Power equipment abnormity identification method and device, electronic equipment and medium

Abstract

The embodiment of the invention discloses a power equipment abnormity identification method and device, electronic equipment and a medium. A specific embodiment of the method comprises the following steps: for each piece of power equipment acquisition information in a power equipment acquisition information set, executing the following exception identification steps: performing feature extraction on each power equipment image in a power equipment image group included in the power equipment acquisition information to generate power equipment image features; inputting the electrical equipment image feature group into a pre-trained electrical equipment appearance anomaly detection model to obtain an electrical equipment appearance anomaly detection result; performing anomaly detection on a power equipment operation data set included in the power equipment acquisition information to generate an operation data anomaly detection result; and in response to determining that the appearance anomaly detection result of the power equipment meets the appearance anomaly alarm condition, sending equipment anomaly alarm information to an associated power equipment maintenance terminal. According to the embodiment, the efficiency and accuracy of detecting the operation data of the power equipment are improved.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the computer field, and specifically to a method, device, electronic device and medium for identifying abnormality of power equipment. Background Art

[0002] With the continuous growth of global energy demand and increasingly severe environmental problems, smart grid technology, as a key technology to achieve efficient energy utilization and optimal configuration, has become an important direction for the development of the global power system. At present, the common method for the operation detection of power equipment is that technicians regularly inspect and repair the power equipment in the power grid area. However, this method reduces the efficiency of power equipment detection, fails to eliminate abnormal power equipment in time, and easily causes power safety hazards.

[0003] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the invention

[0004] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0005] Some embodiments of the present disclosure propose a method, an apparatus, an electronic device, and a computer-readable medium for identifying abnormalities in power equipment to solve one or more of the technical problems mentioned in the above background technology section.

[0006] In a first aspect, some embodiments of the present disclosure provide a method for identifying anomalies in power equipment, the method comprising: in response to receiving an anomaly identification request for power equipment data, obtaining an power equipment collection information set sent by an power equipment information collection terminal, wherein the power equipment collection information in the power equipment collection information set comprises: power equipment name, power equipment location, power equipment image group, and power equipment operation data set; for each power equipment collection information in the power equipment collection information set, performing the following anomaly identification steps: performing feature extraction on each power equipment image in the power equipment image group included in the power equipment collection information to generate power equipment image features and obtain a power equipment image feature group; inputting the power equipment image feature group into a pre-trained power equipment appearance anomaly detection model to obtain a power equipment appearance anomaly detection result; performing anomaly detection on the power equipment operation data set included in the power equipment collection information to generate an operation data anomaly detection result; in response to determining that the power equipment appearance anomaly detection result meets the appearance anomaly alarm condition and / or the operation data anomaly detection result meets the data anomaly alarm condition, sending equipment anomaly alarm information to the associated power equipment maintenance terminal.

[0007] In a second aspect, some embodiments of the present disclosure provide an apparatus for identifying abnormalities in electric power equipment, the apparatus comprising: an acquisition unit, configured to, in response to receiving an electric power equipment data abnormality identification request, acquire an electric power equipment collection information set sent by an electric power equipment information collection terminal, wherein the electric power equipment collection information in the electric power equipment collection information set comprises: electric power equipment name, electric power equipment location, electric power equipment image group, and electric power equipment operation data set; an identification unit, configured to, for each electric power equipment collection information in the electric power equipment collection information set, perform the following abnormality identification steps: perform feature extraction on each electric power equipment image in the electric power equipment image group included in the electric power equipment collection information to generate electric power equipment image features and obtain an electric power equipment image feature group; input the electric power equipment image feature group into a pre-trained electric power equipment appearance abnormality detection model to obtain an electric power equipment appearance abnormality detection result; perform abnormality detection on the electric power equipment operation data set included in the electric power equipment collection information to generate an operation data abnormality detection result; in response to determining that the electric power equipment appearance abnormality detection result meets the appearance abnormality alarm condition and / or the operation data abnormality detection result meets the data abnormality alarm condition, send equipment abnormality alarm information to the associated electric power equipment maintenance terminal.

[0008] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner of the above-mentioned first aspect.

