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 determining the diagnosis reliability of a fault diagnosis model of the multi-split air conditioning system. The multi-split air conditioning system comprises at least one indoor unit; at least one outdoor unit; the controller is configured to obtain current operation data of the multi-split air conditioning system and training data of the fault diagnosis model; determining a first similarity between the current operation data and training data of a fault diagnosis model; based on the first similarity, the diagnosis reliability of the fault diagnosis model is determined, and the diagnosis reliability of the fault diagnosis model is used for representing the reliability degree of fault diagnosis applied to the multi-split air conditioning system based on the fault diagnosis model; and outputting the diagnosis reliability of the fault diagnosis model.

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] Multi-split air conditioning systems have been widely used in commercial, office, residential and other buildings due to their high efficiency, energy saving, comfort and flexibility.

[0003] It is inevitable for multi-split air conditioning systems to fail during use, and in the related art, a fault diagnosis model is often used to diagnose the failure of the multi-split air conditioning system. However, when using the existing fault diagnosis model to diagnose the failure of the multi-split air conditioning system under different operating scenarios, it is difficult to ensure the diagnosis reliability of the multi-split air conditioning system fault diagnosis model.

[0004] Therefore, how to determine the diagnosis reliability of the multi-split air conditioning system fault diagnosis model 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 determining the diagnosis reliability of a multi-split air conditioning system fault diagnosis model.

[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 current operating data of the multi-split air conditioning system and training data of a fault diagnosis model;

[0012] determine a first similarity between the current operating data and the training data of the fault diagnosis model;

[0013] based on the first similarity, determine a diagnosis reliability of the fault diagnosis model, the diagnosis reliability of the fault diagnosis model being used to represent the reliability degree of applying the fault diagnosis model to the multi-split air conditioning system for fault diagnosis;

[0014] output the diagnosis reliability of the fault diagnosis model.

[0015] The technical scheme provided by the embodiments of the present application at least brings the following beneficial effects: According to the similarity between the current operation data of the multi-connected air conditioning system and the training data of the fault diagnosis model, the present application determines the reliability of the fault diagnosis result of the current air conditioner using the fault diagnosis model. The higher the diagnosis reliability is, the higher the consistency between the predicted fault type output by the fault diagnosis model and the actual fault type under the current operation scene is; on the contrary, the lower the diagnosis reliability is, the lower the consistency between the predicted fault type output by the fault diagnosis model and the actual fault type under the current operation scene is. In this way, according to the diagnosis reliability, the user can assist in determining the fault diagnosis of the air conditioner under the unknown operation scene.

[0016] In some embodiments, the controller is configured to determine the diagnosis reliability of the fault diagnosis model based on the first similarity, specifically configured to determine the diagnosis reliability of the fault diagnosis model based on the first similarity and a preset corresponding relationship, and the preset corresponding relationship includes a corresponding relationship between the first similarity and the diagnosis reliability.

[0017] In some embodiments, the preset corresponding relationship is determined by the following steps: obtaining simulation operation data of the multi-connected air conditioning system under different operation scenes; for each set of simulation operation data in the simulation operation data, determining a second similarity between the simulation operation data and the training data of the fault diagnosis model; inputting the simulation operation data into the fault diagnosis model to obtain a fault type output by the fault diagnosis model; determining the diagnosis reliability based on the fault type output by the fault diagnosis model and a preset fault type; and establishing the preset corresponding relationship based on the second similarity and the diagnosis reliability.

[0018] In some embodiments, the different operation scenes include one or more of different set temperatures, different operation conditions, different load rates and different wind speed gears.

[0019] In some embodiments, the controller is configured to determine the first similarity between the current operation data and the training data of the fault diagnosis model, specifically configured to determine a maximum mean difference value between the current operation data and the training data of the fault diagnosis model as the first similarity, and the maximum mean difference value is the maximum value of the difference between the current operation data and the training data of the fault diagnosis model.

[0020] In some embodiments, the controller is further configured to obtain historical operation data of the multi-connected air conditioning system under a plurality of operation scenes; construct a training sample set by taking the historical operation data as training sample data and taking the fault types corresponding to the historical operation data as training sample labels, one sample label corresponding to one sample label; train the original fault diagnosis model based on the training sample set to obtain the fault diagnosis model.

[0021] Secondly, embodiments of this application provide a control method for a multi-split air conditioning system, the method comprising:

[0022] Obtain the current operating data of the multi-split air conditioning system and the training data of the fault diagnosis model;

[0023] Determine the first similarity between the current running data and the training data of the fault diagnosis model;

[0024] Based on the first similarity, the diagnostic reliability of the fault diagnosis model is determined. The diagnostic reliability of the fault diagnosis model is used to characterize the reliability of applying the fault diagnosis model to multi-split air conditioning systems for fault diagnosis.

