided according to the functional requirements of the building. For example, office areas have high personnel density and require stable temperature and moderate airflow; meeting rooms have fluctuating personnel density and require rapid temperature adjustment; corridors have high personnel flow but lower temperature requirements. Based on the layout and control capabilities of air conditioning equipment, areas can be divided. For example, a room served by a fan coil unit can be divided into a zone; multiple adjacent rooms controlled by the same refrigeration unit can be merged into a zone; and the building can be divided into multiple independent control areas to obtain an air conditioning control zone set, which can realize refined and intelligent air conditioning control, optimize energy consumption performance and improve user comfort.

[0028] Furthermore, such as Figure 2As shown, step S100 further includes step S110, defining preliminary control zones based on the functional zoning information and the floor plan; step S120, refining the preliminary control zones according to the coverage of the air conditioning equipment, and establishing the association between the air conditioning equipment and each preliminary control zone based on the adjustment results; step S130, extracting historical indoor image information, and performing functional object recognition based on the historical indoor image information; step S140, performing scene-based correction on the refined preliminary control zones based on the functional object recognition results, and obtaining an air conditioning control zone set.

[0029] Preferably, the functional type of each area is determined according to the functional zoning (e.g., office area, meeting room, etc.). Combined with the floor plan, adjacent areas with the same functional type are merged into preliminary control zones. Then, based on the coverage range of the air conditioning equipment, the preliminary control zones are further subdivided or merged to ensure that each zone is completely covered by one or more air conditioning units. This involves obtaining the location and coverage range of the air conditioning units, checking whether the preliminary control zone is completely covered by the air conditioning units, retaining the zone if so, and splitting or merging the zones based on the equipment coverage range if not. Historical image data of each area within the building is collected through cameras or other image devices. Image recognition technology (e.g., computer vision algorithms) is used to identify functional objects (e.g., people, desks, meeting tables, sofas, etc.) in the images, inferring the actual usage scenario of each area, and obtaining... The functional object recognition results, for example, if an image shows a desk and computer, the area is inferred to be an office area; if an image shows a conference table and projector, the area is inferred to be a conference room. Finally, based on the functional object recognition results, the initial control zones are further revised to better match actual usage scenarios. This includes comparing the functional divisions of the initial control zones with the actual usage scenarios. If they match, the zone is retained; if they do not match, the zone is adjusted according to the actual usage scenarios. For example, if children might run outside a certain area (overflow), the zone's range can be expanded accordingly, such as from the children's playground to the fast-food restaurants surrounding the playground. Ultimately, the air conditioning control zone set is determined, with each zone having a clear functional type and target temperature range, accurately and efficiently meeting users' personalized needs while optimizing energy consumption and improving user comfort.

[0030] Furthermore, step S140 also includes step S141, which involves real-time monitoring of the operating status parameters of each air conditioning unit based on the KNX protocol. If the operating status parameters exceed a preset threshold range or the KNX protocol communication is interrupted, the unit is identified as faulty. Step S142 involves identifying the neighboring air conditioning control zone set of the faulty unit based on the air conditioning control zone set. Step S143 involves using the air conditioning units corresponding to the neighboring air conditioning control zone set as redundant units to compensate for the faulty unit.

[0031] Preferably, an intelligent fault detection and redundancy compensation mechanism ensures stable operation of the air conditioning system even when some equipment fails. Specifically, various sensor devices based on the KNX protocol monitor the operating status parameters of each air conditioning unit in real time, including key parameters such as operating temperature, pressure, current, and power. Then, based on the equipment's technical specifications and historical operating data, the normal range of the operating status parameters (such as temperature range, pressure range, etc.) is set. If the operating status parameters exceed the preset threshold range (e.g., the operating temperature of an air conditioning unit exceeds the set maximum temperature threshold), or if there is a communication failure in the KNX protocol... If a fault occurs (e.g., communication between an air conditioning unit and the KNX network is interrupted, making it impossible to obtain its operating status parameters), the unit is identified as a faulty unit. Then, the neighboring air conditioning control zones of the faulty unit are identified within the air conditioning control zone set; these are the sets of air conditioning control zones adjacent to the zone containing the faulty unit. Finally, the air conditioning units corresponding to these neighboring control zones are designated as redundant units to compensate for the faulty unit. This includes adjusting the operating parameters of the redundant units (e.g., increasing cooling capacity and airflow) to cover the service range of the faulty unit. The adjusted operating parameters are then sent to the redundant units via the KNX network, and corresponding control strategies are executed. Through this intelligent fault detection and redundancy compensation mechanism, the air conditioning system can still operate stably even when some equipment fails, significantly improving the reliability and robustness of the air conditioning system while optimizing energy consumption.

