Multi-split air conditioning system and control method thereof

Abstract

The invention provides a multi-split air conditioning system and a control method thereof, particularly relates to the technical field of air conditioners, and is used for efficiently determining an energy consumption mode of the multi-split air conditioning system at low cost and carrying out fault diagnosis on the multi-split air conditioning system in the energy consumption mode. The multi-split air conditioning system comprises at least one indoor unit; at least one outdoor unit; the controller is configured to obtain operation data of the multi-split air conditioning system under the current working condition; on the basis of the trained energy efficiency recognition model and the operation data under the current working condition, energy consumption modes of the multi-split air conditioning system are determined, and the energy consumption modes comprise a high-energy-efficiency operation mode and a low-energy-efficiency operation mode; under the condition that the energy consumption mode is the low-energy-efficiency operation mode, the fault type of the multi-split air conditioning system is determined based on the trained fault diagnosis model and the operation data under the current working condition; and when the fault type is soft fault operation, outputting indication information of the soft fault operation type.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of air conditioners, and in particular to a multi-split air conditioning system and a control method thereof. BACKGROUND

[0002] At present, multi-split air conditioning systems have been widely applied in entertainment, home and work and other places.

[0003] Due to the complexity of the multi-split air conditioning system control, the uncontrollable actual installation and operation environment, and the inevitable failure after long-time operation, energy consumption is increased. In order to reduce the energy consumption of the multi-split air conditioning system in the operation process and improve the accuracy and efficiency of fault diagnosis, researchers have used machine learning, data mining and other methods to construct a fault detection model, which has become a current research hotspot. However, in the related art, the data-driven fault detection model is mostly only applicable to the detection of a small number of specific fault types, and manual troubleshooting is still required when the multi-split system has a slight fault, resulting in waste of human and material resources.

[0004] Therefore, how to efficiently and at low cost determine the fault type of the multi-split air conditioning system is a problem to be solved. SUMMARY

[0005] Embodiments of the present application provide a multi-split air conditioning system and a control method thereof for efficiently and at low cost determining the fault type of the multi-split air conditioning system.

[0006] To achieve the above-mentioned purpose, the embodiments of the present application adopt the following technical solutions:

[0007] In a first aspect, a multi-split air conditioning system is provided, comprising:

[0008] at least one indoor unit;

[0009] at least one outdoor unit;

[0010] a controller configured to:

[0011] obtain operation data of the multi-split air conditioning system under a current working condition;

[0012] determine an energy use mode of the multi-split air conditioning system based on the trained energy efficiency identification model and the operation data under the current working condition, the energy use mode including a high energy efficiency operation mode and a low energy efficiency operation mode;

[0013] in the case where the energy use mode is the low energy efficiency operation mode, determine a fault type of the multi-split air conditioning system based on the trained fault diagnosis model and the operation data under the current working condition;

[0014] in the case where the fault type is a soft fault operation, output indication information of the type of the soft fault operation.

[0015] The technical scheme provided by the embodiments of the present application at least brings the following beneficial effects: the energy efficiency identification model after training is used to determine the energy use mode of the multi-connected air conditioning system under the current working condition, so as to identify whether the energy use mode of the multi-connected air conditioning system under the current working condition is a high energy efficiency operation mode or a low energy efficiency operation mode. In the case of the energy use mode being a low energy efficiency operation mode, the fault diagnosis model after training is used to determine the specific fault type of the multi-connected air conditioning system, wherein in the case of the fault type being a soft fault operation, the type of the soft fault operation is output, so as to assist the user to understand the type of the soft fault operation of the multi-connected air conditioning system, and then the soft fault is repaired. In this way, the multi-connected air conditioning system is in a high energy efficiency operation mode, and does not need to be repaired, thereby avoiding the waste of human cost caused by frequent manual repair of the multi-connected air conditioning system; in the case of soft fault operation, the user can directly know the fault type of the soft fault operation, and the efficiency of multi-connected air conditioning fault diagnosis can also be improved.

[0016] In some embodiments, the controller is further configured to, in the case of the fault type being a low energy efficiency operation, output a parameter setting scheme corresponding to the operation mode of the multi-connected air conditioning system, the parameter setting scheme being a setting scheme of each operation parameter corresponding to the operation mode with the highest energy efficiency.

[0017] In some embodiments, the controller is further configured to, in the case of the energy use mode being a high energy efficiency operation mode, acquire the current operation mode of the multi-connected air conditioning system and the parameter setting values of each operation parameter of the multi-connected air conditioning system; and determine the plurality of parameter setting values as a parameter setting scheme corresponding to the current operation mode.

[0018] In some embodiments, the current working condition includes one or more of the number of indoor units turned on, the load rate of the multi-connected air conditioning system, the target operation mode of the multi-connected air conditioning system, the indoor environment temperature value, and the outdoor environment temperature value.

[0019] In some embodiments, the energy efficiency identification model after training is obtained by the controller performing the following steps: acquiring historical operation data of the multi-connected air conditioning system under a plurality of working conditions; constructing a first training sample set by taking the historical operation data as first sample data and taking the energy use mode corresponding to the historical operation data as a first sample label, one first sample data corresponding to one first sample label; training the original energy efficiency identification model based on the first training sample set to obtain the energy efficiency identification model after training.

[0020] In some embodiments, the trained fault diagnosis model is obtained by the controller by performing the following steps: obtaining historical operation data of the multi-split air conditioning system under multiple working conditions; constructing a second training sample set by taking the historical operation data as second sample data and taking the fault types corresponding to the historical operation data as second sample labels, one second sample data corresponding to one second sample label; training the original fault diagnosis model based on the second training sample to obtain the trained fault diagnosis model.

