CN115410123A - Artificial intelligence image processing method based on power dispatching - Google Patents
Artificial intelligence image processing method based on power dispatching Download PDFInfo
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Abstract
The invention discloses an artificial intelligence image processing method based on power dispatching, which comprises the following steps: s1, acquiring a video image of power dispatching and uploading the video image to a computer; s2, constructing a sedimentary microfacies recognition model, and establishing a connection relation between the sedimentary microfacies recognition model and a computer; s3, processing the video image in the computer through the deposition micro-phase recognition model, recognizing the needed power equipment graph in the video image, and storing the recognition result into a database; and S4, describing the state of the power equipment corresponding to the power equipment image according to the comparison result, and appointing a regulation and control operation auxiliary decision building through an artificial intelligence algorithm. The invention breaks through the method that curve characteristics must be extracted firstly by utilizing the neural network to identify the sedimentary microfacies, and can automatically and continuously identify the sedimentary microfacies by using a computer to process data according to the model, thereby improving the utilization and identification accuracy of the data information of the original power equipment.
Description
Technical Field
The invention relates to the technical field of power dispatching, in particular to an artificial intelligence image processing method based on power dispatching.
Background
At present, with the improvement of social economy and people living standard, the electricity consumption of the whole society will continuously increase, the increase of the electricity demand becomes a normal state, the prior infrastructure cannot meet the requirements of the current era on the smart grid, the application of an artificial intelligence technology to the grid dispatching control is a brand new attempt, the technologies of load prediction, fault diagnosis, automatic voltage control, natural language processing learning, man-machine interaction and the like in the power system gradually become popular topics, the intelligence level of the current power system cannot meet the requirements, the man-machine interaction technology and the fault diagnosis efficiency need to be improved urgently, the processing problem during dispatching and running needs to be more rapid and accurate, the processing of the data information needs to be more accurate and efficient, the processing of the data information already reaches a certain level, and the method is more important for the processing aspect of the image information in comparison.
Most of image processing systems designed at present are single in function, and can only independently complete some functions under certain fixed occasions, such as independent face recognition, digital recognition and the like, but cannot systematically complete the requirements on image information acquisition, transmission, processing, judgment and learning in a dispatching system, and cannot effectively process required electrical equipment graphs, so that the power dispatching work cannot be effectively expanded.
An effective solution to the problems in the related art has not been proposed yet.
Disclosure of Invention
Aiming at the problems in the related art, the invention provides an artificial intelligence image processing method based on power scheduling, which aims to overcome the technical problems in the prior related art.
Therefore, the invention adopts the following specific technical scheme:
an artificial intelligence image processing method based on power dispatching comprises the following steps:
s1, acquiring a video image of power dispatching and uploading the video image to a computer;
s2, constructing a sedimentary microfacies recognition model, and establishing a connection relation between the sedimentary microfacies recognition model and the computer;
s3, processing the video image in the computer through a deposition micro-phase recognition model, recognizing a required power equipment graph in the video image, and storing a recognition result into a database;
and S4, describing the state of the electric power equipment corresponding to the electric power equipment image according to the comparison result, and appointing a regulation and control operation auxiliary decision building through an artificial intelligence algorithm.
Further, the video images of the power schedule include a power equipment image and a sample image, and the power equipment image and the sample image both include visible light patterns.
Further, the steps of constructing a sedimentary microfacies recognition model and establishing a connection relationship between the sedimentary microfacies recognition model and the computer further comprise:
s21, building a management function and a basic data loading function of the sedimentary microfacies recognition model through a management module;
s22, establishing the sedimentary microfacies recognition model and the computer through an ANN training module;
s23, realizing the recognition function of the deposition micro-phase recognition model through a pattern recognition module, and recognizing the needed power equipment pattern;
s24, automatically drawing a deposition microphotograph according to the recognition result through a graph processing module, and providing functions of editing and processing the image for a user;
and S25, providing a function of inquiring various types of image data appearing in the deposition microfacies recognition model through a data inquiry module.
Further, the configuration module comprises data recording, user management and data source connection.
