CN113010682A - Command ticket system checking method, device and storage medium - Google Patents
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Abstract
The invention discloses a command ticket system checking method, a device and a storage medium, wherein the method comprises the steps of adopting command ticket text analysis to obtain command ticket text content, wherein the command ticket text analysis comprises the steps of firstly constructing a professional basic text word bank in the field of scheduling professional command ticket subdivision, then training the professional basic text word bank by adopting a professional word segmentation technology to form a recognizable command ticket professional phrase, and finally performing part of speech analysis and annotation research; classifying and extracting according to the text content of the command ticket to acquire command ticket structured data; and comparing and checking the structural data with the structural data of the preset information of the automatic master station system and the distribution network OMS system respectively according to the structural data to obtain a comparison and checking result. The invention adopts the command ticket text analysis technology to obtain the structured data of the command ticket text content and improve the command ticket checking accuracy.
Description
Technical Field
The invention relates to the technical field of power grid dispatching operation management, in particular to a command ticket system checking method, device and storage medium.
Background
After the scale of the power grid is rapidly enlarged, a large number of advanced relay protection and safety control devices are put into operation, the power grid dispatching operation management work is complex, the dispatching operation safety problem is increasingly highlighted, and the correctness of a power grid dispatching operation ticket is directly related to the operation safety of the power grid. In the process of automation of scheduling operation, the scheduling command ticket needs to be subjected to 'double check' before execution so as to ensure the compliance and the safety of the scheduling command ticket.
At present, when a dispatching operation order system is manually compiled, equipment maintenance work application orders in a dispatching distribution network OMS system need to be consulted, operation tasks are determined according to the real-time running state of a power grid provided by an intelligent power grid dispatching control system, and then a dispatching order which has strict logic relation, is correct and standard is compiled according to information such as dispatching terms, equipment numbers, power grid operation rules and the like.
Disclosure of Invention
The invention aims to provide a command ticket system checking method, device and storage medium, which adopt a command ticket text analysis technology to obtain structured data of command ticket text content, realize structured translation and description of equipment, actions and matters described in a command ticket and improve the command ticket checking accuracy.
In order to achieve the above object, an embodiment of the present invention provides a method for checking a command ticket system, including:
adopting command ticket text analysis to obtain command ticket text contents, wherein the command ticket text analysis comprises the steps of firstly constructing a professional basic text word bank in a scheduling professional command ticket subdivision field, then training the professional basic text word bank by adopting a professional word segmentation technology to form recognizable command ticket professional phrases, and finally performing part-of-speech analysis and annotation research;
classifying and extracting according to the text content of the command ticket to acquire command ticket structured data;
and comparing and checking the structural data with the structural data of the preset information of the automatic master station system and the distribution network OMS system respectively according to the structural data to obtain a comparison and checking result.
Preferably, the obtaining of the text content of the command ticket by adopting the command ticket text analysis includes the steps of firstly constructing a professional basic text word bank in the field of scheduling professional command ticket subdivision, then training the professional basic text word bank by adopting a professional word segmentation technology to form a recognizable command ticket professional phrase, and finally performing part-of-speech analysis and labeling research, including:
and according to the general word bank and the historical records of the scheduling command tickets, realizing expansion of the word bank by adopting manual labeling and an automatic word segmentation algorithm, and constructing a professional basic text word bank in the field of scheduling professional command ticket subdivision.
Preferably, the obtaining of the text content of the command ticket by adopting the command ticket text analysis includes, firstly, constructing a professional basic text lexicon in a scheduling professional command ticket subdivision field, then training the professional basic text lexicon by adopting a professional word segmentation technology to form a recognizable command ticket professional phrase, and finally performing part-of-speech analysis and labeling research, and further includes:
and constructing an automatic word segmentation compound model of the command ticket text according to a fusion dictionary word segmentation, statistical word segmentation and deep learning word segmentation method, inputting the content of the command ticket text into the automatic word segmentation compound model for training, and acquiring recognition and splitting results of professional words, phrases and long and short sentences.
