Power distribution network image intelligent analysis system

Abstract

The invention relates to a power distribution network image intelligent analysis system, and belongs to the technical field of power system operation and maintenance. Comprising an image acquisition module used for acquiring multi-source heterogeneous image data of power distribution network equipment and lines; the image preprocessing module is used for carrying out standardization, enhancement and regularization processing on the image data; the intelligent analysis core module is used for performing feature extraction, target recognition, anomaly detection, state evaluation and trend prediction on the image based on a deep learning model; the result output and interaction module is used for visually displaying the analysis result, generating a report and triggering early warning; the method is realized based on the system. According to the method, automatic, intelligent and comprehensive analysis of the power distribution network inspection image is realized, the problems of low efficiency, high cost and poor accuracy of traditional manual inspection are effectively overcome, depth state evaluation and risk prediction are performed by fusing multi-source image data, and the intelligent level of operation and maintenance of the power distribution network and the power supply reliability are remarkably improved.

Description

Technical Field

[0001] This invention relates to an intelligent image analysis system for power distribution networks, belonging to the field of power system operation and maintenance technology. Background Technology

[0002] The power distribution network equipment and lines are vast in scale and widely distributed, and their stable operation is directly related to the reliability of power supply. Traditional inspections mainly rely on manual methods, requiring inspectors to go to the site and check the appearance and connection status of equipment visually or with simple instruments. This method has significant shortcomings: First, manual inspections are inefficient, costly, and greatly affected by the environment, weather, and personnel experience; second, inspections of equipment in high-altitude, remote, or dangerous areas are difficult and pose safety risks; third, manual interpretation of images (especially professional images such as infrared and ultraviolet images) is highly subjective, prone to missed detections and misjudgments, making it difficult to achieve early detection and accurate diagnosis of defects.

[0003] With the application of technologies such as drones, high-definition cameras, and robots, automated image acquisition has become possible. However, the analysis of massive amounts of inspection images still largely relies on manual backend review or uses only rudimentary algorithms such as simple image comparison and threshold alarms, resulting in low levels of intelligence. Existing image recognition solutions often target single-type defects or single-modal images (such as visible light only), lacking a comprehensive intelligent analysis system that can integrate multi-source images (visible light, infrared, ultraviolet), cover mainstream equipment and typical defects in the distribution network, and combine automatic identification, in-depth analysis, condition assessment, and trend prediction. Therefore, there is an urgent need for an efficient, accurate, and comprehensive intelligent image analysis solution for distribution networks to achieve the digital and intelligent transformation of operation and maintenance work. Summary of the Invention

[0004] Based on the problems described in the background, the problem to be solved by the present invention is to provide an intelligent image analysis system for power distribution networks to address the aforementioned problems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a power distribution network image intelligent analysis system, comprising: The image acquisition module is used to acquire multi-source heterogeneous image data of power distribution network equipment and lines; An image preprocessing module, connected to the image acquisition module, is used to standardize, enhance, and normalize the acquired image data; The intelligent analysis core module is connected to the image preprocessing module and is used to perform feature extraction, target recognition and state analysis on the preprocessed image based on a deep learning model. The results output and interaction module is connected to the intelligent analysis core module and is used to visualize the analysis results, generate reports, and trigger early warnings.

[0006] Preferably, the image acquisition module includes: A fixed-location high-definition camera group is used to conduct fixed-point and timed monitoring and filming of key equipment and power distribution line corridors within the substation. Mobile inspection devices, including drones equipped with cameras, inspection robots, and handheld smart terminals, are used for mobile, close-range image acquisition of specific areas or equipment. The multi-source heterogeneous image data includes at least visible light images, infrared thermal imaging images, and ultraviolet discharge detection images.

[0007] Preferably, the image preprocessing module includes: The data cleaning unit is used to denoise and filter image data to eliminate environmental interference; The image enhancement unit is used to adjust the contrast, brightness, and sharpness of an image to highlight key features; The image normalization unit is used to normalize the size of images, unify their format, and associate labels to form a standardized image dataset.