[0025] Output the diagnostic reliability of the fault diagnosis model.

[0026] Thirdly, embodiments of this application provide a controller, including: one or more processors; one or more memories; wherein the one or more memories are used to store computer program code, the computer program code including computer instructions, and when the one or more processors execute the computer instructions, the controller executes the control method provided in the second aspect.

[0027] Fourthly, embodiments of this application provide a computer-readable storage medium including computer instructions that, when controlled on a computer, cause the computer to perform the methods provided in the second aspect and possible implementations.

[0028] Fifthly, embodiments of the present invention provide a computer program product that can be directly loaded into a memory and contains software code. After being loaded and executed by a computer, the computer program product can implement the methods provided in the second aspect and possible implementations.

[0029] It should be noted that the aforementioned computer instructions may be stored, in whole or in part, on a computer-readable storage medium. This computer-readable storage medium may be packaged together with the controller's processor, or it may be packaged separately from the controller's processor; this application does not impose any limitations on this.

[0030] The beneficial effects described in aspects two through five of this application can be referred to the analysis of the beneficial effects of aspect one, and will not be repeated here. Attached Figure Description

[0031] The accompanying drawings are provided to further understand the technical solutions of the present invention and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of the present invention and do not constitute a limitation on the technical solutions of the present invention.

[0032] Figure 1A schematic diagram of the structure of a multi-split air conditioning system provided in this application embodiment. Figure 1 ;

[0033] Figure 2 A schematic diagram of the structure of a multi-split air conditioning system provided in this application embodiment. Figure 2 ;

[0034] Figure 3 A schematic diagram of the structure of a multi-split air conditioning system provided in this application embodiment. Figure 3 ;

[0035] Figure 4 A schematic diagram of the structure of a multi-split air conditioning system provided in this application embodiment. Figure 4 ;

[0036] Figure 5 A schematic diagram of the structure of a multi-split air conditioning system provided in this application embodiment. Figure 5 ;

[0037] Figure 6 A schematic diagram of the hardware structure of a controller provided in an embodiment of this application;

[0038] Figure 7 This application provides a schematic diagram of an application scenario for a multi-split air conditioning system.

[0039] Figure 8 A schematic diagram of the hardware structure of a cloud server provided in an embodiment of this application;

[0040] Figure 9 A flowchart illustrating a control method for a multi-split air conditioning system provided in this application embodiment. Figure 1 ;

[0041] Figure 10 This is a schematic diagram of a prompt message provided in an embodiment of this application;

[0042] Figure 11 A flowchart illustrating a control method for a multi-split air conditioning system provided in this application embodiment. Figure 2 ;

[0043] Figure 12 A flowchart illustrating a control method for a multi-split air conditioning system provided in this application embodiment. Figure 3 ;

[0044] Figure 13 A flowchart illustrating a control method for a multi-split air conditioning system provided in this application embodiment. Figure 4 ;

[0045] Figure 14 A flowchart illustrating a control method for a multi-split air conditioning system provided in this application embodiment.Figure 5 . 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 of ordinary skill 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] Furthermore, in the embodiments of this application, the words "exemplary" or "for example" are used to indicate that they are examples, illustrations, or descriptions. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0052] As mentioned in the background technology, multi-split air conditioning systems are widely used in commercial, office, and residential buildings due to their advantages such as high efficiency, energy saving, comfort, and flexibility.

[0053] Faults in multi-split air conditioning systems are inevitable during use, and related technologies often use fault diagnosis models to diagnose these faults. However, when using existing fault diagnosis models to diagnose faults in multi-split air conditioning systems under different operating scenarios, the reliability of the diagnostic model is difficult to guarantee.

[0054] Therefore, determining the diagnostic reliability of a fault diagnosis model for a multi-split air conditioning system is an urgent problem to be solved.

[0055] In view of this, embodiments of this application provide a multi-split air conditioning system and its control method. The reliability of the fault diagnosis result obtained by the fault diagnosis model is determined based on the similarity between the current operating data of the multi-split air conditioning system and the training data of the fault diagnosis model. Higher diagnostic reliability indicates a higher consistency between the predicted fault type output by the fault diagnosis model and the actual fault type in the current operating scenario; conversely, lower diagnostic reliability indicates a lower consistency between the predicted fault type output by the fault diagnosis model and the actual fault type in the current operating scenario. Thus, based on this diagnostic reliability, users can be assisted in determining fault diagnoses for air conditioners in unknown operating scenarios.

