Control method of air conditioning system, controller and air conditioning system

Abstract

According to the control method of the air conditioning system, the controller and the air conditioning system, under the condition of low load demand, the air conditioning system avoids an extreme strategy of complete shutdown or high-load operation in a traditional method through judgment of active load demand data, total environment temperature data and historical target temperature data are further obtained, and the control method of the air conditioning system is provided. And calculating a passive load demand, and executing a first control strategy of low-power-consumption mode operation, so that the temperature of the room or the region corresponding to the sub-controller gradually approaches the historical target temperature. According to the method, energy consumption and equipment abrasion are reduced, and meanwhile, the response efficiency and the user experience of the air conditioning system in the low-load period are remarkably improved by optimizing the water temperature and the system operation state.

Description

Technical Field

[0001] The present invention relates to the technical field of air conditioners, and in particular to a control method, a controller and an air conditioning system. Background Art

[0002] Large water-cooled central air conditioning system is a centralized air conditioning system widely used in commercial buildings, large factories, shopping malls and hotels. Its working principle is to cool or heat water through a chiller as a cold or hot source, and then transport the chilled water or hot water to each terminal device (such as fan coil or air handling unit) through a water pump to achieve space cooling or heating.

[0003] For large water-cooled central air-conditioning systems, there is a technical problem that the control strategy is not appropriate when the load demand is low. Summary of the invention

[0004] The purpose of the present invention is to overcome the above technical deficiencies and provide a control method, a controller and an air-conditioning system for an air-conditioning system to solve the technical problem in the related art that for large water-cooled central air-conditioning systems, when the load demand is low, the control strategy is not appropriate.

[0005] In order to achieve the above technical objectives, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a control method for an air conditioning system, the air conditioning system comprising a main controller and at least one sub-controller in communication with the main controller, the method being applied to the main controller, the method comprising: Determine the current active load demand data of the air conditioning system; Determining whether the active load demand data is less than or equal to a preset load threshold; When the active load demand data is less than or equal to a preset load threshold, obtaining overall ambient temperature data and historical target temperature data; Calculating passive load demand data based on the overall ambient temperature data and the historical target temperature data; Based on the passive load demand data, a preset first control strategy is executed to make the temperature of the room or area corresponding to the sub-controller approach the historical target temperature; wherein the first control strategy is a strategy for controlling the air-conditioning system to operate in a low power consumption mode.

[0006] In a second aspect, the present invention provides a controller, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the above method.

[0007] In a third aspect, the present invention provides an air conditioning system, comprising the above-mentioned controller.

[0008] Beneficial effects: The present invention provides a control method for an air-conditioning system, and proposes a low-power control strategy that links the overall ambient temperature and the historical target temperature to address the problem that the control strategy of a large water-cooled central air-conditioning system is not appropriate when the load demand is low. In the case of low load demand, the air-conditioning system avoids the extreme strategies of completely shutting down or operating at high load in the traditional method by judging the active load demand data, further obtains the overall ambient temperature data and the historical target temperature data, calculates the passive load demand, and executes the first control strategy of operating in low-power mode, so that the temperature of the room or area corresponding to the sub-controller gradually approaches the historical target temperature. This method not only reduces energy consumption and equipment wear, but also significantly improves the response efficiency and user experience of the air-conditioning system during low-load periods by optimizing the water temperature and system operating status. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 is a block diagram of an air conditioning system provided by an embodiment of the present invention; Figure 2 is a flow chart of a control method of an air conditioning system provided by an embodiment of the present invention; Figure 3 It is a flow chart of a control method of an air conditioning system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0010] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0011] In the related art, large-scale water-cooled central air-conditioning systems are widely used in commercial buildings, office buildings and other scenarios that require precise temperature control. The typical architecture of such a large-scale water-cooled central air-conditioning system may include one or more external units (e.g., chillers or heat pumps), circulating water pumps, heat transfer media (water or refrigerant), and terminal devices (e.g., fan coils) distributed in various rooms. The external unit is responsible for generating chilled water or hot water, the circulating water pump drives the heat transfer medium to circulate throughout the system, and the terminal device adjusts the air temperature through the fan and water valve according to the set parameters (target temperature) of the room thermostat to meet the needs of the user.

[0012] In the current large-scale water-cooled central air-conditioning system, the control logic of the outdoor unit and the indoor unit (for example, fan coil unit) is relatively independent. The outdoor unit is mainly responsible for producing hot and cold water, and the fan coil unit at the end of the indoor unit adjusts the fan operation status and the opening and closing of the water valve through the room thermostat to achieve indoor temperature control. However, in the prior art, when the air-conditioning system is under low load demand, equipment such as circulating water pumps and chillers usually adopt a shutdown operation strategy to save energy. However, although this shutdown operation method can reduce energy consumption in a short period of time, it also brings the following significant problems: 1. When user demand increases the next day (for example, the air conditioning system is turned on again during the day), the temperature of the chilled water or hot water may differ greatly from the user's target temperature. This temperature difference causes the system to take a long time to restore the room temperature to the target range, which directly affects the user's comfort.

[0013] 2. When the air conditioning system is started from a completely closed state, the circulating water pump, chiller and other equipment need to operate at a higher load to reduce the gap between the hot and cold water temperatures and the target temperature as quickly as possible. This high-load operation will increase equipment wear and tear and cause a surge in instantaneous energy consumption.

[0014] 3. The shutdown operation strategy ignores the fact that there may still be a small number of rooms (for example, duty rooms, computer rooms, etc.) that require continuous cooling or heating at night, resulting in poor temperature control in these rooms.

[0015] Therefore, for large water-cooled central air-conditioning systems, there is a technical problem that the control strategy is not appropriate when the load demand is low.

[0016] like Figure 1 and Figure 2 As shown, this embodiment provides a control method for an air-conditioning system, wherein the air-conditioning system includes a main controller and at least one sub-controller communicatively connected thereto, and the method is applied to the main controller.

[0017] In this embodiment, the air conditioning system may include: The main controller, as the central control unit of the entire air-conditioning system, is responsible for coordinating and managing the operating status of the entire system.

[0018] Sub-controllers are connected to the main controller, and each sub-controller corresponds to a terminal air conditioning device (such as a fan coil) in a room or area. It can monitor and feedback the real-time ambient temperature of the room, record the target temperature set by the user, and control the operating status of the terminal equipment (such as fans and water valves).

[0019] The circulation pump is responsible for driving the circulation of the heat transfer medium (chilled water or hot water) in the pipe system. It is connected to the main controller and can dynamically adjust the flow rate according to the control strategy issued by the main controller to meet different load requirements.

[0020] Heat source and heat sink equipment, which are used to adjust the temperature of the heat transfer medium and are regulated by the main controller to optimize energy efficiency.

[0021] The terminal device exchanges heat with the room air through a heat transfer medium to achieve room cooling or heating.

[0022] The method comprises: Step S12: Determine the active load demand data of the current air-conditioning system.