[0032] Step S200: Set a target temperature range for each air conditioning control zone, and perform global optimization of the zone target temperature based on the target temperature range to obtain the zone control target.

[0033] Preferably, by scientifically and rationally setting the temperature range for each zone and combining it with the overall building energy consumption optimization goals, the temperature setpoints for each zone are dynamically adjusted to achieve a balance between comfort and energy efficiency. Specifically, based on the temperature requirements and personnel density of different functional zones, a target temperature range is set for each air conditioning control zone, that is, the allowable temperature range is represented by the minimum and maximum temperatures. For example, office areas require a stable temperature and are usually set at 22℃-24℃, meeting rooms have lower temperature requirements due to high personnel density and are usually set at 20℃-22℃, and corridors have lower temperature requirements and are usually set at 18℃-20℃; then, the target temperature range for each zone is met. Within a given temperature range, the target temperature of each zone is adjusted to reduce the overall temperature gradient of the building and lower energy consumption. For example, by adjusting the target temperature of adjacent zones, the temperature difference between different areas within the building is reduced, avoiding energy waste caused by excessive temperature gradients. While ensuring comfort, the operating load of cooling / heating equipment is minimized to reduce overall energy consumption. By optimizing the target temperature of each zone, overload operation of certain equipment is avoided, extending equipment lifespan. Furthermore, the control target for each zone, i.e., the optimal target temperature value for each zone, is determined to achieve a balance between comfort and energy efficiency, thereby significantly reducing the energy consumption of the building's air conditioning system while improving user comfort.

[0034] Furthermore, step S200 also includes step S210, which involves traversing the air conditioning control zone set based on the expert system to match and obtain the target temperature range and temperature sensitivity; step S220, which involves calculating and obtaining the overcurrent coefficient between adjacent zones by combining the air conditioning control zone set with the building structure information; step S230, which involves defining an optimization objective function by using the overcurrent coefficient as the first weight and the temperature sensitivity as the second weight, combined with the temperature gradient operator and the temperature offset operator; step S240, which involves initializing the optimization with the target temperature range as a constraint, obtaining the center value of the target temperature range as the initial condition, optimizing the target temperature by combining the optimization objective function, and redefining the optimal core temperature of the target temperature range; and step S250, which involves outputting the zone control target by using the optimal core temperature as the zone target temperature.

[0035] Preferably, an expert system is used to match and obtain the target temperature range and temperature sensitivity of each air conditioning control zone based on the air conditioning control zone set. The expert system is an intelligent system based on rules and knowledge that can simulate the expert decision-making process. The temperature sensitivity describes how sensitive the zone is to temperature changes (e.g., the conference room is more sensitive to temperature changes and requires more precise control). Then, combined with the air conditioning control zone set and building structure information, the flow coefficient between adjacent zones is calculated. Specifically, the flow area between adjacent zones (e.g., the area of ​​doors and windows) is calculated, and the flow coefficient is calculated based on the air velocity (e.g., measured by sensors or estimated from historical data). That is, the flow coefficient is the product of the flow area and the air velocity.