[0008] Preferably, the intelligent analysis core module includes: The image recognition unit has a built-in trained deep learning recognition model for recognizing device type, nameplate information, and component structure in images; The anomaly detection unit is used to detect surface dirt on equipment, damaged insulators, corroded hardware, loose connections, hanging foreign objects, and abnormal line sag based on image feature analysis. The condition assessment unit is used to combine infrared thermal imaging to analyze connector overheating and partial discharge, and to combine visible light images to assess the appearance integrity of the equipment, and output quantitative or graded condition assessment results. The trend prediction unit is used to analyze the development trend of equipment defects and predict potential failure risks based on historical image sequences and status data.

[0009] Preferably, the deep learning recognition model used by the image recognition unit is a convolutional neural network model, which is trained using a training set containing a large number of labeled power distribution network equipment image samples, and can achieve accurate identification and positioning of multiple categories of transformers, circuit breakers, disconnect switches, instrument transformers, surge arresters, towers, conductors and insulators.

[0010] Preferably, the anomaly detection unit automatically selects and identifies abnormal areas by inputting real-time images into a pre-trained defect detection model. The defect detection model integrates target detection and image segmentation technologies, which can accurately segment targets such as insulator strings, conductors, and towers, and determine whether they have cracks, damage, missing parts, or foreign objects attached.

[0011] Preferably, the state assessment unit includes: The thermal imaging analysis subunit is used to analyze infrared thermal imaging images and identify equipment overheating defects and their severity levels through temperature distribution calculation, temperature difference comparison and historical temperature curve tracking. The appearance evaluation subunit is used to evaluate the appearance of equipment based on visible light images, such as coating peeling, oil leakage, blurred markings, and structural deformation, and to deduct points or give ratings according to preset rules.

[0012] Preferably, the result output and interaction module includes: A visual display interface is used to present analysis results in the form of charts, heat maps, defect annotation maps, and 3D models. The report automatic generation unit is used to generate inspection reports, defect lists, and maintenance recommendations in a structured manner based on the analysis results; The multi-level early warning unit is used to send early warning information to management and maintenance personnel at different levels based on the severity of the anomaly or defect, through system interface pop-ups, SMS, email, or linkage with the production management system.

[0013] Preferred options also include: The data storage and management module is used to classify and store raw image data, preprocessed data, model parameters, analysis process data, and historical result data, and provides multi-dimensional search and statistical functions based on device number, time, defect type, and location.

[0014] Preferably, the intelligent analysis method for distribution network images in the system includes the following steps: S1: Obtain raw image data of the target equipment or line area of ​​the power distribution network through the image acquisition module; S2: The image preprocessing module cleans, enhances, and normalizes the original image data to obtain a standard input image; S3: The image recognition unit of the intelligent analysis core module identifies the device and component in the standard input image; S4: The anomaly detection unit of the intelligent analysis core module performs in-depth analysis on the image based on the recognition results to detect whether there are defects or anomalies of a preset type. S5: The state assessment unit of the intelligent analysis core module assesses the severity of detected anomalies and scores or grades the overall state of the equipment in combination with historical data. S6: The analysis results of steps S3 to S5 are visualized through the result output and interaction module, and a decision is made on whether to generate early warning information or maintenance work order based on the evaluation results.

[0015] The beneficial effects of this invention are: 1. By integrating fixed monitoring and mobile inspection, automatic image data collection is achieved; deep learning models are used to replace manual image analysis, freeing maintenance personnel from the heavy work of reading images and greatly improving the efficiency and automation of inspection work.

[0016] 2. It reduces reliance on extensive on-site manual inspections, lowering labor costs; and significantly improves operational safety by replacing personnel with drones and robots to enter hazardous areas.

[0017] 3. Employing a deep learning model trained on massive amounts of data, it can accurately identify various equipment types and defects (such as rust, damage, overheating, and discharge), reducing the influence of subjective factors and improving the accuracy, consistency, and coverage of defect identification.

[0018] 4. The system does not simply identify defects, but can integrate multimodal image information such as visible light, infrared, and ultraviolet light to comprehensively evaluate and quantitatively classify the equipment status, providing a more comprehensive and reliable diagnostic conclusion than a single judgment.