[0009] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation manner of the above-mentioned first aspect is implemented.

[0010] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: through the power equipment anomaly identification method of some embodiments of the present disclosure, the efficiency and accuracy of the detection of power equipment operation data are improved. First, in response to receiving a request for anomaly identification of power equipment data, the power equipment collection information set sent by the power equipment information collection terminal is obtained, wherein the power equipment collection information in the power equipment collection information set includes: power equipment name, power equipment location, power equipment image group, power equipment operation data set. Then, for each power equipment collection information in the power equipment collection information set, the following abnormality identification steps are performed: feature extraction is performed on each power equipment image in the power equipment image group included in the power equipment collection information to generate power equipment image features and obtain a power equipment image feature group; the power equipment image feature group is input into a pre-trained power equipment appearance anomaly detection model to obtain a power equipment appearance anomaly detection result. In this way, it is detected whether the appearance of the power equipment is abnormal, thereby avoiding damage to the internal hardware facilities. Finally, an abnormality detection is performed on the power equipment operation data set included in the power equipment collection information to generate an abnormality detection result of the operation data; in response to determining that the power equipment appearance abnormality detection result meets the appearance abnormality alarm condition and / or the operation data abnormality detection result meets the data abnormality alarm condition, an equipment abnormality alarm information is sent to the associated power equipment maintenance terminal. Thus, the abnormality type of the operation data of each power equipment operation data can be detected by the power equipment operation data abnormality detection model. The amount of data processing in the model prediction process is reduced, and the waste of computer computing resources is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar figure labels represent the same or similar elements. It should be understood that the drawings are schematic and that the components and elements are not necessarily drawn to scale.

[0012] Figure 1 is a flow chart of some embodiments of the method for identifying abnormality of electric power equipment according to the present disclosure;

[0013] Figure 2 is a flow chart of some embodiments of the apparatus for identifying abnormality of electric power equipment according to the present disclosure;

[0014] Figure 3It is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0015] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0016] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0017] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0018] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0019] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0020] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0021] Figure 1 The flowchart of some embodiments of the method for identifying abnormality of electric power equipment according to the present disclosure is shown. The process 100 of some embodiments of the method for identifying abnormality of electric power equipment according to the present disclosure is shown. The method for identifying abnormality of electric power equipment, applied to an intelligent robot, comprises the following steps:

[0022] Step 101, in response to receiving a request for identifying abnormality in power equipment data, obtaining a power equipment collection information set sent by a power equipment information collection terminal.

[0023] In some embodiments, the execution subject (e.g., computing device) of the power equipment abnormality identification method may obtain the power equipment collection information set sent by the power equipment information collection terminal in response to receiving the power equipment data abnormality identification request. Among them, the power equipment collection information in the power equipment collection information set includes: power equipment name, power equipment location, power equipment image group, power equipment operation data set. The power equipment name may represent the name of the power equipment whose information is collected. For example, the power equipment may be a transformer, a circuit breaker, an electric meter and other equipment. The power equipment may also be a power generation equipment. The power generation equipment is mainly a power station boiler, a steam turbine, a gas turbine, a water turbine, a generator, a transformer, etc. The power equipment data abnormality identification request may refer to a request for abnormal identification of the power equipment collection information. That is, determine whether there is abnormal data in the power equipment collection information. Thereby judging whether the power equipment is abnormal (e.g., the appearance is damaged, the data operation is abnormal). The power equipment information collection terminal may refer to an edge data collection terminal that collects the operation information of each power equipment. The power equipment location may represent the location of the power equipment whose information is collected. The power equipment image group may represent various appearance images (front view, side view, etc.) of an electric equipment. The power equipment operation data set may represent various operation data of the power equipment within a preset time period. For example, the power equipment operation data set may include: electrical parameters, temperature parameters, hydrogen system parameters, and internal cooling water system parameters. For example, the electrical parameters may include: active power, reactive power, stator voltage, stator current, rotor voltage, and rotor current. Temperature parameters may include: stator winding temperature, stator water pipe temperature, stator core temperature, bearing temperature, bearing return oil temperature, etc. Hydrogen system parameters may include: cold hydrogen temperature, hot hydrogen temperature, hydrogen pressure, oil-hydrogen pressure difference, hydrogen side oil and air side oil pressure difference, hydrogen quality (humidity, purity), etc. Internal cooling water system parameters may include: internal cooling water inlet (outlet) water temperature, internal cooling water inlet (outlet) water pressure, water flow rate, and internal cooling water quality (pH value, conductivity).

