CN112348306A - TitanOS artificial intelligence development method and device for power distribution operation inspection - Google Patents
TitanOS artificial intelligence development method and device for power distribution operation inspection Download PDFInfo
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Abstract
The application belongs to the technical field of artificial intelligence, and particularly relates to a TitanOS artificial intelligence development method and device for power distribution operation inspection. The method comprises the following steps: acquiring a power distribution operation and inspection scene and requirements; constructing a training model according to the power distribution operation and inspection scene and requirements; the method comprises the steps of obtaining a field operation video shot by a field camera, identifying key factors in the video, identifying abnormal conditions in the key factors according to the training model, and realizing intelligent supervision of production operation and unmanned intelligent inspection of a station according to an identification result. The artificial intelligence comprehensive performance testing method and the artificial intelligence comprehensive performance testing system have the advantages that various artificial intelligence functions based on power business requirements are provided, a platform comprehensive solution based on artificial intelligence technology is provided, training processes of various artificial intelligence models are organized according to power business requirement scenes, a unified artificial intelligence platform is formed, and the artificial intelligence comprehensive performance which is from data to models and from the models to application and almost has no artificial intelligence professional skill requirements is provided for users.
Description
Technical Field
The application belongs to the technical field of artificial intelligence, and particularly relates to a TitanOS artificial intelligence development method and device for power distribution operation inspection.
Background
The headquarter of the national grid proposes strategic deployment of 'three-type two-network, world-first-class', and the application of artificial intelligence technology in the power industry becomes an important development direction of the national grid.
In recent years, the artificial intelligence technology is developed vigorously, and is applied to the fields of operation, control, management and the like of a power system. The application of the artificial intelligence technology in the power system not only expands the application range of the artificial intelligence technology, but also expands the advantages of the artificial intelligence technology such as automation and high intelligence degree, and promotes the intelligent upgrading of the power industry. The artificial intelligence technology enables the power system to really realize decision intelligence and management intelligence. The standardized AI has the functions of face recognition, voice recognition, character recognition and the like, so that the standardized AI has a single application scene, service logic needs to meet AI standards, service requirements are single, the AI does not need to be updated and upgraded, and only the iceberg with artificial intelligence application is used. With the continuous improvement of artificial intelligence technology, customized AI appears, which has the functions of dressing identification, defect damage detection, signal lamp intelligent decision and the like, and is oriented to a service scene, the model depth is in accordance with service logic, the requirements and scene change are changeable, and the capability of fast iteration close to the service is provided. In the power industry, scenes are special, illumination is changeable, the angle of a camera is changeable, requirements are continuously evolved, and a TitanOS artificial intelligence development platform oriented to power distribution operation and detection is trained through targeted data to meet the complex and changeable scenes and requirements.
At present, under the normal operation and maintenance of a transformer substation or a distribution room, the power distribution cabinet needs to be overhauled sometimes to ensure the normal operation of equipment, and when a work ticket is operated, monitoring personnel, operating personnel and ticket reading personnel are needed, but the identity authority and the work of the personnel are bound to be in compliance or not, and unstable factors exist in the steps or not. In the monitoring process of the guardian, the monitoring effect is also influenced by the psychological quality, the external working environment, the working experience, the skill level, the responsibility of the staff and the like, so that the problem of omission or misreading exists, and if the problem cannot be found, the personal safety of equipment and operators is seriously influenced.
In addition, in the normal operation process of the electrical equipment, the patrol personnel need to stand at a place close to the equipment when patrolling the equipment, certain threats are also brought to the personal safety of the patrol personnel, and the danger is higher when the patrol personnel look up abnormal phenomena, specially patrol in severe weather and find accident causes.
Accordingly, a technical solution is desired to overcome or at least alleviate at least one of the above-mentioned drawbacks of the prior art.
Disclosure of Invention
The application aims to provide a TitanOS artificial intelligence development platform for power distribution operation inspection, so as to solve at least one problem in the prior art.