[0023] In this embodiment, the active load demand data is data representing the active load demand of the air-conditioning system. Specifically, the active load demand can be expressed as the load demand proposed by the user to the air-conditioning system. For example, in a room in an office building, the user turns on the indoor unit and sets the temperature to 20°C (the current ambient temperature of the room is 35°C), then the temperature difference of 15°C is the active load demand. After the sub-controller in the indoor unit of the room receives the user's power-on command and the temperature setting command, it can calculate the active load demand of the room, which is the temperature difference of 15°C. Of course, the sub-controller can also directly send the temperature set by the user and the current ambient temperature of the room to the main controller, and the main controller will assume the task of calculating the active load demand. Accordingly, when the main controller summarizes the active load demands of all sub-controllers, the active load demand of the current air-conditioning system can be obtained.

[0024] In this embodiment, the active load demand data can be represented as temperature difference data corresponding to the sub-controllers included in the current air conditioning system. The temperature data represents the difference between the real-time ambient temperature of the room or area corresponding to the sub-controller and the temperature set by the user.

[0025] In a specific implementation, the active load demand data is composed of temperature difference data corresponding to all sub-controllers in the air-conditioning system. Each temperature difference data reflects the actual cooling or heating demand in the room or area corresponding to the sub-controller. Specifically, this data is represented by the difference between the real-time ambient temperature of the room collected by the sub-controller and the target temperature set by the user through the thermostat.

[0026] In cooling mode, when the room needs cooling, the temperature difference data required by the active load is: Ambient temperature in a room or area - user set temperature; That is, the current ambient temperature of the room or area minus the target temperature set by the user.

[0027] In heating mode, when the room needs to be heated, the temperature difference data required by the active load is: User set temperature - ambient temperature in a room or area; That is, the target temperature set by the user minus the current ambient temperature of the room or area.

[0028] When the calculation result is positive, it means that there is an actual cooling or heating demand (i.e. active load demand) in the room. If the calculation result is negative, it means that the current ambient temperature has reached or exceeded the target temperature, and the active load demand is zero. Each sub-controller calculates its own active load demand data based on the temperature difference of the corresponding room. The main controller summarizes the temperature difference data of all sub-controllers to obtain the total active load demand data of the entire air-conditioning system.

[0029] In a specific scenario example, an office building contains three rooms, each of which is equipped with an independent thermostat (also known as a sub-controller). The following is the current ambient temperature and the user-set temperature: 1. Room 1 Real-time ambient temperature = 30°C; User set temperature = 24℃; Refrigeration demand: temperature difference is 30-24=6℃.

[0030] 2. Room 2 Real-time ambient temperature = 18°C; User set temperature = 22℃; Heating requirement: temperature difference is 22-18=4℃.

[0031] 3. Room 3 Real-time ambient temperature = 26°C; User set temperature = 26°C; No requirement: the temperature difference is 26-26=0℃.

[0032] The main controller summarizes the active load requirements of each sub-controller and obtains: 6℃+4℃+0℃=10℃.

[0033] In this embodiment, when the sub-controller in the air-conditioning system is a thermostat (with weak computing power), the step of determining the active load demand data of the current air-conditioning system may include: First, the main controller sends a data acquisition request to each sub-controller in the air-conditioning system to obtain the ambient temperature data and real-time temperature setting data of the room or area corresponding to the sub-controller.

[0034] Then, the main controller receives the ambient temperature data and real-time temperature setting data returned by each sub-controller in the air-conditioning system.

[0035] Finally, the main controller calculates the active load demand data of the current air-conditioning system based on the ambient temperature data and the real-time temperature setting data.

[0036] It is understandable that in this case, the main controller sends a data acquisition request to all sub-controllers, requesting the sub-controller to return the ambient temperature data and real-time temperature setting data of the corresponding room or area. The sub-controller can collect the ambient temperature of the current room (for example, through the built-in temperature sensor) and the target temperature set by the user. Since the sub-controller has weak computing power, it is only responsible for sending these two data back to the main controller without performing complex calculations. After the main controller receives the ambient temperature data and set temperature data returned by all sub-controllers, it calculates the temperature difference (load demand) of each room. In this scenario, the sub-controller has limited computing power and is mainly responsible for collecting sensor data and executing simple control commands (such as fan switch, water valve adjustment, etc.).

[0037] In this scenario, the sub-controller may be a thermostat, which may include: Microcontroller (MCU); Temperature sensor, used to collect ambient temperature in real time; Communication module, used to communicate with the main controller, can support RS-485 or Modbus protocol; Memory, used to store user set temperature and current sensor data.

[0038] In this embodiment, when the sub-controller in the air-conditioning system has a certain computing capability, the step of determining the active load demand data of the current air-conditioning system may include: First, the main controller sends a data acquisition request to each sub-controller in the air-conditioning system to obtain the load data of the room or area corresponding to the sub-controller.

[0039] Then, the main controller receives the load data returned by each sub-controller in the air-conditioning system.

[0040] Finally, the main controller performs sum calculation on the load data returned by each sub-controller to obtain the active load demand data.

[0041] It is understandable that in this case, the main controller sends a data acquisition request to all sub-controllers, requesting the sub-controller to directly return the load data of the corresponding room or area. On the one hand, the sub-controller collects the ambient temperature of the room and the user-set temperature. On the other hand, the sub-controller completes the calculation of the load demand locally. The main controller can receive the load demand data uploaded by all sub-controllers. The main controller can sum the load demand data of all rooms to obtain the total active load demand data of the current air-conditioning system.

[0042] In this scenario, the sub-controllers may include: The embedded processor has certain data processing performance and may be a high-performance MCU, such as an STM32 processor.

[0043] The temperature sensor is used to collect the ambient temperature in real time; Communication module, used to communicate with the main controller, can support RS-485 or Modbus protocol; Memory, used to store user set temperature and current sensor data.

[0044] In this embodiment, the active load demand data can be expressed as the temperature difference data corresponding to the sub-controller included in the current air-conditioning system, or can also be expressed as: the energy demand data of the room or area. Specifically, the energy demand data of the room or area can be calculated based on the temperature difference data of the room and the room area. For example, for a room of 20 square meters, the ambient temperature is 30°C, the set temperature is 25°C, and the calculated energy demand data is 2kW.

[0045] In this embodiment, the active load demand data can be expressed as temperature difference data corresponding to the sub-controllers included in the current air-conditioning system, or can also be expressed as power-on status data of the room or area. For example, the thermostat of room 1 is currently on, and the thermostat of room 2 is currently off. The main controller summarizes how many rooms in the current air-conditioning system are requesting loads.

[0046] In this embodiment, the active load demand data can be expressed as the temperature difference data corresponding to the sub-controller included in the current air-conditioning system, and can also be expressed as the real-time power consumption data of the room or area. The real-time power consumption data of the room or area can be expressed as the real-time power consumption of the room machine (such as fan coil, fan or electric heater, etc.), which is used to reflect the energy consumption level under the current operating state. For example, the current fan coil operating power of room 1 is 50W, and the electric heating power of room 2 is 1.5kW. The main controller summarizes the total real-time power consumption data.

[0047] In this embodiment, the active load demand data can be expressed as the temperature difference data corresponding to the sub-controller included in the current air-conditioning system, and can also be expressed as: room or area occupancy data. The room or area occupancy data can be expressed as whether there is someone in the room or the number of people in the current room, which is used to determine the actual use of the room. For example, the data can be obtained through an infrared sensor, a pyroelectric sensor or access control system data connected to the sub-controller. For example, there are 5 people in room 1 and no one in room 2. The main controller can adjust the cold / heat distribution to the room accordingly.

