CN117746213A

CN117746213A - Network training method, switch state identification method, device and terminal equipment

Info

Publication number: CN117746213A
Application number: CN202311859021.4A
Authority: CN
Inventors: 李贻凯; 李勇琦; 贺儒飞; 彭煜民; 王文辉; 马一鸣; 李尧; 张豪; 赵增涛; 黄凡旗; 冯易新
Original assignee: Energy Storage Research Institute Of China Southern Power Grid Peak Regulation And Frequency Regulation Power Generation Co ltd; Shaanxi Earth Network Technology Co ltd; Peak and Frequency Regulation Power Generation Co of China Southern Power Grid Co Ltd
Current assignee: Energy Storage Research Institute Of China Southern Power Grid Peak Regulation And Frequency Regulation Power Generation Co ltd; Shaanxi Earth Network Technology Co ltd; Peak and Frequency Regulation Power Generation Co of China Southern Power Grid Co Ltd
Priority date: 2023-12-30
Filing date: 2023-12-30
Publication date: 2024-03-22

Abstract

The application belongs to the field of intelligent monitoring and diagnosis of power systems, and particularly relates to a network training method, a switch state identification device and terminal equipment. Firstly, acquiring an image sample containing a target switch; then, extracting key features of the image sample to construct a first feature map matrix; and finally, training the switch state recognition network based on the first feature map matrix, and establishing a switch state recognition model. According to the intelligent recognition method and the intelligent recognition system, based on the image sample key characteristics and the MobileNet V2 deep learning network, an intelligent recognition algorithm capable of rapidly and accurately recognizing the position state of the switching equipment is constructed, and an algorithm capable of being applied to recognition of the position state of the switching equipment in different types and structures is constructed, so that recognition and judgment of the position state of the opening and closing of the internal conductor of the pumped storage complete equipment are realized.

Description

Network training method, switch state identification method, device and terminal equipment

Technical Field

The application belongs to the field of intelligent monitoring and diagnosis of power systems, and particularly relates to a network training method, a switch state identification device and terminal equipment.

Background

The pumped storage power station is a two-way power station for water discharge power generation and pumped storage, solves the problem of difficult electric energy storage faced by the traditional power station all the time, and can avoid the ineffective waste of electric energy to a great extent. Therefore, the power grid has larger energy storage dependence on the pumped storage power station, and has a great application market in the future. In each pumped storage process, the switching device at the outlet of the generator needs to perform corresponding opening and closing operations. In the multiple opening and closing operations, various problems, such as motor faults, thermal expansion of contacts, abrasion and the like, of the switching equipment are unavoidable, and even safety accidents are caused in serious cases. Therefore, the real-time monitoring of the complete switch equipment is particularly important to quickly and accurately identify the switching-on and switching-off position state and alarm in time for abnormal conditions.

The traditional power station mostly adopts a manual field observation mode for judging the opening and closing state of the high-voltage switch equipment, the method is high in labor cost, time-consuming and labor-consuming, subjective factors have great influence on a judging result, and great personal safety hazards exist in the operation of the high-voltage power station. In recent years, remote video monitoring technology for switchgear is gradually proposed in the market, and trial application is realized in part of power stations. Although the method reduces the danger, time consuming and inconvenient of manual field observation to a great extent, the technology is still immature, and the intelligent recognition algorithm with high efficiency and accuracy is not widely applied at present because of the problems of complex and changeable field actual working conditions, difficult sample data acquisition and the like, and the remote video data still needs to be judged manually under most conditions.

Disclosure of Invention

In order to overcome the problems in the related art, the embodiment of the application provides a network training method, a switch state identification method, a device and terminal equipment.

The application is realized by the following technical scheme:

in a first aspect, an embodiment of the present application provides a network training method, including:

an image sample containing a target switch is acquired.

And extracting key features of the image sample to construct a first feature map matrix.

Based on the first feature diagram matrix, training the switch state recognition network, and establishing a switch state recognition model.

In a possible implementation manner of the first aspect, the extracting key features from the image sample to construct a first feature map matrix includes:

the image sample is divided into a first static side area, a first intermediate dynamic area and a first dynamic side area in sequence in the horizontal direction.

