Multi-split regulation and control method based on temperature and building characteristic parameters

Abstract

The invention discloses a multi-split regulation and control method and device based on temperature and building characteristic parameters, and the method comprises the steps: firstly determining a first thermal load value of a target building according to a first indoor temperature and a first outdoor temperature of the target building at a first time, the building characteristic parameters of the target building, and a first personnel heat dissipation value in the target building; and determining a first starting number, a first set temperature, a first set air speed and a first operation mode of the multi-split air conditioners needing to be regulated and controlled based on the first thermal load value of the target building, the counteractable load value of the single multi-split air conditioner, a preset target temperature and the single load value of the multi-split air conditioner. According to the multi-split air conditioning method based on the temperature and building characteristic parameters, the multi-split air conditioning precision and the energy-saving effect of the multi-split air conditioning are improved.

Description

Technical Field

[0001] This application relates to the field of multi-split air conditioning intelligent control technology, and in particular to a multi-split air conditioning control method based on temperature and building characteristic parameters. Background Technology

[0002] Currently, most multi-split air conditioners are controlled by adjusting the difference between the real-time indoor temperature and the target temperature. For example, when the temperature difference exceeds the set threshold, the multi-split air conditioner is turned on or off, or the temperature setting and fan speed are adjusted.

[0003] However, existing control methods have significant drawbacks: Firstly, they fail to consider the impact of building characteristics on indoor temperature. Different buildings exhibit substantial differences in thermal conductivity, orientation, and window area ratio. For instance, west-facing buildings readily absorb significant solar radiation in summer, leading to a rapid increase in indoor temperature. If control is based solely on temperature differences, frequent start-ups and shutdowns of multi-split air conditioning units and excessive energy consumption will occur. Conversely, north-facing buildings dissipate heat quickly in winter, making it difficult to maintain a stable indoor temperature through temperature difference control alone, thus impacting comfort. Secondly, existing control models are fixed and cannot adaptively adjust to dynamic changes in building heat load, making it difficult to balance energy efficiency and comfort. Consequently, the accuracy of multi-split air conditioning control in related technologies is relatively poor.

[0004] Therefore, how to combine building characteristics to achieve intelligent and precise control of multi-split air conditioning systems has become an urgent technical problem to be solved. Summary of the Invention

[0005] This application provides a multi-split air conditioning control method based on temperature and building characteristic parameters to solve the problem of poor accuracy in multi-split air conditioning control in related technologies.

[0006] In a first aspect, this application provides a multi-split air conditioning control method based on temperature and building characteristic parameters, the method comprising: The first heat load value of the target building is determined based on the first indoor temperature, the first outdoor temperature, the building characteristic parameters of the target building, and the first heat dissipation value of the people in the target building; wherein, the building characteristic parameters include orientation parameters, exterior wall area parameters, window area parameters, and volume parameters. Based on the first heat load value and the offset load value of a single multi-split unit, determine the first number of multi-split units to be turned on; based on the first heat load value, the offset load value of a single multi-split unit, and the preset target temperature, determine the first set temperature of the multi-split unit; based on the first heat load value and the first number of units to be turned on, determine the load value of a single unit; based on the load value range of the single unit load value and the preset correspondence between different load value ranges and wind speeds, determine the first set wind speed of the multi-split unit; based on the indoor humidity of the target building, the first indoor temperature, the first outdoor temperature, the preset target temperature, and the first heat load value, determine the first operating mode of the multi-split unit. The multi-split air conditioner is regulated based on the first number of units turned on, the first set temperature, the first set fan speed, and the first operating mode.

[0007] The above technical solution has the following advantages or beneficial effects: Considering the problem of poor accuracy in multi-split air conditioning control due to simple adjustments based on the difference between real-time indoor temperature and target temperature, this paper proposes a multi-split air conditioning control method based on temperature and building characteristic parameters. This method combines temperature and building characteristic parameters to improve the accuracy of multi-split air conditioning control. Specifically, firstly, based on the first indoor temperature, first outdoor temperature, building characteristic parameters, and first occupant heat dissipation value of the target building at a given time, the first heat load value of the target building is determined. Based on the first heat load value and the offsetting load value of a single multi-split air conditioner, the initial number of multi-split air conditioners to be activated can be determined, avoiding excessive energy waste due to too many activated units and reduced comfort due to too few activated units. Finally, based on the first heat load value, the offsetting load value of a single multi-split air conditioner, and the preset target temperature, the initial set temperature of the multi-split air conditioner is determined. Accurate temperature setting has a significant effect on air conditioning energy saving; reasonable temperature setting helps to improve the balance between comfort and energy saving. Based on the first heat load value and the first number of units in operation, the load value of a single unit is determined. According to the load value range to which the single unit load value belongs, and the preset correspondence between different load value ranges and wind speeds, the first set wind speed of the multi-split air conditioner is determined. The wind speed directly affects the cooling or heating effect of the multi-split air conditioner, thus better meeting the user's comfort requirements while satisfying energy consumption requirements. Based on the indoor humidity of the target building, the first indoor temperature, the first outdoor temperature, the preset target temperature, and the first heat load value, the first operating mode of the multi-split air conditioner is determined. Accurate determination of the operating mode of the multi-split air conditioner has a significant effect on air conditioning energy saving, not only reducing energy consumption but also extending equipment life and improving user comfort. Therefore, the multi-split air conditioner control method based on temperature and building characteristic parameters proposed in this application improves the control accuracy and energy-saving effect of the multi-split air conditioner while also taking into account the user's comfort requirements.

[0008] Secondly, this application provides a multi-split air conditioning control device based on temperature and building characteristic parameters, the device comprising: The first determining module is used to determine the first heat load value of the target building based on the first indoor temperature, the first outdoor temperature, the building characteristic parameters of the target building, and the first heat dissipation value of the people in the target building at a first time; wherein, the building characteristic parameters include orientation parameters, exterior wall area parameters, window area parameters, and volume parameters; The second determining module is used to: determine the first number of multi-split units to be turned on based on the first heat load value and the offset load value of a single multi-split unit; determine the first set temperature of the multi-split unit based on the first heat load value, the offset load value of a single multi-split unit, and a preset target temperature; determine the load value of a single unit based on the first heat load value and the first number of units to be turned on; determine the first set wind speed of the multi-split unit based on the load value range to which the single unit load value belongs and the preset correspondence between different load value ranges and wind speeds; and determine the first operating mode of the multi-split unit based on the indoor humidity of the target building, the first indoor temperature, the first outdoor temperature, the preset target temperature, and the first heat load value. The control module is used to control the multi-split unit according to the first number of units turned on, the first set temperature, the first set fan speed, and the first operating mode.

