CN117541075A - Power grid operation data analysis method and device based on big data and deep learning - Google Patents
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Abstract
The invention relates to a power grid operation data analysis method and device based on big data and deep learning, wherein the method comprises the following steps: acquiring power grid operation data; based on the power grid operation data, carrying out power grid operation abnormality detection by using an abnormality detection model of deep learning; if the power grid operation abnormality is detected, determining a power grid operation abnormality event and an abnormality reason by using an abnormality diagnosis model of deep learning; and outputting an exception solution based on the exception event and the exception cause. According to the method and the device for detecting the power grid operation abnormality, the problem that the accuracy of the power grid operation abnormality detection in the related technology is low is solved, and the effect of improving the accuracy of the power grid operation abnormality detection is achieved.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to a power grid operation data analysis method and device based on big data and deep learning.
Background
The power grid operation data are multiple in variety and large in data quantity, and abnormal power grid operation can be detected by analyzing the power grid operation data. After the power grid operation data are acquired, the power grid operation data are compared with a preset threshold value to judge whether the power grid operation is abnormal, the accuracy of the power grid operation abnormality detection in the mode depends on the accuracy of the threshold value, the setting of the threshold value is usually based on historical operation data or experience data, and the accuracy is not controlled accurately. Therefore, the accuracy of the grid operation abnormality detection in the related art is low.
At present, no effective solution is proposed for the problem of low accuracy of power grid operation anomaly detection in the related art.
Disclosure of Invention
The purpose of the present application is to provide a method, a device, a computer device and a computer readable storage medium for analyzing power grid operation data based on big data and deep learning, so as to at least solve the problem of low accuracy of power grid operation anomaly detection in the related art.
In order to achieve the above purpose, the technical scheme adopted by the application is as follows:
in a first aspect, an embodiment of the present application provides a method for analyzing grid operation data based on big data and deep learning, including:
acquiring power grid operation data;
based on the power grid operation data, carrying out power grid operation abnormality detection by using an abnormality detection model of deep learning;
if the power grid operation abnormality is detected, determining a power grid operation abnormality event and an abnormality reason by using an abnormality diagnosis model of deep learning;
and outputting an exception solution based on the exception event and the exception cause.
In some embodiments, the anomaly detection model includes a traffic classification model and a feature extraction model, wherein the performing the grid operation anomaly detection using the deep-learned anomaly detection model based on the grid operation data includes:
Determining a target business class of the power grid operation data by utilizing the business classification model;
extracting first data features of the power grid operation data by using the feature extraction model;
querying a second data feature corresponding to the target service class from a mapping data table for storing a mapping relation between the service class and the data feature;
judging whether the first data characteristic is consistent with the second data characteristic;
if the first data characteristic is consistent with the second data characteristic, determining that the power grid is normal in operation;
and if the first data characteristic is not consistent with the second data characteristic, determining that the power grid is abnormal in operation.
In some of these embodiments, the determining whether the first data characteristic matches the second data characteristic comprises:
calculating semantic similarity between the first data feature and the second data feature;
judging whether the semantic similarity is larger than a target threshold value or not;
if the semantic similarity is greater than the target threshold, determining that the first data feature is consistent with the second data feature;
and if the semantic similarity is smaller than or equal to the target threshold, determining that the first data characteristic does not accord with the second data characteristic.
In some embodiments, if the power grid operation abnormality is detected, determining the power grid operation abnormality event and the abnormality cause by using the deep learning abnormality diagnosis model includes:
performing word segmentation processing on the first data characteristics to obtain a plurality of segmented words;
inputting the plurality of segmented words into the abnormality diagnosis model, wherein the abnormality diagnosis model outputs segmented words marked as abnormality;
and carrying out semantic combination on the segmented words marked as abnormal to obtain the abnormal event and the abnormal reason.
In some of these embodiments, the constructing of the abnormality diagnostic model includes:
collecting a training data set, wherein the training data set comprises: normal data and abnormal data;
respectively extracting the data characteristics of the normal data and the data characteristics of the abnormal data;
performing word segmentation on the data characteristics of the normal data to obtain a plurality of normal word segments, and performing word segmentation on the data characteristics of the abnormal data to obtain a plurality of abnormal word segments;
training the abnormality diagnosis model by using the plurality of normal words and the plurality of abnormal words, wherein the trained abnormality diagnosis model is used for outputting the abnormal words.