Preferably, the obtaining of the text content of the command ticket by adopting the command ticket text analysis includes, firstly, constructing a professional basic text lexicon in a scheduling professional command ticket subdivision field, then training the professional basic text lexicon by adopting a professional word segmentation technology to form a recognizable command ticket professional phrase, and finally performing part-of-speech analysis and labeling research, and further includes:
and adopting an unsupervised learning and deep learning algorithm, constructing a part-of-speech tagging fusion model according to the recognition and splitting results, and acquiring the text content of the command ticket.
Preferably, the classifying and extracting according to the text content of the command ticket to obtain the command ticket structured data includes:
extracting text features according to the command ticket text content, constructing a classification model according to the text features, automatically distributing class labels by the classification model according to preset command ticket text content, and acquiring command ticket classification text content.
Preferably, the classifying and extracting according to the text content of the command ticket to obtain the command ticket structured data includes:
and constructing a text information intelligent extraction model according to the command ticket classification text content, and extracting key information of the command ticket classification text content by adopting the text information intelligent extraction model to obtain text information, wherein the key information comprises equipment codes, operation types and equipment states in the command ticket.
Preferably, the classifying and extracting according to the text content of the command ticket to obtain the command ticket structured data includes:
the text information intelligent extraction model adopts information merging, redundancy elimination and conflict resolution to carry out standardized processing on the text information, and command ticket structured data are obtained.
Preferably, the classifying and extracting according to the text content of the command ticket to obtain the command ticket structured data includes:
local information extraction is carried out according to text information, a maximum entropy model is selected as a classifier, and a conditional probability model is obtained as follows:
wherein x isnew,iI-1, …, t represents the ith new letter unit, ynew,iAnd i is 1, …, and t represents the ith new annotation unit, and the command ticket structured data is acquired.
The invention also provides a computer terminal device comprising one or more processors and a memory. A memory coupled to the processor for storing one or more programs; when executed by the one or more processors, cause the one or more processors to implement a command ticket system checking method as in any of the embodiments described above.
The present invention also provides a computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the command ticket system checking method according to any of the above embodiments.
According to the method and the device, the technical scheme of command ticket text analysis is adopted, the accuracy and the effectiveness of a file analysis model are improved, the obtained command ticket text contents are classified and extracted, the automatic checking and studying of the device state information in the command ticket and the device state structured data in the automatic master station system and the distribution network OMS system are realized, and the checking accuracy of the command ticket is improved.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used 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 that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart illustrating a command ticket system checking method according to an embodiment of the present invention;
FIG. 2 is a flow chart of a word segmentation method according to another embodiment of the present invention;
fig. 3 is a schematic flow chart of semantic acquisition according to another embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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 invention.
It should be understood that the step numbers used herein are for convenience of description only and are not intended as limitations on the order in which the steps are performed.
It is to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The terms "comprises" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items.
Referring to fig. 1, an embodiment of the present invention provides a method for checking a command ticket system, including:
s101, command ticket text content is obtained through command ticket text analysis, wherein the command ticket text analysis comprises the steps of firstly constructing a professional basic text word bank in a scheduling professional command ticket subdivision field, then training the professional basic text word bank through a professional word segmentation technology to form recognizable command ticket professional phrases, and finally conducting part-of-speech analysis and labeling research.
Specifically, when the command ticket is automatically transmitted to the distribution network OMS system from the WEB end of the main station, the operation composition and transmission content of the command ticket is unstructured text data, and the 'double check' of the command ticket needs to be compared and confirmed based on the structured data of the equipment object, no matter the check of the operation state of the equipment of the automatic main dispatching station or the check of the historical operation data of the corresponding equipment in the distribution network OMS system. Therefore, the application of the command ticket text analysis technology becomes a key technology for dispatching the command ticket to execute automation. The text analysis refers to the precise identification and analysis of text content, and aims to realize the structural translation and description of describing equipment, actions and items in the command ticket through the text analysis, and the method comprises the following specific implementation steps:
step one, constructing a professional basic text word bank in the field of scheduling professional command ticket subdivision.
Specifically, according to the general word bank and the historical records of the scheduling command tickets, the expansion of the word bank is realized by adopting manual labeling and an automatic word segmentation algorithm, and a professional basic text word bank in the field of scheduling professional command ticket subdivision is constructed.
