CN117828486A - Fault power failure early warning method, device, computer equipment and storage medium - Google Patents
Fault power failure early warning method, device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a fault power failure early warning method, a device, computer equipment and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: obtaining metrology data from a data collection platform; the measurement data comprise different types of signals and attribute data corresponding to the signals; identifying a fault power failure signal in the measurement data through a decision tree model; and determining the power equipment corresponding to the fault power failure signal according to the attribute data corresponding to the fault power failure signal, and carrying out early warning on the power equipment. By adopting the method, measurement data are uniformly acquired from the data collection platform, timely synchronization of fault power failure signals is realized, the fault power failure signals are further identified through the decision tree model, the fault power failure signals can be quickly and accurately identified, confusion with power failure signals of planned power failure is avoided, the overall efficiency of fault power failure identification is improved, and the early warning efficiency of corresponding power equipment is improved.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a fault power failure early warning method, a device, a computer device, and a storage medium.
Background
With the rapid development of economy and the continuous upgrading of industry, electric power is one of the most important energy supply modes, and once a power failure event occurs, the production and life of people can be seriously affected. However, a power outage caused by a power utility fault is unavoidable, which requires the utility company to timely perceive the outage event.
At present, when a power failure signal caused by a power facility fault is identified, the power failure signal is easily confused with a power failure signal of a planned power failure, and misjudgment is generated, so that the power company cannot identify a power failure event in time, and the repair and recovery work cannot be carried out at the first time.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a fault outage early warning method, device, computer device and storage medium capable of quickly and accurately identifying a fault outage signal.
In a first aspect, the present application provides a fault outage early warning method, where the method includes:
obtaining metrology data from a data collection platform; the measurement data comprise different types of signals and attribute data corresponding to the signals;
identifying a fault power failure signal in the measurement data through a decision tree model;
and determining the power equipment corresponding to the fault power failure signal according to the attribute data corresponding to the fault power failure signal, and carrying out early warning on the power equipment.
In one embodiment, identifying a fault outage signal in metrology data through a decision tree model includes:
identifying a planned outage signal and an unplanned outage signal in the measurement data through a decision tree model;
and identifying a fault power failure signal in the unplanned power failure signals through the decision tree model.
In one embodiment, identifying planned outage signals and unplanned outage signals in metrology data includes:
matching attribute data corresponding to each signal in the measurement data with preset attribute data in the battery stopping device;
and taking the signals corresponding to the attribute data matched in the measurement data as planned outage signals, and taking the signals corresponding to the attribute data which are not matched as unplanned outage signals.
In one embodiment, identifying a fault outage signal in an unplanned outage signal includes:
determining whether acquisition equipment corresponding to the unplanned power failure signal can acquire the signal at present;
if yes, the unplanned power failure signal is taken as a fault power failure signal.
In one embodiment, the method further comprises:
determining whether the acquisition equipment corresponding to the fault power failure signal can acquire a current signal and/or a voltage signal at present;
if not, determining that the power equipment corresponding to the fault power failure signal has a fault power failure event.
In one embodiment, the pre-warning of the power device includes:
determining an associated user of the power device;
and sending fault power failure early warning information to terminal equipment held by the associated user.
In one embodiment, prior to obtaining metrology data from the data collection platform, the method further comprises:
obtaining measurement data from a metering system through Kafka, and preprocessing the measurement data;
and storing the preprocessed measurement data in a data collection platform.
In a second aspect, the present application further provides a fault power outage early warning device, the device including:
the acquisition module is used for acquiring measurement data from the data collection platform; the measurement data comprise different types of signals and attribute data corresponding to the signals;
the identification module is used for identifying fault power failure signals in the measurement data through the decision tree model;
and the early warning module is used for determining the power equipment corresponding to the fault power failure signal according to the attribute data corresponding to the fault power failure signal and carrying out early warning on the power equipment.
In a third aspect, the present application also provides a computer device comprising a memory storing a computer program and a processor implementing the steps of the method of the first aspect described above when the computer program is executed by the processor.
In a fourth aspect, the present application also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of the first aspect described above.
In a fifth aspect, the present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the method of the first aspect described above.