In a second aspect, an embodiment of the present application provides a power grid operation data analysis device based on big data and deep learning, including:
the acquisition unit is used for acquiring power grid operation data;
the detection unit is used for detecting power grid operation abnormality by using an abnormality detection model of deep learning based on the power grid operation data;
the determining unit is used for determining an abnormal event and an abnormal reason of the power grid operation by using the deep learning abnormal diagnosis model if the abnormal operation of the power grid is detected;
and an output unit for outputting an abnormality solution based on the abnormality event and the abnormality cause.
In some of these embodiments, the anomaly detection model includes a traffic classification model and a feature extraction model, wherein the detection unit includes:
the first determining module is used for determining a target service class of the power grid operation data by utilizing the service classification model;
the extraction module is used for extracting first data features of the power grid operation data by utilizing the feature extraction model;
the query module is used for querying a second data characteristic corresponding to the target service class from a mapping data table for storing the mapping relation between the service class and the data characteristic;
The judging module is used for judging whether the first data characteristic is consistent with the second data characteristic;
the second determining module is used for determining that the power grid runs normally if the first data characteristic accords with the second data characteristic;
and the third determining module is used for determining that the power grid runs abnormally if the first data characteristic is not consistent with the second data characteristic.
In some embodiments, the determining module includes:
a computing sub-module for computing semantic similarity between the first data feature and the second data feature;
the judging submodule is used for judging whether the semantic similarity is larger than a target threshold value or not;
a first determining sub-module configured to determine that the first data feature matches the second data feature if the semantic similarity is greater than the target threshold;
and the second determining submodule is used for determining that the first data characteristic does not accord with the second data characteristic if the semantic similarity is smaller than or equal to the target threshold value.
In a third aspect, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the grid operation data analysis method based on big data and deep learning as described in the first aspect.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the grid operation data analysis method based on big data and deep learning as described in the first aspect above.
Compared with the prior art, the power grid operation data analysis method based on big data and deep learning provided by the embodiment of the application acquires the power grid operation data; based on the power grid operation data, carrying out power grid operation abnormality detection by using an abnormality detection model of deep learning; if the power grid operation abnormality is detected, determining a power grid operation abnormality event and an abnormality reason by using an abnormality diagnosis model of deep learning; based on the abnormal event and the abnormal reason, an abnormal solution is output, the problem that the accuracy of power grid operation abnormality detection is low in the related technology is solved, and the effect of improving the accuracy of power grid operation abnormality detection is achieved.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the other features, objects, and advantages of the application.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
fig. 1 is a block diagram of a mobile terminal according to an embodiment of the present application;
FIG. 2 is a flow chart of a method of grid operation data analysis based on big data and deep learning according to an embodiment of the present application;
FIG. 3 is a block diagram of a grid operation data analysis device based on big data and deep learning according to an embodiment of the present application;
fig. 4 is a schematic hardware structure of a computer device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described and illustrated below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden on the person of ordinary skill in the art based on the embodiments provided herein, are intended to be within the scope of the present application.
It is apparent that the drawings in the following description are only some examples or embodiments of the present application, and it is possible for those of ordinary skill in the art to apply the present application to other similar situations according to these drawings without inventive effort. Moreover, it should be appreciated that while such a development effort might be complex and lengthy, it would nevertheless be a routine undertaking of design, fabrication, or manufacture for those of ordinary skill having the benefit of this disclosure, and thus should not be construed as having the benefit of this disclosure.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is to be expressly and implicitly understood by those of ordinary skill in the art that the embodiments described herein can be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar terms herein do not denote a limitation of quantity, but rather denote the singular or plural. The terms "comprising," "including," "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to only those steps or elements but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. The terms "connected," "coupled," and the like in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as used herein refers to two or more. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., "a and/or B" may mean: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. The terms "first," "second," "third," and the like, as used herein, are merely distinguishing between similar objects and not representing a particular ordering of objects.