[0056] The number of multi-split air conditioning systems provided in this application embodiment can be one, two or more; the number of outdoor units in each multi-split air conditioning system can be one, two or more, and this application embodiment does not impose any restrictions on this.

[0057] To further describe the technical solutions of the embodiments of this application, as follows: Figure 1 The diagram shown is a structural diagram of a multi-split air conditioning system provided in an embodiment of this application.

[0058] like Figure 2 As shown, the multi-split air conditioning system 1 includes at least one outdoor unit 10 and at least one indoor unit 20.

[0059] The following is a detailed explanation of the settings and functions of each component of the outdoor unit.

[0060] In some embodiments, such as Figure 3 As shown, the outdoor unit includes an outdoor heat exchanger 101. One end of the outdoor heat exchanger 101 is connected to the compressor 102 via a four-way reversing valve 103, and the other end is connected to each indoor heat exchanger via connecting pipes. The outdoor heat exchanger 101 is used to facilitate heat exchange between the refrigerant flowing in the heat transfer tubes of the outdoor heat exchanger 101 and the outdoor air.

[0061] In some embodiments, such as Figure 4 As shown, the outdoor unit also includes a compressor 102. The compressor 102 is located between each indoor heat exchanger and the outdoor heat exchanger 101, and is used to provide power for the refrigerant circulation. Taking the refrigeration cycle as an example, the compressor 102 delivers the compressed refrigerant to the outdoor heat exchanger 101 via a four-way reversing valve 103.

[0062] In some embodiments, such as Figure 4 As shown, the outdoor unit also includes a four-way reversing valve 103. The four ports of the four-way reversing valve 103 are respectively connected to the discharge 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 switch between cooling mode and heating mode by changing the flow direction of refrigerant in the system piping.

[0063] In some embodiments, continue as follows Figure 4 As shown, the outdoor unit also includes an outdoor throttling device 104. The outdoor throttling device 104 is located between the outdoor heat exchanger 101 and each indoor heat exchanger, and has the effect of throttling the refrigerant flowing through it to reduce pressure, thereby regulating the refrigerant flow rate in the refrigerant path. Optionally, the outdoor throttling device 104 can be an electronic expansion valve. If the outdoor throttling device 104 decreases its opening, the flow resistance of the refrigerant through it increases. If the outdoor throttling device 104 increases its opening, the flow resistance decreases. Thus, even if the states of other components in the circuit remain unchanged, the refrigerant flow rate to the indoor heat exchanger 201 or the outdoor heat exchanger 101 will change when the opening of the outdoor throttling device 104 changes.

[0064] In some embodiments, continue as follows Figure 5 As shown, the outdoor unit also 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 contain the refrigerant in the return liquid section of the refrigerant passage, preventing liquid slugging of the compressor 102.

[0065] The following is a detailed explanation of the settings and functions of each component of the indoor unit.

[0066] In some embodiments, such as Figure 5 As shown, the indoor unit includes an indoor heat exchanger 201. The indoor heat exchanger 201 is used to exchange heat between the refrigerant flowing in the heat transfer tubes of the indoor heat exchanger 201 and the indoor air.

[0067] In some embodiments, continue as follows Figure 6As shown, the indoor unit also includes an indoor throttling device 202. The indoor throttling device 202 is connected to the indoor heat exchanger 201 and is used to throttle the refrigerant flowing through it, thereby reducing pressure and regulating the refrigerant flow rate in the refrigerant passage. Optionally, the indoor throttling device 202 can be an electronic expansion valve.

[0068] In this application, the multi-split air conditioning system executes the refrigeration cycle by using a compressor 102, an outdoor heat exchanger 101, a throttling device 104, an indoor heat exchanger 201, and a 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 the conditioned and heat-exchanged air.

[0069] Indoor heat exchanger 201 and outdoor heat exchanger 101 are used as condensers or evaporators. When indoor heat exchanger 201 is used as a condenser, the multi-split air conditioning system 1 is used as a heater in heating mode. When indoor heat exchanger 201 is used as an evaporator, the multi-split air conditioning system 1 is used as a cooler in cooling mode.

[0070] In some embodiments, the multi-split air conditioning system 1 further includes a controller 300. For example... Figure 7 As shown, 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.

[0071] In some embodiments, the controller 300 can be used to acquire the current operating data of the multi-split air conditioning system and the training data of the fault diagnosis model; determine the first similarity between the current operating data and the training data of the fault diagnosis model; determine the diagnostic reliability of the fault diagnosis model based on the first similarity, wherein the diagnostic reliability of the fault diagnosis model is used to characterize the reliability of applying the fault diagnosis model to the multi-split air conditioning system for fault diagnosis; and output the diagnostic reliability of the fault diagnosis model.