And respectively carrying out feature enhancement processing on the first static side region, the first middle dynamic region and the first dynamic side region, and generating a plurality of target images with different feature parameters by each region image, namely realizing feature expansion of a single sample, wherein each target image comprises key features of different regions of the target switch.

Each target image is subjected to graying processing, and size normalization is performed.

And longitudinally splicing the first static side region, the first middle dynamic region and the first dynamic side region in each target image to obtain a first feature map matrix.

In a possible implementation manner of the first aspect, the training the switch state identification network based on the first feature map matrix, and building a switch state identification model includes:

and marking the position states of the switches in the first feature map matrix as in-place opening, in-place closing and fault states respectively, obtaining a sample data set, and dividing the sample data set into a training set and a verification set according to the proportion.

And building a MobileNet V2 network, and modifying the full-connection layer structure and the output category of the MobileNet V2 network.

Based on the training set and the verification set, training the switch state recognition network, and establishing a switch state recognition model.

In a second aspect, an embodiment of the present application provides a method for identifying a switch state, including:

and acquiring an image to be identified, wherein the image to be identified comprises a switch to be identified.

The image sample is divided into a second static side region, a second intermediate dynamic region and a second dynamic side region in order in the horizontal direction.

And respectively carrying out gray scale treatment on the second static side region, the second middle dynamic region and the second dynamic side region, and carrying out size normalization.

And longitudinally splicing a second static side region, a second middle dynamic region and a second dynamic side region in each target image to obtain a second feature map matrix.

And inputting the second feature map matrix into the established switch state identification model to obtain the position state of the switch to be identified.

In a third aspect, an embodiment of the present application provides a training apparatus for a switch state identification network, including:

and the first acquisition module is used for acquiring an image sample containing the target switch.

And the feature extraction module is used for extracting key features of the image sample and constructing a first feature map matrix.

The switch state recognition model building module is used for training the switch state recognition network based on the first feature diagram matrix and building a switch state recognition model.

In a fourth aspect, an embodiment of the present application provides a switch state identifying device, including:

the second acquisition module is used for acquiring an image to be identified, and the image to be identified comprises a switch to be identified.

The image segmentation module is used for sequentially segmenting the image sample into a second static side area, a second middle dynamic area and a second dynamic side area in the horizontal direction.

And the image normalization module is used for respectively carrying out gray scale processing on the second static side area, the second middle dynamic area and the second dynamic side area and carrying out size normalization.

And the image splicing module is used for longitudinally splicing the second static side area, the second middle dynamic area and the second dynamic side area in each target image to obtain a second feature map matrix.

The switch state determining module is used for inputting the second feature diagram matrix into the established switch state identification model to obtain the position state of the switch to be identified.

In a fifth aspect, embodiments of the present application provide a terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the method according to any one of the first aspects when executing the computer program.

In a sixth aspect, embodiments of the present application provide a computer readable storage medium storing a computer program which, when executed by a processor, implements a method according to any one of the first aspects.

In a seventh aspect, embodiments of the present application provide a computer program product for, when run on a terminal device, causing the terminal device to perform the method of any one of the first aspects.

It will be appreciated that the advantages of the second to seventh aspects may be found in the relevant description of the first aspect, and are not described here again.

Compared with the prior art, the embodiment of the application has the beneficial effects that:

in the embodiment of the application, an image sample containing a target switch is first acquired. And then, extracting key features of the image sample, and constructing a first feature map matrix. And finally, training the switch state recognition network based on the first feature map matrix, and establishing a switch state recognition model. According to the intelligent recognition method and the intelligent recognition system, based on the image sample key characteristics and the neural network, an intelligent recognition algorithm capable of rapidly and accurately recognizing the position state of the switching equipment is constructed, and an algorithm capable of being applied to recognition of the position state of the switching equipment in different types and structures is constructed, so that recognition and judgment of the position state of the opening and closing of the internal conductor of the pumping energy storage complete switch equipment are realized.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required for the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a schematic view of an application scenario provided in an embodiment of the present application.

Fig. 2 is a flow chart of a network training method according to an embodiment of the present application.

Fig. 3 is a flowchart of a network training method according to an embodiment of the present application.

