CN113823269A - Method for automatically storing power grid dispatching command based on voice recognition - Google Patents
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- G10L15/00—Speech recognition
- G10L15/02—Feature extraction for speech recognition; Selection of recognition unit
- G10L2015/025—Phonemes, fenemes or fenones being the recognition units
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Abstract
The invention discloses a method for automatically storing a power grid dispatching command based on voice recognition, which comprises the following steps of: the feature extraction module extracts the voice feature signals, carries out comprehensive processing and sends the comprehensive processing result to the neural network module; the neural network module receives the comprehensive processing result sent by the feature extraction module, identifies the comprehensive processing result and sends the identification result to the command matching module; the command matching module receives the identification result sent by the neural network module, performs matching and sends the matching result to the scheduling command recording module; and the scheduling command recording module receives and stores the matching result sent by the command matching module. The dispatching command can be automatically stored, and the workload of dispatching personnel for manually recording the dispatching command is reduced.
Description
Technical Field
The invention belongs to the technical field of voice recognition, and particularly relates to a method for automatically storing a power grid dispatching command based on voice recognition.
Background
With the continuous development of the application of the power industry in recent years, the complexity of the power grid structure is increasing day by day, and the power dispatching communication center is used as a neural center for the operation, management and control of the power grid, so that the power dispatching communication center bears an important responsibility for guaranteeing the safe operation of the power grid and is also a key factor for influencing the smooth operation of the power grid and the efficient optimal configuration of resources. However, although each link in the power system tends to be more and more intelligent and digital, the automation level and reliability thereof have been greatly improved, people still play an indispensable role in the power system. Because the existing power grid surpasses the traditional power grid in the aspects of equipment quantity, complexity and the like, the workload, the working complexity and the working pressure of dispatchers are increased gradually, and even dispatchers with good training and rich experience are likely to enter a fatigue state, so careless mistakes occur in the middle links with complicated processes. At present, telephone is often adopted for communication in the power grid dispatching work, manual recording is needed to be carried out in a log system after oral notification, and the telephone communication content is recorded and stored. The accuracy of the telephone information is affected due to the compact time of receiving and making telephone calls, the unsatisfactory field communication environment, dialect accents of dispatchers and field personnel and the like.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a method for automatically storing a power grid dispatching command based on voice recognition, which realizes the automatic recognition of the dispatching command and the storage.
In order to achieve the purpose, the invention adopts the following technical scheme:
s101, a feature extraction module extracts voice feature signals, performs comprehensive processing, and sends a comprehensive processing result to a neural network module;
s102, the neural network module receives the comprehensive processing result sent by the feature extraction module, identifies the comprehensive processing result and sends the identification result to the command matching module;
s103, receiving the identification result sent by the neural network module by the command matching module, matching and sending the matching result to the scheduling command recording module;
s104, the scheduling command recording module receives and stores the matching result sent by the command matching module;
and S101 to S104, the command matching module matches the recognition result with the scheduling command phoneme label library and sends the matching result to the scheduling command recording module for storage.
Further, the feature extraction module performs comprehensive processing on the voice feature signal, and the comprehensive processing mode includes: pre-emphasis, framing, windowing and fast fourier transform; the feature extraction module carries out comprehensive processing on the voice feature signals to generate speech and speech pattern signals, and sends the speech and speech pattern signals to the neural network module.
Further, the neural network module receives the comprehensive processing result sent by the feature extraction module, and the neural network module identifies the language map.
Further, the neural network module identifies a first phoneme in the speech graph as a first phoneme label, a second phoneme as a second phoneme label, and a third phoneme as a third phoneme label … …; the neural network module identifies the first combination of phonemes as a first phoneme label sequence, the neural network module identifies the second combination of phonemes as a second phoneme label sequence, and the neural network module identifies the third combination of phonemes as a third phoneme label sequence … … the neural network module identifies the nth combination of phonemes as an nth phoneme label sequence.
