Building air conditioning system partition control method based on KNX technology

Abstract

The invention discloses a building air conditioning system partition control method based on the KNX technology, and relates to the related technical field of building air conditioning systems.The method comprises the steps that building structure information and air conditioning system topological information are obtained based on the KNX protocol, and an air conditioning control partition set is divided; a target temperature interval is set, and global optimization of partition target temperature is carried out; according to real-time data collected by indoor and outdoor temperature sensors, the reference personnel density and a partition control target are combined, and partition control parameters of all air conditioner control partitions are initialized; and based on the personnel flow data, the air conditioner operation parameters of all the air conditioner control subareas are dynamically adjusted, and the personnel flow direction is predicted to execute prospective regulation and control. The technical problems that in the prior art, fine regulation and control are difficult to conduct according to actual requirements of different functional areas in a building, consequently, energy of an air conditioning system is wasted, and the comfort degree of a user is poor are solved, and the technical effects of reducing air conditioner energy consumption and improving the comfort degree of the user are achieved.

Description

Technical Field

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

[0002] With the acceleration of urbanization and the continuous expansion of building scale, the problem of building energy consumption has become increasingly prominent. As the main component of building energy consumption, the air conditioning system accounts for a large proportion of the total building energy consumption. Modern buildings usually have complex building structures and diverse functional zones, such as office areas, meeting rooms, rest areas, etc. Each functional zone has different requirements for environmental parameters such as temperature and humidity. Traditional air conditioning system control methods often adopt centralized control or simple zoning control methods, making it difficult to achieve refined regulation of different functional areas in the building according to the building structure, personnel distribution, and environmental changes. This not only causes energy waste but also may affect the comfort experience of users. With the diversification of building functions and the complexity of personnel flow, traditional control methods can no longer meet the needs of modern buildings.

[0003] In the current related technologies, there are technical problems such as difficulty in achieving refined regulation according to the actual needs of different functional areas in the building, resulting in energy waste in the air conditioning system and poor user comfort. Summary of the Invention

[0004] This application provides a method for zoning control of a building air conditioning system based on KNX technology, which solves the technical problems in the prior art that it is difficult to achieve refined regulation according to the actual needs of different functional areas in the building, resulting in energy waste in the air conditioning system and poor user comfort, and achieves the technical effects of reducing air conditioning energy consumption and improving user comfort.

[0005] This application provides a method for zoning control of a building air conditioning system based on KNX technology, including: obtaining building structure information and air conditioning system topology information of the building based on the KNX protocol, and dividing an 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 globally optimizing the zoned target temperature based on the target temperature range to obtain a zoning control target; initializing the zoning control parameters of each air conditioning control zone according to the real-time data collected by indoor and outdoor temperature sensors, in combination with the reference personnel density and the zoning control target, where the zoning control parameters include set temperature, cooling and heating capacity, and air volume parameters; dynamically adjusting the air conditioning operation parameters of each air conditioning control zone based on the 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 manner, the method for partitioning and controlling a building air-conditioning system based on KNX technology further performs the following processing: obtaining building structure information and air-conditioning system topology information based on the KNX protocol, where the building structure information includes a floor plan and functional partition information, and the air-conditioning system topology information includes the positions of air-conditioning devices, connection relationships, and coverage ranges.

[0007] In a possible implementation manner, the method for partitioning and controlling a building air-conditioning system based on KNX technology further performs the following processing: defining preliminary control partitions based on the functional partition information and the floor plan; refining and adjusting the preliminary control partitions according to the coverage ranges of the air-conditioning devices, and establishing an association relationship between the air-conditioning devices and each preliminary control partition based on the adjustment results; extracting historical indoor image information, and performing functional object recognition according to the historical indoor image information; based on the functional object recognition results, performing scene-based correction on the refined and adjusted preliminary control partitions to obtain a set of air-conditioning control partitions.

[0008] In a possible implementation manner, the method for partitioning and controlling a building air-conditioning system based on KNX technology further performs the following processing: traversing the set of air-conditioning control partitions based on an expert system to match and obtain a target temperature range and temperature sensitivity; calculating and obtaining an overcurrent coefficient between adjacent partitions by combining the set of air-conditioning control partitions and the building structure information; defining an optimization objective function by using the overcurrent coefficient as the first weight, the temperature sensitivity as the second weight, and combining a temperature gradient operator and a temperature offset operator; performing optimization initialization with the target temperature range as a constraint, and obtaining the central value of the target temperature range as an initial condition, and performing target temperature optimization by combining the optimization objective function to redefine the optimal core temperature of the target temperature range; using the optimal core temperature as the partition target temperature and outputting it as the partition control target.