Fig. 4 is a flowchart of a network training method according to an embodiment of the present application.

Fig. 5 is a schematic structural diagram of a training device of a switch state recognition network according to an embodiment of the present application.

Fig. 6 is a flowchart of a switch state identifying method according to an embodiment of the present application.

Fig. 7 is a schematic structural diagram of a switching state identifying device according to an embodiment of the present application.

Fig. 8 is a schematic structural diagram of a terminal device according to an embodiment of the present application.

Fig. 9 is a schematic structural diagram of a computer to which the network training method or the switch state identifying method according to an embodiment of the present application is applied.

Fig. 10 is a schematic image sample segmentation diagram of a network training method according to an embodiment of the present application.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It should also be understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.

As used in this specification and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".

In addition, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.

Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.

Based on the above-mentioned problems, in the network training method in the embodiment of the present application, the present application first obtains an image sample including a target switch. And then, extracting key features of the image sample, and constructing a first feature map matrix. And finally, training the switch state recognition network based on the first feature map matrix. The intelligent recognition algorithm capable of rapidly and accurately recognizing the position state of the switching equipment is constructed based on the key features of the image sample and the neural network.

For example, the embodiments of the present application may be applied to an exemplary scenario as shown in fig. 1, in which the server 30 is first used to train the switch state recognition network through image samples, the image capturing device 20 is used to capture images of the switch 10 located in the capture area, and the captured images including the switch 10 are transmitted to the server 30. The server 30 recognizes the position state of the network recognition switch through the trained switch state based on the image transmitted from the image capturing apparatus 20.

It should be noted that the above application scenario is taken as an exemplary illustration, and is not limited to the application scenario when the embodiments of the present application are implemented, and in fact, the embodiments of the present application may also be applied to other application scenarios. For example, in other exemplary application scenarios, an image of the operator selection switch 10 may be sent to the image capture device 20, etc.

In order to better understand the solution of the present invention by those skilled in the art, the technical solution of the embodiment of the present application will be clearly and completely described below with reference to fig. 1, and it is obvious that the described embodiment is only a part of embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Fig. 2 is a schematic flowchart of a network training method according to an embodiment of the present application, and referring to fig. 2, the network training method is described in detail as follows:

in step 101, an image sample containing a target switch is acquired.

The image sample in this step is a training sample of the training switch identification network, and may be directly input by a user or may be acquired by the image acquisition device 20, which is not limited in this embodiment of the present application.

It should be noted that the image samples in this step are internal images of the pumped storage complete switch of different types and structures under different working conditions. The image capturing device 20 may be image information of the inside of the switch that is captured in real time by a visible light video sensor mounted on the switch device.

In step 102, key feature extraction is performed on an image sample to construct a first feature map matrix.

The internal structural designs of the pumped storage complete sets of switches of different types are different, and in order to enable the network model to adapt to different types of switch equipment, image samples of the switches are segmented, feature enhanced and reconstructed, and then extraction and construction of key features in the image samples are achieved.

Referring to fig. 3, in some embodiments, the step 102 implementation may be:

in step 1021, the image sample is divided into a first static side region, a first intermediate dynamic region, and a first dynamic side region in order in the horizontal direction.

For example, as shown in fig. 10, the image sample of the switch is equally divided into three parts in the X-axis direction and into three or four parts in the Y-axis direction, wherein the area of the first static side area in the X-axis direction is (0, 1/3), and the area in the Y-axis direction is (1/3, 2/3). The first intermediate dynamic region has a region in the X-axis direction of (1/3, 2/3), and the first intermediate dynamic region has a region in the Y-axis direction of (1/4, 3/4). The first movement-side region has a region in the X-axis direction of (2/3, 1), and the first movement-side region has a region in the Y-axis direction of (1/3, 2/3). It should be noted that the first static side area, the first intermediate dynamic area, and the first dynamic side area may be obtained by other methods, which are not limited in the embodiment of the present application.

In step 1022, feature enhancement processing is performed on the first static side region, the first intermediate dynamic region, and the first dynamic side region, and each region image generates multiple target images with different feature parameters, that is, feature expansion of a single sample is implemented, where each target image includes key features of different regions of the target switch.

