Methods, devices and electronic equipment for detecting the operating status of power-consuming equipment

Abstract

This disclosure relates to a method, apparatus, and electronic device for detecting the operating status of power-consuming equipment. The method for detecting the operating status of power-consuming equipment includes: acquiring a current characteristic map of the power-consuming equipment; determining the derivative of the current characteristic map; determining the equipment type of the power-consuming equipment and a target detection method corresponding to the equipment type based on the derivative of the current characteristic map; and detecting the operating status of the power-consuming equipment based on the target detection method. The method for detecting the operating status of power-consuming equipment provided by this disclosure can not only detect the operating status of power-consuming equipment but also improve the management efficiency of power-consuming equipment.

Description

Technical Field

[0001] This disclosure relates to the field of equipment testing technology, and in particular to a method, apparatus and electronic equipment for detecting the operating status of power-consuming equipment. Background Technology

[0002] In related technologies, power-consuming devices are typically installed in enterprises, factories, laboratories, and other locations, requiring management. Currently, the management of these devices usually involves detecting their on / off status to manage them accordingly. However, even when powered on, these devices may be in different operating states, such as working or standby. For example, a power consumption tester might be in either power consumption testing (working) or standby mode. Managing power-consuming devices solely based on their on / off status leads to low management efficiency due to insufficient data. Therefore, a method for detecting the operating status of power-consuming devices is urgently needed. Summary of the Invention

[0003] This disclosure provides a method, apparatus, electronic device, and storage medium for detecting the operating status of power-consuming equipment. The technical solution of this disclosure is as follows:

[0004] According to a first aspect of the present disclosure, a method for detecting the operating status of a power-consuming device is provided, comprising:

[0005] Obtain the current characteristic map of power-consuming equipment;

[0006] Determine the derivative of the current characteristic graph;

[0007] Based on the derivative of the current characteristic map, determine the device type of the power-consuming device and the target detection method corresponding to the device type;

[0008] The operating status of the power-consuming equipment is detected based on the target detection method described above.

[0009] In one possible implementation, determining the derivative of the current characteristic graph includes:

[0010] The current feature map is smoothed and denoised to obtain the processed current feature map;

[0011] Calculate the derivative of the processed current characteristic graph; wherein the derivative is the first derivative.

[0012] In one possible implementation, the method for determining the device type of the power-consuming device and the target detection method corresponding to the device type based on the derivative of the current characteristic map includes:

[0013] Based on the derivative of the processed current characteristic graph, determine the first derivative curve of the processed current characteristic graph;

[0014] Determine the changes in the first derivative curve;

[0015] Based on the changes in the first derivative curve, the equipment type of the power-consuming equipment and the target detection method corresponding to the equipment type are determined.

[0016] In one possible implementation, the operating state includes a working state;

[0017] Determining the change of the first derivative curve includes:

[0018] The first derivative curve is input into a preset waveform recognition model; wherein, the preset waveform recognition model is a neural network model trained based on the first derivative curve of the current characteristic map of the power-consuming device of the preset device type under different working states, and the preset device type includes the device type of the power-consuming device;

[0019] The changes in the first derivative curve are determined by the preset waveform recognition model.

[0020] In one possible implementation, determining the change of the first derivative curve through the preset waveform recognition model includes at least one of the following:

[0021] When the derivative value in the first derivative curve is zero or the derivative value in the first derivative curve is greater than a first preset derivative threshold, the preset waveform recognition model determines the change of the first derivative curve as a first change and determines the power-consuming device as a first device type.

[0022] When the first derivative curve shows an upward trend, the preset waveform recognition model determines the change of the first derivative curve as a second change and determines the power-consuming device as a second device type.

[0023] If the derivative value in the first derivative curve is greater than the second preset derivative threshold, the preset waveform recognition model determines the change of the first derivative curve as a third change and determines the power-consuming device as a third device type; wherein, the second preset derivative threshold is less than the first preset derivative threshold;

[0024] When the range of derivative values ​​in the first derivative curve exceeds a preset derivative range, the preset waveform recognition model determines the change of the first derivative curve as a fourth change and identifies the power-consuming device as a fourth device type; wherein, any derivative value within the preset derivative range is less than the second preset derivative threshold.

[0025] In one possible implementation, the method for determining the target detection method for the power-consuming device based on the change of the first derivative curve includes at least one of the following:

[0026] When the change of the first derivative curve is the first change and the power-consuming device is the first device type, the target detection method for detecting the power-consuming device is determined to be the threshold judgment method.

[0027] When the change of the first derivative curve is the second change and the power-consuming device is the second type of device, the target detection method for detecting the power-consuming device is determined to be the arithmetic mean method.

[0028] When the change of the first derivative curve is the third change and the power-consuming device is the third type of device, the target detection method for detecting the power-consuming device is determined to be the mean square error method.

[0029] When the change of the first derivative curve is the fourth change case and the power-consuming device is the fourth device type, the target detection method for detecting the power-consuming device is determined to be the template matching method.