[0024] Step 102: for each piece of electric power equipment collection information in the electric power equipment collection information set, perform the following abnormality identification steps:

[0025] Step 1021: extract features from each power equipment image in the power equipment image group included in the power equipment collection information to generate power equipment image features, thereby obtaining a power equipment image feature group.

[0026] In some embodiments, the execution entity may perform feature extraction on each power equipment image in the power equipment image group included in the power equipment acquisition information to generate power equipment image features and obtain a power equipment image feature group.

[0027] In practice, the execution subject may extract features from each power equipment image in the power equipment image group included in the power equipment collection information through the following steps to generate power equipment image features:

[0028] In the first step, the power equipment image is subjected to image denoising to generate a denoised power equipment image. The power equipment image can be subjected to image denoising by an image denoising algorithm to generate a denoised power equipment image. For example, the image denoising algorithm can be a mean filter, a statistical sorting filter, or a non-local mean denoising algorithm.

[0029] In the second step, the denoised power equipment image is input into a pre-trained power equipment image feature extraction model to obtain power equipment image features. The power equipment image feature extraction model can be a 2D-based convolutional neural network model. For example, the power equipment image feature extraction model can be a fully connected network model. The power equipment image features can characterize the characteristic semantic content of the power equipment in the power equipment image.

[0030] The power equipment image feature extraction model can be trained through the following steps:

[0031] The first step is to fine-tune the initial power equipment image feature extraction model based on the pre-trained large model to generate a fine-tuned power equipment image feature extraction model. The pre-trained large model can be a large model for power equipment image feature extraction. For example, first, the above-mentioned execution subject can obtain an initial power equipment image sample set. Then, using the pre-trained large model, generate a power equipment feature information label set corresponding to the initial power equipment image sample set. Next, use the power equipment feature information label set as the label set and the initial power equipment image sample set as the training sample set to train the above-mentioned initial power equipment image feature extraction model to generate a fine-tuned power equipment image feature extraction model.

[0032] The second step is to obtain a sample set of power equipment images. The power equipment image samples include sample power equipment images and real equipment images. The sample power equipment images can be equipment input images related to the appearance of the power equipment. The real equipment images can be images of the appearance surface of the power equipment obtained by real photography.

[0033] The third step is to select a target power equipment image sample from the power equipment image sample set. An power equipment image sample can be randomly selected from the power equipment image sample set as the target power equipment image sample.

[0034] Step 4: For the target power equipment image sample, perform the following training steps:

[0035] 1. Input the sample power equipment image included in the target power equipment image sample into the fine-tuning power equipment image feature extraction model to generate the sample power equipment image features.

[0036] 2. Input the sample power equipment image features into the initial power equipment image generation model to generate a power equipment prediction image. The initial power equipment image generation model may be a neural network model for generating power equipment images that has not yet been trained. For example, the initial power equipment image generation model may be a multi-layer series of convolutional layers.

[0037] 3. Determine whether the model training is completed based on the predicted image of the power equipment and the corresponding real equipment image. As an example, first, the above-mentioned execution subject can use the difference in image semantic content between the predicted image of the power equipment and the corresponding real equipment image as difference information. Then, the difference information is input into the loss function to generate image loss information. Next, in response to determining that the loss information in the image loss information sequence where the image loss information is located tends to a predetermined value, determine that the model training is completed. In response to determining that the loss information in the image loss information sequence where the image loss information is located does not tend to a predetermined value, determine that the model training is not completed.

[0038] 4. In response to the completion of the training, the fine-tuned electric power equipment image feature extraction model is determined as the trained electric power equipment image feature extraction model.