The embodiment provides a mobile terminal. Fig. 1 is a block diagram of a mobile terminal according to an embodiment of the present application. As shown in fig. 1, the mobile terminal includes: radio Frequency (RF) circuit 110, memory 120, input unit 130, display unit 140, sensor 150, audio circuit 160, wireless fidelity (wireless fidelity, wiFi) module 170, processor 180, and power supply 190. Those skilled in the art will appreciate that the mobile terminal structure shown in fig. 1 is not limiting of the mobile terminal and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
The following describes the components of the mobile terminal in detail with reference to fig. 1:
the RF circuit 110 may be used for receiving and transmitting signals during the process of receiving and transmitting information or communication, specifically, after receiving downlink information of the base station, the downlink information is processed by the processor 180; in addition, the data of the design uplink is sent to the base station. Typically, RF circuitry includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier (Low Noise Amplifier, simply referred to as an LNA), a duplexer, and the like. In addition, RF circuit 110 may also communicate with networks and other devices via wireless communications. The wireless communication may use any communication standard or protocol, including but not limited to global system for mobile communications (Global System of Mobile communication, abbreviated GSM), general packet radio service (General Packet Radio Service, abbreviated GPRS), code division multiple access (Code Division Multiple Access, abbreviated CDMA), wideband code division multiple access (Wideband Code Division Multiple Access, abbreviated WCDMA), long term evolution (Long Term Evolution, abbreviated LTE), email, short message service (Short Messaging Service, abbreviated SMS), and the like.
The memory 120 may be used to store software programs and modules, and the processor 180 performs various functional applications and data processing of the mobile terminal by executing the software programs and modules stored in the memory 120. The memory 120 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebooks, etc.) created according to the use of the mobile terminal, etc. In addition, memory 120 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.
The input unit 130 may be used to receive input numeric or character information and to generate key signal inputs related to user settings and function control of the mobile terminal. In particular, the input unit 130 may include a touch panel 131 and other input devices 132. The touch panel 131, also referred to as a touch screen, may collect touch operations thereon or thereabout by a user (e.g., operations of the user on the touch panel 131 or thereabout by using any suitable object or accessory such as a finger, a stylus, etc.), and drive the corresponding connection device according to a predetermined program. Alternatively, the touch panel 131 may include two parts of a touch detection device and a touch controller. The touch detection device detects the touch azimuth of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch detection device and converts it into touch point coordinates, which are then sent to the processor 180, and can receive commands from the processor 180 and execute them. In addition, the touch panel 131 may be implemented in various types such as resistive, capacitive, infrared, and surface acoustic wave. The input unit 130 may include other input devices 132 in addition to the touch panel 131. In particular, other input devices 132 may include, but are not limited to, one or more of a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, mouse, joystick, etc.
The display unit 140 may be used to display information input by a user or information provided to the user and various menus of the mobile terminal. The display unit 140 may include a display panel 141, and alternatively, the display panel 141 may be configured in the form of a liquid crystal display (Liquid Crystal Display, abbreviated as LCD), an Organic Light-Emitting Diode (OLED), or the like. Further, the touch panel 131 may cover the display panel 141, and when the touch panel 131 detects a touch operation thereon or thereabout, the touch panel is transferred to the processor 180 to determine the type of the touch event, and then the processor 180 provides a corresponding visual output on the display panel 141 according to the type of the touch event. Although in fig. 1, the touch panel 131 and the display panel 141 implement the input and output functions of the mobile terminal as two independent components, in some embodiments, the touch panel 131 and the display panel 141 may be integrated to implement the input and output functions of the mobile terminal.
The mobile terminal may also include at least one sensor 150, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor that may adjust the brightness of the display panel 141 according to the brightness of ambient light, and a proximity sensor that may turn off the display panel 141 and/or the backlight when the mobile terminal moves to the ear. As one of the motion sensors, the accelerometer sensor can detect the acceleration in all directions (generally three axes), and can detect the gravity and direction when stationary, and can be used for recognizing the application of the gesture of the mobile terminal (such as horizontal and vertical screen switching, related games, magnetometer gesture calibration), vibration recognition related functions (such as pedometer and knocking), and the like; other sensors such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc. that may also be configured with the mobile terminal are not described in detail herein.