[0030] According to a second aspect of the present disclosure, an operating status detection device for power-consuming equipment is provided, comprising:

[0031] The acquisition module is used to acquire the current characteristic map of power-consuming devices;

[0032] The first determining module is used to determine the derivative of the current characteristic graph;

[0033] The second determining module is used to determine the device type of the power-consuming device and the target detection method corresponding to the device type based on the derivative of the current characteristic map;

[0034] The detection module is used to detect the operating status of the power-consuming equipment based on the target detection method.

[0035] In one possible implementation, the first determining module includes:

[0036] A noise reduction unit is used to perform smooth noise reduction processing on the current feature map to obtain a processed current feature map.

[0037] A calculation unit is used to calculate the derivative of the processed current characteristic map; wherein the derivative is the first derivative.

[0038] In one possible implementation, the second determining module includes:

[0039] The first determining unit is used to determine the first derivative curve of the processed current characteristic graph based on the derivative of the processed current characteristic graph.

[0040] The second determining unit is used to determine the changes in the first derivative curve;

[0041] The third determining unit is used to determine the equipment type of the power-consuming equipment and the target detection method corresponding to the equipment type based on the change of the first derivative curve.

[0042] In one possible implementation, the operating state includes a working state;

[0043] The second determining unit includes:

[0044] An input subunit is used to input the first derivative curve into a preset waveform recognition model; wherein, the preset waveform recognition model is a neural network model trained based on the first derivative curve of the current characteristic map of a power-consuming device of a preset device type under different working states, and the preset device type includes the device type of the power-consuming device;

[0045] A sub-unit is defined to determine the changes in the first derivative curve using the preset waveform recognition model.

[0046] In one possible implementation, the determining subunit is configured to perform at least one of the following:

[0047] When the derivative value in the first derivative curve is zero or the derivative value in the first derivative curve is greater than a first preset derivative threshold, the preset waveform recognition model determines the change of the first derivative curve as a first change and determines the power-consuming device as a first device type.

[0048] When the first derivative curve shows an upward trend, the preset waveform recognition model determines the change of the first derivative curve as a second change and determines the power-consuming device as a second device type.

[0049] If the derivative value in the first derivative curve is greater than the second preset derivative threshold, the preset waveform recognition model determines the change of the first derivative curve as a third change and determines the power-consuming device as a third device type; wherein, the second preset derivative threshold is less than the first preset derivative threshold;

[0050] When the range of derivative values ​​in the first derivative curve exceeds a preset derivative range, the preset waveform recognition model determines the change of the first derivative curve as a fourth change and identifies the power-consuming device as a fourth device type; wherein, any derivative value within the preset derivative range is less than the second preset derivative threshold.

[0051] In one possible implementation, the third determining unit is configured to perform at least one of the following:

[0052] When the change of the first derivative curve is the first change and the power-consuming device is the first device type, the target detection method for detecting the power-consuming device is determined to be the threshold judgment method.

[0053] When the change of the first derivative curve is the second change and the power-consuming device is the second type of device, the target detection method for detecting the power-consuming device is determined to be the arithmetic mean method.

[0054] When the change of the first derivative curve is the third change and the power-consuming device is the third type of device, the target detection method for detecting the power-consuming device is determined to be the mean square error method.

[0055] When the change of the first derivative curve is the fourth change case and the power-consuming device is the fourth device type, the target detection method for detecting the power-consuming device is determined to be the template matching method.

[0056] According to a third aspect of the present disclosure, an electronic device is provided, comprising:

[0057] processor;

[0058] Memory used to store the processor's executable instructions;

[0059] The processor is configured to execute the instructions to implement the method for detecting the operating status of a power-consuming device as described in any one of the first aspects.

[0060] According to a fourth aspect of the present disclosure, a storage medium is provided that, when instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to perform the power-consuming device operation status detection method as described in any one of the first aspects.

[0061] According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the method for detecting the operating status of a power-consuming device as described in any one aspect of the first aspect.

[0062] The technical solutions provided by the embodiments of this disclosure have at least the following beneficial effects:

[0063] In the embodiments of this disclosure, the derivative of the current characteristic map of the power-consuming device is determined by acquiring the current characteristic map; the device type and the corresponding target detection method are determined based on the derivative of the current characteristic map; and the operating status of the power-consuming device is detected based on the target detection method. In this way, on the one hand, the operating status of the power-consuming device can be detected based on its current characteristic map, and the detected operating status can be relatively accurate. On the other hand, it can also provide a more sufficient data foundation for the management of power-consuming devices, improving the management efficiency of power-consuming devices.

[0064] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0065] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0066] Figure 1 This is a flowchart illustrating a method for detecting the operating status of power-consuming equipment according to an embodiment of this disclosure.

[0067] Figure 2 This is a schematic diagram of a processed current characteristic map provided in an embodiment of this disclosure.

[0068] Figure 3 This is a schematic diagram illustrating the establishment and application process of a preset waveform recognition model provided in an embodiment of this disclosure.

[0069] Figure 4 This is a schematic diagram of a first derivative curve provided in an embodiment of this disclosure.

[0070] Figure 5 This is a schematic diagram of another first derivative curve provided in an embodiment of this disclosure.

[0071] Figure 6 This is a schematic diagram of another first derivative curve provided in an embodiment of this disclosure.

[0072] Figure 7 This is a schematic diagram of another first derivative curve provided in an embodiment of this disclosure.