[0009] In a possible implementation manner, the method for partitioning and controlling a building air-conditioning system based on KNX technology further performs the following processing: obtaining temperature sensor data inside and outside the room through the KNX protocol; obtaining historical pedestrian flow data of a target building, and statistically analyzing to determine a reference personnel density, where the reference personnel density is a weighted combination of a fixed personnel density and a flowing personnel density; inputting 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 amounts and the air volume parameters of each air-conditioning control partition; using the partition control target as a set temperature, and defining the partition control parameters by combining the cooling and heating amounts and the air volume parameters.

[0010] In a possible implementation, the method for zoning control of a building air conditioning system based on KNX technology further performs the following processing: Based on the population migration probability matrix of the target building and in combination with the personnel flow data, generate the probability distribution of the inflow zones; according to the generated probability distribution of the inflow zones, perform forward-looking regulation on the personnel inflow zones.

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

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

[0013] In a possible implementation, the method for zoning control of a building air conditioning system based on KNX technology further performs the following processing: Based on the KNX protocol, monitor the operating state parameters of each air conditioning unit in real time. If the operating state parameters exceed the preset threshold range or the KNX protocol communication is interrupted, then determine it as a faulty unit; based on the air conditioning control zone set, identify the set of neighboring air conditioning control zones of the faulty unit; use the air conditioning units corresponding to the set of neighboring air conditioning control zones as redundant units to compensate for the faulty unit.

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

[0015] The method for zoning control of a building air conditioning system based on KNX technology proposed in this application obtains building structure information and air conditioning system topology information based on the KNX protocol, and divides the air conditioning control zoning set; sets the target temperature range, and conducts global optimization of the zoned target temperatures to obtain the zoning control targets; initializes the zoning control parameters of each air conditioning control zone according to the real-time data collected by indoor and outdoor temperature sensors, in combination with the reference occupancy density and the zoning control targets; dynamically adjusts the air conditioning operation parameters of each air conditioning control zone based on the occupancy flow data, and predicts the occupancy flow direction to perform proactive control. This solves the technical problems in the prior art that it is difficult to perform refined control according to the actual needs of different functional areas in the building, resulting in energy waste in the air conditioning system and poor user comfort, and achieves the technical effects of reducing air conditioning energy consumption and improving user comfort. Brief Description of the Drawings

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings of the embodiments of the present invention will be briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the systems according to the embodiments of the present application. It should be understood that the operations in the front or below do not necessarily need to be executed precisely in sequence. On the contrary, as needed, they can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several operations can be removed from these processes.

[0017] Figure 1 It is a schematic flowchart of the method for zoning control of a building air conditioning system based on KNX technology provided by an embodiment of this application; Figure 2 It is a schematic flowchart of dividing the air conditioning control zoning set in the method for zoning control of a building air conditioning system based on KNX technology provided by an embodiment of this application. Detailed Embodiments

[0018] The above description is only an overview of the technical solutions of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the description. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the following specifically gives the detailed embodiments of this application.

[0019] In order to make the purpose, technical solutions and advantages of this application clearer, the following will further describe this application in detail with reference to the drawings. The described embodiments should not be regarded as limitations of this application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this application.

[0020] In the following description, "some embodiments" are involved, which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict. The terms "first / second" involved are only used to distinguish similar objects and do not represent a specific order for the 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 comprising a series of steps does not necessarily have to be limited to those steps clearly listed, but may include other steps not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application.

[0021] The embodiment of this application provides a method for zoning control of a building air-conditioning system based on KNX technology, as Figure 1 shown, the method includes: Step S100, 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.

[0022] Step S100 further includes obtaining building structure information and air-conditioning system topology information based on the KNX protocol, where the building structure information includes a floor plan and functional partition information, and the air-conditioning system topology information includes the positions, connection relationships, and coverage ranges of air-conditioning devices.

