Distribution network inspection image analysis method and system based on deep learning algorithm

Abstract

The invention provides a distribution network inspection image analysis method and system based on a deep learning algorithm, and the method comprises the steps: collecting a plurality of pieces of defect image data, and carrying out the manual marking of each piece of defect image data, so as to obtain preliminary training data; constructing a pre-training model, and training the pre-training model by using the preliminary training data to obtain an image processing model; performing model enhancement on the image processing model through multi-size training and a feature pyramid to obtain an image processing optimization model; and performing image acquisition through image acquisition equipment to obtain corresponding image data, performing image preprocessing on the image data to obtain a preprocessed image, and inputting the preprocessed image into the image processing optimization model for image analysis to obtain a corresponding analysis result.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and particularly to a method and system for analyzing distribution network inspection images based on deep learning algorithms. Background Art

[0002] With the deepening of the power system reform, the competition in the power construction market has become increasingly fierce. Currently, for the inspection of distribution network projects, images are usually analyzed manually, and the analysis results are used as a reference for inspection. However, with the increasing complexity and scale of distribution equipment, manual image analysis is not only time-consuming and laborious, but also prone to misjudgment and missed judgment, resulting in the inability to timely detect and handle equipment hidden dangers. At the same time, in the face of a large number of images, it is impossible to generate an analysis report in a short time. The main manifestations are as follows: 1. Inconsistency in defect identification caused by subjective factors Manual defect identification is affected by subjective factors such as the experience, knowledge, and judgment of inspectors. Different inspectors may make different judgments on the same problem based on their own understandings, resulting in inconsistencies in inspection results.

[0003] 2. Low efficiency and error-proneness in manually processing inspection records Inspectors manually sort out inspection records, file them, and write defect reports. This process is cumbersome and time-consuming, involving a large amount of data input, classification, analysis, and report generation. Due to the easy omission or error in manual operations, the accuracy and integrity of inspection results are affected, further increasing the work burden.

[0004] 3. Low inspection efficiency and high labor cost Traditional manual inspection methods usually require a large amount of manpower and time. Inspectors rely on experience for manual inspection. Such a method is not only slow, but also unable to cover every detail comprehensively.

[0005] 4. Many recognition errors and poor recognition accuracy Manual inspection is easily affected by environmental factors, personnel fatigue, etc., resulting in missed inspections or misinspections, and thus unable to timely identify potential faults of equipment.

[0006] 5. High maintenance cost and many potential risks If defects are not detected in time, it will lead to equipment failures and even power outages, thereby increasing the maintenance cost and posing potential threats to the stability and security of power supply. Summary of the Invention

[0007] Embodiments of this application provide a method and system for analyzing distribution network inspection images based on deep learning algorithms to at least solve the deficiencies in the above related technologies.

[0008] In a first aspect, an embodiment of the present application provides a method for analyzing distribution network inspection images based on a deep learning algorithm, including the following steps: Step 1: Collect a number of defect image data, and manually annotate each of the defect image data to obtain preliminary training data; Step 2: Construct a pre-trained model, and use the preliminary training data to train the pre-trained model to obtain an image processing model; Step 3: Enhance the model of the image processing model through multi-size training and a feature pyramid to obtain an optimized image processing model; Step 4: Collect image data through an image acquisition device to obtain corresponding image data, perform image preprocessing on the image data to obtain a preprocessed image, and input the preprocessed image into the optimized image processing model for image analysis to obtain corresponding analysis results.

[0009] Further, the step 1 includes: Extract defect information of distribution network equipment, where the defect information includes insulator damage, pole corrosion, and wire slack; Manually annotate each of the defect images based on the defect information to obtain preliminary training data.

[0010] Further, the step 2 includes: Construct a YOLO pre-trained model, and adjust the feature extraction layer of the YOLO pre-trained model in combination with transfer learning to improve the identification ability of the YOLO pre-trained model for specific defects; Input the preliminary training data into the YOLO pre-trained model, and use a deep learning algorithm to optimize the model of the YOLO pre-trained model to obtain an image processing model.