A speaker 161 in the audio circuit 160 and a microphone 162 may provide an audio interface between the user and the mobile terminal. The audio circuit 160 may transmit the received electrical signal converted from audio data to the speaker 161, and the electrical signal is converted into a sound signal by the speaker 161 to be output; on the other hand, the microphone 162 converts the collected sound signal into an electrical signal, receives the electrical signal from the audio circuit 160, converts the electrical signal into audio data, outputs the audio data to the processor 180 for processing, transmits the audio data to, for example, another mobile terminal via the RF circuit 110, or outputs the audio data to the memory 120 for further processing.
WiFi belongs to a short-distance wireless transmission technology, and a mobile terminal can help a user to send and receive emails, browse webpages, access streaming media and the like through the WiFi module 170, so that wireless broadband Internet access is provided for the user. Although fig. 1 shows a WiFi module 170, it will be understood that it does not belong to the necessary configuration of the mobile terminal, and may be omitted entirely or replaced with other short-range wireless transmission modules, such as Zigbee modules, WAPI modules, or the like, as desired within the scope of not changing the essence of the invention.
The processor 180 is a control center of the mobile terminal, connects various parts of the entire mobile terminal using various interfaces and lines, and performs various functions of the mobile terminal and processes data by running or executing software programs and/or modules stored in the memory 120 and calling data stored in the memory 120, thereby performing overall monitoring of the mobile terminal. Optionally, the processor 180 may include one or more processing units; preferably, the processor 180 may integrate an application processor that primarily handles operating systems, user interfaces, applications, etc., with a modem processor that primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 180.
The mobile terminal also includes a power supply 190 (e.g., a battery) for powering the various components, which may be logically connected to the processor 180 via a power management system so as to provide for the management of charge, discharge, and power consumption by the power management system.
Although not shown, the mobile terminal may further include a camera, a bluetooth module, etc., which will not be described herein.
In this embodiment, the processor 180 is configured to:
acquiring power grid operation data;
based on the power grid operation data, carrying out power grid operation abnormality detection by using an abnormality detection model of deep learning;
if the power grid operation abnormality is detected, determining a power grid operation abnormality event and an abnormality reason by using an abnormality diagnosis model of deep learning;
and outputting an exception solution based on the exception event and the exception cause.
In some of these embodiments, the processor 180 is further configured to:
determining a target business class of the power grid operation data by utilizing the business classification model;
extracting first data features of the power grid operation data by using the feature extraction model;
querying a second data feature corresponding to the target service class from a mapping data table for storing a mapping relation between the service class and the data feature;
Judging whether the first data characteristic is consistent with the second data characteristic;
if the first data characteristic is consistent with the second data characteristic, determining that the power grid is normal in operation;
and if the first data characteristic is not consistent with the second data characteristic, determining that the power grid is abnormal in operation.
In some of these embodiments, the processor 180 is further configured to:
calculating semantic similarity between the first data feature and the second data feature;
judging whether the semantic similarity is larger than a target threshold value or not;
if the semantic similarity is greater than the target threshold, determining that the first data feature is consistent with the second data feature;
and if the semantic similarity is smaller than or equal to the target threshold, determining that the first data characteristic does not accord with the second data characteristic.
In some of these embodiments, the processor 180 is further configured to:
performing word segmentation processing on the first data characteristics to obtain a plurality of segmented words;
inputting the plurality of segmented words into the abnormality diagnosis model, wherein the abnormality diagnosis model outputs segmented words marked as abnormality;
and carrying out semantic combination on the segmented words marked as abnormal to obtain the abnormal event and the abnormal reason.
In some of these embodiments, the processor 180 is further configured to:
collecting a training data set, wherein the training data set comprises: normal data and abnormal data;
respectively extracting the data characteristics of the normal data and the data characteristics of the abnormal data;
performing word segmentation on the data characteristics of the normal data to obtain a plurality of normal word segments, and performing word segmentation on the data characteristics of the abnormal data to obtain a plurality of abnormal word segments;
training the abnormality diagnosis model by using the plurality of normal words and the plurality of abnormal words, wherein the trained abnormality diagnosis model is used for outputting the abnormal words.