Further, the first phoneme combination comprises a first phoneme, a second phoneme, a fourth phoneme and a fifth phoneme; the second phoneme combination comprises a second phoneme, a third phoneme, a fourth phoneme, a fifth phoneme and a sixth phoneme; the third phoneme combination comprises a fourth phoneme, a fifth phoneme and a sixth phoneme; the Nth phoneme comprises a first phoneme, a second phoneme and a third phoneme … … Nth phoneme; the first phoneme label sequence comprises a first phoneme label, a second phoneme label, a fourth phoneme label and a fifth phoneme label; the second phoneme label sequence comprises a second phoneme label, a third phoneme label, a fourth phoneme label, a fifth phoneme label and a sixth phoneme label; the third phoneme label sequence comprises a fourth phoneme label, a fifth phoneme label and a sixth phoneme label; the sequence of nth phoneme tags includes a first phoneme tag, a second phoneme tag, and a third phoneme tag … … nth phoneme tag.
Further, the command matching module adopts a dynamic time warping algorithm for matching.
Further, the command matching module matches the first phoneme label, the second phoneme label and the third phoneme label … … with the scheduling command phoneme label library.
Further, the scheduling command phoneme label library includes a scheduling command first phoneme label, a scheduling command second phoneme label, a scheduling command third phoneme label … … scheduling command nth phoneme label.
Further, the first phoneme label is successfully matched with the first phoneme label of the scheduling command, the command matching module sends the matching result of the first phoneme label of the scheduling command to the scheduling command recording module, the second phoneme label is successfully matched with the second phoneme label of the scheduling command, the command matching module sends the matching result of the second phoneme label of the scheduling command to the scheduling command recording module, the third phoneme label is successfully matched with the third phoneme label of the scheduling command, the command matching module sends the matching result of the third phoneme label of the scheduling command to the scheduling command recording module … …, the nth phoneme label is successfully matched with the nth phoneme label of the scheduling command, and the command matching module sends the matching result of the nth phoneme label of the scheduling command to the scheduling command recording module.
Further, the command matching module matches the first phoneme label sequence, the second phoneme label sequence, the third phoneme label sequence … … the nth phoneme label sequence with the scheduling command phoneme label library.
Further, the scheduling command phoneme label library further includes a scheduling command first phoneme label sequence, a scheduling command second phoneme label sequence, a scheduling command third phoneme label sequence … … scheduling command nth phoneme label sequence.
Further, the first phoneme label sequence is successfully matched with the first phoneme label sequence of the scheduling command, the command matching module sends a matching result of the first phoneme label sequence of the scheduling command to the scheduling command recording module, the second phoneme label sequence is successfully matched with the second phoneme label sequence of the scheduling command, the command matching module sends a matching result of the second phoneme label sequence of the scheduling command to the scheduling command recording module, the third phoneme label sequence is successfully matched with a third phoneme label sequence of the scheduling command, the command matching module sends the matching result of the third phoneme label sequence of the scheduling command to the scheduling command recording module … …, the nth phoneme label sequence is successfully matched with the nth phoneme label sequence of the scheduling command, and the command matching module sends the matching result of the nth phoneme label sequence of the scheduling command to the scheduling command recording module.
Further, the scheduling command recording module receives the matching result sent by the command matching module, and stores the scheduling command first phoneme label matching result, the scheduling command second phoneme label matching result, the scheduling command third phoneme label matching result … … and the scheduling command Nth phoneme label matching result; and the scheduling command recording module stores the matching result of the scheduling command first phoneme label sequence, the matching result of the scheduling command second phoneme label sequence and the matching result of the scheduling command third phoneme label sequence … ….
The invention has the beneficial effects that: a method for automatically storing a power grid dispatching command based on voice recognition is characterized in that a neural network module receives and recognizes a comprehensive processing result sent by a feature extraction module, the neural network module sends the recognition result to a command matching module, the command matching module matches the recognition result with a dispatching command phoneme label library and sends the matching result to a dispatching command recording module for storage, and the workload of dispatching personnel for manually recording the dispatching command is reduced.
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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 used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic step diagram of a method for automatically saving a power grid dispatching command based on voice recognition according to the present invention.