[0072] In some embodiments, the controller 300 can also be used to determine the diagnostic reliability of the fault diagnosis model based on a first similarity and a preset correspondence, wherein the preset correspondence includes the correspondence between the first similarity and the diagnostic reliability of the fault diagnosis model.

[0073] In some embodiments, the controller 300 can also be used to acquire simulated operating data of the multi-split air conditioning system under different operating scenarios; for each set of simulated operating data in each set of simulated operating data, determine the second similarity between the simulated operating data and the training data of the fault diagnosis model; input the simulated operating data into the fault diagnosis model to obtain the fault type output by the fault diagnosis model; determine the diagnostic reliability of the fault diagnosis model based on the fault type output by the fault diagnosis model and the preset fault type; and establish a preset correspondence based on the second similarity and the diagnostic reliability of the fault diagnosis model.

[0074] In some embodiments, the controller 300 may also be used to determine the maximum mean difference between the current running data and the training data of the fault diagnosis model as the first similarity, wherein the maximum mean difference is the maximum value of the difference between the current running data and the training data of the fault diagnosis model.

[0075] In some embodiments, the controller 300 can also be used to acquire historical operating data of the multi-split air conditioning system under multiple operating scenarios; use the historical operating data as training sample data, use the fault type corresponding to the historical operating data as training sample label, construct a training sample set, with one sample label corresponding to one sample label; and train the original fault diagnosis model based on the training sample set to obtain the fault diagnosis model.

[0076] The aforementioned controller 300 refers to a device that can generate operation control signals based on instruction opcodes and timing signals, instructing the multi-split air conditioning system 1 to execute control commands. Exemplarily, the controller 300 can be a central processing unit (CPU), a network processor (NP), a digital signal processor (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 circuits, devices, or software modules; this application embodiment does not impose any limitations on this.

[0077] In some embodiments, the controller 300 can be a microcontroller unit (MCU). An MCU, also known as a single-chip microcomputer, is a chip-level computer that integrates a central processing unit (CPU) with appropriately reduced frequency and specifications, along with peripheral interfaces such as memory, timer, USB, A / D converter, UART, PLC, DMA, and even LCD driver circuitry, all onto a single chip. This allows for different combinations of control for various applications.

[0078] In addition, the controller 300 can be used 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 can operate to achieve each predetermined function of the multi-split air conditioning system 1.

[0079] It is understood that the structures illustrated in the embodiments of the present invention do not constitute a specific limitation on the multi-split air conditioning system. In other embodiments of this application, the multi-split air conditioning system may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0080] Figure 7 This is a schematic diagram illustrating an application scenario of a multi-split air conditioning system provided in this application according to an exemplary embodiment. For example... Figure 8 As shown, the application scenario includes multiple multi-split air conditioning systems, such as the first multi-split air conditioning system 100 and the second multi-split air conditioning system 200, as well as a cloud server 400.

[0081] The cloud server 400 has a communication connection with the first multi-split air conditioning system 100 and the second multi-split air conditioning system 200.

[0082] 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 parameters such as temperature, humidity, and airflow rate of the ambient air inside a building or structure.

[0083] In some embodiments, cloud server 400 may be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks, and big data servers. This application does not impose any special restrictions on the specific form of cloud server 400.

[0084] In some embodiments, the first multi-split air conditioning system 100 and the second multi-split air conditioning system 200 can send their own operating data to the cloud server 400 so that the cloud server 400 can detect faults in the first multi-split air conditioning system 100 and the second multi-split air conditioning system 200 based on the operating data.

[0085] Figure 8 This is a schematic diagram of the hardware structure of a cloud server provided in accordance with an exemplary embodiment of this application. Figure 8 As shown, the cloud server 400 includes a communicator 401, a memory 402, and a processor 403.

[0086] In some embodiments, the communicator 401 is used to establish communication connections with other network entities, such as establishing a communication connection with the first multi-split air conditioning system 100. The communicator 401 may include a radio frequency (RF) module, a cellular module, a wireless fidelity (WIFI) module, and a GPS module, etc. Taking an RF module as an example, the RF module can be used for signal reception and transmission; specifically, it sends received information to the processor 403 for processing; additionally, it transmits the signals generated by the processor 403. Typically, the RF circuit may include, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low-noise amplifier (LNA), a duplexer, etc.