[0019] 5. The system not only reports current defects, but also analyzes defect development trends based on historical data and predicts potential fault risks, thereby promoting the transformation of operation and maintenance mode from post-maintenance and periodic maintenance to predictive maintenance, which helps to avoid faults and improve power grid reliability.

[0020] 6. It automatically generates structured reports and visualized analysis results, enabling effective management and knowledge accumulation of inspection data, and providing data-driven support for asset lifecycle management and operation and maintenance decision optimization. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the framework of the present invention; Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0022] The embodiments of the present invention will be further described below with reference to the accompanying drawings: Example 1: As Figure 1 As shown, the present invention provides a power distribution network image intelligent analysis system, comprising: The image acquisition module is used to acquire multi-source heterogeneous image data of power distribution network equipment and lines; The image acquisition module, as the system's sensing front end, is responsible for actively or passively collecting various visual data from the power distribution network site.

[0023] An image preprocessing module, connected to the image acquisition module, is used to standardize, enhance, and normalize the acquired image data; The image preprocessing module is directly connected to the image acquisition module. Its core function is to "clean" and "regulate" the raw image data to eliminate noise, unify the format, and enhance features, providing high-quality input data for subsequent intelligent analysis.

[0024] The intelligent analysis core module is connected to the image preprocessing module and is used to perform feature extraction, target recognition and state analysis on the preprocessed image based on a deep learning model. The intelligent analysis core module is the central hub of this system. It receives preprocessed image data and uses embedded deep learning algorithms to perform complex pattern recognition and state analysis, thus achieving a leap from "seeing" to "understanding".

[0025] The results output and interaction module is connected to the intelligent analysis core module and is used to visualize the analysis results, generate reports, and trigger early warnings.

[0026] The result output and interaction module serves as a bridge between the system and operations and maintenance personnel. It transforms the results of machine analysis into visualized charts, executable reports, and timely early warning information, thereby directly empowering actual operations and maintenance decisions and operations with the conclusions of intelligent analysis.

[0027] The four modules of this invention are connected in sequence and work together to realize the automated and intelligent analysis of power distribution network image data, solving the problems of low efficiency and poor accuracy of traditional manual inspection.

[0028] The image acquisition module includes: A fixed-location high-definition camera group is used to conduct fixed-point and timed monitoring and filming of key equipment and power distribution line corridors within the substation. Mobile inspection devices, including drones equipped with cameras, inspection robots, and handheld smart terminals, are used for mobile, close-range image acquisition of specific areas or equipment. The multi-source heterogeneous image data includes at least visible light images, infrared thermal imaging images, and ultraviolet discharge detection images.

[0029] This module adopts a three-dimensional data collection strategy that combines "fixed deployment" and "mobile inspection".

[0030] Specifically, fixed high-definition camera groups constitute a routine monitoring network, usually installed indoors and outdoors in substations and on transmission line towers, to achieve 24 / 7 uninterrupted image capture of key equipment (such as transformers and circuit breakers) and key line corridors, forming baseline data.

[0031] Mobile inspection devices provide flexible and efficient supplementary means: drones are suitable for rapid inspection of large-scale line corridors and high-altitude equipment; inspection robots can enter substation equipment areas for close-range fine scanning; and handheld smart terminals facilitate fixed-point photography by maintenance personnel during special inspections and maintenance.

[0032] In particular, the system integrates multi-source heterogeneous image data, combining visible light images reflecting appearance status, infrared thermal imaging images revealing temperature anomalies and overload faults, and ultraviolet discharge detection images used to detect insulation defects such as partial discharge. This provides a rich data foundation for subsequent multi-dimensional comprehensive analysis and greatly enhances the comprehensiveness of status perception.

[0033] The image preprocessing module includes: The data cleaning unit is used to denoise and filter image data to eliminate environmental interference; The image enhancement unit is used to adjust the contrast, brightness, and sharpness of an image to highlight key features; The image normalization unit is used to normalize the size of images, unify their format, and associate labels to form a standardized image dataset.

[0034] This module is equivalent to the system's "data quality inspection and processing workshop".

[0035] The data cleaning unit first processes the original image, using algorithms such as median filtering and Gaussian filtering to remove random noise, rain, snow, fog interference, and stripes caused by electromagnetic interference, ensuring the purity of the data.