The technical scheme of the application is as follows:
the first aspect of the application provides a TitanOS artificial intelligence development method for power distribution operation inspection, which comprises the following steps:
acquiring a power distribution operation and inspection scene and requirements;
constructing a training model according to the power distribution operation and inspection scene and requirements;
the method comprises the steps of obtaining a field operation video shot by a field camera, identifying key factors in the video, identifying abnormal conditions in the key factors according to the training model, and realizing intelligent production operation supervision and unmanned intelligent station inspection according to an identification result.
Optionally, the power distribution inspection scene and the demand include: personnel identification, dressing identification, dangerous behavior identification, instrument identification, foreign body identification and operation specification identification.
Optionally, the constructing a training model according to the power distribution inspection scene and the requirement includes:
formulating a training flow according to the power distribution operation and inspection scene and the requirement;
carrying out distribution and operation inspection scenes and demands;
after the import and the labeling of the related data sets are completed, training is started;
a training model is obtained.
Optionally, the training process sequentially includes selecting a training data set, model training, model selection, model rectification, and rectification evaluation.
Optionally, obtaining a training model according to the power distribution inspection scene and the requirement further includes:
and encrypting the training model.
Optionally, the acquiring a field operation video shot by a field camera, identifying key factors in the video, analyzing the key factors according to the training model, determining whether an abnormality exists, and implementing intelligent supervision of production operation according to a determination result includes:
automatically adjusting the orientation and the focal length of the field camera;
acquiring a field operation video shot by a field camera based on a network video protocol;
analyzing the obtained video and identifying key factors in the video;
judging whether the key factors are consistent with the operation contents in the training model, reporting to scheduling management personnel when abnormality occurs in the field operation process, giving an alarm to the field personnel, and operating the field personnel according to the operation progress of the field personnel;
and recording the whole process of the field operation for the checking of the scheduling manager.
Optionally, the acquiring a field operation video shot by a field camera, identifying key factors in the video, analyzing the key factors according to the training model, judging whether an abnormality exists, and implementing unmanned intelligent tour of a site according to a judgment result includes:
automatically adjusting the orientation and the focal length of the field camera;
acquiring a field operation video shot by a field camera based on a network video protocol;
analyzing the obtained video and identifying key factors in the video;
and judging whether the key factors are consistent with the operation contents in the training model, generating a relevant record when an abnormal event occurs in the field operation process, and reporting the abnormal event.
A second aspect of the present application provides a TitanOS artificial intelligence development apparatus for distribution electric operating system, based on the above-mentioned TitanOS artificial intelligence development method for distribution electric operating system, including:
the data acquisition module is used for acquiring a power distribution operation and detection scene and requirements;
the training model acquisition module is used for constructing a training model according to the power distribution operation and inspection scene and the requirement;
the production operation intelligent supervision module is used for acquiring a field operation video shot by a field camera, identifying key factors in the video, identifying abnormal conditions in the key factors according to the training model and realizing the production operation intelligent supervision according to an identification result;
and the site unmanned intelligent patrol module is used for acquiring a field operation video shot by a field camera, identifying key factors in the video, identifying abnormal conditions in the key factors according to the training model, and realizing site unmanned intelligent patrol according to an identification result.
A third aspect of the present application provides a computer device, including a processor, a memory, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the TitanOS artificial intelligence development method in power distribution-oriented operation test as described above.
A fourth aspect of the present application provides a readable storage medium storing a computer program, which when executed by a processor, is configured to implement the method for TitanOS artificial intelligence development in power distribution-oriented operation inspection as described above.
The invention has at least the following beneficial technical effects:
the TitanOS artificial intelligence development method oriented to power distribution operation and inspection provides a platform comprehensive solution based on an artificial intelligence technology based on various artificial intelligence functions of power business requirements, organizes each artificial intelligence model training process according to power business requirement scenes, adds and combines more functions from the artificial intelligence model training process to form a unified artificial intelligence platform, and provides artificial intelligence comprehensive capacity which can realize the professional skill requirements of almost no artificial intelligence from data to model and from model to application to a user.