[0023] Preferably, the KNX technology is an international open building automation standard dedicated to device control and automation management in intelligent buildings, and is widely used in multiple fields such as lighting control, heating, ventilation, and air conditioning (HVAC), security systems, energy management, and curtain control. It supports multiple communication media (such as twisted pair, power line, radio frequency, and IP network), aiming to achieve interconnection and intelligent control between different devices through a unified communication protocol. The main goals of the KNX technology are to improve the energy efficiency of buildings, user comfort, as well as the flexibility and scalability of the system. Through the KNX protocol, the system can obtain real-time building structure information, air-conditioning system topology information, and data of various sensors of the building, and perform intelligent zoning control based on this information.

[0024] Preferably, data interaction and integration between the building automation system, building structure, and air conditioning system equipment are achieved through the KNX protocol. Then, according to the functional zoning of the building and the layout of air conditioning equipment, the building is divided into multiple independent air conditioning control areas (i.e., the air conditioning control partition set) to achieve refined and intelligent air conditioning control. Among them, the KNX building automation communication protocol can connect various devices in the building (such as sensors, controllers, actuators, etc.) to a unified network. Specifically, environmental data of each room is obtained through sensors (such as temperature sensors, humidity sensors) in the KNX network, functional zoning information of the building is obtained through controllers in the KNX network, and building structure information of the building is obtained through the building management system in the KNX network, including floor plans and functional zoning of the building. Among them, the floor plan is the layout diagram of each floor of the building, showing the spatial layout (such as the location and size of spaces such as rooms, stairs, elevators, floor distribution, and passage layout), wall materials, window and door positions, etc.; the functional zoning information refers to the functional division of each area in the building, such as office areas, meeting rooms, rest areas, corridors, etc.

[0025] 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 relationship between devices in the air conditioning system, including the layout of air conditioning equipment (such as chillers, fan coils, cooling towers, air ducts, etc.), the connection relationship between devices, and the control logic of devices. Among them, the air conditioning system topology information refers to the data describing the layout of air conditioning equipment and its connection relationship. For example, obtain the location of the chiller and the area it controls, obtain the distribution of fan coils and the rooms they serve, obtain the layout of air ducts and the fan coils they are connected to, and obtain the status and operating parameters of auxiliary equipment such as cooling towers and water pumps. Then, according to the functional zoning of the building and the topological structure of the air conditioning system, the building is divided into multiple independent control areas, and each area can independently control parameters such as temperature, humidity, and air volume. Specifically, the areas are divided according to the functional requirements of the building. For example, the office area has a high personnel density and requires stable temperature and moderate air volume; the meeting room has a large fluctuation in personnel density and requires rapid temperature adjustment; the corridor has a large flow of people but has a low temperature requirement. The areas are divided according to the layout and control ability of air conditioning equipment. For example, the rooms served by a certain fan coil can be divided into one partition; multiple adjacent rooms controlled by the same chiller can be combined into one partition. Then, the building is divided into multiple independent control areas to obtain the air conditioning control partition set, which can achieve refined and intelligent air conditioning control, optimize energy consumption performance, and improve user comfort.

[0026] Furthermore, as Figure 2As shown, step S100 further includes step S110 of defining a preliminary control partition based on the function partition information and the floor plan; step S120 of refining and adjusting the preliminary control partition according to the coverage of the air conditioning equipment, and establishing an association relationship between the air conditioning equipment and each preliminary control partition based on the adjustment result; step S130 of extracting historical indoor image information and performing function object recognition according to the historical indoor image information; step S140 of performing scene correction on the refined and adjusted preliminary control partition based on the function object recognition result to obtain an air conditioning control partition set.

[0027] Preferably, the function types of each area (such as office area, meeting room, etc.) are determined according to the function partition, and combined with the floor plan, adjacent areas with the same function type are merged into a preliminary control partition. Then, according to the coverage of the air conditioning equipment, the preliminary control partition is further divided or merged to ensure that each partition is completely covered by one or more air conditioning equipment, that is, the location and coverage of the air conditioning equipment are obtained, and it is checked whether the preliminary control partition is completely covered by the air conditioning equipment. If so, the partition is retained; if not, the partition is split or merged according to the equipment coverage; the historical image data of each area in the building collected by cameras or other image devices is used to identify the function objects (such as people, desks, meeting tables, sofas, etc.) in the image through image recognition technology (such as computer vision algorithms), and the actual usage scenarios of each area are inferred to obtain the function object recognition result. For example, if desks and computers are recognized in the image, it is inferred that the area is an office area; if a meeting table and a projector are recognized in the image, it is inferred that the area is a meeting room; finally, the preliminary control partition is further corrected according to the function object recognition result to make it more in line with the actual usage scenario, including comparing whether the function division of the preliminary control partition is consistent with the actual usage scenario. If it is consistent, the partition is retained; if not, the partition is adjusted according to the actual usage scenario. For example, if children will run to a certain range outside the corresponding partition (overflow), the partition range can be expanded accordingly, such as from the children's playground to the fast food restaurant around the children's playground; finally, the air conditioning control partition set is determined, and each partition has a clear function type and target temperature range, which can accurately and efficiently meet the personalized needs of users, while optimizing energy consumption performance and improving user comfort.