[0009] Thirdly, this application provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, used to execute a program stored in memory, implements the method described.

[0010] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described herein.

[0011] Fifthly, this application provides a computer program product comprising an executable program that is executed by a processor to implement the method described. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A schematic diagram of the multi-unit control process based on temperature and building characteristic parameters provided for this application; Figure 2 A schematic diagram illustrating the process for determining the first heat load value of the target building, provided for this application; Figure 3 A schematic diagram of the wind speed adjustment process provided for this application; Figure 4 The dynamic control flowchart provided for this application; Figure 5 The feedback optimization flowchart provided for this application; Figure 6 A schematic diagram of the multi-unit control device based on temperature and building characteristic parameters provided for this application; Figure 7 A schematic diagram of the electronic device structure provided in this application. Detailed Implementation

[0014] To make the objectives and implementation methods of this application clearer, the exemplary implementation methods of this application will be clearly and completely described below with reference to the accompanying drawings of the exemplary embodiments of this application. Obviously, the exemplary embodiments described are only some embodiments of this application, and not all embodiments.

[0015] It should be noted that the brief descriptions of terms in this application are only for the convenience of understanding the embodiments described below, and are not intended to limit the embodiments of this application. Unless otherwise stated, these terms should be understood in their ordinary and common meaning.

[0016] The terms "first," "second," "third," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar or related objects or entities, and do not necessarily imply a specific order or sequence, unless otherwise specified. It should be understood that such terms are interchangeable where appropriate.

[0017] The terms “comprising” and “having”, and any variations thereof, are intended to cover but not exclude inclusion, for example, a product or device that includes a range of components is not necessarily limited to all of the components that are clearly listed, but may include other components that are not clearly listed or that are inherent to such product or device.

[0018] The term "module" refers to any known or subsequently developed hardware, software, firmware, artificial intelligence, fuzzy logic, or combination of hardware and / or software code that is capable of performing the functions associated with that element.

[0019] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0020] For ease of explanation, the above description has been provided in conjunction with specific embodiments. However, the above exemplary discussion is not intended to be exhaustive or to limit the embodiments to the specific forms disclosed above. Various modifications and variations can be obtained based on the above teachings. The selection and description of the above embodiments are for the purpose of better explaining the principles and practical applications, thereby enabling those skilled in the art to better utilize the described embodiments and various different variations of embodiments suitable for specific use considerations.

[0021] This application relates to intelligent control technology for central air conditioning multi-split systems, specifically to an intelligent control method and system for multi-split systems based on indoor temperature feedback and building characteristics.

[0022] This application incorporates characteristic parameters such as building thermal conductivity coefficient and orientation coefficient to construct a real-time building heat load calculation model; dynamically adjusts the start / stop threshold, set temperature, wind speed and mode of multi-split air conditioning units based on the real-time building heat load value; and adds a machine learning feedback optimization module to continuously correct the parameters of the real-time building heat load calculation model.

[0023] This application controls the adjustment accuracy error within ±0.5℃, improving comfort; reduces energy consumption of multi-split units, resulting in significant energy savings; reduces frequent start-ups and shutdowns of equipment, extends service life, and lowers operation and maintenance costs.

[0024] Figure 1 The schematic diagram of the multi-split air conditioning control process based on temperature and building characteristic parameters provided in this application includes the following steps: S101: Determine the first heat load value of the target building based on the first indoor temperature, the first outdoor temperature, the building characteristic parameters of the target building, and the first heat dissipation value of the people in the target building; wherein, the building characteristic parameters include orientation parameters, exterior wall area parameters, window area parameters, and volume parameters; S102: Determine the first number of multi-split units to be turned on based on the first heat load value and the offset load value of a single multi-split unit; determine the first set temperature of the multi-split unit based on the first heat load value, the offset load value of a single multi-split unit, and a preset target temperature; determine the load value of a single unit based on the first heat load value and the first number of units to be turned on; determine the first set wind speed of the multi-split unit based on the load value range to which the single unit load value belongs and the preset correspondence between different load value ranges and wind speeds; determine the first operating mode of the multi-split unit based on the indoor humidity of the target building, the first indoor temperature, the first outdoor temperature, the preset target temperature, and the first heat load value. S103: Adjust the multi-split unit according to the first number of units turned on, the first set temperature, the first set fan speed, and the first operating mode.

[0025] The multi-split air conditioning control method based on temperature and building characteristic parameters provided in this application is applied to electronic devices, which can be smart terminals, servers, or multi-split air conditioners.

[0026] When controlling a multi-split air conditioning system, the first step is to obtain the first indoor temperature, the first outdoor temperature, and the first heat dissipation value per person in the target building at a given time. The target building refers to the building where the multi-split air conditioning system is located. Obtaining the first heat dissipation value per person involves obtaining the number of people in the target building at the given time, and then multiplying the number of people by a preset heat dissipation value per person as the first heat dissipation value.

[0027] Obtain the building characteristic parameters of the target building, including orientation parameters, exterior wall area parameters, window area parameters, and volume parameters. Based on the first indoor temperature, first outdoor temperature of the target building at a first time, the building characteristic parameters of the target building, and the first heat dissipation value of occupants within the target building, determine the first heat load value of the target building.

[0028] Specifically, Figure 2 The schematic diagram provided in this application illustrates the process of determining the first heat load value of a target building. The process of determining the first heat load value of the target building based on the first indoor temperature, the first outdoor temperature, the building characteristic parameters of the target building, and the first heat dissipation value of occupants within the target building at a first time includes the following steps: S201: Determine the thermal conductivity coefficient K and the equivalent heat transfer area S based on the exterior wall area parameters and window area parameters of the target building; determine the orientation coefficient D based on the orientation parameters of the target building; For example, if the exterior wall area accounts for 70% of the total area and the window area accounts for 70% of the total area, then the thermal conductivity coefficient K = 1.5 × 0.7 + 3.0 × 0.3 = 1.95 W / (m²) (℃). 1.5 is the preset thermal conductivity coefficient of the exterior wall, and 3.0 is the preset thermal conductivity coefficient of the South Lake. The equivalent heat transfer area S is the sum of the exterior wall area and the window area. For example, the orientation coefficient is 1.2 for west or east facing; 1.0 for south facing; and 0.8 for north facing.