[0036] Preferably, an optimization objective function is defined using the overcurrent coefficient as the first weight and temperature sensitivity as the second weight, combined with temperature gradient and temperature offset operators, to measure the operating effect of the air conditioning system. The first term, multiplying the overcurrent coefficient by the temperature gradient, reduces the temperature gradient between adjacent zones, avoiding energy waste caused by excessive temperature differences. The second term, multiplying the temperature sensitivity by the temperature offset, reduces temperature fluctuations in temperature-sensitive zones, improving user comfort. The optimization objective function is initialized with the target temperature range (the allowable temperature range for each zone) as a constraint, calculating the initial temperature value for each zone. An optimization algorithm (such as linear programming or genetic algorithm) is used to find the minimum value of the optimization objective function, obtaining the optimal core temperature for each zone—the temperature value that minimizes the optimization objective function. This optimal core temperature is ultimately used as the target temperature for each zone and as the control target for the air conditioning control system, applied to the air conditioning equipment in each zone. This achieves a balance between comfort and energy saving, thereby reducing the energy consumption of the building's air conditioning system while improving user comfort.

[0037] Step S300: Based on the real-time data collected by indoor and outdoor temperature sensors, and combined with the baseline personnel density and the zoning control target, initialize the zoning control parameters of each air conditioning control zone, wherein the zoning control parameters include set temperature, cooling / heating capacity and air volume parameters.

[0038] Preferably, by using real-time environmental data and preset personnel density information, initial operating parameters are set for each air conditioning control zone, including set temperature, cooling / heating capacity, and airflow. The baseline personnel density refers to the number of fixed and mobile personnel in each zone. Temperature, cooling / heating capacity, and airflow parameters are the basic configuration for air conditioning system startup or reset, aiming to quickly provide a comfortable environment. Specifically, indoor and outdoor temperature data are collected from indoor and outdoor temperature sensors via a KNX network to provide the current environmental status. Then, combined with the baseline personnel density and the determined zone control target, the zone control parameters for each air conditioning control zone are initialized, including calculating the initial set temperature for each zone; calculating the required cooling / heating capacity for each zone based on the indoor / outdoor temperature difference and personnel density (e.g., if the indoor temperature is 25℃, the target temperature is 22.5℃, and the personnel density is high, a larger cooling capacity is required); and calculating the required airflow for each zone based on personnel density and temperature adjustment needs (e.g., areas with high personnel density require a larger airflow to ensure air circulation and temperature uniformity, while areas with low personnel density can appropriately reduce airflow to lower energy consumption). This ensures that the air conditioning system quickly provides a comfortable environment during startup or reset.

[0039] Furthermore, step S300 also includes step S310, acquiring indoor and outdoor temperature sensor data via the KNX protocol; step S320, acquiring historical pedestrian traffic data of the target building and statistically analyzing it to determine a baseline pedestrian density, wherein the baseline pedestrian density is a weighted combination of fixed pedestrian density and mobile pedestrian density; step S330, inputting the temperature sensor data, the baseline pedestrian density, and the zoning control target into a preset heat load model to calculate the cooling / heating capacity and airflow parameters of each air conditioning control zone; step S340, defining the zoning control parameters based on the zoning control target as the set temperature, combined with the cooling / heating capacity and airflow parameters.

[0040] Preferably, multiple temperature sensors inside and outside the building are connected and controlled based on the KNX protocol to acquire indoor and outdoor temperature sensor data. Historical pedestrian flow data is collected through building entrance and exit sensors (such as infrared sensors, cameras, access control systems, etc.), and statistical analysis is performed to derive the baseline personnel density, which is a weighted combination of the fixed personnel density (such as the number of permanent staff in the office area) and the transient personnel density (such as the number of temporary visitors in the meeting room) of each zone. Then, the temperature sensor data, the baseline personnel density, and the zone control target are input into a preset heat load model to calculate the cooling and heating and airflow parameters required to maintain the target temperature of a certain zone. Specifically, this is done through formulas. The calculation of heating and cooling is performed, where Q is the total heat, U is the heat transfer coefficient representing the insulation performance of walls, windows, etc., and A represents the surface area of ​​the zone. It's the temperature difference between indoors and outdoors. It is the heat load generated by people. This indicates the heat load generated by equipment (such as computers and printers). This represents the heat load generated by lighting tubes, etc.; expressed by the formula. Calculate the air volume, where V represents the required air volume. It is air density. It is the specific heat capacity of air; finally, the zone control target is used as the set temperature, and combined with the cooling and heating and air volume parameters calculated by the heat load model, precise operating parameters are set for each air conditioning control zone to achieve a balance between comfort and energy saving, thereby significantly improving the operating efficiency of the air conditioning system and reducing energy consumption.

