CN112434079A - Secondary equipment abnormity discrimination decision method and device based on big data - Google Patents
Secondary equipment abnormity discrimination decision method and device based on big data Download PDFInfo
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Abstract
The invention relates to a secondary equipment abnormity discrimination decision method and a device based on big data, wherein the method comprises the following steps: acquiring alarm information of secondary equipment, and analyzing digitalized abnormal information contained in the alarm information; calling an abnormality judgment expert library, inputting the digital abnormal information into the abnormality judgment expert library, and paraphrasing the digital abnormal information by the abnormality judgment expert library to obtain paraphrasing information; traversing the abnormality judgment expert base, and finding out an abnormality processing strategy corresponding to the paraphrasing information so as to realize an abnormality judgment decision of the secondary equipment; the exception handling strategy comprises an exception reason and an exception handling method. The invention solves the problems that the failure reason of the secondary equipment cannot be quickly analyzed and the failure solving time is long at present.
Description
Technical Field
The invention relates to the technical field of intelligent substations, in particular to a secondary equipment abnormity discrimination decision method, a device, equipment and a storage medium based on big data.
Background
With the large-scale operation of intelligent substations in recent years, the proportion of intelligent substations in the whole power grid system is also increasing, and this will become a mainstream trend of development of the future power grid system. In an intelligent substation, a digital communication transmission mode represented by Ethernet replaces a traditional hard-wired loop, and the mode has the main advantages that: all transmission signals can be exchanged and shared.
At present, an intelligent substation which is continuously developed and built has a three-layer two-network structure and consists of a plurality of secondary devices (such as an intelligent terminal, a merging unit, a relay protection device, a measurement and control device, a wave recording device and other secondary devices), when a fault occurs, a fault reason is generally analyzed by a running and inspection person according to own experience, a specific abnormal processing method is formulated according to the fault reason, so that the time for solving the fault is long, and the workload of a worker is increased.
Disclosure of Invention
In view of the above, it is necessary to provide a secondary device abnormality determination method, apparatus, device and storage medium based on big data, so as to solve the problem that the failure cause of the secondary device cannot be analyzed quickly and the failure resolution time is long.
In a first aspect, the invention provides a secondary equipment abnormity discrimination decision method based on big data, which comprises the following steps:
acquiring alarm information of secondary equipment, and analyzing digitalized abnormal information contained in the alarm information;
calling an abnormality judgment expert library, inputting the digital abnormal information into the abnormality judgment expert library, and paraphrasing the digital abnormal information by the abnormality judgment expert library to obtain paraphrasing information;
traversing the abnormality judgment expert base, and finding out an abnormality processing strategy corresponding to the paraphrasing information so as to realize an abnormality judgment decision of the secondary equipment; the exception handling strategy comprises an exception reason and an exception handling method.
Preferably, in the secondary equipment abnormality judgment and decision method based on big data, the abnormality judgment expert database at least includes the following data: the system comprises digitalized information, paraphrase information corresponding to the digitalized information one by one, and exception handling strategies corresponding to the paraphrase information one by one.
Preferably, in the secondary equipment abnormality judgment and decision method based on big data, the paraphrase information at least includes a fault name, a fault equipment, a fault type and a fault location.
Preferably, in the secondary equipment abnormality judgment and decision method based on big data, the step of calling an abnormality judgment expert library, inputting the digitized abnormality information into the abnormality judgment expert library, and paraphrasing the digitized abnormality information by the abnormality judgment expert library to obtain paraphrase information includes:
calling an abnormality judgment expert library, and inputting the digital abnormality information into the abnormality judgment expert library;
traversing the digital information prestored in the abnormal judgment expert library until the digital information corresponding to the digital abnormal information is found;
and calling out paraphrase information corresponding to the found digital information to obtain paraphrase information of the digital abnormal information.
Preferably, in the secondary equipment abnormality judgment decision method based on big data, the specific establishment method of the abnormality judgment expert database is as follows:
acquiring the digital information of the existing fault handling case, establishing the corresponding relation between the digital information and the paraphrase information by combining with the operation rule knowledge base, and establishing the corresponding relation between the paraphrase information and the exception handling strategy according to the exception handling strategy in the existing fault handling case.