[0029] S202: Determine the first influence coefficient based on the orientation parameter and the first time; determine the solar radiation heat gain ΔQ_solar based on the external wall area parameter, window area parameter, solar radiation value and the first influence coefficient; Electronic devices can pre-store the influence coefficients corresponding to different orientation parameters at different times, thereby determining the orientation parameters of the target building and the first influence coefficient at the first time.

[0030] ΔQ_Sun = (Window area parameter × 1 + Exterior wall area parameter × 0.8) × Solar radiation value × First influence coefficient.

[0031] S203: Determine the rate of change of indoor temperature of the target building, and determine the air heat capacity parameter = C×ρ×V×(dT1 / dt) based on the rate of change of indoor temperature dT1 / dt, air specific heat capacity C, air density ρ, and volume parameter V. The rate of change of indoor temperature, dT1 / dt, is calculated as: (after T - before T) / time interval, in degrees Celsius per second. For example, if T rises from 25°C to 26°C within 10 minutes, then dT1 / dt = 1 / 10 / 60°C / s.

[0032] S204: Determine the first heat load value ΔQ of the target building based on the first indoor temperature T1, the first outdoor temperature T2, the thermal conductivity coefficient K, the equivalent heat transfer area S, the orientation coefficient D, the solar radiation heat gain ΔQ_solar, the air heat capacity parameter, and the first personnel heat dissipation value ΔQ_person.

[0033] Specifically, ΔQ = K×D×(T2 - T1)×S + ΔQ_Sun + C×ρ×V×(dT1 / dt) + ΔQ_Person.

[0034] In this application, the first number of multi-split units to be started is determined based on the first heat load value and the offset load value of a single multi-split unit.

[0035] Based on the building's heat load (ΔQ) and the load (Q_ref) that can be offset during steady-state operation of a single multi-split air conditioner, typically 10% to 15% of the rated power, the calculation is as follows: The first number of units to be started, N_start, is calculated as: N_start = ceil (|ΔQ| / Q_ref) (ceil: round up). Q_ref is the offsetting load value of a single multi-split unit, and |ΔQ| is the absolute value of the first heat load value.

[0036] Constraints: 1 ≤ N_open ≤ N_max (N_max is the maximum number of multi-split air conditioners in a room).

[0037] Note: If the power-on conditions are not met, the same logic will be used for the next power-on check.

[0038] The first set temperature of the multi-split unit is determined based on the first heat load value, the offset load value of the single multi-split unit, and the preset target temperature.

[0039] The initial set temperature is determined based on the target temperature T0 and the current first heat load value ΔQ.

[0040] For example, in cooling mode: T_initial setting = T0 - min (0.5℃, 0.2×|ΔQ| / Q_ref); Heating mode: T_initial = T0 + min (0.5℃, 0.2×|ΔQ| / Q_ref).

[0041] Based on the first heat load value and the first number of units started, the load value of a single unit is determined. Based on the load value range to which the single unit load value belongs and the preset correspondence between different load value ranges and wind speeds, the first set wind speed F_initial of the multi-split unit is determined.

[0042] The ratio of the first heat load value to the first number of units started is determined as the single unit load value ΔQ_single.

[0043] For example: ΔQ_single > 500W (high load): F_initial = high wind; 200W≤ΔQ_single≤500W (medium load): F_initial = medium load; ΔQ_single < 200W (low load): F_initial = low wind.

[0044] The first operating mode of the multi-split air conditioner is determined based on the indoor humidity of the target building, the first indoor temperature, the first outdoor temperature, the preset target temperature, and the first heat load value.

[0045] The system offers four operating modes: cooling, heating, dehumidification, and ventilation. Based on real-time environmental parameters, the system determines the appropriate operating mode according to the following rules: When multiple conditions are met simultaneously, the mode is executed in the following order of priority: dehumidification > cooling > heating > ventilation. The operating modes are determined as shown in Table 1 below.

[0046] Table 1

[0047] Table 1 is used for both initial mode settings during system startup and dynamic mode evaluation during operation. When changes in environmental parameters are detected that cause the current mode to no longer be optimal, the system will reassess based on this table and switch to a new mode after the stability conditions are met.

[0048] Considering the problem of poor accuracy in multi-split air conditioning control due to simple adjustments based on the difference between real-time indoor temperature and target temperature, this paper proposes a multi-split air conditioning control method based on temperature and building characteristic parameters. This method combines temperature and building characteristic parameters to improve the accuracy of multi-split air conditioning control. Specifically, firstly, based on the first indoor temperature, first outdoor temperature, building characteristic parameters, and first occupant heat dissipation value of the target building at a given time, the first heat load value of the target building is determined. Based on the first heat load value and the offsetting load value of a single multi-split air conditioner, the initial number of multi-split air conditioners to be activated can be determined, avoiding excessive energy waste due to too many activated units and reduced comfort due to too few activated units. Finally, based on the first heat load value, the offsetting load value of a single multi-split air conditioner, and the preset target temperature, the initial set temperature of the multi-split air conditioner is determined. Accurate temperature setting has a significant effect on air conditioning energy saving; reasonable temperature setting helps to improve the balance between comfort and energy saving. Based on the first heat load value and the first number of units in operation, the load value of a single unit is determined. According to the load value range to which the single unit load value belongs, and the preset correspondence between different load value ranges and wind speeds, the first set wind speed of the multi-split air conditioner is determined. The wind speed directly affects the cooling or heating effect of the multi-split air conditioner, thus better meeting the user's comfort requirements while satisfying energy consumption requirements. Based on the indoor humidity of the target building, the first indoor temperature, the first outdoor temperature, the preset target temperature, and the first heat load value, the first operating mode of the multi-split air conditioner is determined. Accurate determination of the operating mode of the multi-split air conditioner has a significant effect on air conditioning energy saving, not only reducing energy consumption but also extending equipment life and improving user comfort. Therefore, the multi-split air conditioner control method based on temperature and building characteristic parameters proposed in this application improves the control accuracy and energy-saving effect of the multi-split air conditioner while also taking into account the user's comfort requirements.

[0049] In this application, the method further includes: According to the preset control cycle, if the second indoor temperature T3 of the target building at the second time, the preset target temperature T0, the second heat load value ΔQ_actual of the target building at the second time, the offset load value Q_ref of a single multi-split unit, the first number of units N_on, and the rate of change of indoor temperature dT1 / dt of the target building at the second time simultaneously satisfy: T3-T0>ΔT_moving+ w1×|ΔQ_real| / Q_ref; |ΔQ_actual| ≥ Q_ref × N_on × w2; dT1 / dt > 0; Then increase the number of operating multi - split units based on the first number of operating units; where, ΔT_dynamic is the dynamic threshold, w1 is the preset first coefficient, and w2 is the preset second coefficient; Otherwise, do not adjust the first number of operating units.