[0041] Furthermore, step S300 also includes step S350, acquiring personnel behavior images of each air conditioning control zone through the KNX protocol and in conjunction with a preset acquisition period; step S360, performing motion feature recognition based on the personnel behavior images, and evaluating personnel comfort based on the motion features; step S370, if the personnel comfort deviates from a preset comfort range, dynamically adjusting the zone control target of the corresponding air conditioning control zone according to the direction of deviation of the personnel comfort.

[0042] Step S370 further includes step S371, if the personnel comfort level is greater than the upper limit of the comfort range, then the upper limit of the target temperature range of the corresponding air conditioning control zone is reduced; step S372, if the personnel comfort level is less than the lower limit of the comfort range, then the lower limit of the target temperature range of the corresponding air conditioning control zone is increased.

[0043] Preferably, based on a preset acquisition cycle, sensor and camera data within the building are collected using the KNX protocol to connect and control the images of people's movements and postures in each zone. The preset acquisition cycle refers to the time interval (e.g., once every 5 minutes). Then, action feature recognition is performed based on the images of people's behavior. That is, through image recognition technology (such as computer vision algorithms), the behavioral characteristics of people (such as posture, frequency of movement, facial expressions, etc.) are analyzed, including inferring the comfort state of people, such as whether they feel too hot, too cold, or uncomfortable, whether they move frequently, whether they frown, wipe sweat, etc., and the comfort level of people is evaluated based on the action feature recognition results. For example, if people are detected wiping sweat frequently, it is inferred that they feel too hot; if people are detected curling up, it is inferred that they feel too cold.

[0044] Preferably, the system compares the user's comfort level with a preset comfort range. If the comfort level is within the range, the current control parameters are maintained. If the comfort level deviates from the range, the control parameters such as the target temperature, cooling / heating capacity, and airflow of the zone are adjusted according to the direction of deviation. Specifically, if the user's comfort level is greater than the upper limit of the comfort range, the upper limit of the target temperature range for the corresponding air conditioning control zone is lowered; if the user's comfort level is less than the lower limit of the comfort range, the lower limit of the target temperature range for the corresponding air conditioning control zone is raised. This includes lowering the target temperature and increasing cooling capacity and airflow if the user feels too hot, and raising the target temperature and increasing heating capacity and airflow if the user feels too cold. Here, the comfort range refers to the temperature range in which the user feels comfortable, usually expressed as the lowest and highest temperatures. The preset comfort range refers to the comfort range (such as temperature range, humidity range, etc.) set according to the zone's function and usage requirements. The deviation direction refers to the direction in which the user's comfort level deviates from the comfort range (such as overheating or overcooling). This improves the user's comfort experience, thereby enhancing the intelligence level of the air conditioning system and optimizing energy consumption.

[0045] Step S400: Based on the personnel flow data collected by the building entrance and exit sensors, dynamically adjust the air conditioning operation parameters of each air conditioning control zone, and predict the personnel flow direction to perform forward-looking regulation.

[0046] Step S400 further includes, at least, starting backup equipment, adjusting heating / cooling temperature and airflow.