[0028] Further, step S140 further includes step S141 of real-time monitoring the operating state parameters of each air conditioning unit based on the KNX protocol. If the operating state parameters exceed the preset threshold range or the KNX protocol communication is interrupted, it is determined as a faulty unit; step S142 of identifying the neighborhood air conditioning control partition set of the faulty unit based on the air conditioning control partition set; step S143 of using the air conditioning units corresponding to the neighborhood air conditioning control partition set as redundant units to compensate for the faulty unit.

[0029] Preferably, through an intelligent fault detection and redundancy compensation mechanism, it is ensured that the air conditioning system can still operate stably when some equipment fails. Specifically, based on various sensor devices in the KNX protocol, the operating state parameters of each air conditioning unit are monitored in real time, including key parameters such as the operating temperature, pressure, current, and power of the air conditioning unit; then, according to the technical specifications and historical operating data of the equipment, the normal range of the set operating state parameters (such as temperature range, pressure range, etc.) is set. If the operating state parameters exceed the preset threshold range (for example, the operating temperature of a certain air conditioning unit exceeds the set maximum temperature threshold), or the KNX protocol communication is interrupted (for example, the communication between a certain air conditioning unit and the KNX network is interrupted and its operating state parameters cannot be obtained), then this unit is determined as a faulty unit; then, in the air conditioning control partition, the set of neighboring air conditioning control partitions of the faulty unit is centrally identified, that is, the set of air conditioning control partitions adjacent to the partition where the faulty unit is located; finally, the air conditioning units corresponding to the set of neighboring air conditioning control partitions are set as redundant units for compensating the faulty unit, including adjusting the operating parameters of the redundant units (such as increasing the cooling capacity, air volume, etc.) to cover the service range of the faulty unit; through the KNX network, the adjusted operating parameters are sent to the redundant units to execute the corresponding control strategies. Through the intelligent fault detection and redundancy compensation mechanism, it is ensured that the air conditioning system can still operate stably when some equipment fails, thereby significantly improving the reliability and robustness of the air conditioning system and optimizing the energy consumption performance at the same time.

[0030] Step S200, set a target temperature range for each of the air conditioning control partitions, and globally optimize the partition target temperature based on the target temperature range to obtain a partition control target.

[0031] Preferably, by scientifically and reasonably setting the temperature range of each zone and combining with the overall building energy consumption optimization goal, the temperature set values of each zone are dynamically adjusted to achieve the balance between comfort and energy saving. Specifically, according to the temperature requirements of different functional zones, personnel density, etc., a target temperature range is set for each air-conditioning control zone, that is, the allowable temperature range is represented by the lowest temperature and the highest temperature. For example, the office area requires a stable temperature and is usually set to 22°C - 24°C. Since the meeting room is densely populated, the temperature requirement is lower and is usually set to 20°C - 22°C. The corridor has a lower temperature requirement and is usually set to 18°C - 20°C. Then, on the premise of meeting the target temperature range of each zone, the target temperature of each zone is adjusted to reduce the overall temperature gradient of the building and reduce energy consumption. For example, by adjusting the target temperature of adjacent zones, the temperature difference between different areas in the building is reduced, and energy waste caused by too large a temperature gradient is avoided. On the premise of meeting comfort, the operation load of the refrigeration / heating equipment is minimized as much as possible to reduce the overall energy consumption. By optimizing the target temperature of each zone, the overloading operation of some equipment is avoided, and the equipment life is extended. Furthermore, the control target of each zone is determined, that is, the optimal target temperature value of each zone, to achieve the balance between comfort and energy saving, thereby significantly reducing the energy consumption of the building air-conditioning system and improving the comfort of users at the same time.