Wherein, this step carries out sample expansion while carrying out feature enhancement.

Specifically, the brightness and saturation change processing within a specified threshold range can be performed, or random noise can be added by adopting a signal processing mode, and one image sample can be expanded into a plurality of images with different characteristic parameters.

In step 1023, each target image is subjected to graying processing, and size normalization is performed.

Wherein the size may be normalized by a size transformation.

In step 1024, the first static side area, the first intermediate dynamic area, and the first dynamic side area in each target image are longitudinally spliced to obtain a first feature map matrix.

It should be noted that, in this step, when the longitudinal stitching is performed, the first static side area, the first middle dynamic area, and the first dynamic side area of the same target image may be longitudinally stitched, or the first static side area, the first middle dynamic area, and the first dynamic side area in different target images may be longitudinally stitched.

When the longitudinal splicing is completed, the positions of the first static side area and the first dynamic side area are randomly changed. After the longitudinal splicing is completed, a new characteristic diagram is obtained, and the angle transformation of the specified threshold range can be carried out on the new characteristic diagram, so that the sample characteristics are adapted to the change working condition of the actual installation angle of the sensor.

In step 103, the switch state recognition network is trained based on the first feature map matrix, and a switch state recognition model is established.

The method comprises the steps of building and training a switching state identification network based on a MobileNet V2.

As shown in fig. 4, in some embodiments, step 103 may be implemented as:

in step 1031, the position states of the switches in the first feature map matrix are respectively marked as a switching-on-position state, a switching-off-position state and a fault state, a sample data set is obtained, and the sample data set is divided into a training set and a verification set in proportion.

The on-state flag may be "0", the off-state flag may be "1", and the fault state flag may be "2", which is not limited in this embodiment of the present application.

Wherein the ratio of the training set to the verification set is 4:1. The embodiments of the present application are not limited in this regard.

It should be noted that after the position states of the switches in the first feature map matrix are respectively marked as the in-place state of opening and closing, and the fault state, different types of samples can be subjected to equalization processing, so that the sample data sets are uniformly distributed.

In step 1032, a MobileNet V2 network is set up, and the full connection layer structure and output class of the MobileNet V2 network are modified.

Wherein, the output category of the MobileNet V2 network can be modified to 3 as the output of the network model.

In step 1033, the switch state identification network is trained based on the training set and the verification set to build a switch state identification model.

Before the switch state recognition network is trained, parameters such as the set iteration times of the network model, a learning rate adjustment strategy, a weight updating method and the like can be used.

It should be noted that in this step, the training set is used to train the model, the training set is input to the switch state recognition network, and the verification set uses cross verification to select the optimal model as the switch state recognition model.

According to the network training method, firstly, an image sample containing a target switch is obtained. And then, extracting key features of the image sample, and constructing a first feature map matrix. And finally, training the switch state recognition network based on the first feature map matrix, and establishing a switch state recognition model. According to the intelligent recognition method and the intelligent recognition system, based on the image sample key characteristics and the neural network, an intelligent recognition algorithm capable of rapidly and accurately recognizing the position state of the switching equipment is constructed, and an algorithm capable of being applied to recognition of the position state of the switching equipment in different types and structures is constructed, so that recognition and judgment of the position state of the opening and closing of the internal conductor of the pumping energy storage complete switch equipment are realized.

It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic of each process, and should not limit the implementation process of the embodiment of the present application in any way.

Corresponding to the network training method described in the above embodiments, fig. 5 shows a block diagram of a training device of the switch state recognition network provided in the embodiment of the present application, and for convenience of explanation, only a portion relevant to the embodiment of the present application is shown.

Referring to fig. 5, a training apparatus of a switch state recognition network in an embodiment of the present application may include a first acquisition module 201, a feature extraction module 202, and a training module 203.

A first acquisition module 201, configured to acquire an image sample including a target switch.

The feature extraction module 202 is configured to perform key feature extraction on the image sample, and construct a first feature map matrix.

The switch state identification model building module 203 is configured to train the switch state identification network based on the first feature map matrix, and build a switch state identification model.