Drawings
FIG. 1 is a flowchart of a TitanOS artificial intelligence development method in power distribution operation inspection according to an embodiment of the present application;
fig. 2 is a diagram of a production operation intelligent supervision framework of a TitanOS artificial intelligence development method in power distribution operation inspection according to an embodiment of the present application;
fig. 3 is a framework diagram of site unmanned intelligent patrol for a TitanOS artificial intelligence development method in power distribution operation inspection according to an embodiment of the present application.
Detailed Description
In order to make the implementation objects, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be described in more detail below with reference to the drawings in the embodiments of the present application. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are a subset of the embodiments in the present application and not all embodiments in the present application. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application. Embodiments of the present application will be described in detail below with reference to the accompanying drawings.
The present application is described in further detail below with reference to fig. 1 to 3.
The first aspect of the application provides a TitanOS artificial intelligence development method for power distribution operation inspection, which comprises the following steps:
acquiring a power distribution operation and inspection scene and requirements;
constructing a training model according to a power distribution operation and inspection scene and requirements;
the method comprises the steps of obtaining a field operation video shot by a field camera, identifying key factors in the video, identifying abnormal conditions in the key factors according to a training model, and realizing intelligent supervision of production operation and unmanned intelligent inspection of a site according to an identification result.
According to the TitanOS artificial intelligence development method oriented to power distribution operation inspection, power distribution operation inspection scenes and requirements comprise: personnel identification, dressing identification, dangerous behavior identification, instrument identification, foreign body identification and operation specification identification. Wherein, the dressing identification can comprise smoking identification, work clothes identification, safety helmet identification, glove identification and the like. The meter identification may include identification of switches, live displays, electrical indicating devices, pressure plates, five-prevention coded locks, object prevention switches, and the like.
The TitanOS artificial intelligence development method oriented to power distribution operation inspection comprises the following steps of: the method comprises the following steps that a training flow is formulated according to power distribution operation and inspection scenes and requirements, different scenes in a business have different data and different training models due to factors such as camera angles and illumination conditions, customized training needs to be carried out aiming at various scenes, and the training flow sequentially comprises the steps of training data set selection, model training, model selection, model correction and correction evaluation; carrying out distribution and operation inspection scenes and demands; and after the import and the labeling of the related data sets are completed, training is started to obtain a training model. In this embodiment, after the training model is constructed according to the power distribution operation and maintenance scenario and the requirement, the method further includes: the training model is encrypted. After encryption, all computing resources in each computing node managed in training are managed globally, all storage resources in each computing node managed in training are managed globally, and in addition, all data in an artificial intelligent training platform, especially training and verification data, are managed.
According to the TitanOS artificial intelligence development method oriented to power distribution operation inspection, the production operation intelligent supervision mainly achieves artificial intelligence recognition and analysis of the production operation process, the artificial intelligence technology is used for analyzing and recognizing key factors such as people, equipment and operation in the video based on the field operation video shot by the field camera, and then the operation correctness is judged by combining the content requirements of the work ticket and the operation ticket. And correspondingly reporting the field condition, reminding field personnel and recording the whole operation process according to the judgment result. In this embodiment, the intelligent supervision of the production operation mainly includes the following functions: the system comprises the control of a field camera, video reading and analysis based on a network video protocol, an artificial intelligence function, a two-ticket comparison verification function, an event report and field reminding, and the whole process recording of production operation. Specifically, firstly, the control of a field camera is realized, and the automatic adjustment of the orientation, the focal length and the like of the camera is realized according to the content of a work ticket and an operation ticket so as to shoot key points of an operator, operated equipment and the like; the method comprises the steps of realizing video reading and analysis based on a network video protocol, and acquiring videos shot by a camera through a network for artificial intelligent analysis and judgment; acquiring a field operation video shot by a field camera, analyzing the acquired field operation video by adopting an artificial intelligence function, and identifying key factors in the video, including dressing and insulation protection of identification personnel, equipment operated by the identification personnel and operation experienced by the operated equipment; the two tickets are compared and checked, the key factors are analyzed according to the training model, whether the two tickets are consistent or not is judged, when abnormality occurs in the field operation process, a scheduling manager is reported, field personnel are alarmed, and the field personnel are operated according to the operation progress of the field personnel; and recording the whole process of the field operation for the checking of the scheduling manager.