The embodiment provides a power grid operation data analysis method based on big data and deep learning. Fig. 2 is a flowchart of a method for analyzing grid operation data based on big data and deep learning according to an embodiment of the present application, as shown in fig. 2, the flowchart includes the following steps:
step S201, acquiring power grid operation data;
step S202, based on the power grid operation data, performing power grid operation abnormality detection by using an abnormality detection model of deep learning;
step S203, if the power grid operation abnormality is detected, determining an abnormal event and an abnormal cause of the power grid operation by using an abnormality diagnosis model of deep learning;
Step S204, outputting an abnormality solution based on the abnormality event and the abnormality cause.
In the above steps, the grid operation data may include, but is not limited to: grid topology, geographic location, service type, environmental parameters, device model, device acquisition data, and the like. The deep-learning anomaly detection model and the deep-learning anomaly diagnosis model are pre-constructed according to historical power grid operation data. Optionally, the anomaly detection model includes a traffic classification model and a feature extraction model.
In some embodiments, the step S202 may further include performing the power grid operation anomaly detection using a deep-learning anomaly detection model based on the power grid operation data:
determining a target business class of the power grid operation data by utilizing the business classification model;
extracting first data features of the power grid operation data by using the feature extraction model;
querying a second data feature corresponding to the target service class from a mapping data table for storing a mapping relation between the service class and the data feature;
judging whether the first data characteristic is consistent with the second data characteristic;
if the first data characteristic is consistent with the second data characteristic, determining that the power grid is normal in operation;
And if the first data characteristic is not consistent with the second data characteristic, determining that the power grid is abnormal in operation.
Optionally, if the first data feature and the second data feature are adjusted to be word expressions, the determining whether the first data feature and the second data feature match may include:
calculating semantic similarity between the first data feature and the second data feature;
judging whether the semantic similarity is larger than a target threshold value or not;
if the semantic similarity is greater than the target threshold, determining that the first data feature is consistent with the second data feature;
and if the semantic similarity is smaller than or equal to the target threshold, determining that the first data characteristic does not accord with the second data characteristic.
Optionally, if the first data feature and the second data feature are adjusted to be numerical expressions, the determining whether the first data feature and the second data feature match may include:
judging whether the difference between the first data feature and the second data feature is lower than an error reference value;
if the difference between the first data feature and the second data feature is lower than an error reference value, determining that the first data feature is consistent with the second data feature;
And if the difference value between the first data characteristic and the second data characteristic is not lower than an error reference value, determining that the first data characteristic and the second data characteristic do not accord.
According to the embodiment of the application, the power grid operation data are analyzed by using the deep learning abnormality detection model, whether the power grid operation is abnormal or not is detected, and compared with the traditional threshold detection, the accuracy of power grid operation abnormality detection can be improved.
In some of these embodiments, the process of constructing the anomaly diagnostic model may include:
collecting a training data set, wherein the training data set comprises: normal data and abnormal data;
respectively extracting the data characteristics of the normal data and the data characteristics of the abnormal data;
performing word segmentation on the data characteristics of the normal data to obtain a plurality of normal word segments, and performing word segmentation on the data characteristics of the abnormal data to obtain a plurality of abnormal word segments;
training the abnormality diagnosis model by using the plurality of normal words and the plurality of abnormal words, wherein the trained abnormality diagnosis model is used for outputting the abnormal words.
In some embodiments, if the power grid operation abnormality is detected, determining the power grid operation abnormality event and the abnormality cause by using the deep learning abnormality diagnosis model may include:
Performing word segmentation processing on the first data characteristics to obtain a plurality of segmented words;
inputting the plurality of segmented words into the abnormality diagnosis model, wherein the abnormality diagnosis model outputs segmented words marked as abnormality;
and carrying out semantic combination on the segmented words marked as abnormal to obtain the abnormal event and the abnormal reason.
According to the embodiment of the application, after the power grid operation abnormality is determined by using the abnormality detection model, the power grid operation abnormality event and the abnormality reason are determined by using the abnormality diagnosis model with deep learning, and the abnormality solution is output based on the abnormality event and the abnormality reason, so that power grid staff can be helped to rapidly and accurately process the abnormality, and the abnormality fault processing efficiency of the staff is improved.
