CN117934367A - Image processing method, device, electronic equipment and storage medium - Google Patents

Abstract

The invention discloses an image processing method, an image processing device, electronic equipment and a storage medium. The specific scheme is as follows: acquiring an image to be processed comprising at least one device to be monitored; the method comprises the steps of performing image enhancement processing on an image to be processed to obtain an image to be analyzed, and inputting the image to be analyzed into an image recognition model to obtain target characteristics of the image to be analyzed; determining target information of equipment to be monitored by carrying out feature comparison processing on the target features and a power distribution network equipment diagram of at least one power distribution network equipment under at least one preset condition; the target information comprises at least one of a target position, a target situation and a target fault type of the equipment to be monitored. According to the method and the device for detecting the faults of the equipment to be monitored, the images to be processed are analyzed and processed, so that the target information of the equipment to be monitored is obtained, the fault detection of the equipment to be monitored is improved, and the fault repairing efficiency of the equipment to be monitored is improved.

Description

Image processing method, device, electronic equipment and storage medium

Technical Field

The present invention relates to the field of image processing technologies, and in particular, to an image processing method, an image processing device, an electronic device, and a storage medium.

Background

The distribution network is the part of the power system that is connected to the consumers. Safe, reliable, full and high-quality electric power is a precondition for economic, rapid, stable and healthy development. However, as severe weather typified by typhoons, lightning, high temperature, ice coating, and strong convection weather frequently occurs, distribution networks deployed in outdoor environments, and particularly overhead lines, are exposed to interference from severe weather for a long period of time. Therefore, real-time monitoring is required to be carried out on the power distribution network equipment so as to ensure that the power distribution network equipment can be overhauled in time when faults are caused by severe weather.

At present, monitoring of power distribution network equipment mainly comprises the steps of arranging power grid related personnel to periodically patrol the power distribution network equipment and observing the running state of the power distribution network equipment, but the mode is difficult to discover whether the power distribution network equipment fails at the first time, so that the power distribution network equipment is low in maintenance efficiency, and the risk of power transmission interruption is possibly caused.

Disclosure of Invention

The invention provides an image processing method, an image processing device, electronic equipment and a storage medium, solves the problem of low repair efficiency of power distribution network equipment, improves fault detection of equipment to be monitored, and is beneficial to improving the efficiency of repairing faults of the equipment to be monitored.

According to an aspect of the present invention, there is provided an image processing method, which is applied to a power distribution network, including:

acquiring an image to be processed comprising at least one device to be monitored;

The method comprises the steps of performing image enhancement processing on an image to be processed to obtain an image to be analyzed, and inputting the image to be analyzed into an image recognition model to obtain target characteristics of the image to be analyzed;

determining target information of equipment to be monitored by carrying out feature comparison processing on the target features and a power distribution network equipment diagram of at least one power distribution network equipment under at least one preset condition;

the target information comprises at least one of a target position, a target situation and a target fault type of the equipment to be monitored.

According to another aspect of the present invention, there is provided an image processing apparatus for use in a power distribution network, comprising:

The image acquisition module is used for acquiring an image to be processed comprising at least one device to be monitored;

The target feature acquisition module is used for obtaining an image to be analyzed by carrying out image enhancement processing on the image to be processed, and inputting the image to be analyzed into the image recognition model so as to obtain target features of the image to be analyzed;

The target information determining module is used for determining target information of the equipment to be monitored by carrying out feature comparison processing on the target features and the power distribution network equipment diagram of the at least one power distribution network equipment under at least one preset condition; the target information comprises at least one of a target position, a target situation and a target fault type of the equipment to be monitored.

According to another aspect of the present invention, there is provided an electronic device including:

At least one processor; and

A memory communicatively coupled to the at least one processor; wherein,

The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the image processing method of any one of the embodiments of the present invention.

According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute an image processing method of any one of the embodiments of the present invention.

According to the technical scheme, the image to be processed comprising at least one device to be monitored is obtained; performing image enhancement processing on the image to be processed to obtain an image to be analyzed, and inputting the image to be analyzed into an image recognition model to obtain target characteristics of the image to be analyzed; determining target information of equipment to be monitored by carrying out feature comparison processing on the target features and a power distribution network equipment diagram of at least one power distribution network equipment under at least one preset condition; the target information comprises at least one of a target position, a target situation and a target fault type of the equipment to be monitored, the problem of low repair efficiency of the power distribution network equipment is solved, fault detection of the equipment to be monitored is improved, fault repair efficiency of the equipment to be monitored is improved, and accordingly risk of power transmission interruption caused by damage of the power distribution network equipment is avoided.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.