[0050] The method further includes: According to a preset regulation period, if the second indoor temperature T3 of the second time - targeted building, the preset target temperature T0, the second actual heat load value ΔQ_actual of the second time - targeted building, the load - offset value Q_ref of a single multi - split unit, the first number of operating units N_on, and the indoor temperature change rate dT1 / dt of the second time - targeted building simultaneously satisfy: T0 - T3 > ΔT_dynamic; |ΔQ_actual| ≤ Q_ref × N_on × w3; dT1 / dt < 0; N_on > 1; Then reduce the number of operating multi - split units based on the first number of operating units; where, ΔT_dynamic is the dynamic threshold, and w3 is the preset third coefficient; Otherwise, do not adjust the first number of operating units.

[0051] The method further includes: According to a preset regulation period, if the second indoor temperature T3 of the second time - targeted building, the preset target temperature T0, the second actual heat load value ΔQ_actual of the second time - targeted building, the load - offset value Q_ref of a single multi - split unit, and the first heat load value ΔQ satisfy: If T3 > T0, the set temperature of the multi - split unit is adjusted to T_set_dynamic = T_set_initial - min(a °C, w4×|ΔQ_actual - ΔQ| / Q_ref); If T3 < T0, the set temperature of the multi - split unit is adjusted to T_set_dynamic = T_set_initial + min(a °C, w4×|ΔQ_actual - ΔQ| / Q_ref); T_set_initial is the first set temperature, w4 is the preset fourth coefficient, and a is the preset first adjustment temperature.

[0052] Figure 3 The following is a schematic diagram of the wind speed adjustment process provided by this application, including the following steps: S301: Determine the heat deviation index based on the second indoor temperature T3 of the second time - targeted building, the preset target temperature T0, the single - unit load value ΔQ_single, and the load - offset value Q_ref of a single multi - split unit; S302: Based on the index range to which the thermal deviation index belongs and the preset correspondence between different index ranges and wind speed, determine the second set wind speed of the multi-split unit and adjust the wind speed of the multi-split unit to the second set wind speed.

[0053] Specifically, the thermal deviation index is determined by the formula |T3 - T0| + 0.2 × (ΔQ_single / Q_ref) based on the second indoor temperature T3 of the target building at the second time, the preset target temperature T0, the single unit load value ΔQ_single, and the offset load value Q_ref of a single multi-split unit.

[0054] In this application, before adjusting the multi-split air conditioner at the second time, the method further includes: According to the preset control cycle, if the second indoor temperature T3 of the target building at the second time and the preset target temperature T0 satisfy |T3-T0|>ΔT_moment, the subsequent control process of the multi-split air conditioning system at the second time is triggered; or If the second heat load value ΔQ_actual of the target building at the second time and the first heat load value ΔQ satisfy: (|ΔQ_real-ΔQ| / ΔQ) > m; then the subsequent process of regulating the multi-unit system at the second time is triggered; where m is the preset first proportional threshold.

[0055] Specifically, the dynamic threshold ΔT_dynamic is determined based on the load value range to which the first heat load value ΔQ belongs and the preset correspondence between different load value ranges and dynamic thresholds. The larger the load value range includes, the larger the corresponding dynamic threshold.

[0056] The method further includes: According to the preset correction cycle, the first heat load value ΔQ of the target building is corrected through the following steps: S1: Determine the actual heat load data for the historical time period ΔQ_actual = ΔQ_air conditioning + (C × ρ × V) × dT1 / dt_actual; ΔQ_air conditioning = number of units started (N_on) × real-time power of a single multi-split unit (P_single) × 1000 (converted to watts); dT1 / dt_actual: actual rate of change of indoor temperature; S2: Calculate the model prediction value ΔQ_predicted = |K × D × (T2 - T1) × S + ΔQ_sun + (C × ρ × V) × dT1 / dt_actual + ΔQ_person|; S3: Calculate the relative error = |ΔQ_predicted - ΔQ_actual| / ΔQ_actual; if the relative error is greater than the preset second proportional threshold, proceed to S4; S4: Using ΔQ_true as the dependent variable, construct a linear model: ΔQ_real ≈ x1 × ((T2-T1) × S) + x2 × ΔQ_solar + x3 × (dT1 / dt_actual) + x4 × number of people; data fitting is performed using the least squares method to obtain x1, x2, x3, and x4; the first heat load value ΔQ is corrected to ΔQ_real; Where x1: equivalent heat transfer coefficient (≈ K × D); x2: response coefficient of solar radiation heat gain; x3: air heat capacity correlation coefficient (≈ C×ρ×V / 60); x4: actual heat dissipation per person (W / person).

[0057] Figure 4 The dynamic control flowchart provided for this application, such as Figure 4 As shown, data is collected at times T1 and T2; the heat load ΔQ is calculated, and it is determined whether the start-up conditions are met. If not, the data collection steps for T1 and T2 are continued. If yes, the initial number of units started, the set temperature, and the fan speed are calculated; the equipment is started and commands are issued; the dynamic control cycle is entered; it is determined whether the temperature difference / load triggers control. If yes, the on / off state, temperature, and fan speed of the multi-split air conditioner are dynamically controlled; it is determined whether to continue operation. If yes, the dynamic control cycle is entered again. If not, the process ends.

[0058] It should be noted that the air conditioning operating temperature, fan speed and mode determined in this application are applicable to each multi-split air conditioner. In other words, all multi-split air conditioners that are turned on will operate according to the determined temperature, fan speed and mode.

[0059] Figure 5 The feedback optimization flowchart provided for this application, such as Figure 5 As shown, the process involves: filtering valid data; calculating the actual heat load ΔQ_actual; calculating the predicted heat load ΔQ_predicted; calculating the average error of the heat load; determining whether the average error is greater than 15%; if not, the process ends; if so, using multiple linear regression to fit the model parameters; correcting the model parameters, and the process ends.

[0060] The following section provides a detailed explanation of the multi-unit control process based on temperature and building characteristic parameters provided in this application, using specific examples.

[0061] 1. Data collection.

[0062] ① Indoor temperature (T1): The temperature is collected once every 3 minutes using a temperature sensor, and the average value is taken from multiple sensors.

[0063] ②Outdoor temperature (T2): The temperature is collected once every 3 minutes using a temperature sensor, and the average value is taken from multiple sensors.

[0064] 2. Heat load calculation.