[0047] Preferably, by monitoring the flow of people within the building in real time, the operating status of the air conditioning system can be adjusted in advance to meet the changing environmental demands. This includes dynamically adjusting the air conditioning parameters of the current zones, and achieving proactive control through means such as activating backup equipment and adjusting cooling / heating capacity and airflow. This ensures that all zones within the building are always in a comfortable state. Specifically, sensors installed at building entrances and exits (such as infrared sensors, cameras, access control systems, etc.) collect real-time data on people entering and exiting, including the number of people, flow direction, and time distribution. Based on the real-time personnel flow data, the operating parameters of the air conditioning in each zone are adjusted, including set temperature, cooling / heating capacity, and airflow. For example, when the number of people in a certain zone increases... To meet the needs of new occupants, the cooling / heating capacity and airflow of a given zone are increased. Conversely, when the number of people in a zone decreases, the cooling / heating capacity and airflow of that zone are reduced to lower energy consumption. Furthermore, by combining historical data and using predictive algorithms (such as machine learning models and time series analysis), the flow direction and temporal distribution of people are analyzed, and the operating status of the air conditioning system is adjusted in advance to meet the changing environmental demands. When a large influx of people is predicted into a zone, backup chiller units and cooling towers are activated in advance to meet the sudden increase in cooling / heating demand. Based on the prediction results, the cooling / heating capacity and airflow of the target zone are adjusted in advance to ensure that the environment is already at a comfortable level when people arrive. Through proactive regulation of the air conditioning system, the building's various zones are kept in a comfortable state at all times, while energy consumption is optimized.

[0048] Furthermore, step S400 also includes step S410, generating an inflow partition probability distribution based on the population migration probability matrix of the target building and the population flow data; step S420, performing forward-looking regulation of the population inflow partition according to the generated inflow partition probability distribution.

[0049] Preferably, based on historical personnel flow data of the target building, the personnel flow frequency between each zone is statistically analyzed, the proportion of flow frequency is calculated, and a population migration probability matrix is ​​generated. Each element of the matrix represents the probability of migration from one zone to another. Combined with the personnel flow data, an inflow zone probability distribution is generated. Specifically, the personnel distribution data at the current moment (i.e., the number of people in each zone) is obtained, and the population migration probability matrix is ​​used to calculate the probability of personnel inflow into each zone in the future, thereby obtaining the inflow zone probability distribution. Then, based on the inflow zone probability distribution, the personnel inflow zone is proactively controlled. That is, the operation status of the air conditioning system is adjusted in advance to meet the upcoming changes in environmental demand. For example, when it is predicted that a large number of people will flow into a certain zone, the backup refrigeration units, cooling towers, and other equipment are started in advance to meet the sudden increase in cooling / heating demand. Based on the prediction results, the cooling and heating capacity and air volume of the target zone are adjusted in advance to ensure that the environment has reached a comfortable state when people flow in. Before the inflow of people, the target zone is preheated or precooled in advance to reduce temperature fluctuations.

[0050] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application. In some cases, the actions or steps described in this application can be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for partition control of a building air conditioning system based on KNX technology, characterized in that, The method comprises: Obtaining building structure information and air conditioning system topology information based on the KNX protocol, and dividing an air conditioning control partition set based on the building structure information and the air conditioning system topology information; Setting a target temperature interval for each air conditioning control partition, and performing global optimization of the partition target temperature based on the target temperature interval to obtain a partition control target; Initializing the partition control parameters of each air conditioning control partition according to real-time data collected by indoor and outdoor temperature sensors, in combination with a reference personnel density and the partition control target, wherein the partition control parameters include set temperature, cold and heat quantity, and air volume parameters; Based on personnel flow data collected by building entrance and exit sensors, dynamically adjusting air conditioning operating parameters of each air conditioning control partition, and predicting personnel flow direction to perform forward-looking regulation and control; Setting a target temperature interval for each air conditioning control partition, and performing global optimization of the partition target temperature based on the target temperature interval to obtain a partition control target, comprising: Based on an expert system, traversing the air conditioning control partition set, and matching to obtain a target temperature interval and a temperature sensitivity; In combination with the air conditioning control partition set and the building structure information, calculating and obtaining an overcurrent coefficient between adjacent partitions, specifically, calculating the overcurrent area between adjacent partitions, and calculating the overcurrent coefficient according to the air flow rate, the overcurrent coefficient being the product of the overcurrent area and the air flow rate; Taking the overcurrent coefficient as a first weight, and taking the temperature sensitivity as a second weight, defining an optimization objective function in combination with a temperature gradient operator and a temperature offset operator; Optimizing and initializing with the target temperature interval as a constraint, and obtaining the center value of the target temperature interval as an initial condition, performing target temperature optimization in combination with the optimization objective function, and redefining the optimal core temperature of the target temperature interval as a partition target temperature; Taking the optimal core temperature as the partition target temperature, and outputting the partition control target.