[0039] In step 5, in response to the training not being completed, the fine-tuning power equipment image feature extraction model and the initial power equipment image generation model are trained according to the power equipment prediction image and the corresponding real equipment image, so as to generate a trained power equipment image feature extraction model and a trained power equipment image generation model. For example, the execution subject may perform model training on the fine-tuning power equipment image feature extraction model and the initial power equipment image generation model according to the image loss information of the power equipment prediction image and the corresponding real equipment image, using a back propagation method, so as to generate a trained power equipment image feature extraction model and a trained power equipment image generation model.

[0040] In the sixth step, the target power equipment image samples are reselected from the unselected power equipment image samples, the trained power equipment image feature extraction model is used as the fine-tuning power equipment image feature extraction model, and the trained power equipment image generation model is used as the power equipment image generation model, and the training step is performed again.

[0041] Step 1022: input the electric power equipment image feature group into a pre-trained electric power equipment appearance anomaly detection model to obtain an electric power equipment appearance anomaly detection result.

[0042] In some embodiments, the above-mentioned execution entity may input the power equipment image feature group into a pre-trained power equipment appearance anomaly detection model to obtain a power equipment appearance anomaly detection result. The power equipment appearance anomaly detection model may refer to a pre-trained neural network model that takes the power equipment image feature group as input and the power equipment appearance anomaly detection result as output. The power equipment appearance anomaly detection result may indicate whether the appearance of the power equipment corresponding to the power equipment image feature group is abnormal. For example, the appearance anomaly of the power equipment may indicate rust / deformation / depression on the surface of the power equipment appearance. For example, the power equipment appearance anomaly detection model may refer to a convolutional neural network (CNN), a generative adversarial network (GAN), or a long short-term memory network (LSTM). For another example, the power equipment appearance anomaly detection model may refer to a model based on color feature analysis, a model based on texture feature analysis, or a model based on shape feature analysis.

[0043] Models based on color feature analysis: Color is one of the important features for object appearance detection. Common color feature analysis methods include color histogram, color filtering, and color segmentation. Color histogram generates a color distribution histogram by counting the color values ​​of each pixel in the image. Color filtering detects specific colors in the image by defining different color filters. Color segmentation segments the image according to color features1.

[0044] Models based on texture feature analysis: Texture is another important feature in object appearance detection, which is used to describe the surface features of an object. Texture feature analysis methods are usually implemented by performing operations such as texture filtering, texture segmentation, and texture matching on images. Texture filtering enhances or suppresses the texture information in an image by defining a texture filter, while texture segmentation separates the texture area in an image, and texture matching performs object appearance detection and classification by comparing the texture information in different images1.

[0045] Model based on shape feature analysis: Shape is another important feature of object appearance detection, which can be identified and classified by analyzing the shape features of the object. Shape feature analysis methods include edge detection, contour extraction and shape description. Edge detection extracts the contour of the object by detecting the edges in the image, contour extraction extracts the contour of the object in the image, and shape description mathematically describes the shape features of the object.

[0046] Step 1023: Perform anomaly detection on the electric power equipment operation data set included in the electric power equipment collection information to generate an operation data anomaly detection result.

[0047] In some embodiments, the above-mentioned execution subject may perform anomaly detection on the power equipment operation data set included in the power equipment collection information to generate an anomaly detection result of the operation data. For example, each power equipment operation data may be monitored through a pre-set power equipment operation index operation interval table to generate an anomaly detection result of the operation data. The anomaly detection result of the operation data may indicate whether there is power equipment operation data that is not within the corresponding power equipment operation index operation interval in the power equipment operation data set. One power equipment operation data corresponds to one power equipment operation index operation interval. The power equipment operation index operation interval may indicate the normal operating parameter interval of a certain power equipment operation index.

[0048] In practice, the above-mentioned execution entity can input the power equipment operation data set into a pre-trained power equipment operation data anomaly detection model to obtain the power equipment operation data anomaly detection result.

[0049] The power equipment operation data anomaly detection model can be trained through the following steps:

[0050] The first step is to obtain a sample set of power equipment operation information. The power equipment operation information sample includes: a power equipment operation sub-data group. The power equipment operation sub-data can be data on power operation collected by power monitoring equipment within a preset time period. The power monitoring equipment includes: smart meters and power load collectors. The above-mentioned power equipment operation sub-data can be data used to determine the type of abnormal operation.