Further, the establishing of the depositional microfacies recognition model and the computer by the ANN training module further comprises the following steps of:
s221, setting a network structure, training parameters and working parameters of the artificial neural network through the structure of the ANN module;
s222, completing maintenance of training samples and training work of the neural network, providing addition, deletion and modification operations on the training samples, and starting and completing a training process of the artificial neural network;
and S223, testing the successfully trained neural network, and verifying the recognition rate of the neural network.
Further, the identifying function of the deposition micro-phase identification model by the pattern identification module and the identification of the required power equipment pattern further include the following steps:
s231, the computer acquires the interception of the site real-time acquisition video where the electric power equipment image is located from the small-layer database according to the electric power equipment image and the sample image to be subjected to deposition microphase identification, intercepts 5-10 frames of pictures every second for processing, screens out key frames and then conducts digital data,
s232, coding the digital data, sending the coded data into a BP neural network, carrying out deposition microfacies identification through the BP neural network, and finally storing an identification result into a deposition microfacies identification result library;
and S233, when the situation that the classification cannot be accurately carried out exists, the BP neural network can automatically identify the fuzzy pattern, mark the fuzzy pattern and carry out manual identification in the deposition microphase manual identification.
Further, the step of automatically drawing the sedimentary microphotograph according to the recognition result through the graph processing module and providing the function of editing and processing the image by the user further comprises the following steps:
s241, automatically generating a deposition microphase band on the graph according to the characteristic parameter extraction of the graph of the power equipment, the state of the power equipment and the identified deposition microphase result, and distinguishing different deposition microphases;
s242, the user edits the generated image and performs corresponding storage processing;
and S243, opening the stored sedimentary microphase graph for editing processing, and setting legends of various sedimentary microphases in the image.
Further, the data query module queries the power equipment image, the characteristic parameters of the sample image and the state of the power equipment corresponding to the power equipment image.
Further, the processing the video image in the computer through the deposition micro-phase recognition model, recognizing the needed power equipment graph in the video image, and storing the recognition result into a database further comprises the following steps:
s31, setting the font, symbol, size and color of the legend of each deposition micro item in a computer interface;
s32, by using the function of drawing the sedimentary microphase diagram, a user can generate the sedimentary microphase diagram of any specified power equipment;
and S33, after the automatic drawing work of the computer is finished, a user can manually add and draw the sedimentary microphase belt according to the power use distribution condition in the current power equipment, and the graphic editing and processing functions of the computer are enriched.
Further, the step of describing the state of the power equipment corresponding to the power equipment image according to the comparison result and specifying the auxiliary regulation and control operation decision through an artificial intelligence algorithm further comprises the following steps:
s41, describing the state of the electric power equipment corresponding to the electric power equipment image according to a comparison result, extracting a compressed picture for face recognition of a scheduling system to complete identity verification of a scheduling person;
and S42, maintaining voltage stability and completing deep reinforcement learning work of the power dispatching system by carrying out load flow calculation and regional load compensation through equipment identification, character identification and fault diagnosis, and providing a regulation and control operation auxiliary decision suggestion for a dispatcher to refer and select through an artificial intelligence algorithm.
The invention has the beneficial effects that: the method breaks through the way that curve characteristics must be extracted firstly by utilizing a neural network to identify the sedimentary microfacies in the past, and can automatically and continuously identify the sedimentary microfacies by utilizing a computer to process data according to a model, thereby simplifying the process of traditional pretreatment and improving the utilization and identification accuracy of original power equipment data information; in the invention, a user can freely customize the network structure, the training parameters and the working parameters of the neural network model; a user can manually intervene in the network training process in real time in the network training process; the user can modify the conclusion of automatic identification according to the data logging curve automatically drawn by the artificial intelligent graphic processing method and the self knowledge and experience; the artificial intelligent graphic processing method of the invention provides a strong image processing function, so that a user can edit and process the automatically generated sedimentary microphotograph.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a flowchart of an artificial intelligence image processing method based on power scheduling according to an embodiment of the present invention.
Detailed Description
For further explanation of the various embodiments, the drawings which form a part of the disclosure and which are incorporated in and constitute a part of this specification, illustrate embodiments and, together with the description, serve to explain the principles of operation of the embodiments, and to enable others of ordinary skill in the art to understand the various embodiments and advantages of the invention, and, by reference to these figures, reference is made to the accompanying drawings, which are not to scale and wherein like reference numerals generally refer to like elements.