Fig. 6 is a schematic flowchart of a switch state identification method according to an embodiment of the present application, and referring to fig. 6, the switch state identification method is described in detail as follows:

In step 501, an image to be identified is acquired, the image to be identified including a switch to be identified.

The image to be identified in this step may be directly input by the user, or may be acquired by the image acquisition device 20, which is not limited in this embodiment of the present application.

It should be noted that the images to be identified in the step are internal images of the pumped storage complete switch with different types and structures under different working conditions. The image capturing device 20 may be image information of the inside of the switch that is captured in real time by a visible light video sensor mounted on the switch device.

In step 502, the image sample is divided into a second static side region, a second intermediate dynamic region, and a second dynamic side region in order in the horizontal direction.

In step 503, the second static side region, the second intermediate dynamic region, and the second dynamic side region are respectively subjected to graying processing, and are subjected to size normalization.

In step 504, the second static side area, the second middle dynamic area and the second dynamic side area in each target image are longitudinally spliced to obtain a second feature map matrix.

In step 505, the second feature map matrix is input into the established switch state recognition model, so as to obtain the position state of the switch to be recognized.

Fig. 7 shows a block diagram of a switch state recognition device according to an embodiment of the present application, and for convenience of explanation, only a portion related to the embodiment of the present application is shown.

Referring to fig. 7, the switching state identifying device in the embodiment of the present application may include a second acquisition module 601, an image segmentation module 602, an image normalization module 603, an image stitching module 604, and a switching state determining module 605.

The second acquisition module 601 is configured to acquire an image to be identified, where the image to be identified includes a switch to be identified.

The image segmentation module 602 is configured to segment the image sample into a second static side region, a second middle dynamic region, and a second dynamic side region in sequence in a horizontal direction.

The image normalization module 603 is configured to perform grayscale processing on the second static side region, the second intermediate dynamic region, and the second dynamic side region, and perform size normalization.

The image stitching module 604 is configured to perform longitudinal stitching on the second static side area, the second middle dynamic area, and the second dynamic side area in each target image, so as to obtain a second feature map matrix.

The switch state determining module 605 is configured to input the second feature map matrix into the established switch state identification model, and obtain the position state of the switch to be identified.

It should be noted that, because the content of information interaction and execution process between the above devices/units is based on the same concept as the method embodiment of the present application, specific functions and technical effects thereof may be referred to in the method embodiment section, and will not be described herein again.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.

The embodiment of the present application further provides a terminal device, referring to fig. 8, the terminal device 300 may include: at least one processor 310, a memory 320 and a computer program stored in the memory 320 and executable on the at least one processor 310, the processor 310 implementing the steps of any of the various method embodiments described above when executing the computer program.

By way of example, a computer program may be partitioned into one or more modules/units that are stored in memory 320 and executed by processor 310 to complete the present application. The one or more modules/units may be a series of computer program segments capable of performing specific functions for describing the execution of the computer program in the terminal device 300.

It will be appreciated by those skilled in the art that the figures are merely examples of terminal devices and are not limiting of terminal devices and may include more or fewer components than shown, or may combine certain components, or different components, such as input and output devices, network access devices, buses, etc.

The processor 310 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 320 may be an internal storage unit of the terminal device, or may be an external storage device of the terminal device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card), or the like. The memory 320 is used for storing the computer program and other programs and data required by the terminal device. The memory 320 may also be used to temporarily store data that has been output or is to be output.

The bus may be an industry standard architecture (Industry Standard Architecture, ISA) bus, an external device interconnect (Peripheral Component, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, the buses in the drawings of the present application are not limited to only one bus or one type of bus.

The network training method or the switch state identification method provided by the embodiment of the application can be applied to terminal equipment such as computers, tablet computers, notebook computers, netbooks, personal digital assistants (personal digital assistant, PDA) and the like, and the embodiment of the application does not limit the specific types of the terminal equipment.

Taking the terminal device as a computer as an example. Fig. 9 is a block diagram showing a part of the structure of a computer provided with an embodiment of the present application. Referring to fig. 9, a computer includes: communication circuitry 410, memory 420, input unit 430, display unit 440, audio circuitry 450, wireless fidelity (wireless fidelity, wiFi) module 460, processor 470, and power supply 480. Those skilled in the art will appreciate that the computer architecture shown in fig. 9 is not limiting and that more or fewer components than shown may be included, or that certain components may be combined, or that different arrangements of components may be provided.