[0032] Further, step S200 further includes step S210, based on the expert system, traversing the air-conditioning control zone set to match and obtain the target temperature range and temperature sensitivity; step S220, combining the air-conditioning control zone set with the building structure information of the building to calculate and obtain the overcurrent coefficient between adjacent zones; step S230, taking the overcurrent coefficient as the first weight and the temperature sensitivity as the second weight, combining the temperature gradient operator and the temperature offset operator to define the optimization objective function; step S240, performing optimization initialization with the target temperature range as the constraint, and obtaining the central value of the target temperature range as the initial condition, and combining the optimization objective function to optimize the target temperature to redefine the optimal core temperature of the target temperature range; step S250, taking the optimal core temperature as the zone target temperature and outputting it as the zone control target.

[0033] Preferably, based on the air-conditioning control partition set, an expert system is used to match and obtain the target temperature range and temperature sensitivity of each air-conditioning control partition. The expert system is an intelligent system based on rules and knowledge that can simulate the expert decision-making process. The temperature sensitivity describes the sensitivity of the partition to temperature changes (for example, a meeting room is more sensitive to temperature changes and requires more precise control). Then, in combination with the air-conditioning control partition set and the building structure information, the overflow coefficient between adjacent partitions is calculated. Specifically, the overflow area (such as the area of doors and windows) between adjacent partitions is calculated, and based on the air velocity (such as measured by sensors or estimated from historical data), the overflow coefficient is calculated, that is, the overflow coefficient is the product of the overflow area and the air velocity.

[0034] Preferably, taking the overflow coefficient as the first weight and the temperature sensitivity as the second weight, in combination with the temperature gradient operator and the temperature offset operator, an optimization objective function is defined to measure the operation effect of the air-conditioning system. Among them, multiplying the overflow coefficient by the temperature gradient is used as the first term to reduce the temperature gradient between adjacent partitions and avoid energy waste caused by excessive temperature differences; multiplying the temperature sensitivity by the temperature offset is used as the second term to reduce the temperature fluctuation of the temperature-sensitive partition and improve user comfort; with the target temperature range (the allowable temperature range for each partition) as the constraint condition, the optimization objective function is initialized, and the initial temperature values of each partition are calculated; through optimization algorithms (such as linear programming, genetic algorithms, etc.), the minimum value of the optimization objective function is solved to obtain the optimal core temperature of each partition, that is, the temperature value that minimizes the optimization objective function. Finally, the optimal core temperature is used as the partition target temperature and as the control target of the air-conditioning control system for the air-conditioning equipment in each partition to achieve a balance between comfort and energy conservation, thereby reducing the energy consumption of the building air-conditioning system and improving user comfort at the same time.

[0035] Step S300, according to the real-time data collected by the indoor and outdoor temperature sensors, in combination with the benchmark personnel density and the partition control target, initialize the partition control parameters of each air-conditioning control partition, where the partition control parameters include set temperature, cooling and heating capacity, and air volume parameters.

[0036] Preferably, based on real-time environmental data and preset personnel density information, initial operating parameters are set for each air-conditioning control zone, including set temperature, cooling and heating capacity, air volume, etc. Among them, the reference personnel density refers to the fixed number of personnel and the number of mobile personnel in each zone. The temperature, cooling and heating capacity, and air volume parameters are the basic configurations when the air-conditioning system is started or reset, aiming to quickly provide a comfortable environment. Specifically, temperature sensors inside and outside the building collect temperature data through the KNX network to provide the current environmental status. Then, combined with the reference personnel density and the determined zone control target, the zone control parameters of each air-conditioning control zone are initialized, including calculating the initial set temperature of each zone; calculating the cooling and heating capacity required for each zone according to the temperature difference between inside and outside and the personnel density. For example, if the indoor temperature is 25 °C, the target temperature is 22.5 °C, and the personnel density is high, a larger cooling capacity is required; calculating the air volume required for each zone according to the personnel density and temperature adjustment requirements. For example, areas with a high personnel density require a larger air volume to ensure air circulation and uniform temperature, and areas with a low personnel density can appropriately reduce the air volume to reduce energy consumption; thus ensuring that the air-conditioning system quickly provides a comfortable environment when starting or resetting.