Drawings

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

Fig. 1 is a flowchart of an image processing method according to an embodiment of the present invention;

FIG. 2 is a flowchart of an image processing method according to an embodiment of the present invention;

fig. 3 is an exemplary diagram of an image processing method provided by an embodiment of the present invention;

Fig. 4 is a schematic structural view of an image processing apparatus according to an embodiment of the present invention;

Fig. 5 is a schematic structural diagram of an electronic device implementing an image processing method according to an embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some 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 present invention without making any inventive effort, shall fall within the scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example 1

Fig. 1 is a flowchart of an image processing method provided in an embodiment of the present invention, where the method may be performed by an image processing apparatus, which may be implemented in hardware and/or software, and the image processing apparatus may be configured in an electronic device such as a mobile phone, a computer, or a server, where an image of a device to be monitored is analyzed and processed to obtain fault information of the device. As shown in fig. 1, the method includes:

s110, acquiring a to-be-processed image comprising at least one to-be-monitored device.

In a power distribution network, a number of different types of power distribution network equipment are typically involved, such as transformers, circuit breakers, distribution lines, etc. In order to ensure the normal operation of the power distribution network equipment, at least one power distribution network equipment can be monitored by using a corresponding camera device. The currently monitored power distribution network equipment can be used as equipment to be monitored. The image of the equipment to be monitored acquired by the camera device is the image to be processed. Alternatively, the image pickup device may be a monitoring camera, an unmanned aerial vehicle, or the like, which is not limited in this embodiment.

Specifically, the image acquisition device can be used for acquiring the image of at least one device to be monitored in the power distribution network so as to obtain the image to be processed corresponding to the device to be monitored, so that whether the device to be monitored has faults or not and the corresponding fault types can be analyzed based on the image to be processed.

S120, performing image enhancement processing on the image to be processed to obtain an image to be analyzed, and inputting the image to be analyzed into an image recognition model to obtain target characteristics of the image to be analyzed.

In the embodiment of the invention, the image enhancement processing can be a mode of processing the image to be processed by adopting a certain technical means so as to improve the quality of the image to be processed. Correspondingly, an image obtained after the image enhancement processing is carried out on the image to be processed is recorded as an image to be analyzed. The image recognition model can be a model which is trained in advance and can be used for recognizing and processing the image to be analyzed. The target feature is an output feature obtained after processing the image to be analyzed based on the image recognition model. Optionally, the target features may include color features and texture features of the image to be analyzed, shape features of the device to be monitored in the image to be analyzed, and the embodiment is not limited thereto.

Specifically, the obtained image to be processed is subjected to image enhancement processing so as to obtain the image to be analyzed, which is higher in quality and more convenient for subsequent image recognition. And inputting the image to be analyzed into a pre-trained image recognition model, and outputting target features corresponding to the image to be analyzed based on the image recognition model.

Wherein the image recognition model also needs to be trained in advance before the image recognition model is used for processing the image to be analyzed. First, a plurality of training sample images may be acquired. The training sample image comprises equipment to be monitored and theoretical target characteristics corresponding to the equipment to be monitored. The training sample image may be a sample image obtained in a power distribution network device image recognition training library. Further, the image enhancement processing may be performed on the training sample image, so as to obtain the corresponding actual target feature based on the training sample image after the enhancement processing being input into the image recognition model to be trained.

In general, the model parameters of the image recognition model to be trained are initial parameters or default parameters, and when the image recognition model to be trained is trained, each model parameter in the model can be corrected based on the actual target characteristics output by the image recognition model to be trained, that is, the loss value of the image recognition model to be trained can be corrected, so that the trained image recognition model is obtained. The loss value is a difference value between the actual output target characteristic and the theoretical target characteristic.