[0087] In some embodiments, memory 402 can be used to store software programs and data. Processor 403 executes various functions of cloud server 400 and data processing by running software programs or data stored in memory 402. Memory 402 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Memory 402 stores an operating system that enables processor 403 to run. In this application, memory 402 may store the operating system and various applications, and may also store code that executes the control method of the multi-split air conditioning system provided in the embodiments of this application.

[0088] In some embodiments, the processor 403 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0089] Those skilled in the art will understand that Figure 9 The hardware structure shown does not constitute a limitation on cloud servers. Cloud servers may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0090] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0091] like Figure 10 As shown, this application provides a control method for a multi-split air conditioning system, applied to the controller of the multi-split air conditioning system. The method includes the following steps S1-S4:

[0092] S1. Obtain the current operating data of the multi-split air conditioning system and the training data of the fault diagnosis model.

[0093] As one possible approach, the operating data of the multi-split air conditioning system under current conditions can be obtained in real time through the communication device of the multi-split air conditioning system.

[0094] Optionally, the operating conditions include, but are not limited to, one or more of the following: the number of indoor units in operation, the load rate of the multi-split air conditioning system, the target cooling / heating capacity of the multi-split air conditioning system, the indoor ambient temperature, and the outdoor ambient temperature. Operating data includes, but is not limited to, one or more of the following: compressor suction temperature, compressor discharge temperature, compressor suction pressure, compressor discharge pressure, evaporator inlet temperature, evaporator outlet temperature, compressor frequency, compressor current, and the opening degree of the throttling device.

[0095] As one possible implementation, training data for the fault diagnosis model can be obtained from a database that stores training data in a multi-split air conditioning system.

[0096] As another possible implementation, the training data for the fault diagnosis model can be obtained from the storage of a cloud server.

[0097] The training data can be historical operating data of multi-split air conditioning systems.

[0098] S2. Determine the first similarity between the current running data and the training data of the fault diagnosis model.

[0099] In some embodiments, step S2 above can be specifically implemented as: determining the maximum mean difference between the current running data and the training data of the fault diagnosis model as the first similarity.

[0100] The maximum mean discrepancy (MMD) is the maximum difference between the current running data and the training data of the fault diagnosis model. The MMD is a nonparametric statistic used to compare the differences between two probability distributions. The basic idea of ​​MMD is to find a function (f) in the reproducing kernel Hilbert space (RKHS) that maximizes the expected difference between the two distributions on this function; the maximum value of this expected difference is the MMD value. The calculation of the MMD value relies on a kernel function (such as a Gaussian kernel) that maps the original data to a high-dimensional feature space, making it easier to distinguish different data distributions in the high-dimensional space. The calculation of the MMD value typically involves calculating the kernel matrix between the samples of the two distributions and then using these kernel matrices to calculate the squared value of the MMD. The smaller the MMD value, the more similar the two distributions are under the considered kernel function and RKHS.

[0101] For example, the current running data and training data are treated as data samples from two different distributions. The mean of the function values ​​of the data samples from different distributions on the function f is determined using a continuous function f in the sample space. The difference between the means of the two function values ​​is used to obtain the MMD value. The MMD value between the current running data and the training data of the fault diagnosis model is determined as the first similarity.

[0102] S3. Based on the first similarity, determine the diagnostic reliability of the fault diagnosis model.

[0103] Among them, the diagnostic reliability of the fault diagnosis model is used to characterize the reliability of applying the fault diagnosis model to the fault diagnosis of multi-split air conditioning systems.

[0104] Understandably, the higher the diagnostic reliability, the higher the consistency between the predicted fault type output by the fault diagnosis model and the actual fault type in the current operating scenario; conversely, the lower the diagnostic reliability, the lower the consistency between the predicted fault type output by the fault diagnosis model and the actual fault type in the current operating scenario.

[0105] As one possible implementation, step S3 above can be specifically implemented as determining the diagnostic reliability of the fault diagnosis model based on the first similarity and the preset correspondence.

[0106] The preset correspondence includes the correspondence between the first similarity and the diagnostic reliability.

[0107] S4. Output the diagnostic reliability of the fault diagnosis model.

[0108] For example, such as Figure 9 As shown, the output message "The reliability of the fault diagnosis model is 80%" is displayed.

[0109] Figure 11 The illustrated embodiments offer at least the following advantages: This application determines the reliability of the fault diagnosis results obtained by the fault diagnosis model based on the similarity between the current operating data of the multi-split air conditioning system and the training data of the fault diagnosis model. Higher diagnostic reliability means a higher consistency between the predicted fault type output by the fault diagnosis model and the actual fault type in the current operating scenario; conversely, lower diagnostic reliability means a lower consistency between the predicted fault type output by the fault diagnosis model and the actual fault type in the current operating scenario. Thus, this diagnostic reliability can assist users in determining fault diagnoses for air conditioners operating in unknown scenarios.

