Fault diagnosis method and device, air conditioner, equipment and storage medium

Abstract

The invention relates to a fault diagnosis method and device, an air conditioner, equipment and a storage medium. The method comprises the following steps: according to a preset load opening sequence, sequentially opening target loads in the intelligent equipment; in the process of sequentially starting the target load, detecting the starting state of the intelligent equipment; when it is detected that the starting state of the intelligent device is in an abnormal reset state, a preset first mark is reserved, and target loads in the intelligent device are turned on and off one by one; in the process of switching on and off the target loads one by one, the starting state of the intelligent equipment is detected; when it is detected that the starting state of the intelligent device is in an abnormal reset state, a preset second mark is reserved; when it is detected that the starting state of the intelligent device is the normal starting state all the time, the second mark is cleared; and performing fault diagnosis according to the retention states of the first mark and the second mark. According to the invention, the fault diagnosis efficiency is improved through the load starting sequence and the abnormal reset detection mechanism.

Description

Technical Field

[0001] This application relates to the field of fault diagnosis technology, and in particular to a fault diagnosis method, apparatus, air conditioner, equipment and storage medium. Background Technology

[0002] With the increasing popularity of smart homes, users are using smart devices more and more frequently. However, as smart devices are used continuously, their components may age and cause abnormal status issues, which may trigger the reset protection mechanism to prevent damage to the smart devices.

[0003] For example, as air conditioners are used for longer periods, they may experience abnormal conditions due to aging of the compressor, fan, solenoid coil, and other loads. For instance, a short circuit between the turns of the solenoid coil may cause abnormal voltage in the air conditioner. When the mainboard detects an abnormal condition, it will force the air conditioner system to reset and restart. After a brief power outage, the abnormal load in the air conditioner can clear the temporary abnormality and restore it to normal.

[0004] In some cases, while reset protection mechanisms can restore smart devices to normal operation, they cannot repair faults in the load itself. Therefore, when a smart device detects an abnormal status, it will issue an error message to facilitate troubleshooting as quickly as possible. Traditional troubleshooting methods require on-site service from technicians who individually power on loads and observe the abnormal loads that triggered the reset protection mechanism. However, traditional troubleshooting relies on manual experience for fault diagnosis, which is time-consuming and labor-intensive. Furthermore, it may require a second on-site service visit due to parts shortages, severely impacting the user experience. Summary of the Invention

[0005] This application provides a fault diagnosis method, apparatus, air conditioner, equipment, and storage medium to solve the problem that traditional fault diagnosis methods rely on human experience and the diagnosis process is time-consuming and labor-intensive.

[0006] To address the aforementioned technical problems, the technical solution of this application is provided through the following embodiments:

[0007] This application provides a fault diagnosis method, comprising: sequentially activating target loads in a smart device according to a pre-set load activation order; detecting the startup state of the smart device during the sequential activation of the target loads; if an abnormal reset state is detected in the startup state of the smart device, retaining a preset first flag and switching the target loads in the smart device on and off one by one; detecting the startup state of the smart device during the switching of the target loads on and off one by one; if an abnormal reset state is detected in the startup state of the smart device, retaining a preset second flag; if the startup state of the smart device is always in a normal startup state, clearing the second flag; and performing fault diagnosis based on the retention states of the first flag and the second flag.

[0008] The detection of the startup state of the smart device includes: after receiving a control command corresponding to the target load to be started, storing the memory time corresponding to the target load into a preset memory chip; starting the target load according to the control command; wherein the memory time and the startup time of the target load are separated by a preset time interval; if it is determined that the smart device has been reset, determining the startup time corresponding to the target load according to the time interval and the memory time stored in the memory chip; determining the time difference between the startup time corresponding to the target load and the reset completion time of the smart device; if the target action duration corresponding to the target load is greater than the time difference, determining that the startup state of the smart device when starting the target load is an abnormal reset state.

[0009] Wherein, the target action duration corresponding to the target load is greater than or equal to the expected action duration corresponding to the target load; wherein, the expected action duration refers to the longest time from the start of the target load to the operating state indicated by the control command.

[0010] The fault diagnosis based on the retention status of the first and second flags includes: when the first flag is retained and the second flag is cleared, generating fault diagnosis information corresponding to multiple complex faults based on preset fault description information; when the first flag is retained and the second flag is retained, generating fault diagnosis information corresponding to a single load factor based on information about the target load that causes the smart device to have an abnormal reset state during the process of switching target loads one by one.

[0011] The procedure, prior to sequentially activating the target loads in the smart device according to a pre-set load activation order, further includes: in each activation operation, sequentially activating the loads in the smart device according to the load activation order; during the sequential activation of the loads, detecting the startup status of the smart device; if an abnormal reset state is detected in the startup status of the smart device, identifying each load that has been indicated to be activated as a candidate load; wherein, after the smart device resets, the loads in the smart device are activated again according to the load activation order, and during the sequential activation of the loads, the startup status of the smart device is detected; if an abnormal reset state is detected in the startup status of the smart device for a consecutive preset number of rounds, and the load causing the abnormal reset state of the smart device is the same in each round, identifying each candidate load as a target load.

[0012] The method further includes: generating a fault prompt message when an abnormal reset state is detected in each round of the smart device's startup state; or, when an abnormal reset state is detected in both consecutive rounds of the smart device's startup state, generating a fault prompt message if it is determined that the load causing the abnormal reset state in the latter round is the same as the load causing the abnormal reset state in the former round; and deleting the generated fault prompt message if it is determined that the load causing the abnormal reset state in the latter round is different from the load causing the abnormal reset state in the former round.