Specifically, when the model parameters in the image recognition model to be trained are corrected by using the loss values, the loss function can be converged to be a training target, for example, whether the training error is smaller than a preset error, whether the error change tends to be stable, or whether the current iteration number is equal to the preset number. If the detection reaches the convergence condition, for example, the training error of the loss function is smaller than the preset error, or the error change trend tends to be stable, the training of the image recognition model to be trained is completed, and at the moment, the iterative training can be stopped. If the current condition is detected not to be met, other training sample images can be further obtained to train the image recognition model to be trained continuously until the training error of the loss function is within a preset range. When the training error of the loss function reaches convergence, a training-completed image recognition model can be obtained, namely, after the image to be analyzed corresponding to the equipment to be monitored is input into the image recognition model, the target feature corresponding to the image to be analyzed can be accurately obtained.

S130, determining target information of equipment to be monitored by performing feature comparison processing on the target features and a power distribution network equipment diagram of at least one power distribution network equipment under at least one preset condition.

The target information comprises at least one of a target position, a target situation and a target fault type of the equipment to be monitored.

In the embodiment of the invention, the power distribution network equipment diagram under the preset condition can be understood as a sample image when equipment is in fault, wherein the power distribution network equipment diagram and equipment to be monitored are of the same equipment type. The power distribution network equipment graph can be obtained from a power distribution network equipment image recognition training library. The preset condition may be a condition in which the distribution network equipment encounters a fault. The target information may include a target location of the device to be monitored, a target situation, and a corresponding target fault type. The target location may be a geographic location where the device to be monitored is located. The target situation may be a state or condition in which the device to be monitored is located. The target fault type may be a fault type in which the device to be monitored fails.

Specifically, after the target feature is obtained, the equipment type of the equipment to be monitored can be determined according to the target feature, and the equipment image when the equipment image is in failure, namely the equipment image of the power distribution network under the preset condition, which is the same equipment type, is obtained from the power distribution network equipment image recognition training library according to the equipment type. The method comprises the steps that a distribution network equipment diagram of at least one distribution network equipment under at least one preset condition can be obtained, so that accurate comparison of corresponding characteristics can be realized. Further, based on feature comparison analysis of the features contained in the power distribution network equipment diagram and the target features, the target position, the target situation and the corresponding target fault type of the equipment to be monitored are determined.

According to the technical scheme, an image to be processed comprising at least one device to be monitored is obtained; performing image enhancement processing on the image to be processed to obtain an image to be analyzed, and inputting the image to be analyzed into an image recognition model to obtain target characteristics of the image to be analyzed; determining target information of equipment to be monitored by carrying out feature comparison processing on the target features and a power distribution network equipment diagram of at least one power distribution network equipment under at least one preset condition; the target information comprises at least one of a target position, a target situation and a target fault type of the equipment to be monitored, the problem of low repair efficiency of the power distribution network equipment is solved, fault detection of the equipment to be monitored is improved, fault repair efficiency of the equipment to be monitored is improved, and accordingly risk of power transmission interruption caused by damage of the power distribution network equipment is avoided.

Example two

Fig. 2 is a flowchart of an image processing method according to an embodiment of the present invention, which is a preferred embodiment of the above-described embodiments. The specific implementation manner can be seen in the technical scheme of the embodiment. Wherein, the technical terms identical to or corresponding to the above embodiments are not repeated herein. As shown in fig. 2, the method includes:

S210, acquiring a to-be-processed image comprising at least one to-be-monitored device based on at least one camera device deployed in a power distribution network area.

In an embodiment of the present invention, the power distribution network area may be an area containing at least one power distribution network device. The power distribution network area can be an area divided according to actual requirements.

Specifically, in the power distribution network, some camera devices, such as monitoring cameras, are deployed to observe the working state and the operation condition of the power distribution network equipment. And acquiring images of at least one device to be monitored according to at least one camera arranged in the power distribution network area so as to obtain the images to be processed corresponding to the device to be monitored.

As illustrated by way of example in fig. 3. Fig. 3 is an exemplary diagram of an image processing method. In fig. 3, an image collection module may be used to collect disaster images of the power distribution network equipment to be monitored in real time. The image collecting module comprises at least one image pickup device. And the disaster-affected image of the power distribution network equipment to be monitored is the image to be processed of the equipment to be monitored.

S220, removing noise information in the image to be processed through Gaussian filtering processing of the image to be processed, and performing content enhancement processing on the image to be processed with the noise information removed to obtain an image to be analyzed.