In a second aspect, the present invention further provides a secondary device anomaly decision device based on big data, including:
the digital abnormal information acquisition module is used for acquiring the alarm information of the secondary equipment and analyzing the digital abnormal information contained in the alarm information;
the paraphrase information acquisition module is used for calling an abnormality judgment expert library, inputting the digital abnormal information into the abnormality judgment expert library, and performing paraphrase on the digital abnormal information by the abnormality judgment expert library to obtain paraphrase information;
the abnormity discrimination decision module is used for traversing the abnormity discrimination expert database and finding out an abnormity processing strategy corresponding to the paraphrase information so as to realize abnormity discrimination decision of the secondary equipment; the exception handling strategy comprises an exception reason and an exception handling method.
Preferably, in the secondary equipment abnormality judgment and decision device based on big data, the abnormality judgment expert database at least includes the following data: the system comprises digitalized information, paraphrase information corresponding to the digitalized information one by one, and exception handling strategies corresponding to the paraphrase information one by one.
Preferably, in the secondary equipment abnormality judgment and decision device based on big data, the paraphrase information at least includes a fault name, a fault equipment, a fault type, and a fault location.
Preferably, in the secondary device abnormality judgment and decision device based on big data, the paraphrase information obtaining module is specifically configured to:
calling an abnormality judgment expert library, and inputting the digital abnormality information into the abnormality judgment expert library;
traversing the digital information prestored in the abnormal judgment expert library until the digital information corresponding to the digital abnormal information is found;
and calling out paraphrase information corresponding to the found digital information to obtain paraphrase information of the digital abnormal information.
In a third aspect, the present invention further provides a secondary device anomaly decision device based on big data, including: a processor and a memory;
the memory has stored thereon a computer readable program executable by the processor;
the processor, when executing the computer readable program, implements the steps in the big data based secondary device anomaly decision method as described above.
In a fourth aspect, the present invention also provides a computer-readable storage medium, where one or more programs are stored, and the one or more programs are executable by one or more processors to implement the steps in the big data based secondary device anomaly decision method as described above.
[ PROBLEMS ] the present invention
According to the secondary equipment abnormity discrimination decision method, the device, the equipment and the storage medium based on the big data, the digitalized abnormity information is extracted from the alarm information, and then the digitalized abnormity information is directly input into the abnormity discrimination expert base, so that an abnormity processing strategy corresponding to the digitalized abnormity information can be directly obtained from the abnormity discrimination expert base, reference is provided for overhaul of maintainers, the working intensity of the maintainers is reduced, and the working efficiency is improved.
Drawings
FIG. 1 is a flowchart of a secondary device anomaly decision method based on big data according to a preferred embodiment of the present invention;
FIG. 2 is a schematic diagram of an operating environment of a secondary device anomaly decision-making process based on big data according to a preferred embodiment of the present invention.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
Referring to fig. 1, a secondary device abnormality judgment decision method based on big data according to an embodiment of the present invention includes the following steps:
s100, acquiring alarm information of the secondary equipment, and analyzing digitalized abnormal information contained in the alarm information.
Specifically, when the secondary device fails, the secondary device automatically sends out alarm information, the alarm information is contained in information such as self-check information, time setting state information, alarm information obtained by monitoring an MMS message, a GOOSE message and an SV message in a communication network, and the like of the secondary device, the alarm information contains digitized abnormal information, the digitized abnormal information can be one section or multiple sections of abnormal codes, the abnormal codes are defined before the system is established, and each section of abnormal code has unique paraphrasing information such as a fault name, fault equipment, a fault type, a fault location and the like, so that the invention can directly make an abnormality judgment decision according to the digitized abnormal information.
S200, calling an abnormality judgment expert library, inputting the digital abnormal information into the abnormality judgment expert library, and paraphrasing the digital abnormal information by the abnormality judgment expert library to obtain paraphrasing information.