[0036] The image enhancement unit addresses image quality issues caused by the acquisition environment (such as insufficient lighting, backlighting, and strong reflections) by selectively enhancing key features such as texture and edges of the target device or region and suppressing irrelevant backgrounds through techniques such as histogram equalization, contrast stretching, and homomorphic filtering.

[0037] The image shaving unit performs standardization operations, uniformly adjusting the size of all input images to the fixed resolution required by the model (e.g., 512x512 pixels), converting images from different sources and formats (e.g., JPEG, PNG, RAW) into a unified tensor format, and accurately associating the images with their metadata (e.g., device ID, shooting time, GPS location), ultimately forming a standardized image dataset. This ensures the consistency of subsequent deep learning model inputs and is a key preprocessing step to guarantee the accuracy and stability of model analysis.

[0038] The intelligent analysis core module includes: The image recognition unit has a built-in trained deep learning recognition model for recognizing device type, nameplate information, and component structure in images; The anomaly detection unit is used to detect surface dirt on equipment, damaged insulators, corroded hardware, loose connections, hanging foreign objects, and abnormal line sag based on image feature analysis. The condition assessment unit is used to combine infrared thermal imaging to analyze connector overheating and partial discharge, and to combine visible light images to assess the appearance integrity of the equipment, and output quantitative or graded condition assessment results. The trend prediction unit is used to analyze the development trend of equipment defects and predict potential failure risks based on historical image sequences and status data.

[0039] This module is the central embodiment of the system's intelligence, containing four progressively layered analysis units.

[0040] The image recognition unit is equipped with a pre-trained deep learning model (such as Faster R-CNN, YOLO series), which can quickly and accurately identify and locate specific equipment types and components such as "transformer", "insulator string", and "wire clamp" from complex backgrounds, just like an experienced expert, laying the target foundation for subsequent targeted analysis.

[0041] Based on the identification, the anomaly detection unit further uses target detection and semantic segmentation technologies (such as MaskR-CNN) to perform a "physical examination" on specific components, automatically detecting defects such as cracks on the surface of insulators, corrosion of metal parts, and foreign objects hanging on conductors, and accurately marking the abnormal areas with bounding boxes or pixel-level masks.

[0042] The condition assessment unit then "diagnoses" the detected anomalies by integrating various information: for example, it analyzes the temperature difference and trend of the hot spots by combining infrared images to give a level of "general overheating" or "severe overheating"; and it assesses the corrosion area by combining visible light images to determine the degree of corrosion.

[0043] The trend prediction unit introduces a time dimension. By analyzing image sequences and condition assessment results of the same equipment at different historical periods, it uses time series analysis or regression models to predict the evolution speed of defects and assess the probability of them evolving into failures, thereby achieving a leap from "condition-based maintenance" to predictive maintenance.

[0044] The image recognition unit uses a deep learning recognition model, which is a convolutional neural network model. It is trained using a training set containing a large number of labeled images of power distribution network equipment, and can achieve accurate identification and positioning of multiple categories of transformers, circuit breakers, disconnect switches, instrument transformers, surge arresters, towers, conductors and insulators.

[0045] The model is trained through supervised learning: First, a large-scale, professionally labeled training set of power distribution network equipment images needs to be constructed. Each image in the training set is accurately labeled with the equipment category it contains and its location bounding box in the image.

[0046] During training, the model automatically learns a hierarchical representation from pixels to semantic features (such as shape, texture, and contextual relationships) through multi-layer convolution, pooling, and other operations.

[0047] After thorough training, the model is able to accurately identify and locate multiple categories of input field images. For example, it can not only distinguish between tall "towers" and slender "conductors", but also accurately identify specific equipment such as "pin insulators", "suspension insulators", and "lightning arresters", and output their category labels and location coordinates with high confidence.

[0048] This deep learning-based recognition method has stronger generalization and anti-interference capabilities compared to traditional methods based on template matching or manual features.

[0049] The anomaly detection unit automatically selects and identifies abnormal areas by inputting real-time images into a pre-trained defect detection model. The defect detection model integrates target detection and image segmentation technologies, which can accurately segment targets such as insulator strings, conductors, and towers, and determine whether they have cracks, damage, missing parts, or foreign objects attached.

