Work state detection method, device, apparatus and storage medium

Abstract

The application discloses a working state detection method, device and equipment and a storage medium, and belongs to the field of intelligent electrical appliances. The method comprises the following steps: a plurality of output powers and a plurality of specified performance parameters of an intelligent electrical appliance in a preset working time period are acquired first, then a first state curve is determined according to the plurality of output powers and the plurality of specified performance parameters, then the first state curve is matched with a plurality of second state curves respectively to obtain a matching result, and finally the working state of a power device in the intelligent electrical appliance is determined according to the matching result. The first state curve is a corresponding relationship curve of the output power and the specified performance parameter of the intelligent electrical appliance, and the plurality of second state curves are corresponding relationship curves of the output power and the specified performance parameter of sample intelligent electrical appliances under different working conditions, wherein the working conditions comprise an input voltage and an aging degree. In this way, the working state of the power device in the intelligent electrical appliance can be detected.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent electrical appliances, and in particular to a working state detection method and device, equipment and a storage medium. BACKGROUND

[0002] With the continuous improvement of people's daily life quality, intelligent electrical appliances have been widely used in people's daily life. Intelligent electrical appliances include power devices, and appropriate power control strategies can be used to control the power devices to adjust the performance parameters of the intelligent electrical appliances and realize the related functions of the intelligent electrical appliances. However, the power devices include abnormal working states such as voltage fluctuation, aging degree and failure. If the same power control strategy is used to control the power devices in different working states, the performance parameters of the intelligent electrical appliances may be different, and the performance parameters directly affect the working performance of the intelligent electrical appliances. For example, when the intelligent electrical appliance is a water dispenser, the performance parameter is the working temperature. If the same power control strategy is used to control the power device in the water dispenser in different working states, the working temperature of the water dispenser may be different, and the working temperature that is too high may cause safety hazards, and the working temperature that is too low may not achieve the expected effect.

[0003] Since the performance parameters directly affect the working performance of the intelligent electrical appliances, it is of great significance to detect the working state of the power device before using the power control strategy to control the power device. However, there is little research on the detection method of the working state of the power device in the prior art. SUMMARY

[0004] The present application provides a working state detection method, device, equipment and storage medium, which can realize the detection of the working state of the power device. The technical solution is as follows:

[0005] In a first aspect, a working state detection method is provided, the method comprising:

[0006] obtaining a plurality of output powers and a plurality of specified performance parameters of an intelligent electrical appliance in a preset working time period;

[0007] determining a first state curve according to the plurality of output powers and the plurality of specified performance parameters, the first state curve being a corresponding relationship curve of the output power and the specified performance parameter of the intelligent electrical appliance;

[0008] matching the first state curve with a plurality of second state curves respectively to obtain a matching result, the plurality of second state curves being corresponding relationship curves of the output power and the specified performance parameter of a sample intelligent electrical appliance under different working conditions, the working conditions including input voltage and aging degree;

[0009] According to the matching result, a working state of a power device in the smart electrical appliance is determined, the working state being a normal state or an abnormal state, the abnormal state including one or more of a voltage fluctuation state, an aging state, and a fault state.

[0010] As an example, the determining, according to the matching result, of the working state of the power device in the smart electrical appliance includes:

[0011] If the matching result is that the first state curve matches a target second state curve in the plurality of second state curves, the working state is determined as the normal state or as one or more of the voltage fluctuation state and the aging state according to the target second state curve;

[0012] If the matching result is that the first state curve does not match any second state curve in the plurality of second state curves, the working state is determined as the fault state.

[0013] As an example, the determining, according to the target second state curve, of the working state as the normal state or as one or more of the voltage fluctuation state and the aging state includes:

[0014] A working condition corresponding to the target second state curve is determined, the working condition corresponding to the target second state curve including a target input voltage and a target aging degree;

[0015] If a difference between the target input voltage and a standard voltage is greater than or equal to a voltage threshold, the working state is determined as the voltage fluctuation state;

[0016] If the target aging degree is greater than or equal to an aging degree threshold, the working state is determined as the aging state;

[0017] If the difference between the target input voltage and the standard voltage is less than the voltage threshold, and the target aging degree is less than the aging degree threshold, the working state is determined as the normal state.

[0018] As an example, the matching, respectively, of the first state curve with a plurality of second state curves to obtain a matching result includes:

[0019] A curve feature of the first state curve is determined;

[0020] The curve feature of the first state curve is matched with a curve feature of each second state curve in the plurality of second state curves to obtain a similarity between the first state curve and each second state curve in the plurality of second state curves;

[0021] if the similarity between the first state curve and each of the plurality of second state curves is less than a similarity threshold, determining that the first state curve does not match any of the plurality of second state curves;

[0022] if the similarity between the first state curve and a target second state curve of the plurality of second state curves is greater than or equal to the similarity threshold, determining that the first state curve matches the target second state curve, the target second state curve being the second state curve of the plurality of second state curves having the greatest similarity to the first state curve.

[0023] As an example, before the matching the curve feature of the first state curve with the curve feature of each of the plurality of second state curves, the method further comprises:

[0024] obtaining a feature data set, the feature data set comprising the curve feature of each of the plurality of second state curves and the working condition corresponding to each of the plurality of second state curves.

[0025] As an example, before the obtaining the feature data set, the method further comprises:

[0026] determining the plurality of second state curves, the plurality of second state curves being the corresponding relationship curves of the output power and the specified performance parameter of the sample intelligent electric appliance under different working conditions;

[0027] determining the curve feature of each of the plurality of second state curves;

[0028] storing the curve feature of each of the plurality of second state curves and the working condition corresponding to each of the plurality of second state curves into the feature data set.

[0029] As an example, after the determining the working state of the power device in the intelligent electric appliance according to the matching result, the method further comprises:

[0030] adjusting the power control strategy of the power device according to the working state, so as to adjust the specified performance parameter of the intelligent electric appliance.

[0031] In a second aspect, a working state detection device is provided, the device comprising:

[0032] a first obtaining module, configured to obtain a plurality of output powers and a plurality of specified performance parameters of the intelligent electric appliance within a preset working time period;

[0033] The first determining module is used to determine a first state curve based on the plurality of output powers and the plurality of specified performance parameters, wherein the first state curve is a curve showing the correspondence between the output power and the specified performance parameters of the smart appliance.