And step two, training the professional basic text word bank by adopting a professional word segmentation technology to form recognizable command ticket professional phrases.
Referring to fig. 2, specifically, an automatic word segmentation composite model of the command ticket text is constructed according to a fusion dictionary word segmentation, statistical word segmentation and deep learning word segmentation method, and the content of the command ticket text is input into the automatic word segmentation composite model for training to obtain recognition and splitting results of professional words, phrases and long and short sentences.
The specific operation process is as follows:
marking by a manual operation command, wherein the marking set is as follows: { B, E, M, S }, where B is the beginning of a word, E is the end of a word, M is the middle of a word, and S is a single word, for ease of understanding, by way of example: you S now B should be B to the S baby B, M garden E, and S.
After the four types of labels are adopted to mark the content of the order ticket, an HMM can be established by a statistical method, and a Hidden Markov Model (HMM) is a statistical Model and is used for describing a Markov process containing Hidden unknown parameters. The difficulty is to determine the implicit parameters of the process from the observable parameters. These parameters are then used for further analysis, such as pattern recognition. In this embodiment, a 2-gram (bigram) language model, that is, a 1-order HMM is constructed, the label classification of each character is only affected by the classification of the previous character, and a state transition matrix a and a mixing matrix B of the HMM are solved, where:
Aij＝P(Cj|Ci)＝P(Ci|Cj)/P(Ci)＝Count(Ci|Cj)/Count(Ci)；
Bij＝P(Oj|Ci)＝P(Oi|Cj)/P(Ci)＝Count(Oi|Cj)/Count(Ci)；
in the formula, C ═ B, E, M, S }, O ═ { character set }, and Count represents frequency. Wherein, in the calculation of BijMeanwhile, due to the sparsity of data, many characters do not appear in the training set, which results in the result with probability 0 appearing in B, and in order to fix this problem, this embodiment adopts the data smoothing technique of adding 1, that is:
Bij＝P(Oj|Ci)＝(Count(Oi|Cj)+1)/Count(Ci)；
setting an initial vector PiThe HMM model is constructed, but because the HMM model has an output independent assumption, the context cannot be taken into the feature design, and the available range of the feature is limited, so that in combination with the deep learning method, the context is infinitely taken into consideration in the feature, and in order to consider all the history information, the bidirectional LSTM is selected as the skeleton model of the sequence annotation.
Referring to fig. 2, the chinese word segmentation includes a dictionary-based method including a matching judgment-based method, a maximum matching method, and a full segmentation path selection, a statistics-based method including a sequence labeling-based method, a BMS representation (viewing data bits are enterprise big data service providers), and HMMs, CRF models, and a CRF + BiLSTM network, and a trial learning-based method including word vector pre-training and a CRF + BiLSTM network. The sentence features extracted by the HMM model are used as bidirectional input, and the hidden layer of the forward LSTM is output as h1Hidden layer output h of reverse LSTM2Splicing to obtain a complete hidden state sequence ht＝[h1；h2]And then calculating the probability of each layer of features by utilizing a softmax layer, and sequencing the features according to the probability, wherein the calculation of the softmax value is as follows:
in the formula, SiFor the softmax value, i denotes the ith element, for a total of t elements.
And step three, performing part-of-speech analysis and annotation research.
Specifically, an unsupervised learning and deep learning algorithm is adopted, a part-of-speech tagging fusion model is constructed according to the recognition and splitting results, the text content of the command ticket is obtained, accurate tagging of the command ticket text is achieved, and data materials are provided for higher-level text processing and analysis in the following steps.
The text parsing technology adopted by the invention is a high-level word segmentation model combined with a deep learning algorithm, and word vectors are used as initial input, so that the similarity information among words can be accurately described, and the part-of-speech tagging result can be further improved. The feature extraction process of the traditional part-of-speech tagging method mainly combines words in a fixed context window manually, and the deep learning method can automatically utilize a nonlinear activation function to complete the goal. In the technical research process, the accuracy and the effectiveness of a file analysis model are spirally improved by adopting an advanced word segmentation technology and a scheduling command ticket mass data phase structure and gradually verifying and word labeling mode.