[0073] Figure 8 This is a block diagram of an operating status detection device for power-consuming equipment provided in an embodiment of this disclosure.

[0074] Figure 9This is a block diagram of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0075] To enable those skilled in the art to better understand the technical solutions of this disclosure, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0076] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0077] The following detailed description, with reference to the accompanying drawings, describes the method, apparatus, electronic device, and storage medium for detecting the operating status of power-consuming equipment provided in the embodiments of this disclosure.

[0078] Figure 1 This is a flowchart illustrating a method for detecting the operating status of power-consuming devices according to an embodiment of this disclosure. This method can be applied to electronic devices. Figure 1 As shown, the method for detecting the operating status of power-consuming equipment may include the following steps.

[0079] In step S101, the current characteristic map of the power-consuming device is obtained.

[0080] In this embodiment of the disclosure, when detecting the operating status of a power-consuming device, a current characteristic map of the device can be obtained first. This current characteristic map can be a current curve, i.e., a current waveform. For example, the current characteristic map of the power-consuming device can be acquired using a power tracking device, and then obtained from the power tracking device. The power-consuming device can be, for example, any power-consuming device whose operating status needs to be detected, such as a vibration table, a drop tester, a flow chamber, a temperature chamber, a light box, a power consumption tester, a surge generator, a comprehensive tester, or an oscilloscope.

[0081] In step S102, the derivative of the current characteristic graph is determined.

[0082] In this embodiment of the disclosure, after obtaining the current characteristic map of the power-consuming device, the derivative of the current characteristic map can be determined. For example, the derivative of the current characteristic map of the power-consuming device can be calculated using a derivative calculation formula, that is, the derivative of the power-consuming device at each point can be calculated.

[0083] In step S103, the device type of the power-consuming equipment and the target detection method corresponding to the device type are determined based on the derivative of the current characteristic diagram.

[0084] In this embodiment, after determining the derivative of the current characteristic graph, the device type of the power-consuming device and the appropriate detection method for detecting the operating status of that device type can be determined based on the derivative of the current characteristic graph. This detection method is the target detection method. For example, the target detection method can be any method such as threshold judgment, arithmetic mean, standard deviation, or template matching. For example, considering that the derivative at a certain point can represent the rate of change near that point, determining the device type of the power-consuming device and the corresponding target detection method based on the derivative of the current characteristic graph can actually be based on the changes in the current characteristic graph to determine the device type of the power-consuming device and the corresponding target detection method. The specific process will be described in detail in subsequent embodiments.

[0085] In step S104, the operating status of power-consuming equipment is detected based on the target detection method.

[0086] In the embodiments of this disclosure, after determining the target detection method for detecting power-consuming devices based on the derivative of the current characteristic graph, the operating state of the power-consuming devices can be detected based on the target detection method. For example, assuming the target detection method is a threshold judgment method, the operating state of the power-consuming devices can be detected based on the threshold judgment method. The operating state can be a working state, a standby state, etc.

[0087] In the embodiments of this disclosure, the derivative of the current characteristic map of the power-consuming device is determined by acquiring the current characteristic map; the device type and the corresponding target detection method are determined based on the derivative of the current characteristic map; and the operating status of the power-consuming device is detected based on the target detection method. In this way, on the one hand, the operating status of the power-consuming device can be detected based on its current characteristic map, and the detected operating status can be relatively accurate. On the other hand, it can also provide a more sufficient data foundation for the management of power-consuming devices, improving the efficiency of power consumption device management.

[0088] In some possible implementations, determining the derivative of the current characteristic map may include the following processing:

[0089] The current feature map is smoothed and denoised to obtain the processed current feature map.

[0090] Calculate the first derivative of the processed current characteristic graph.

[0091] In embodiments of this disclosure, when determining the derivative of the current feature map, the current feature map can first be smoothed and denoised. For example, a rolling window algorithm can be used to scroll through the data in the current feature map and calculate the average value of the data within the window. For discrete data in the current feature map, the convolve method of NumPy (Numerical Python) can be used to process the data, obtaining a discrete convolution of two one-dimensional sequences, which serves as the processed current feature map. For example, refer to... Figure 2 , Figure 2 This is a schematic diagram of a processed current characteristic map provided in an embodiment of this disclosure. Figure 2 The horizontal axis is used to indicate a point, and the vertical axis is the discrete convolution corresponding to each point. Then, the first derivative of the processed current feature map can be calculated, where the derivative calculation formula is as shown in formula (1).

[0092]

[0093] Where x0 represents the independent variable, which can be any time point in the current characteristic graph; f′(x0) represents the first derivative at x0; Δx represents the increment of the independent variable; and Δy represents the increment of the dependent variable, that is, the increment of the current value in the current characteristic graph within Δx.

[0094] In a further possible implementation, the target detection method for power-consuming equipment can be determined based on the change of the first derivative. Accordingly, the specific implementation method for determining the target detection method for power-consuming equipment based on the derivative of the current characteristic diagram in step S103 above can be as follows:

[0095] Based on the derivative of the processed current characteristic map, the first derivative curve of the processed current characteristic map is determined;

[0096] Determine how the first derivative curve changes;

[0097] Based on the changes in the first derivative curve, determine the equipment type of the power-consuming equipment and the corresponding target detection method.