[0037] Further, step S300 further includes step S310, obtaining temperature sensor data inside and outside the building through the KNX protocol; step S320, obtaining historical pedestrian flow data of the target building and statistically analyzing to determine the reference personnel density, where the reference personnel density is a weighted combination of the fixed personnel density and the mobile personnel density; step S330, inputting the temperature sensor data, the reference personnel density, and the zone control target into a preset heat load model to calculate the cooling and heating capacity and the air volume parameters of each air-conditioning control zone; step S340, using the zone control target as the set temperature and defining the zone control parameters in combination with the cooling and heating capacity and the air volume parameters.

[0038] Preferably, multiple temperature sensors inside and outside the building are connected and controlled based on the KNX protocol to obtain temperature sensor data inside and outside the building. Historical pedestrian flow data is collected through building entrance and exit sensors (such as infrared sensors, cameras, access control systems, etc.) and statistically analyzed to obtain the reference personnel density, that is, a weighted combination of the fixed personnel density (such as the number of fixed staff in the office area) and the mobile personnel density (such as the number of temporary visitors in the meeting room) in each zone; then the temperature sensor data, the reference personnel density, and the zone control target are input into a preset heat load model to calculate the cooling and heating capacity and the air volume parameters required to maintain the target temperature of a certain zone. Specifically, the cooling and heating capacity is calculated through the formula , where Q is the total heat, U is the heat transfer coefficient, indicating the heat insulation performance of walls, windows, etc., A represents the surface area of the zone, is the temperature difference between inside and outside, is the heat load generated by personnel. Indicates the heat load generated by devices (such as computers, printers), Indicates the heat load generated by lighting tubes, etc.; The air volume is calculated through the formula where V represents the required air volume, is the air density, is the specific heat capacity of air; Finally, the zoning control target is used as the set temperature, and combined with the cold and heat quantities and air volume parameters calculated by the heat load model, precise operating parameters are set for each air-conditioning control zone to achieve the balance between comfort and energy conservation, thereby significantly improving the operating efficiency of the air-conditioning system and reducing energy consumption at the same time.

[0039] Further, step S300 further includes step S350, collecting the personnel behavior images of each air-conditioning control zone through the KNX protocol in combination with a preset acquisition period; step S360, performing action feature recognition based on the personnel behavior images and evaluating the personnel comfort according to the action features; step S370, if the personnel comfort deviates from the preset comfort range, then dynamically adjust the zoning control target of the corresponding air-conditioning control zone according to the deviation direction of the personnel comfort.

[0040] Step S370 further includes step S371, if the personnel comfort is greater than the upper limit of the comfort range, then reduce the upper limit of the target temperature range of the corresponding air-conditioning control zone; step S372, if the personnel comfort is less than the lower limit of the comfort range, then increase the lower limit of the target temperature range of the corresponding air-conditioning control zone.

[0041] Preferably, based on a preset acquisition period, using the KNX protocol to connect and control sensors and cameras in the building, etc., collect image data such as the actions and postures of personnel in each zone, where the preset acquisition period refers to the time interval (such as collecting once every 5 minutes), and then perform action feature recognition based on the personnel behavior images, that is, through image recognition technology (such as computer vision algorithms), analyze the behavior characteristics of personnel (such as postures, action frequencies, expressions, etc.), including inferring the comfort state of personnel, such as whether they feel overheated, cold or uncomfortable, whether they are active frequently, whether they show frowning, sweating, etc., and evaluate the personnel comfort according to the action feature recognition results. For example, if it is detected that a person sweats frequently, it is inferred that they feel overheated; if it is detected that a person curls up, it is inferred that they feel cold.

[0042] Preferably, compare the personnel comfort level with a preset comfort level range. If the comfort level is within the range, keep the current control parameters. If the comfort level deviates from the range, adjust the control parameters such as the target temperature, cooling and heating capacity, and air volume of the partition according to the deviation direction. Specifically, if the personnel comfort level is greater than the upper limit of the comfort level range, lower the upper limit of the target temperature range of the corresponding air-conditioning control partition; if the personnel comfort level is less than the lower limit of the comfort level range, raise the lower limit of the target temperature range of the corresponding air-conditioning control partition, that is, if the personnel feel overheated, lower the target temperature, increase the cooling capacity and air volume, and if the personnel feel cold, raise the target temperature, increase the heating capacity and air volume; wherein, the comfort level range refers to the temperature range in which the user feels comfortable, usually represented by the lowest temperature and the highest temperature; the preset comfort level range refers to the comfort level range set according to the partition function and usage requirements (such as temperature range, humidity range, etc.), and the deviation direction refers to the direction in which the personnel comfort level deviates from the comfort level range (such as overheating or cold); to enhance the user's comfort experience, thereby enhancing the intelligent level of the air-conditioning system and optimizing the energy consumption performance at the same time.