[0013] This application embodiment also provides a fault diagnosis device, including: a load activation module, used to sequentially activate target loads in a smart device according to a preset load activation order; a first detection module, used to detect the startup state of the smart device during the sequential activation of target loads; if an abnormal reset state is detected in the startup state of the smart device, a preset first flag is retained and the target loads in the smart device are switched on and off one by one; a second detection module, used to detect the startup state of the smart device during the switching on and off of target loads one by one; if an abnormal reset state is detected in the startup state of the smart device, a preset second flag is retained; if the startup state of the smart device is always in a normal startup state, the second flag is cleared; and a fault diagnosis module, used to perform fault diagnosis based on the retention states of the first flag and the second flag.

[0014] This application also provides an air conditioner that applies the fault diagnosis method described in any of the above embodiments.

[0015] This application also provides a fault diagnosis device, including: at least one communication interface; at least one bus connected to the at least one communication interface; at least one processor connected to the at least one bus; and at least one memory connected to the at least one bus, wherein the processor is configured to execute a fault diagnosis program stored in the memory to implement the fault diagnosis method described above.

[0016] This application also provides a computer-readable storage medium storing computer-executable instructions, which are executed to implement the fault diagnosis method described in any of the above claims.

[0017] Compared with the prior art, the technical solution provided in this application has the following advantages: The method provided in this application can sequentially turn on the target loads in the smart device according to a preset load turn-on sequence; during the sequential turn-on of the target loads, the startup status of the smart device is detected; if an abnormal reset state is detected in the startup status of the smart device, a preset first flag is retained and the target loads in the smart device are turned on and off one by one; during the turn-on of the target loads one by one, the startup status of the smart device is detected; if an abnormal reset state is detected in the startup status of the smart device, a preset second flag is retained; if the startup status of the smart device is always in a normal startup state, the second flag is cleared; and fault diagnosis is performed based on the retention status of the first flag and the second flag. This application, through a preset load turn-on sequence and an abnormal reset detection mechanism, replaces the inefficient fault diagnosis method of traditional manual troubleshooting, reduces the dependence of fault diagnosis on manual maintenance experience, and through secondary detection and flag retention, can accurately distinguish between single load faults or multi-load combined faults, avoid misjudgment, improve the overall efficiency of fault diagnosis, and optimize the user experience. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0021] Figure 1 This is a flowchart of a fault diagnosis method according to an embodiment of this application;

[0022] Figure 2 This is a flowchart illustrating the steps for determining the target load according to an embodiment of this application;

[0023] Figure 3 This is a flowchart of the startup state detection steps according to an embodiment of this application;

[0024] Figure 4 This is a structural diagram of a smart device according to an embodiment of this application;

[0025] Figure 5 This is a structural diagram of a fault diagnosis device according to an embodiment of this application;

[0026] Figure 6 This is a structural diagram of a fault diagnosis device according to an embodiment of this application. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, 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, 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.

[0028] The following disclosure provides numerous different embodiments or examples for implementing various structures of this application. To simplify the disclosure, specific examples of components and arrangements are described below. These are merely examples and are not intended to limit the scope of this application. Furthermore, reference numerals and / or letters may be repeated in different examples. Such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0029] This application provides a fault diagnosis method. For example... Figure 1 The diagram shown is a flowchart of a fault diagnosis method according to an embodiment of this application.

[0030] Step S110: Sequentially turn on the target loads in the smart devices according to the preset load turn-on order.

[0031] The load activation sequence refers to the order in which the various loads within a smart device are activated. During the startup process, the various loads within the smart device are activated sequentially according to their activation sequence.

[0032] The types of smart devices include, but are not limited to, air conditioners. When a smart device is an air conditioner, the load within it includes, but is not limited to, compressors, fans, electromagnetic coils, and sensors.

[0033] The target load refers to the load in the smart device that needs to be diagnosed. You can set each load in the smart device as the target load, or you can set each load that was already running before the smart device was abnormally reset as the target load.

[0034] This application embodiment avoids the problem of current surges or mechanical conflicts caused by starting multiple target loads at the same time by orderly starting the target load, thereby reducing the risk of abnormality of smart devices and providing a logical basis for subsequent fault diagnosis.

[0035] Step S120: During the process of sequentially turning on the target loads, the startup status of the smart device is detected; if an abnormal reset state is detected in the startup status of the smart device, a preset first flag is retained and the target loads in the smart device are turned on and off one by one.

[0036] Startup state refers to the state of a smart device during the startup process. The types of startup states include, but are not limited to, abnormal reset state.

[0037] An abnormal reset state refers to the state in which a smart device is reset due to an abnormal load during the sequential activation of each target load.

[0038] The first flag is used to indicate that the smart device has encountered an abnormal reset state during the sequential activation of each target load.

[0039] Retaining the first flag refers to setting or retaining a pre-set first flag. Further, the first flag can be set after the smart device experiences an abnormal reset. Alternatively, the first flag can be set before sequentially starting the target loads; if the smart device experiences an abnormal reset during the sequential starting of each target load, the first flag is retained; otherwise, it is cleared. Alternatively, the first flag can be set when starting the last target load; if the smart device experiences an abnormal reset, the first flag is retained; otherwise, it is cleared.

[0040] Switching target loads one by one means turning the target loads on and off sequentially. That is, for each target load in the smart device, first turn on one target load, then turn it off, then turn on the next target load, then turn it off, and so on. Furthermore, the target loads can be switched on and off one by one according to the above-described load activation sequence.

[0041] In this embodiment, during the sequential activation of target loads, if no abnormal reset state is detected in the startup state of the smart device, it can be determined that the smart device has started normally. If an abnormal reset state is detected in the startup state of the smart device, since there is at least one target load in the activated state, the target load causing the abnormal reset of the smart device may be one or more of the already activated target loads. That is, multiple load factors may cause the abnormal reset of the smart device. Therefore, the abnormal suspicion can be recorded by retaining a first flag. Subsequently, the fault range can be quickly narrowed down by combining the operation method of switching loads one by one, avoiding the inefficiency of manual troubleshooting, and reducing secondary damage caused by continuous abnormal operation of the smart device.