[0098] In the embodiments of this disclosure, considering that the derivative at a certain point can be used to indicate the rate of change near that point, and the curves of each point in the current characteristic graph can indicate the overall change of the current characteristic graph, and that the current characteristic graphs of different types of power-consuming devices are different under different operating states, then the changes in the first derivative curves of the current characteristic graphs of different types of power-consuming devices under different operating states will also be different. Therefore, when determining the device type of the power-consuming device to be detected and the target detection method corresponding to the device type based on the derivative of the current characteristic graph, the first derivative curve of the processed current characteristic graph can be determined firstly based on the first derivative of the processed current characteristic graph, that is, the first derivative of each point in the current characteristic graph. For example, the first derivatives of each point in the current characteristic graph can be connected to form a first derivative curve. Then, the change of the first derivative curve can be determined, for example, the magnitude of the change of the first derivative curve, whether the first derivative curve is rising or falling, the size of the distribution range of the derivative value of the first derivative curve, etc. Afterwards, the device type of the power-consuming device to be detected and the target detection method corresponding to the device type can be determined based on the change of the first derivative curve.

[0099] Thus, since the first derivative curves of the current characteristic diagrams of different types of power-consuming equipment change differently under different operating conditions, the changes in the first derivative curves of the current characteristic diagrams of different power-consuming equipment can reflect the operating status of different equipment types. Therefore, determining the equipment type of power-consuming equipment and the corresponding target detection method based on the changes in the first derivative curves of the current characteristic diagram can improve the accuracy of the determined target detection method and the accuracy of the operating status of power-consuming equipment detected by the target detection method.

[0100] In some possible implementations, the changes in the first derivative curve of the current characteristic graph can be determined using a neural network model. The specific processing method can be as follows:

[0101] Input the first derivative curve into the preset waveform recognition model;

[0102] The changes in the first derivative curve are determined by a preset waveform recognition model.

[0103] The preset waveform recognition model can be a neural network model trained based on the first derivative curves of the current characteristic diagrams of power-consuming devices of a preset device type under different operating states. The preset device type can include the device types of the aforementioned power-consuming devices. The different operating states can be, for example, high-load operating state, low-load operating state, standby state, etc., or other state classifications. For example, for a power consumption tester, the operating states can be divided into test state, standby state, etc.

[0104] In the embodiments of this disclosure, the first derivative curves of the current characteristic graphs of power-consuming devices of a preset device type, including the aforementioned power-consuming devices, under different operating states can be obtained in advance. These first derivative curves are determined as training samples, and a preset waveform recognition model is established based on these training samples. This preset waveform recognition model can identify which change condition the first derivative curve corresponds to. Thus, when determining the change condition of the first derivative curve of the current characteristic graph, the first derivative curve can be input into the preset waveform recognition model. The preset waveform recognition model performs waveform recognition on the first derivative curve to determine the change condition of the first derivative curve.

[0105] For example, the process of a preset waveform recognition model identifying changes in the first derivative curve can actually be a waveform classification and recognition process. For instance... Figure 3 As shown, Figure 3 This is a schematic diagram illustrating the establishment and application process of a preset waveform recognition model provided in an embodiment of this disclosure. Figure 3 In the text, 'waveform template establishment' can represent the process of establishing a preset waveform recognition model, and 'wavelength classification and recognition' can represent the application process of the preset waveform recognition model.

[0106] Reference Figure 3 In the process of establishing waveform templates, i.e., establishing preset waveform recognition models, waveform feature extraction can be performed first. During this process, sample waveforms can be collected from a database. Specifically, current characteristic diagrams of any type of power-consuming device (including the aforementioned device types) under different operating states can be collected from the database as sample waveforms. Then, waveform feature extraction can be performed on these sample waveforms. For example, the sample waveform can be converted from an analog signal to a discrete signal to obtain a current characteristic diagram (which can also be understood as a current curve). For instance, the analog signal to discrete signal conversion can be performed using Discrete Cosine Transform (DCT), and the first derivative waveform of the current characteristic diagram can be extracted.

[0107] Next, the waveform template can be established using the first-order derivative waveform extracted in the previous step. Specifically, a backpropagation (BP) neural network model can be built based on the first-order derivative curve. This model can include an input layer, hidden layers, and an output layer. The BP neural network model is initialized and trained. After training, this model can be designated as the preset waveform recognition model for waveform classification. Considering that model training typically involves model validation to ensure the accuracy of the output, repeated adjustments and training are common during the training process. Understandably, the creation and establishment of the preset waveform recognition model is similar to the principles of existing model creation and establishment, and will not be elaborated further here.

[0108] Subsequently, waveform classification and recognition can be performed using a preset waveform recognition model. This process begins by acquiring the waveform data to be measured, i.e., obtaining the current characteristic information of the power-consuming equipment, and then processing this information to obtain a current characteristic graph. Next, waveform feature extraction can be performed, i.e., determining the first derivative curve of the current characteristic graph. Finally, the first derivative curve is input into the preset waveform recognition model, which identifies changes in the first derivative curve and outputs these changes, i.e., the waveform classification result of the first derivative curve.