[0050] The core of this unit is a pre-trained defect detection model, which is innovative in that it integrates object detection and image segmentation techniques.

[0051] In terms of process, the model first completes the detection and localization of overall targets such as "insulator strings" and "conductors" (i.e., "component-level"). Then, within the located target area, the model activates pixel-level semantic segmentation, which can finely delineate the substructures of the insulator string, such as the porcelain skirts, steel caps, and conductor bodies.

[0052] Based on this, the model analyzes the texture, continuity, and shape features of the segmented sub-regions to determine whether there are defects of a preset category. For example, it analyzes the texture continuity of the ceramic skirt segmentation region to determine cracks, analyzes the color and texture of the steel cap region to determine corrosion, and detects the outline of foreign objects attached to the wire segmentation region to determine foreign object attachment.

[0053] This "detection-segmentation-precise judgment" process enables anomaly detection not only to discover defects, but also to accurately describe the shape, size and location of the defects, providing extremely detailed information for subsequent quantitative assessment and maintenance guidance.

[0054] The status assessment unit includes: The thermal imaging analysis subunit is used to analyze infrared thermal imaging images and identify equipment overheating defects and their severity levels through temperature distribution calculation, temperature difference comparison and historical temperature curve tracking. The appearance evaluation subunit is used to evaluate the appearance of equipment based on visible light images, such as coating peeling, oil leakage, blurred markings, and structural deformation, and to deduct points or give ratings according to preset rules.

[0055] This unit contains two parallel specialized analysis subunits, which process different types of data to arrive at comprehensive evaluation conclusions.

[0056] The thermal imaging analysis subunit is specifically designed for processing infrared thermal imaging images. Its workflow is as follows: First, it extracts temperature field data from the image; then, it calculates the temperature distribution, generating temperature contour maps or pseudo-color maps; next, it performs temperature difference comparisons, calculating the temperature difference between the device itself and the background environment, between similar devices in different phases, or between different components of the device itself—this is a crucial basis for determining overheating; finally, it combines historical temperature curve tracking to observe the temperature change trend of specific measuring points over time.

[0057] Based on the above analysis, this sub-unit can identify the type of heat generation defect (such as poor connection, increased internal loss, etc.) and, according to the preset temperature difference threshold and trend slope, give a severity level such as "normal", "caution", "abnormal" and "severe".

[0058] The appearance evaluation subunit focuses on visible light images. Based on the results of image recognition and anomaly detection, it deducts points or assigns ratings according to a set of preset rules.

[0059] For example, the rules might stipulate that: 10 points will be deducted if the area of ​​coating peeling exceeds 5% of the equipment surface area; and the discovery of oil leaks will be directly rated as "serious condition". The evaluation results of the two sub-units are finally combined to form a unified, quantitative health status score or risk level for the equipment.

[0060] The result output and interaction module includes: A visual display interface is used to present analysis results in the form of charts, heat maps, defect annotation maps, and 3D models. The report automatic generation unit is used to generate inspection reports, defect lists, and maintenance recommendations in a structured manner based on the analysis results; The multi-level early warning unit is used to send early warning information to management and maintenance personnel at different levels based on the severity of the anomaly or defect, through system interface pop-ups, SMS, email, or linkage with the production management system.

[0061] This module is the ultimate embodiment of the system's value.

[0062] The visualization interface uses advanced graphical technology to display data not only in the form of lists and charts, but more importantly, it can directly overlay defect annotation maps (i.e., the original map that shows the location of defects with boxes or highlights) and heat maps (infrared temperature distribution) onto the 3D model or panoramic view of the equipment, realizing an intuitive mapping between analysis results and physical equipment, making it clear to maintenance personnel at a glance.

[0063] The automatic report generation unit automatically fills in the preset report template based on the structured data of the analysis results, and instantly generates a standardized inspection report containing a defect list, location photos, severity, and handling suggestions, which greatly reduces the workload of copywriting.