And S102, classifying and extracting according to the text content of the command ticket, and acquiring command ticket structured data.
Referring to fig. 3, specifically, based on the result obtained in step S101, a command ticket multi-system checking and studying model is constructed by using algorithms such as machine learning and deep learning, structured content of the command ticket is classified and information is extracted, and automatic checking and studying of device state information in the command ticket and device state structured data in the automatic master station system and the distribution network OMS system are realized.
Firstly, text features are extracted according to the text content of the command ticket, feature conversion and dimension reduction are carried out, a classification model is built according to the text features, the classification model automatically distributes class labels according to the preset text content of the command ticket, and the classified text content of the command ticket is obtained.
Secondly, semantic acquisition is divided into three parts, namely named entity identification including entity boundary identification and entity classification, relationship extraction including relationship detection and relationship classification, event extraction including event type identification and event element filling, and semantic acquisition is performed according to rule compilation and machine learning. The method comprises the steps of constructing a text information intelligent extraction model according to command ticket classification text contents, extracting key information from the command ticket classification text contents by adopting the text information intelligent extraction model, and obtaining text information, wherein the key information comprises equipment codes, operation types and equipment states in command tickets, and the text information intelligent extraction model adopts information merging, redundancy elimination and conflict resolution to carry out standardized processing on the text information, so that command ticket structured data are obtained.
The method for constructing the intelligent extraction model of the text information comprises the following steps:
the text information extraction comprises a learning stage and an extraction stage, in the learning stage, some labeled data sets exist, and the sample comprises word units and labeled units, as follows:
in the formula, the part x is a character unit, the part y is a labeling unit, and as only local information extraction needs to be carried out on the part { B, E, M } of the text information, a first-order Markov property is assumed to exist among different labels, local information extraction is carried out according to the text information, a maximum entropy model is selected as a classifier, and the conditional probability model is obtained as follows:
wherein x isnew,iI-1, …, t represents the ith new letter unit, ynew,iI is 1, …, t represents the ith newThe marking unit acquires the command ticket structured data.
And S103, comparing and checking the structural data with the structural data of the preset information of the automatic master station system and the distribution network OMS system respectively according to the structural data to obtain a comparison and checking result.
Specifically, structured data classified and extracted according to the content of the command ticket is compared with information of an automatic master station system and a distribution network OMS (operation management system) system to check the evidence and correct error information.
The invention provides technical support for 'double check' before the execution of a scheduling command ticket in the automatic process of scheduling operation, namely 'double check' of the command ticket, wherein the first check means that whether the equipment operation described in the command ticket is matched with the current operation information of the equipment in a scheduling automation master station or not and simultaneously carries out system-level check with the equipment operation information recorded in a historical command ticket recorded by a distribution network OMS system. At present, two system-level checks are mainly performed by manually identifying the content of a command ticket and manually logging in two application systems for manual comparison and confirmation, so that the efficiency is low and the working quality is not high. By applying the text mining technology, the method can realize the rapid analysis and mining of the unstructured data, intelligently compare the unstructured data with the structured data of a key system, and realize the conversion of a scheduling operation process from a manual mode to automatic execution by a machine. The method can adapt to various operation requirements under various wiring modes and operation modes in the power grid, and can realize quick and correct verification, thereby greatly improving the working efficiency of dispatchers and reducing the labor intensity; the problem of scheduling instruction compilation completely rely on manual work is solved to through the comprehensive electric wire netting safety management and control level that promotes of scheduling instruction safety check technique, and can check the operation sequence of operation order automatically, very big reduction manual operation's work load, improved operating personnel's work efficiency and the intelligent level of electric wire netting operation, very big promotion the safe operation level of electric wire netting.
The invention provides a computer terminal device comprising one or more processors and a memory. The memory is coupled to the processor for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the command ticket system checking method as in any of the above embodiments.
The processor is used for controlling the overall operation of the computer terminal equipment so as to complete all or part of the steps of the command ticket system checking method. The memory is used to store various types of data to support the operation at the computer terminal device, which data may include, for example, instructions for any application or method operating on the computer terminal device, as well as application-related data. The Memory may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.