[0034] The matching module is used to match the first state curve with multiple second state curves respectively to obtain the matching result. The multiple second state curves are the corresponding relationship curves between the output power and specified performance parameters of the sample smart appliance under different working conditions. The working conditions include input voltage and aging degree.

[0035] The second determining module is used to determine the operating state of the power device in the smart appliance based on the matching result. The operating state is either a normal state or an abnormal state. The abnormal state includes one or more of the following: voltage fluctuation state, aging state, and fault state.

[0036] As an example, the second determining module is further configured to determine the working state as the normal state, or as one or more of the voltage fluctuation state and the aging state, based on the target second state curve if the matching result is that the first state curve matches the target second state curve among the plurality of second state curves;

[0037] If the matching result is that the first state curve does not match any of the multiple second state curves, then the working state is determined to be the fault state.

[0038] As an example, the second determining module is also used to determine the operating conditions corresponding to the target second state curve, the operating conditions corresponding to the target second state curve including the target input voltage and the target aging degree;

[0039] If the difference between the target input voltage and the standard voltage is greater than or equal to the voltage threshold, then the operating state is determined to be the voltage fluctuation state.

[0040] If the target aging degree is greater than or equal to the aging degree threshold, then the working state is determined to be the aging state;

[0041] If the difference between the target input voltage and the standard voltage is less than the voltage threshold, and the target aging degree is less than the aging degree threshold, then the working state is determined to be the normal state.

[0042] As an example, the matching module is also used to determine the curve characteristics of the first state curve;

[0043] The curve features of the first state curve are matched with the curve features of each of the plurality of second state curves to obtain the similarity between the first state curve and each of the plurality of second state curves.

[0044] If the similarity between the first state curve and each of the plurality of second state curves is less than the similarity threshold, then it is determined that the first state curve does not match any of the plurality of second state curves.

[0045] If the similarity between the first state curve and the target second state curve among the plurality of second state curves is greater than or equal to the similarity threshold, then the first state curve is determined to match the target second state curve, wherein the target second state curve is the second state curve among the plurality of second state curves that has the greatest similarity to the first state curve.

[0046] As an example, the device further includes a second acquisition module for acquiring a feature dataset, the feature dataset including the curve features of each of the plurality of second state curves, and the operating conditions corresponding to each of the plurality of second state curves.

[0047] As an example, the device further includes a third determining module, a fourth determining module, and a storage module:

[0048] The third determining module is used to determine the plurality of second state curves, which are the curves showing the correspondence between the output power and specified performance parameters of the sample smart appliance under different working conditions;

[0049] The fourth determining module is used to determine the curve characteristics of each of the plurality of second state curves;

[0050] The storage module is used to store the curve features of each of the multiple second state curves, as well as the corresponding working conditions of each second state curve, into the feature dataset.

[0051] As an example, the device further includes an adjustment module for adjusting the power control strategy of the power device according to the operating state, so as to adjust the specified performance parameters of the smart appliance.

[0052] Thirdly, a computer device is provided, the computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the above-described working state detection method.

[0053] Fourthly, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described working state detection method.

[0054] The beneficial effects of the technical solutions provided in this application are:

[0055] In this embodiment, multiple output powers and multiple specified performance parameters of the smart appliance within a preset working time period can be acquired first. Then, based on these multiple output powers and specified performance parameters, a first state curve is determined. The first state curve is a correspondence curve between the smart appliance's output power and the specified performance parameters. The first state curve is then matched with multiple second state curves to obtain matching results. Based on the matching results, the operating state of the power devices in the smart appliance is determined. The multiple second state curves are correspondence curves between the sample smart appliance's output power and specified performance parameters under different working conditions. Working conditions include input voltage and aging degree, and the operating state is either normal or abnormal. Abnormal states include one or more of voltage fluctuation, aging, and fault states. Thus, the operating state of the power devices in the smart appliance can be detected by matching the acquired correspondence curve between the smart appliance's output power and specified performance parameters with the multiple correspondence curves between the sample smart appliance's output power and specified performance parameters under different working states. Moreover, this detection method acquires the smart appliance's output power and specified performance parameters in real time during operation, and the operating state of the power devices can be determined based on these output power and specified performance parameters. The detection method is simple and highly efficient. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a flowchart of a working status detection method provided in an embodiment of this application;

[0058] Figure 2 This is a schematic diagram of the structure of a working status detection device provided in an embodiment of this application;

[0059] Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0061] It should be understood that "multiple" as mentioned in this application refers to two or more. In the description of this application, unless otherwise stated, " / " indicates "or," for example, A / B can mean A or B; "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist, for example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, to facilitate a clear description of the technical solutions of this application, the terms "first," "second," etc., are used to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or execution order, and that "first," "second," etc., do not necessarily imply differences.

[0062] Before providing a detailed explanation of the embodiments of this application, the application scenarios of these embodiments will be described first.

[0063] The operating state detection method provided in this application can be applied to scenarios where the operating state of power devices in smart appliances is detected. Here, the smart appliance is defined as an appliance that uses a suitable power control strategy to control the power devices, thereby modulating the performance parameters of the smart appliance and realizing its related functions. This application does not limit the scope of the smart appliance.

[0064] For example, a smart appliance might be a water dispenser, with its performance parameter being its operating temperature. The power devices in a water dispenser can operate in normal or abnormal states. Abnormal states can include one or more of the following: voltage fluctuations, aging, and malfunctions. If the same power control strategy is used to control these power devices under different operating states, the water dispenser's operating temperature may vary. The operating temperature directly affects the performance of the smart appliance.

[0065] For example, the operating state of power devices includes voltage fluctuation. If a fixed power control strategy is used, the output power of the power device may fluctuate wildly, making it difficult to accurately control the water dispenser's operating temperature and thus hindering the precise functioning of the appliance. Furthermore, the fluctuating output power could lead to uncontrolled temperature, posing a safety hazard such as scalding to users or causing a fire. Alternatively, the operating state of power devices may include aging. Under different aging conditions, a fixed power control strategy may result in varying output power. These differences in output power will lead to varying operating temperatures in the water dispenser. Excessive operating temperature may pose a safety hazard, while insufficient operating temperature may fail to achieve the desired effect.