In the embodiment of the invention, gaussian filtering is an image processing technology, and the purpose of reducing noise of an image to be processed is achieved by convolving the image to be processed by using a Gaussian function. Noise information in the image to be processed may include background noise around the device to be monitored, light interference, etc. Such noise information may obscure, distort or otherwise artifact the image to be processed, resulting in an inability to accurately identify information in the image to be processed. The content enhancement processing may refer to a process of enhancing an image to be processed to improve its quality by a series of technical means. These techniques may include contrast enhancement, sharpening, color correction, etc., which are not limited by the present embodiment.

Specifically, after the image to be processed is obtained, gaussian filtering processing is performed on the image to be processed by utilizing a Gaussian function, so that the purpose of eliminating noise information in the image to be processed is achieved. And carrying out content enhancement processing on the image to be processed with noise information removed, so as to improve the quality and the identifiability of the image to be processed, and further obtain the image to be analyzed.

For example, in combination with the above example, after the image collecting module obtains the disaster-affected image, that is, the image to be processed, of the power distribution network device to be monitored, the image collecting module may transmit the disaster-affected image to the image enhancing module. The image enhancement module is connected with the image collection module. The image enhancement module is a module for denoising and enhancing the image to be processed. In the image enhancement module, the image to be processed can be decomposed and reconstructed by using a wavelet transformation algorithm to remove noise information in the image to be processed, and meanwhile, image details in the image to be processed are reserved. And then, smoothing the image to be processed by utilizing a bilateral filtering algorithm, so that the image to be processed is more natural, residual noise is removed, the quality of the image to be processed is improved, and the brightness and the definition of the image to be processed are ensured. The wavelet transformation algorithm is a technology for enhancing details of an image and removing noise by carrying out multi-scale wavelet decomposition on the image to be processed to obtain details and contour information of the image to be processed under different scales. The bilateral filtering algorithm is a nonlinear filter based on Gaussian distribution, and can be used for noise reduction and smoothing of an image to be processed. After the above processing, a to-be-processed image with noise information removed can be obtained. Further, the image to be processed can be subjected to content enhancement processing to obtain the image to be analyzed.

Optionally, performing content enhancement processing on the image to be processed with noise information removed to obtain an image to be analyzed, including: carrying out histogram equalization pretreatment on the image to be processed to obtain an image to be used; and inputting the image to be used into a low-illumination image enhancement network to obtain an image to be analyzed with enhanced image characteristics in the image to be used.

In the embodiment of the invention, the histogram equalization preprocessing may be a mode of preprocessing the image to be processed by using a histogram equalization algorithm, wherein the histogram equalization algorithm is used for changing a gray level histogram corresponding to the image to be processed into a uniformly distributed histogram, and keeping the brightness of the image to be processed unchanged. Through equalization processing, the contrast of the image to be processed can be enhanced, and meanwhile, the overall brightness of the image to be processed is more uniform and stable. After the image to be processed is subjected to the pretreatment, the image to be used can be obtained. The low-illumination image enhancement network is a network model for processing an image to be used, aiming at improving the visual effect and quality of the image to be used.

Specifically, the image to be processed with noise information removed is subjected to histogram equalization pretreatment, so that the brightness and contrast of the image to be processed are improved. And taking the preprocessed image to be processed as an image to be used. Further, the image to be used is input into a low-illumination image enhancement network, so that image features in the image to be used are enhanced based on the low-illumination image enhancement network, and an image to be analyzed is obtained.

For example, in combination with the above example, the image to be processed may be subjected to histogram equalization preprocessing. Meanwhile, a low-illumination image enhancement network based on Residual-Unet is constructed, and network training is completed. Further, the image to be processed and the image to be used which is subjected to histogram equalization pretreatment are combined and input into a trained low-illumination image enhancement network based on Residual-Unet, and the image with enhanced image characteristics is output by the low-illumination image enhancement network based on Residual-Unet to serve as the image to be analyzed.

S230, inputting the image to be analyzed into an image recognition model to obtain target characteristics of the image to be analyzed.

Specifically, the obtained image to be analyzed is input into an image recognition model, so that feature extraction is carried out on the image to be analyzed based on the image recognition model, and target features of the image to be analyzed are obtained.