According to the TitanOS artificial intelligence development method oriented to the power distribution operation and inspection, unmanned intelligent inspection of a station can be divided into a computer interface and a mobile phone interface, the same function is provided for dispatching management personnel to know the current condition of a power distribution station room and the abnormal events, so that the management personnel can know and process the current condition and the abnormal events, and the function of looking up the records of the historical events is provided. The unmanned intelligent patrol process for realizing the station mainly comprises the following steps: automatically adjusting the orientation and the focal length of the field camera according to the contents of the work ticket and the operation ticket; acquiring a field operation video shot by a field camera based on a network video protocol; analyzing the obtained video and identifying key factors in the video; and judging whether the key factors are consistent with the operation contents in the training model, generating a relevant record when an abnormal event occurs in the field operation process, and reporting the abnormal event. In this embodiment, determining whether the key factors are consistent with the job content in the training model includes: the method comprises the steps of identifying and judging abnormal personnel, analyzing and judging whether clothing is in compliance or not, analyzing and judging whether dangerous behaviors such as smoking exist or not, analyzing and judging whether equipment instruments are displaced or not, analyzing and judging whether foreign matters enter or not, identifying operation specifications and the like.
A second aspect of the present application provides a TitanOS artificial intelligence development apparatus for power distribution operation inspection, based on the above TitanOS artificial intelligence development method for power distribution operation inspection, including:
the data acquisition module is used for acquiring a power distribution operation and detection scene and requirements;
the training model acquisition module is used for constructing a training model according to the power distribution operation and inspection scene and requirements;
the production operation intelligent supervision module is used for acquiring a field operation video shot by a field camera, identifying key factors in the video, identifying abnormal conditions in the key factors according to a training model, and realizing production operation intelligent supervision according to an identification result;
and the site unmanned intelligent patrol module is used for acquiring a field operation video shot by a field camera, identifying key factors in the video, identifying abnormal conditions in the key factors according to the training model and realizing site unmanned intelligent patrol according to the identification result.
In the production operation intelligent supervision module, the main functions are the issuing of the operation ticket and the task playback after the work is finished, after the issued work order is clicked to start work, when field operating personnel operate, the auxiliary judgment of the operation can be carried out, after the error operation occurs, the system can automatically prompt that the error occurs, after the operation is finished, the completion condition of the work ticket every time can be checked, and the management is convenient to carry out at the background. In the unmanned intelligent patrol module of the station, the main function is to patrol the state of the instrument on the power distribution cabinet and the identity authority and dressing of the indoor staff of the power distribution station at a fixed point. The content of patrolling divide into in the show personnel dress and the tour of foreign matter and the tour of switch board cabinet body instrument state information, when patrolling, the system inspects out off-spec's matter and reports.
A third aspect of the present application provides a computer device, including a processor, a memory, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the TitanOS artificial intelligence development method in power distribution oriented operation inspection as described above.
A fourth aspect of the present application provides a readable storage medium storing a computer program, which when executed by a processor, is used to implement the TitanOS artificial intelligence development method in power distribution oriented operation inspection as above.
The application discloses TitanOS artificial intelligence development method and device for distribution operation inspection, utilize TitanOS artificial intelligence development platform control camera, realize incessant the patrolling and examining of equipment in the station room, through the pointer change of observing each panel board on the cabinet body and the bright or dark of each status indicator lamp, each cupboard state in the station room is normal to the colour etc. ensure, can also carry out in the station room and check the dress of staff, avoid appearing the safety problem because of dress noncompliance, managers can realize the remote real time monitoring to equipment through Osprey platform system, and the inspection to personnel's authority and dress. The application can greatly enhance the working safety and the management and control performance of the distribution room, and reduce the probability of occurrence of human errors.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A TitanOS artificial intelligence development method oriented to power distribution operation inspection is characterized by comprising the following steps:
acquiring a power distribution operation and inspection scene and requirements;
constructing a training model according to the power distribution operation and inspection scene and requirements;
the method comprises the steps of obtaining a field operation video shot by a field camera, identifying key factors in the video, identifying abnormal conditions in the key factors according to the training model, and realizing intelligent production operation supervision and unmanned intelligent station inspection according to an identification result.