[0066] Therefore, monitoring the operating status of smart appliances such as water dispensers is of great significance. For example, by monitoring the operating status of a water dispenser, the power control strategy of the power devices within the dispenser can be adjusted accordingly, thereby adjusting the operating temperature to meet the expected results.

[0067] It should be noted that the smart appliance mentioned above is a water dispenser, and the performance parameter of operating temperature is only an example and not a limitation on smart appliances and performance parameters. For example, a smart appliance could also be a fan, and the corresponding performance parameter could be speed.

[0068] Next, the working status detection method provided in the embodiments of this application will be described.

[0069] Please refer to Figure 1 , Figure 1 This is a flowchart of a working status detection method provided in an embodiment of this application. The working status detection method provided in this embodiment of the application can be applied to a first device, which can be a terminal device or a server. The first device can be installed in the smart appliance to be detected or can be installed outside the smart appliance to be detected. This embodiment of the application does not limit the installation location of the first device.

[0070] Please see Figure 1 The method includes the following steps:

[0071] Step 101: The first device acquires multiple output powers and multiple specified performance parameters of the smart appliance within a preset working time period.

[0072] Among them, smart appliances may include power devices. Appropriate power control strategies can be used to control the power devices in smart appliances to adjust the performance parameters of smart appliances and realize the relevant functions of smart appliances.

[0073] Power devices are mainly used in electronic power switching, power conversion, power amplification, or line protection, and are essential electronic components in power control circuits and power switching circuits. Power devices can be MOSFETs (Metal-Oxide-Semiconductor Field-Effect Transistors) or IGBTs (Insulated Gate Bipolar Transistors), etc.

[0074] The specified performance parameters reflect the working performance of the smart appliance. Different smart appliances may have different specified performance parameters. For example, a water dispenser might have its operating temperature as its specified performance parameter, enabling it to heat the water. Conversely, a fan might have its speed as its specified performance parameter, enabling it to generate air for cooling.

[0075] The preset working time period can refer to any continuous time period during which the smart appliance is working, such as 10ms or 20ms. Alternatively, the preset working time period can also include multiple non-contiguous time periods; this application embodiment does not limit the preset working time period. For example, the smart appliance can be a water dispenser, and the preset working time period can refer to any 20ms time period during which the water dispenser is working. For ease of explanation, the preset working time period in this application embodiment can be referred to as the first preset working time period.

[0076] The smart appliance may also include a data acquisition circuit, which is used to acquire the operating parameters of the smart appliance. This acquisition circuit can be an existing circuit within the smart appliance or an additional circuit added to it. The operating parameters can be the smart appliance's operating voltage, operating current, or specified performance parameters. Thus, the first device can obtain multiple output powers and multiple specified performance parameters of the smart appliance within a preset operating time period through the acquisition circuit.

[0077] As an example, the acquisition circuit can be one or more of the following: voltage acquisition circuit, current acquisition circuit, and specified performance parameter acquisition circuit.

[0078] For example, a smart appliance includes a voltage acquisition circuit. This circuit can acquire multiple output voltages within a preset operating time period when a first preset condition is met. A first device can acquire these multiple output voltages and determine the multiple output powers of the smart appliance within the preset operating time period based on these voltages. The first preset condition can be a preset time interval or the smart appliance transitioning from a power-off state to a power-on state.

[0079] Alternatively, the smart appliance includes a current acquisition circuit, which can acquire multiple output currents within a preset working time period when a first preset condition is met. The first device can acquire the multiple output currents acquired by the current acquisition circuit and determine the multiple output powers of the smart appliance within the preset working time period based on the multiple output currents.

[0080] Of course, smart appliances can also include voltage acquisition circuits and current acquisition circuits. The first device can determine multiple output powers based on multiple output voltages acquired by the voltage acquisition circuit and multiple output currents acquired by the current acquisition circuit.

[0081] For example, a smart appliance includes a specified performance parameter acquisition circuit. This circuit can acquire multiple specified performance parameters over a preset operating time period when a first preset condition is met. The first device can then acquire these multiple specified performance parameters acquired by the circuit. The specified performance parameter could be operating temperature, and the acquisition circuit could be a temperature acquisition circuit. Alternatively, the specified performance parameter could be rotational speed, and the acquisition circuit could be a rotational speed acquisition circuit.

[0082] As an example, a smart appliance may include a first device, which may be an embedded processor with processing capabilities. The first device and the acquisition circuit can be connected via wired or wireless means. The first device can acquire the operating parameters of the smart appliance collected by the acquisition circuit through wired or wireless communication, and thereby determine multiple output powers and multiple specified performance parameters based on the operating parameters of the smart appliance. Alternatively, the first device may be located outside the smart appliance, which may also include a communication device that enables wireless or wired communication. The first device can acquire the operating parameters of the smart appliance collected by the acquisition circuit through the communication device, and thereby determine multiple output powers and multiple specified performance parameters based on the operating parameters of the smart appliance.

[0083] As an example, the first device can also acquire multiple output powers and multiple specified performance parameters of the smart appliance within a preset working time period, and then acquire multiple output powers and multiple specified performance parameters again at each preset working time period. For example, the first device can first acquire multiple output powers and multiple specified performance parameters of the smart appliance within a first preset working time period, and then continue to acquire multiple output powers and multiple specified performance parameters of the smart appliance within a second preset working time period at each preset working time period, so as to continuously determine the current working state of the power device in the smart device based on the current output power and specified performance parameters of the smart appliance.

[0084] Step 102: The first device determines a first state curve based on multiple output powers and multiple specified performance parameters. The first state curve is the correspondence curve between the output power of the smart appliance and the specified performance parameters.

[0085] The first device can plot a first state curve in a two-dimensional coordinate system based on multiple output powers and multiple specified performance parameters, where the X-axis of the two-dimensional coordinate system represents the output power and the Y-axis represents the specified performance parameters.