[0048] In this embodiment, in addition to being represented by the temperature difference data corresponding to the sub-controller included in the current air-conditioning system, the active load demand data can also be represented as: the power data required by the air-conditioning system to achieve the temperature difference. In this embodiment, the active load demand data can be represented by converting the temperature difference data of the room into the power data of the air-conditioning system required to achieve the temperature difference. The power data reflects the actual energy level that the air-conditioning system needs to consume in order to meet the current load demand, which can be measured in kilowatts (kW). It can be understood that compared with the temperature difference data, the power data can directly reflect the actual load power required by the air-conditioning system, which is convenient for accurate matching with equipment parameters (for example, chiller power, circulating water pump flow rate). For example, when the total power demand is 4.14kW, the main controller can adjust the operating power of the chiller to a level close to this value.

[0049] Step S14: Determine whether the active load demand data is less than or equal to a preset load threshold.

[0050] In this embodiment, the load threshold can be a pre-set load threshold, which is used to determine the current working state of the air-conditioning system, whether it is in a high-load working state during the daytime or a low-load period in the middle of the night (for example, the air-conditioning system is configured in an office building).

[0051] In this embodiment, the load threshold can be pre-configured based on the design capacity of the air conditioning system. Specifically, the cooling / heating capacity of the air conditioning system is directly related to the actual temperature difference requirement. For example, if the overall temperature difference corresponding to the maximum cooling capacity of the air conditioning system is 20°C, the load threshold can be set to 10%-20% of the temperature difference (i.e., 2°C-4°C) to identify low-load scenarios.

[0052] In this embodiment, the load threshold can be pre-configured based on historical temperature difference demand data. Specifically, by analyzing historical operation data, for example, if the temperature difference demand of all rooms in an office building is usually less than 3°C at night (low load period), the load threshold can be set to 3°C.

[0053] In this embodiment, the load threshold can be pre-configured based on the number of rooms and the proportion of rooms powered on. Specifically, in the air conditioning system, when only some rooms are powered on, the total temperature difference of the active load demand will be lower. The load threshold can be dynamically set in combination with the proportion of powered-on rooms.

[0054] In a specific implementation scheme, the air conditioning system of a large office building is designed to provide a maximum cooling capacity of 500 kW, and the load threshold can be set to 10% of the maximum cooling capacity.

[0055] Step S16: When the active load demand data is less than or equal to a preset load threshold, the overall ambient temperature data and the historical target temperature data are obtained.

[0056] In this embodiment, the situation where the active load demand data is less than or equal to the preset load threshold can be represented as a low load demand situation. For example, for an office building, the early hours of the night are a period of low load demand. For example, when the external ambient temperature is close to the user-set temperature, the load demand of the air-conditioning system will be greatly reduced. For example, in the morning and evening of spring and autumn, the outside temperature may be close to the human body's comfortable temperature, so the user's demand for cooling or heating is relatively low.

[0057] In this embodiment, the overall ambient temperature data may be used to characterize the overall temperature of the environment in which the air conditioning system is located. For example, it may be the temperature of the weather forecast for the area in which the air conditioning system is located, it may be the average temperature of the indoor ambient temperature of the air conditioning system, it may be the outdoor ambient temperature of the air conditioning system, and so on.

[0058] In this implementation, the main controller may obtain the overall ambient temperature data in the following manner.

[0059] First, the main controller sends a first data acquisition request to the weather forecast service interface; wherein the first data acquisition request is used to acquire current ambient temperature data provided by the weather forecast service interface.

[0060] Then, the main controller receives the current ambient temperature data returned by the weather forecast service interface, and uses the current ambient temperature data as the overall ambient temperature data.

[0061] In this implementation, the main controller may obtain the overall ambient temperature data in the following manner.

[0062] First, the main controller sends a request to the outdoor temperature sensor built into the air conditioning system to obtain the outdoor ambient temperature.

[0063] Then, the main controller receives the outdoor ambient temperature returned by the outdoor temperature sensor.

[0064] For example, the current ambient temperature detected by the outdoor temperature sensor is 35° C., and the main controller uses this temperature as the overall ambient temperature data.

[0065] In this embodiment, the main controller can obtain the current indoor room ambient temperature data from each sub-controller (such as a thermostat) in the air-conditioning system, and average these data to obtain the average indoor ambient temperature of the entire air-conditioning system as the overall ambient temperature data.

[0066] In this embodiment, the main controller can be integrated with a building management system (BMS) to obtain outdoor ambient temperature data directly from the environmental monitoring module of the BMS. It is understandable that the BMS often continuously monitors the ambient temperature around the building and stores the data in a central system for the air conditioning system to call.

[0067] In this embodiment, the main controller can obtain the geographical location of the air-conditioning system by accessing the geographic positioning service (GPS module), and obtain the real-time ambient temperature data of the location in combination with the online weather service (online map service).

[0068] In this embodiment, the main controller can predict the current ambient temperature through a time series model based on historical ambient temperature data (for example, the outdoor temperature change trend in the past 7 days or the past month), and use this as the overall ambient temperature data.

[0069] In this embodiment, the historical target temperature data may be data used to characterize the temperature demand preferences of one or more users of the air conditioning system in historical time. Specifically, the historical target temperature data reflects the target temperature records set by the user through the sub-controller (such as the thermostat) in different time periods in the past.

[0070] In this embodiment, the main controller can obtain historical target temperature data in the following manner.

[0071] First, the main controller extracts the historical temperature setting data corresponding to each sub-controller from a preset storage device to obtain a historical temperature setting data set.

[0072] In this real-time mode, the historical temperature setting data set may include user-set temperatures of multiple sub-controllers at different historical times, for example, user-set temperatures within a week or user-set temperatures within a month.

[0073] Specifically, the main controller can extract the historical temperature setting data associated with each sub-controller from a preset storage device (e.g., a local database, cloud storage, or a distributed file system). The historical temperature setting data of each sub-controller reflects the target temperature records of its corresponding room or area in different time periods in the past.

[0074] Then the main controller directly combines the historical temperature setting data as the historical target temperature data.

[0075] Specifically, the main controller may directly randomly select a historical temperature setting data from the historical temperature setting data set as the historical target temperature data.

[0076] Alternatively, the main controller executes a preset calculation method based on the historical temperature setting data set to obtain the historical target temperature data.

[0077] Specifically, the main controller may average the historical temperature setting data set. Specifically, the main controller may first calculate the average value of the historical temperature setting data of each main controller, and then further average the average values ​​of each main controller.

[0078] The main controller can calculate the weighted average of the historical temperature setting data set. The main controller can perform weighted processing on each sub-controller of the historical temperature setting data set, so that the sub-controllers with larger weights (such as rooms in important areas or rooms with high usage frequency) have a greater impact on the final historical target temperature data. More specifically, the main controller weights the historical temperature setting data set of each sub-controller according to the time period or setting frequency. Then, the main controller weights the weighted average of each sub-controller according to the importance of the room (such as area, frequency of use). Through weighted processing, the main controller can more reasonably consider the importance of different rooms or time periods, and improve the applicability and accuracy of the temperature target.

[0079] In this embodiment, the main controller may also utilize a time series analysis method to extract a time trend from a historical temperature setting data set and predict the current or future target temperature.