2. The TitanOS artificial intelligence development method oriented to power distribution operation inspection according to claim 1, wherein the power distribution operation inspection scene and requirements include: personnel identification, dressing identification, dangerous behavior identification, instrument identification, foreign body identification and operation specification identification.
3. The TitanOS artificial intelligence development method oriented to power distribution operation inspection according to claim 1, wherein the building of the training model according to the power distribution operation inspection scene and the requirement comprises:
formulating a training flow according to the power distribution operation and inspection scene and the requirement;
carrying out distribution and operation inspection scenes and demands;
after the import and the labeling of the related data sets are completed, training is started;
a training model is obtained.
4. The TitanOS artificial intelligence development method oriented to power distribution operation inspection according to claim 3, wherein the training process sequentially comprises selection of a training data set, model training, model selection, model rectification and rectification evaluation.
5. The TitanOS artificial intelligence development method oriented to power distribution operation inspection according to claim 3, wherein the obtaining of the training model according to the power distribution operation inspection scene and the requirement further comprises:
and encrypting the training model.
6. The TitanOS artificial intelligence development method oriented to power distribution operation inspection according to claim 1, wherein the obtaining of a field operation video shot by a field camera, the identification of key factors in the video, the analysis of the key factors according to the training model, the judgment of whether an abnormality exists, and the realization of intelligent supervision of production operation according to the judgment result comprises:
automatically adjusting the orientation and the focal length of the field camera;
acquiring a field operation video shot by a field camera based on a network video protocol;
analyzing the obtained video and identifying key factors in the video;
judging whether the key factors are consistent with the operation contents in the training model, reporting to scheduling management personnel when abnormality occurs in the field operation process, giving an alarm to the field personnel, and operating the field personnel according to the operation progress of the field personnel;
and recording the whole process of the field operation for the checking of the scheduling manager.
7. The TitanOS artificial intelligence development method for power distribution operation inspection according to claim 6, wherein the obtaining of a field operation video shot by a field camera, the identification of key factors in the video, the analysis of the key factors according to the training model, the judgment of whether an abnormality exists, and the realization of unmanned intelligent inspection of a site according to the judgment result comprises:
automatically adjusting the orientation and the focal length of the field camera;
acquiring a field operation video shot by a field camera based on a network video protocol;
analyzing the obtained video and identifying key factors in the video;
and judging whether the key factors are consistent with the operation contents in the training model, generating a relevant record when an abnormal event occurs in the field operation process, and reporting the abnormal event.
8. The utility model provides a TitanOS artificial intelligence development device towards in distribution fortune is examined, its characterized in that includes:
the data acquisition module is used for acquiring a power distribution operation and detection scene and requirements;
the training model acquisition module is used for constructing a training model according to the power distribution operation and inspection scene and the requirement;
the production operation intelligent supervision module is used for acquiring a field operation video shot by a field camera, identifying key factors in the video, identifying abnormal conditions in the key factors according to the training model and realizing the production operation intelligent supervision according to an identification result;
and the site unmanned intelligent patrol module is used for acquiring a field operation video shot by a field camera, identifying key factors in the video, identifying abnormal conditions in the key factors according to the training model, and realizing site unmanned intelligent patrol according to an identification result.
9. A computer device comprising a processor, a memory, and a computer program stored on the memory and executable on the processor, the processor executing the computer program for implementing the TitanOS artificial intelligence development method in power distribution oriented operation test of any one of claims 1-7.
10. A readable storage medium storing a computer program, wherein the computer program, when executed by a processor, is configured to implement the method for TitanOS artificial intelligence development in power distribution-oriented runtime inspection according to any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117009164A (en) * | 2023-08-15 | 2023-11-07 | 江苏流枢阁科技有限公司 | Method and device for evaluating artificial intelligence solution |
| CN117171694A (en) * | 2023-11-02 | 2023-12-05 | 北京龙德缘电力科技发展有限公司 | Distribution scene safety identification system based on AI technology |
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