Detailed Description
The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
The embodiments of the present disclosure are described below with specific examples, and other advantages and effects of the present disclosure will be readily apparent to those skilled in the art from the disclosure in the specification. The disclosure may be embodied or carried out in various other specific embodiments, and various modifications and changes may be made in the details within the description without departing from the spirit of the disclosure. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
The first embodiment is as follows:
s101, a feature extraction module extracts voice feature signals, performs comprehensive processing, and sends a comprehensive processing result to a neural network module;
the feature extraction module extracts the voice feature signal, the feature extraction module carries out comprehensive processing on the voice feature signal, and the comprehensive processing mode comprises the following steps: pre-emphasis, framing, windowing and fast fourier transform; the feature extraction module carries out comprehensive processing on the voice feature signals to generate speech and speech pattern signals, and sends the speech and speech pattern signals to the neural network module.
S102, the neural network module receives the comprehensive processing result sent by the feature extraction module, identifies the comprehensive processing result and sends the identification result to the command matching module;
the neural network module receives the comprehensive processing result sent by the feature extraction module, the neural network module identifies the speech pattern, the neural network module identifies a first phoneme in the speech pattern as a first phoneme label, a second phoneme in the speech pattern as a second phoneme label, and a third phoneme in the speech pattern as a third phoneme label … …; the neural network module recognizes the first phoneme combination as a first phoneme label sequence, the neural network module recognizes the second phoneme combination as a second phoneme label sequence, and the neural network module recognizes the third phoneme combination as a third phoneme label sequence … … the neural network module recognizes the nth phoneme combination as an nth phoneme label sequence; the first phoneme combination comprises a first phoneme, a second phoneme, a fourth phoneme and a fifth phoneme; the second phoneme combination comprises a second phoneme, a third phoneme, a fourth phoneme, a fifth phoneme and a sixth phoneme; the third phoneme combination comprises a fourth phoneme, a fifth phoneme and a sixth phoneme; the Nth phoneme comprises a first phoneme, a second phoneme and a third phoneme … … Nth phoneme; the first phoneme label sequence comprises a first phoneme label, a second phoneme label, a fourth phoneme label and a fifth phoneme label; the second phoneme label sequence comprises a second phoneme label, a third phoneme label, a fourth phoneme label, a fifth phoneme label and a sixth phoneme label; the third phoneme label sequence comprises a fourth phoneme label, a fifth phoneme label and a sixth phoneme label; the nth phoneme label sequence includes a first phoneme label, a second phoneme label, a third phoneme label … … nth phoneme label; and the neural network module sends the identification result to the command matching module.
S103, receiving the identification result sent by the neural network module by the command matching module, matching and sending the matching result to the scheduling command recording module;
the command matching module receives the recognition result sent by the neural network module, and the command matching module adopts a dynamic time warping algorithm for matching; the command matching module matches the first phoneme label, the second phoneme label and the third phoneme label … … with the scheduling command phoneme label library; scheduling command phoneme label library includes scheduling command first phoneme label, scheduling command second phoneme label, scheduling command third phoneme label … … scheduling command nth phoneme label; the first phoneme label is successfully matched with a first phoneme label of a scheduling command, the command matching module sends a matching result of the first phoneme label of the scheduling command to the scheduling command recording module, the second phoneme label is successfully matched with a second phoneme label of the scheduling command, the command matching module sends a matching result of the second phoneme label of the scheduling command to the scheduling command recording module, the third phoneme label is successfully matched with a third phoneme label of the scheduling command, the command matching module sends the matching result of the third phoneme label of the scheduling command to the scheduling command recording module … …, the Nth phoneme label is successfully matched with the Nth phoneme label of the scheduling command, and the command matching module sends the matching result of the Nth phoneme label of the scheduling command to the scheduling command recording module; the command matching module matches the first phoneme label sequence, the second phoneme label sequence and the third phoneme label sequence … … with a scheduling command phoneme label library; scheduling command phoneme label library further comprises a scheduling command first phoneme label sequence, a scheduling command second phoneme label sequence, a scheduling command third phoneme label sequence … … scheduling command Nth phoneme label sequence; the first phoneme label sequence is successfully matched with the first phoneme label sequence of the scheduling command, the command matching module sends a matching result of the first phoneme label sequence of the scheduling command to the scheduling command recording module, the second phoneme label sequence is successfully matched with the second phoneme label sequence of the scheduling command, the command matching module sends a matching result of the second phoneme label sequence of the scheduling command to the scheduling command recording module, the third phoneme label sequence is successfully matched with the third phoneme label sequence of the scheduling command, the command matching module sends a matching result of the third phoneme label sequence of the scheduling command to the scheduling command recording module … …, the Nth phoneme label sequence is successfully matched with the Nth phoneme label sequence of the scheduling command, and the command matching module sends a matching sequence result of the Nth phoneme label of the scheduling command to the scheduling command recording module.