According to the embodiment of the invention, an artificial intelligence image processing method based on power scheduling is provided.
Referring to the drawings and the detailed description, the invention will be further explained, as shown in fig. 1-a, according to an embodiment of the invention, an artificial intelligence image processing method based on power scheduling, the artificial intelligence image processing method includes the following steps:
s1, acquiring a video image of power dispatching and uploading the video image to a computer;
in one embodiment, the video images of the power schedule include a power equipment image and a sample image, and the power equipment image and the sample image both include visible light graphics.
S2, constructing a sedimentary microfacies recognition model, and establishing a connection relation between the sedimentary microfacies recognition model and the computer;
in one embodiment, the building a depositional microfacies identification model and establishing the connection relationship between the depositional microfacies identification model and the computer further comprises the following steps:
s21, building a management function and a basic data loading function of the sedimentary microfacies recognition model through a management module;
s22, establishing the sedimentary microfacies recognition model and the computer through an ANN training module;
s23, realizing the recognition function of the deposition micro-phase recognition model through a mode recognition module, and recognizing a required power equipment graph;
and S24, automatically drawing a deposition microphase diagram according to the recognition result through a graphic processing module, and providing the functions of editing and processing the image for a user.
S25, providing a query function for various image data appearing in the sedimentary microfacies recognition model through a data query module;
in one embodiment, the configuration module includes data logging, user management, and data source connections.
In one embodiment, the establishing the depositional microfacies recognition model and the computer through the ANN training module further comprises the following steps:
s221, setting a network structure, training parameters and working parameters of the artificial neural network through the structure of the ANN module;
s222, completing maintenance of training samples and training work of the neural network, providing addition, deletion and modification operations on the training samples, and starting and completing a training process of the artificial neural network;
s223, testing the successfully trained neural network, and verifying the recognition rate of the neural network;
in one embodiment, the identifying, by the pattern recognition module, the deposition micro-phase recognition model, and identifying the desired power device pattern further includes:
s231, the computer acquires the interception of the site real-time collected video where the electric power equipment image is located from the small-layer database according to the electric power equipment image and the sample image to be subjected to deposition microphase identification, intercepts 5-10 frames of pictures every second for processing, screens out key frames and then performs digital data,
s232, coding the digital data, sending the coded data into a BP neural network, carrying out deposition microphase identification through the BP neural network, and finally storing an identification result into a deposition microphase identification result library;
s233, when the situation that the classification cannot be accurately carried out exists, the BP neural network can automatically identify the fuzzy pattern, mark the fuzzy pattern and carry out manual identification in the deposition microphase manual identification;
in one embodiment, the automatically drawing, by the graphics processing module, the deposition microphase graph according to the recognition result, and the function of providing the user to edit and process the image further includes the following steps:
s241, automatically generating a deposition microphase band on the graph according to the characteristic parameter extraction of the graph of the power equipment, the state of the power equipment and the identified deposition microphase result, and distinguishing different deposition microphases;
s242, the user edits the generated image and performs corresponding storage processing;
s243, opening the stored deposition microphase graph for editing, and setting legends of various deposition microphase in the image;
in one embodiment, the data query module queries the power equipment image, the characteristic parameters of the sample image and the state of the power equipment corresponding to the power equipment image.
In the specific application, the sedimentary microfacies recognition model is used for pre-processing and converting a digital logging curve and stratum parameters into a binary lattice image mode on the aspect of logging curve feature extraction, the binary lattice image mode is directly input into a network after lattice data coding and compression, and the network automatically extracts and memorizes the stratum mode features represented by the curve. The invention breaks through the method that curve characteristics must be extracted firstly by utilizing a neural network to identify the sedimentary microfacies in the prior art, and can automatically and continuously identify the sedimentary microfacies by utilizing a computer to process data according to a model, thereby simplifying the traditional preprocessing process and improving the utilization and identification accuracy of the original power equipment data information.
In addition, the ANN model is in a topological structure, an artificial neural network realized by the ANN model belongs to a feedback-free forward ANN, and the adopted learning algorithm is an inclusive correction method (an improved algorithm based on the traditional error inverse propagation algorithm), namely a learning mode guided by a teacher is adopted.