[0109] In this way, compared with manually determining the changes in the first derivative curve, using a neural network model to determine the changes in the first derivative curve can, on the one hand, effectively reduce the time consumption and improve the efficiency of determining the changes in the first derivative curve, thereby effectively improving the efficiency of determining the operating status of power-consuming equipment; on the other hand, it can also effectively reduce manpower consumption and reduce labor costs.

[0110] In one possible implementation, the specific way to determine the change of the first derivative curve by using a preset waveform recognition model may include at least one of the following:

[0111] When the derivative value in the first derivative curve is zero or the derivative value in the first derivative curve is greater than the first preset derivative threshold, the change in the first derivative curve is determined as the first change situation and the power-consuming equipment is determined as the first equipment type by the preset waveform recognition model;

[0112] When the first derivative curve shows an upward trend, the change in the first derivative curve is determined as the second change situation and the power-consuming equipment is determined as the second equipment type by using a preset waveform recognition model;

[0113] If the derivative value in the first derivative curve is greater than the second preset derivative threshold, the change of the first derivative curve is determined to be the third change and the power-consuming equipment is determined to be the third equipment type by the preset waveform recognition model;

[0114] When the range of derivative values ​​in the first derivative curve exceeds the preset derivative range, the change in the first derivative curve is determined to be the fourth change situation and the power-consuming equipment is determined to be the fourth equipment type by the preset waveform recognition model.

[0115] The specific values ​​of the first preset derivative threshold and the second preset derivative threshold, as well as the specific range of the preset derivative range, can be set according to actual conditions. The second preset derivative threshold is less than the first preset derivative threshold, and any derivative value within the preset derivative range is less than the second preset derivative threshold. The first change, the second change, the third change, and the fourth change represent the changes corresponding to different waveforms of the first derivative curve, respectively. The first device type, the second device type, the third device type, and the fourth device type represent the device type of the power-consuming device corresponding to each change.

[0116] In embodiments of this disclosure, it is assumed that the derivative value in the first derivative curve is zero or the derivative value in the first derivative curve is greater than a first preset derivative threshold. For example, Figure 4 This is a schematic diagram of a first derivative curve provided in an embodiment of this disclosure, such as... Figure 4 As shown, in the first derivative curve, the derivative value is zero or a large value (greater than the first preset derivative threshold). For example, if... Figure 4 If the minimum value of the first derivative curve is zero, then the first preset derivative threshold can be set to less than zero. Figure 4 The minimum derivative value excluding zero, or slightly smaller, can be set according to actual needs. At this time, the change in the first derivative curve can be considered relatively obvious. The preset waveform recognition model can determine this change as the first change, that is, the change in the first derivative curve is the first change. Furthermore, it can be determined that the equipment type of the above-mentioned power-consuming equipment is the equipment type with a relatively obvious change in the first derivative curve, that is, the first equipment type.

[0117] Assume the first derivative curve shows an upward trend. For example, Figure 5 This is a schematic diagram of another first derivative curve provided in an embodiment of this disclosure, such as... Figure 5 As shown, although the first derivative curve does not rise continuously, it is generally on an upward trend. At this time, the preset waveform recognition model can determine that this change is the second change, that is, the change of the first derivative curve is the second change, and it can determine that the equipment type of the above-mentioned power-consuming equipment is the equipment type with an upward trend in the first derivative curve, that is, the second equipment type.

[0118] Assuming the derivative values ​​in the first derivative curve are greater than a second preset derivative threshold, this could mean that all derivative values ​​in the first derivative curve are greater than the second preset derivative threshold, or it could mean that a certain percentage of the values ​​are greater than the second preset derivative threshold. The specific value of the second preset derivative threshold can be set according to actual needs. For example, Figure 6 This is a schematic diagram of another first derivative curve provided in an embodiment of this disclosure, as shown below. Figure 6 As shown, the derivative values ​​in the first derivative curve are mostly large, meaning they are mostly greater than the second preset threshold, indicating that the change in the first derivative curve is significant. At this point, the preset waveform recognition model can determine that this change is the third type of change, i.e., the change in the first derivative curve is the third type of change. Furthermore, it can determine that the equipment type of the aforementioned power-consuming device is the type of equipment with a significant change in the first derivative curve, i.e., the third equipment type.

[0119] Assuming the range of derivative values ​​in the first derivative curve exceeds a preset derivative range, the preset derivative range can be set according to actual needs. For example, Figure 7 This is a schematic diagram of another first derivative curve provided in an embodiment of this disclosure, such as... Figure 7 As shown, the derivative values ​​of the first derivative curve have a wide distribution range (exceeding the preset distribution range), and the changes are relatively unclear. At this time, the preset waveform recognition model can determine that this change is the fourth change situation, that is, the change situation of the first derivative curve is the fourth change situation, and it can determine that the equipment type of the above-mentioned power-consuming equipment is the equipment type with a wide distribution range of the derivative values ​​of the first derivative curve, that is, the fourth equipment type.