[0051] The second step is to select a target electric equipment operation information sample from the electric equipment operation information sample set. An electric equipment operation information sample can be randomly selected from the electric equipment operation information sample set as the target electric equipment operation information sample.

[0052] The third step is to perform operation abnormality type identification processing on each power equipment operation sub-data included in the target power equipment operation information sample to obtain an operation data abnormality type group. For example, the above-mentioned operation data abnormality type can be a type indicating abnormal operation data behavior. The above-mentioned operation data abnormality type can be: the operation data is less than the minimum threshold, or the operation data is greater than the maximum threshold.

[0053] The third step may include the following sub-steps:

[0054] In the first sub-step, for each power equipment operation sub-data, the following processing steps are performed:

[0055] 1. Acquire the detection task information corresponding to the power equipment operation sub-data. For example, the detection task information may be a programming language code for performing operation anomaly detection on the power equipment operation sub-data.

[0056] 2. Execute the detection task corresponding to the detection task information to perform type anomaly detection on the power equipment operation sub-data to generate a data anomaly type. For example, when the task detection information is an operation data correlation numerical sequence, the execution subject can execute the detection task corresponding to the operation data correlation numerical sequence to perform power consumption anomaly detection on the power equipment operation sub-data to obtain the corresponding data anomaly type. The data anomaly type can be a representation of "power equipment operation sub-data anomaly" or a representation of "power equipment operation sub-data normal".

[0057] In the second sub-step, the data abnormality type that meets the preset abnormality type condition among the data abnormality types is determined as the operation data abnormality type to obtain an operation data abnormality type group. The abnormality type condition may be that the data abnormality type represents abnormality in the operation sub-data of the power equipment.

[0058] The fourth step is to train the initial power equipment operation data anomaly detection model according to the power equipment operation sub-data group and the operation data anomaly type group included in the target power equipment operation information sample to obtain a trained power equipment operation data anomaly detection model.

[0059] The fourth step may include the following sub-steps:

[0060] The first sub-step is to perform the following processing steps for each electric power equipment operation sub-data in the electric power equipment operation sub-data group:

[0061] 1. Determine the operation data abnormality type corresponding to the power equipment operation sub-data in the operation data abnormality type group as the reference operation data abnormality type.

[0062] 2. Combine the power equipment operation sub-data and the reference operation data abnormality type into a power equipment operation sub-data sample.

[0063] The second sub-step is to select a target power equipment operation sub-data sample from each power equipment operation sub-data sample. For example, one power equipment operation sub-data sample can be randomly selected from each power equipment operation sub-data sample as the target power equipment operation sub-data sample.

[0064] The third sub-step is to input the power equipment operation sub-data included in the target power equipment operation sub-data sample into the initial power equipment operation data anomaly detection model to obtain the initial power equipment operation data anomaly detection result.

[0065] The fourth sub-step is to determine the loss value between the initial power equipment operation data anomaly detection result and the corresponding reference operation data anomaly type. For example, the loss value between the initial power equipment operation data anomaly detection result and the corresponding reference operation data anomaly type can be determined by a preset damage function.

[0066] The fifth sub-step is, in response to determining that the loss value is less than or equal to the preset loss value, determining the initial power equipment operation data anomaly detection model as the trained power equipment operation data anomaly detection model.

[0067] Therefore, the abnormal operation data type of each power equipment operation data can be detected by the power equipment operation data abnormality detection model, which reduces the amount of data processing in the model prediction process and reduces the waste of computer computing resources.

[0068] Step 1024, in response to determining that the power equipment appearance abnormality detection result meets the appearance abnormality alarm condition and / or the operation data abnormality detection result meets the data abnormality alarm condition, send equipment abnormality alarm information to the associated power equipment maintenance terminal.