[0042] Step S130: During the process of switching target loads one by one, the startup status of the smart device is detected; if an abnormal reset state is detected in the startup status of the smart device, a preset second flag is retained; if the startup status of the smart device is always in a normal startup state, the second flag is cleared.

[0043] The second flag is used to identify that during the process of switching each target load one by one, the target load of the last switch triggered an abnormal reset state of the smart device.

[0044] Retaining the second flag refers to setting a second flag or retaining a pre-set second flag. Furthermore, during the process of switching each target load on and off, a second flag is set before each target load is turned on. If the smart device experiences an abnormal reset state after turning on the target load, the second flag is retained; otherwise, it is cleared.

[0045] In this embodiment, during the process of switching each target load on and off one by one, since only one target load is activated at a time, it can be determined that the target load causing the abnormal reset of the smart device is the most recently activated target load. That is, a single load factor causes the abnormal reset of the smart device. Therefore, during fault diagnosis, the abnormal target load can be located by retaining a second flag. Thus, this embodiment can improve the accuracy of fault location and avoid misjudgment through secondary detection and flag retention.

[0046] Step S140: Perform fault diagnosis based on the retention status of the first flag and the second flag.

[0047] Fault diagnosis information is generated based on the retention status of the first and second flags.

[0048] Fault diagnostic information refers to the information used to indicate a suspected faulty load. Fault diagnostic information includes, but is not limited to, information about the suspected faulty load. The load information can be the load's identifier or a fault code associated with the load. The load identifier can be the load's name or a unique identifier.

[0049] The types of fault diagnosis information include, but are not limited to: fault diagnosis information corresponding to single load factors and fault diagnosis information corresponding to multiple load factors. A single load factor refers to a smart device reset caused by a single load. Multiple load factors refer to a smart device reset caused by a combination of problems with multiple loads.

[0050] When the first flag is retained and the second flag is cleared, fault diagnosis information corresponding to multiple load factors can be generated based on preset fault description information. The fault description information describes the abnormal state that caused the smart device to be abnormally reset. Furthermore, the fault description information pre-set for the abnormal state that caused the smart device to be abnormally reset can be queried.

[0051] For example, if only the first flag is retained, the abnormal state that causes the smart device to be abnormally reset is that the power load is greater than the preset charge threshold. The fault description information corresponding to this abnormal state is that the power load is too large. The fault diagnosis information generated based on the fault description information is: the power load is too large due to the combination of multiple load factors, which causes the device to reset.

[0052] If both the first and second flags are retained, fault diagnosis information corresponding to a single load factor can be generated based on the information of the target loads that caused the intelligent device to experience an abnormal reset during the process of switching target loads one by one. Furthermore, if both the first and second flags are retained, it can be clearly determined that a single load anomaly caused the intelligent device to be abnormally reset. Since the cause of the abnormal reset is generally the last target load to be switched on, fault diagnosis information can be generated based on the information of the last target load switched during the process of switching target loads one by one. Furthermore, the fault diagnosis information can also carry fault description information corresponding to the abnormal state that caused the intelligent device to be abnormally reset.

[0053] For example: If both the first and second flags are retained, during the process of switching the target loads one by one, the last target load to be turned on is the electromagnetic coil. The abnormal state that causes the smart device to be abnormally reset is overcurrent protection. The fault description information corresponding to overcurrent protection is load short circuit. Based on the fact that the last target load to be turned on is the electromagnetic coil and the fault type is load short circuit, the generated fault diagnosis information is that the electromagnetic coil short circuit caused the device to reset.

[0054] In this embodiment, target loads in a smart device are sequentially activated according to a pre-set load activation order. During the sequential activation of the target loads, the startup status of the smart device is detected. If an abnormal reset state is detected in the startup status of the smart device, a preset first flag is retained, and the target loads in the smart device are switched on and off one by one. During the switching on and off of the target loads one by one, the startup status of the smart device is detected. If an abnormal reset state is detected in the startup status of the smart device, a preset second flag is retained. If the startup status of the smart device is consistently in a normal startup state, the second flag is cleared. Fault diagnosis is performed based on the retention status of the first and second flags. This embodiment replaces the inefficient traditional manual troubleshooting method with a preset load activation order and abnormal reset detection mechanism, reducing the reliance on manual repair experience in fault diagnosis. Furthermore, through secondary detection and flag retention, it can accurately distinguish between single-load faults and multi-load combined faults, avoiding misjudgments, thus improving the overall efficiency of fault diagnosis and optimizing the user experience.

[0055] To make the embodiments of this application clearer, the fault diagnosis method of the embodiments of this application will be further described below.

[0056] In this embodiment of the application, before sequentially activating the target loads in the smart device, it is possible to first determine which loads in the smart device are the target loads. For example... Figure 2 The diagram shown is a flowchart of the steps for determining the target load according to an embodiment of this application.

[0057] Step S210: In each round of startup operation, the loads in the smart device are started sequentially according to the load startup order; during the sequential startup of the loads, the startup status of the smart device is detected.

[0058] The load activation sequence in this step is the same as the load activation sequence described above.

[0059] Turning on a smart device once corresponds to one round of startup operation. Each round of startup operation refers to sequentially turning on each load within the smart device according to the load startup order. Furthermore, if an abnormal reset state is detected in the smart device during the startup process, i.e., during the sequential startup of each load, the next round of startup operation will begin after the smart device has completed the reset.

[0060] The startup states of smart devices include, but are not limited to: normal startup state, normal reset state, and abnormal reset state.