The following describes each component of the computer in detail with reference to the drawings:

the communication circuit 410 may be used to receive and transmit signals during a message or a call, and in particular, after receiving an image sample transmitted by the image capturing device, the signal is processed by the processor 470. In addition, an image acquisition instruction is sent to the image acquisition apparatus. Typically, the communication circuitry includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier (Low Noise Amplifier, LNA), a duplexer, and the like. In addition, the communication circuit 410 may also communicate with networks and other devices through wireless communication. The wireless communications may use any communication standard or protocol including, but not limited to, global system for mobile communications (Global System of Mobile communication, GSM), general packet radio service (General Packet Radio Service, GPRS), code division multiple access (Code Division Multiple Access, CDMA), wideband code division multiple access (Wideband Code Division Multiple Access, WCDMA), long term evolution (Long Term Evolution, LTE)), email, short message service (Short Messaging Service, SMS), and the like.

The memory 420 may be used to store software programs and modules, and the processor 470 performs various functional applications and data processing of the computer by executing the software programs and modules stored in the memory 420. The memory 420 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, application programs required for at least one function (such as a sound playing function, an image playing function, etc.), and the like. The storage data area may store data created according to the use of the computer (such as audio data, phonebooks, etc.), and the like. In addition, memory 420 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.

The input unit 430 may be used to receive input numeric or character information and to generate key signal inputs related to user settings and function control of the computer. In particular, the input unit 430 may include a touch panel 431 and other input devices 432. The touch panel 431, also referred to as a touch screen, may collect touch operations thereon or thereabout by a user (e.g., operations of the user on the touch panel 431 or thereabout using any suitable object or accessory such as a finger, a stylus, etc.), and drive the corresponding connection device according to a predetermined program. Alternatively, the touch panel 431 may include two parts of a touch detection device and a touch controller. The touch detection device detects the touch direction of a user, detects signals brought by touch operation and transmits the signals to the touch controller. The touch controller receives touch information from the touch detection device and converts it into touch point coordinates, which are then sent to the processor 470 and can receive commands from the processor 470 and execute them. In addition, the touch panel 431 may be implemented in various types such as resistive, capacitive, infrared, and surface acoustic wave. The input unit 430 may include other input devices 432 in addition to the touch panel 431. In particular, other input devices 432 may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, mouse, joystick, etc.

The display unit 440 may be used to display information input by a user or information provided to the user as well as various menus of a computer. The display unit 440 may include a display panel 441, and optionally, the display panel 441 may be configured in the form of a liquid crystal display (Liquid Crystal Display, LCD), an Organic Light-Emitting Diode (OLED), or the like. Further, the touch panel 431 may cover the display panel 441, and when the touch panel 431 detects a touch operation thereon or nearby, the touch operation is transmitted to the processor 470 to determine the type of the touch event, and then the processor 470 provides a corresponding visual output on the display panel 441 according to the type of the touch event. Although in the figures, the touch panel 431 and the display panel 441 are two independent components to implement the input and input functions of the computer, in some embodiments, the touch panel 431 and the display panel 441 may be integrated to implement the input and output functions of the computer.

Audio circuitry 450 may provide an audio interface between a user and a computer. The audio circuit 450 may convert the received audio data into an electrical signal, transmit the electrical signal to a speaker, and convert the electrical signal into a sound signal for output by the speaker. On the other hand, the microphone converts the collected sound signals into electrical signals, which are received by the audio circuit 450 and converted into audio data, which are processed by the audio data output processor 470 for transmission to, for example, another computer via the communication circuit 410, or which are output to the memory 420 for further processing.

WiFi belongs to a short-distance wireless transmission technology, and a computer can help a user to send and receive emails, browse webpages, access streaming media and the like through the WiFi module 460, so that wireless broadband Internet access is provided for the user. Although fig. 9 shows a WiFi module 460, it is understood that it does not belong to the essential constitution of a computer, and can be omitted entirely as required within the scope of not changing the essence of the invention.

