WO2023082061A1 - Procédé de répartition visuelle d'agent intelligent basé sur un traitement d'image à réalité augmentée - Google Patents

Procédé de répartition visuelle d'agent intelligent basé sur un traitement d'image à réalité augmentée Download PDF

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Publication number
WO2023082061A1
WO2023082061A1 PCT/CN2021/129629 CN2021129629W WO2023082061A1 WO 2023082061 A1 WO2023082061 A1 WO 2023082061A1 CN 2021129629 W CN2021129629 W CN 2021129629W WO 2023082061 A1 WO2023082061 A1 WO 2023082061A1
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WIPO (PCT)
Prior art keywords
image processing
augmented reality
instruction
scheduling
reality image
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Ceased
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PCT/CN2021/129629
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English (en)
Chinese (zh)
Inventor
陈奂
虢韬
周海
吕政�
方曦
肖林
黄玉辉
林先堪
龙燕
黄磊
汪适
喻群
张宇红
张亚维
吴寿长
禹天润
邹胤
杨秋
艾丹
田川
杨兴武
潘飞
冀红超
黄志清
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Guizhou Power Grid Co Ltd
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Guizhou Power Grid Co Ltd
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Priority to PCT/CN2021/129629 priority Critical patent/WO2023082061A1/fr
Publication of WO2023082061A1 publication Critical patent/WO2023082061A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Definitions