[0061] Normal startup status means that the smart device operates without any abnormalities during the sequential startup of each load.

[0062] A normal reset state refers to the state in which the smart device resets due to non-fault reasons during the sequential startup of various loads. Non-fault reasons include, but are not limited to: user-initiated restarts and restarts triggered by software updates.

[0063] An abnormal reset state refers to the state in which a smart device resets due to an abnormal load during the sequential activation of various loads. Abnormal loads include, but are not limited to, load short circuits and load overloads.

[0064] Step S220: Determine whether the smart device experiences an abnormal reset state during the sequential load activation process; if yes, proceed to step S230; if no, proceed to step S260.

[0065] Step S230: If an abnormal reset state is detected in the startup state of the smart device, each load that has been indicated to be turned on is identified as a candidate load.

[0066] Candidate loads refer to loads that were already running before the smart device was abnormally reset.

[0067] After the smart device is reset, the next startup operation begins. The loads in the smart device are started up sequentially according to the load startup order. During the sequential startup of the loads, the startup status of the smart device is detected.

[0068] Step S240: Determine whether the abnormal reset state of the smart device is detected in a consecutive preset number of rounds, and whether the load that causes the smart device to experience an abnormal reset state is the same in each round; if yes, then proceed to step S250; if no, then proceed to step S260.

[0069] The load that causes a smart device to enter an abnormal reset state is usually the last load that was turned on before the smart device was abnormally reset.

[0070] Step S250: If an abnormal reset state is detected in the startup state of the smart device in a consecutive preset number of rounds, and the load that causes the abnormal reset state of the smart device is the same in each round, then each of the candidate loads is determined as the target load.

[0071] The target load is the load to be detected that causes an abnormal reset of the smart device in a preset round.

[0072] The preset number of rounds can be an empirical value or a value determined through experiments. For example, if the preset number of rounds is 3, and the smart device is abnormally reset after being turned on to the same load for three consecutive rounds, then that load and all loads preceding it can be identified as target loads. If the smart device is abnormally reset for three consecutive rounds, but the load that triggers the abnormal reset is different each time, then each load can be identified as a normal load.

[0073] If the smart device is detected to be in an abnormal reset state during startup for a series of preset rounds, it indicates that the abnormal reset state is not accidental. If the load that causes the smart device to be in an abnormal reset state is the same in each round, it means that the same load has been reproduced in multiple startup operations, which is why the load and the loads that were started before it are very likely to be the cause of the abnormal reset. Therefore, the load and the loads that were started before it are identified as the target loads.

[0074] Step S260: Determine each of the candidate loads as a normal load.

[0075] Normal load is the load that does not trigger an abnormal reset of the smart device in the preset cycle.

[0076] In this embodiment, after the smart device is reset, a new round of power-on operations is performed. In this new round, the same load activation sequence is used to ensure the reproducibility of the abnormal reset, thus providing a basis for accurately locating the abnormal load. During the reproduction process, if the smart device is abnormally reset after activating the same load, this load and all loads preceding it can be identified as target loads. Identifying the target load narrows down the scope of subsequent troubleshooting.

[0077] In this embodiment of the application, during the execution of multiple rounds of activation operations, a fault prompt operation can be performed based on the detection results.

[0078] In one scenario, a fault message can be generated each time an abnormal reset is detected in the startup state of the smart device. That is, a fault message is generated every time an abnormal reset of the smart device is detected.

[0079] In another scenario, if an abnormal reset state is detected in both the initial and final startup states of the smart device, and if it is determined that the load causing the abnormal reset state in the latter case is the same as the load causing the abnormal reset state in the former case, a fault message is generated; if it is determined that the load causing the abnormal reset state in the latter case is different from the load causing the abnormal reset state in the former case, the generated fault message is cleared.

[0080] The fault message should at least indicate information about the load that is suspected of causing the smart device to enter an abnormal reset state. This fault message can also indicate the number of consecutive cycles in which the smart device has entered an abnormal reset state. The information about the load that caused the abnormal reset state can be the identifier of the last load that was turned on before the smart device was abnormally reset, or the fault code associated with that load.

[0081] In this embodiment of the application, a fault diagnosis command can be sent to the smart device according to the fault prompt information; based on the fault diagnosis command, the target load in the smart device is turned on sequentially according to the preset load turn-on order.

[0082] The fault diagnosis instruction is used to instruct the flow of executing the fault diagnosis method of the embodiments of this application.

[0083] Furthermore, the fault message can be sent to a preset display device for display. The type of display device includes, but is not limited to, the display of a user terminal and a smart device. If an abnormal reset state is detected in the startup state of the smart device for a consecutive preset number of rounds, and the load causing the abnormal reset state is the same in each round, a preset fault self-diagnosis button can be displayed on the display device. The user can trigger the fault self-diagnosis button based on the fault message. After the fault self-diagnosis button is triggered, a fault diagnosis command is sent to the smart device. Upon receiving the fault diagnosis command, the smart device executes... Figure 1 The fault diagnosis method shown is as follows: Target loads in the smart device are sequentially turned on according to a pre-set load turn-on order; during the sequential turn-on of the target loads, the startup status of the smart device is detected; if an abnormal reset state is detected in the startup status of the smart device, a preset first flag is retained, and the target loads in the smart device are turned on and off one by one; during the turn-on of the target loads one by one, the startup status of the smart device is detected; if an abnormal reset state is detected in the startup status of the smart device, a preset second flag is retained; if the startup status of the smart device is always in a normal startup state, the second flag is cleared; fault diagnosis information is generated based on the retention status of the first flag and the second flag.