[0086] As an example, the first device can first plot points in a two-dimensional coordinate system based on multiple output powers and multiple specified performance parameters to obtain multiple plotted points, and then perform curve fitting on the multiple plotted points to obtain a first state curve. The curve fitting method can be least squares method, polynomial curve fitting, or Bézier curve fitting, etc. This application does not limit the method for determining the first state curve based on multiple output powers and multiple specified performance parameters.

[0087] After acquiring the first state curve, the first device can also extract features from the first state curve to obtain the curve features of the first state curve. The curve features of the first state curve indicate the output power characteristics and specified performance parameter characteristics of the smart appliance within a preset working time period.

[0088] Step 103: The first device matches the first state curve with multiple second state curves respectively to obtain the matching results. The multiple second state curves are the corresponding curves of the output power and specified performance parameters of the sample smart appliance under different working conditions. The working conditions include input voltage and aging degree.

[0089] The input voltage refers to the voltage input to the smart appliance, and the aging level indicates the operating time of the power devices in the smart appliance. Generally, the longer the operating time of a smart appliance, the lower its aging level. For example, for a smart appliance manufactured by a manufacturer but not in use, the operating time of its power devices is 0, and its aging level is 0%.

[0090] As an example, the degree of aging can be determined by the operating time of power devices, or it can be determined in other ways. For instance, for electrical parameters related to the lifespan of power devices, these parameters can be acquired under specific conditions, and the degree of aging of smart appliances can be determined based on changes in these parameters. For example, given an input voltage and power control strategy, the output voltage of the smart appliance can be collected, and the degree of aging can be determined by comparing the collected output voltage with the initial output voltage at which the aging level was 0% at the time of manufacture.

[0091] Different operating conditions refer to different input voltages and different aging levels. For example, different operating conditions may include: (first input voltage, first aging level), (first input voltage, second aging level), (second input voltage, first aging level), and (second input voltage, second aging level).

[0092] As an example, the first device can match the curve features of a first state curve with the curve features of multiple second state curves to obtain a matching result. For example, the first device can obtain the matching result through the following steps:

[0093] Step 1031: Determine the curve characteristics of the first state curve.

[0094] For example, the first device performs feature extraction on the first state curve to obtain the curve features of the first state curve. The curve feature extraction method can be the DP (Douglas-Peucher) algorithm or the DP algorithm with radial constraints, etc. The embodiments of this application do not limit the method for determining the curve features of the first state curve.

[0095] Step 1032: Match the curve features of the first state curve with the curve features of each of the multiple second state curves to obtain the similarity between the first state curve and each of the multiple second state curves.

[0096] Specifically, the curve features of each of the multiple second-state curves can be pre-stored in a feature dataset; that is, the feature dataset includes the curve features of each of the multiple second-state curves. Additionally, the feature dataset may also include the operating conditions corresponding to each of the multiple second-state curves, which may include input voltage and aging degree. In other words, the feature dataset includes the curve features of each of the multiple second-state curves, as well as the corresponding operating conditions.

[0097] Thus, before matching the curve features of the first state curve with the curve features of each of the multiple second state curves, the first device can first acquire a feature dataset, and then match the curve features of the first state curve with the curve features of each of the multiple second state curves in the acquired feature dataset.

[0098] As an example, the feature dataset can be pre-stored in the first device. In this way, after determining the curve features of the first state curve, the first device can match the curve features of the first state curve with the curve features of multiple second state curves in the stored feature dataset.

[0099] As an example, a method for determining the curve features of each of multiple second-state curves in a feature dataset may include the following steps:

[0100] Step 1) Determine multiple second-state curves.

[0101] Among them, multiple second-state curves are the corresponding curves of the output power and specified performance parameters of the sample smart appliances under different working conditions.

[0102] The method for determining each of the multiple second-state curves is the same as that for determining the first-state curve. For example, under different operating conditions, multiple output powers and multiple specified performance parameters of the sample smart appliance are first obtained within a third preset operating time period. Then, multiple second-state curves are determined based on these multiple output powers and specified performance parameters. The different operating conditions refer to different output voltages and different degrees of aging. That is, the multiple second-state curves are obtained from the sample smart appliance under operating conditions of voltage fluctuation, aging, and normal operation.

[0103] The third preset working time period can refer to any continuous time period during which the sample smart appliance is in operation, or it can include multiple non-contiguous time periods. The third preset working time period can be the same as or different from the first preset working time period. For example, the third preset working time period can be longer than the first preset working time period.

[0104] As an example, different operating conditions refer to different input voltages and different aging levels. For instance, different operating conditions could include: (first input voltage, first aging level), (first input voltage, second aging level), (second input voltage, first aging level), and (second input voltage, second aging level).

[0105] For example, based on the voltage acquisition interval and aging acquisition interval, under different combinations of input voltage and different aging degrees, multiple output powers and multiple specified performance parameters of the sample smart appliance can be obtained within a third preset working time period.

[0106] As an example, when the input voltage of the sample smart appliances is fixed, multiple output powers and multiple specified performance parameters of sample smart appliances with different aging levels can be obtained within a third preset working time period according to the aging collection interval, thereby determining multiple second state curves. For example, if the aging collection interval is 20%, multiple output powers and multiple specified performance parameters of sample smart appliances with 0%, 20%, 40%, 60%, and 80% aging levels can be obtained within the third preset working time period. Of course, the aging collection interval can also be other than that specified in the embodiments of this application.

[0107] The input voltage of the sample smart appliance can also be multiple. For example, different input voltages can be input to the sample smart appliance according to the voltage acquisition interval. For example, the voltage acquisition interval can be 20V, and voltages of 180V, 200V, 220V, 240V, and 260V can be input to the sample smart appliance respectively. Of course, the voltage acquisition interval can also be other, and this application embodiment does not limit it.

[0108] For example, the voltage sampling interval can be 20V, the aging sampling interval is 20%, and different operating conditions can include: (180V, 0%), (180V, 20%), (180V, 40%), (180V, 60%), (180V, 80%), (200V, 0%), (200V, 20%), (200V, 40%), (200V, 60%), (200V, 80%), (220V, 0%). ), (220v, 20%), (220v, 40%), (220v, 60%), (220v, 80%), (240v, 0%), (240v, 20%), (240v, 40%), (240v, 60%), (240v, 80%), (260v, 0%), (260v, 20%), (260v, 40%), (260v, 60%) and (260v, 80%).