[0120] In a further possible embodiment, the specific implementation of the target detection method for detecting power-consuming equipment based on the change of the first derivative curve of the current characteristic graph in the above steps may include at least one of the following:

[0121] When the change of the first derivative curve is the first change case, the target detection method for detecting power-consuming equipment is determined to be the threshold judgment method;

[0122] When the change of the first derivative curve is the second change case, the target detection method for detecting power-consuming equipment is determined to be the arithmetic mean method;

[0123] When the change of the first derivative curve is the third change case, the target detection method for detecting power-consuming equipment is determined to be the mean square error method;

[0124] When the first derivative curve changes to the fourth variation case, the target detection method for detecting power-consuming equipment is determined to be the template matching method.

[0125] In the embodiments of this application, after determining which change the first derivative curve belongs to, the detection method corresponding to that change can be determined as the target detection method. The correspondence between different change scenarios and different detection methods can be pre-set.

[0126] Specifically, assuming the change in the first derivative curve represents the first change scenario, the power-consuming device can be considered as one with a large difference in current variation and a small rate of change. Examples include vibration tables and drop test machines. A threshold judgment method corresponding to this first change scenario can be obtained and used as the target detection method. This threshold judgment method can determine the operating state of the power-consuming device based on pre-set current or power thresholds for different device types under different operating states. Taking an air conditioner as an example, a power threshold for cooling can be set as W1, and a heat preservation threshold as W2. Therefore, when the air conditioner's power value is greater than W1, it is confirmed that the air conditioner is in cooling mode; when the power value is greater than W2 but less than W1, it is confirmed that the air conditioner is in heat preservation mode.

[0127] Assuming the change in the first derivative curve represents the second change scenario, the power-consuming equipment can be considered to have small differences in current variation and a small rate of change. Examples include flow boxes, temperature chambers, and light boxes. The arithmetic mean method corresponding to the second change scenario can be obtained and used as the target detection method. Specifically, the arithmetic mean method involves calculating the actual average current or power over a period of time, comparing this average current or power with pre-set threshold values ​​for the average current or power of different equipment types under different operating conditions, and thus determining the operating state of the power-consuming equipment.

[0128] Assuming the change in the first derivative curve represents the third scenario, the power-consuming device can be considered to have a large rate of current change. Examples include power consumption testers and surge generators. The mean square error (MSE) method corresponding to this third scenario can be used as the target detection method. Specifically, the MSE method involves calculating the actual current or power MSE of the power-consuming device over a period of time, comparing this MSE with pre-set threshold values ​​for different types of power-consuming devices under different operating conditions, and thus determining the device's operating state.

[0129] Assuming the change in the first derivative curve represents the fourth change scenario, the power-consuming device can be considered a device with no typical characteristics in current change. For example, this could be a comprehensive test instrument or oscilloscope. A template matching method corresponding to the fourth change scenario can be obtained, and this method can be designated as the target detection method. Specifically, the template matching method can pre-set current waveform templates or power waveform templates for different types of power-consuming devices under different operating states. The actual current waveform or power waveform of the power-consuming device over a period of time is matched with these templates to determine the device's operating state. Taking power waveform templates including template 1 for operating state 1, template 2 for operating state 2, and template 3 for operating state 3 as an example, assuming the power waveform of the power-consuming device matches template 2, it can be confirmed that the device is in operating state 2.

[0130] In this way, different detection methods that correspond to the current characteristics of power-consuming equipment can be determined based on the different changes in the first derivative curve, thereby further improving the accuracy of the detection results.

[0131] Figure 8 This is a block diagram of an operating status detection device for power-consuming equipment provided in an embodiment of this disclosure. (Refer to...) Figure 8 The power-consuming equipment's operating status detection device 800 may include:

[0132] The acquisition module 810 can be used to acquire the current characteristic map of power-consuming equipment;

[0133] The first determining module 820 can be used to determine the derivative of the current characteristic graph;

[0134] The second determining module 830 can be used to determine the device type of the power-consuming device and the target detection method corresponding to the device type based on the derivative of the current characteristic map.

[0135] The detection module 840 can be used to detect the operating status of the power-consuming equipment based on the target detection method.

[0136] In one possible implementation, the first determining module 820 may include:

[0137] The noise reduction unit can be used to smooth and reduce the noise of the current feature map to obtain the processed current feature map.

[0138] The calculation unit can be used to calculate the derivative of the processed current characteristic map; wherein the derivative is the first derivative.

[0139] In one possible implementation, the second determining module 830 may include:

[0140] The first determining unit can be used to determine the first derivative curve of the processed current characteristic graph based on the derivative of the processed current characteristic graph.

[0141] The second determining unit can be used to determine the changes in the first derivative curve;

[0142] The third determining unit can be used to determine the equipment type of the power-consuming equipment and the target detection method corresponding to the equipment type based on the change of the first derivative curve.

[0143] In one possible implementation, the operating state may include a working state;

[0144] The second determining unit may include:

[0145] The input subunit can be used to input the first derivative curve into a preset waveform recognition model; wherein, the preset waveform recognition model is a neural network model trained based on the first derivative curve of the current characteristic map of the power-consuming device of a preset device type under different working states, and the preset device type includes the device type of the power-consuming device;

[0146] The sub-unit can be used to determine the changes in the first derivative curve through the preset waveform recognition model.