The fault power failure early warning method, the fault power failure early warning device, the computer equipment and the storage medium acquire measurement data from the data collection platform; the measurement data comprise different types of signals and attribute data corresponding to the signals; identifying a fault power failure signal in the measurement data through a decision tree model; and determining the power equipment corresponding to the fault power failure signal according to the attribute data corresponding to the fault power failure signal, and carrying out early warning on the power equipment. According to the method and the system, measurement data are uniformly acquired from the data collection platform, timely synchronization of fault power failure signals is achieved, then the fault power failure signals are identified through the decision tree model, the fault power failure signals can be quickly and accurately identified, confusion with power failure signals of planned power failure is avoided, overall efficiency of fault power failure identification is improved, and early warning efficiency of corresponding power equipment is improved.
Drawings
FIG. 1 is an application scenario diagram of a fault outage early warning method in one embodiment;
FIG. 2 is a flow chart of a fault outage early warning method according to an embodiment;
FIG. 3 is a flow diagram of identifying a fault power failure signal in one embodiment;
FIG. 4 is a flow chart of sending fault outage warning information in one embodiment;
FIG. 5 is a flow chart illustrating a method for storing metrology data in accordance with one embodiment;
FIG. 6 is a flow chart of a fault outage early warning method according to another embodiment;
FIG. 7 is a block diagram illustrating a fault outage warning device according to one embodiment;
FIG. 8 is a block diagram illustrating a fault outage warning apparatus according to another embodiment;
FIG. 9 is a block diagram of a fault outage warning device according to yet another embodiment;
FIG. 10 is a block diagram of a fault outage warning device according to yet another embodiment;
FIG. 11 is a block diagram of a computer device implementing a fault outage early warning method according to one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail 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.
The fault power failure early warning method provided by the embodiment of the application can be applied to an application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data, such as metrology data, that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. The server 104 obtains metrology data from the data collection platform; identifying a fault power failure signal in the measurement data through a decision tree model; determining power equipment corresponding to the fault power failure signal according to attribute data corresponding to the fault power failure signal, and carrying out early warning on the power equipment; the measurement data comprises different types of signals and attribute data corresponding to the signals. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices, and portable wearable devices, where the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart vehicle devices, and the like. The portable wearable device may be a smart watch, smart bracelet, headset, or the like. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.
In one embodiment, as shown in fig. 2, a fault power failure early warning method is provided, which includes the following steps:
s201, measuring data are obtained from a data collection platform.
The measurement data comprises different types of signals and attribute data corresponding to the signals. For example, the metrology data may include fault outage signals, planned outage signals, loss of voltage data, loss of current data, voltage out-of-limit data, and the like. Specifically, for the fault outage signal and the planned outage signal, the corresponding attribute data may include at least one of outage time, outage associated line, associated user, and predicted double-power time.
The data collection platform is used for collecting various measurement data from a metering system in the electric power field in real time so as to directly obtain the various measurement data when the server executes a fault power failure early warning task, thereby ensuring that the measurement data collected by a plurality of collection devices are synchronized in time.
S202, identifying fault power failure signals in the measurement data through a decision tree model.
The decision tree model is realized through a decision tree algorithm, and the decision tree algorithm is a classification processing algorithm in the form of a tree structure, such as a binary tree and a multi-way tree. Specifically, the decision tree model is composed of nodes and branches, the nodes are divided into root nodes, internal nodes and leaf nodes, recursion is performed from the root nodes to the leaf nodes, and classification is performed at each internal node.
The fault power failure signal refers to a signal caused by a fault of an electric device, and the electric device can be a device in the electric power field such as a transformer, and optionally, the electric device can be further extended to be a facility in the electric power field such as a power transmission line.
And pre-building a decision tree model in the electric power field according to different types of signals in each measurement data and attribute data corresponding to each signal, so that a server can identify a fault power failure signal directly through the pre-built decision tree model.
For example, each item of measurement data is input into the decision tree model in a unified way, that is, each item of measurement data is used as a root node in the decision tree model, and classified through each internal node until the leaf node corresponding to each item of measurement data is determined, that is, the type corresponding to each item of measurement data is determined. The internal node may be preset to determine whether a power outage is planned, whether a device fault is collected, whether a power outage has occurred due to a power device fault, and the like. Generally, the type corresponding to each measurement data can be understood as the type of signal in each measurement data.
And S203, determining the power equipment corresponding to the fault power failure signal according to the attribute data corresponding to the fault power failure signal, and carrying out early warning on the power equipment.
Under the condition that the attribute data corresponding to the fault power failure signal comprises the associated power equipment, the power equipment corresponding to the fault power failure signal can be directly determined; when the attribute data corresponding to the fault power failure signal does not include the associated power equipment, the power equipment corresponding to the fault power failure signal can be indirectly determined according to other data in the attribute data, such as a power failure associated line and the like.