[0109] As an example, with an input voltage of 180V, multiple output powers and multiple specified performance parameters of a sample smart appliance with 0% aging are obtained during a third preset operating time period. Based on these multiple output powers and multiple specified performance parameters, a second state curve can be determined. The operating conditions corresponding to this determined second state curve are an input voltage of 180V and an aging level of 0%.

[0110] Alternatively, with an input voltage of 180V, multiple output powers and multiple specified performance parameters of a sample smart appliance with a 20% aging level can be obtained within a third preset operating time period. Based on these multiple output powers and multiple specified performance parameters, a second state curve can be determined. The operating conditions corresponding to this determined second state curve are an input voltage of 180V and an aging level of 20%.

[0111] Of course, the input voltage mentioned above can also be other values, and the aging level of the sample smart appliance can also be other values. Thus, based on the voltage acquisition interval and the aging acquisition interval, under different combinations of input voltage and aging levels, multiple output powers and multiple specified performance parameters of the sample smart appliance within the third preset working time period can be obtained.

[0112] As an example, the method for obtaining multiple output powers and multiple specified performance parameters of the sample smart voltage within the third preset working time period can refer to step 101 above, and will not be repeated here.

[0113] As an example, after obtaining multiple output powers and multiple specified performance parameters of the sample smart voltage, the process of determining multiple second state curves based on the multiple output powers and multiple specified performance parameters can refer to step 102 above, which will not be repeated here.

[0114] Furthermore, after identifying multiple second-state curves, these curves can be filtered to remove similar ones. For example, similar second-state curves can be filtered based on their curve characteristics.

[0115] Step 2) Determine the curve characteristics of each of the multiple second-state curves.

[0116] The curve characteristics of the second state curve indicate the output power characteristics and specified performance parameter characteristics of the sample smart appliance within the third preset working time period.

[0117] For example, feature extraction is performed on each second-state curve to obtain the curve features of each second-state curve. The curve feature extraction method can refer to step 1031 above, and will not be repeated here.

[0118] Step 3) Store the curve features of each of the multiple second-state curves, as well as the corresponding operating conditions, into a feature dataset. For example, the feature dataset may include: (first curve features, first input voltage, first aging degree), (second curve features, first input voltage, second aging degree), (third curve features, second input voltage, first aging degree), and (fourth curve features, second input voltage, second aging degree).

[0119] Operating conditions include input voltage and degree of aging.

[0120] In this way, the curve features of each determined second state curve and the corresponding operating conditions can be stored in the feature dataset. This allows for the subsequent matching of the curve features of the current first state curve of the smart appliance with each second state curve to determine the current operating conditions of the smart appliance. Based on the determined operating conditions, the operating state of the power device can then be determined.

[0121] In the method for determining the curve features of each of the multiple second-state curves included in the feature dataset, steps 1)-3) can be performed by a first device or other computer devices. This application embodiment does not limit the executing entity for determining the feature dataset. For example, after other computer devices obtain the feature dataset through steps 1)-3), the first device can store the feature dataset obtained by the other computer devices, thereby matching the curve features of the first-state curves with the curve features of the multiple second-state curves included in the stored feature dataset.

[0122] As an example, the similarity between the curve features of the first state curve and the curve features of each second state curve can be calculated to obtain the similarity between the first state curve and each second state curve. The similarity between the first state curve and each second state curve can be the Euclidean distance, Chebyshev distance, or Manhattan distance between the two curve features, etc. This application does not limit the method for determining the similarity between the first state curve and each second state curve.

[0123] Step 1033: If the similarity between the first state curve and each of the multiple second state curves is less than the similarity threshold, then it is determined that the first state curve does not match any of the multiple second state curves.

[0124] The similarity threshold is a pre-set threshold used to determine whether two curves match. If the similarity between the first state curve and the second state curve is less than the similarity threshold, then the first state curve and the second state curve are determined to be mismatched. If the similarity between the first state curve and the second state curve is greater than or equal to the similarity threshold, then the first state curve and the second state curve are determined to match.

[0125] If the similarity between the first state curve and each second state curve is less than the similarity threshold, then the curve feature of the first state curve is determined to be far from the curve feature of each second state curve, and the matching result is that the first state curve does not match any of the multiple second state curves.

[0126] Step 1034: If the similarity between the first state curve and the target second state curve among multiple second state curves is greater than or equal to the similarity threshold, then the first state curve is determined to match the target second state curve, and the target second state curve is the second state curve with the highest similarity to the first state curve among multiple second state curves.

[0127] If at least one of the multiple second-state curves has a similarity to the first-state curve greater than or equal to a similarity threshold, then the curve feature of the first-state curve is determined to be close to the curve feature of each of the at least one second-state curves, and the first-state curve is determined to match each of the at least one second-state curves.

[0128] As an example, the second state curve with the highest similarity to the first state curve among at least one second state curve can be used as the target second state curve. In this case, the matching result is that the first state curve matches the target second state curve among multiple second state curves.

[0129] In addition, among the curve features of multiple second state curves, the curve feature of the target second state curve is the smallest distance from the curve feature of the first state curve, and the working conditions corresponding to the target second state curve are the same as the current working conditions of the smart appliance.

[0130] In addition, among the curve features of multiple second state curves, the curve feature of the target second state curve is the smallest distance from the curve feature of the first state curve, and the working conditions corresponding to the target second state curve are the same as the current working conditions of the smart appliance.

[0131] Step 104: The first device determines the operating status of the power devices in the smart appliance based on the matching results.

[0132] The operating status is either normal or abnormal. Abnormal status includes one or more of the following: voltage fluctuation, aging, and fault.

[0133] As an example, the first device can determine the operating state of the power device through the following steps:

[0134] Step 1041: If the matching result is that the first state curve matches the target second state curve among multiple second state curves, then the working state is determined to be either the normal state, or one or more of the voltage fluctuation state and aging state based on the target second state curve.