The ANN model provides an interface for developers, so that the developers can set various parameters of the ANN, including the number of hidden layers, the number of hidden layer nodes, calculation formula parameters and the like. A practical artificial neural network can be implemented: setting corresponding parameters, starting a training process and carrying out pattern recognition by using a network.
S3, processing the video image in the computer through a deposition micro-phase recognition model, recognizing a required power equipment graph in the video image, and storing a recognition result into a database;
s31, setting the font, symbol, size and color of the legend of each deposition micro item in a computer interface;
s32, using a function of drawing a sedimentary microphase diagram, a user can generate a sedimentary microphase diagram of any specified power equipment;
and S33, after the automatic drawing work of the computer is finished, a user can manually add and draw the sedimentary microphase belt according to the power use distribution condition in the current power equipment, and the graphic editing and processing functions of the computer are enriched.
S4, describing the state of the power equipment corresponding to the power equipment image according to a comparison result, and appointing a regulation and control operation auxiliary decision building through an artificial intelligence algorithm;
in one embodiment, the processing the video image in the computer through the deposition microphase identification model, identifying the required power equipment pattern in the video image, and storing the identification result in the database further comprises the following steps:
in one embodiment, the describing the state of the power device corresponding to the power device image according to the comparison result and specifying the auxiliary control operation decision through the artificial intelligence algorithm further includes the following steps:
s41, describing the state of the electric power equipment corresponding to the electric power equipment image according to a comparison result, extracting a compressed picture for face recognition of a scheduling system to complete identity verification of a scheduling person;
and S42, performing load flow calculation and regional load compensation through equipment identification, character identification and fault diagnosis to maintain voltage stability, completing the work of deep reinforcement learning of the power dispatching system, and providing a regulation and control operation auxiliary decision suggestion through an artificial intelligence algorithm for reference selection of a dispatcher.
In conclusion, by means of the technical scheme, the method breaks through the conventional method that curve characteristics must be extracted firstly by utilizing a neural network to identify the sedimentary microfacies, can automatically and continuously identify the sedimentary microfacies by utilizing a computer to process data according to a model, simplifies the traditional preprocessing process, and improves the utilization and identification accuracy of the original power equipment data information; in the invention, a user can freely customize the network structure, the training parameters and the working parameters of the neural network model; a user can manually intervene in the network training process in real time in the network training process; the user can modify the conclusion of automatic identification according to the data logging curve automatically drawn by the artificial intelligent graphic processing method and the self knowledge and experience; the artificial intelligent graphic processing method of the invention provides a strong image processing function, so that a user can edit and process the automatically generated sedimentary microphotograph.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. An artificial intelligence image processing method based on power dispatching is characterized by comprising the following steps:
s1, acquiring a video image of power dispatching and uploading the video image to a computer;
s2, constructing a sedimentary microfacies recognition model, and establishing a connection relation between the sedimentary microfacies recognition model and the computer;
s3, processing the video image in the computer through a deposition micro-phase recognition model, recognizing a required power equipment graph in the video image, and storing a recognition result into a database;
and S4, describing the state of the power equipment corresponding to the power equipment image according to the comparison result, and appointing a regulation and control operation auxiliary decision building through an artificial intelligence algorithm.
2. The method according to claim 1, wherein the video images of the power schedule include power equipment images and sample images, and the power equipment images and the sample images each include visible light patterns.
3. The artificial intelligence image processing method based on power dispatching of claim 1, wherein the constructing a depositional microfacies recognition model and establishing a connection relationship between the depositional microfacies recognition model and the computer further comprises the following steps:
s21, building a management function and a basic data loading function of the sedimentary microfacies recognition model through a management module;
s22, establishing the sedimentary microfacies recognition model and the computer through an ANN training module;
s23, realizing the recognition function of the deposition micro-phase recognition model through a mode recognition module, and recognizing a required power equipment graph;
s24, automatically drawing a deposition microphase diagram according to the recognition result through a graphic processing module, and providing the functions of editing and processing the image for a user;
and S25, providing a function of inquiring various image data appearing in the sedimentary microfacies recognition model through a data inquiry module.