2. The KNX technology-based zone control method for a building air conditioning system according to claim 1, wherein, Obtaining building structure information and air conditioning system topology information based on the KNX protocol, wherein the building structure information includes floor plan and functional partition information, and the air conditioning system topology information includes air conditioning equipment position, connection relationship, and coverage range.

3. The KNX technology-based building air conditioning system zoning control method according to claim 2, wherein, Based on the building structure information and the air conditioning system topology information, dividing an air conditioning control partition set, comprising: Defining preliminary control partitions based on the functional partition information and the floor plan; According to the coverage range of the air conditioning equipment, refining and adjusting the preliminary control partitions, and establishing an association relationship between the air conditioning equipment and each preliminary control partition based on the adjustment result; Extracting historical indoor image information, and performing functional object recognition according to the historical indoor image information; Based on the functional object recognition result, performing scene-based correction on the refined and adjusted preliminary control partitions to obtain an air conditioning control partition set.

4. The KNX technology-based zone control method for a building air conditioning system according to claim 1, wherein, Initializing the partition control parameters of each air conditioning control partition according to real-time data collected by indoor and outdoor temperature sensors, in combination with a reference personnel density and the partition control target, wherein the partition control parameters include set temperature, cold and heat quantity, and air volume parameters, comprising: Obtaining indoor and outdoor temperature sensor data through the KNX protocol; Acquire historical passenger flow data of a target building, and statistically analyze and determine a reference personnel density, wherein the reference personnel density is a weighted combination of a fixed personnel density and a flowing personnel density; Input the temperature sensor data, the reference personnel density, and the partition control target into a preset heat load model to calculate the cooling and heating capacity and the air volume parameter of each air conditioning control partition; Set the partition control target as a set temperature, and define the partition control parameter in combination with the cooling and heating capacity and the air volume parameter.

5. The KNX technology-based zone control method for a building air conditioning system according to claim 4, wherein, Based on personnel flow data collected by building entrance and exit sensors, dynamically adjust air conditioning operating parameters of each air conditioning control partition, and predict personnel flow direction to perform forward-looking regulation and control, including: Based on a crowd migration probability matrix of a target building, generate inflow partition probability distribution in combination with the personnel flow data; According to the generated inflow partition probability distribution, perform forward-looking regulation and control on personnel inflow partitions.

6. The KNX technology-based zone control method for a building air conditioning system according to claim 1, wherein, Further comprising: Collect personnel behavior images of each air conditioning control partition in combination with a preset collection period through a KNX protocol; Perform action feature recognition based on the personnel behavior images, and evaluate personnel comfort level according to the action features; If the personnel comfort level deviates from a preset comfort level interval, dynamically adjust the partition control target of the corresponding air conditioning control partition according to the deviation direction of the personnel comfort level.

7. The KNX technology-based zone control method of a building air conditioning system according to claim 6, wherein, Dynamically adjusting the partition control target of the corresponding air conditioning control partition according to the deviation direction of the personnel comfort level further comprises: If the personnel comfort level is greater than the upper limit of the comfort level interval, reduce the upper limit of the target temperature interval of the corresponding air conditioning control partition; If the personnel comfort level is less than the lower limit of the comfort level interval, increase the lower limit of the target temperature interval of the corresponding air conditioning control partition.

8. The KNX technology-based zone control method for a building air conditioning system according to claim 1, wherein, Further comprising: Based on the KNX protocol, real-time monitor operating state parameters of each air conditioning unit, and if the operating state parameters exceed a preset threshold range or KNX protocol communication is interrupted, determine that it is a faulty unit; Based on the set of air conditioning control partitions, identify a set of neighborhood air conditioning control partitions of the faulty unit; Take air conditioning units corresponding to the set of neighborhood air conditioning control partitions as redundant units to compensate for the faulty unit.

9. The KNX technology-based zone control method for a building air conditioning system according to claim 5, wherein, The forward-looking regulation and control at least includes starting a backup device, adjusting cooling and heating capacity, and adjusting air volume.

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