[0021] In a second aspect, the embodiments of the present application provide a control method of a multi-split air conditioning system, which comprises:

[0022] obtaining operation data of the multi-split air conditioning system under a current working condition;

[0023] determining an energy use mode of the multi-split air conditioning system based on the trained energy efficiency identification model and the operation data under the current working condition, the energy use mode comprising a high energy efficiency operation mode and a low energy efficiency operation mode;

[0024] in a case where the energy use mode is the low energy efficiency operation mode, determining a fault type of the multi-split air conditioning system based on the trained fault diagnosis model and the operation data under the current working condition;

[0025] in a case where the fault type is a soft fault operation, outputting indication information of the type of the soft fault operation.

[0026] In a third aspect, the embodiments of the present application provide a controller, comprising: one or more processors; one or more memories; wherein the one or more memories are configured to store computer program codes, the computer program codes comprising computer instructions, when the one or more processors execute the computer instructions, the controller performs the control method provided in the second aspect.

[0027] In a fourth aspect, the embodiments of the present application provide a computer readable storage medium, the computer readable storage medium comprising computer instructions, when the computer instructions control a computer, the computer executes the method provided in the second aspect and possible implementation manners.

[0028] In a fifth aspect, the embodiments of the present application provide a computer program product, which can be directly loaded into a memory and contains software codes, the computer program product is loaded and executed by a computer to realize the method provided in the second aspect and possible implementation manners.

[0029] It should be noted that the above computer instructions can be stored in the computer readable storage medium in whole or in part. The computer readable storage medium can be packaged together with the processor of the controller or packaged separately from the processor of the controller, and the present application does not limit this.

[0030] The beneficial effects of the second to fifth aspects described in the present application can be analyzed with reference to the beneficial effects of the first aspect, which will not be repeated here. BRIEF DESCRIPTION OF DRAWINGS

[0031] The accompanying drawings are included to provide a further understanding of the technical solutions of the present application, and constitute a part of the specification, and are used together with the embodiments of the present application to explain the technical solutions of the present application, and do not constitute a limitation on the technical solutions of the present application.

[0032] Figure 1 A structural diagram of a multi-split air conditioning system provided for an embodiment of the present application Figure 1 ;

[0033] Figure 2 A structural diagram of a multi-split air conditioning system provided for an embodiment of the present application Figure 2 ;

[0034] Figure 3 A structural diagram of a multi-split air conditioning system provided for an embodiment of the present application Figure 3 ;

[0035] Figure 4 A structural diagram of a multi-split air conditioning system provided for an embodiment of the present application Figure 4 ;

[0036] Figure 5 A structural diagram of a multi-split air conditioning system provided for an embodiment of the present application Figure 5 ;

[0037] Figure 6 A hardware structural diagram of a controller provided for an embodiment of the present application

[0038] Figure 7 An application scenario diagram of a multi-split air conditioning system provided for an embodiment of the present application

[0039] Figure 8 A hardware structural diagram of a cloud server provided for an embodiment of the present application

[0040] Figure 9 A flow diagram of a control method of a multi-split air conditioning system provided for an embodiment of the present application Figure 1 ;

[0041] Figure 10 A flow diagram of a control method of a multi-split air conditioning system provided for an embodiment of the present application Figure 2 ;

[0042] Figure 11 A use method diagram of an energy efficiency identification model provided for an embodiment of the present application

[0043] Figure 12 A schematic diagram illustrating the training process of a fault diagnosis model provided in an embodiment of this application;

[0044] Figure 13 A schematic diagram illustrating the process of determining a fault diagnosis model provided in an embodiment of this application;

[0045] Figure 14 A flowchart illustrating a control method for a multi-split air conditioning system provided in this application embodiment. Figure 3 . Detailed Implementation

[0046] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0047] In the description of this invention, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0048] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0049] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0050] The terms “comprising” and “having”, and any variations thereof, used in the description of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0051] In addition, the words "exemplary" and "for example" are used herein to mean serving as an example, instance, or illustration. Any implementation described herein as "exemplary" or as an "example" is not necessarily to be construed as preferred or advantageous over other implementations. Rather, use of the words "exemplary" or "example" is intended to present concepts in a concrete manner.

[0052] For ease of understanding, first, some terms or basic concepts of the technology involved in the embodiments of the present application are briefly introduced and described.

[0053] (I) Soft failure

[0054] According to the degree of failure, the failure types of the air conditioning system can be divided into soft failure and hard failure. The soft failure refers to the performance degradation or partial failure of the air conditioning equipment or element caused by fatigue, corrosion or wear during use, and the air conditioning system can still run after the soft failure, but the energy consumption increases. Soft failure usually needs a certain time to appear, and the detection difficulty is also greater, such as valve leakage, water pipe scaling, etc. The hard failure refers to the complete failure of the air conditioning equipment or element, such as fan failure to start, valve jamming, etc. Hard failure is usually sudden and destructive, and the air conditioning system cannot run normally after hard failure.

[0055] (II) Sub-learner and integrated learner

[0056] The sub-learner is the basic component of the integrated learner, which is usually generated from the training data by existing learning algorithms. The sub-learner can be generated by the same algorithm, or can be generated by different algorithms. The integrated learner is to complete the learning task by constructing and combining multiple sub-learners, and to improve the overall performance by combining the prediction results of multiple sub-learners, so as to improve the generalization ability and stability, and make the performance of the integrated learner better than any single sub-learner.

[0057] The above is the introduction of some concepts involved in the embodiments of the present application, which will not be described below.

[0058] As described in the background, at present, the multi-split air conditioning system has been widely used in entertainment, home and work, etc.