Further, the power equipment is pre-warned. For example, a repair notification is sent to a repair person in charge of the power equipment, so that repair recovery work of the power equipment is performed at the first time.
According to the scheme, measurement data are obtained from the data collection platform; the measurement data comprise different types of signals and attribute data corresponding to the signals; identifying a fault power failure signal in the measurement data through a decision tree model; and determining the power equipment corresponding to the fault power failure signal according to the attribute data corresponding to the fault power failure signal, and carrying out early warning on the power equipment. According to the embodiment, the measurement data are uniformly acquired from the data collection platform, so that timely synchronization of fault power failure signals is realized, the fault power failure signals are further identified through the decision tree model, the fault power failure signals can be quickly and accurately identified, confusion with power failure signals of planned power failure is avoided, the overall efficiency of fault power failure identification is improved, and the early warning efficiency of corresponding power equipment is improved.
In order to quickly and accurately identify the outage signal, in one embodiment, the planned outage signal may be identified first, and then the outage signal may be identified, as shown in fig. 3, based on the above embodiment, S202 includes:
s301, identifying a planned outage signal and an unplanned outage signal in the measurement data through a decision tree model.
The planned power outage signal is a signal caused by planned power outage, and it can be understood that the planned power outage is made during daily maintenance operation and power grid upgrading and reconstruction of power equipment in order to ensure safe and reliable operation of the power grid. Accordingly, the unplanned power outage signal is distinguished from the planned power outage signal, and refers to a signal that is not caused by a planned power outage.
Because the planned outage is determined manually, the attribute data corresponding to the planned outage signal can be uploaded to the server when each planned outage occurs, so that the server can identify the planned outage signal from various measurement data according to the attribute data corresponding to the planned outage signal through the decision tree model.
Optionally, the uppermost internal node in the decision tree model is used for classifying the planned outage signal and the unplanned outage signal.
In an alternative embodiment, matching attribute data corresponding to each signal in the measurement data with attribute data in a preset battery stopping state; and taking the signals corresponding to the attribute data matched in the measurement data as planned outage signals, and taking the signals corresponding to the attribute data which are not matched as unplanned outage signals.
For example, when each power outage is planned, the attribute data corresponding to the power outage planning signal, such as the power outage time, the power outage related line, the related user, the predicted power restoration time and the like, are uploaded to the server for the server to store in a preset battery outage. Further, when the fault power failure early warning task is executed, attribute data contained in each item of measurement data is directly compared with attribute data stored in advance in a battery which is shut down, if the comparison is successful, that is, matched attribute data exists, it is indicated that a signal corresponding to the matched attribute data is caused by planned power failure, the signal is a planned power failure signal, otherwise, a signal corresponding to the unmatched attribute data is an unplanned power failure signal.
Optionally, when a plurality of items of attribute data corresponding to one signal exist, each item of attribute data is matched, and the corresponding signal is used as the planned power failure signal.
Optionally, the priority of each item of attribute data is preset, when a plurality of items of attribute data corresponding to one signal exist, matching is performed according to the priority, attribute data with high priority corresponds to higher weight, the overall matching degree of each item of attribute data is calculated through weighting, the overall matching degree is compared with a matching degree threshold, and a planned outage signal and an unplanned outage signal are identified according to a comparison result.
S302, identifying a fault power failure signal in the unplanned power failure signals through a decision tree model.
And the server identifies the fault power failure signal from all the unplanned power failure signals according to the attribute data corresponding to the unplanned power failure signals through the decision tree model.
Optionally, the internal nodes at the lower layer in the decision tree model are used for classifying fault outage signals.
In an alternative embodiment, determining whether the acquisition device corresponding to the unplanned power outage signal is currently capable of acquiring the signal; if yes, the unplanned power failure signal is taken as a fault power failure signal.
The acquisition equipment corresponding to the unplanned power failure signal refers to metering equipment for acquiring the unplanned power failure signal.
When the server operates the decision tree model to identify an internal node of a fault power failure signal in the unplanned power failure signal, a signal which is currently acquired by acquisition equipment and corresponds to the unplanned power failure signal is acquired in real time, if a related signal can be acquired, the acquisition equipment which corresponds to the unplanned power failure signal is determined to be capable of acquiring the signal currently, namely, the acquisition equipment operates normally, the unplanned power failure signal is not caused by the fault of the acquisition equipment but caused by the fault of the power equipment, and the unplanned power failure signal is used as the fault power failure signal.