[0065] The building heat load (ΔQ, unit: W) is calculated based on the building's characteristic parameters, using the following formula: ΔQ = K×D×(T2 - T1)×S + ΔQ_Sun + C×ρ×V×(dT1 / dt) + ΔQ_Man.

[0066] The detailed meanings of each parameter in the formula are as follows: ① Building heat transfer term: K×D×(T2 - T1)×S quantifies “the heat exchange between the interior and exterior of the building envelope (exterior walls, windows, etc.).

[0067]

[0068] Table 2

[0069] ②Solar radiation term: ΔQ_Solar quantification "Heat generated by solar radiation".

[0070]

[0071] Table 3

[0072] ③ Air heat capacity term: C×ρ×V×(dT1 / dt) quantifies "the heat required for a change in indoor air temperature".

[0073]

[0074] Table 4

[0075] ④ Heat dissipation by personnel: ΔQ_person is the amount of heat generated by the metabolism of personnel in the room.

[0076]

[0077] Table 5

[0078] Whether to start the machine is determined based on the current heat load ΔQ and the temperature difference between the actual temperature T1 and the target temperature T0.

[0079] Power-on requirements: Condition 1: Actual temperature T1 - target temperature T0 ≥ initial temperature difference threshold (default 1 degree); Condition 2: Initial heat load |ΔQ| ≥ minimum load threshold (default 200W); The machine can only be turned on if both of the above conditions are met.

[0080] Dynamic settings for the number of units turned on, temperature, and fan speed: After 10 minutes of operation, dynamic adjustment is performed based on the conditions (to prevent frequent adjustments due to excessive temperature difference between indoor and target temperatures at startup). The dynamic adjustment is not executed at a fixed cycle, but includes two mechanisms: "temperature difference trigger" and "heat load change trigger" (or relationship, adjustment is performed when either is met).

[0081] Temperature difference trigger: When the difference between the real-time indoor temperature (T3) and the target temperature (T0) (|T3-T0|) exceeds the "dynamic threshold (ΔT_dynamic)", regulation is triggered; ΔT_dynamic; set according to the initial heat load (ΔQ): Low load (|ΔQ| < 200W): ΔT_dynamic = 0.3℃ (small temperature difference means regulation to maintain stability); Medium load (200W≤|ΔQ|≤500W): ΔT_dynamic = 0.5℃ (balanced stability and operating frequency); High load (|ΔQ|>500W): ΔT_dynamic = 0.8℃ (allow a slightly larger temperature difference, avoid frequent adjustments).

[0082] Heat load change trigger: When the difference between the real-time heat load (ΔQ_real) and the initial heat load (ΔQ) (|ΔQ_real-ΔQ| / ΔQ) exceeds 20%, control is triggered (e.g., a sudden increase in personnel or a sudden change in outdoor temperature leading to load change). Both mechanisms can be triggered if either one is met, with a minimum adjustment interval of 10 minutes.

[0083] Once the trigger conditions are met, the number of units turned on, the set temperature, and the fan speed will be adjusted according to the following rules: The number of devices powered on is dynamically adjusted (N_dynamic): The number of machines switched on and off is dynamically adjusted based on three conditions: temperature difference, load, and temperature trend.

[0084] Increase the number of machines that can be started: Taking refrigeration as an example, an additional unit should be added when the following conditions are met: ①Temperature conditions: T3-T0>ΔT_dynamic+ 0.2×|ΔQ_actual| / Q_ref (At high load, the temperature rise lags behind the load increase. This formula allows the starting threshold to increase synchronously with the load, avoiding false starting due to small temperature differences at low load, and also ensuring that the temperature deviation is large enough before starting at high load, thus balancing temperature control and energy saving). ② Load condition: |ΔQ_actual| ≥ Q_ref × N_on × 1.1 (Actual load > current total cooling capacity × 1.1, N_on: number of units in operation); ③ Trend condition: dT1 / dt>0; Adjustment rules: Only add 1 standby unit each time, and for the newly added equipment, it is necessary to preferentially select the multi-connected air conditioner with the "shortest cumulative operation time" (load balancing) to avoid some equipment operating at high load for a long time.

[0085] Reduce the number of units in operation: Taking refrigeration as an example, when the following conditions are met simultaneously, turn off one unit: ① Temperature condition: T0 - T3 > ΔT_moving (when the load is insufficient during shutdown, the excess capacity of the equipment directly cools, and the temperature has no lag. It can be judged only through the temperature deviation without load compensation); ② Load condition: |ΔQ_actual| ≤ Q_ref × N_operating × 0.4 (actual load ≤ current total refrigeration capacity × 0.4, N_operating: number of units in operation); ③ Trend condition: dT1 / dt < 0; ④ Current number of units in operation > 1 (at least keep 1 unit to maintain the basic temperature); Adjustment rules: Only turn off 1 unit each time, and preferentially turn off the multi-connected air conditioner with the "longest cumulative operation time".

[0086] Dynamic adjustment of the set temperature (T_set_dynamic): Based on whether the actual temperature T1 reaches the target temperature T0 for regulation, and the regulation value increases or decreases based on the initial set temperature, to prevent continuous reduction of the set temperature resulting in excessive refrigeration or large temperature difference fluctuations when the temperature fails to reach the standard.

[0087] Refrigeration mode: If T3 > T0 (temperature fails to reach the standard): T_set_dynamic = T_set_initial - min(0.5°C, 0.1 × |ΔQ_actual - ΔQ| / Q_ref); If T3 < T0 (excessive refrigeration): T_set_dynamic = T_set_initial + min(0.5°C, 0.1 × |ΔQ_actual - ΔQ| / Q_ref); Constraints: T_set_dynamic ≥ 22°C (to avoid excessive refrigeration), and the adjustment amplitude each time ≤ 0.5°C (to prevent sudden temperature changes); Example: Initial T_set_initial = 24.2°C (high load), ΔQ_actual increases by 200W compared to ΔQ_initial, then T_set_dynamic = 24.2 - min(0.5, 0.1 × 200 / 100) = 24.2 - 0.2 = 24.0°C.

[0088] Heating mode: If T3 < T0 (temperature fails to reach the standard): T_set_dynamic = T_set_initial + min(0.5°C, 0.1 × |ΔQ_actual - ΔQ| / Q_ref); If T3 > T0 (overheating): T_set_actual = T_set_initial - min (0.5℃, 0.1×|ΔQ_actual - ΔQ| / Q_ref); Constraints: T_setup ≤ 24℃ (to avoid overheating), and each adjustment increment ≤ 0.5℃.