[0011] Further, the step 3 includes: Enhance the model of the image processing model through multi-scale training and a feature pyramid to obtain an enhanced image processing model; Use non-maximum suppression to reduce the overlapping boxes of each image defect in the enhanced image processing model to obtain the corresponding optimized image processing model.

[0012] Further, the step 4 includes: Perform noise reduction processing on the image data, and perform illumination adjustment on the image data after noise reduction processing to obtain a preprocessed image; Use the image processing model to identify defects in the preprocessed image, and generate a corresponding analysis report based on the defect identification results, where the analysis report includes defect information, maintenance information, and priority information.

[0013] In a second aspect, the present invention also proposes a distribution network inspection image analysis system based on a deep learning algorithm, including: A data annotation module, configured to collect a plurality of defect image data and perform manual annotation on each of the defect image data to obtain preliminary training data; A model training module, configured to construct a pre-trained model and use the preliminary training data to train the pre-trained model to obtain an image processing model; A model enhancement module, configured to perform model enhancement on the image processing model through multi-scale training and a feature pyramid to obtain an optimized image processing model; An image analysis module, configured to collect image data through an image acquisition device to obtain corresponding image data, perform image preprocessing on the image data to obtain a preprocessed image, and input the preprocessed image into the optimized image processing model for image analysis to obtain corresponding analysis results.

[0014] Further, the data annotation module includes: An information extraction unit, configured to extract defect information of distribution network equipment, where the defect information includes insulator damage, tower corrosion, and conductor slack; A manual annotation unit, configured to perform manual annotation on each of the defect images based on the defect information to obtain preliminary training data.

[0015] Further, the model training module includes: A model construction unit, configured to construct a YOLO pre-trained model, and adjust the feature extraction layer of the YOLO pre-trained model in combination with transfer learning to improve the identification ability of the YOLO pre-trained model for specific defects; A model training unit, configured to input the preliminary training data into the YOLO pre-trained model and use a deep learning algorithm to optimize the YOLO pre-trained model to obtain an image processing model.

[0016] Further, the model enhancement module includes: A model enhancement unit, configured to perform model enhancement on the image processing model through multi-scale training and a feature pyramid to obtain an enhanced image processing model; A model optimization unit, configured to use non-maximum suppression to reduce the overlapping boxes of each image defect in the enhanced image processing model to obtain a corresponding optimized image processing model.

[0017] Further, the image analysis module includes: A preprocessing unit, configured to perform noise reduction processing on the image data and perform illumination adjustment on the image data after noise reduction processing to obtain a preprocessed image; An image analysis unit for performing defect recognition on the pre - processed image by the image processing model and generating a corresponding analysis report based on the defect recognition result, where the analysis report includes defect information, maintenance information, and priority information.

[0018] Compared with the related art, the power distribution inspection image analysis method and system based on deep learning algorithm provided by the embodiments of the present application use the YOLO deep learning algorithm to efficiently and accurately identify various equipment defects in the power distribution inspection images, accurately locate the defect positions, reduce the need for manual intervention, eliminate the errors and missed detection risks of manual recognition, automatically generate a structured defect report according to the recognition result, including defect type, location, and severity information, ensure the timeliness and accuracy of the report, significantly improve the automation and intelligence level of power distribution inspection, enhance the operation and maintenance efficiency and decision - making accuracy, and provide strong technical support for the refined management of power distribution operation and maintenance.

[0019] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more comprehensible. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the present application and form a part of the present application. The illustrative embodiments and descriptions thereof are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings: Figure 1 It is a flowchart of the power distribution inspection image analysis method based on deep learning algorithm in the first embodiment of the present invention; Figure 2 is Figure 1 a detailed flowchart of step S101 in Figure 3 is Figure 1 a detailed flowchart of step S102 in Figure 4 is Figure 1 a detailed flowchart of step S103 in Figure 5 is Figure 1 a detailed flowchart of step S104 in Figure 6 It is a structural block diagram of the power distribution inspection image analysis system based on deep learning algorithm in the second embodiment of the present invention.