[0110] In some embodiments, such as Figure 12 As shown, the above-mentioned preset correspondence is determined by the following steps S31-S35:

[0111] S31. Obtain simulated operation data of the multi-split air conditioning system under different operating scenarios.

[0112] In some embodiments, different operating scenarios may include different set temperatures, which may include set temperatures for low-temperature cooling, high-temperature cooling, and cooling under extreme conditions.

[0113] In some embodiments, different operating scenarios may also include different operating conditions.

[0114] In some embodiments, different operating scenarios may also include different load rates.

[0115] In some embodiments, different operating scenarios may also include different wind speed settings.

[0116] In some embodiments, different operating scenarios may also include different lengths of air conditioning piping.

[0117] As one possible approach, simulated operating data of multi-split air conditioning systems under different operating scenarios can be obtained from a cloud platform.

[0118] For example, if the historical operating data pertains to a low-temperature cooling scenario, the operating conditions of the multi-split air conditioning system under set temperature scenarios such as high-temperature cooling or cooling under extreme conditions can be simulated.

[0119] S32. For each set of simulated running data in each set of simulated running data, determine the second similarity between the simulated running data and the training data of the fault diagnosis model.

[0120] As one possible approach, the second similarity is determined by the maximum mean difference between each set of simulated running data and the training data of the fault diagnosis model.

[0121] For example, the simulated running data and training data are divided into two data samples with different distributions. The mean of the function values ​​of the data samples with different distributions on the function f is determined using a continuous function f in the sample space. The difference between the means of the two function values ​​is used to obtain the MMD value. The MMD value between the simulated running data and the training data of the fault diagnosis model is determined as the second similarity.

[0122] In this way, by determining the MMD value between each set of simulated operating data and the training data of the fault diagnosis model under each operating scenario, the similarity between the operating data of the multi-split air conditioning system and the training data of the fault diagnosis model under each operating scenario can be obtained.

[0123] S33. Input the simulation operation data into the fault diagnosis model to obtain the fault type output by the fault diagnosis model.

[0124] For example, fault types may include compressor lack of refrigerant, compressor overload shutdown, temperature detection failure, etc.

[0125] S34. Determine the diagnostic reliability based on the fault type output by the fault diagnosis model and the preset fault type.

[0126] As one possible approach, diagnostic reliability can be determined using accuracy (ACC) as an evaluation metric.

[0127] For example, for each operating scenario, simulated operating data is input into the fault diagnosis model. If the output fault type matches the preset fault type, the fault diagnosis model is determined to be a positive prediction for the simulated operating data, i.e., the prediction result is correct. If the output fault type does not match the preset fault type, the fault diagnosis model is determined to be a negative prediction for the simulated operating data, i.e., the prediction result is incorrect. The diagnostic reliability is determined based on the proportion of positive predictions among multiple sets of prediction results.

[0128] In some embodiments, users can customize the relationship between fault diagnosis model accuracy (ACC) and diagnostic reliability.

[0129] For example, if the ACC value is greater than 80%, the diagnostic results obtained by applying the fault diagnosis model to the current multi-split air conditioning system are considered reliable.

[0130] This allows users to intuitively understand the accuracy of the fault diagnosis model and the reliability of its application in current multi-split air conditioning systems.

[0131] S35. Based on the second similarity and diagnostic reliability, establish a preset correspondence.

[0132] For example, the pre-defined correspondence between the second similarity and diagnostic reliability can be expressed as the following formula:

[0133] ACC = f(MMD)

[0134] Where ACC represents diagnostic reliability, MMD represents second similarity, and f() represents function f.

[0135] As can be seen from the above embodiments, in order to enhance the generalization ability of the fault diagnosis model in different operating scenarios, this application embodiment obtains the simulated operating data of the multi-split air conditioning system in different operating scenarios, provides diverse data conditions for the fault diagnosis model, and determines the preset correspondence between the second similarity and the diagnostic reliability in different operating scenarios.

[0136] In some embodiments, such as Figure 13 As shown, the control method for the multi-split air conditioning system provided in this application further includes the following steps S5-S7:

[0137] S5. Obtain historical operating data of the multi-split air conditioning system under multiple operating scenarios.

[0138] It should be noted that the historical operating data of the multi-split air conditioning system under multiple operating scenarios can be the historical operating data of multiple multi-split air conditioning systems of the same model under multiple different operating scenarios; it can also be the historical operating data of multiple multi-split air conditioning systems of the same model under multiple identical operating scenarios; it can also be the historical operating data of multiple multi-split air conditioning systems of different models under multiple different operating scenarios; or it can also be the historical operating data of multiple multi-split air conditioning systems of the same model under multiple identical operating scenarios. This application embodiment does not impose any restrictions on this.