Illustratively, in combination with the above example, as shown in fig. 3, after image denoising and image enhancement are performed on an image to be processed by the image enhancement module, the image to be analyzed obtained after the processing is input to the image recognition module. The image recognition module is connected with the image enhancement module and the image recognition training library and is used for extracting target characteristics of equipment to be monitored in the image to be analyzed based on the disaster-affected image, namely the image to be analyzed, of the power distribution network equipment after image enhancement. The image recognition module is a module that performs recognition processing based on the image information obtained and the image after the image preprocessing. The image information acquisition is realized in an image collection module, and the image preprocessing is realized based on an image enhancement module.

S240, determining the device type of the device to be monitored based on the target characteristics.

In the embodiment of the invention, the device type can be defined according to different classification standards. For example, power distribution network devices may be classified into power transmission devices, power distribution devices, and power consumption devices according to their functional uses; according to the structural characteristics of the power distribution network equipment, the power distribution network equipment can be further divided into primary equipment and secondary equipment, and the embodiment does not limit the equipment types of the power distribution network equipment.

Specifically, according to the target characteristics and the equipment types of the power distribution network defined in the actual demands, the equipment types corresponding to the equipment to be monitored are determined.

By way of example, in combination with the above example, the image recognition module determines the device type of the device to be monitored according to the obtained target feature after image feature extraction, that is, the image classification and discrimination in fig. 3 is implemented.

S250, a distribution network equipment diagram of at least one distribution network equipment corresponding to the equipment type under at least one preset condition is called.

Specifically, after determining the device type of the device to be monitored, a power distribution network device diagram of at least one power distribution network similar to the device type thereof under at least one preset condition may be obtained from a corresponding database, for example, a power distribution network device image recognition training library.

For example, in combination with the above example, after the image recognition module recognizes the device type of the device to be monitored, each sample image, that is, the power distribution network device graph, which is the same as the device type of the device to be monitored, may be obtained from the image recognition training library, so as to perform subsequent feature comparison based on the power distribution network device graph. In the image recognition training library, namely the power distribution network equipment image recognition training library, sample images of each power distribution network equipment when encountering faults are stored, and each sample image is marked with the equipment type of the corresponding power distribution network equipment.

S260, determining target information based on the equipment diagram features and the target features of the power distribution network equipment diagram.

In the embodiment of the invention, the equipment map features can comprise color features, texture features, shape features and the like of the power distribution network equipment in the power distribution network equipment map.

Specifically, according to the obtained equipment diagram characteristics of the power distribution network equipment diagram, comparing and analyzing the equipment diagram characteristics with target characteristics of the image to be analyzed. And finding a distribution network equipment diagram closest to or similar to the target characteristics of the distribution network equipment diagram in at least one distribution network equipment diagram, and acquiring the target information such as the position, the situation and the fault type of the distribution network equipment based on the distribution network equipment diagram, so that related personnel can timely maintain the equipment to be monitored based on the target information.

Optionally, determining the target information based on the device map feature and the target feature of the power distribution network device map includes: determining target equipment graph characteristics through matching processing of the equipment graph characteristics and the target characteristics; determining target power distribution network equipment corresponding to the characteristics of the target equipment map, determining the target position and the target situation of the target power distribution network equipment, and determining target information; wherein the target situation includes at least one of a fire situation, a flood situation, and an abnormal working situation.

In an embodiment of the present invention, the target device graph feature may be a feature that most closely matches the target feature. The target power distribution network device may be a power distribution network device contained in a target device graph. The target situation may be a situation when the target power distribution network device encounters a fault, and the target situation may include at least one of a fire situation, a flood situation, and an abnormal operation situation.

Specifically, matching processing is performed based on the equipment diagram features and the target features of at least one power distribution network equipment diagram, the power distribution network equipment diagram with the most similar matching is determined, and the equipment diagram features are used as the target equipment diagram features. And determining the target power distribution equipment in the corresponding power distribution network equipment diagram according to the characteristics of the target equipment diagram. Thus, the target position and the target situation of the target power distribution network equipment are obtained, and the target information is obtained based on the information integration. Wherein the target situation includes at least one of a fire situation, a flood situation, and an abnormal working situation.

Illustratively, in combination with the above example, after the image recognition module determines the device type using the image recognition training library, the image recognition module determines the target device map feature in combination with the target feature according to the device map feature of the sample picture in the image recognition training library, which is the same as the device type of the sample picture. And then, inputting the target equipment graph characteristics into a data analysis module as a recognition result. The data analysis module is connected with the image recognition module and the image collection module. The data analysis module analyzes the received identification result and outputs corresponding target information. For example, the target information may include information such as a situation that the target power distribution network device has abnormal operation due to a device failure, and a target position.