[0021] The following specific embodiments will further illustrate the present invention in conjunction with the above - mentioned drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] To make the objectives, technical solutions and advantages of this application more clear and understandable, the following describes and explains this application in combination with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments provided in this application without creative efforts belong to the scope of protection of this application.

[0023] Obviously, the accompanying drawings in the following description are only some examples or embodiments of this application. For those of ordinary skill in the art, without creative efforts, this application can also be applied to other similar scenarios based on these drawings. In addition, it can also be understood that although the efforts made in such a development process may be complex and lengthy, for those of ordinary skill in the art related to the content disclosed in this application, some design, manufacturing or production changes based on the technical content disclosed in this application are only conventional technical means and should not be understood as the content disclosed in this application being insufficient.

[0024] Referring to "embodiments" in this application means that the specific features, structures or characteristics described in combination with the embodiments can be included in at least one embodiment of this application. The appearance of this phrase in various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those of ordinary skill in the art explicitly and implicitly understand that the embodiments described in this application can be combined with other embodiments without conflict.

[0025] Unless otherwise defined, the technical terms or scientific terms involved in this application should have the ordinary meaning understood by those with ordinary skills in the technical field to which this application belongs. The words such as "a", "an", "one kind", "the" and the like involved in this application do not indicate a quantity limitation and can represent a single or plural number. The terms "including", "comprising", "having" and any variations thereof involved in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product or device including a series of steps or modules (units) is not limited to the listed steps or units, but may further include unlisted steps or units, or may further include other steps or units inherent to these processes, methods, products or devices. The terms "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. Embodiment 1

[0026] Please refer to Figure 1, shown is a method for analyzing distribution network inspection images based on deep learning algorithms in the first embodiment of the present invention. The method specifically includes steps S101 to S104: S101, collect a number of defect image data, and perform manual annotation on each of the defect image data to obtain preliminary training data; Further, please refer to Figure 2 , the step S101 specifically includes steps S1011~S1012: S1011, extract defect information of distribution network equipment, where the defect information includes insulator damage, pole corrosion, and wire slack; S1012, perform manual annotation on each of the defect images based on the defect information to obtain preliminary training data.

[0027] In specific implementation, use a drone to take device images, collect a large number of defect-containing image data, perform real-time defect detection on the defect-containing image data, identify various common distribution network defects including broken wires, insulation damage, corrosion, etc., and perform manual annotation on each defect image based on the defect information to obtain preliminary training data.

[0028] S102, construct a pre-trained model, and use the preliminary training data to train the pre-trained model to obtain an image processing model; Further, please refer to Figure 3 , the step S102 specifically includes steps S1021~S1022: S1021, construct a YOLO pre-trained model, and adjust the feature extraction layer of the YOLO pre-trained model in combination with transfer learning to improve the identification ability of the YOLO pre-trained model for specific defects; S1022, input the preliminary training data into the YOLO pre-trained model, and use deep learning algorithms to optimize the YOLO pre-trained model to obtain an image processing model.

[0029] In specific implementation, use a YOLO pre-trained model, adjust the feature extraction layer in combination with transfer learning, improve the model's identification ability for specific defects, and at the same time use data augmentation techniques in the algorithm to enhance the robustness of the model, adapt to different environments and lighting conditions, and finally construct an efficient and reliable defect library to support automated inspection and generate accurate defect identification reports.

[0030] The YOLO deep learning algorithm processes images by accurately identifying device features in the images and locating defects, completing target recognition and classification. To improve the recognition accuracy and real-time performance of the model, a training dataset for the characteristics of distribution network devices is adopted, and the model is optimized so that it can identify various types of defects. Through deep learning technology, the YOLO deep learning algorithm can quickly locate the position of defects in the image and accurately output each defect type, defect level, and defect description.

[0031] S103. Enhance the image processing model through multi-scale training and feature pyramids to obtain an optimized image processing model. Further, please refer to Figure 4 The step S103 specifically includes steps S1031 to S1032: S1031. Enhance the image processing model through multi-scale training and feature pyramids to obtain an enhanced image processing model. S1032. Use non-maximum suppression to reduce the overlapping boxes of each image defect in the enhanced image processing model to obtain the corresponding optimized image processing model.