Optionally, if the related signal cannot be called, it is determined that the acquisition device corresponding to the unplanned power failure signal cannot acquire the signal currently, that is, the acquisition device itself operates abnormally, the unplanned power failure signal is caused by the failure of the acquisition device, and the unplanned power failure signal is used as the unplanned power failure signal.
In this embodiment, the planned outage signal is identified through the decision tree model, and then the fault outage signal is identified from other measurement data, so that the planned outage signal and the fault outage signal can be distinguished more accurately, misjudgment is avoided, meanwhile, the newly built decision tree model is more attached to the characteristics of various measurement data in the power field, and the fault outage signal can be identified more rapidly, so that the overall efficiency of fault outage identification is improved.
As an optional implementation manner in this embodiment, it is determined whether the collection device corresponding to the fault power failure signal can collect the current signal and/or the voltage signal currently; if not, determining that the power equipment corresponding to the fault power failure signal has a fault power failure event.
The lowest internal node in the decision tree model is used for classifying the power equipment failure event and the power equipment failure event.
And when the server runs the decision tree model to identify whether the power equipment corresponding to the fault power failure signal has the internal node of the fault power failure event, the server invokes the current collected signal of the collection equipment corresponding to the fault power failure signal in real time, and if the current signal and/or the voltage signal cannot be invoked, the power equipment corresponding to the fault power failure signal is determined to have the fault power failure event.
Therefore, the accuracy of identifying the fault stop signal is ensured by further judging the fault power failure signal.
In order to timely perform the fault outage early warning, in one embodiment, as shown in fig. 4, the step S203 includes:
s401, determining an associated user of the power equipment.
The associated user of the power equipment can be the user of the power failure area caused by the power equipment failure, and also can be the rush repair personnel responsible for the power equipment.
Optionally, the associated user of the power equipment is obtained from the data processing platform, or the associated user corresponding to the fault power failure signal is directly obtained from the attribute data corresponding to the fault power failure signal.
S402, fault power failure early warning information is sent to terminal equipment held by an associated user.
The fault power failure early warning information can comprise information such as power failure time, expected power restoration time and the like, and optionally, the fault power failure early warning information is generated according to attribute information corresponding to a fault power failure signal. The fault power failure early warning information can also comprise information such as a power failure influence range, a power failure reason and the like.
The server sends fault power failure early warning information to the terminal equipment held by the relevant user in a short message mode and the like so as to inform the user in the power failure area, achieve the purpose of soothing the emotion of the user, inform emergency repair personnel, timely repair the power facilities with faults and assist in guiding the emergency repair operation.
Optionally, the server uploads the real-time progress of the rush-repair to an application in the electric power field, so that a user accesses the application to inquire the real-time progress of the rush-repair.
In this embodiment, in time carry out trouble outage early warning to different associated users, avoid bringing discontentment for the user in power failure area because of the trouble outage, help the rush-repair personnel to carry out the rush-repair to the electric power facility that breaks down simultaneously the first time, ensure the normal operating of people's production life.
In order to quickly identify the fault outage signal, in an embodiment, measurement data collected by different areas and different types of collection devices may be synchronized through a data collection platform, as shown in fig. 5, and before S201, the fault outage early warning method may further include:
s501, measuring data are acquired from a metering system through Kafka, and the measuring data are preprocessed.
Wherein Kafka is a message middleware for timely synchronizing various measurement data. The Kafka has higher throughput compared with other message middleware, can process PB (byte) level data, has lower delay, and is suitable for measuring data with larger synchronous data quantity.
And extracting each measurement data from the metering system through Kafka, and further preprocessing each measurement data. The preprocessing comprises data cleaning, missing value processing, outlier processing and normalization processing.
Specifically, the data cleaning refers to removing repeated and erroneous measurement data; the missing value processing can be realized by interpolation or deletion and the like; outlier processing may be based on statistical methods or machine learning methods to detect and repair outliers in each item of metrology data; the normalization process refers to converting each measurement data into a unified measurement range, such as a 0-1 interval.
For example, the Z-Score method is used for outlier processing, and the minimum normalization MinMaxScaler method is used for normalization processing to ensure the reliability of each measurement data.
S502, the preprocessed measurement data is stored in a data collection platform.