Specifically, the abnormality expert database is established according to the processed faults and is updated in real time, and the relevant information of fault processing is stored in the abnormality expert database every time the fault is processed, so as to perfect the function of the abnormality expert database. Specifically, the abnormality judgment expert database at least includes the following data: the system comprises digitalized information, paraphrase information corresponding to the digitalized information one by one, and exception handling strategies corresponding to the paraphrase information one by one. The paraphrase information at least comprises a fault name, fault equipment, a fault type and a fault position. In specific implementation, the method for establishing the abnormality judgment expert database comprises the following steps:
acquiring the digital information of the existing fault handling case, establishing the corresponding relation between the digital information and the paraphrase information by combining with the operation rule knowledge base, and establishing the corresponding relation between the paraphrase information and the exception handling strategy according to the exception handling strategy in the existing fault handling case.
Specifically, the operation rule knowledge base is a series of rules (such as code rules and the like) preset by a worker before the system is established, and is a basis for judging an abnormality, each piece of different digital information sent out in the system has unique paraphrase information, so that a corresponding relation between the digital information and the paraphrase information can be established, namely a first decision tree, the digital information and the paraphrase information are in one-to-one correspondence, so that the digital information is input, the paraphrase information corresponding to the digital information can be quickly found, and an abnormality processing strategy corresponding to the paraphrase information is an abnormality reason and an abnormality processing method analyzed by the worker after the abnormality processing, after the worker finishes the fault processing, the abnormality reason and the abnormality processing method corresponding to the paraphrase information are stored and recorded and then input into an abnormality judgment expert base, so that the abnormality judgment expert base has a second decision tree, the second decision tree is a corresponding relation tree of paraphrase information and an exception handling strategy. It should be noted that one exception handling policy may correspond to a plurality of paraphrase information, but each paraphrase information may correspond to only one digitized information.
After the establishment of the abnormality judgment expert database is completed, the subsequent abnormality judgment can quickly find out paraphrase information corresponding to the digitized abnormality information by inputting the digitized abnormality information into the abnormality judgment expert database for identification, specifically, the step S200 specifically includes:
calling an abnormality judgment expert library, and inputting the digital abnormality information into the abnormality judgment expert library;
traversing the digital information prestored in the abnormal judgment expert library until the digital information corresponding to the digital abnormal information is found;
and calling out paraphrase information corresponding to the found digital information to obtain paraphrase information of the digital abnormal information.
In other words, when digital abnormal information is input, the abnormal judgment expert base is quickly traversed to find the digital information consistent with the abnormal judgment expert base, and the paraphrase information corresponding to the digital information can be quickly found from the first decision tree.
S300, traversing the abnormality judgment expert base, and finding out an abnormality processing strategy corresponding to the paraphrase information so as to realize an abnormality judgment decision of the secondary equipment; the exception handling strategy comprises an exception reason and an exception handling method.
Specifically, after the paraphrase information is found, the exception handling strategy corresponding to the paraphrase information can be quickly found from the second decision tree, and then the exception handling strategy is displayed to the user, so that the user can quickly perform exception handling according to the exception handling strategy. When the exception handling strategy consistent with the paraphrase information is not found, the system prompts a user to handle exception by himself at the moment. After the fault is processed, all information of the fault is recorded into an abnormality judgment expert library by a user, and an abnormality judgment decision strategy can be quickly obtained when the fault occurs next time.
The invention extracts the digital abnormal information from the warning information, then directly inputs the digital abnormal information into the abnormal judgment expert database, and can directly obtain the abnormal processing strategy corresponding to the digital abnormal information from the abnormal judgment expert database, thereby providing reference for the overhaul of maintainers, reducing the working strength of the maintainers and improving the working efficiency.
Based on the above secondary equipment abnormity discrimination decision method based on big data, the invention also correspondingly provides a secondary equipment abnormity discrimination decision device based on big data, which comprises:
the digital abnormal information acquisition module is used for acquiring the alarm information of the secondary equipment and analyzing the digital abnormal information contained in the alarm information;
the paraphrase information acquisition module is used for calling an abnormality judgment expert library, inputting the digital abnormal information into the abnormality judgment expert library, and performing paraphrase on the digital abnormal information by the abnormality judgment expert library to obtain paraphrase information;
the abnormity discrimination decision module is used for traversing the abnormity discrimination expert database and finding out an abnormity processing strategy corresponding to the paraphrase information so as to realize abnormity discrimination decision of the secondary equipment; the exception handling strategy comprises an exception reason and an exception handling method.