[0084] In this embodiment, the startup state of the smart device must be detected both during the determination of the target load and during the fault self-diagnosis process. The following description uses the fault self-diagnosis process as an example to illustrate the detection process of the smart device's startup state. Further, during the sequential activation of the target loads, the startup state of the smart device when activating each target load can be determined; if it is determined that the startup state of the smart device when activating a certain target load is an abnormal reset state, then it is determined that the startup state of the smart device has an abnormal reset state. The following description details the process of detecting the startup state of the smart device when activating each target load.

[0085] like Figure 3 The diagram shown is a flowchart of the startup state detection steps according to an embodiment of this application.

[0086] Step S310: After receiving the control command corresponding to the target load to be activated, the memory time corresponding to the target load is stored in a preset memory chip.

[0087] The target load to be started refers to the target load that needs to be started at present.

[0088] Control commands are used to instruct a load to be turned on in a smart device.

[0089] The memory time refers to the timestamp of the first operation information stored for the target load. In this step, the memory time can be the timestamp of the control command corresponding to the target load. The timestamp can be the system time provided by the clock chip.

[0090] The memory chip is used to record operational information for the target load. Furthermore, the memory chip is used to store at least the memory time corresponding to the target load. The memory chip can be a non-volatile storage medium, so the information in the memory chip will not be lost after the smart device is reset.

[0091] Step S320: Start the target load according to the control command; wherein, the system time and the start time of the target load are separated by a preset time interval.

[0092] If the smart device is reset when the target load is turned on, the start time of the target load cannot be stored in the memory chip. To address this issue, this application embodiment can write the memory time corresponding to the target load into the memory chip, and then turn on the target load after a preset time interval. In this way, even if the smart device is reset when the target load is turned on, the start time of the target load can be calculated based on the memory time corresponding to the target device and the time interval used. That is, the start time is equal to the memory time plus the time interval.

[0093] Step S330: Determine whether the smart device has been reset; if yes, proceed to step S340; if no, proceed to step S390.

[0094] Step S340: If it is determined that the smart device has been reset, the startup time corresponding to the target load is determined according to the time interval and the memory time stored in the memory chip.

[0095] The startup time for the target load is equal to the memory time for the target load plus the time interval used to start the target load.

[0096] Step S350: Determine the time difference between the startup time corresponding to the target load and the reset completion time of the smart device.

[0097] Reset completion time refers to the time required for a smart device to reset and restart.

[0098] Step S360: Determine whether the target action duration corresponding to the target load is greater than the time difference; if yes, proceed to step S370; if no, proceed to step S380.

[0099] The target action duration refers to the longest action duration required for the target load to start normally.

[0100] The target action duration corresponding to the target load is greater than or equal to the expected action duration corresponding to the target load; wherein, the expected action duration refers to the longest time from start-up of the target load to reaching the operating state indicated by the control command. For example, if the target load is a fan, the expected action duration of the fan is the longest time from start-up to maximum speed, which is 10 seconds.

[0101] Furthermore, for different load individuals under the same load type, the actual action time required to start varies. The historical action time corresponding to multiple load individuals of the same load type can be obtained. The historical action time refers to the measured action time required for the load to start normally. The average value of each historical action time corresponding to the load type is calculated, and the average value is determined as the expected action time corresponding to the load type.

[0102] Furthermore, considering the varying startup speeds of individual loads under different load types, the actual startup time fluctuates. Therefore, for loads in Category 1, the target startup time equals the expected startup time; for loads in Category 2, the target startup time is longer than the expected startup time. Specifically, loads in Category 1 have slower startup speeds than those in Category 2. Moreover, for loads with slow startup speeds (Category 1), the startup time fluctuation is small, allowing the expected startup time to be used as the target startup time, ensuring sensitive fault diagnosis. For loads with fast startup speeds (Category 2), the startup time fluctuation is large, so setting the target startup time to be longer than the expected startup time, using a relaxed target startup time, ensures accurate fault diagnosis.

[0103] When the target action duration corresponding to the target load is greater than the expected action duration corresponding to the target load, the target action duration = k * expected action duration + a; where k is the scaling factor and a is the control parameter. Both k and a are empirical values ​​or values ​​obtained through experiments. For example, if k is 2 and a is 5, then the target action duration = 2 * expected action duration + 5.

[0104] If the target action duration corresponding to the target load is greater than the time difference, it means that the smart device has already reset before the target load reaches the maximum action duration required for normal startup. This situation may be due to a reset caused by an abnormal target load, i.e., the smart device has been abnormally reset. Therefore, it is necessary to prioritize troubleshooting the target load whose target action duration is greater than the time difference. For example, after the target load is turned on, it may mechanically jam, causing the smart device's reset protection mechanism to be triggered before the target load has completed the startup operation.

[0105] If the duration of the target action corresponding to the target load is less than or equal to the time difference, it means that the smart device only completed the reset after the target load was normally started. This situation may be a normal reset performed by the smart device. For example, a reset caused by software-level reasons.

[0106] Step S370: If the duration of the target action corresponding to the target load is greater than the time difference, determine that the startup state of the smart device when the target load is turned on is an abnormal reset state.

[0107] If it is determined that the intelligent device is in an abnormal reset state when activating the current target load, then the abnormal reset state of the intelligent device's startup state is confirmed. At this point, during the fault diagnosis phase, control commands for the next target load to be activated can be received; during the target fault determination phase, the next round of detection can begin.

[0108] Step S380: If the target action duration corresponding to the target load is less than or equal to the time difference, determine that the startup state of the smart device when the target load is turned on is a normal reset state.

[0109] Step S390: If it is determined that the smart device has not been reset, the startup state of the smart device when the target load is turned on is determined to be a normal startup state.

[0110] In this embodiment of the application, the startup state detection step in the process of determining the target load can refer to Figure 3 The steps are simple: just replace the target load with the load.