[0069] In some embodiments, the above-mentioned execution subject may send equipment abnormality alarm information to the associated power equipment maintenance terminal in response to determining that the power equipment appearance abnormality detection result meets the appearance abnormality alarm condition and / or the operation data abnormality detection result meets the data abnormality alarm condition. For example, the appearance abnormality alarm condition may be: the power equipment appearance abnormality detection result indicates that the power equipment appearance is abnormal. The data abnormality alarm condition may be: the operation data abnormality detection result indicates that the power equipment operation data is abnormal. The power equipment maintenance terminal may refer to a terminal for maintenance monitoring of the power equipment. The equipment abnormality alarm information may be information indicating that the power equipment is abnormal and needs to be repaired.

[0070] Further references Figure 2 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of an apparatus for identifying abnormality of an electric device. These embodiments of the apparatus for identifying abnormality of an electric device are similar to Figure 1 Corresponding to the method embodiments shown, the power equipment abnormality identification device can be specifically applied to various electronic devices.

[0071] like Figure 2As shown, the apparatus 200 for identifying abnormality of electric power equipment in some embodiments includes: an acquisition unit 201 and an identification unit 202 . Among them, the acquisition unit 201 is configured to, in response to receiving a request for identifying anomalies in power equipment data, acquire a power equipment collection information set sent by a power equipment information collection terminal, wherein the power equipment collection information in the power equipment collection information set includes: power equipment name, power equipment location, power equipment image group, and power equipment operation data set; the identification unit 202 is configured to perform the following anomaly identification steps for each power equipment collection information in the power equipment collection information set: perform feature extraction on each power equipment image in the power equipment image group included in the power equipment collection information to generate power equipment image features and obtain a power equipment image feature group; input the power equipment image feature group into a pre-trained power equipment appearance anomaly detection model to obtain a power equipment appearance anomaly detection result; perform anomaly detection on the power equipment operation data set included in the power equipment collection information to generate an operation data anomaly detection result; in response to determining that the power equipment appearance anomaly detection result meets the appearance anomaly alarm condition and / or the operation data anomaly detection result meets the data anomaly alarm condition, send equipment anomaly alarm information to the associated power equipment maintenance terminal.

[0072] It can be understood that the units described in the power equipment abnormality identification device 200 are similar to those described in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the power equipment abnormality identification device 200 and the units included therein, and will not be described in detail here.

[0073] Reference below Figure 3 , which shows a schematic diagram of the structure of an electronic device (e.g., a computing device) 300 suitable for implementing some embodiments of the present disclosure. The electronic devices in some embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (e.g., vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0074] like Figure 3As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0075] Typically, the following devices may be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication devices 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0076] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0077] It should be noted that the computer-readable medium recorded in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0078] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0079] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: in response to receiving a request for identifying anomalies in power equipment data, obtains a power equipment collection information set sent by a power equipment information collection terminal, wherein the power equipment collection information in the power equipment collection information set includes: a power equipment name, a power equipment location, a power equipment image group, and a power equipment operation data set; for each power equipment collection information in the power equipment collection information set, performs the following anomaly identification steps: performs feature extraction on each power equipment image in the power equipment image group included in the power equipment collection information to generate power equipment image features and obtain a power equipment image feature group; inputs the power equipment image feature group into a pre-trained power equipment appearance anomaly detection model to obtain a power equipment appearance anomaly detection result; performs anomaly detection on the power equipment operation data set included in the power equipment collection information to generate an operation data anomaly detection result; in response to determining that the power equipment appearance anomaly detection result meets the appearance anomaly alarm condition and / or the operation data anomaly detection result meets the data anomaly alarm condition, sends equipment anomaly alarm information to the associated power equipment maintenance terminal.

[0080] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0081] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0082] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The described units may also be provided in a processor, for example, may be described as: a processor comprising: an acquisition unit and an identification unit. The names of these units do not constitute a limitation on the units themselves in certain circumstances, for example, the acquisition unit may also be described as "a unit for acquiring a power equipment collection information set sent by a power equipment information collection terminal in response to receiving a request for identifying an abnormality in power equipment data".

[0083] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0084] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) and the technical solutions formed.