[0043] 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 partition, and predict the personnel flow direction to perform forward-looking regulation.

[0044] Step S400 further includes that the forward-looking regulation at least includes starting backup equipment, adjusting the cooling and heating capacity, and the air volume.

[0045] Preferably, by monitoring the personnel flow in the building in real time, the operating state of the air conditioning system is adjusted in advance to meet the upcoming changing environmental requirements, including dynamically adjusting the air conditioning parameters of the current zone. Through means such as starting backup equipment, adjusting cooling and heating capacity and air volume, forward-looking control is achieved to ensure that each zone in the building is always in a comfortable state. Specifically, through sensors installed at the entrances and exits of the building (such as infrared sensors, cameras, access control systems, etc.), the entry and exit data of personnel are collected in real time, including the number of personnel, flow direction and time distribution. According to the real-time personnel flow data, the air conditioning operation parameters of each zone are adjusted, including the set temperature, cooling and heating capacity, and air volume. For example, when the number of personnel in a certain zone increases, the cooling / heating capacity and air volume of this zone are increased to meet the needs of the newly added personnel; when the number of personnel in a certain zone decreases, the cooling / heating capacity and air volume of this zone are reduced to reduce energy consumption; and combined with historical data, through prediction algorithms (such as machine learning models, time series analysis, etc.), the flow direction and time distribution of personnel are analyzed, and the operating state of the air conditioning system is adjusted in advance to meet the upcoming changing environmental requirements. When it is predicted that a large number of personnel will flow into a certain zone, backup refrigeration units, cooling towers and other equipment are started in advance to meet the sudden increase in cooling / heating demand; according to 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 the personnel flow in. Through the forward-looking control of the air conditioning system, it is ensured that each zone in the building is always in a comfortable state, while optimizing the energy consumption performance.

[0046] Further, step S400 further includes step S410 of generating a probability distribution of the inflow zone based on the population migration probability matrix of the target building and combining the personnel flow data; step S420 of performing forward-looking control on the personnel inflow zone according to the generated probability distribution of the inflow zone.

[0047] Preferably, based on the historical personnel flow data of the target building, the personnel flow frequency between each partition is statistically analyzed, the ratio of the flow frequencies is calculated, and a population migration probability matrix is generated. Each element of the matrix represents the probability of flowing from one partition to another partition. Then, in combination with the personnel flow data, an inflow partition probability distribution is generated. Specifically, the personnel distribution data at the current moment (i.e., the number of personnel in each partition) is obtained, and using the population migration probability matrix, the personnel inflow probability of each partition within a future period of time is calculated, and then the inflow partition probability distribution is obtained; then, according to the inflow partition probability distribution, prospective regulation of the personnel inflow partition is carried out, that is, the operating state of the air conditioning system is adjusted in advance to meet the upcoming changing environmental requirements. For example, when it is predicted that a large number of personnel will flow into a certain partition, standby refrigeration units, cooling towers and other equipment are started in advance to meet the sudden increase in cooling / heating demand; according to the prediction results, the cooling and heating capacity and air volume of the target partition are adjusted in advance to ensure that the environment has reached a comfortable state when the personnel flow in; before the personnel flow in, the target partition is preheated or precooled in advance to reduce temperature fluctuations.

[0048] The above specific implementation manners do not constitute a limitation on the protection scope of the present 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 principle of the present application shall be included within the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be executed in a different order from that in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A zoning control method for a building air conditioning system based on KNX technology, characterized in that, The method includes: 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 range for each of the air conditioning control partitions, and globally optimizing the partition target temperatures based on the target temperature range to obtain partition control targets; Initializing the partition control parameters of each air conditioning control partition according to the real-time data collected by indoor and outdoor temperature sensors, in combination with a reference personnel density and the partition control targets, where the partition control parameters include set temperature, cooling and heating capacity, and air volume parameters; Dynamically adjusting the air conditioning operation parameters of each air conditioning control partition based on the personnel flow data collected by building entrance and exit sensors, and predicting the personnel flow direction to perform proactive regulation.