[0059] Due to the complexity of the multi-split air conditioning system control, the actual installation and operation environment is uncontrollable, and it is difficult to avoid failure under long-time operation. In order to improve the accuracy and efficiency of air conditioning fault diagnosis, researchers use machine learning, data mining and other methods to build fault detection models, which has become a research hotspot. However, in related technologies, the data-driven fault detection model is mostly suitable for the detection of a small number of specific fault types, and manual troubleshooting is still required when the multi-split air conditioning system has a slight fault, which causes waste of human and material resources. For example, the frequency of soft failure of the multi-split air conditioning system in actual operation is not high, and manual troubleshooting and maintenance will cost a lot of manpower if soft failure occurs.

[0060] Therefore, how to efficiently and low-cost determine the fault type of the multi-split air conditioning system is a problem to be solved.

[0061] Therefore, the energy efficiency recognition model is trained to determine the energy use mode of the multi-split air conditioning system under the current working condition, so as to identify whether the energy use mode of the multi-split air conditioning system under the current working condition is a high energy efficiency operation mode or a low energy efficiency operation mode. In the case of low energy efficiency operation mode, the trained fault diagnosis model is used to determine the specific fault type of the multi-split air conditioning system, wherein in the case of soft failure operation, the type of soft failure operation is output, so as to assist the user to understand the type of soft failure operation of the multi-split air conditioning system, and then the soft failure is repaired. In this way, the multi-split air conditioning system in the high energy efficiency operation mode does not need to be repaired, avoiding the waste of human cost caused by frequent manual repair of the multi-split air conditioning system; in the case of soft failure operation, the user can directly know the fault type of the soft failure operation, which can also improve the efficiency of multi-split air conditioning fault diagnosis.

[0062] The number of the multi-split air conditioning system provided by the embodiment of the present application can be one, two or more than two; the outdoor unit and the outdoor unit of each multi-split air conditioning system can be one, two or more than two, and the embodiment of the present application does not make any limitation.

[0063] In order to further describe the technical solutions of the embodiments of the present application, as shown in Figure 1 The structure of a multi-split air conditioning system provided by the embodiment of the present application is shown in the figure.

[0064] As shown in Figure 2 The multi-split air conditioning system 1 includes at least one outdoor unit 10 and at least one indoor unit 20.

[0065] The setting and function of each component of the outdoor unit are described in detail below.

[0066] In some embodiments, asFigure 3 As shown, the outdoor unit includes an outdoor heat exchanger 101. One end of the outdoor heat exchanger 101 is connected to a compressor 102 through a four-way reversing valve 103, and the other end is connected to each indoor heat exchanger through a connection pipe. The outdoor heat exchanger 101 is used for heat exchange between the refrigerant flowing in the heat transfer pipe of the outdoor heat exchanger 101 and outdoor air.

[0067] In some embodiments, as shown in FIG. 1, the outdoor unit further includes a compressor 102. The compressor 102 is arranged between each indoor heat exchanger and the outdoor heat exchanger 101, and is used to provide power for refrigerant circulation. Taking a refrigeration cycle as an example, the compressor 102 delivers the compressed refrigerant to the outdoor heat exchanger 101 through the four-way reversing valve 103. Figure 4

[0068] In some embodiments, as shown in FIG. 1, the outdoor unit further includes a four-way reversing valve 103. The four ports of the four-way reversing valve 103 are respectively connected to the exhaust port of the compressor 102, the outdoor heat exchanger 101, the suction port of the compressor 102, and each indoor heat exchanger. The four-way reversing valve 103 is used to realize mutual conversion between the refrigeration mode and the heating mode by changing the flow direction of the refrigerant in the system pipeline. Figure 4

[0069] In some embodiments, as shown in FIG. 1, the outdoor unit further includes an outdoor throttling device 104. The outdoor throttling device 104 is arranged between the outdoor heat exchanger 101 and each indoor heat exchanger, and has the effect of throttling and reducing the pressure of the refrigerant flowing through the outdoor throttling device 104, and is used to adjust the refrigerant flow of the refrigerant passage. Optionally, the outdoor throttling device 104 can be an electronic expansion valve. If the opening degree of the outdoor throttling device 104 is reduced, the flow path resistance of the refrigerant passing through the outdoor throttling device 104 increases. If the opening degree of the outdoor throttling device 104 is increased, the flow path resistance of the refrigerant passing through the outdoor throttling device 104 decreases. In this way, even if the states of other devices in the circuit remain unchanged, when the opening degree of the outdoor throttling device 104 changes, the refrigerant flow to the indoor heat exchanger 201 or the outdoor heat exchanger 101 will also change. Figure 4

[0070] In some embodiments, as shown in FIG. 1, the outdoor unit further includes a gas-liquid separator 105. The gas-liquid separator 105 is connected to the suction port of the compressor 102, and is used to accommodate the refrigerant of the liquid return part in the refrigerant passage, and prevent liquid impact on the compressor 102. Figure 5

[0071] The arrangement and functions of each component of the indoor unit are described in detail below.

[0072] In some embodiments, as shown in FIG. 1, the indoor unit further includes a compressor 102. The compressor 102 is arranged between each indoor heat exchanger and the outdoor heat exchanger 101, and is used to provide power for refrigerant circulation. Taking a refrigeration cycle as an example, the compressor 102 delivers the compressed refrigerant to the outdoor heat exchanger 101 through the four-way reversing valve 103. Figure 5 ​​​​As shown, the indoor unit includes an indoor heat exchanger 201. The indoor heat exchanger 201 is configured to exchange heat between the refrigerant flowing in the heat transfer pipe of the indoor heat exchanger 201 and indoor air.