Specifically, the data collection platform stores the preprocessed measurement data, performs data encryption, backup and recovery on each preprocessed measurement data, and performs access control on each preprocessed measurement data, and when the server acquires the measurement data from the data collection platform, the server needs to have corresponding access control authority, so that the data security of the measurement data is ensured.
In this embodiment, the accuracy of the fault power failure signal of the subsequent identification can be improved by preprocessing the measurement data, and each item of measurement data is timely synchronized through the data collection platform, so that the efficiency of the fault power failure signal of the subsequent identification can be ensured, and the early warning efficiency of the corresponding power equipment is improved.
In one embodiment, a training step of a decision tree model is provided, specifically comprising:
historical metrology data is collected, for example, metrology data for the past three months. After preprocessing the historical measurement data, a training set is generated from 80% of the measurement data, and a test set is generated from the other 20% of the measurement data.
Further, training the initial decision tree model by using a training set, and testing the trained decision tree model by using a testing set. For example, the calculation performance and accuracy of the trained decision tree model are verified by adopting methods such as cross verification or performance index verification, wherein the cross verification can be 5-fold cross verification, and the performance index verification can be realized by calculating data such as mean square error, average absolute error and the like.
In this embodiment, the model parameters are continuously adjusted by comparing the recognition result of the decision tree model with the actual result, so as to improve the recognition accuracy of the decision tree model.
In one embodiment, an alternative example of a fault outage early warning method is provided, as shown in fig. 6, the fault outage early warning method includes the following steps:
s601, measuring data are acquired from a metering system through Kafka, and the measuring data are preprocessed.
S602, the preprocessed measurement data is stored in a data collection platform.
S603, obtaining measurement data from the data collection platform.
The measurement data comprises different types of signals and attribute data corresponding to the signals.
S604, matching the attribute data corresponding to each signal in the measurement data with the attribute data in the preset battery stopping.
S605, signals corresponding to the matched attribute data in the measurement data are taken as planned outage signals, and signals corresponding to the unmatched attribute data are taken as unplanned outage signals.
And S606, when the condition that the acquisition equipment corresponding to the unplanned power failure signal can acquire the signal currently is determined, the unplanned power failure signal is taken as a fault power failure signal.
Optionally, determining whether the acquisition equipment corresponding to the fault power failure signal can acquire the current signal and/or the voltage signal at present; if not, determining that the power equipment corresponding to the fault power failure signal has a fault power failure event.
S607, determining the power equipment corresponding to the fault power failure signal and the associated user of the power equipment according to the attribute data corresponding to the fault power failure signal.
And S608, fault power failure early warning information is sent to the terminal equipment held by the associated user.
The specific process of the above steps may refer to the description of the above method embodiments, and its implementation principle and technical effects are similar, and are not repeated herein.
It should be understood that, although the steps in the flowcharts related to the above embodiments are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a fault power failure early warning device for realizing the fault power failure early warning method. The implementation scheme of the device for solving the problem is similar to that described in the above method, so the specific limitation of one or more embodiments of the fault outage early warning device provided below can be referred to the limitation of the fault outage early warning method above, and will not be repeated here.
In one embodiment, as shown in fig. 7, there is provided a fault outage early warning device 1, including an acquisition module 10, an identification module 20 and an early warning module 30, wherein:
an acquisition module 10 for acquiring metrology data from the data collection platform.
The measurement data comprises different types of signals and attribute data corresponding to the signals.
The identifying module 20 is configured to identify the fault outage signal in the measurement data through the decision tree model.
And the early warning module 30 is used for determining the power equipment corresponding to the fault power failure signal according to the attribute data corresponding to the fault power failure signal and carrying out early warning on the power equipment.
In one embodiment, on the basis of fig. 7, as shown in fig. 8, the identification module 20 may include:
the first identifying unit 21 is configured to identify the planned outage signal and the unplanned outage signal in the measurement data by using the decision tree model.
The second identifying unit 22 is configured to identify, by using the decision tree model, a fault outage signal among the unplanned outage signals.
In one embodiment, the first identifying unit 21 may include:
and the matching subunit is used for matching the attribute data corresponding to each signal in the measurement data with the attribute data in the preset battery stopping.
The first recognition subunit is configured to take a signal corresponding to the attribute data that is matched in the measurement data as a planned outage signal, and take a signal corresponding to the attribute data that is not matched as an unplanned outage signal.