[0147] In one possible implementation, the determining subunit can be used to perform at least one of the following:

[0148] When the derivative value in the first derivative curve is zero or the derivative value in the first derivative curve is greater than a first preset derivative threshold, the preset waveform recognition model determines the change of the first derivative curve as a first change and determines the power-consuming device as a first device type.

[0149] When the first derivative curve shows an upward trend, the preset waveform recognition model determines the change of the first derivative curve as a second change and determines the power-consuming device as a second device type.

[0150] If the derivative value in the first derivative curve is greater than the second preset derivative threshold, the preset waveform recognition model determines the change of the first derivative curve as a third change and determines the power-consuming device as a third device type; wherein, the second preset derivative threshold is less than the first preset derivative threshold;

[0151] When the range of derivative values ​​in the first derivative curve exceeds a preset derivative range, the preset waveform recognition model determines the change of the first derivative curve as a fourth change and identifies the power-consuming device as a fourth device type; wherein, any derivative value within the preset derivative range is less than the second preset derivative threshold.

[0152] In one possible implementation, the third determining unit can be used to perform at least one of the following:

[0153] When the change of the first derivative curve is the first change and the power-consuming device is the first device type, the target detection method for detecting the power-consuming device is determined to be the threshold judgment method.

[0154] When the change of the first derivative curve is the second change and the power-consuming device is the second type of device, the target detection method for detecting the power-consuming device is determined to be the arithmetic mean method.

[0155] When the change of the first derivative curve is the third change and the power-consuming device is the third type of device, the target detection method for detecting the power-consuming device is determined to be the mean square error method.

[0156] When the change of the first derivative curve is the fourth change case and the power-consuming device is the fourth device type, the target detection method for detecting the power-consuming device is determined to be the template matching method.

[0157] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0158] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0159] Figure 9 A schematic block diagram of an example electronic device 900 that can be used to implement embodiments of the present disclosure is shown. The electronic device 900 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0160] like Figure 9As shown, the electronic device 900 includes a computing unit 901, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 902 or a computer program loaded from a storage unit 908 into a random access memory (RAM) 903. The RAM 903 may also store various programs and data required for the operation of the device 900. The computing unit 901, ROM 902, and RAM 903 are interconnected via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0161] Multiple components in electronic device 900 are connected to I / O interface 905, including: input unit 906, such as keyboard, mouse, etc.; output unit 907, such as various types of displays, speakers, etc.; storage unit 908, such as disk, optical disk, etc.; and communication unit 909, such as network card, modem, wireless transceiver, etc. Communication unit 909 allows electronic device 900 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0162] The computing unit 901 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 901 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 901 performs the various methods and processes described above, such as the method for detecting the operating status of a power-consuming device. For example, in some embodiments, the method for detecting the operating status of a power-consuming device can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 908. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 900 via ROM 902 and / or communication unit 909. When the computer program is loaded into RAM 903 and executed by the computing unit 901, one or more steps of the method for detecting the operating status of a power-consuming device described above can be performed. Alternatively, in other embodiments, the computing unit 901 may be configured to perform a method for detecting the operating status of a power-consuming device by any other suitable means (e.g., by means of firmware).

[0163] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0164] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0165] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0166] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0167] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), the Internet, and blockchain networks.

[0168] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service ecosystem, addressing the shortcomings of traditional physical hosts and VPS (Virtual Private Server, or simply "VPS") services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0169] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0170] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method of detecting an operating state of an electric power consuming device, characterized by, include: Obtain the current characteristic map of power-consuming equipment; Determine the derivative of the current characteristic graph; Based on the derivative of the current characteristic graph, the device type of the power-consuming device and the target detection method corresponding to the device type are determined; wherein, the device type is a type classified according to the waveform of the current derivative. The operating status of the power-consuming equipment is detected based on the target detection method described above.

2. The method of claim 1, wherein Determining the derivative of the current characteristic graph includes: The current feature map is smoothed and denoised to obtain the processed current feature map; Calculate the derivative of the processed current characteristic graph; wherein the derivative is the first derivative.

3. The method of claim 2, wherein The method for determining the device type of the power-consuming equipment and the corresponding target detection method based on the derivative of the current characteristic map includes: Based on the derivative of the processed current characteristic graph, determine the first derivative curve of the processed current characteristic graph; Determine the changes in the first derivative curve; Based on the changes in the first derivative curve, the equipment type of the power-consuming equipment and the target detection method corresponding to the equipment type are determined.

4. The method of claim 3, wherein The operating status includes the working status; Determining the change of the first derivative curve includes: The first derivative curve is input into a preset waveform recognition model; wherein, the preset waveform recognition model is a neural network model trained based on the first derivative curve of the current characteristic map of the power-consuming device of the preset device type under different working states, and the preset device type includes the device type of the power-consuming device; The changes in the first derivative curve are determined by the preset waveform recognition model.