[0073] In some embodiments, the method further includes Figure 6 As shown, the indoor unit further includes an indoor throttling device 202. The indoor throttling device 202 is connected to the indoor heat exchanger 201 and is configured to throttle the refrigerant flowing through the indoor throttling device 202 to achieve a pressure reduction effect, thereby adjusting the refrigerant flow in the refrigerant passage. Optionally, the indoor throttling device 202 can be an electronic expansion valve.

[0074] In the present application, the multi-split air conditioning system performs a refrigeration cycle of the multi-split air conditioning system by using the compressor 102, the outdoor heat exchanger 101, the throttling device 104, the indoor heat exchanger 201, and the four-way reversing valve 103 as a refrigerant circulation loop. The refrigeration cycle includes a series of processes involving compression, condensation, expansion, and evaporation, and supplies refrigerant to air that has been adjusted and heat exchanged.

[0075] The indoor heat exchanger 201 and the outdoor heat exchanger 101 function as a condenser or an evaporator. When the indoor heat exchanger 201 functions as a condenser, the multi-split air conditioning system 1 functions as a heater in a heating mode, and when the indoor heat exchanger 201 functions as an evaporator, the multi-split air conditioning system 1 functions as a cooler in a cooling mode.

[0076] In some embodiments, the multi-split air conditioning system 1 further includes a controller 300. As shown, Figure 7 The controller 300 is electrically connected to the outdoor heat exchanger 101, the compressor 102, the four-way reversing valve 103, the outdoor throttling device 104, the gas-liquid separator 105, the indoor heat exchanger 201, and the indoor throttling device 202.

[0077] In some embodiments, the controller 300 can be configured to acquire operation data of the multi-split air conditioning system under a current working condition; determine an energy consumption mode of the multi-split air conditioning system based on the trained energy efficiency identification model and the operation data under the current working condition, the energy consumption mode including a high energy efficiency operation mode and a low energy efficiency operation mode; in a case where the energy consumption mode is the low energy efficiency operation mode, determine a fault type of the multi-split air conditioning system based on the trained fault diagnosis model and the operation data under the current working condition; and in a case where the fault type is a soft fault operation, output indication information of the type of the soft fault operation.

[0078] In some embodiments, the controller 300 can be further configured to, in a case where the fault type is a low energy efficiency operation, output a parameter setting scheme corresponding to an operation mode of the multi-split air conditioning system, the parameter setting scheme being a setting scheme of each operation parameter corresponding to the operation mode with the highest energy efficiency.

[0079] In some embodiments, the controller 300 can also be configured to, in the case that the energy consumption mode is the high energy efficiency operation mode, acquire a current operation mode of the multi-split air conditioning system and parameter setting values of various operation parameters of the multi-split air conditioning system, and determine that the plurality of parameter setting values are a parameter setting scheme corresponding to the current operation mode.

[0080] In some embodiments, the controller 300 can also be configured to acquire historical operation data of the multi-split air conditioning system under a plurality of working conditions, construct a first training sample set by taking the historical operation data as first sample data and taking energy consumption modes corresponding to the historical operation data as first sample labels, one first sample data corresponding to one first sample label, and train the original energy efficiency identification model based on the first training sample set to obtain the trained energy efficiency identification model.

[0081] In some embodiments, the controller 300 can also be configured to acquire historical operation data of the multi-split air conditioning system under a plurality of working conditions, construct a second training sample set by taking the historical operation data as second sample data and taking fault types corresponding to the historical operation data as second sample labels, one second sample data corresponding to one second sample label, and train the original fault diagnosis model based on the second training sample to obtain the trained fault diagnosis model.

[0082] The controller 300 described above refers to a device that can generate operation control signals according to instruction operation codes and timing signals to instruct the multi-split air conditioning system 1 to execute control instructions. Exemplarily, the controller 300 can be a central processing unit (CPU), a general-purpose processor network processor (NP), a digital signal processing (DSP), a microprocessor, a microcontroller, a programmable logic device (PLD), or any combination thereof. The controller can also be other devices with processing functions, such as a circuit, a device, or a software module, and the embodiments of the present application do not make any limitation thereto.

[0083] In some embodiments, the controller 300 can be a microcontroller unit (MCU). The MCU, also known as a single chip microcomputer or a single-chip microprocessor, is a chip-level computer that integrates a central processing unit, memory, timers, USB, A / D conversion, UART, PLC, DMA, and even LCD driving circuits on a single chip for different application scenarios to make different combinations of control.

[0084] In addition, the controller 300 can be configured to control the operation of each component in the multi-split air conditioning system 1, so that each component of the multi-split air conditioning system 1 operates to achieve each predetermined function of the multi-split air conditioning system 1.

[0085] It can be understood that the structure shown in the embodiments of the present application does not constitute a specific limitation on the multi-split air conditioning system. In other embodiments of the present application, the multi-split air conditioning system can include more or fewer components than shown, or combine certain components, or split certain components, or different component arrangements. The components shown can be implemented in hardware, software, or a combination of software and hardware.

[0086] Figure 7 An application scenario of a multi-split air conditioning system according to an exemplary embodiment of the present application is shown. As shown in the figure, the application scenario includes a plurality of multi-split air conditioning systems, such as a first multi-split air conditioning system 100 and a second multi-split air conditioning system 200, and a cloud server 400. Figure 8

[0087] Among them, the cloud server 400 is in communication connection with the first multi-split air conditioning system 100 and the second multi-split air conditioning system 200.

[0088] In some embodiments, the first multi-split air conditioning system 100 and the second multi-split air conditioning system 200 are devices for adjusting and controlling the temperature, humidity, flow rate, etc. of the air in the environment of a building or structure.