In an exemplary embodiment, the computer terminal Device may be implemented by one or more Application Specific 1 integrated circuits (AS 1C), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components, and is configured to perform the above command ticket system checking method and achieve technical effects consistent with the above method.
In another exemplary embodiment, there is also provided a computer readable storage medium comprising program instructions which, when executed by a processor, implement the steps of the command ticket system checking method in any one of the above embodiments. For example, the computer readable storage medium may be the above-mentioned memory including program instructions executable by a processor of a computer terminal device to perform the above-mentioned order ticket system checking method, and achieve the technical effects consistent with the above-mentioned method.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. A command ticket system checking method is characterized by comprising the following steps:
adopting command ticket text analysis to obtain command ticket text contents, wherein the command ticket text analysis comprises the steps of firstly constructing a professional basic text word bank in a scheduling professional command ticket subdivision field, then training the professional basic text word bank by adopting a professional word segmentation technology to form recognizable command ticket professional phrases, and finally performing part-of-speech analysis and annotation research;
classifying and extracting according to the text content of the command ticket to acquire command ticket structured data;
and comparing and checking the structural data with the structural data of the preset information of the automatic master station system and the distribution network OMS system respectively according to the structural data to obtain a comparison and checking result.
2. The command ticket system checking method of claim 1, wherein the command ticket text parsing is used to obtain command ticket text content, and the command ticket text parsing comprises the steps of firstly constructing a professional basic text lexicon in a scheduling professional command ticket subdivision field, then training the professional basic text lexicon by using a professional word segmentation technology to form a recognizable command ticket professional phrase, and finally performing part-of-speech analysis and annotation research, wherein the step comprises the steps of:
and according to the general word bank and the historical records of the scheduling command tickets, realizing expansion of the word bank by adopting manual labeling and an automatic word segmentation algorithm, and constructing a professional basic text word bank in the field of scheduling professional command ticket subdivision.
3. The command ticket system checking method of claim 2, wherein the command ticket text parsing is used to obtain command ticket text content, and the command ticket text parsing comprises the steps of firstly constructing a professional basic text word bank in a scheduling professional command ticket subdivision field, then training the professional basic text word bank by using a professional word segmentation technology to form a recognizable command ticket professional phrase, and finally performing part-of-speech analysis and annotation research, and further comprising:
and constructing an automatic word segmentation compound model of the command ticket text according to a fusion dictionary word segmentation, statistical word segmentation and deep learning word segmentation method, inputting the content of the command ticket text into the automatic word segmentation compound model for training, and acquiring recognition and splitting results of professional words, phrases and long and short sentences.
4. The command ticket system checking method of claim 3, wherein the command ticket text parsing is used to obtain command ticket text content, and the command ticket text parsing comprises the steps of firstly constructing a professional basic text word bank in a scheduling professional command ticket subdivision field, then training the professional basic text word bank by using a professional word segmentation technology to form a recognizable command ticket professional phrase, and finally performing part-of-speech analysis and annotation research, and further comprising:
and adopting an unsupervised learning and deep learning algorithm, constructing a part-of-speech tagging fusion model according to the recognition and splitting results, and acquiring the text content of the command ticket.
5. The command ticket system checking method of claim 1, wherein the classifying and extracting according to the command ticket text content to obtain the command ticket structured data comprises:
extracting text features according to the command ticket text content, constructing a classification model according to the text features, automatically distributing class labels by the classification model according to preset command ticket text content, and acquiring command ticket classification text content.
6. The command ticket system checking method of claim 5, wherein the classifying and extracting according to the command ticket text content to obtain the command ticket structured data comprises:
and constructing a text information intelligent extraction model according to the command ticket classification text content, and extracting key information of the command ticket classification text content by adopting the text information intelligent extraction model to obtain text information, wherein the key information comprises equipment codes, operation types and equipment states in the command ticket.
7. The command ticket system checking method of claim 6, wherein the classifying and extracting according to the command ticket text content to obtain the command ticket structured data comprises:
the text information intelligent extraction model adopts information merging, redundancy elimination and conflict resolution to carry out standardized processing on the text information, and command ticket structured data are obtained.