5. The method for detecting the operating status of power-consuming equipment according to claim 4, characterized in that, Determining the change of the first derivative curve through the preset waveform recognition model includes at least one of the following: When the derivative value in the first derivative curve is zero or the derivative value in the first derivative curve is greater than a first preset derivative threshold, the preset waveform recognition model determines the change of the first derivative curve as a first change and determines the power-consuming device as a first device type. When the first derivative curve shows an upward trend, the preset waveform recognition model determines the change of the first derivative curve as a second change and determines the power-consuming device as a second device type. If the derivative value in the first derivative curve is greater than the second preset derivative threshold, the preset waveform recognition model determines the change of the first derivative curve as a third change and determines the power-consuming device as a third device type; wherein, the second preset derivative threshold is less than the first preset derivative threshold; When the range of derivative values ​​in the first derivative curve exceeds a preset derivative range, the preset waveform recognition model determines the change of the first derivative curve as a fourth change and identifies the power-consuming device as a fourth device type; wherein, any derivative value within the preset derivative range is less than the second preset derivative threshold.

6. The method for detecting the operating status of power-consuming equipment according to claim 5, characterized in that, The method for determining the target detection method for the power-consuming equipment based on the change of the first derivative curve includes at least one of the following: When the change of the first derivative curve is the first change and the power-consuming device is the first device type, the target detection method for detecting the power-consuming device is determined to be the threshold judgment method. When the change of the first derivative curve is the second change and the power-consuming device is the second type of device, the target detection method for detecting the power-consuming device is determined to be the arithmetic mean method. When the change of the first derivative curve is the third change and the power-consuming device is the third type of device, the target detection method for detecting the power-consuming device is determined to be the mean square error method. When the change of the first derivative curve is the fourth change case and the power-consuming device is the fourth device type, the target detection method for detecting the power-consuming device is determined to be the template matching method.

7. A device for detecting the operating status of power-consuming equipment, characterized in that, include: The acquisition module is used to acquire the current characteristic map of power-consuming devices; The first determining module is used to determine the derivative of the current characteristic graph; The second determining module is used to determine the device type of the power-consuming device and the target detection method corresponding to the device type based on the derivative of the current characteristic graph; wherein, the device type is a type classified according to the waveform of the current derivative; The detection module is used to detect the operating status of the power-consuming equipment based on the target detection method.

8. The device for detecting the operating status of power-consuming equipment according to claim 7, characterized in that, The first determining module includes: A noise reduction unit is used to perform smooth noise reduction processing on the current feature map to obtain a processed current feature map. A calculation unit is used to calculate the derivative of the processed current characteristic map; wherein the derivative is the first derivative.

9. The device for detecting the operating status of power-consuming equipment according to claim 8, characterized in that, The second determining module includes: The first determining unit is used to determine the first derivative curve of the processed current characteristic graph based on the derivative of the processed current characteristic graph. The second determining unit is used to determine the changes in the first derivative curve; The third determining unit is used to determine the equipment type of the power-consuming equipment and the target detection method corresponding to the equipment type based on the change of the first derivative curve.

10. The device for detecting the operating status of power-consuming equipment according to claim 9, characterized in that, The operating status includes the working status; The second determining unit includes: An input subunit is used to input the first derivative curve into a preset waveform recognition model; wherein, the preset waveform recognition model is a neural network model trained based on the first derivative curve of the current characteristic map of a power-consuming device of a preset device type under different working states, and the preset device type includes the device type of the power-consuming device; A sub-unit is defined to determine the changes in the first derivative curve using the preset waveform recognition model.

11. The device for detecting the operating status of power-consuming equipment according to claim 10, characterized in that, The determining subunit is configured to perform at least one of the following: When the derivative value in the first derivative curve is zero or the derivative value in the first derivative curve is greater than a first preset derivative threshold, the preset waveform recognition model determines the change of the first derivative curve as a first change and determines the power-consuming device as a first device type. When the first derivative curve shows an upward trend, the preset waveform recognition model determines the change of the first derivative curve as a second change and determines the power-consuming device as a second device type. If the derivative value in the first derivative curve is greater than the second preset derivative threshold, the preset waveform recognition model determines the change of the first derivative curve as a third change and determines the power-consuming device as a third device type; wherein, the second preset derivative threshold is less than the first preset derivative threshold; When the range of derivative values ​​in the first derivative curve exceeds a preset derivative range, the preset waveform recognition model determines the change of the first derivative curve as a fourth change and identifies the power-consuming device as a fourth device type; wherein, any derivative value within the preset derivative range is less than the second preset derivative threshold.

12. The device for detecting the operating status of power-consuming equipment according to claim 11, characterized in that, The third determining unit is configured to perform at least one of the following: When the change of the first derivative curve is the first change and the power-consuming device is the first device type, the target detection method for detecting the power-consuming device is determined to be the threshold judgment method. When the change of the first derivative curve is the second change and the power-consuming device is the second type of device, the target detection method for detecting the power-consuming device is determined to be the arithmetic mean method. When the change of the first derivative curve is the third change and the power-consuming device is the third type of device, the target detection method for detecting the power-consuming device is determined to be the mean square error method. When the change of the first derivative curve is the fourth change case and the power-consuming device is the fourth device type, the target detection method for detecting the power-consuming device is determined to be the template matching method.

13. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the method for detecting the operating status of a power-consuming device as described in any one of claims 1 to 6.

14. A storage medium, wherein when instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the power-consuming device operation status detection method as described in any one of claims 1 to 6.

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