[0139] As one possible approach, the historical operating data of the fault diagnosis model can be obtained from a database that stores historical operating data of the multi-split air conditioning system.

[0140] As another possible implementation, historical running data of the fault diagnosis model can be obtained from the cloud server's storage.

[0141] In some embodiments, the control method for a multi-split air conditioning system provided in this application may further include the following steps after obtaining historical operating data of the multi-split air conditioning system under multiple operating scenarios: preprocessing the historical operating data to obtain a historical operating dataset with uniform time intervals.

[0142] As one possible approach, Lagrange interpolation is used to preprocess the operational data of multi-split air conditioning systems with different acquisition time intervals in the cloud platform. All historical operational datasets are processed at the same time interval, and the operational data at new time points is obtained by substituting the operational data from adjacent time points in the original historical datasets into the Lagrange function. Because the time intervals between the acquired data are relatively short, the data error after interpolation is small, thus achieving uniformity in the data time intervals.

[0143] The basic idea of ​​Lagrange interpolation is to construct a polynomial with known function values ​​at several different points, such that the polynomial takes values ​​at these points that are exactly equal to the known function values.

[0144] S6. Use historical operating data as training sample data and the fault types corresponding to the historical operating data as training sample labels to construct a training sample set.

[0145] One sample label corresponds to one sample label.

[0146] S7. Train the original fault diagnosis model based on the training sample set to obtain the fault diagnosis model.

[0147] As one possible implementation, such as Figure 14 As shown, training the original fault diagnosis model based on the training sample set includes the following steps:

[0148] Sa1. Input each set of historical operating data in the training sample set into the original fault diagnosis model to obtain the predicted fault type corresponding to each set of historical operating data in the training sample set.

[0149] Sa2. Determine the loss value of the training sample set based on the predicted fault type corresponding to each set of historical operating data and the actual fault type of each set of historical operating data.

[0150] Sa3. Determine whether the fault diagnosis model has converged based on the loss value of the training sample set.

[0151] Sa4. If not, update the weight parameters in the fault diagnosis model based on the loss value of the training sample set, and continue to execute step Sa1 to input each set of historical running data in the training sample set into the original fault diagnosis model to obtain the predicted fault type corresponding to each set of historical running data in the training sample set.

[0152] Sa5. If so, determine the current fault diagnosis model as the trained fault diagnosis model.

[0153] It should be noted that the fault diagnosis model in this application embodiment is a trained fault diagnosis model.

[0154] In some embodiments, after obtaining the trained fault diagnosis model, the following steps may be further included: using historical operational data as test sample data; constructing a test sample set by using the fault types corresponding to the historical data as test sample labels; wherein one test sample data corresponds to one test sample label; and testing the trained fault diagnosis model using the test sample set.

[0155] The aforementioned test sample set includes multiple test samples with different historical operating data. Each test sample includes the fault types of the historical operating data, and each test sample is assigned a fault type label, which is used to represent the actual fault type of the historical operating data.

[0156] It should be noted that the historical running data in the test sample set differs from the historical running data in the training sample set. The training sample set is used to train the fault diagnosis model and learn fault characteristics; the test sample set is used to verify the accuracy of the fault diagnosis model.

[0157] Optionally, 70% of the historical running data in the historical running dataset can be selected as the training sample set, and the remaining 30% of the historical running data in the historical running dataset can be selected as the test sample set.

[0158] The following is combined ​ The control method of the multi-split air conditioning system provided in this application is described in detail.

[0159] On the equipment side, a multi-split air conditioning system includes n devices: device 1, device 2, ..., device n. The multi-split air conditioning system uploads operational data for the current operating scenario to the cloud platform in real time.

[0160] For the cloud platform, operational data sent by the multi-split air conditioning system is received and stored in the database. The operational data in the database is preprocessed and divided into multiple historical operational data training sets. The fault diagnosis model is trained using these historical operational data training sets. Simulated operational data for multiple operational scenarios is generated. For the simulated operational data in the first operational scenario, MMD1 and ACC1 are determined; for the simulated operational data in the second operational scenario, MMD2 and ACC2 are determined; and for the simulated operational data in the nth operational scenario, MMDn and ACCn are determined. Further, a preset correspondence ACC = f(MMD) is fitted to obtain the result.

[0161] For the user end, the diagnostic reliability of fault diagnosis for the current running data is determined based on ACC = f(MMD).