[0089] In some embodiments, the cloud server 400 can be a cloud server that provides cloud services, cloud databases, cloud computing, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks, big data servers, and other basic cloud computing services. The specific form of the cloud server 400 is not specially limited in the present application.

[0090] In some embodiments, the first multi-split air conditioning system 100 and the second multi-split air conditioning system 200 can send their own operation data to the cloud server 400, so that the cloud server 400 detects the faults of the first multi-split air conditioning system 100 and the second multi-split air conditioning system 200 according to the operation data of the first multi-split air conditioning system 100 and the second multi-split air conditioning system 200.

[0091] Figure 8 A hardware structure of a cloud server according to an exemplary embodiment of the present application is shown. As shown in the figure, the cloud server 400 includes a communicator 401, a memory 402, and a processor 403. Figure 8

[0092] ​​In some embodiments, the communicator 401 is configured to establish a communication connection with other network entities, for example, a first multi-connected air conditioning system 100. The communicator 401 can include a radio frequency (RF) module, a cellular module, a wireless fidelity (WIFI) module, and a GPS module, etc. Taking the RF module as an example, the RF module can be configured to receive and send signals, in particular, to send the received information to the processor 403 for processing, and to send the signals generated by the processor 403. Generally, the RF circuit can include, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier (LNA), a duplexer, etc.

[0093] In some embodiments, the memory 402 can be configured to store software programs and data. The processor 403 can execute various functions and data processing of the cloud server 400 by running the software programs or data stored in the memory 402. The memory 402 can include a high-speed random access memory, and can further include a non-volatile memory, for example, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state memory device. The memory 402 stores an operating system that enables the processor 403 to run. In this application, the memory 402 can store an operating system and various application programs, and can further store codes for executing the control method of the multi-connected air conditioning system provided in the embodiments of the present application.

[0094] In some embodiments, the processor 403 can be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present disclosure, for example, one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs).

[0095] Those skilled in the art can understand that the hardware structure shown in the above Figure 9 The hardware structure shown in the above

[0096] The embodiments of the present application will be specifically described below with reference to the accompanying drawings.

[0097] As Figure 9As shown, the embodiment of the present application provides a control method of a multi-split air conditioning system, applied to a controller of the multi-split air conditioning system, which comprises the following steps S1-S4:

[0098] S1, obtaining running data of the multi-split air conditioning system under a current working condition.

[0099] The current working condition includes but is not limited to one or more of the number of indoor units turned on, the load rate of the multi-split air conditioning system, the target refrigeration / heat of the multi-split air conditioning system, the indoor environment temperature value, and the outdoor environment temperature value. The running data includes but is not limited to one or more of the compressor suction temperature, the compressor discharge temperature, the compressor suction pressure, the compressor discharge pressure, the evaporator inlet temperature value, the evaporator outlet temperature value, the compressor frequency, the compressor current, and the opening degree of the throttling device.

[0100] Optionally, the running data of the multi-split air conditioning system under the current working condition is obtained in real time through a communication device of the multi-split air conditioning system.

[0101] S2, determining the energy use mode of the multi-split air conditioning system based on the trained energy efficiency identification model and the running data under the current working condition.

[0102] The energy use mode includes a high energy efficiency operation mode and a low energy efficiency operation mode. The high energy efficiency operation mode refers to a working state in which the multi-split air conditioning system has a relatively high energy consumption efficiency during operation, i.e., generates more cold / heat with the same power consumption. The low energy efficiency operation mode refers to a working state in which the multi-split air conditioning system has a relatively low energy consumption efficiency during operation, i.e., generates less cold / heat with the same power consumption.

[0103] For example, the running data under the current working condition is input into the trained energy efficiency identification model, and the output result of the energy efficiency identification model can indicate that the energy use mode of the multi-split air conditioning system is the high energy efficiency operation mode or the low energy efficiency operation mode.

[0104] S3, in the case where the energy use mode is the low energy efficiency operation mode, determining the fault type of the multi-split air conditioning system based on the trained fault diagnosis model and the running data under the current working condition.

[0105] The fault type of the multi-split air conditioning system can include soft fault operation and low energy efficiency operation.

[0106] For example, the running data under the current working condition is input into the trained fault diagnosis model, and the output result of the fault diagnosis model can indicate that the fault type of the multi-split air conditioning system is soft fault operation or low energy efficiency operation.

[0107] S4, in the case where the fault type is soft fault operation, outputting the indication information of the type of soft fault operation.

[0108] The indication information of the type of the soft fault operation is used to indicate a specific fault type of the soft fault operation. The type of the soft fault operation includes, but is not limited to, one or more of a sensor fault, a dirty filter screen of an indoor unit, a dirty filter screen of an outdoor unit, an aging of an indoor heat exchanger, and an aging of an outdoor heat exchanger.

[0109] Figure 10 The embodiments shown at least bring the following beneficial effects: The energy consumption mode of the multi-split air conditioning system under the current working condition is determined by the trained energy efficiency identification model, so as to identify whether the energy consumption mode of the multi-split air conditioning system under the current working condition is a high energy efficiency operation mode or a low energy efficiency operation mode. In the case of the energy consumption mode being the low energy efficiency operation mode, the specific fault type of the multi-split air conditioning system is determined by the trained fault diagnosis model. In the case of the fault type being the soft fault operation, indication information of the type of the soft fault operation is output, so as to assist the user to understand the type of the soft fault operation of the multi-split air conditioning system, and then the soft fault is repaired. In this way, the multi-split air conditioning system is in the high energy efficiency operation mode, and does not need to be repaired, thereby avoiding the waste of human cost caused by frequent manual repair of the multi-split air conditioning system. In the case of the soft fault operation, the user can directly know the fault type of the soft fault operation, and the efficiency of the multi-split air conditioning fault diagnosis can also be improved.