It should be noted that the steps illustrated in the above-described flow or flow diagrams of the figures may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flow diagrams, in some cases, the steps illustrated or described may be performed in an order other than that illustrated herein.
The embodiment provides a power grid operation data analysis device based on big data and deep learning, which is used for realizing the above embodiment and the preferred implementation manner, and is not described in detail. As used below, the terms "module," "unit," "sub-unit," and the like may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
Fig. 3 is a block diagram of a power grid operation data analysis device based on big data and deep learning according to an embodiment of the present application, and as shown in fig. 3, the device includes:
an acquiring unit 31, configured to acquire grid operation data;
a detection unit 32, configured to perform power grid operation anomaly detection by using a deep learning anomaly detection model based on the power grid operation data;
a determining unit 33, configured to determine an abnormal event and an abnormal cause of the power grid operation by using an abnormal diagnosis model of deep learning if the power grid operation abnormality is detected;
an output unit 34 for outputting an abnormality solution based on the abnormality event and the abnormality cause.
In some of these embodiments, the anomaly detection model includes a traffic classification model and a feature extraction model, wherein the detection unit 32 includes:
the first determining module is used for determining a target service class of the power grid operation data by utilizing the service classification model;
the extraction module is used for extracting first data features of the power grid operation data by utilizing the feature extraction model;
the query module is used for querying a second data characteristic corresponding to the target service class from a mapping data table for storing the mapping relation between the service class and the data characteristic;
The judging module is used for judging whether the first data characteristic is consistent with the second data characteristic;
the second determining module is used for determining that the power grid runs normally if the first data characteristic accords with the second data characteristic;
and the third determining module is used for determining that the power grid runs abnormally if the first data characteristic is not consistent with the second data characteristic.
In some embodiments, the determining module includes:
a computing sub-module for computing semantic similarity between the first data feature and the second data feature;
the judging submodule is used for judging whether the semantic similarity is larger than a target threshold value or not;
a first determining sub-module configured to determine that the first data feature matches the second data feature if the semantic similarity is greater than the target threshold;
and the second determining submodule is used for determining that the first data characteristic does not accord with the second data characteristic if the semantic similarity is smaller than or equal to the target threshold value.
In some of these embodiments, the determining unit 33 includes:
the word segmentation module is used for carrying out word segmentation processing on the first data characteristics to obtain a plurality of segmented words;
The input module is used for inputting the plurality of segmented words into the abnormality diagnosis model, and the abnormality diagnosis model outputs segmented words marked as abnormality;
and the combination module is used for carrying out semantic combination on the word segmentation marked as abnormal to obtain the abnormal event and the abnormal cause.
In some of these embodiments, the apparatus further comprises a build unit, wherein the build unit comprises:
the system comprises an acquisition module, a data acquisition module and a data processing module, wherein the acquisition module is used for acquiring a training data set, and the training data set comprises: normal data and abnormal data;
the extraction module is used for respectively extracting the data characteristics of the normal data and the data characteristics of the abnormal data;
the processing module is used for performing word segmentation processing on the data characteristics of the normal data to obtain a plurality of normal word segments, and performing word segmentation processing on the data characteristics of the abnormal data to obtain a plurality of abnormal word segments;
the training module is used for training the abnormality diagnosis model by utilizing the plurality of normal words and the plurality of abnormal words, wherein the trained abnormality diagnosis model is used for outputting the abnormal words.
The above-described respective modules may be functional modules or program modules, and may be implemented by software or hardware. For modules implemented in hardware, the various modules described above may be located in the same processor; or the above modules may be located in different processors in any combination.
Embodiments provide a computer device. The power grid operation data analysis method based on big data and deep learning in combination with the embodiment of the application can be realized by computer equipment. Fig. 4 is a schematic hardware structure of a computer device according to an embodiment of the present application.
The computer device may include a processor 41 and a memory 42 storing computer program instructions.
In particular, the processor 41 may include a Central Processing Unit (CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, abbreviated as ASIC), or may be configured to implement one or more integrated circuits of embodiments of the present application.