[0080] It is understandable that the main purpose of step S16 is to provide necessary data support for the air conditioning system to formulate a more energy-saving and efficient operation strategy by obtaining the overall ambient temperature data and the historical target temperature data when the load demand is low (that is, the active load demand data is less than or equal to the preset load threshold). When the air conditioning system is in low load demand (for example, at night, during non-working hours, or when some rooms are closed), the user has less demand for temperature control. In this case, the air conditioning system does not need to maintain high load operation, but can switch to a low power consumption mode, such as increasing the cold water temperature or reducing the flow rate of the circulating water pump. However, to formulate a low power consumption strategy, it is necessary to understand the current overall ambient temperature data and historical target temperature data. The overall ambient temperature data can reflect the overall temperature situation of the air conditioning system (such as outdoor temperature, average indoor temperature), and provide a basis for the air conditioning system to adjust parameters such as water temperature and wind speed. The historical target temperature data can reflect the user's historical temperature control preferences and guide the air conditioning system to adjust the temperature to the range of the user's habits (for example, the historical temperature preference may be 26°C).

[0081] During low-load demand periods (such as at night), although the current demand is low, there may be a surge in load demand in the future (such as the next day during the day). Obtaining the overall ambient temperature data and historical target temperature data can help the main controller adjust the air conditioning system in advance (for example, adjust the cold water temperature close to the target temperature) to respond quickly when the actual user load increases, avoiding the system from increasing operating time and energy consumption due to large temperature difference adjustments.

[0082] Understandably, simply reducing the power consumption of the air conditioning system may cause users to wait longer to reach the target temperature when the load increases. However, obtaining historical target temperature data can help the system to accurately control and adjust the temperature to the user's habitual range, thereby ensuring user comfort while saving energy.

[0083] Step S18: Calculate passive load demand data based on the overall ambient temperature data and the historical target temperature data.

[0084] In this embodiment, the passive load demand data can be expressed as predicted user load demand data. The passive load demand data is not a load demand directly proposed by the user, but is load demand data calculated by the main control based on the overall ambient temperature data and historical target temperature data.

[0085] Specifically, passive load demand data is a predictive load demand that can be used to characterize the operating load required by the air-conditioning system to achieve the user's historically accustomed comfortable temperature under low load demand conditions. This data is not an immediate demand directly raised by the user, but the result of the main controller analyzing the overall ambient temperature data and historical target temperature data based on calculations or model predictions. Passive load demand data reflects the ability of the air-conditioning system to passively adjust to maintain a suitable temperature during low load demand periods (such as nighttime, non-working hours, etc.).

[0086] It is understandable that passive load demand data can be used to predict future demand. For example, the air conditioning system may not be operated at night (no user gives temperature settings), but the water temperature or fan status still needs to be adjusted to quickly respond to user needs before the load rises in the early morning. Passive load demand data helps the air conditioning system actively maintain a target temperature close to the user's habits under low load conditions, thereby reducing the temperature adjustment time when the user operates the air conditioning and improving comfort.

[0087] In this implementation, the temperature difference between the overall ambient temperature data and the historical target temperature data may be used as the passive load demand data.

[0088] In this embodiment, the power data required to achieve the temperature difference (i.e., the passive load demand data expressed in the form of power) may also be further calculated based on the temperature difference data. Specifically, the main controller may calculate the power demand of a single room based on the temperature difference, and then the power demands of all sub-controllers (rooms) may be aggregated to obtain the overall passive load power demand data.

[0089] Specifically, in this embodiment, when the historical temperature setting data is directly combined as the historical target temperature data, the main controller can calculate the passive load demand data based on the overall ambient temperature data and the historical target temperature data by the following method.

[0090] First, the main controller traverses the historical temperature setting data set based on the overall ambient temperature data, thereby calculating a first temperature difference data set; wherein the first temperature difference data set includes multiple first temperature difference data, and the first temperature difference data represents the difference between the historical temperature setting data and the overall ambient temperature data.

[0091] Then, the main controller performs a summation calculation on the first temperature difference data set to obtain passive load demand data.

[0092] In this embodiment, when a preset calculation method is executed based on the historical temperature setting data set to obtain the historical target temperature data, the main controller can calculate the passive load demand data based on the overall ambient temperature data and the historical target temperature data by the following method.

[0093] First, the main controller calculates unit passive load demand data based on the overall ambient temperature data and the historical target temperature data; Then, the main controller multiplies the unit passive load demand data by the number of sub-controllers to obtain the passive load demand data.

[0094] Step S10: Based on the passive load demand data, execute a preset first control strategy to make the temperature of the room or area corresponding to the sub-controller approach the historical target temperature; wherein the first control strategy is a strategy for controlling the air-conditioning system to operate in a low power consumption mode.

[0095] It is understandable that when the active load demand data is low (for example, in the middle of the night in an office building), the air-conditioning system can be actively adjusted in low-power mode to approach the user's historical target temperature by executing the preset first control strategy, although most thermostats may be turned off at this time, and the main controller may not be able to collect the ambient temperature of all rooms in real time (the thermostat is turned off, and the temperature collection function of the thermostat cannot be called). Of course, if the air-conditioning system can be equipped with a temperature collection device in each room separately to collect the ambient temperature of each room in real time, and the temperature collection device can communicate with the main controller, then even in the case of low active load demand data (for example, in the middle of the night in an office building), the main controller can also obtain the ambient temperature of each room.

[0096] In this scenario, the main controller can adjust the operating parameters including the temperature of the heat transfer medium, the flow rate of the circulating pump, the opening and closing of the control valve, the wind speed of the indoor unit, etc. through predictive control, mainly relying on the passive load demand data and the global control capability of the air conditioning system, so as to gradually bring the ambient temperature of the room where the temperature data is not collected closer to the historical target temperature. Specifically, at midnight in an office building, most rooms are not in use, the thermostat is turned off, and the active load demand data of the air conditioning system is low. In a summer environment, the current temperature of most rooms may be higher than the user's historical target temperature (for example, the historical target temperature is 25°C, while the room may currently be 28°C or higher). Although the main controller cannot directly sense the real-time ambient temperature of each room, it can predict the temperature deviation of each room based on the passive load demand data (calculated from the overall ambient temperature and the historical target temperature). In low power mode, the air conditioning system gradually reduces the room temperature to or close to the historical target temperature, ensuring that the user's comfort needs can be quickly met in the early morning or when the user turns the air conditioner back on.

[0097] In this embodiment, the main controller may execute a preset first control strategy based on the passive load demand data in the following manner.

[0098] 1. The main controller controls the outdoor unit to adjust the temperature of the heat transfer medium.

[0099] Specifically, in the low power consumption mode, the main controller adjusts the temperature of the cold water or hot water generated by the air conditioner outdoor unit (such as a chiller or a heat pump) according to the passive load demand data.

[0100] 2. The main controller controls the circulation pump and valve to adjust the flow rate of the heat transfer medium.

[0101] Specifically, in low-power mode, the main controller optimizes the circulation of the heat transfer medium in the pipe network by controlling the flow rate of the circulation pump and the opening of the valve, ensuring that the room temperature in the building gradually approaches the historical target temperature. More specifically, the main controller controls the speed of the circulation pump based on the passive load demand data to reduce the flow rate of the heat transfer medium to a level suitable for low-load demand. For some room areas (such as duty rooms or computer rooms), the main controller can control the opening and closing of valves to preferentially guide the heat transfer medium to flow into areas that require cooling / heating. In order to achieve this low-power mode, the main controller can reduce the flow rate to 20%-50% of the normal load demand.