In one embodiment, the second identifying unit 22 may include:
and the judging subunit is used for determining whether the acquisition equipment corresponding to the unplanned power failure signal can acquire the signal currently.
And the second identification subunit is used for taking the unplanned power failure signal as a fault power failure signal under the condition that the acquisition equipment corresponding to the unplanned power failure signal can acquire the signal currently.
In one embodiment, the fault outage early warning apparatus 1 may further include:
the event judging module is used for determining whether the acquisition equipment corresponding to the fault power failure signal can acquire the current signal and/or the voltage signal currently, and determining that the power equipment corresponding to the fault power failure signal has a fault power failure event under the condition that the acquisition equipment corresponding to the fault power failure signal cannot acquire the current signal and/or the voltage signal currently.
In one embodiment, on the basis of fig. 7, as shown in fig. 9, the early warning module 30 may include:
the user determination unit 31 is configured to determine an associated user of the power device.
And the early warning unit 32 is used for sending fault power failure early warning information to the terminal equipment held by the associated user.
In one embodiment, on the basis of fig. 7, as shown in fig. 10, the fault outage early warning device 1 may further include:
the preprocessing module 40 is configured to acquire measurement data from the metrology system through Kafka, and perform preprocessing on the measurement data.
The storage module 50 is configured to store the preprocessed metrology data in the data collection platform.
All or part of each module in the fault power failure early warning device can be realized by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 11. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used to store metrology data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by the processor, implements a fault outage early warning method.
It will be appreciated by those skilled in the art that the structure shown in fig. 11 is merely a block diagram of a portion of the structure associated with the present application and is not limiting of the computer device to which the present application applies, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided, including a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the fault outage early warning method described above when the computer program is executed.
In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, which when executed by a processor implements the steps of the fault outage warning method described above.
In one embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the fault outage warning method described above.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above 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 foregoing examples represent only a few embodiments of the present application, which are described in more detail and are not thereby to be construed as limiting the scope of the present application. 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 shall be subject to the appended claims.
Claims (10)
1. The fault power failure early warning method is characterized by comprising the following steps of:
obtaining metrology data from a data collection platform; the measurement data comprise signals of different types and attribute data corresponding to the signals;
identifying a fault power failure signal in the measurement data through a decision tree model;
and determining the power equipment corresponding to the fault power failure signal according to the attribute data corresponding to the fault power failure signal, and carrying out early warning on the power equipment.
2. The method of claim 1, wherein identifying a fault outage signal in the metrology data via a decision tree model comprises:
identifying a planned outage signal and an unplanned outage signal in the measurement data through a decision tree model;
and identifying a fault power failure signal in the unplanned power failure signal through the decision tree model.
3. The method of claim 2, wherein the identifying planned outages and unplanned outages in the metrology data comprises:
matching attribute data corresponding to each signal in the measurement data with preset attribute data in a battery stopping state;
and taking the signals corresponding to the attribute data matched in the measurement data as planned outage signals, and taking the signals corresponding to the attribute data which are not matched as unplanned outage signals.
4. The method of claim 2, wherein the identifying a fault outage signal in the unplanned outage signal comprises:
determining whether the acquisition equipment corresponding to the unplanned power failure signal can acquire a signal currently;
if yes, the unplanned power failure signal is used as a fault power failure signal.
5. The method according to claim 4, wherein the method further comprises:
determining whether the acquisition equipment corresponding to the fault power failure signal can acquire a current signal and/or a voltage signal at present;
if not, determining that the power equipment corresponding to the fault power failure signal has a fault power failure event.
6. The method of claim 1, wherein the pre-warning the electrical device comprises:
determining an associated user of the power device;
and sending fault power failure early warning information to terminal equipment held by the associated user.
7. The method of claim 1, wherein prior to obtaining metrology data from a data collection platform, the method further comprises:
obtaining measurement data from a metering system through a Kafka, and preprocessing the measurement data;
and storing the preprocessed measurement data in the data collection platform.
8. A fault power outage early warning device, the device comprising:
the acquisition module is used for acquiring measurement data from the data collection platform; the measurement data comprise signals of different types and attribute data corresponding to the signals;
the identification module is used for identifying fault power failure signals in the measurement data through a decision tree model;
and the early warning module is used for determining the power equipment corresponding to the fault power failure signal according to the attribute data corresponding to the fault power failure signal and carrying out early warning on the power equipment.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1-7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1-7.
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