[0110] In some embodiments, the control method of the multi-split air conditioning system provided by the application further includes the following steps:

[0111] In the case of the fault type being the low energy efficiency operation, a parameter setting scheme corresponding to the operation mode of the multi-split air conditioning system is output. The parameter setting scheme is a setting scheme of each operation parameter corresponding to the operation mode and having the highest energy efficiency.

[0112] The low energy efficiency operation refers to a case where the multi-split air conditioning system can normally operate, but has a low operation energy efficiency. The parameter setting scheme can include, but is not limited to, one or more of a set temperature of the multi-split air conditioning system, a set air volume of the multi-split air conditioning system, and an operation mode of the multi-split air conditioning system.

[0113] Optionally, the operation mode includes refrigeration / heat of the multi-split air conditioning system.

[0114] As a possible implementation manner, in the case that the output result of the fault diagnosis model is no fault, the fault type is determined to be the low energy efficiency operation.

[0115] As can be known from the above embodiments, in the case that the fault type is low energy efficiency operation, the multi-split air conditioning system does not have a hard fault that causes the air conditioner to fail to operate normally, nor does it have a soft fault. In this regard, the application outputs the parameter setting scheme corresponding to the current operation mode to recommend to the user the setting scheme of each operation parameter with the highest energy efficiency, thereby assisting the user to optimize the setting of the operation parameters of the multi-split air conditioning system, and further improving the energy efficiency of the multi-split air conditioning system.

[0116] In some embodiments, the control method of the multi-split air conditioning system provided by the application further includes the following steps: in the case that the energy use mode is a high energy efficiency operation mode, obtaining the current operation mode of the multi-split air conditioning system and the parameter setting values of each operation parameter of the multi-split air conditioning system; determining the plurality of parameter setting values as a parameter setting scheme corresponding to the current operation mode.

[0117] As can be known from the above embodiments, the operation parameters set by the multi-split air conditioning system are different under different operation modes. In the case that the energy use mode of the multi-split air conditioning system is a high energy efficiency operation mode, it indicates that the operation parameters set by the current multi-split air conditioning system can make the multi-split air conditioning system in a state with higher energy efficiency. In this regard, the plurality of parameter setting values are taken as a parameter setting scheme corresponding to the current operation mode, and the parameter setting scheme can be taken as a parameter optimization scheme recommended to the user.

[0118] In some embodiments, as shown in Figure 11 The trained energy efficiency identification model in the application can be obtained by the controller performing the following steps S10-S30:

[0119] S10, obtain historical operation data of the multi-split air conditioning system under a plurality of working conditions.

[0120] Optionally, the memory of the multi-split air conditioning system stores historical operation data under a plurality of working conditions, and the historical operation data of the multi-split air conditioning system under a plurality of working conditions is obtained from the memory.

[0121] It should be noted that the historical operation data stored in the memory can be historical operation data of multi-split air conditioning systems of the same model under different working conditions, historical operation data of multi-split air conditioning systems of different models under different working conditions, or historical operation data of multi-split air conditioning systems of the same model under the same working conditions, and the embodiments of the application do not limit this.

[0122] S20, the historical operation data is taken as first sample data, and the energy use mode corresponding to the historical operation data is taken as a first sample label to construct a first training sample set.

[0123] Among them, one first sample data corresponds to one first sample label.

[0124] S30, training the original energy efficiency identification model based on the first training sample set to obtain a trained energy efficiency identification model.

[0125] As a possible implementation, training the original energy efficiency identification model based on the first training sample set comprises the following steps:

[0126] Sa1, inputting each target historical running data in the first training sample set into the original energy efficiency identification model to obtain a predicted result of the energy consumption mode corresponding to each target historical running data in the first training sample set.

[0127] Sa2, determining a loss value of the first training sample set according to the predicted result of the energy consumption mode corresponding to each target historical running data in the first training sample set and a real result of the energy consumption mode of each target historical running data in the first training sample.

[0128] Sa3, judging whether the energy efficiency identification model converges according to the loss value of the first training sample set.

[0129] Sa4, if not, updating a weight parameter in the energy efficiency identification model according to the loss value of the first training sample set, and continuing to perform step Sa1 of inputting each target historical running data in the first training sample set into the original energy efficiency identification model to obtain a predicted result of the energy consumption mode corresponding to each target historical running data in the first training sample set.

[0130] Sa5, if yes, determining the current energy efficiency identification model as the trained energy efficiency identification model.

[0131] In some embodiments, after obtaining the trained energy efficiency identification model, the following step can be further included: constructing a first test sample set by taking the historical running data as first sample data and taking the energy consumption mode corresponding to the historical running data as a first sample label. Each first sample data corresponds to one first sample label. The trained energy efficiency identification model is tested through the first test sample set.

[0132] The first test sample set includes a plurality of samples of different historical running data, each sample includes an energy consumption mode of target historical running data, and each sample is provided with an energy consumption mode label used to represent a real energy consumption mode of the target historical running data.

[0133] It should be noted that the target historical running data in the first test sample set is different from the target historical running data in the first training sample set.

[0134] The use method of the energy efficiency identification model provided in the present application will be described below. Figure 12

[0135] ​The historical running data of the multi-connected air conditioning system under multiple working conditions from inside and outside the laboratory is collected as an experimental data set.

[0136] The samples in the data set are divided into high energy efficiency running mode and low energy efficiency running mode through data clustering analysis of the energy efficiency identification model.