[0102] 3. The main controller controls the indoor unit to adjust the operating status of the terminal fan coil unit.

[0103] Specifically, in low-power mode, the operation of the indoor fan coil unit needs to match the cold water temperature and flow rate of the outdoor unit to further reduce the room temperature. Therefore, even if the thermostat is turned off, the main controller can intermittently open the fans and water valves of some indoor units in the room according to the predicted passive load demand data to guide the temperature to gradually decrease. In order to achieve this low-power mode, the main controller can adjust the operation mode of the fan to a low speed gear. And control the water valve to open intermittently to reduce energy consumption.

[0104] This embodiment provides a control method for an air-conditioning system, and proposes a low-power control strategy that links the overall ambient temperature and the historical target temperature to address the problem that the control strategy of a large water-cooled central air-conditioning system is not appropriate when the load demand is low. In the case of low load demand, the air-conditioning system avoids the extreme strategies of completely shutting down or operating at high load in the traditional method by judging the active load demand data, further obtains the overall ambient temperature data and the historical target temperature data, calculates the passive load demand, and executes the first control strategy of operating in low-power mode, so that the temperature of the room or area corresponding to the sub-controller gradually approaches the historical target temperature. This method not only reduces energy consumption and equipment wear, but also significantly improves the response efficiency and user experience of the air-conditioning system during low-load periods by optimizing the water temperature and system operating status, thereby effectively solving the problems of energy waste and slow response speed of the air-conditioning system in the prior art.

[0105] In some embodiments, the step of obtaining overall ambient temperature data includes: Step S162: Send a first data acquisition request to the weather forecast service interface; wherein the first data acquisition request is used to obtain current ambient temperature data provided by the weather forecast service interface.

[0106] In this embodiment, the weather forecast service interface can be an external data service interface for providing real-time ambient temperature, meteorological data and forecast information. The interface allows the main controller of the air conditioning system to communicate with the meteorological data service provider through the network to obtain the required current ambient temperature data.

[0107] In this implementation, the first data acquisition request may include: API Key, used to authenticate access rights; Geographic location parameters, such as latitude and longitude; The data type parameter specifies the type of data to be obtained, for example, real-time temperature or short-term weather forecast.

[0108] Step S164: receiving the current ambient temperature data returned by the weather forecast service interface, and using the current ambient temperature data as the overall ambient temperature data.

[0109] In this implementation, by sending a first data acquisition request to the weather forecast service interface and receiving the returned current ambient temperature data as the overall ambient temperature data, the technical problem that the air-conditioning system is difficult to directly obtain the outdoor ambient temperature in low-load demand scenarios is effectively solved. Through the external weather forecast service interface, the main controller can obtain accurate ambient temperature information in real time without relying on environmental sensors deployed on site, which not only reduces the system hardware cost and maintenance complexity, but also improves the accuracy and real-time nature of environmental data. At the same time, by using weather forecast data, the ambient temperature of the overall area can be better reflected, providing a reliable basis for subsequent passive load demand calculations and system operation strategies, thereby achieving more efficient and energy-saving system control.

[0110] like Figure 3 As shown, in some embodiments, the air conditioning system includes a plurality of sub-controllers; the step of obtaining historical target temperature data includes: Step S166: extracting the historical temperature setting data corresponding to each sub-controller from a preset storage device to obtain a historical temperature setting data set.

[0111] In this embodiment, the storage device may be a local storage device, such as storage hardware directly deployed in a main controller of the air-conditioning system or other related devices.

[0112] In this embodiment, the storage device may be a distributed storage system.

[0113] In this embodiment, the storage device may be a cloud storage platform.

[0114] In this embodiment, the historical temperature setting data may be data of the temperature setting of the indoor unit (sub-controller) by the user within a week, or within a month.

[0115] Step S168: directly taking the historical temperature setting data set as the historical target temperature data; Step S1610: Alternatively, based on the historical temperature setting data set, a preset calculation method is executed to obtain the historical target temperature data.

[0116] In this implementation, the preset calculation method may be a calculation method for obtaining an average value.

[0117] In this implementation, the preset calculation method may be a calculation method for obtaining a weighted average value.

[0118] In this embodiment, the preset calculation method may be to input the historical temperature setting data set into a preset analysis or prediction model to generate historical target temperature data.

[0119] Specifically, the preset analysis or prediction model may be a time series model, such as a moving average model, a linear regression model, or an ARIMA model.

[0120] The preset analysis or prediction model may be a machine learning model, for example, a support vector machine (SVM) model, a neural network model, or a random forest model.

[0121] In this embodiment, the historical target temperature data is obtained by executing a preset calculation method based on the historical temperature setting data set. The historical target temperature data may be historical target temperature data corresponding to one sub-controller (for example, one sub-controller may correspond to an average of historical target temperatures), and the historical target temperature data may be historical target temperature data corresponding to all sub-controllers (all sub-controllers only correspond to an average of historical target temperatures).

[0122] In this embodiment, by extracting the historical temperature setting data corresponding to the sub-controller from a preset storage device and directly using it as the historical target temperature data or generating the historical target temperature data based on a preset calculation method, the dynamic adaptation requirements of the air-conditioning system to the historical user temperature preferences can be flexibly met. Directly using the historical temperature setting data set can quickly restore the user's historical temperature control habits, reduce the calculation complexity, and is suitable for simple scenarios; while extracting a more representative target temperature through a preset calculation method (such as average, weighted average or time series analysis) can more accurately reflect the user's long-term temperature control demand trend. In low-load demand scenarios, this method provides the air-conditioning system with reliable temperature control reference data, enabling it to adjust the temperature in advance, achieve energy-saving operation while ensuring user comfort, and further improve the system's intelligence level and response efficiency.

[0123] In some embodiments, when the historical temperature setting data is directly used as the historical target temperature data, the step of calculating the passive load demand data based on the overall ambient temperature data and the historical target temperature data includes: Step S182: Based on the overall ambient temperature data, traverse the historical temperature setting data set to calculate a first temperature difference data set; wherein the first temperature difference data set includes a plurality of first temperature difference data, and the first temperature difference data represents the difference between the historical temperature setting data and the overall ambient temperature data.

[0124] In this embodiment, based on the overall ambient temperature data, the step of traversing the historical temperature setting data set to calculate the first temperature difference data set may include: First, the controller averages the historical temperature setting data corresponding to each sub-controller in the historical temperature setting data set to obtain the mean value of the historical temperature setting data corresponding to each sub-controller (for example, the mean value within a week). It can be understood that when the historical temperature setting data set only includes the historical temperature setting data of the sub-controller at a past time point, or the temperature setting data of each sub-controller has not changed in the past period of time. For example, in the past week, the temperature setting data of each sub-controller has not been adjusted and has always been 25 degrees Celsius. In this case, it is not possible to not average.

[0125] Then, the controller traverses the mean values ​​corresponding to the sub-controllers, and subtracts the mean value from the overall ambient temperature data, thereby obtaining a first temperature difference data set.

[0126] It can be understood that, in this case, the first temperature difference data set includes the temperature difference data of each sub-controller, which is the difference between the historical temperature setting data of each sub-controller and the overall ambient temperature data.