[0135] The feature dataset includes the operating conditions corresponding to each of the multiple second-state curves, including the input voltage and the degree of aging.

[0136] As an example, the operating conditions corresponding to the target second state curve are the same as the current operating conditions of the smart appliance. The first device can first determine the operating conditions corresponding to the target second state curve, that is, determine the current operating conditions of the smart device, and then determine the operating state of the power device as normal state, or one or more of voltage fluctuation state and aging state based on the current operating conditions of the smart device.

[0137] For example, the first device can determine the operating conditions corresponding to the target second state curve from the feature dataset. The operating conditions corresponding to the target second state curve in the feature dataset may include the target input voltage and the target aging degree, that is, the current operating conditions of the smart device include the target input voltage and the target aging degree.

[0138] For example, determining the operating state of a power device as normal, or as one or more of voltage fluctuation and aging states based on the current operating conditions of the smart device can be achieved through the following steps:

[0139] Step 1) If the difference between the target input voltage and the standard voltage is greater than or equal to the voltage threshold, then the working state is determined to be voltage fluctuation state.

[0140] The standard voltage can be preset, such as the rated input voltage of smart appliances, which is usually 220V.

[0141] The voltage threshold is a pre-set threshold. If the difference between the target input voltage and the standard voltage is less than the voltage threshold, the target input voltage is determined to be within the range of the standard voltage, meaning the operating state is not a voltage fluctuation state. If the difference between the target input voltage and the standard voltage is greater than or equal to the voltage threshold, the operating state is determined to be a voltage fluctuation state.

[0142] For example, the voltage threshold can be 10V, and the standard voltage is 220V. If the target input voltage is within the range of [210V, 230V], that is, if the difference between the target input voltage and the standard voltage is less than the voltage threshold, then the operating state is determined to be not a voltage fluctuation state. If the target input voltage is not within the range of [210V, 230V], that is, if the difference between the target input voltage and the standard voltage is greater than or equal to the voltage threshold, then the operating state is determined to be a voltage fluctuation state.

[0143] Step 2) If the target aging degree is greater than or equal to the aging degree threshold, then the working state is determined to be the aging state.

[0144] Among them, the aging degree threshold is a preset threshold. For example, the aging degree threshold can be set manually according to the actual situation.

[0145] For example, if the target aging level is determined to be greater than or equal to the aging level threshold, then the working state is determined to be an aging state. If the target aging level is less than the aging level threshold, then the working state is determined not to be an aging state.

[0146] As an example, the aging threshold can be 10%. If the target aging level is greater than or equal to 10%, the working state is determined to be an aging state. If the target aging level is less than 10%, the working state is determined not to be an aging state. Of course, the aging threshold can be other than 10%, and this application embodiment does not limit the aging threshold.

[0147] Step 3) If the difference between the target input voltage and the standard voltage is less than the voltage threshold, and the target aging degree is less than the aging degree threshold, then the working state is determined to be normal.

[0148] The normal state refers to the input voltage being near the standard voltage and the degree of aging being less than the aging threshold.

[0149] Step 1042: If the matching result is that the first state curve does not match any of the multiple second state curves, then the working state is determined to be a fault state.

[0150] Since the multiple second-state curves are the corresponding curves of the output power and specified performance parameters of the sample smart appliance under different working conditions, that is, since the multiple second-state curves are obtained from the working conditions of the sample smart appliance under voltage fluctuation state, aging state and normal state, if there is no second-state curve that matches the first-state curve among the multiple second-state curves, it can be determined that the working state is not the voltage fluctuation state, aging state and normal state, but a fault state.

[0151] As an example, after determining that the operating state is faulty, the first device can issue an alarm message. The alarm message is used to indicate that the power device of the smart appliance has failed, so that relevant personnel can quickly take corresponding actions, improve maintainability, and improve repair efficiency and after-sales service quality.

[0152] The method of issuing alarm information may include displaying alarm information or issuing alarm sounds, and this application embodiment does not limit this.

[0153] As an example, the first device can not only determine the operating status of power devices, but also the operating parameters of smart appliances. For instance, it can determine the voltage fluctuation parameters and aging level of smart appliances.

[0154] In addition, after determining the operating state of the power device, the first device can adjust the power control strategy of the power device according to the operating state in order to adjust the specified performance parameters of the smart appliance.

[0155] The power control strategy includes multiple control parameters. The first device can adjust the specified performance parameters of the smart appliance based on the values ​​of these control parameters. These control parameters may include voltage fluctuation parameters and aging degree. The first device can adjust the power control strategy of the power device based on different voltage fluctuation parameters and aging degrees, thereby adjusting the specified performance parameters of the smart appliance.

[0156] For example, the first device can first determine the input voltage and aging degree of the power device based on the working status, then determine the voltage fluctuation parameters of the power device based on the input voltage, and adjust the control parameters of the power control strategy based on the voltage fluctuation parameters and aging degree.

[0157] As an example, if the operating state is normal, or one or more of voltage fluctuation and aging states, then the current operating conditions of the smart appliance are the target input voltage and the target aging degree; that is, the input voltage of the power device is the target input voltage, and the aging degree of the power device is the target aging degree. If the operating state is fault state, then the input voltage and aging degree of the power device can be the previously determined target input voltage and target aging degree.

[0158] The voltage fluctuation parameters of the power device can be determined based on the numerical relationship between the input voltage of the power device and the standard voltage. The numerical relationship can be a difference or a ratio, etc., and this application does not limit this.

[0159] As an example, after determining the operating state of the power device, the first device can also repeat steps 101-104 to continuously determine the operating state of the power device. This allows for timely adjustment of the power control strategy based on the operating state, thereby adjusting the specified performance parameters of the smart appliance. This avoids the problem of the specified performance parameters of the smart appliance being too high or too low due to using the same power control strategy under different operating states. Specifically, excessively high specified performance parameters may pose safety hazards, while excessively low specified performance parameters may fail to achieve the expected results.

[0160] As an example, after the first device has determined the working state multiple times in succession, it can also predict the working state of the power device in advance. This allows it to adjust the power control strategy of the power device in advance based on the predicted working state, adjust the specified performance parameters of the smart appliance, avoid the problem of power device runaway caused by frequent changes in the specified performance parameters that are too high or too low, and improve the stability of the smart appliance.