Optionally, the method further comprises: generating early warning information based on the target information and at least one video frame of the corresponding equipment to be monitored, and sending the early warning information to the target terminal equipment so as to enable a user corresponding to the target terminal equipment to maintain the equipment to be monitored.

In the embodiment of the present invention, at least one video frame may be a video frame of the device to be monitored, which is acquired after the device to be monitored fails. The early warning information can be information for prompting related personnel that the equipment to be monitored has faults. The early warning information may include target information, at least one video frame of the device to be monitored, and other information for prompting, which is not limited in this embodiment. The target terminal device may be an electronic device such as a mobile phone or a computer. The early warning information may be sent to the target terminal device in a manner of telephone, short message, mail, etc., which is not limited in this embodiment.

Specifically, after determining the target information, that is, after judging that the target power distribution equipment, that is, the equipment to be monitored has a fault, the image capturing device may be used to capture an image or record a video of the equipment to be monitored, so as to obtain at least one video frame of the equipment to be monitored. And generating corresponding early warning information according to the target information and at least one video frame. And the early warning information is sent to the target terminal equipment in a telephone, short message, mail and other modes, so that a user corresponding to the target terminal equipment can timely maintain the equipment to be monitored according to the early warning information, and the risk of power transmission interruption caused by the failure of the equipment to be monitored is reduced.

In an exemplary embodiment, in combination with the above-mentioned example, after the data analysis module determines that the device to be monitored has a device fault and determines the target information, the data analysis module may control the early warning module to record the target information, and control the image collecting module to perform image capturing or video recording on the device to be monitored, so as to obtain at least one corresponding video frame. Further, the early warning module generates early warning information according to the recorded target information and at least one video frame acquired by the image collecting module. And the early warning information is sent to target terminal equipment of the relevant user, so that the relevant user can maintain the equipment to be monitored in time according to the early warning information. The early warning module is connected with the data analysis module and is used for locating the early warning position, sending early warning information and starting emergency response.

The early warning module can support various early warning modes of web page display early warning information, sound and light, telephone and short message, and comprises a real-time warning module and an early warning module, wherein the real-time warning module adopts a joint warning mode, a user needs to define threshold parameters of equipment monitoring equipment in a machine room in a self-defined mode in advance when the equipment parameters are abnormal, the joint image capturing module captures pictures and records videos of power distribution network equipment working abnormally, and starts real-time warning, the early warning module adopts an image recognition technology to perform early warning, and mainly performs comparison analysis according to sample images of the power distribution network equipment stored in image recognition training, and images to be processed acquired by the image collecting module and images to be analyzed which are analyzed by the image identifying module, and the early warning is started when an output result is abnormal.

In addition, after the early warning information is obtained and the equipment to be monitored is maintained, the target information, the early warning event, the image to be processed, the image to be analyzed and other data of the equipment to be monitored can be stored in the storage module. The device information in fig. 3 is target information of the device to be monitored, the captured image data is the image data to be processed, and the image identification data is the data corresponding to the image to be analyzed. In addition, the storage module is connected with the early warning module and the image acquisition module. The storage module supports two modes of SQLite database storage and local hard disk storage, locally stored early warning data and monitoring data can be stored in different catalogues, subdirectories are respectively divided according to monitored power distribution network equipment and cameras, and a user can perform user-defined information query, wherein the user-defined information comprises early warning equipment, time periods, places, early warning types and early warning level information.

According to the technical scheme, an image to be processed comprising at least one device to be monitored is acquired based on at least one camera device deployed in a power distribution network area; removing noise information in the image to be processed through Gaussian filtering processing of the image to be processed, and carrying out content enhancement processing on the image to be processed with the noise information removed to obtain an image to be analyzed; inputting the image to be analyzed into an image recognition model to obtain target characteristics of the image to be analyzed; further, based on the target characteristics, the equipment type of the equipment to be monitored is determined, and the power distribution network equipment diagram of at least one power distribution network equipment corresponding to the equipment type under at least one preset condition is called, so that the target information is determined based on the equipment diagram characteristics and the target characteristics of the power distribution network equipment diagram, the problem of low repair efficiency of the power distribution network equipment is solved, the fault detection of the equipment to be monitored is improved, the fault repair efficiency of the equipment to be monitored is improved, and the risk of power transmission interruption caused by damage of the power distribution network equipment is avoided.