[0089] Dynamic wind speed adjustment (F_dynamic): The wind speed is adjusted based on the degree to which the indoor temperature deviates from the target (|T3 - T0|) and the heat load ΔQ_of a single device.

[0090] The wind speed setpoint is determined based on the thermal deviation index, according to the following rules:

[0091] Table 6

[0092] Feedback optimization (adjusted daily): By analyzing historical operating data, the accuracy of the current heat load model is assessed, and key parameters (such as thermal conductivity and heat dissipation from personnel) are automatically corrected to make model predictions more accurate, thereby improving the precision and energy efficiency of multi-split air conditioning systems. Current heat load model: ΔQ = K×D×(T2 - T1)×S + ΔQ_Sun + C×ρ×V×(dT1 / dt) + ΔQ_Person; Core principle (conservation of energy): The building's actual heat load = actual air conditioning output power + heat required to warm the indoor air, i.e.: ΔQ_real = ΔQ_air conditioning + C × ρ × V × (dT1 / dt_actual).

[0093] Data collection: Environmental and operational data are collected every 5 minutes.

[0094] Table 7

[0095] Feedback is used to optimize the execution process, which is executed daily at 2:00 AM.

[0096] Step 1: Filter valid historical data segments.

[0097] Select data that meets the following criteria within the past 24 hours: ② The air conditioner is in operation (N ≥ 1); ②The data is complete and without any missing data; Step 2: Calculate the true heat load ΔQ_true for each data point.

[0098] ΔQ_real = ΔQ_air conditioning + (1005 × 1.2 × V) × dT1 / dt_actual; ΔQ_air conditioner = Number of units in operation (N_on) × Real-time power per unit (P_single) × 1000 (converted to watts); dT1 / dt_actual: The actual rate of change of indoor temperature.

[0099] Step 3: Calculate the model prediction value ΔQ_prediction.

[0100] ΔQ_predicted = |K × D × (T2 - T1) × S + ΔQ_Sun (before adjustment) + (1005 × 1.2 × V) × dT1 / dt_actual + number of people × q_person|; K: Thermal conductivity (W / (㎡·℃)); D: Orientation coefficient (unitless); S: Equivalent heat transfer area (㎡); q_person: Heat dissipation per person (initial value 100 W / person).

[0101] Step 4: Calculate the model error.

[0102] Calculate the relative error for each data point: Error = |ΔQ_predicted - ΔQ_actual| / ΔQ_actual; If the average error is greater than 15%, parameter correction will be initiated.

[0103] Step 5: Parameter identification (linear regression method).

[0104] Treating ΔQ_actual as the dependent variable, construct a linear model: ΔQ_real ≈ x1 × ((T2-T1) × S) + x2 × ΔQ_sun + x3 × (dT1 / dt_actual) + x4 × number of people; where: x1: Equivalent heat transfer coefficient (≈ K × D); x2: Response coefficient of solar radiation heat gain; x3: Air heat capacity correlation coefficient (ideal value ≈ 1005 × 1.2 × V / 60); x4: Actual heat dissipation per person (W / person); The least squares method was used to fit the historical data to find the optimal x1, x2, x3, and x4.

[0105] Step 6: Update model parameters.

[0106] Write the calculation results into the control system configuration:

[0107] Table 8

[0108] The final formula is revised to: ΔQ = K×D×(T2 - T1)×S + ΔQ_Sun + α×C×ρ×V×(dT1 / dt) + ΔQ_Man.

[0109] Figure 6 The schematic diagram of the multi-split air conditioning control device based on temperature and building characteristic parameters provided in this application includes: The first determining module 11 is used to determine the first heat load value of the target building based on the first indoor temperature, the first outdoor temperature, the building characteristic parameters of the target building, and the first heat dissipation value of the people in the target building at a first time; wherein, the building characteristic parameters include orientation parameters, exterior wall area parameters, window area parameters, and volume parameters. The second determining module 12 is used to determine the first number of multi-split units to be turned on based on the first heat load value and the offset load value of a single multi-split unit; to determine the first set temperature of the multi-split unit based on the first heat load value, the offset load value of a single multi-split unit, and a preset target temperature; to determine the load value of a single unit based on the first heat load value and the first number of units to be turned on; to determine the first set wind speed of the multi-split unit based on the load value range to which the single unit load value belongs and the preset correspondence between different load value ranges and wind speeds; and to determine the first operating mode of the multi-split unit based on the indoor humidity of the target building, the first indoor temperature, the first outdoor temperature, the preset target temperature, and the first heat load value. The control module 13 is used to control the multi-split unit according to the first number of units turned on, the first set temperature, the first set wind speed and the first operating mode.

[0110] The first determining module 11 is specifically used to determine the thermal conductivity coefficient K and the equivalent heat transfer area S based on the exterior wall area parameters and window area parameters of the target building; and to determine the orientation coefficient D based on the orientation parameters of the target building. Based on the orientation parameter and the first time, a first influence coefficient is determined; based on the external wall area parameter, window area parameter, solar radiation value and the first influence coefficient, the solar radiation heat gain ΔQ_solar is determined. Determine the rate of change of indoor temperature of the target building, and based on the rate of change of indoor temperature dT1 / dt, specific heat capacity of air C, air density ρ, and volume parameter V, determine the air heat capacity parameter = C×ρ×V×(dT1 / dt); Based on the first indoor temperature T1, the first outdoor temperature T2, the thermal conductivity K, the equivalent heat transfer area S, the orientation coefficient D, the solar radiation heat gain ΔQ_solar, the air heat capacity parameter, and the first occupant heat dissipation value ΔQ_person, the first heat load value ΔQ of the target building is determined by the following formula: ΔQ = K×D×(T2 - T1)×S + ΔQ_Sun + C×ρ×V×(dT1 / dt) + ΔQ_Man.

[0111] The control module 13 is also used to, according to a preset control cycle, determine if the second indoor temperature T3 of the target building at the second time, the preset target temperature T0, the second heat load value ΔQ_real of the target building at the second time, the offset load value Q_ref of a single multi-split unit, the first number of units N_on, and the rate of change of the indoor temperature of the target building at the second time, dT1 / dt, simultaneously satisfy the following: T3-T0>ΔT_moving+ w1×|ΔQ_real| / Q_ref; |ΔQ_real|≥ Q_ref × N_open×w2; dT1 / dt>0; Then, based on the first number of units to be powered on, the number of units to be powered on in a multi-unit configuration is increased; where ΔT_dynamic is the dynamic threshold, w1 is the preset first coefficient, and w2 is the preset second coefficient; Otherwise, the first number of devices to be powered on will not be adjusted.