[0032] In specific implementation, through multi-scale training and feature pyramids, the sensitivity of the model to small objects is enhanced. YOLO outputs the classification and precise position of each defect. By using non-maximum suppression to reduce overlapping boxes, the detection accuracy is improved. The drone images are preprocessed to solve noise and lighting problems, thus ensuring the efficient recognition of the model in complex environments.

[0033] S104. Collect images through an image acquisition device to obtain corresponding image data, perform image preprocessing on the image data to obtain a preprocessed image, and input the preprocessed image into the optimized image processing model for image analysis to obtain corresponding analysis results.

[0034] Further, please refer to Figure 5 The step S104 specifically includes steps S1041 to S1042: S1041. Perform noise reduction processing on the image data and adjust the lighting of the image data after noise reduction processing to obtain a preprocessed image. S1042. Use the image processing model to identify defects in the preprocessed image and generate a corresponding analysis report based on the defect recognition results. The analysis report includes defect information, maintenance information, and priority information.

[0035] In specific implementation, based on the defect information identified by the YOLO deep learning algorithm and combined with the attribute data of the device, a detailed defect analysis report can be automatically generated. The report content includes the defect type, occurrence location, and defect degree. Specifically, the YOLO deep learning algorithm is used to automatically detect defects and automatically mark the defects. After defect detection, the platform automatically generates an analysis report and outputs a complete report including defect details, maintenance suggestions, priority, etc. according to the set parameters.

[0036] In this embodiment, according to the severity of the defect, it is automatically determined whether maintenance is required, and corresponding suggestions are put forward. To ensure the accuracy and integrity of the report, intelligent analysis is carried out through a rule engine to generate a report format that meets the specifications.

[0037] In this embodiment, through basic data analysis and requirement analysis, the inspection work process of the distribution network, the inspection task frequency, and the actual usage of inspection equipment are investigated. Combining the operation characteristics of the distribution network and the application of UAV technology, the requirements for automatically generating defect analysis reports based on the YOLO deep learning algorithm are clarified. On this basis, combined with the requirements for visible light image acquisition, an image acquisition specification for UAV inspection is formulated, including standards such as shooting angle, resolution, and shooting period, to ensure that the acquired image data can provide high-quality input for subsequent defect detection and analysis.

[0038] Through algorithm optimization, the defect recognition accuracy in different environmental light conditions, complex backgrounds, and poor image quality is improved. At the same time, image preprocessing and data augmentation technologies are studied to ensure that the algorithm can work stably in various inspection scenarios, providing a reliable technical guarantee for automatic defect report generation.

[0039] Combined with the research results of the YOLO deep learning algorithm, the automatic generation of defect analysis reports for visible light images of distribution network inspection is carried out. The method for automatically generating defect analysis reports for visible light images of distribution network inspection using the YOLO deep learning algorithm performs real-time defect detection on the visible light images taken by the UAV, and identifies various common distribution network defects including wire breakage, insulation damage, corrosion, etc. According to the detection results, a defect analysis report is automatically generated, including information such as the type, location, severity, and repair suggestions of the defect, facilitating the inspection personnel to obtain information in a timely manner and make decisions quickly, thereby improving the efficiency and accuracy of distribution network inspection.

[0040] In this embodiment, by automatically analyzing visible light images, various types of defects in the distribution network can be accurately identified, and a detailed defect analysis report can be automatically generated. Different from the traditional manual inspection method, the YOLO algorithm can process a large amount of image data in real time, quickly detect problems such as cracks, corrosion, and broken wires, eliminating the errors and missed inspection risks of manual identification. According to information such as the type, location, and severity of the defects, a standardized report is automatically generated. The report content includes defect descriptions, repair suggestions, priorities, etc., greatly improving the efficiency and accuracy of inspections. The automated report generation enables rapid response to defect diagnosis and repair decisions, and also simplifies the storage and query of historical inspection data through data management, facilitating long-term equipment maintenance and trend analysis. The application of the YOLO deep learning algorithm not only reduces the time cost and error rate of manual inspections, but also improves the processing speed of inspection data, greatly enhancing the intelligent level of distribution network operation and maintenance.