Memory 42 may include, among other things, mass storage for data or instructions. By way of example, and not limitation, memory 42 may comprise a Hard Disk Drive (HDD), floppy Disk Drive, solid state Drive (Solid State Drive, SSD), flash memory, optical Disk, magneto-optical Disk, tape, or universal serial bus (Universal Serial Bus, USB) Drive, or a combination of two or more of the foregoing. The memory 42 may include removable or non-removable (or fixed) media, where appropriate. The memory 42 may be internal or external to the data processing apparatus, where appropriate. In a particular embodiment, the memory 42 is a Non-Volatile (Non-Volatile) memory. In a particular embodiment, the Memory 42 includes Read-Only Memory (ROM) and random access Memory (Random Access Memory, RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (Programmable Read-Only Memory, abbreviated PROM), an erasable PROM (Erasable Programmable Read-Only Memory, abbreviated EPROM), an electrically erasable PROM (Electrically Erasable Programmable Read-Only Memory, abbreviated EEPROM), an electrically rewritable ROM (Electrically Alterable Read-Only Memory, abbreviated EAROM), or a FLASH Memory (FLASH), or a combination of two or more of these. The RAM may be Static Random-Access Memory (SRAM) or dynamic Random-Access Memory (Dynamic Random Access Memory DRAM), where the DRAM may be a fast page mode dynamic Random-Access Memory (Fast Page Mode Dynamic Random Access Memory FPMDRAM), extended data output dynamic Random-Access Memory (Extended Date Out Dynamic Random Access Memory EDODRAM), synchronous dynamic Random-Access Memory (Synchronous Dynamic Random-Access Memory SDRAM), or the like, as appropriate.
Memory 42 may be used to store or cache various data files that need to be processed and/or communicated, as well as possible computer program instructions for execution by processor 41.
The processor 41 reads and executes the computer program instructions stored in the memory 42 to implement any of the grid operation data analysis methods based on big data and deep learning in the above embodiments.
In some of these embodiments, the computer device may also include a communication interface 43 and a bus 40. As shown in fig. 4, the processor 41, the memory 42, and the communication interface 43 are connected to each other through the bus 40 and perform communication with each other.
The communication interface 43 is used to enable communication between various modules, devices, units and/or units in embodiments of the application. The communication interface 43 may also enable communication with other components such as: and the external equipment, the image/data acquisition equipment, the database, the external storage, the image/data processing workstation and the like are used for data communication.
Bus 40 includes hardware, software, or both, that couple components of the computer device to one another. Bus 40 includes, but is not limited to, at least one of: data Bus (Data Bus), address Bus (Address Bus), control Bus (Control Bus), expansion Bus (Expansion Bus), local Bus (Local Bus). By way of example, and not limitation, bus 40 may include a graphics acceleration interface (Accelerated Graphics Port), AGP or other graphics Bus, an enhanced industry standard architecture (Extended Industry Standard Architecture, EISA) Bus, front Side Bus (FSB), hyperTransport (HT) interconnect, industry standard architecture (Industry Standard Architecture, ISA) Bus, radio bandwidth (InfiniBand) interconnect, low Pin Count (LPC) Bus, memory Bus, micro channel architecture (Micro Channel Architecture, MCa) Bus, peripheral component interconnect (Peripheral Component Interconnect, PCI) Bus, PCI-Express (PCI-X) Bus, serial advanced technology attachment (Serial Advanced Technology Attachment, SATA) Bus, video electronics standards association local (Video Electronics Standards Association Local Bus, VLB) Bus, or other suitable Bus, or a combination of two or more of these. Bus 40 may include one or more buses, where appropriate. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.
In addition, in combination with the grid operation data analysis method based on big data and deep learning in the above embodiments, the embodiments of the present application may provide a computer readable storage medium for implementation. The computer readable storage medium has stored thereon computer program instructions; the computer program instructions, when executed by the processor, implement any of the grid operation data analysis methods of the above embodiments based on big data and deep learning.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (10)
1. The utility model provides a power grid operation data analysis method based on big data and deep learning, which is characterized by comprising the following steps:
acquiring power grid operation data;
based on the power grid operation data, carrying out power grid operation abnormality detection by using an abnormality detection model of deep learning;
if the power grid operation abnormality is detected, determining a power grid operation abnormality event and an abnormality reason by using an abnormality diagnosis model of deep learning;
and outputting an exception solution based on the exception event and the exception cause.