2. The zoning control method for a building air conditioning system based on KNX technology according to claim 1, wherein Obtaining building structure information and air conditioning system topology information based on the KNX protocol, where the building structure information includes a floor plan and functional partition information, and the air conditioning system topology information includes the positions, connection relationships, and coverage ranges of air conditioning equipment.

3. The zoning control method for a building air-conditioning system based on KNX technology according to claim 2, wherein, Dividing an air conditioning control partition set based on the building structure information and the air conditioning system topology information, including: Defining preliminary control partitions based on the functional partition information and the floor plan; Refining and adjusting the preliminary control partitions according to the coverage range of the air conditioning equipment, and establishing an association relationship between the air conditioning equipment and each preliminary control partition based on the adjustment results; Extracting historical indoor image information, and performing functional object recognition according to the historical indoor image information; Performing scenario-based correction on the refined and adjusted preliminary control partitions based on the functional object recognition results to obtain an air conditioning control partition set.

4. The zoning control method for a building air conditioning system based on KNX technology according to claim 3, wherein, Setting a target temperature range for each of the air conditioning control partitions, and globally optimizing the partition target temperatures based on the target temperature range to obtain partition control targets, including: Traversing the air conditioning control partition set based on an expert system to match and obtain the target temperature range and temperature sensitivity; Calculating and obtaining the overcurrent coefficient between adjacent partitions in combination with the air conditioning control partition set and the building structure information; Defining an optimization objective function by using the overcurrent coefficient as the first weight, the temperature sensitivity as the second weight, in combination with a temperature gradient operator and a temperature offset operator; Performing optimization initialization with the target temperature range as a constraint, obtaining the central value of the target temperature range as an initial condition, and performing target temperature optimization in combination with the optimization objective function to redefine the optimal core temperature of the target temperature range; Taking the optimal core temperature as the partition target temperature and outputting it as the partition control target.

5. The zoning control method for a building air-conditioning system based on KNX technology according to claim 4, characterized in that, Initializing the partition control parameters of each air conditioning control partition according to the real-time data collected by indoor and outdoor temperature sensors, in combination with a reference personnel density and the partition control targets, where the partition control parameters include set temperature, cooling and heating capacity, and air volume parameters, including: Obtaining the temperature sensor data indoors and outdoors through the KNX protocol; Obtaining the historical pedestrian flow data of the target building, and statistically analyzing and determining the reference personnel density, where 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 zoning control target into a preset heat load model to calculate the cooling and heating amounts and the air volume parameters for each air-conditioning control zone. Define the zoning control parameters with the zoning control target as the set temperature, in combination with the cooling and heating amounts and the air volume parameters.

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

7. The zoning control method of the building air conditioning system based on KNX technology according to claim 1, characterized in that, It also includes: Collect the personnel behavior images of each air-conditioning control zone through the KNX protocol in combination with a preset collection period. Perform action feature recognition based on the personnel behavior images and evaluate the personnel comfort level according to the action features. If the personnel comfort level deviates from the preset comfort interval, dynamically adjust the zoning control target of the corresponding air-conditioning control zone according to the deviation direction of the personnel comfort level.

8. The method for zoning control of a building air conditioning system based on KNX technology according to claim 7, wherein Dynamically adjusting the zoning control target of the corresponding air-conditioning control zone according to the deviation direction of the personnel comfort level also includes: If the personnel comfort level is greater than the upper limit of the comfort interval, lower the upper limit of the target temperature interval of the corresponding air-conditioning control zone. If the personnel comfort level is less than the lower limit of the comfort interval, raise the lower limit of the target temperature interval of the corresponding air-conditioning control zone.

9. The zoning control method for a building air conditioning system based on KNX technology according to claim 1, characterized in that, It also includes: Based on the KNX protocol, monitor the operation state parameters of each air-conditioning unit in real time. If the operation state parameters exceed the preset threshold range or the KNX protocol communication is interrupted, it is determined as a faulty unit. Based on the air-conditioning control zone set, identify the neighboring air-conditioning control zone set of the faulty unit. Use the air-conditioning units corresponding to the neighboring air-conditioning control zone set as redundant units to compensate for the faulty unit.

10. The zoning control method for a building air-conditioning system based on KNX technology according to claim 6, characterized in that, The forward-looking regulation at least includes starting backup equipment, adjusting the cooling and heating amounts and the air volume.

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