[0041] In summary, the distribution network inspection image analysis method based on the deep learning algorithm in the above embodiments of the present invention uses the YOLO deep learning algorithm to efficiently and accurately identify various types of equipment defects in the distribution network inspection images, accurately locate the defect positions, reduces the need for manual intervention, eliminates the errors and missed inspection risks of manual identification, automatically generates a structured defect report according to the recognition results, including defect type, location, and severity information, ensures the timeliness and accuracy of the report, significantly improves the automation and intelligent level of distribution network inspections, improves the operation and maintenance efficiency and decision-making accuracy, and provides strong technical support for the refined management of distribution network operation and maintenance. Embodiment 2

[0042] On the other hand, the present invention also proposes a distribution network inspection image analysis system based on the deep learning algorithm. Please refer to Figure 6 , which shows a distribution network inspection image analysis system based on the deep learning algorithm in the second embodiment of the present invention, including: A data annotation module 11, configured to collect a number of defect image data and perform manual annotation on each of the defect image data to obtain preliminary training data; Further, the data annotation module 11 includes: An information extraction unit, configured to extract defect information of distribution network equipment, where the defect information includes insulator damage, pole corrosion, and wire slack; A manual annotation unit, configured to perform manual annotation on each of the defect images based on the defect information to obtain preliminary training data.

[0043] A model training module 12, configured to construct a pre-trained model and use the preliminary training data to train the pre-trained model to obtain an image processing model; Further, the model training module 12 includes: A model construction unit for constructing a YOLO pre-trained model and adjusting the feature extraction layer of the YOLO pre-trained model in combination with transfer learning to improve the identification ability of the YOLO pre-trained model for specific defects; A model training unit for inputting the preliminary training data into the YOLO pre-trained model and optimizing the model of the YOLO pre-trained model using a deep learning algorithm to obtain an image processing model.

[0044] A model enhancement module 13 for enhancing the image processing model through multi-size training and a feature pyramid to obtain an optimized image processing model; Further, the model enhancement module 13 includes: A model enhancement unit for enhancing the image processing model through multi-scale training and a feature pyramid to obtain an enhanced image processing model; A model optimization unit for reducing the overlapping boxes of each image defect in the enhanced image processing model using non-maximum suppression to obtain a corresponding optimized image processing model.

[0045] An image analysis module 14 for collecting images through an image acquisition device to obtain corresponding image data, preprocessing the image data to obtain a preprocessed image, and inputting the preprocessed image into the optimized image processing model for image analysis to obtain a corresponding analysis result.

[0046] Further, the image analysis module 14 includes: A preprocessing unit for denoising the image data and adjusting the illumination of the denoised image data to obtain a preprocessed image; An image analysis unit for identifying defects in the preprocessed image using the image processing model and generating a corresponding analysis report based on the defect identification result, where the analysis report includes defect information, maintenance information, and priority information.

[0047] The functions or operation steps implemented when the above modules are executed are substantially the same as those in the above method embodiments, and will not be described in detail here.

[0048] A distribution network inspection image analysis system based on a deep learning algorithm provided by an embodiment of the present invention has the same implementation principle and technical effects as those in the foregoing method embodiments. For a brief description, for parts not mentioned in the system embodiment, reference may be made to the corresponding content in the foregoing method embodiments.

[0049] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0050] The above-described embodiments merely represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A distribution network inspection image analysis method based on deep learning algorithm, characterized in that: The following steps are involved: Step 1: Collect a number of defect image data, and manually annotate each of the defect image data to obtain preliminary training data; Step 2: construct a pre-training model, and use the preliminary training data to train the pre-training model to obtain an image processing model; Step 3: Enhance the image processing model through multi-scale training and feature pyramid to obtain an image processing optimization model; Step 4: Capture images through an image acquisition device to obtain corresponding image data, and preprocess the image data to obtain a preprocessed image, and input the preprocessed image into the image processing optimization model for image analysis to obtain corresponding analysis results.