[0064] The multi-level early warning unit is key to the proactive operation and maintenance of the entire system. It has an embedded early warning rule engine that can automatically trigger early warnings at different levels and through different channels based on the severity (e.g., general, severe, critical) and type of the anomaly: for example, a general defect generates a to-do task on the system interface; a severe defect automatically sends an SMS to the team leader; and a critical defect (e.g., an imminent wire breakage) simultaneously triggers an interface pop-up, a telephone notification, and directly generates an emergency repair work order in the production management system (e.g., PMS).

[0065] This tiered and coordinated early warning mechanism ensures that risk information can be delivered to the relevant responsible persons in a timely and accurate manner.

[0066] Also includes: The data storage and management module is used to classify and store raw image data, preprocessed data, model parameters, analysis process data, and historical result data, and provides multi-dimensional search and statistical functions based on device number, time, defect type, and location.

[0067] This module is a comprehensive data warehouse responsible for classifying and storing data throughout the system's entire lifecycle: including raw image data without any processing, preprocessed data, model parameters of deep learning models, intermediate features and logs during the analysis process, and the final generated historical results data.

[0068] More importantly, this module provides powerful data governance capabilities, supporting multi-dimensional retrieval and statistics. Maintenance personnel can use it like an advanced search engine, combining query conditions such as: "Retrieve all infrared images of transformer with device number 'A-105' containing 'joint overheating' defects in the past year," or "Statistics on the distribution of 'insulator damage' defects in all lines this month."

[0069] This capability not only facilitates fault tracing and historical data analysis, but also provides valuable data resources for optimizing inspection plans, assessing equipment family defects, and continuously training and optimizing AI models, enabling the system to have the potential for continuous self-evolution.

[0070] like Figure 2 As shown, the intelligent analysis method for distribution network images in the system includes the following steps: S1: Obtain raw image data of the target equipment or line area of ​​the power distribution network through the image acquisition module; S2: The image preprocessing module cleans, enhances, and normalizes the original image data to obtain a standard input image; S3: The image recognition unit of the intelligent analysis core module identifies the device and component in the standard input image; S4: The anomaly detection unit of the intelligent analysis core module performs in-depth analysis on the image based on the recognition results to detect whether there are defects or anomalies of a preset type. S5: The state assessment unit of the intelligent analysis core module assesses the severity of detected anomalies and scores or grades the overall state of the equipment in combination with historical data. S6: The analysis results of steps S3 to S5 are visualized through the result output and interaction module, and a decision is made on whether to generate early warning information or maintenance work order based on the evaluation results.

[0071] This method embodies a complete analytical logic chain: Step S1 is the starting point for data acquisition, clarifying that the object of analysis is a specific "target device or line area".

[0072] Step S2 is the data preparation stage, which ensures the reliability of the analysis input through standardized preprocessing.

[0073] Steps S3 (identification) and S4 (detection) constitute the core analysis process, which first "identifies the object" and then "diagnoses the problem," demonstrating rigorous logic.

[0074] Step S5 (assessment) is a quantitative diagnosis of the "condition". Combining historical data makes the assessment results more dynamic and predictive.

[0075] Step S6 (output) is the closed loop of the entire process, transforming the analysis conclusions of machine intelligence into actual productivity, whether it is visualized information for human decision-making or early warnings and work orders that drive automated processes.

Claims

1. A power distribution network image intelligent analysis system, characterized in that, include: The image acquisition module is used to acquire multi-source heterogeneous image data of power distribution network equipment and lines; An image preprocessing module, connected to the image acquisition module, is used to standardize, enhance, and normalize the acquired image data; The intelligent analysis core module is connected to the image preprocessing module and is used to perform feature extraction, target recognition and state analysis on the preprocessed image based on a deep learning model. The results output and interaction module is connected to the intelligent analysis core module and is used to visualize the analysis results, generate reports, and trigger early warnings.

2. The intelligent image analysis system for power distribution networks according to claim 1, characterized in that, The image acquisition module includes: A fixed-location high-definition camera group is used to conduct fixed-point and timed monitoring and filming of key equipment and power distribution line corridors within the substation. Mobile inspection devices, including drones equipped with cameras, inspection robots, and handheld smart terminals, are used for mobile, close-range image acquisition of specific areas or equipment. The multi-source heterogeneous image data includes at least visible light images, infrared thermal imaging images, and ultraviolet discharge detection images.