[0162] As can be seen, the above mainly describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the above functions, the embodiments of this application provide corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the modules and algorithm steps of the various examples described in the embodiments disclosed herein, the embodiments of this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in a hardware or computer software-driven hardware manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention.

[0163] This application embodiment can divide the controller into functional modules according to the above method example. For example, each function can be divided into a separate functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. Optionally, the module division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0164] This application also provides a computer-readable storage medium including computer-executable instructions that, when run on a computer, cause the computer to execute any of the control methods for a multi-split air conditioning system provided in the above embodiments.

[0165] This application also provides a computer program product containing computer execution instructions, which, when run on a computer, causes the computer to execute any of the control methods for a multi-split air conditioning system provided in the above embodiments.

[0166] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer-executable instructions. When these computer-executable instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer-executable instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer-executable instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks, SSDs).

[0167] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0168] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely illustrative descriptions of the application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and modifications of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and modifications.

[0169] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the 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 current operating data of the multi-split air conditioning system and the training data of the fault diagnosis model; Determine the first similarity between the current running data and the training data of the fault diagnosis model; Based on the first similarity, the diagnostic reliability of the fault diagnosis model is determined. The diagnostic reliability of the fault diagnosis model is used to characterize the reliability of applying the fault diagnosis model to the multi-split air conditioning system for fault diagnosis. Output the diagnostic reliability of the fault diagnosis model.

2. The multi-split air conditioning system according to claim 1, characterized in that, The controller is specifically configured to determine the diagnostic reliability of the fault diagnosis model based on the first similarity. Based on the first similarity and the preset correspondence, the diagnostic reliability of the fault diagnosis model is determined, wherein the preset correspondence includes the correspondence between the first similarity and the diagnostic reliability of the fault diagnosis model.

3. The multi-split air conditioning system according to claim 2, characterized in that, The preset correspondence is determined by the following steps: Obtain simulated operating data of the multi-split air conditioning system under different operating scenarios; For each set of simulated running data in each set of simulated running data, determine a second similarity between the simulated running data and the training data of the fault diagnosis model; The simulated operation data is input into the fault diagnosis model to obtain the fault type output by the fault diagnosis model; Based on the fault type output by the fault diagnosis model and the preset fault type, the diagnostic reliability of the fault diagnosis model is determined. Based on the second similarity and the diagnostic reliability of the fault diagnosis model, the preset correspondence is established.

4. The multi-split air conditioning system according to claim 3, characterized in that, The different operating scenarios include one or more of the following: different set temperatures, different operating conditions, different load rates, and different wind speed levels.

5. The multi-split air conditioning system according to claim 1, characterized in that, The determination of the first similarity between the current running data and the training data of the fault diagnosis model is specifically configured as follows: The maximum mean difference between the current running data and the training data of the fault diagnosis model is determined as the first similarity, where the maximum mean difference is the maximum value of the difference between the current running data and the training data of the fault diagnosis model.

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

7. A control method for a multi-split air conditioning system, characterized in that, The method includes: Obtain the current operating data of the multi-split air conditioning system and the training data of the fault diagnosis model; Determine the first similarity between the current running data and the training data of the fault diagnosis model; Based on the first similarity, the diagnostic reliability of the fault diagnosis model is determined. The diagnostic reliability of the fault diagnosis model is used to characterize the reliability of applying the fault diagnosis model to the multi-split air conditioning system for fault diagnosis. Output the diagnostic reliability of the fault diagnosis model.

8. The method according to claim 7, characterized in that, Determining the diagnostic reliability of the fault diagnosis model based on the first similarity includes: Based on the first similarity and the preset correspondence, the diagnostic reliability of the fault diagnosis model is determined, wherein the preset correspondence includes the correspondence between the first similarity and the diagnostic reliability of the fault diagnosis model.

9. The method according to claim 8, characterized in that, The preset correspondence is determined by the following steps: Obtain simulated operating data of the multi-split air conditioning system under different operating scenarios; For each set of simulated running data in each set of simulated running data, determine a second similarity between the simulated running data and the training data of the fault diagnosis model; The simulated operation data is input into the fault diagnosis model to obtain the fault type output by the fault diagnosis model; Based on the fault type output by the fault diagnosis model and the preset fault type, the diagnostic reliability of the fault diagnosis model is determined. Based on the second similarity and the diagnostic reliability of the fault diagnosis model, the preset correspondence is established.

10. The method according to claim 9, characterized in that, The different operating scenarios include one or more of the following: different set temperatures, different operating conditions, different load rates, and different wind speed levels.

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