[0137] The running data of the multi-connected air conditioner under the current working condition is identified by the energy efficiency identification model.

[0138] When the identification result is the high energy efficiency mode, the running data of the multi-connected air conditioner under the current working condition is stored.

[0139] When the identification result is the low energy efficiency mode, the fault type of the multi-connected air conditioning system is determined through the fault diagnosis model.

[0140] In some embodiments, as shown in Figure 13 The trained fault diagnosis model in the present application is obtained by the controller executing the following steps S100-S300:

[0141] S100, obtaining the historical running data of the multi-connected air conditioning system under multiple working conditions.

[0142] Step S100 can be the same as the implementation mode of step S10 described above, which will not be repeated here.

[0143] S200, the historical running data is taken as the second sample data, and the fault type corresponding to the historical running data is taken as the second sample label, and a second training sample set is constructed.

[0144] Each second sample data corresponds to a second sample label.

[0145] S300, training the original fault diagnosis model based on the second training sample to obtain the trained fault diagnosis model.

[0146] As a possible implementation mode, training the original fault diagnosis model based on the second training sample set includes the following steps:

[0147] Sb1, input each target historical running data in the second training sample set into the original fault diagnosis model to obtain the prediction result of the fault type corresponding to each target historical running data in the second training sample set.

[0148] Sb2, according to the prediction result of the fault type corresponding to each target historical running data in the second training sample set and the true result of the fault type of the target historical running data in each second training sample, the loss value of the second training sample set is determined.

[0149] Sb3, determining whether the fault diagnosis model converges according to the loss value of the second training sample set.

[0150] Sb4, if not, updating the weight parameters in the fault diagnosis model according to the loss value of the second training sample set, and continuing to perform step Sa1, inputting each target historical operation data in the second training sample set into the original fault diagnosis model to obtain the prediction result of the fault type corresponding to each target historical operation data in the second training sample set.

[0151] Sb5, if yes, determining the current fault diagnosis model as the trained fault diagnosis model.

[0152] In some embodiments, after obtaining the trained fault diagnosis model, the following steps can also be included: constructing a second test sample set by taking the historical operation data as the second sample data and taking the fault type corresponding to the historical operation data as the second sample label. One second sample data corresponds to one second sample label. The trained fault diagnosis model is tested through the second test sample set.

[0153] The above-mentioned second test sample set includes a plurality of samples of different historical operation data, each sample including a fault type of target historical operation data, and each sample being provided with a fault type label for indicating the real fault type of the target historical operation data.

[0154] It should be noted that the target historical operation data in the second test sample set is different from the target historical operation data in the second training sample set.

[0155] The determination process of the fault diagnosis model provided by the present application will be described below. Figure 14

[0156] The historical operation data of the multi-split air conditioning system under a plurality of working conditions from inside and outside the laboratory are collected as experimental data sets.

[0157] The data in the data set are trained by the sub-learner to obtain the output result of the sub-learner. The sub-learner includes but is not limited to one or more of random forest algorithm, support vector machine, multiple linear regression, Gaussian regression, artificial neural network.

[0158] The output result of the sub-learner and the data in the data set are input into the ensemble learner to obtain the trained fault diagnosis model.

[0159] The control method of the multi-split air conditioning system provided by the present application will be described below. ​

[0160] The operation data of the multi-split air conditioning system under the current working condition are obtained.​​

[0161] inputting the running data under the current working condition into the energy efficiency identification model to determine the current energy consumption mode;

[0162] if the current energy consumption mode is the high energy efficiency running mode, outputting the instruction information of "normal running", and storing the running data under the current working condition into the database.

[0163] if the current energy consumption mode is the low energy efficiency running mode, inputting the running data under the current working condition into the fault diagnosis model to determine the current fault type.

[0164] if the current fault type is the soft fault running, outputting the prompt information of "fault: XX", and recording the feedback information of the user.

[0165] if the current fault type is the no fault running, outputting the prompt information of "low energy efficiency running", obtaining the parameter setting scheme (energy saving scheme) corresponding to the current running mode from the database, and outputting the parameter setting scheme.

[0166] It should be noted that the steps of determining the current energy consumption mode and determining the current fault type can be periodically executed by the controller. In the case that the current energy consumption mode is the high energy efficiency running mode, the controller continues to determine the energy consumption mode of the next period through the energy efficiency identification model in the next period. In the case that the current energy consumption mode is the low energy efficiency running mode, the controller stops determining the energy consumption mode of the next period through the energy efficiency identification model until the controller receives the instruction of enabling the energy mode identification.

[0167] It can be seen that the above mainly introduces the scheme provided by the embodiments of the present application from the perspective of method. To implement the above functions, the embodiments of the present application provide corresponding hardware structures and / or software modules for executing various functions. Those skilled in the art should easily realize that, in combination with the modules and algorithm steps of the examples described in the embodiments disclosed in the present text, the embodiments of the present application can be realized in the form of hardware or the combination of hardware and computer software. Whether a certain function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0168] The embodiments of the present application can divide the functional modules of the controller according to the above method examples, for example, each functional module can be divided according to each function, or two or more functions can be integrated in one processing module. The above integrated module can be realized in the form of hardware or in the form of software functional module. Optionally, the division of the modules in the embodiments of the present application is illustrative, and is only a logical function division. When actually implemented, there can be another division manner.

[0169] The embodiment of the present application further provides a computer readable storage medium comprising computer execution instructions, which, when running on a computer, causes the computer to execute the control method of the multi-split air conditioning system according to any one of the above embodiments.

[0170] The embodiment of the present application further provides a computer program product comprising computer execution instructions, which, when running on a computer, causes the computer to execute the control method of the multi-split air conditioning system according to any one of the above embodiments.