[0111] In this embodiment of the application, after the process of sequentially turning on the target load and turning on and off the target load one by one is completed, fault diagnosis information can be generated based on the retention status of the first flag and the second flag.

[0112] When the first flag is retained and the second flag is cleared, fault diagnosis information corresponding to multiple complex factors is generated based on preset fault description information.

[0113] When the first flag is retained and the second flag is retained, fault diagnosis information corresponding to a single load factor is generated based on the information of the target load that causes the smart device to enter an abnormal reset state during the process of switching target loads one by one.

[0114] The following provides a specific example to describe the fault diagnosis method of this application embodiment. For example... Figure 4 The diagram shown is a structural diagram of a smart device according to an embodiment of this application.

[0115] The smart device includes: a microcontroller unit (MCU), multiple loads, a memory chip, and a clock chip.

[0116] The memory chip is a non-volatile storage chip that can communicate with a microprocessor. The microprocessor can write data to the memory chip, and the data can be retained even when the smart device loses power.

[0117] The clock chip has a timekeeping function and can communicate with the microprocessor to provide the microprocessor with system time.

[0118] When the smart device is an air conditioner, the types of loads include, but are not limited to: compressor, fan, four-way valve, and electronic expansion valve.

[0119] The microprocessor can also receive multiple input signals, including but not limited to: ambient temperature, exhaust temperature, and outer pipe temperature.

[0120] In this embodiment, the microprocessor is the execution entity. When the power-on conditions are met, the microprocessor, according to a preset load activation sequence, first activates the electronic expansion valve, then the fan after 10 seconds, then the compressor after 30 seconds, and finally the four-way valve (e.g., for heating) after 60 seconds. After activating all loads, the fan, compressor, and electronic expansion valve can be continuously adjusted. When the microprocessor detects that the intelligent device meets the power-off conditions, it gradually shuts down all loads according to a preset load shutdown sequence. In addition to the aforementioned loads, the intelligent device may also have electric heating elements, such as the compressor's electric heating element and the chassis's electric heating element, which will switch on and off based on input outer loop and exhaust conditions. Therefore, the electric heating element can be added as an option to the load activation sequence.

[0121] As smart devices are used continuously, the load gradually ages, and this aging load is prone to malfunctions during the smart device's startup phase, causing the smart device to be abnormally reset. Since resetting can only temporarily resolve the load malfunction and cannot eliminate the root cause of the load failure, the microprocessor can execute the steps of the fault diagnosis method of this application embodiment based on the above-described structural diagram of the smart device:

[0122] Step 1: To detect abnormal loads, we can first proceed to the target load determination stage. In this stage, each load can be activated sequentially. For each load to be activated, before activating it, the control command instructing the load to be activated is written to the memory chip, and the system time (memory time) provided by the clock chip is recorded, forming a record entry corresponding to that load. Timing starts from this memorized time, and when a preset time interval is reached, the load is activated. Furthermore, we can reread the memory chip to check if the control command and memorized time have been successfully written. If they have been successfully written and the preset time interval (e.g., 1 second) has elapsed, the load is activated.

[0123] Step 2: If the microprocessor continues to operate normally after the load is enabled, the load's enabled status can be written to the corresponding record entry in the memory chip, and the system time at this time can also be recorded as the load's enabled time. If the unit (intelligent device) is reset and restarted after the load is enabled, the memory entries stored in the memory chip are read to form the record information corresponding to this round of startup operations. The record information includes the control commands, memory times, enabled status, and enabled times corresponding to all loads before the unit was reset. Since the last enabled load may not have completed enabling before the unit is reset, the record entry for the last enabled load may only include the control commands and memory times corresponding to that last enabled load.

[0124] Step 3: In the case of the unit being reset and restarted, calculate the sum of the memory time and the preset time interval corresponding to the last load that was turned on, and obtain the start time corresponding to the last load that was turned on; and calculate the time difference Trest between the start time and the unit's reset completion time.

[0125] Each load has a corresponding expected action time TMAX after it is turned on to reach its maximum power or complete the action instructed by the control command. Since the actual action time of the load fluctuates, the target action time can be set to TMAX*2+5. ​​For example, an electric heating strip, being a purely resistive load, theoretically reaches its maximum power within 1 second (expected action time) after receiving a control command. Considering the fluctuation of its actual action time, the target action time for the electric heating strip can be set to 7 seconds (1*2+5=7). Another example is an adjustable fan. Theoretically, it takes 10 seconds (expected action time) to reach its maximum speed after receiving a control command. Considering the fluctuation of its actual action time, the target action time for the fan can be set to 25 seconds (10*2+5=25).

[0126] Step 4: Determine whether the target action duration corresponding to the last load to be started is greater than the time difference Trest. If so, it means that the unit has already completed the reset before the load is started, and the unit's reset is considered to be an abnormal reset. Thus, it can be determined that the unit's startup state when starting the load is an abnormal reset state. Otherwise, the reset is considered to be a normal reset.

[0127] Step 5: Each time the unit resets, a new record will be generated. If the unit is determined to be abnormally reset in 3 consecutive records (i.e., the intelligent device is detected to be in an abnormal reset state in a consecutive preset number of rounds), and the load corresponding to the last record entry in each record is the same load, then the load and all loads that were turned on before the load are taken as the target load.

[0128] Furthermore, a fault message can be generated based on the load information and sent to the corresponding display device of the smart device. Specifically, the fault code associated with the load information can be identified, and a fault message carrying that fault code can be generated. After seeing the fault message, the user can choose to report a problem or continue using the smart device; the microprocessor will not shut down due to the abnormal record. If the load does not trigger a unit reset again after the smart device is turned on next time, the fault message can be cleared.