[0161] For example, after the first device has determined the working state multiple times, it can predict the working state for the next time period based on the multiple determined working states, obtain the predicted working state for the next time period, and adjust the power control strategy for the next time period based on the predicted working state.

[0162] For example, after the second device has determined its working status multiple times, it can assess the aging rate based on the degree of aging and predict the aging status and degree of aging in advance based on the aging rate.

[0163] In this embodiment, multiple output powers and multiple specified performance parameters of the smart appliance within a preset working time period can be acquired first. Then, based on these multiple output powers and specified performance parameters, a first state curve is determined. The first state curve is a correspondence curve between the smart appliance's output power and the specified performance parameters. The first state curve is then matched with multiple second state curves to obtain matching results. Based on the matching results, the operating state of the power devices in the smart appliance is determined. The multiple second state curves are correspondence curves between the sample smart appliance's output power and specified performance parameters under different working conditions. Working conditions include input voltage and aging degree, and the operating state is either normal or abnormal. Abnormal states include one or more of voltage fluctuation, aging, and fault states. Thus, the operating state of the power devices in the smart appliance can be detected by matching the acquired correspondence curve between the smart appliance's output power and specified performance parameters with the multiple correspondence curves between the sample smart appliance's output power and specified performance parameters under different working states. Moreover, this detection method acquires the smart appliance's output power and specified performance parameters in real time during operation, and the operating state of the power devices can be determined based on these output power and specified performance parameters. The detection method is simple and highly efficient.

[0164] Figure 2 This is a schematic diagram of a working status detection device provided in an embodiment of this application. The working status detection device can be implemented as part or all of a computer device by software, hardware, or a combination of both. This computer device can be described below. Figure 3 The computer equipment shown. See also Figure 2 The device includes: a first acquisition module 201, a first determination module 202, a matching module 203, and a second determination module 204.

[0165] The first acquisition module 201 is used to acquire multiple output powers and multiple specified performance parameters of the smart appliance within a preset working time period.

[0166] The first determining module 202 is used to determine a first state curve based on multiple output powers and multiple specified performance parameters. The first state curve is a curve showing the correspondence between the output power of the smart appliance and the specified performance parameters.

[0167] The matching module 203 is used to match the first state curve with multiple second state curves respectively to obtain the matching result. The multiple second state curves are the corresponding curves of the output power and specified performance parameters of the sample smart appliance under different working conditions. The working conditions include input voltage and aging degree.

[0168] The second determining module 204 is used to determine the operating state of the power device in the smart appliance based on the matching result. The operating state is either normal or abnormal. The abnormal state includes one or more of the following: voltage fluctuation state, aging state, and fault state.

[0169] As an example, the second determining module 204 is also used to determine the working state as normal state, or one or more of voltage fluctuation state and aging state based on the target second state curve if the matching result is that the first state curve matches the target second state curve among multiple second state curves.

[0170] If the matching result is that the first state curve does not match any of the multiple second state curves, then the working state is determined to be a fault state.

[0171] As an example, the second determining module 204 is also used to determine the operating conditions corresponding to the target second state curve, which include the target input voltage and the target aging degree.

[0172] If the difference between the target input voltage and the standard voltage is greater than or equal to the voltage threshold, the operating state is determined to be a voltage fluctuation state.

[0173] If the target aging degree is greater than or equal to the aging degree threshold, the working state is determined to be the aging state;

[0174] If the difference between the target input voltage and the standard voltage is less than the voltage threshold, and the target aging degree is less than the aging degree threshold, then the working state is determined to be normal.

[0175] As an example, the matching module 203 is also used to determine the curve characteristics of the first state curve;

[0176] The curve features of the first state curve are matched with the curve features of each of the multiple second state curves to obtain the similarity between the first state curve and each of the multiple second state curves.

[0177] If the similarity between the first state curve and each of the multiple second state curves is less than the similarity threshold, then it is determined that the first state curve does not match any of the multiple second state curves.

[0178] If the similarity between the first state curve and the target second state curve among multiple second state curves is greater than or equal to the similarity threshold, then the first state curve is determined to match the target second state curve, and the target second state curve is the second state curve with the highest similarity to the first state curve among multiple second state curves.

[0179] As an example, the device further includes a second acquisition module for acquiring a feature dataset, the feature dataset including the curve features of each of the multiple second state curves, and the operating conditions corresponding to each of the multiple second state curves.

[0180] As an example, the device further includes a third determining module, a fourth determining module, and a storage module:

[0181] The third determination module is used to determine multiple second state curves, which are the curves showing the correspondence between the output power and specified performance parameters of the sample smart appliance under different working conditions.

[0182] The fourth determination module is used to determine the curve characteristics of each of the multiple second-state curves;

[0183] The storage module is used to store the curve features of each of the multiple second-state curves, as well as the corresponding working conditions of each second-state curve, into the feature dataset.

[0184] As an example, the device also includes an adjustment module for adjusting the power control strategy of the power devices according to the operating state to adjust specified performance parameters of the smart appliance.

[0185] It should be noted that the working status detection device provided in the above embodiments is only an example of the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0186] The functional units and modules in the above embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of the embodiments of this application.

[0187] The working status detection device and the working status detection method provided in the above embodiments belong to the same concept. The specific working process and technical effects of the units and modules in the above embodiments can be found in the method embodiments section, and will not be repeated here.

[0188] Please refer to Figure 3 , Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Figure 3As shown, the computer device includes: a processor 301, a memory 302, and a computer program 303 stored in the memory 302 and executable on the processor 301. When the processor 301 executes the computer program 303, it implements the steps in the working state detection method in the above embodiments.

[0189] The computer equipment can be the above. Figure 1 The first device in the embodiment. In specific implementations, the computer device may be a terminal or a server, etc., and the embodiments of this application do not limit the type of computer device. Those skilled in the art will understand that Figure 3 The examples of computer devices are merely examples and do not constitute a limitation on computer devices. They may include more or fewer components than shown in the illustration, or combinations of certain components, or different components, such as input / output devices, network access devices, etc.