Preferably, in the secondary equipment abnormality judgment and decision device based on big data, the abnormality judgment expert database at least includes the following data: the system comprises digitalized information, paraphrase information corresponding to the digitalized information one by one, and exception handling strategies corresponding to the paraphrase information one by one.
Preferably, in the secondary equipment abnormality judgment and decision device based on big data, the paraphrase information at least includes a fault name, a fault equipment, a fault type, and a fault location.
Preferably, in the secondary device abnormality judgment and decision device based on big data, the paraphrase information obtaining module is specifically configured to:
calling an abnormality judgment expert library, and inputting the digital abnormality information into the abnormality judgment expert library;
traversing the digital information prestored in the abnormal judgment expert library until the digital information corresponding to the digital abnormal information is found;
and calling out paraphrase information corresponding to the found digital information to obtain paraphrase information of the digital abnormal information.
Since the secondary device abnormality judgment decision method based on big data has been described in detail above, it is not described herein again.
As shown in fig. 2, based on the above secondary device abnormality judgment and decision method based on big data, the present invention also provides a secondary device abnormality judgment and decision device based on big data, where the secondary device abnormality judgment and decision device based on big data may be a mobile terminal, a desktop computer, a notebook computer, a palm computer, a server, or other computing devices. The big data-based secondary equipment abnormity judgment and decision-making equipment comprises a processor 10, a memory 20 and a display 30. Fig. 2 shows only some of the components of the big data based secondary equipment anomaly decision making apparatus, but it should be understood that not all of the shown components are required to be implemented, and more or fewer components may be implemented instead.
The storage 20 may be an internal storage unit of the big data based secondary device abnormality judgment decision device in some embodiments, for example, a hard disk or a memory of the big data based secondary device abnormality judgment decision device. The memory 20 may also be an external storage device of the big data based secondary device abnormality determination device in other embodiments, for example, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like provided on the big data based secondary device abnormality determination device. Further, the memory 20 may include both an internal storage unit and an external storage device of the secondary device abnormality judgment decision device based on big data. The memory 20 is used for storing application software installed in the big data based secondary device abnormality judgment decision device and various types of data, such as program codes of the big data based secondary device abnormality judgment decision device. The memory 20 may also be used to temporarily store data that has been output or is to be output. In one embodiment, the memory 20 stores a big data-based secondary device abnormality determination decision program 40, and the big data-based secondary device abnormality determination decision program 40 can be executed by the processor 10, so as to implement the big data-based secondary device abnormality determination decision method according to the embodiments of the present application.
The processor 10 may be, in some embodiments, a Central Processing Unit (CPU), a microprocessor or other data Processing chip, and is configured to run program codes stored in the memory 20 or process data, for example, execute the secondary device anomaly decision method based on big data.
The display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch panel, or the like in some embodiments. The display 30 is used for displaying information of the secondary equipment abnormity judgment decision equipment based on big data and displaying a visual user interface. The components 10-30 of the big data based secondary equipment anomaly decision making device communicate with each other through a system bus.
In an embodiment, when the processor 10 executes the secondary device anomaly decision-making program 40 based on big data in the memory 20, the steps in the secondary device anomaly decision-making method based on big data according to the above embodiment are implemented, and since the above description has been made in detail for the secondary device anomaly decision-making method based on big data, the detailed description is omitted here.
In summary, according to the secondary equipment abnormality judgment decision method, the device, the equipment and the storage medium based on the big data, the digitalized abnormal information is extracted from the alarm information, and then the digitalized abnormal information is directly input into the abnormality judgment expert base, so that the abnormality processing strategy corresponding to the digitalized abnormal information can be directly obtained from the abnormality judgment expert base, reference is provided for the overhaul of the overhaul personnel, the working strength of the overhaul personnel is reduced, and the working efficiency is improved.