  • the invention relates to the technical field of electric power dispatching, in particular to a visual dispatching method for intelligent agents based on augmented reality image processing.
  • Power scheduling is a very important link in the power system.
  • the normal operation of the power system is inseparable from the real-time scheduling between the power supply company and the load.
  • the maintenance and repair of power equipment also requires power scheduling.
  • the most dangerous part of power dispatching is the on-site operation of power equipment. Once misoperation occurs, especially in high-voltage systems, very serious personal injury accidents will occur.
  • Augmented reality Augmented reality
  • Augmented reality technology is a new technology that "seamlessly" integrates real world information and virtual world information. Taste, touch, etc.), through computer and other science and technology, simulate and then superimpose, apply virtual information to the real world, and be perceived by human senses, so as to achieve a sensory experience beyond reality.
  • the real environment and virtual objects are superimposed on the same screen or space in real time.
  • Augmented reality technology not only shows real world information, but also displays virtual information at the same time, and the two kinds of information complement and superimpose each other.
  • users use AR equipment to multiplex the real world and computer graphics, and then they can see the real world surrounding it.
  • smart wearable devices based on AR technology have been applied in the power industry, and innovative solutions have been proposed to solve traditional problems in the power system.
  • the present invention provides a visual scheduling method for intelligent agents based on augmented reality image processing, which can solve the problems of cumbersome operation steps for equipment operators and insufficient accuracy and safety of scheduling instructions in the power scheduling process in the prior art.
  • the present invention provides the following technical solutions: including collecting dispatcher's dispatching instructions and operating instructions of operation tickets, textualizing dispatching instructions through image recognition technology, and standardizing and decomposing textual instructions; on-site operators according to The decomposed scheduling instruction finds the corresponding operating device, and scans the QR code on the operating device through the AR device to read the information of the operating device, and then automatically compares the information; if the information is compared correctly, the on-site operation
  • the personnel will take the security measures before the operation of the operation equipment, take photos of the security measures and upload them, and automatically judge whether the on-site security measures are standardized through AI image recognition; if the on-site security measures are standardized, then pass the
  • the AR device visualizes the operation instructions, and the operator operates the operation equipment according to the illustrated operation instructions.
  • the operation equipment is photographed and uploaded;
  • the security measures are described and checked;
  • the operating device is photographed and uploaded, and the key image of the image collected by the AR device is screened out and compressed and transmitted by DCT (discrete cosine transform technology); decompressed in the terminal server;
  • DCT discrete cosine transform technology
  • the Plasian operator image sharpening technology processes the compressed video images, classifies them, learns them, and stores them; the processed images are used in the application of the dispatching system, and artificial intelligence algorithms are used to propose control and operation assistance.
  • the standardized decomposition of the text instructions includes,
  • Decomposition result action + voltage level + device name.
  • the AR device includes AR glasses, an intelligent image acquisition device, a 5G mobile phone, and the like.
  • the information comparison also includes checking whether the operation device and the The corresponding situation of the scheduling instruction.
  • the security measures include setting isolation fences and hanging warning signs.
  • the AI image recognition includes preprocessing images, installing Tensorflow and Pillow libraries; defining convolutional network models, loss functions and An optimizer; execute image recognition training to obtain convolutional network model parameters; use the convolutional network model to perform the image recognition.
  • taking pictures and uploading the security measures includes taking pictures of the security measures through an AR device and uploading them.
  • the images required for the collection of power scheduling include all the images collected by the AR device and are directly stored in the local server, and automatically overwrites every fifteen days.
  • the screening out of the key images collected by the AR device and compressing includes the selection of the key images collected by the AR device is
  • the images in the real-time collection of the on-site AR equipment are processed by similarity filtering, and the duplicate images are filtered out, and then the DCT image compression processing is performed and transmitted.
  • the transmission includes that the transmission method is to use 4G, 5G, SMS, etc. for transmission, which is suitable for short-distance , Medium-distance and long-distance transmission.
  • the improved Laplacian image sharpening technology includes the compressed key image passing through the scheduling terminal Perform decompression processing, use threshold denoising technology to perform noise reduction processing on the key image, and then perform Laplacian image enhancement.
  • the selection of the threshold value varies with different application scenarios and different brightness.
  • the filtered images are classified and stored, including compressed images collected and transmitted by the AR device, Perform decompression processing, use the improved Laplacian operator image sharpening technology for image processing and analysis, and classify and store the processed pictures in the intelligent agent terminal server.
  • the artificial intelligence algorithm is used to provide an auxiliary decision-making suggestion for regulation and operation, including the artificial intelligence algorithm, and extracting the compressed image , and can further maintain system voltage stability through equipment identification, text recognition, and fault diagnosis for power flow calculation and regional load compensation, and complete the deep reinforcement learning work of the power dispatching system, and propose auxiliary decision-making suggestions for regulation and operation through artificial intelligence algorithms for dispatching Staff reference selection.
  • the transmission of the operation completion instruction includes that when the operation instruction is sent to the AR device, voice broadcast operation content is required And use the AR device to mark the part of the device that needs to be operated; after the operation is completed, the AR device needs to be used to collect the password of the on-site operator to complete the operation, and give feedback information at the same time.
  • the system includes that the system is a system in which text instructions and pictures correspond to each other; the steps of constructing the system are as follows: : First, take pictures of electrical equipment of different models, categories, and functions in sequence according to different switching states; then match the pictures of the equipment with the text one by one.
  • the present invention combines the instruction content of the operation ticket with the operation device itself for visual processing, and the dispatcher and the operator establish contact through the AR device. Even the operator who is not familiar with the state of the device can scan the QR code, AI image recognition technology and image processing technology can also quickly locate the equipment to be operated and issue instructions for correct operation; the possibility of mistransmission of information is less than the existing traditional mode of manual broadcast, and the operation speed has also been greatly improved; At the same time, the present invention uses the remote expert cooperation function of the AR device to transmit the real-time on-site picture, and through the cooperation of multiple experts, the actual problems on the site can be solved online, and the AR device can be used to inform the operator in time of the part of the equipment that needs to be operated, so as to realize remote scheduling deal with.
  • Fig. 1 is a schematic flow chart of the image processing part described in the first embodiment of the present invention
  • FIG. 2 is a schematic flow diagram of the method for visually dispatching intelligent agents based on augmented reality image processing described in the second embodiment of the present invention
  • Fig. 3 is a schematic diagram of the operation instruction visualization process of the augmented reality image processing-based intelligent agent visualization scheduling method described in the second embodiment of the present invention