Claims

1. A method for identifying abnormality of electric power equipment, comprising: In response to receiving a request for identifying abnormal power equipment data, obtaining a power equipment collection information set sent by a power equipment information collection terminal, wherein the power equipment collection information in the power equipment collection information set includes: a power equipment name, a power equipment location, a power equipment image group, and a power equipment operation data set; For each piece of electric power equipment collection information in the electric power equipment collection information set, the following abnormality identification steps are performed: Extracting features from each power equipment image in the power equipment image group included in the power equipment collection information to generate power equipment image features, thereby obtaining a power equipment image feature group; Inputting the electric power equipment image feature group into a pre-trained electric power equipment appearance anomaly detection model to obtain an electric power equipment appearance anomaly detection result; Performing anomaly detection on the power equipment operation data set included in the power equipment collection information to generate an anomaly detection result of the operation data; In response to determining that the electric equipment appearance abnormality detection result meets the appearance abnormality alarm condition and / or the operation data abnormality detection result meets the data abnormality alarm condition, equipment abnormality alarm information is sent to the associated electric equipment maintenance terminal.

2. The method according to claim 1, wherein: The extracting features of each electric equipment image in the electric equipment image group included in the electric equipment collection information to generate electric equipment image features includes: Performing image denoising processing on the electric power equipment image to generate a denoised electric power equipment image; The denoised power equipment image is input into a pre-trained power equipment image feature extraction model to obtain the power equipment image features.

3. The method according to claim 1, wherein: The performing anomaly detection on the electric power equipment operation data set included in the electric power equipment collection information to generate an anomaly detection result of the operation data includes: The electric power equipment operation data set is input into a pre-trained electric power equipment operation data anomaly detection model to obtain an electric power equipment operation data anomaly detection result.

4. The method according to claim 3, wherein: Before performing anomaly detection on the electric power equipment operation data set included in the electric power equipment collection information to generate an anomaly detection result of the operation data, the method further includes: Acquire a sample set of power equipment operation information, wherein the power equipment operation information sample includes: a power equipment operation sub-data group; Selecting a target electric power equipment operation information sample from the electric power equipment operation information sample set; Performing operation abnormality type identification processing on each power equipment operation sub-data included in the target power equipment operation information sample to obtain an operation data abnormality type group; According to the power equipment operation sub-data group and the operation data anomaly type group included in the target power equipment operation information sample, the initial power equipment operation data anomaly detection model is trained to obtain a trained power equipment operation data anomaly detection model.

5. The method according to claim 4, wherein: The step of performing operation abnormality type identification processing on each power equipment operation sub-data included in the target power equipment operation information sample to obtain an operation data abnormality type group includes: For each power equipment running sub-data, perform the following processing steps: Acquire detection task information corresponding to the power equipment operation sub-data; Executing the detection task corresponding to the detection task information to perform type anomaly detection on the power equipment operation sub-data to generate a data anomaly type; The data anomaly type that meets the preset anomaly type condition among the various data anomaly types is determined as the operation data anomaly type to obtain an operation data anomaly type group.

6. A device for identifying abnormality of electric power equipment, comprising: an acquisition unit, configured to acquire, in response to receiving a request for identifying abnormal data of electric power equipment, an electric power equipment acquisition information set sent by an electric power equipment information acquisition terminal, wherein the electric power equipment acquisition information in the electric power equipment acquisition information set includes: an electric power equipment name, an electric power equipment location, an electric power equipment image group, and an electric power equipment operation data set; The identification unit is configured to perform the following abnormality identification steps for each power equipment collection information in the power equipment collection information set: perform feature extraction on each power equipment image in the power equipment image group included in the power equipment collection information to generate power equipment image features and obtain a power equipment image feature group; input the power equipment image feature group into a pre-trained power equipment appearance abnormality detection model to obtain a power equipment appearance abnormality detection result; perform abnormality detection on the power equipment operation data set included in the power equipment collection information to generate an operation data abnormality detection result; in response to determining that the power equipment appearance abnormality detection result meets the appearance abnormality alarm condition and / or the operation data abnormality detection result meets the data abnormality alarm condition, send equipment abnormality alarm information to the associated power equipment maintenance terminal.

7. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 5.

8. A computer readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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