Example III

Fig. 4 is a schematic structural diagram of an image processing apparatus according to an embodiment of the present invention. As shown in fig. 4, the apparatus includes: an image acquisition module 310, a target feature acquisition module 320, and a target information determination module 330.

An image acquisition module 310, configured to acquire a to-be-processed image including at least one to-be-monitored device; the target feature obtaining module 320 is configured to obtain an image to be analyzed by performing image enhancement processing on the image to be processed, and input the image to be analyzed into the image recognition model to obtain target features of the image to be analyzed; the target information determining module 330 is configured to determine target information of the device to be monitored by performing feature comparison processing on the target feature and a power distribution network device diagram of at least one power distribution network device under at least one preset condition; the target information comprises at least one of a target position, a target situation and a target fault type of the equipment to be monitored.

According to the technical scheme, an image to be processed comprising at least one device to be monitored is obtained; performing image enhancement processing on the image to be processed to obtain an image to be analyzed, and inputting the image to be analyzed into an image recognition model to obtain target characteristics of the image to be analyzed; determining target information of equipment to be monitored by carrying out feature comparison processing on the target features and a power distribution network equipment diagram of at least one power distribution network equipment under at least one preset condition; the target information comprises at least one of a target position, a target situation and a target fault type of the equipment to be monitored, the problem of low repair efficiency of the power distribution network equipment is solved, fault detection of the equipment to be monitored is improved, fault repair efficiency of the equipment to be monitored is improved, and accordingly risk of power transmission interruption caused by damage of the power distribution network equipment is avoided.

On the basis of the above embodiment, optionally, the image acquisition module is configured to acquire the image to be processed including the at least one device to be monitored based on at least one camera device deployed in the power distribution network area.

Optionally, the target feature acquisition module includes: the image acquisition unit to be analyzed is used for removing noise information in the image to be processed through Gaussian filtering processing of the image to be processed, and carrying out content enhancement processing on the image to be processed with the noise information removed to obtain the image to be analyzed; the target feature acquisition unit is used for inputting the image to be analyzed into the image recognition model to obtain the target feature of the image to be analyzed.

Optionally, the image acquisition unit to be analyzed includes: the image to be used acquisition subunit is used for carrying out histogram equalization pretreatment on the image to be processed to obtain the image to be used; the image to be analyzed obtaining subunit is used for inputting the image to be used into the low-illumination image enhancement network to obtain the image to be analyzed with enhanced image characteristics in the image to be used.

Optionally, the target information determining module includes: the device type determining unit is used for determining the device type of the device to be monitored based on the target characteristics; the device diagram calling unit is used for calling a power distribution network device diagram of at least one power distribution network device corresponding to the device type under at least one preset condition; and the target information determining unit is used for determining target information based on the equipment diagram characteristics and the target characteristics of the power distribution network equipment diagram.

Optionally, the target information determining unit includes: the equipment graph feature determining subunit is used for determining target equipment graph features through matching processing of the equipment graph features and the target features; the target information determining subunit is used for determining target power distribution network equipment corresponding to the target equipment graph characteristics, determining target positions and target situations of the target power distribution network equipment and determining target information; wherein the target situation includes at least one of a fire situation, a flood situation, and an abnormal working situation.

Optionally, the apparatus further comprises: and the early warning information generation module is used for generating early warning information based on the target information and at least one video frame of the corresponding equipment to be monitored, and sending the early warning information to the target terminal equipment so as to enable a user corresponding to the target terminal equipment to maintain the equipment to be monitored.

The image processing device provided by the embodiment of the invention can execute the image processing method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

Example IV

Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM12 and the RAM13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the respective methods and processes described above, for example, an image processing method.