  • Fig. 4 is the comparison of the distribution network image of the third embodiment of the present invention after three processing methods, wherein (a) is the compressed color image, (b) is the unimproved Laplacian image sharpness (c) is the effect diagram of the improved Laplacian image sharpening technology image processing.
  • one embodiment or “an embodiment” referred to herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. "In one embodiment” appearing in different places in this specification does not all refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.
  • installation, connection, connection should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integrated connection; it can also be a mechanical connection, an electrical connection or a direct connection.
  • a connection can also be an indirect connection through an intermediary, or it can be an internal communication between two elements.
  • FIG. 1 it is the first embodiment of the present invention, which provides a method for processing images collected by an AR device. Including: screening out the key images of the images collected by the AR device and compressing and transmitting them; processing the compressed video images through the improved Laplacian image sharpening technology, and classifying, learning and storing them; The processed pictures are used in the application of the dispatching system, and the artificial intelligence algorithm is used to propose auxiliary decision-making suggestions for control and operation for the dispatcher to refer to and choose.
  • this embodiment provides a kind of intelligent agent visual scheduling method based on augmented reality image processing, including:
  • S1 Collect the scheduling instructions of the dispatcher and the operation instructions of the operation ticket, textualize the dispatching instructions through speech recognition technology, and standardize and decompose the text instructions.
  • Training Analyze the speech feature parameters in advance, make a speech template, and store it in the speech parameter library.
  • Distortion Measures There must be a standard for comparison, which is the "distortion measure" between the speech feature parameter vectors.
  • Decomposition result action + voltage level + device name.
  • S2 On-site operators find the corresponding operating equipment according to the decomposed scheduling instructions, and scan the QR code on the operating equipment through the AR device, and then read the information of the operating equipment, and then automatically compare the information.
  • the switch name is automatically compared with the text on the equipment information. If the content is the same, the automatic comparison is correct, otherwise the automatic comparison fails.
  • the on-site operators will take security measures before the operation of the operating equipment, and use the AR device to take pictures of the security measures and upload them, and automatically judge whether the on-site security measures are standardized through AI (Artificial Intelligence) image recognition.
  • AI Artificial Intelligence
  • a positioning label can be pasted on the device in the early stage to ensure the speed and accuracy of AI image recognition; if the information comparison is incorrect, the AR device will correspond to the display device incorrectly. The operator should re-check whether the information on the operation ticket is consistent with the actual information on site, and then scan the QR code again until the information corresponds to each other before the system proceeds.
  • the steps of AI image recognition are as follows:
  • the program code for installing Tensorflow and pillow libraries is as follows:
  • pool0 tf.layers.max_pooling2d(conv0,[2,2],[2,2])
  • pool1 tf.layers.max_pooling2d(conv1,[2,2],[2,2])
  • Training needs to use sess.run(tf.global_variables_initializer()) to initialize parameters. After training, saver.save(sess,model_path) needs to be used to save model parameters.
  • the test needs to use saver.restore(sess, model_path) to read the parameters.
  • the operation instructions will be graphically displayed through the AR device, and the operation equipment will be operated according to the illustrated operation instructions. After the operation is completed, the operation equipment will be photographed and uploaded; otherwise, the security measures will be rearranged and examine.
  • security measures include setting up isolation fences and hanging warning signs.
  • S3 Use the AR device to take photos of the operating equipment and upload them, and then automatically judge whether the operating equipment status meets the operating instructions through AI image recognition.
  • the system will sequentially send the illustrated operation instructions to the AR device according to the operation ticket instructions.
  • Use the AR device to take photos and upload them again, and then use the AI image recognition function to automatically judge whether the operation device status meets the operation instructions. Only when the operation is consistent with the standard operation picture, the system will issue the next operation ticket instruction to the AR device.
  • the AR device can use the high-speed and low-latency characteristics of the 5G network to transmit the on-site images to the dispatcher in time, and the temporary operation plan can be communicated immediately after the professional judges to the operator.
  • the system is a system in which text instructions and pictures correspond to each other; the steps to build the system are: 1 take pictures of electrical equipment of different models, categories, and functions in sequence according to different switch states; 2 combine the pictures of the equipment with the text One to one correspondence.
  • the system issues operation instructions, it needs to broadcast the operation content by voice and mark the part of the equipment that needs to be operated through the AR device; after the operation is completed, it needs to use the AR device to collect the password of the on-site operator to complete the operation, and give feedback information at the same time.
  • the operating device satisfies the operating instruction, it is judged whether all the operating instructions are completed; otherwise, the operating device is re-operated.
  • the artificial intelligence dispatching system will send the completion instruction to the AR device, and inform the on-site operator that the operation of the dispatch instruction equipment has been completed.
  • the AR device has the capability of a remote expert collaboration system, and can conduct multi-person calls when the network communication is good. Operators can use AR equipment to timely put forward collaboration requirements to remote experts, combined with 5G technology, to achieve real-time technical support, reduce downtime losses, and reduce travel expenses.
  • Fig. 4 it is the third embodiment of the present invention.
  • the present embodiment adopts the traditional technical scheme and the method of the present invention to carry out comparative tests, and compares the test results by means of scientific demonstration to verify the method the real effect it has.
  • test environment is to select C++ engine and java database for testing, and randomly select 100 pictures in the distribution network image to carry out image processing test, in order to verify the benefits of the present invention Effect, image processing of distribution network image compression, image processing of traditional Laplacian image sharpening technology and image processing of Laplacian image sharpening technology improved by this method, three methods of processing After comparing the images, refer to Figure 4 for the results.
  • Figure 4 is the comparison of distribution network images after three processing methods, where (a) is the compressed color image, and (b) is the image processing effect of the unimproved Laplacian operator image sharpening technology , (c) is the effect diagram of the improved Laplacian image sharpening technology image processing, it can be clearly seen that the image processing using the method of the present invention has a higher image definition, and only the compressed image is clear The degree is the lowest, and this method has a better effect of image processing due to the elimination of noise interference.
  • this embodiment chooses the traditional technical scheme and adopts this method to conduct a comparative test, and compares the test results by means of scientific demonstration to verify the real effect of this method.
  • the traditional technical solution uses the traditional purely manual operation, that is, the dispatcher communicates the instructions one by one to the on-site personnel through the phone, and the operator needs to repeat the instruction; after the operation is completed, the operator and the dispatcher also need to complete the instruction. Repeat it twice again.
  • This technology is time-consuming and labor-intensive. It not only requires dispatchers and operators to be extremely familiar with the site and equipment, but also is affected by external factors such as environmental noise and communication interference.
  • the traditional technical solution and this method will be used to execute the operation ticket respectively. The situation is compared in real time.
  • Table 1 A comparison table of the results of executing operation tickets using two different methods.