[0127] Step S184: performing a summation calculation on the first temperature difference data set to obtain passive load demand data.

[0128] In a specific embodiment, the main controller can extract the historical temperature setting data set of each sub-controller from a preset storage device. For example: The historical temperature setting data set of sub-controller 1 is: 24℃, 25℃, 25℃, 26℃; The historical temperature setting data set of sub-controller 2 is: 26℃, 27℃, 26℃, 28℃; The historical temperature setting data set of sub-controller 3 is: 22℃, 23℃, 23℃, 24℃.

[0129] The main controller can calculate the average value of the historical temperature setting data of each sub-controller. If the historical temperature setting data set contains data at multiple time points (such as records within a week), the average value of each sub-controller is calculated as the representative value. For example: The average value of sub-controller 1 is: 25℃; The mean value of sub-controller 2 is: 26.75℃; The average value of sub-controller 3 is: 23℃.

[0130] It is understandable that if the historical temperature setting data set only contains the value of a single time point (for example, the temperature setting has always been 25° C. in the past week), there is no need for an average value, and the value is directly taken.

[0131] Finally, the main controller can calculate the difference between the average value of each sub-controller and the overall ambient temperature data. If the overall ambient temperature data is 30°C, then: Temperature difference of sub-controller 1: −5°C; Temperature difference of sub-controller 2: −3.25°C; Temperature difference of sub-controller 3: −7°C.

[0132] In this embodiment, by directly using the historical temperature setting data set as the historical target temperature data, the main controller can quickly calculate the passive load demand data, thereby simplifying the calculation process and improving the operating efficiency. Specifically, by traversing the historical temperature setting data set, calculating the difference between each historical temperature setting value and the overall ambient temperature, forming a first temperature difference data set, and then summing the temperature difference set to obtain the passive load demand data. This method can make full use of historical temperature control data and quickly evaluate the potential temperature control requirements of the system in low-load demand scenarios. It not only reduces additional complex calculations, but also dynamically reflects the load deviation between the system and the target state. In addition, this method is suitable for complex scenarios with multiple users and multiple rooms, and provides reliable data support for low-power optimization and efficient operation of air-conditioning systems, further improving the intelligence level and energy-saving effect of the system.

[0133] In some embodiments, when a preset calculation method is executed based on the historical temperature setting data set to obtain the historical target temperature data, the step of calculating the passive load demand data based on the overall ambient temperature data and the historical target temperature data includes: Calculating unit passive load demand data based on the overall ambient temperature data and the historical target temperature data; The passive load demand data is obtained by multiplying the unit passive load demand data by the number of sub-controllers.

[0134] In this embodiment, a unified historical target temperature data is calculated by first executing a preset calculation method (such as averaging) based on a set of historical temperature setting data, thereby simplifying the separate calculation process for the passive load requirements of multiple sub-controllers. Subsequently, based on the overall ambient temperature data and the historical target temperature data, a unit passive load demand data is calculated to directly represent the passive load demand characteristics of all sub-controllers, thereby avoiding the complexity of calculating the load of each sub-controller one by one. Finally, the unit passive load demand data is multiplied by the total number of sub-controllers to quickly obtain the passive load demand data of the system as a whole. The beneficial effect of this method is that, in low load demand scenarios, the calculation steps are greatly simplified by the unified historical target temperature and unit load data, while ensuring the accuracy and globality of the results. This method is applicable to multi-sub-controller scenarios, can improve the operating efficiency of the air-conditioning system, reduce the complexity of calculations, and provide reliable data support for subsequent system energy-saving optimization.

[0135] In some implementations, the step of determining active load demand data of the current air conditioning system includes: Step S122: issuing a second data acquisition request to the sub-controller; wherein the second data acquisition request is a request for acquiring the ambient temperature data and real-time temperature setting data of the room or area corresponding to the sub-controller.

[0136] Step S124: receiving the ambient temperature data and real-time temperature setting data returned by the sub-controller; Step S126: Calculate active load demand data of the current air-conditioning system based on the ambient temperature data and the real-time temperature setting data.

[0137] In this embodiment, by sending a second data acquisition request to the sub-controller to obtain the ambient temperature data and real-time temperature setting data of each room or area, the main controller can grasp the current temperature control requirements of the area corresponding to each sub-controller in real time. Subsequently, the active load demand data is calculated based on the ambient temperature data and the real-time temperature setting data, thereby realizing the accurate quantification of the overall load demand of the current air-conditioning system. The beneficial effect is that this method can dynamically perceive the real-time operation requirements of the air-conditioning system and ensure the accuracy of the system load data; at the same time, it provides a reliable basis for the optimization of subsequent operation strategies (such as water pump flow rate adjustment, chiller power distribution, etc.), thereby avoiding resource waste and improving energy efficiency. Through data interaction and centralized calculation of distributed sub-controllers, this method further enhances the intelligence level and responsiveness of the air-conditioning system.

[0138] In some implementations, the step of calculating active load demand data of the current air conditioning system based on the ambient temperature data and the real-time temperature setting data includes: Step S1262: When the active load demand data represents cooling load, the real-time temperature setting data is subtracted from the ambient temperature data and a preset temperature control interval deviation value is added to obtain the active load demand data corresponding to a single sub-controller, and then the active load demand data corresponding to all sub-controllers are summed to obtain the active load demand data of the current air-conditioning system.

[0139] In this embodiment, the ambient temperature data (Tambient) and real-time temperature setting data (Tsetting) received from the sub-controller represent the actual temperature of the current room and the target temperature set by the user. And using the preset temperature control maintenance interval deviation value (ΔT), it can represent the temperature fluctuation range (e.g., 1°C-2°C) allowed by the system to prevent frequent start and stop.

[0140] For each sub-controller, the cooling load is calculated by subtracting the real-time temperature setting value from the ambient temperature and adding the temperature control holding interval deviation value: F single cooling = T environment - T setting + ΔT If the calculated result is less than 0 (i.e. the current temperature is already below the target temperature range), the load value is set to 0, indicating that no further cooling is required.

[0141] The controller traverses all sub-controllers, sums the cooling load demand data of each sub-controller, and obtains the overall active load demand data of the current air-conditioning system.

[0142] Step S1264: When the active load demand data represents the heating load, the real-time temperature setting data is subtracted from the ambient temperature data and the preset temperature control interval deviation value is added to obtain the active load demand data corresponding to a single sub-controller, and then the active load demand data corresponding to all sub-controllers are summed to obtain the active load demand data of the current air-conditioning system.

[0143] In this embodiment, the main controller receives ambient temperature data (Tambient) and real-time temperature setting data (Tsetting) from the sub-controller, and uses a preset temperature control holding interval deviation value (ΔT) to indicate the temperature fluctuation range (e.g., 1°C-2°C) allowed by the system to prevent frequent start-stop.

[0144] For each sub-controller, the real-time temperature setting data is subtracted from the ambient temperature, and then the temperature control holding interval deviation value is added to calculate the heating load: F heating only = T setting - T environment + ΔT If the calculated result is less than 0 (i.e. the current temperature is already higher than the target temperature range), the load value is set to 0, indicating that no further heating is required.

[0145] Traverse all sub-controllers, sum the heating load demand data of each sub-controller, and obtain the overall active load demand data of the current air-conditioning system.