[0129] Step 6: In addition to reporting a problem, users can also choose to enter the fault self-diagnosis process. The fault self-diagnosis process includes a sequential detection phase and a single-load detection phase. In the sequential detection phase, each target load can be activated sequentially according to the load activation order described above. When the last target load is activated, a first flag TEST_ALL is set in the memory chip. If the unit is not reset after the target action time corresponding to the target load is reached, the first flag TEST_ALL is canceled; if the unit is reset, the first flag TEST_ALL is retained and the single-load detection phase begins.

[0130] Furthermore, while canceling the first flag TEST_ALL, the already generated fault message is also canceled.

[0131] Step 7: In the single load detection phase, each target load is switched on and off one by one: When a target load is turned on, the second flag TEST_SINGLE is recorded in the memory chip. If the unit is not reset after the target action time corresponding to the target load is reached, the second flag TEST_SINGLE is canceled and the target load is turned off; if the unit is reset, the second flag TEST_SINGLE is retained and the process proceeds to the next step.

[0132] Step 8: Generate fault diagnosis information based on the recorded results of the first flag TEST_ALL and the second flag TEST_SINGLE.

[0133] If both the first flag TEST_ALL and the second flag TEST_SINGLE are present, it indicates that the unit reset was caused by the last load that was turned on during the single load detection phase. Fault diagnosis information can be generated based on the fault code corresponding to the last load that was turned on, so that maintenance personnel can clearly know which load triggered the reset. This allows them to prepare the relevant after-sales parts for the load before on-site service for easy replacement and repair.

[0134] If only the first flag TEST_ALL is present, it may be a reset caused by multiple loads combined, such as a reset caused by excessive power supply load. In this case, fault diagnosis information can be generated to indicate a multi-target load combined fault, so as to prompt maintenance personnel to conduct further on-site analysis.

[0135] If neither the first flag TEST_ALL nor the second flag TEST_SINGLE is present, it indicates that the fault message generated during the target load determination phase may be a false alarm, and the fault message can be canceled.

[0136] This application also provides a fault diagnosis device. For example... Figure 5 The diagram shown is a structural diagram of a fault diagnosis device according to an embodiment of this application.

[0137] The fault diagnosis device includes:

[0138] The load activation module 510 is used to sequentially activate the target loads in the smart device according to a pre-set load activation sequence.

[0139] The first detection module 520 is used to detect the startup status of the smart device during the process of sequentially turning on the target load; if an abnormal reset state is detected in the startup status of the smart device, a preset first flag is retained and the target loads in the smart device are turned on and off one by one.

[0140] The second detection module 530 is used to detect the startup status of the smart device during the process of switching target loads one by one; if an abnormal reset state is detected in the startup status of the smart device, a preset second flag is retained; if the startup status of the smart device is always detected to be a normal startup state, the second flag is cleared.

[0141] The fault diagnosis module 540 is used to perform fault diagnosis based on the retention status of the first flag and the second flag.

[0142] The functions of the apparatus described in this application embodiment have been described in the above method embodiments. Therefore, for any parts not detailed in the description of this embodiment, please refer to the relevant descriptions in the foregoing embodiments, which will not be repeated here.

[0143] This application also provides an air conditioner. In this application embodiment, the air conditioner applies the above-described fault diagnosis method.

[0144] This application also provides a fault diagnosis device, such as... Figure 6 The diagram shown is a structural diagram of a fault diagnosis device according to an embodiment of this application.

[0145] The fault diagnosis device includes a processor 610, a communication interface 620, a memory 630, and a communication bus 640. The processor 610, communication interface 620, and memory 630 communicate with each other via the communication bus 640.

[0146] The memory 630 is used to store computer programs.

[0147] In one embodiment of this application, when the processor 610 executes the program stored in the memory 630, it implements the fault diagnosis method provided in any of the foregoing method embodiments, including: sequentially turning on target loads in the smart device according to a preset load turn-on order; detecting the startup state of the smart device during the sequential turn-on of the target loads; if an abnormal reset state is detected in the startup state of the smart device, retaining a preset first flag and turning on and off the target loads in the smart device one by one; detecting the startup state of the smart device during the turn-on of the target loads one by one; if an abnormal reset state is detected in the startup state of the smart device, retaining a preset second flag; if the startup state of the smart device is always in a normal startup state, clearing the second flag; and performing fault diagnosis based on the retention states of the first flag and the second flag.

[0148] The detection of the startup state of the smart device includes: after receiving a control command corresponding to the target load to be started, storing the memory time corresponding to the target load into a preset memory chip; starting the target load according to the control command; wherein the memory time and the startup time of the target load are separated by a preset time interval; if it is determined that the smart device has been reset, determining the startup time corresponding to the target load according to the time interval and the memory time stored in the memory chip; determining the time difference between the startup time corresponding to the target load and the reset completion time of the smart device; if the target action duration corresponding to the target load is greater than the time difference, determining that the startup state of the smart device when starting the target load is an abnormal reset state.

[0149] Wherein, the target action duration corresponding to the target load is greater than or equal to the expected action duration corresponding to the target load; wherein, the expected action duration refers to the longest time from the start of the target load to the operating state indicated by the control command.

[0150] The fault diagnosis based on the retention status of the first and second flags includes: when the first flag is retained and the second flag is cleared, generating fault diagnosis information corresponding to multiple complex factors based on preset fault description information; when the first flag is retained and the second flag is retained, generating fault diagnosis information corresponding to a single load factor based on information about the target load that causes the smart device to have an abnormal reset state during the process of switching target loads one by one.

[0151] The procedure, prior to sequentially activating the target loads in the smart device according to a pre-set load activation order, further includes: in each activation operation, sequentially activating the loads in the smart device according to the load activation order; during the sequential activation of the loads, detecting the startup status of the smart device; if an abnormal reset state is detected in the startup status of the smart device, identifying each load that has been indicated to be activated as a candidate load; wherein, after the smart device resets, the loads in the smart device are activated again according to the load activation order, and during the sequential activation of the loads, the startup status of the smart device is detected; if an abnormal reset state is detected in the startup status of the smart device for a consecutive preset number of rounds, and the load causing the abnormal reset state of the smart device is the same in each round, identifying each candidate load as a target load.