[0112] The control module 13 is also used to, according to a preset control cycle, determine if the second indoor temperature T3 of the target building at the second time, the preset target temperature T0, the second heat load value ΔQ_real of the target building at the second time, the offset load value Q_ref of a single multi-split unit, the first number of units N_on, and the rate of change of the indoor temperature of the target building at the second time, dT1 / dt, simultaneously satisfy the following: T0-T3>ΔT_movement; |ΔQ_real|≤ Q_ref × N_open×w3; dT1 / dt < 0; N_open > 1; Then, based on the first number of units to be powered on, the number of units to be powered on in the multi-unit configuration is reduced; where ΔT_dynamic is the dynamic threshold and w3 is the preset third coefficient; Otherwise, the first number of devices to be powered on will not be adjusted.

[0113] The control module 13 is also used to determine the target indoor temperature T3 of the target building at the second time, the preset target temperature T0, the second heat load value ΔQ_real of the target building at the second time, the offset load value Q_ref of a single multi-split unit, and the first heat load value ΔQ according to a preset control cycle, provided that: If T3 > T0, the set temperature of the multi-connected unit is adjusted to T_set_dynamic = T_set_initial - min(a °C, w4 × |ΔQ_actual - ΔQ| / Q_ref); If T3 < T0, the set temperature of the multi-connected unit is adjusted to T_set_dynamic = T_set_initial + min(a °C, w4 × |ΔQ_actual - ΔQ| / Q_ref); T_set_initial is the first set temperature, w4 is the preset fourth coefficient, and a is the preset first adjustment temperature.

[0114] The control module 13 is further configured to determine a heat deviation index according to the second indoor temperature T3 of the target building at the second time, the preset target temperature T0, the single-unit load value ΔQ_single, and the offset load value Q_ref of a single multi-connected unit through the formula |T3 - T0| + 0.2 × (ΔQ_single / Q_ref) according to a preset control period; According to the index range to which the heat deviation index belongs and the corresponding relationship between different preset index ranges and wind speeds, determine the second set wind speed of the multi-connected unit, and adjust the wind speed of the multi-connected unit to the second set wind speed.

[0115] The control module 13 is further configured to trigger the subsequent process of controlling the multi-connected unit at the second time according to a preset control period if the second indoor temperature T3 of the target building at the second time and the preset target temperature T0 satisfy |T3 - T0| > ΔT_dynamic; or

[0116] If the second heat load value ΔQ_actual of the target building at the second time and the first heat load value ΔQ satisfy: (|ΔQ_actual - ΔQ| / ΔQ) > m; then trigger the subsequent process of controlling the multi-connected unit at the second time; where m is the preset first proportional threshold.

[0117] Among them, according to the load value range to which the first heat load value ΔQ belongs and the corresponding relationship between different preset load value ranges and dynamic thresholds, determine the dynamic threshold ΔT_dynamic. The larger the load value included in the load value range, the larger the corresponding dynamic threshold.

[0118] The control module 13 is further configured to correct the first heat load value ΔQ of the target building according to the following steps according to a preset correction period: S1: Determine the true heat load data ΔQ_true in the historical time period = ΔQ_air conditioner + (C × ρ × V) × dT1 / dt_actual; ΔQ_air conditioner = the first number of units turned on (N_on) × the real-time power of a single multi-connected unit (P_single) × 1000 (converted to watts); dT1 / dt_actual: the actual indoor temperature change rate; S2: Calculate the model prediction value ΔQ_predicted = |K × D × (T2 - T1) × S + ΔQ_sun + (C × ρ × V) × dT1 / dt_actual + ΔQ_person|; S3: Calculate the relative error = |ΔQ_predicted - ΔQ_actual| / ΔQ_actual; if the relative error is greater than the preset second proportional threshold, proceed to S4; S4: Using ΔQ_true as the dependent variable, construct a linear model: ΔQ_real ≈ x1 × ((T2-T1) × S) + x2 × ΔQ_solar + x3 × (dT1 / dt_actual) + x4 × number of people; data fitting is performed using the least squares method to obtain x1, x2, x3, and x4; the first heat load value ΔQ is corrected to ΔQ_real; Where x1: equivalent heat transfer coefficient (≈ K × D); x2: response coefficient of solar radiation heat gain; x3: air heat capacity correlation coefficient (≈ C×ρ×V / 60); x4: actual heat dissipation per person (W / person).

[0119] This application also provides an electronic device, such as Figure 7 As shown, it includes: processor 21, communication interface 22, memory 23 and communication bus 24, wherein processor 21, communication interface 22 and memory 23 communicate with each other through communication bus 24; The memory 23 stores a computer program, which, when executed by the processor 21, causes the processor 21 to perform any of the above method steps.

[0120] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0121] Communication interface 22 is used for communication between the above-mentioned electronic device and other devices.

[0122] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0123] The processors mentioned above can be general-purpose processors, including central processing units, network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits, field-programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0124] This application also provides a computer-readable storage medium storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform any of the above method steps.

[0125] This application provides a computer program product, which includes an executable program that, when executed by a processor, implements the method described herein.

[0126] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0127] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A multi-split air conditioning control method based on temperature and building characteristic parameters, characterized in that, The method includes: The first heat load value of the target building is determined based on the first indoor temperature, the first outdoor temperature, the building characteristic parameters of the target building, and the first heat dissipation value of the people in the target building; wherein, the building characteristic parameters include orientation parameters, exterior wall area parameters, window area parameters, and volume parameters. Based on the first heat load value and the offset load value of a single multi-split unit, determine the first number of multi-split units to be turned on; based on the first heat load value, the offset load value of a single multi-split unit, and the preset target temperature, determine the first set temperature of the multi-split unit; based on the first heat load value and the first number of units to be turned on, determine the load value of a single unit; based on the load value range to which the single unit load value belongs and the preset correspondence between different load value ranges and wind speeds, determine the first set wind speed of the multi-split unit; based on the indoor humidity of the target building, the first indoor temperature, the first outdoor temperature, the preset target temperature, and the first heat load value, determine the first operating mode of the multi-split unit. The multi-split air conditioner is regulated based on the first number of units turned on, the first set temperature, the first set fan speed, and the first operating mode.