3. The intelligent image analysis system for power distribution networks according to claim 1, characterized in that, The image preprocessing module includes: The data cleaning unit is used to denoise and filter image data to eliminate environmental interference; The image enhancement unit is used to adjust the contrast, brightness, and sharpness of an image to highlight key features; The image normalization unit is used to normalize the size of images, unify their format, and associate labels to form a standardized image dataset.

4. The intelligent image analysis system for power distribution networks according to claim 1, characterized in that, The intelligent analysis core module includes: The image recognition unit has a built-in trained deep learning recognition model for recognizing device type, nameplate information, and component structure in images; The anomaly detection unit is used to detect surface dirt on equipment, damaged insulators, corroded hardware, loose connections, hanging foreign objects, and abnormal line sag based on image feature analysis. The condition assessment unit is used to combine infrared thermal imaging to analyze connector overheating and partial discharge, and to combine visible light images to assess the appearance integrity of the equipment, and output quantitative or graded condition assessment results. The trend prediction unit is used to analyze the development trend of equipment defects and predict potential failure risks based on historical image sequences and status data.

5. The intelligent image analysis system for power distribution networks according to claim 1, characterized in that, The image recognition unit uses a deep learning recognition model, which is a convolutional neural network model. It is trained using a training set containing a large number of labeled images of power distribution network equipment, and can achieve accurate identification and positioning of multiple categories of transformers, circuit breakers, disconnect switches, instrument transformers, surge arresters, towers, conductors and insulators.

6. The intelligent image analysis system for power distribution networks according to claim 1, characterized in that, The anomaly detection unit automatically selects and identifies abnormal areas by inputting real-time images into a pre-trained defect detection model. The defect detection model integrates target detection and image segmentation technologies, which can accurately segment targets such as insulator strings, conductors, and towers, and determine whether they have cracks, damage, missing parts, or foreign objects attached.

7. The intelligent image analysis system for power distribution networks according to claim 1, characterized in that, The status assessment unit includes: The thermal imaging analysis subunit is used to analyze infrared thermal imaging images and identify equipment overheating defects and their severity levels through temperature distribution calculation, temperature difference comparison and historical temperature curve tracking. The appearance evaluation subunit is used to evaluate the appearance of equipment based on visible light images, such as coating peeling, oil leakage, blurred markings, and structural deformation, and to deduct points or give ratings according to preset rules.

8. The intelligent image analysis system for power distribution networks according to claim 1, characterized in that, The result output and interaction module includes: A visual display interface is used to present analysis results in the form of charts, heat maps, defect annotation maps, and 3D models. The report automatic generation unit is used to generate inspection reports, defect lists, and maintenance recommendations in a structured manner based on the analysis results; The multi-level early warning unit is used to send early warning information to management and maintenance personnel at different levels based on the severity of the anomaly or defect, through system interface pop-ups, SMS, email, or linkage with the production management system.

9. The intelligent image analysis system for power distribution networks according to claim 1, characterized in that, Also includes: The data storage and management module is used to classify and store raw image data, preprocessed data, model parameters, analysis process data, and historical result data, and provides multi-dimensional search and statistical functions based on device number, time, defect type, and location.

10. The intelligent image analysis system for power distribution networks according to claim 1, characterized in that, The intelligent analysis method for distribution network images in the system includes the following steps: S1: Obtain raw image data of the target equipment or line area of ​​the power distribution network through the image acquisition module; S2: The image preprocessing module cleans, enhances, and normalizes the original image data to obtain a standard input image; S3: The image recognition unit of the intelligent analysis core module identifies the device and component in the standard input image; S4: The anomaly detection unit of the intelligent analysis core module performs in-depth analysis on the image based on the recognition results to detect whether there are defects or anomalies of a preset type. S5: The state assessment unit of the intelligent analysis core module assesses the severity of detected anomalies and scores or grades the overall state of the equipment in combination with historical data. S6: The analysis results of steps S3 to S5 are visualized through the result output and interaction module, and a decision is made on whether to generate early warning information or maintenance work order based on the evaluation results.

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