S104, the scheduling command recording module receives and stores the matching result sent by the command matching module;
the scheduling command recording module receives the matching result sent by the command matching module, and stores a scheduling command first phoneme label matching result, a scheduling command second phoneme label matching result and a scheduling command third phoneme label matching result … …; and the scheduling command recording module stores the matching result of the scheduling command first phoneme label sequence, the matching result of the scheduling command second phoneme label sequence and the matching result of the scheduling command third phoneme label sequence … ….
In the description of the present invention, it should be noted that the terms "first", "second", "third", "fourth", "fifth", "sixth" and "nth" are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
The above description is for the purpose of illustrating embodiments of the invention and is not intended to limit the invention, and it will be apparent to those skilled in the art that any modification, equivalent replacement, or improvement made without departing from the spirit and principle of the invention shall fall within the protection scope of the invention.
Claims (8)
1. A method for automatically storing a power grid dispatching command based on voice recognition is characterized by comprising the following steps:
s101, a feature extraction module extracts voice feature signals, performs comprehensive processing, and sends a comprehensive processing result to a neural network module;
s102, the neural network module receives and identifies the comprehensive processing result sent by the feature extraction module, and sends the identification result to the command matching module;
s103, the command matching module receives the identification result sent by the neural network module, matches the identification result and sends the matching result to the scheduling command recording module;
and S104, the scheduling command recording module receives and stores the matching result sent by the command matching module.
2. The method for automatically storing the dispatching command of the power grid based on the voice recognition as claimed in claim 1, wherein the feature extraction module performs comprehensive processing on the voice feature signal to generate a speech-pattern signal.
3. The method for automatically saving the dispatching command of the power grid based on the voice recognition as claimed in claim 1 or 2, wherein the comprehensive processing modes comprise pre-emphasis, framing, windowing and fast Fourier transformation.
4. The method for automatic saving of power grid dispatching commands based on speech recognition as claimed in claim 1, wherein the neural network module recognizes phonemes in a speech spectrogram as phoneme labels, and recognizes combinations of phonemes in the speech spectrogram as a sequence of phoneme labels.
5. The method for automatic power grid dispatching command saving based on speech recognition of claim 1, wherein the command matching module matches phoneme labels and phoneme label sequences with a dispatching command phoneme label library.
6. The method for automatic preservation of power grid dispatching commands based on speech recognition as claimed in claim 5, wherein said library of dispatching command phoneme labels comprises dispatching command phoneme labels and a sequence of dispatching command phoneme labels.
7. The method for automatically saving power grid dispatching commands based on speech recognition as claimed in claim 1 or 5, wherein the command matching module matches phoneme tags with dispatching command phoneme tags, and the command matching module matches phoneme tag sequences with dispatching command phoneme tag sequences.
8. The method for automatically storing the power grid dispatching command based on the voice recognition as claimed in claim 1, wherein the dispatching command recording module stores the phoneme label matching result and the phoneme label sequence matching result.
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| CN202111044322.2A CN113823269A (en) | 2021-09-07 | 2021-09-07 | Method for automatically storing power grid dispatching command based on voice recognition |
| PCT/CN2022/115880 WO2023036014A1 (en) | 2021-09-07 | 2022-08-30 | Method for automatically saving power grid scheduling command on basis of voice recognition |
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| WO2023036014A1 (en) * | 2021-09-07 | 2023-03-16 | 广西电网有限责任公司贺州供电局 | Method for automatically saving power grid scheduling command on basis of voice recognition |
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