[0146] In this embodiment, by respectively targeting the cooling load and heating load scenarios, based on the ambient temperature data and real-time temperature setting data of the sub-controller, combined with the preset temperature control interval deviation value, the active load demand data of a single sub-controller is accurately calculated, and the overall active load demand data of the current air-conditioning system is further summarized. The beneficial effect of this method is that by clearly distinguishing between cooling and heating demands, it can adapt to the load characteristics under different operating scenarios and ensure the accuracy of load calculation; combined with the adjustment of the temperature control interval deviation value, it can be more in line with the actual operating conditions and avoid energy waste caused by frequent start-stop or excessive adjustment. In addition, by calculating the loads of the sub-controllers one by one and summarizing them globally, the real-time load demand of the entire air-conditioning system can be dynamically reflected, providing accurate data support for the adjustment of subsequent operating strategies (such as cold and heat source power allocation, water pump flow rate adjustment, etc.). This load calculation method enhances the operational intelligence and responsiveness of the air-conditioning system, while improving the overall energy efficiency.

[0147] In some embodiments, when the active load demand data is greater than a preset load threshold, the method further includes: Step S112: Based on the active load demand data, executing a preset second control strategy; wherein the second control strategy is a strategy for controlling the air-conditioning system to operate in an optimal efficiency mode.

[0148] In this embodiment, the preset second control strategy may be: The main controller determines a target flow rate from a preset heat transfer medium flow rate table based at least on the active load demand data; wherein the target flow rate is a flow rate that enables the air conditioning system to operate in an optimal efficiency mode while satisfying the current active load demand data; The main controller controls the operating state of the circulation pump so that the circulation pump adjusts the flow rate of the heat transfer medium to a target flow rate.

[0149] In this embodiment, the preset second control strategy may also be: the main controller dynamically adjusts the target temperature of the heat transfer medium (cold water / hot water) according to the current active load demand data to ensure that the cold and hot sources operate in the optimal efficiency range. Specifically, the target temperature is determined based on the active load demand data and the preset temperature-load relationship table. For example, when the active load demand is high, the cold water temperature is reduced to meet the rapid cooling demand (such as 7°C); when the active load demand is low, the cold water temperature can be increased to reduce the energy consumption of the chiller (such as 10°C).

[0150] In this embodiment, the preset second control strategy may also be: the main controller dynamically adjusts the valve opening of each regional sub-controller to match the specific requirements of each region in the current active load demand data. Specifically, the valve opening is proportional to the active load demand of each room or region. For example, in a high-load area (such as a load share of 70%), the valve is fully opened; in a low-load area (such as a load share of 10%), the valve is partially closed (such as an opening of only 20%).

[0151] In this embodiment, the preset second control strategy may also be: the main controller dynamically controls the operating state of the indoor fan of each room, which may include the speed and start-stop frequency. When the room load demand is high (such as a temperature difference of 10°C), the fan runs at a high speed (such as 3000RPM); when the room load demand is low (such as a temperature difference of 3°C), the fan runs at a low speed (such as 1200RPM).

[0152] In this embodiment, the preset second control strategy may also be: the main controller dynamically activates or shuts down cold and heat source equipment such as chillers and heat pump modules according to active load demand data.

[0153] In this embodiment, when the active load demand data is greater than the preset load threshold, the air-conditioning system operates the air-conditioning system in the optimal efficiency mode by executing the preset second control strategy. This strategy can dynamically adjust the operating parameters of the air-conditioning system (for example, water pump flow rate, chiller power, etc.) according to the current load demand, ensuring that the air-conditioning system operates at the highest energy efficiency ratio in high-load demand scenarios. The beneficial effect is that through optimized control for high-load demand scenarios, this method can effectively avoid energy waste or performance degradation caused by overload operation of equipment, while reducing equipment wear and extending the service life of the system. In addition, while meeting user needs, the air-conditioning system operates in the optimal efficiency mode, which can also significantly reduce operating costs, improve the overall reliability and intelligence level of the system, and provide a guarantee for efficient temperature control in high-load scenarios.

[0154] In some embodiments, the air conditioning system further comprises a circulation pump, the circulation pump is communicatively connected to the main controller, and the circulation pump is used to control the flow rate of the heat transfer medium in the air conditioning system; The step of executing a preset second control strategy based on the active load demand data comprises: Step S1122: Determine a target flow rate from a preset heat transfer medium flow rate table based at least on the active load demand data; wherein the target flow rate is a flow rate that enables the air conditioning system to operate in an optimal efficiency mode while satisfying the current active load demand data.

[0155] In this embodiment, the main controller may determine the target flow rate from a preset heat transfer medium flow rate table based on the active load demand data.

[0156] Specifically, in this case, the preset heat transfer medium flow rate table may be: It can be understood that in this scenario, when the active load demand is 20kW, the main controller selects a target flow rate of 10.0m³ / h according to the flow rate table to meet the demand and optimize energy efficiency.

[0157] In this embodiment, the main controller can determine the target flow rate from a preset heat transfer medium flow rate table based on the active load demand data and the overall ambient temperature data.

[0158] Specifically, in this case, the preset heat transfer medium flow rate table may be: It can be understood that in this scenario, when the active load demand is 20kW and the ambient temperature is 35°C, the target flow rate is 10.0m³ / h; when the ambient temperature is lower (25°C), the target flow rate is reduced to 8.0m³ / h.

[0159] In this embodiment, the main controller can determine the target flow rate from a preset heat transfer medium flow rate table based on the active load demand data, the overall ambient temperature data and the real-time target temperature data.

[0160] In this embodiment, the main controller can determine the target flow rate from a preset heat transfer medium flow rate table based on the active load demand data, the overall ambient temperature data, the real-time target temperature data, the number or proportion of sub-controllers in the startup state.

[0161] Step S1124: controlling the operating state of the circulation pump so that the circulation pump adjusts the flow rate of the heat transfer medium to the target flow rate.

[0162] In this embodiment, by determining the target flow rate from the preset heat transfer medium flow rate table based on the current active load demand data, and controlling the operating state of the circulation pump, the flow rate of the heat transfer medium is dynamically adjusted to the target flow rate, which can accurately match the real-time load demand of the air-conditioning system. The beneficial effect of this method is that through the dynamic adjustment of the target flow rate, it is ensured that the heat transfer medium flow rate can not only meet the current cooling or heating needs, but also avoid energy waste and equipment wear caused by excessive flow rate; at the same time, by adopting the preset flow rate table, it can ensure that the circulation pump always operates in the optimal efficiency range, thereby improving the energy efficiency ratio (COP / EER) of the entire air-conditioning system. In addition, this dynamic adjustment strategy based on load demand can also balance the efficiency of cold and heat transfer and system stability, reduce operating costs, and extend the service life of the circulation pump and other equipment in the system, providing an important technical guarantee for efficient and energy-saving air-conditioning operation.

[0163] In some embodiments, the step of determining the target flow rate from a preset heat transfer medium flow rate table based at least on the active load demand data comprises: Determining a target flow rate from a preset heat transfer medium flow rate table based on the active load demand data; Determine whether the target flow rate is greater than or equal to the current power-on ratio multiplied by a preset maximum flow rate; When the target flow rate is greater than or equal to the current power-on ratio multiplied by the preset maximum flow rate, the target flow rate is limited to the current power-on ratio multiplied by the preset maximum flow rate; wherein the current power-on ratio refers to the ratio of the number of sub-controllers that are currently in the on state to the number of all sub-controllers.