In some embodiments, the image processing method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM12 and/or the communication unit 19. When the computer program is loaded into the RAM13 and executed by the processor 11, one or more steps of the image processing method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the image processing method in any other suitable way (e.g. by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

The computer program for implementing the image processing method of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

Example five

The embodiment of the invention also provides a computer readable storage medium, the computer readable storage medium storing computer instructions for causing a processor to execute an image processing method, the method comprising:

Acquiring an image to be processed comprising at least one device to be monitored; the method comprises the steps of performing image enhancement processing on an image to be processed to obtain an image to be analyzed, and inputting the image to be analyzed into an image recognition model to obtain target characteristics of the image to be analyzed; determining target information of equipment to be monitored by carrying out feature comparison processing on the target features and a power distribution network equipment diagram of at least one power distribution network equipment under at least one preset condition; the target information comprises at least one of a target position, a target situation and a target fault type of the equipment to be monitored.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

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

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.

The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims (10)

1. The image processing method is characterized by being applied to a power distribution network and comprising the following steps of:

acquiring an image to be processed comprising at least one device to be monitored;

the image to be analyzed is obtained through image enhancement processing on the image to be processed, and the image to be analyzed is input into an image recognition model to obtain target characteristics of the image to be analyzed;

determining target information of the equipment to be monitored by carrying out feature comparison processing on the target features and a power distribution network equipment diagram of at least one power distribution network equipment under at least one preset condition;

The target information comprises at least one of a target position, a target situation and a target fault type of the equipment to be monitored.

2. The method of claim 1, wherein the acquiring a to-be-processed image including at least one to-be-monitored device comprises:

an image to be processed including at least one device to be monitored is acquired based on at least one camera device deployed in an area of the distribution network.

3. The method according to claim 1, wherein the obtaining the image to be analyzed by performing image enhancement processing on the image to be processed, and inputting the image to be analyzed into an image recognition model to obtain the target feature of the image to be analyzed, includes:

removing noise information in the image to be processed through Gaussian filtering processing of the image to be processed, and carrying out content enhancement processing on the image to be processed with noise information removed to obtain the image to be analyzed;

Inputting the image to be analyzed into the image recognition model to obtain target characteristics of the image to be analyzed.

4. A method according to claim 3, wherein the content enhancement processing is performed on the image to be processed from which noise information is removed, so as to obtain the image to be analyzed, and the method comprises:

Performing histogram equalization pretreatment on the image to be processed to obtain an image to be used;

and inputting the image to be used into a low-illumination image enhancement network to obtain an image to be analyzed with enhanced image characteristics in the image to be used.

5. The method according to claim 1, wherein the determining the target information of the device to be monitored by performing feature comparison processing on the target feature and a power distribution network device diagram of at least one power distribution network device under at least one preset condition includes:

Determining the equipment type of the equipment to be monitored based on the target characteristics;

invoking a distribution network equipment diagram of at least one distribution network equipment corresponding to the equipment type under at least one preset condition;

And determining the target information based on the equipment diagram characteristics of the power distribution network equipment diagram and the target characteristics.

6. The method of claim 5, wherein the determining the target information based on the device map features of the power distribution network device map and the target features comprises:

determining target equipment graph characteristics through matching processing of the equipment graph characteristics and the target characteristics;

determining target power distribution network equipment corresponding to the target equipment graph characteristics, determining target positions and target situations of the target power distribution network equipment, and determining the target information;

wherein the target situation includes at least one of a fire situation, a flood situation, and an abnormal working situation.

7. The method as recited in claim 1, further comprising:

Generating early warning information based on the target information and at least one video frame of the corresponding equipment to be monitored, and sending the early warning information to target terminal equipment so that a user corresponding to the target terminal equipment maintains the equipment to be monitored.

8. An image processing apparatus, for use in a power distribution network, comprising:

The image acquisition module is used for acquiring an image to be processed comprising at least one device to be monitored;

The target feature acquisition module is used for obtaining an image to be analyzed by carrying out image enhancement processing on the image to be processed, and inputting the image to be analyzed into an image recognition model so as to obtain target features of the image to be analyzed;

the target information determining module is used for determining target information of the equipment to be monitored by carrying out feature comparison processing on the target features and the power distribution network equipment diagram of the at least one power distribution network equipment under at least one preset condition; the target information comprises at least one of a target position, a target situation and a target fault type of the equipment to be monitored.

9. An electronic device, the electronic device comprising:

At least one processor; and

A memory communicatively coupled to the at least one processor; wherein,

The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the image processing method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores computer instructions for causing a processor to implement the image processing method of any one of claims 1-7 when executed.