2. The method of claim 1, wherein the anomaly detection model comprises a business classification model and a feature extraction model, wherein the grid operation anomaly detection using a deep-learned anomaly detection model based on the grid operation data comprises:
determining a target business class of the power grid operation data by utilizing the business classification model;
extracting first data features of the power grid operation data by using the feature extraction model;
querying a second data feature corresponding to the target service class from a mapping data table for storing a mapping relation between the service class and the data feature;
judging whether the first data characteristic is consistent with the second data characteristic;
If the first data characteristic is consistent with the second data characteristic, determining that the power grid is normal in operation;
and if the first data characteristic is not consistent with the second data characteristic, determining that the power grid is abnormal in operation.
3. The method of claim 2, wherein said determining whether the first data characteristic matches the second data characteristic comprises:
calculating semantic similarity between the first data feature and the second data feature;
judging whether the semantic similarity is larger than a target threshold value or not;
if the semantic similarity is greater than the target threshold, determining that the first data feature is consistent with the second data feature;
and if the semantic similarity is smaller than or equal to the target threshold, determining that the first data characteristic does not accord with the second data characteristic.
4. The method of claim 2, wherein determining grid operational anomalies and causes using a deep-learned anomaly diagnostic model if grid operational anomalies are detected comprises:
performing word segmentation processing on the first data characteristics to obtain a plurality of segmented words;
inputting the plurality of segmented words into the abnormality diagnosis model, wherein the abnormality diagnosis model outputs segmented words marked as abnormality;
And carrying out semantic combination on the segmented words marked as abnormal to obtain the abnormal event and the abnormal reason.
5. The method of claim 4, wherein the constructing of the abnormality diagnostic model includes:
collecting a training data set, wherein the training data set comprises: normal data and abnormal data;
respectively extracting the data characteristics of the normal data and the data characteristics of the abnormal data;
performing word segmentation on the data characteristics of the normal data to obtain a plurality of normal word segments, and performing word segmentation on the data characteristics of the abnormal data to obtain a plurality of abnormal word segments;
training the abnormality diagnosis model by using the plurality of normal words and the plurality of abnormal words, wherein the trained abnormality diagnosis model is used for outputting the abnormal words.
6. A grid operation data analysis device based on big data and deep learning, characterized by comprising:
the acquisition unit is used for acquiring power grid operation data;
the detection unit is used for detecting power grid operation abnormality by using an abnormality detection model of deep learning based on the power grid operation data;
the determining unit is used for determining an abnormal event and an abnormal reason of the power grid operation by using the deep learning abnormal diagnosis model if the abnormal operation of the power grid is detected;
And an output unit for outputting an abnormality solution based on the abnormality event and the abnormality cause.
7. The apparatus of claim 6, wherein the anomaly detection model comprises a traffic classification model and a feature extraction model, wherein the detection unit comprises:
the first determining module is used for determining a target service class of the power grid operation data by utilizing the service classification model;
the extraction module is used for extracting first data features of the power grid operation data by utilizing the feature extraction model;
the query module is used for querying a second data characteristic corresponding to the target service class from a mapping data table for storing the mapping relation between the service class and the data characteristic;
the judging module is used for judging whether the first data characteristic is consistent with the second data characteristic;
the second determining module is used for determining that the power grid runs normally if the first data characteristic accords with the second data characteristic;
and the third determining module is used for determining that the power grid runs abnormally if the first data characteristic is not consistent with the second data characteristic.
8. The apparatus of claim 7, wherein the determining module comprises:
A computing sub-module for computing semantic similarity between the first data feature and the second data feature;
the judging submodule is used for judging whether the semantic similarity is larger than a target threshold value or not;
a first determining sub-module configured to determine that the first data feature matches the second data feature if the semantic similarity is greater than the target threshold;
and the second determining submodule is used for determining that the first data characteristic does not accord with the second data characteristic if the semantic similarity is smaller than or equal to the target threshold value.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 5 when executing the computer program.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any one of claims 1 to 5.
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