[0190] Processor 301 can be a Central Processing Unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0191] In some embodiments, memory 302 may be an internal storage unit of a computer device, such as a hard disk or RAM. In other embodiments, memory 302 may be an external storage device of a computer device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc. Furthermore, memory 302 may include both internal and external storage units. Memory 302 is used to store the operating system, applications, boot loader, data, and other programs. Memory 302 may also be used to temporarily store data that has been output or will be output.

[0192] This application also provides a computer device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the steps in any of the above method embodiments.

[0193] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps in the various method embodiments described above.

[0194] This application provides a computer program product that, when run on a computer, causes the computer to perform the steps described in the various method embodiments above.

[0195] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above method embodiments of this application can be implemented by a computer program instructing related hardware. This computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate form. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to a photographing device / terminal device, a recording medium, a computer memory, ROM (Read-Only Memory), RAM (Random Access Memory), CD-ROM (Compact Disc Read-Only Memory), magnetic tape, floppy disk, and optical data storage devices. The computer-readable storage medium mentioned in this application can be a non-volatile storage medium; in other words, it can be a non-transient storage medium.

[0196] It should be understood that all or part of the steps of the above embodiments can be implemented by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented in whole or in part as a computer program product. The computer program product includes one or more computer instructions. The computer instructions can be stored in the above-described computer-readable storage medium.

[0197] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0198] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software 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 application.

[0199] In the embodiments provided in this application, it should be understood that the disclosed apparatus / computer devices and methods can be implemented in other ways. For example, the apparatus / computer device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0200] 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0201] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for detecting working status, characterized in that, The method includes: Acquire multiple output powers and multiple specified performance parameters of smart appliances within a preset working time period; Based on the plurality of output powers and the plurality of specified performance parameters, a first state curve is determined, wherein the first state curve is the correspondence curve between the output power and the specified performance parameters of the smart appliance; The first state curve is matched with multiple second state curves respectively to obtain the matching result. The multiple second state curves are the corresponding relationship curves between the output power and specified performance parameters of the sample smart appliance under different working conditions. The working conditions include input voltage and aging degree. Based on the matching results, the operating state of the power devices in the smart appliance is determined. The operating state is either a normal state or an abnormal state. The abnormal state includes one or more of the following: voltage fluctuation state, aging state, and fault state. If the matching result is that the first state curve matches the target second state curve among the plurality of second state curves, then the operating conditions corresponding to the target second state curve are determined. The operating conditions corresponding to the target second state curve include the target input voltage and the target aging degree. If the difference between the target input voltage and the standard voltage is greater than or equal to a voltage threshold, then the operating state is determined to be the voltage fluctuation state. If the target aging degree is greater than or equal to an aging degree threshold, then the operating state is determined to be the aging state. If the difference between the target input voltage and the standard voltage is less than the voltage threshold, and the target aging degree is less than the aging degree threshold, then the operating state is determined to be the normal state. If the matching result is that the first state curve does not match any of the multiple second state curves, then the working state is determined to be the fault state.

2. The method as described in claim 1, characterized in that, The step of matching the first state curve with multiple second state curves to obtain matching results includes: Determine the curve characteristics of the first state curve; The curve features of the first state curve are matched with the curve features of each of the plurality of second state curves to obtain the similarity between the first state curve and each of the plurality of second state curves. If the similarity between the first state curve and each of the plurality of second state curves is less than the similarity threshold, then it is determined that the first state curve does not match any of the plurality of second state curves. If the similarity between the first state curve and the target second state curve among the plurality of second state curves is greater than or equal to the similarity threshold, then the first state curve is determined to match the target second state curve, wherein the target second state curve is the second state curve among the plurality of second state curves that has the greatest similarity to the first state curve.

3. The method as described in claim 2, characterized in that, Before matching the curve features of the first state curve with the curve features of each of the plurality of second state curves, the method further includes: Obtain a feature dataset, which includes the curve features of each of the plurality of second state curves and the working conditions corresponding to each of the plurality of second state curves.

4. The method as described in claim 3, characterized in that, Before obtaining the feature dataset, the method further includes: The plurality of second state curves are determined, which are the curves showing the correspondence between the output power and specified performance parameters of the sample smart appliance under different working conditions; Determine the curve characteristics of each of the plurality of second state curves; The curve features of each of the multiple second state curves, and the corresponding working conditions of each second state curve, are stored in the feature dataset.

5. The method according to any one of claims 1-4, characterized in that, After determining the operating state of the power devices in the smart appliance based on the matching result, the method further includes: Based on the operating state, the power control strategy of the power device is adjusted to adjust the specified performance parameters of the smart appliance.

6. A working status detection device, characterized in that, The device includes: The acquisition module is used to acquire multiple output powers and multiple specified performance parameters of smart appliances within a preset working time period; The first determining module is used to determine a first state curve based on the plurality of output powers and the plurality of specified performance parameters, wherein the first state curve is a curve showing the correspondence between the output power and the specified performance parameters of the smart appliance. The matching module is used to match the first state curve with multiple second state curves respectively to obtain the matching result. The multiple second state curves are the corresponding relationship curves between the output power and specified performance parameters of the sample smart appliance under different working conditions. The working conditions include input voltage and aging degree. The second determining module is used to determine the operating state of the power device in the smart appliance based on the matching result. The operating state is a normal state or an abnormal state. The abnormal state includes one or more of voltage fluctuation state, aging state and fault state. If the matching result is that the first state curve matches the target second state curve among the plurality of second state curves, then the operating conditions corresponding to the target second state curve are determined. The operating conditions corresponding to the target second state curve include the target input voltage and the target aging degree. If the difference between the target input voltage and the standard voltage is greater than or equal to a voltage threshold, then the operating state is determined to be the voltage fluctuation state. If the target aging degree is greater than or equal to an aging degree threshold, then the operating state is determined to be the aging state. If the difference between the target input voltage and the standard voltage is less than the voltage threshold, and the target aging degree is less than the aging degree threshold, then the operating state is determined to be the normal state. If the matching result is that the first state curve does not match any of the multiple second state curves, then the working state is determined to be the fault state.

7. A computer device, characterized in that, The computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 5.

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