2. The distribution network inspection image analysis method based on deep learning algorithm according to claim 1 is characterized in that: The step one comprises: Extracting defect information of distribution network equipment, wherein the defect information includes insulator damage, tower corrosion, and conductor looseness; Manually annotate each of the defect images based on the defect information to obtain preliminary training data.

3. The distribution network inspection image analysis method based on deep learning algorithm according to claim 1 is characterized in that: The second step comprises: Construct a YOLO pre-trained model, and adjust the feature extraction layer of the YOLO pre-trained model in combination with transfer learning to improve the YOLO pre-trained model's ability to identify specific defects; The preliminary training data is input into the YOLO pre-training model, and the YOLO pre-training model is optimized using a deep learning algorithm to obtain an image processing model.

4. The distribution network inspection image analysis method based on deep learning algorithm according to claim 1 is characterized in that: The step three comprises: Performing model enhancement on the image processing model through multi-scale training and feature pyramid to obtain an image processing enhanced model; Non-maximum suppression is used to reduce the overlapping frame of each image defect in the image processing enhancement model to obtain a corresponding image processing optimization model.

5. The distribution network inspection image analysis method based on deep learning algorithm according to claim 1 is characterized in that: The fourth step comprises: Performing noise reduction processing on the image data, and performing illumination adjustment on the image data after the noise reduction processing to obtain a preprocessed image; The image processing model is used to perform defect recognition on the pre-processed image, and a corresponding analysis report is generated based on the defect recognition result, wherein the analysis report includes defect information, maintenance information and priority information.

6. A distribution network inspection image analysis system based on deep learning algorithm, characterized in that: include: A data annotation module, used for collecting a number of defect image data and manually annotating each of the defect image data to obtain preliminary training data; A model training module, used for constructing a pre-training model and training the pre-training model using the preliminary training data to obtain an image processing model; A model enhancement module, used to enhance the image processing model through multi-scale training and feature pyramid to obtain an image processing optimization model; The image analysis module is used to acquire images through an image acquisition device to obtain corresponding image data, and to perform image preprocessing on the image data to obtain a preprocessed image, and to input the preprocessed image into the image processing optimization model for image analysis to obtain a corresponding analysis result.

7. The distribution network inspection image analysis system based on deep learning algorithm according to claim 6 is characterized in that: The data annotation module includes: An information extraction unit, used to extract defect information of distribution network equipment, wherein the defect information includes insulator damage, tower corrosion, and conductor looseness; The manual labeling unit is used to manually label each defect image based on the defect information to obtain preliminary training data.

8. The distribution network inspection image analysis system based on deep learning algorithm according to claim 6, characterized in that: The model training module includes: A model building unit, used to build a YOLO pre-training model, and adjust the feature extraction layer of the YOLO pre-training model in combination with transfer learning to improve the YOLO pre-training model's ability to identify specific defects; The model training unit is used to input the preliminary training data into the YOLO pre-training model, and optimize the YOLO pre-training model using a deep learning algorithm to obtain an image processing model.

9. The distribution network inspection image analysis system based on deep learning algorithm according to claim 6, characterized in that: The model enhancement module includes: A model enhancement unit, used to enhance the image processing model through multi-scale training and feature pyramid to obtain an image processing enhanced model; The model optimization unit is used to reduce the overlapping frame of each image defect in the image processing enhancement model by using non-maximum suppression to obtain the corresponding image processing optimization model.

10. The distribution network inspection image analysis system based on deep learning algorithm according to claim 6, characterized in that: The image analysis module comprises: A preprocessing unit, used to perform noise reduction processing on the image data, and perform illumination adjustment on the image data after the noise reduction processing to obtain a preprocessed image; The image analysis unit is used to use the image processing model to perform defect recognition on the pre-processed image, and generate a corresponding analysis report based on the defect recognition result, wherein the analysis report includes defect information, maintenance information and priority information.

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