8. The command ticket system checking method of claim 7, wherein the classifying and extracting according to the command ticket text content to obtain the command ticket structured data comprises:
local information extraction is carried out according to text information, a maximum entropy model is selected as a classifier, and a conditional probability model is obtained as follows:
wherein x isnew,iI-1, …, t represents the ith new letter unit, ynew,iAnd i is 1, …, and t represents the ith new annotation unit, and the command ticket structured data is acquired.
9. A computer terminal device, comprising:
one or more processors;
a memory coupled to the processor for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the command ticket system checking method of any one of claims 1 to 8.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a command ticket system checking method according to any one of claims 1 to 8.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114139941A (en) * | 2021-11-30 | 2022-03-04 | 广东电网有限责任公司 | Distribution network scheduling operation command ticket checking method and device, electronic equipment and medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108021552A (en) * | 2017-11-09 | 2018-05-11 | 国网浙江省电力公司电力科学研究院 | A kind of power system operation ticket method for extracting content and system |
| CN109710947A (en) * | 2019-01-22 | 2019-05-03 | 福建亿榕信息技术有限公司 | Power specialty word stock generating method and device |
| CN109726915A (en) * | 2018-12-27 | 2019-05-07 | 广东电网有限责任公司 | Equipment state automatic checking method, apparatus and electronic equipment |
| CN109740837A (en) * | 2018-11-20 | 2019-05-10 | 海南电网有限责任公司 | Based on regulating and controlling integrated power grid and save ground network to issue an order and safe related function method |
| CN110991179A (en) * | 2019-11-13 | 2020-04-10 | 国网山东省电力公司临沂供电公司 | Semantic analysis method based on electric power professional term |
| CN112232078A (en) * | 2020-10-10 | 2021-01-15 | 云南电网有限责任公司昆明供电局 | Scheduling operation ticket auditing method based on bidirectional GRU and attention mechanism |
| CN112396399A (en) * | 2020-11-23 | 2021-02-23 | 海南电网有限责任公司东方供电局 | Distribution network operation ticket simulation method based on natural language processing technology |
-
2021
- 2021-03-29 CN CN202110336997.8A patent/CN113010682A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108021552A (en) * | 2017-11-09 | 2018-05-11 | 国网浙江省电力公司电力科学研究院 | A kind of power system operation ticket method for extracting content and system |
| CN109740837A (en) * | 2018-11-20 | 2019-05-10 | 海南电网有限责任公司 | Based on regulating and controlling integrated power grid and save ground network to issue an order and safe related function method |
| CN109726915A (en) * | 2018-12-27 | 2019-05-07 | 广东电网有限责任公司 | Equipment state automatic checking method, apparatus and electronic equipment |
| CN109710947A (en) * | 2019-01-22 | 2019-05-03 | 福建亿榕信息技术有限公司 | Power specialty word stock generating method and device |
| CN110991179A (en) * | 2019-11-13 | 2020-04-10 | 国网山东省电力公司临沂供电公司 | Semantic analysis method based on electric power professional term |
| CN112232078A (en) * | 2020-10-10 | 2021-01-15 | 云南电网有限责任公司昆明供电局 | Scheduling operation ticket auditing method based on bidirectional GRU and attention mechanism |
| CN112396399A (en) * | 2020-11-23 | 2021-02-23 | 海南电网有限责任公司东方供电局 | Distribution network operation ticket simulation method based on natural language processing technology |
Non-Patent Citations (2)
| Title |
|---|
| 刘胜文，等;: "电网设备程序化操作在贵港电网的实践与应用", 《广西电力》, vol. 43, no. 2, pages 62 - 66 * |
| 曹靖, 等;: "基于语义框架的电网缺陷文本挖掘技术及其应用", 电网技术, vol. 41, no. 2, pages 637 - 643 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114139941A (en) * | 2021-11-30 | 2022-03-04 | 广东电网有限责任公司 | Distribution network scheduling operation command ticket checking method and device, electronic equipment and medium |
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