Processor 470 is the control center of the computer, and uses various interfaces and lines to connect the various parts of the entire computer, and by running or executing software programs and/or modules stored in memory 420, and invoking data stored in memory 420, performs various functions of the computer and processes the data, thereby performing overall monitoring of the computer. In the alternative, processor 470 may include one or more processing units. Preferably, the processor 470 may integrate an application processor that primarily handles operating systems, user interfaces, applications, etc., with a modem processor that primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into processor 470.

The computer also includes a power supply 480 (e.g., a battery) for powering the various components, and preferably the power supply 480 can be logically connected to the processor 470 via a power management system so as to perform functions such as managing charge, discharge, and power consumption via the power management system.

Embodiments of the present application also provide a computer readable storage medium storing a computer program that, when executed by a processor, implements steps in various embodiments of the network training method or the switch state identification method described above.

Embodiments of the present application provide a computer program product that, when executed on a mobile terminal, causes the mobile terminal to perform steps in each of the embodiments of the network training method or the switch state identification method described above.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application implements all or part of the flow of the method of the above embodiments, and may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing device/terminal apparatus, recording medium, computer Memory, read-Only Memory (ROM), random access Memory (RAM, random Access Memory), electrical carrier signals, telecommunications signals, and software distribution media. Such as a U-disk, removable hard disk, magnetic or optical disk, etc. In some jurisdictions, computer readable media may not be electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus/network device and method may be implemented in other manners. For example, the apparatus/network device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical functional division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

The above embodiments are only for illustrating the technical solution of the present application, and are not limiting. Although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some of the technical features thereof can be replaced by equivalents. Such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.

Claims

1. A method of network training, comprising:

acquiring an image sample containing a target switch;

extracting key features of the image sample to construct a first feature map matrix;

2. The method of claim 1, wherein the performing key feature extraction on the image sample to construct a first feature map matrix comprises:

dividing an image sample into a first static side area, a first middle dynamic area and a first dynamic side area in sequence in the horizontal direction;

respectively carrying out feature enhancement processing on the first static side region, the first middle dynamic region and the first dynamic side region, and generating a plurality of target images with different feature parameters by each region image, namely realizing feature expansion of a single sample, wherein each target image comprises key features of different regions of the target switch;

carrying out graying treatment on each target image and carrying out size normalization;

3. The method of claim 2, wherein training the switch state identification network based on the first feature map matrix to build a switch state identification model comprises:

marking the position states of the switches in the first feature map matrix as on-off in-place, on-off in-place and fault states respectively, obtaining a sample data set, and dividing the sample data set into a training set and a verification set according to a proportion;

Building a MobileNet V2 network, and modifying the full-connection layer structure and the output category of the MobileNet V2 network;

4. A method for identifying a switch state, comprising:

acquiring an image to be identified, wherein the image to be identified comprises a switch to be identified;

dividing the image sample into a second static side area, a second middle dynamic area and a second dynamic side area in turn in the horizontal direction;

respectively carrying out gray scale treatment on the second static side region, the second middle dynamic region and the second dynamic side region, and carrying out size normalization;

longitudinally splicing a second static side region, a second middle dynamic region and a second dynamic side region in each target image to obtain a second feature map matrix;

5. A training device for a switch state identification network, comprising:

the first acquisition module is used for acquiring an image sample containing a target switch;

the feature extraction module is used for extracting key features of the image sample and constructing a first feature map matrix;

6. A switch state identification device, comprising:

the second acquisition module is used for acquiring an image to be identified, wherein the image to be identified comprises a switch to be identified;

the image segmentation module is used for sequentially segmenting the image sample into a second static side area, a second middle dynamic area and a second dynamic side area in the horizontal direction;

the image normalization module is used for respectively carrying out gray scale treatment on the second static side area, the second middle dynamic area and the second dynamic side area and carrying out size normalization;

the image splicing module is used for longitudinally splicing the second static side area, the second middle dynamic area and the second dynamic side area in each target image to obtain a second feature map matrix;

7. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 4 when executing the computer program.

8. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the method according to any one of claims 1 to 4.