[0171] In the above embodiments, the implementation can be achieved by software, hardware, firmware or any combination thereof, entirely or partially. When implemented by software, the implementation can be in the form of a computer program product, entirely or partially. The computer program product includes one or more computer execution instructions. When the computer execution instructions are loaded and executed on a computer, the entire or partial process or function according to the embodiments of the present application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. The computer execution instructions can be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer execution instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) mode. The computer readable storage medium can be any available medium that can be accessed by a computer or a data storage device including one or more servers, data centers, etc. integrated with the medium. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk (SSD)), etc.

[0172] Although the present application is described herein in conjunction with various embodiments, other variations of the disclosed embodiments can be understood and implemented by those skilled in the art through viewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "one" does not exclude a plurality. A single processor or other unit can implement several functions listed in the claims. Some measures are described in mutually different dependent claims, but this does not mean that these measures cannot be combined to produce a good result.

[0173] Although the present application has been described in connection with certain specific features and embodiments thereof, it is to be understood that it is intended to cover all modifications and variations of this application which are within the scope of the appended claims and their equivalents. Accordingly, the description and drawings are to be regarded as illustrative in nature and not as restrictive.

[0174] The above description is only specific embodiments of the present application, but the protection scope of the present application is not limited to this. Any changes or replacements within the technical scope disclosed by the present application should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A multi-split air conditioning system, characterized in that, include: At least one indoor unit; At least one outdoor unit; The controller is configured as follows: Obtain the operating data of the multi-split air conditioning system under the current operating conditions; Based on the trained energy efficiency identification model and the operating data under the current operating conditions, the energy consumption mode of the multi-split air conditioning system is determined, including a high energy efficiency operation mode and a low energy efficiency operation mode. When the energy consumption mode is the low-energy-efficiency operation mode, the fault type of the multi-split air conditioning system is determined based on the trained fault diagnosis model and the operating data under the current operating conditions. When the fault type is a soft fault operation, output the indication information of the type of soft fault operation.

2. The multi-split air conditioning system according to claim 1, characterized in that, The controller is also configured to: When the fault type is low energy efficiency operation, output the parameter setting scheme corresponding to the operation mode of the multi-split air conditioning system. The parameter setting scheme is the setting scheme of each operating parameter with the highest energy efficiency corresponding to the operation mode.

3. The multi-split air conditioning system according to claim 1, characterized in that, The controller is also configured to: When the energy consumption mode is the high-efficiency operation mode, the current operation mode of the multi-split air conditioning system and the parameter setting values ​​of each operation parameter of the multi-split air conditioning system are obtained. Determine multiple parameter settings as parameter setting schemes corresponding to the current operating mode.

4. The multi-split air conditioning system according to claim 1, characterized in that, The current operating conditions include one or more of the following: the number of indoor units turned on, the load rate of the multi-split air conditioning system, the target operating mode of the multi-split air conditioning system, the indoor ambient temperature value, and the outdoor ambient temperature value.

5. The multi-split air conditioning system according to claim 1, characterized in that, The trained energy efficiency identification model is obtained by the controller performing the following steps: Acquire historical operating data of the multi-split air conditioning system under multiple operating conditions; The historical operating data is used as the first sample data, and the energy consumption mode corresponding to the historical operating data is used as the first sample label to construct a first training sample set, with one first sample data corresponding to one first sample label. The original energy efficiency identification model is trained based on the first training sample set to obtain the trained energy efficiency identification model.

6. The multi-split air conditioning system according to claim 1, characterized in that, The trained fault diagnosis model is obtained by the controller performing the following steps: Acquire historical operating data of the multi-split air conditioning system under multiple operating conditions; The historical operating data is used as the second sample data, and the fault type corresponding to the historical operating data is used as the second sample label to construct a second training sample set, with one second sample data corresponding to one second sample label. The original fault diagnosis model is trained based on the second training sample to obtain the trained fault diagnosis model.

7. A control method for a multi-split air conditioning system, characterized in that, The method includes: Obtain the operating data of the multi-split air conditioning system under the current operating conditions; Based on the trained energy efficiency identification model and the operating data under the current operating conditions, the energy consumption mode of the multi-split air conditioning system is determined, including a high energy efficiency operation mode and a low energy efficiency operation mode. When the energy consumption mode is the low-energy-efficiency operation mode, the fault type of the multi-split air conditioning system is determined based on the trained fault diagnosis model and the operating data under the current operating conditions. When the fault type is a soft fault operation, output the indication information of the type of soft fault operation.

8. The method according to claim 7, characterized in that, The method further includes: When the fault type is low energy efficiency operation, output the parameter setting scheme corresponding to the operation mode of the multi-split air conditioning system. The parameter setting scheme is the setting scheme of each operating parameter with the highest energy efficiency corresponding to the operation mode.

9. The method according to claim 7, characterized in that, The method further includes: When the energy consumption mode is the high-efficiency operation mode, the current operation mode of the multi-split air conditioning system and the parameter setting values ​​of each operation parameter of the multi-split air conditioning system are obtained. Determine multiple parameter settings as parameter setting schemes corresponding to the current operating mode.

10. The method according to claim 7, characterized in that, The trained energy efficiency identification model is obtained through the following steps: Acquire historical operating data of the multi-split air conditioning system under multiple operating conditions; The historical operating data is used as the first sample data, and the energy consumption mode corresponding to the historical operating data is used as the first sample label to construct a first training sample set, with one first sample data corresponding to one first sample label. The original energy efficiency identification model is trained based on the first training sample set to obtain the trained energy efficiency identification model.

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