[0164] When the target flow rate is less than the current startup ratio multiplied by the preset maximum flow rate, the target flow rate determined from the preset heat transfer medium flow rate table is used as the final target flow rate.

[0165] In this embodiment, by determining the target flow rate from the preset heat transfer medium flow rate table based on the active load demand data, and further limiting or confirming the target flow rate in combination with the current startup ratio, the operating efficiency and energy utilization of the system can be dynamically balanced. The beneficial effect of this method is that when the target flow rate exceeds the current startup ratio multiplied by the preset maximum flow rate, the restriction can effectively avoid the excessive flow rate caused by the high load demand of a single or a few sub-controllers, and reduce unnecessary energy waste; and when the target flow rate is lower than the limit, the original target flow rate is directly used to ensure the response accuracy of the air-conditioning system. In addition, by incorporating the startup ratio of the sub-controller into the calculation, the current actual load state of the system can be more accurately reflected, thereby improving the flexibility and scientificity of the flow rate adjustment. This strategy not only ensures the rationality of the heat transfer medium flow rate, but also can achieve high efficiency and energy saving while meeting the load demand, extend the service life of the equipment and reduce the operating cost, and provide important technical support for the efficient operation of the air-conditioning system.

[0166] According to an embodiment of the present invention, a controller is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the above method.

[0167] According to an embodiment of the present invention, an air conditioning system is provided, comprising the above-mentioned controller.

[0168] According to an embodiment of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a computer, the computer executes the method described in any of the above embodiments.

[0169] According to an embodiment of the present invention, a computer program product including instructions is also provided. When the instructions are executed by a computer, the computer executes a method in any one of the above embodiments.

[0170] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0171] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, and this embodiment will not be described in detail here.

[0172] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0173] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0174] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A control method for an air conditioning system, characterized in that: The air conditioning system comprises a main controller and at least one sub-controller in communication with the main controller, and the method is applied to the main controller, and the method comprises: Determine the current active load demand data of the air conditioning system; Determining whether the active load demand data is less than or equal to a preset load threshold; When the active load demand data is less than or equal to a preset load threshold, obtaining overall ambient temperature data and historical target temperature data; Calculating passive load demand data based on the overall ambient temperature data and the historical target temperature data; Based on the passive load demand data, a preset first control strategy is executed to make the temperature of the room or area corresponding to the sub-controller approach the historical target temperature; wherein the first control strategy is a strategy for controlling the air-conditioning system to operate in a low power consumption mode.

2. The method according to claim 1, characterized in that The step of obtaining overall ambient temperature data comprises: Sending a first data acquisition request to the weather forecast service interface; wherein the first data acquisition request is used to obtain current ambient temperature data provided by the weather forecast service interface; The current ambient temperature data returned by the weather forecast service interface is received, and the current ambient temperature data is used as the overall ambient temperature data.

3. The method according to claim 1, characterized in that The air conditioning system includes a plurality of sub-controllers; the step of obtaining historical target temperature data includes: Extracting historical temperature setting data corresponding to each sub-controller from a preset storage device to obtain a historical temperature setting data set; directly taking the historical temperature setting data set as the historical target temperature data; or, Based on the historical temperature setting data set, a preset calculation method is executed to obtain the historical target temperature data.

4. The method according to claim 3, characterized in that In the case where the historical temperature setting data is directly used as the historical target temperature data, the step of calculating the passive load demand data based on the overall ambient temperature data and the historical target temperature data includes: Based on the overall ambient temperature data, the historical temperature setting data set is traversed to calculate a first temperature difference data set; wherein the first temperature difference data set includes a plurality of first temperature difference data, and the first temperature difference data represents the difference between the historical temperature setting data and the overall ambient temperature data; A sum calculation is performed on the first temperature difference data set to obtain passive load demand data.

5. The method according to claim 3, characterized in that: In the case where the historical target temperature data is obtained by executing a preset calculation method based on the historical temperature setting data set, the step of calculating the passive load demand data based on the overall ambient temperature data and the historical target temperature data includes: Calculating unit passive load demand data based on the overall ambient temperature data and the historical target temperature data; The passive load demand data is obtained by multiplying the unit passive load demand data by the number of sub-controllers.

6. The method according to claim 1, characterized in that The step of determining the active load demand data of the current air conditioning system includes: Sending a second data acquisition request to the sub-controller; wherein the second data acquisition request is a request for acquiring ambient temperature data and real-time temperature setting data of the room or area corresponding to the sub-controller; Receive ambient temperature data and real-time temperature setting data returned by the sub-controller; Based on the ambient temperature data and the real-time temperature setting data, active load demand data of the current air-conditioning system is calculated.

7. The method according to claim 6, characterized in that The step of calculating the active load demand data of the current air-conditioning system based on the ambient temperature data and the real-time temperature setting data comprises: In the case where the active load demand data represents the cooling load, the active load demand data corresponding to a single sub-controller is obtained by subtracting the real-time temperature setting data from the ambient temperature data and adding a preset temperature control holding interval deviation value, and then the active load demand data corresponding to all sub-controllers are summed to obtain the active load demand data of the current air-conditioning system; In the case where the active load demand data represents the heating load, the real-time temperature setting data is subtracted from the ambient temperature data and the preset temperature control maintenance interval deviation value is added to obtain the active load demand data corresponding to a single sub-controller, and then the active load demand data corresponding to all sub-controllers are summed to obtain the active load demand data of the current air-conditioning system.

8. The method according to claim 6, characterized in that When the active load demand data is greater than a preset load threshold, the method further includes: Based on the active load demand data, a preset second control strategy is executed; wherein the second control strategy is a strategy for controlling the air-conditioning system to operate in an optimal efficiency mode.

9. The method according to claim 8, characterized in that The air conditioning system further comprises a circulation pump, the circulation pump is communicatively connected with the main controller, and the circulation pump is used to control the flow rate of the heat transfer medium in the air conditioning system; The step of executing a preset second control strategy based on the active load demand data comprises: At least based on the active load demand data, determine a target flow rate from a preset heat transfer medium flow rate table; wherein the target flow rate is a flow rate that enables the air conditioning system to operate in an optimal efficiency mode while satisfying the current active load demand data; The operating state of the circulation pump is controlled so that the circulation pump adjusts the flow rate of the heat transfer medium to a target flow rate.

10. The method according to claim 9, characterized in that The step of determining the target flow rate from a preset heat transfer medium flow rate table based at least on the active load demand data comprises: Based on the active load demand data, determining a target flow rate from a preset heat transfer medium flow rate table; Determine whether the target flow rate is greater than or equal to the current power-on ratio multiplied by a preset maximum flow rate; When the target flow rate is greater than or equal to the current power-on ratio multiplied by the preset maximum flow rate, the target flow rate is limited to the current power-on ratio multiplied by the preset maximum flow rate; wherein the current power-on ratio refers to the ratio of the number of sub-controllers currently in the on state to the number of all sub-controllers; When the target flow rate is less than the current startup ratio multiplied by the preset maximum flow rate, the target flow rate determined from the preset heat transfer medium flow rate table is used as the final target flow rate.

11. A controller, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 10.

12. An air conditioning system, characterized in that: Comprising a controller as claimed in claim 11.

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