Of course, it will be understood by those skilled in the art that all or part of the processes of the methods of the above embodiments may be implemented by a computer program instructing relevant hardware (such as a processor, a controller, etc.), and the program may be stored in a computer readable storage medium, and when executed, the program may include the processes of the above method embodiments. The storage medium may be a memory, a magnetic disk, an optical disk, etc.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (10)
1. A secondary equipment abnormity discrimination decision method based on big data is characterized by comprising the following steps:
acquiring alarm information of secondary equipment, and analyzing digitalized abnormal information contained in the alarm information;
calling an abnormality judgment expert library, inputting the digital abnormal information into the abnormality judgment expert library, and paraphrasing the digital abnormal information by the abnormality judgment expert library to obtain paraphrasing information;
traversing the abnormality judgment expert base, and finding out an abnormality processing strategy corresponding to the paraphrasing information so as to realize an abnormality judgment decision of the secondary equipment; the exception handling strategy comprises an exception reason and an exception handling method.
2. The big data-based secondary equipment abnormality judgment decision method according to claim 1, wherein the abnormality judgment expert database at least comprises the following data: the system comprises digitalized information, paraphrase information corresponding to the digitalized information one by one, and exception handling strategies corresponding to the paraphrase information one by one.
3. The big-data-based secondary equipment abnormality judgment and decision method according to claim 2, wherein the paraphrase information at least comprises a fault name, a fault equipment, a fault type and a fault location.
4. The big-data-based secondary equipment abnormality judgment decision method according to claim 3, wherein the step of calling an abnormality judgment expert database, inputting the digitized abnormality information into the abnormality judgment expert database, and paraphrasing the digitized abnormality information by the abnormality judgment expert database to obtain paraphrasing information comprises:
calling an abnormality judgment expert library, and inputting the digital abnormality information into the abnormality judgment expert library;
traversing the digital information prestored in the abnormal judgment expert library until the digital information corresponding to the digital abnormal information is found;
and calling out paraphrase information corresponding to the found digital information to obtain paraphrase information of the digital abnormal information.
5. The big data-based secondary equipment abnormality judgment decision method according to claim 1, wherein the specific establishment method of the abnormality judgment expert database is as follows:
acquiring the digital information of the existing fault handling case, establishing the corresponding relation between the digital information and the paraphrase information by combining with the operation rule knowledge base, and establishing the corresponding relation between the paraphrase information and the exception handling strategy according to the exception handling strategy in the existing fault handling case.
6. A secondary equipment abnormity discrimination decision device based on big data is characterized by comprising:
the digital abnormal information acquisition module is used for acquiring the alarm information of the secondary equipment and analyzing the digital abnormal information contained in the alarm information;
the paraphrase information acquisition module is used for calling an abnormality judgment expert library, inputting the digital abnormal information into the abnormality judgment expert library, and performing paraphrase on the digital abnormal information by the abnormality judgment expert library to obtain paraphrase information;
the abnormity discrimination decision module is used for traversing the abnormity discrimination expert database and finding out an abnormity processing strategy corresponding to the paraphrase information so as to realize abnormity discrimination decision of the secondary equipment; the exception handling strategy comprises an exception reason and an exception handling method.
7. The big-data-based secondary equipment abnormality judgment decision device according to claim 6, wherein the abnormality judgment expert database at least comprises the following data: the system comprises digitalized information, paraphrase information corresponding to the digitalized information one by one, and exception handling strategies corresponding to the paraphrase information one by one.
8. The big-data-based secondary equipment abnormality judgment and decision device according to claim 7, wherein the paraphrasing information at least includes a fault name, a fault equipment, a fault type and a fault location.
9. A secondary equipment abnormity discrimination decision-making equipment based on big data is characterized by comprising: a processor and a memory;
the memory has stored thereon a computer readable program executable by the processor;
the processor, when executing the computer readable program, implements the steps in the big data based secondary device anomaly decision making method according to any one of claims 1-5.
10. A computer-readable storage medium storing one or more programs, the one or more programs being executable by one or more processors to implement the steps of the big data based secondary device anomaly decision making method as claimed in any one of claims 1 to 5.
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