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Abstract

La présente invention divulgue un procédé de répartition visuelle d'agent intelligent basé sur un traitement d'image à réalité augmentée (AR). Le procédé comprend : la collecte d'une instruction de répartition d'un répartiteur et d'une instruction d'opération d'un ticket d'opération, la textualisation de l'instruction de répartition au moyen d'une technologie de reconnaissance d'image, et la réalisation d'une décomposition standardisée sur une instruction textuelle; par un opérateur local, la recherche d'un dispositif d'opération correspondant selon l'instruction de répartition décomposée et le balayage d'un code bidimensionnel sur le dispositif d'opération au moyen d'un dispositif AR, puis la lecture d'informations du dispositif d'opération, et après cela, la réalisation automatique d'une comparaison d'informations; si la comparaison indique que les informations sont correctes, la prise, par l'opérateur local, de mesures de protection de sécurité sur le dispositif d'opération avant une opération, la prise d'images des mesures de protection de sécurité et leur téléchargement, et le fait de déterminer automatiquement, au moyen d'une reconnaissance d'image par intelligence artificielle (AI), si les mesures de protection de sécurité de champ sont standard; si les mesures de protection de sécurité de champ sont standard, l'illustration de l'instruction d'opération au moyen du dispositif AR, et l'utilisation, par un opérateur, du dispositif d'opération selon l'instruction d'opération illustrée, la prise d'images du dispositif d'opération et leur téléchargement après que l'opération est accomplie; sinon, la reconfiguration des mesures de protection de sécurité et leur vérification; la prise d'images du dispositif d'opération et leur téléchargement, le filtrage de données clés d'une image collectée par le dispositif AR, et la réalisation d'une transmission compressée par transformée en cosinus discrète (DCT); la réalisation d'une décompression dans un serveur de terminal; le traitement d'une image vidéo compressée au moyen d'une technologie de netteté d'image de Laplace améliorée, et la classification et l'apprentissage de l'image vidéo compressée et son stockage; l'utilisation de l'image traitée pour une application d'un système de répartition et la proposition d'une suggestion de prise de décision par un assistant d'opération de régulation et de commande au moyen d'un algorithme AI pour référence et sélection par le répartiteur; ensuite, le fait de déterminer automatiquement, au moyen d'une reconnaissance d'image AI, si l'état du dispositif d'opération satisfait l'instruction d'opération; si tel est le cas, le fait de déterminer si l'instruction d'opération est entièrement accomplie; sinon, la réutilisation du dispositif d'opération; et si l'instruction d'opération est entièrement accomplie, l'envoi, par le système, d'une instruction d'accomplissement au dispositif AR de façon à informer l'opérateur local que l'opération de l'instruction de répartition par le dispositif a été accomplie. Au moyen de la présente invention, la mauvaise transmissibilité d'informations est relativement faible, le problème d'opérations complexes et fastidieuses dans des systèmes de répartition d'énergie électrique existants est efficacement résolu, et la formation de nouveaux employés peut être facilitée au moyen d'une opération visuelle, ce qui améliore la flexibilité de fonctionnement et la sécurité d'un système de répartition d'énergie électrique.
PCT/CN2021/129629 2021-11-09 2021-11-09 Procédé de répartition visuelle d'agent intelligent basé sur un traitement d'image à réalité augmentée Ceased WO2023082061A1 (fr)

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CN116468990A (zh) * 2023-06-08 2023-07-21 中海智（北京）科技有限公司 一种基于集中判图的任务随机派发智能管理系统及方法
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CN120124001A (zh) * 2025-05-12 2025-06-10 合肥优晟电力科技有限公司 基于安全知识库的电网操作票智能生成方法及系统

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CN116468990A (zh) * 2023-06-08 2023-07-21 中海智（北京）科技有限公司 一种基于集中判图的任务随机派发智能管理系统及方法
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CN117932972A (zh) * 2024-03-15 2024-04-26 南京凯奥思数据技术有限公司 基于web应用于设备状态算法模型的可视化建模平台及方法
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CN120124001A (zh) * 2025-05-12 2025-06-10 合肥优晟电力科技有限公司 基于安全知识库的电网操作票智能生成方法及系统

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