[0152] The method further includes: generating a fault prompt message when an abnormal reset state is detected in each round of the smart device's startup state; or, when an abnormal reset state is detected in both consecutive rounds of the smart device's startup state, generating a fault prompt message if it is determined that the load causing the abnormal reset state in the latter round is the same as the load causing the abnormal reset state in the former round; and deleting the generated fault prompt message if it is determined that the load causing the abnormal reset state in the latter round is different from the load causing the abnormal reset state in the former round.

[0153] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the fault diagnosis method provided in any of the foregoing method embodiments. Since the fault diagnosis method has already been described in detail above, any omissions in the description of this embodiment can be found in the relevant descriptions in the foregoing embodiments, and will not be repeated here.

[0154] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0155] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software plus a general-purpose hardware platform, or of course, using hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0156] It should be understood that the terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “described” as used herein may also include the plural forms. The terms “comprising,” “including,” “containing,” and “having” are inclusive and therefore indicate the presence of the stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not construed as requiring them to be performed in a particular order described or illustrated unless the order of performance is explicitly indicated. It should also be understood that additional or alternative steps may be used.

[0157] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A fault diagnosis method, characterized in that, include: The target loads in the smart devices are turned on sequentially according to the pre-set load activation order. During the process of sequentially starting the target load, the startup status of the smart device is detected; If an abnormal reset state is detected in the startup state of the smart device, a preset first flag is retained and the target load in the smart device is switched on and off one by one. During the process of switching on and off target loads one by one, the startup status of the smart device is detected; If an abnormal reset state is detected in the startup state of the smart device, a preset second flag is retained; If the smart device is detected to be in a normal startup state at all times, the second flag is cleared; Fault diagnosis is performed based on the retention status of the first and second flags.

2. The method according to claim 1, characterized in that, The detection of the startup status of the smart device includes: After receiving the control command corresponding to the target load to be activated, the memory time corresponding to the target load is stored in a preset memory chip; The target load is activated according to the control command; wherein, the memory time and the activation time of the target load are separated by a preset time interval; If it is determined that the smart device has been reset, the startup time corresponding to the target load is determined based on the time interval and the memory time stored in the memory chip. Determine the time difference between the startup time corresponding to the target load and the reset completion time of the smart device; If the duration of the target action corresponding to the target load is greater than the time difference, the startup state of the smart device when the target load is turned on is determined to be an abnormal reset state.

3. The method according to claim 2, characterized in that, The target action duration corresponding to the target load is greater than or equal to the expected action duration corresponding to the target load; wherein, the expected action duration refers to the longest time from the start of the target load to the operating state indicated by the control command.

4. The method according to claim 1, characterized in that, The fault diagnosis based on the retention status of the first and second flags includes: When the first flag is retained and the second flag is cleared, fault diagnosis information corresponding to multiple load factors is generated based on preset fault description information. When the first flag is retained and the second flag is retained, fault diagnosis information corresponding to a single load factor is generated based on the information of the target load that causes the smart device to enter an abnormal reset state during the process of switching target loads one by one.

5. The method according to claim 1, characterized in that, Before sequentially activating the target loads in the smart devices according to a pre-set load activation order, the process further includes: In each round of startup operation, the loads in the smart device are started sequentially according to the load startup order; during the sequential startup of the loads, the startup status of the smart device is detected. If an abnormal reset state is detected in the startup state of the smart device, each load that has been indicated to be turned on is identified as a candidate load; wherein, after the smart device is reset, the loads in the smart device are turned on again in the order of load turn-on, and the startup state of the smart device is detected during the sequential turn-on process. If an abnormal reset state is detected in the startup state of the smart device in a consecutive preset number of rounds, and the load that causes the abnormal reset state of the smart device is the same in each round, each of the candidate loads is determined as the target load.

6. The method according to claim 5, characterized in that, The method further includes: If an abnormal reset state is detected in the startup state of the smart device in each round, a fault prompt message is generated; or, If an abnormal reset state is detected in both the initial and final startup states of the smart device, and if it is determined that the load causing the abnormal reset state in the latter case is the same as the load causing the abnormal reset state in the former case, a fault message is generated; if it is determined that the load causing the abnormal reset state in the latter case is different from the load causing the abnormal reset state in the former case, the generated fault message is cleared.

7. A fault diagnosis device, characterized in that, include: The load activation module is used to sequentially activate the target loads in the smart devices according to a pre-set load activation order. The first detection module is used to detect the startup status of the smart device during the process of sequentially starting the target load; If an abnormal reset state is detected in the startup state of the smart device, a preset first flag is retained and the target load in the smart device is switched on and off one by one. The second detection module is used to detect the startup status of the smart device during the process of switching the target loads one by one. If an abnormal reset state is detected in the startup state of the smart device, a preset second flag is retained; If the smart device is detected to be in a normal startup state at all times, the second flag is cleared; The fault diagnosis module is used to perform fault diagnosis based on the retention status of the first flag and the second flag.

8. An air conditioner, characterized in that, The air conditioner uses the fault diagnosis method according to any one of claims 1-6.

9. A fault diagnosis device, characterized in that, include: At least one communication interface; At least one bus connected to the at least one communication interface; At least one processor connected to the at least one bus; At least one memory connected to the at least one bus, wherein the processor is configured to execute a fault diagnosis program stored in the memory to implement the fault diagnosis method according to any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which are executed to implement the fault diagnosis method according to any one of claims 1-6.

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