2. The method as described in claim 1, characterized in that, The step of determining the first heat load value of the target building based on the first indoor temperature, the first outdoor temperature, the building characteristic parameters of the target building, and the first heat dissipation value of the people in the target building includes: Based on the exterior wall area parameters and window area parameters of the target building, determine the thermal conductivity coefficient K and the equivalent heat transfer area S; based on the orientation parameters of the target building, determine the orientation coefficient D. Based on the orientation parameter and the first time, a first influence coefficient is determined; based on the external wall area parameter, window area parameter, solar radiation value and the first influence coefficient, the solar radiation heat gain ΔQ_solar is determined. Determine the rate of change of indoor temperature of the target building, and based on the rate of change of indoor temperature dT1 / dt, specific heat capacity of air C, air density ρ, and volume parameter V, determine the air heat capacity parameter = C×ρ×V×(dT1 / dt); Based on the first indoor temperature T1, the first outdoor temperature T2, the thermal conductivity K, the equivalent heat transfer area S, the orientation coefficient D, the solar radiation heat gain ΔQ_solar, the air heat capacity parameter, and the first occupant heat dissipation value ΔQ_person, the first heat load value ΔQ of the target building is determined by the following formula: ΔQ = K×D×(T2 - T1)×S + ΔQ_Sun + C×ρ×V×(dT1 / dt) + ΔQ_Man.

3. The method as described in claim 1, characterized in that, The method further includes: According to the preset control cycle, if the second indoor temperature T3 of the target building at the second time, the preset target temperature T0, the second heat load value ΔQ_actual of the target building at the second time, the offset load value Q_ref of a single multi-split unit, the first number of units N_on, and the rate of change of indoor temperature dT1 / dt of the target building at the second time simultaneously satisfy: T3-T0>ΔT_moving+ w1×|ΔQ_real| / Q_ref; |ΔQ_real|≥ Q_ref × N_open×w2; dT1 / dt > 0; Then increase the number of multi-connected units turned on based on the first number of units turned on; where ΔT_moving is the dynamic threshold, w1 is the preset first coefficient, and w2 is the preset second coefficient; Otherwise, do not adjust the first number of units turned on.

4. The method as described in claim 1, characterized in that, The method further includes: According to a preset control period, if the second indoor temperature T3 of the target building at the second time, the preset target temperature T0, the second actual heat load value ΔQ_actual of the target building at the second time, the load value Q_ref that can be offset by a single multi-connected unit, the first number of units turned on N_on, and the indoor temperature change rate dT1 / dt of the target building at the second time simultaneously satisfy: T0 - T3 > ΔT_moving; |ΔQ_actual| ≤ Q_ref × N_on × w3; dT1 / dt < 0; N_on > 1; Then reduce the number of multi-connected units turned on based on the first number of units turned on; where ΔT_moving is the dynamic threshold, and w3 is the preset third coefficient; Otherwise, do not adjust the first number of units turned on.

5. The method as described in claim 1, characterized in that, The method further includes: According to a preset control period, if the second indoor temperature T3 of the target building at the second time, the preset target temperature T0, the second actual heat load value ΔQ_actual of the target building at the second time, the load value Q_ref that can be offset by a single multi-connected unit, and the first heat load value ΔQ satisfy: When T3 > T0, the set temperature of the multi-connected unit is adjusted to T_set_moving = T_set_initial - min(a °C, w4 × |ΔQ_actual - ΔQ| / Q_ref); When T3 < T0, the set temperature of the multi-connected unit is adjusted to T_set_moving = T_set_initial + min(a °C, w4 × |ΔQ_actual - ΔQ| / Q_ref); T_set_initial is the first set temperature, w4 is the preset fourth coefficient, and a is the preset first adjustment temperature.

6. The method as described in claim 1, characterized in that, The method further includes: According to a preset control period, based on the second indoor temperature T3 of the target building at the second time, the preset target temperature T0, the single-load value ΔQ_single, and the load value Q_ref that can be offset by a single multi-connected unit, determine the heat deviation index through the formula |T3 - T0| + 0.2 × (ΔQ_single / Q_ref); According to the index range to which the heat deviation index belongs and the corresponding relationship between the preset different index ranges and the wind speed, determine the second set wind speed of the multi-connected unit, and adjust the wind speed of the multi-connected unit to the second set wind speed.

7. The method according to any one of claims 3 to 6, characterized in that, Before controlling the multi-connected unit at the second time, the method further includes: According to a preset control period, if the second indoor temperature T3 of the target building at the second time and the preset target temperature T0 satisfy |T3 - T0| > ΔT_moving, trigger the subsequent process of controlling the multi-connected unit at the second time; or If the second actual heat load value ΔQ_actual of the target building at the second time and the first heat load value ΔQ satisfy: (|ΔQ_actual - ΔQ| / ΔQ) > m; then trigger the subsequent process of controlling the multi-connected unit at the second time; where m is the preset first proportional threshold.

8. The method as described in claim 7, characterized in that, Based on the load value range to which the first heat load value ΔQ belongs, and the preset correspondence between different load value ranges and dynamic thresholds, the dynamic threshold ΔT_dynamic is determined. The larger the load value range includes, the larger the corresponding dynamic threshold.

9. The method as described in claim 2, characterized in that, The method further includes: According to the preset correction cycle, the first heat load value ΔQ of the target building is corrected through the following steps: S1: Determine the actual heat load data for the historical time period ΔQ_actual = ΔQ_air conditioning + (C × ρ × V) × dT1 / dt_actual; ΔQ_air conditioning = number of units started (N_on) × real-time power of a single multi-split unit (P_single) × 1000 (converted to watts); dT1 / dt_actual: actual rate of change of indoor temperature; S2: Calculate the model prediction value ΔQ_predicted = |K × D × (T2 - T1) × S + ΔQ_sun + (C ×ρ × V) × dT1 / dt_actual + ΔQ_person|; S3: Calculate the relative error = |ΔQ_predicted - ΔQ_actual| / ΔQ_actual; if the relative error is greater than the preset second proportional threshold, proceed to S4; S4: Using ΔQ_true as the dependent variable, construct a linear model: ΔQ_real ≈ x1 × ((T2-T1) × S) + x2 × ΔQ_solar + x3 × (dT1 / dt_actual) + x4 × number of people; data fitting is performed using the least squares method to obtain x1, x2, x3, and x4; the first heat load value ΔQ is corrected to ΔQ_real; Where x1: equivalent heat transfer coefficient (≈ K × D); x2: response coefficient of solar radiation heat gain; x3: air heat capacity correlation coefficient (≈ C×ρ×V / 60); x4: actual heat dissipation per person (W / person).

10. A multi-split air conditioner, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the method described in any one of claims 1-9.

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