4. The method of claim 3, wherein the configuration module comprises data logging, user management and data source connection.
5. The artificial intelligence image processing method based on power scheduling of claim 3, wherein the establishing of the depositional microfacies recognition model and the computer through the ANN training module further comprises the following steps:
s221, setting a network structure, training parameters and working parameters of the artificial neural network through the structure of the ANN module;
s222, completing maintenance of training samples and training work of the neural network, providing addition, deletion and modification operations on the training samples, and starting and completing a training process of the artificial neural network;
and S223, testing the successfully trained neural network, and verifying the recognition rate of the neural network.
6. The artificial intelligence image processing method based on power scheduling of claim 3, wherein the identifying function of the depositional microfacies identification model is realized through a pattern recognition module, and the identifying of the required power equipment graph further comprises the following steps:
s231, the computer acquires the interception of the site real-time acquisition video where the electric power equipment image is located from the small-layer database according to the electric power equipment image and the sample image to be subjected to deposition microphase identification, intercepts 5-10 frames of pictures every second for processing, screens out key frames and then conducts digital data,
s232, coding the digital data, sending the coded data into a BP neural network, carrying out deposition microfacies identification through the BP neural network, and finally storing an identification result into a deposition microfacies identification result library;
and S233, when the situation that the classification cannot be accurately carried out exists, the BP neural network can automatically identify the fuzzy pattern, mark the fuzzy pattern and carry out manual identification in the deposition microphase manual identification.
7. The method as claimed in claim 3, wherein the step of automatically drawing the deposition microphase graph according to the recognition result through the graph processing module, and the step of providing the user with the function of editing and processing the image further comprises the following steps:
s241, automatically generating a deposition microphase band on the graph according to the characteristic parameter extraction of the graph of the power equipment, the state of the power equipment and the identified deposition microphase result, and distinguishing different deposition microphases;
s242, the user edits the generated image and performs corresponding storage processing;
and S243, opening the stored sedimentary microphase graph for editing processing, and setting legends of various sedimentary microphase graphs in the image.
8. The method according to claim 3, wherein the data query module queries the power device image, the characteristic parameters of the sample image, and the state of the power device corresponding to the power device image.
9. The method according to claim 1, wherein the processing the video image in the computer through the depositional microfacies recognition model, recognizing the required power equipment pattern in the video image, and storing the recognition result in the database further comprises the following steps:
s31, setting the font, symbol, size and color of the legend of each deposition micro item in a computer interface;
s32, by using the function of drawing the sedimentary microphase diagram, a user can generate the sedimentary microphase diagram of any specified power equipment;
and S33, after the automatic drawing work of the computer is finished, a user can manually add and draw the sedimentary microphase belt according to the power use distribution condition in the current power equipment, and the graphic editing processing function of the computer is enriched.
10. The artificial intelligence image processing method based on power scheduling of claim 1, wherein the step of describing the state of the power equipment corresponding to the power equipment image according to the comparison result and specifying the auxiliary decision of regulation and control operation through an artificial intelligence algorithm further comprises the steps of:
s41, describing the state of the power equipment corresponding to the power equipment image according to the comparison result, extracting the compressed picture, and using the compressed picture for face recognition of a scheduling system to complete identity verification of scheduling personnel;
and S42, maintaining voltage stability and completing deep reinforcement learning work of the power dispatching system by carrying out load flow calculation and regional load compensation through equipment identification, character identification and fault diagnosis, and providing a regulation and control operation auxiliary decision suggestion for a dispatcher to refer and select through an artificial intelligence algorithm.
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| CN116469058A (en) * | 2023-06-19 | 2023-07-21 | 安徽省国家电投和新电力技术研究有限公司 | Method and system for monitoring fire hazard of photovoltaic power station based on computer vision |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116469058A (en) * | 2023-06-19 | 2023-07-21 | 安徽省国家电投和新电力技术研究有限公司 | Method and system for monitoring fire hazard of photovoltaic power station based on computer vision |
| CN116469058B (en) * | 2023-06-19 | 2023-09-26 | 安徽省国家电投和新电力技术研究有限公司 | Method and system for monitoring fire hazard of photovoltaic power station based on computer vision |
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