CN117590149A - Fault solution generation method, device and equipment based on big data technology - Google Patents
Fault solution generation method, device and equipment based on big data technology Download PDFInfo
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Abstract
The application relates to a fault solution generating method, a fault solution generating device, a fault solution generating computer device, a fault solution storing medium and a fault solution generating computer program product based on big data technology, which can be used in the big data technology field. The method comprises the following steps: acquiring equipment parameters of power grid fault equipment; the equipment parameters carry equipment identifiers of power grid fault equipment; inquiring an operation logic diagram of power grid fault equipment from an equipment database according to the equipment identification; according to the operation logic diagram and the equipment parameters, performing fault prediction processing on the power grid fault equipment to obtain a fault prediction result of the power grid fault equipment; the fault prediction result comprises a fault prediction position of power grid fault equipment and a fault solution of the fault prediction position; and generating a fault solution of the power grid fault equipment according to the fault prediction result. By adopting the method, the generation efficiency of the fault solution can be improved.
Description
Technical Field
The present application relates to the field of big data technologies, and in particular, to a method, an apparatus, a computer device, a storage medium, and a computer program product for generating a fault solution based on a big data technology.
Background
With the development of power technology, the scale of the power grid is continuously expanding, and the number and types of power grid equipment are also continuously increasing. In the event of a faulty device in the power network, a fault solution for the faulty device needs to be generated quickly in order to improve the stability of the operation of the power network. Therefore, how to efficiently generate a failure solution becomes an important research direction.
In the conventional technology, after an engineering technician arrives at the site to detect the fault equipment, a fault solution is manually formulated; however, generating the fault solution in this manner requires more manual processing time, resulting in lower efficiency of generating the fault solution.
Disclosure of Invention
Based on this, it is necessary to provide a failure solution generating method, apparatus, computer device, computer readable storage medium and computer program product based on big data technology capable of improving failure solution generating efficiency, in view of the above technical problems.
In a first aspect, the present application provides a method for generating a fault solution based on big data technology.
The method comprises the following steps:
acquiring equipment parameters of power grid fault equipment; the equipment parameter carries an equipment identifier of the power grid fault equipment;
According to the equipment identification, inquiring an operation logic diagram of the power grid fault equipment from an equipment database;
according to the operation logic diagram and the equipment parameters, performing fault prediction processing on the power grid fault equipment to obtain a fault prediction result of the power grid fault equipment; the fault prediction result comprises a fault prediction position of the power grid fault equipment and a fault solution of the fault prediction position;
and generating a fault solution of the power grid fault equipment according to the fault prediction result.
In one embodiment, the performing, according to the operation logic diagram and the device parameter, a fault prediction process on the power grid fault device to obtain a fault prediction result of the power grid fault device includes:
inputting the operation logic diagram and the equipment parameters into a pre-trained fault prediction model to perform fault prediction processing to obtain a fault prediction position of the power grid fault equipment, and a fault solution and a standby solution of the fault prediction position;
and taking the fault prediction position, the fault solution and the standby solution of the fault prediction position as a fault prediction result of the power grid fault equipment.
In one embodiment, the operation logic diagram includes a plurality of operation logic points;
and inputting the operation logic diagram and the equipment parameters into a pre-trained fault prediction model to perform fault prediction processing to obtain a fault prediction position of the power grid fault equipment, and a fault solution and a standby solution of the fault prediction position, wherein the fault solution and the standby solution comprise the following steps:
identifying the operation logic diagram to obtain the execution sequence, the historical fault information and the element information of the operation logic points;
and inputting the equipment parameters, the execution sequence of the operation logic points, the historical fault information and the element information into a pre-trained fault prediction model to perform fault prediction processing, so as to obtain a fault prediction position of the power grid fault equipment, and a fault solution and a standby solution of the fault prediction position.
In one embodiment, after generating the fault solution of the grid fault device according to the fault prediction result, the method further includes:
generating a fault resolution work order of the power grid fault equipment according to the fault resolution scheme of the power grid fault equipment and the geographical position information of the power grid fault equipment;
And sending the fault resolution worksheet to a target maintenance terminal.
In one embodiment, before the failure resolution worksheet is sent to the target maintenance terminal, the method further comprises:
selecting a maintenance terminal with corresponding geographic position information matched with the geographic position information of the power grid fault equipment from a plurality of maintenance terminals as a target maintenance terminal,
or selecting a maintenance terminal with a corresponding terminal identifier matched with the equipment identifier of the power grid fault equipment from the plurality of maintenance terminals as the target maintenance terminal.
In one embodiment, after the failure resolution worksheet is sent to the target maintenance terminal, the method further comprises:
receiving a fault detection result sent by the target maintenance terminal;
generating a corresponding replacement element work order according to the fault detection result under the condition that the fault detection result indicates replacement elements; the replacement element worksheet is used for assisting corresponding personnel in carrying out replacement element processing on the power grid fault equipment.
In one embodiment, before generating the fault solution of the grid fault device according to the fault prediction result, the method further includes:
Transmitting the equipment parameters to a processing terminal associated with the power grid fault equipment; the processing terminal is used for carrying out fault prediction processing on the power grid fault equipment according to the equipment parameters to obtain a first fault solution of the power grid fault equipment;
receiving the first fault solution sent by the processing terminal;
the generating a fault solution of the power grid fault equipment according to the fault prediction result comprises the following steps:
generating a second fault solution of the power grid fault equipment according to the fault prediction result;
and determining the fault solution of the power grid fault equipment according to the first fault solution and the second fault solution.
In a second aspect, the present application also provides a fault solution generating device based on big data technology. The device comprises:
the parameter acquisition module is used for acquiring equipment parameters of power grid fault equipment; the equipment parameter carries an equipment identifier of the power grid fault equipment;
the logic query module is used for querying an operation logic diagram of the power grid fault equipment from an equipment database according to the equipment identifier;
The fault prediction module is used for performing fault prediction processing on the power grid fault equipment according to the operation logic diagram and the equipment parameters to obtain a fault prediction result of the power grid fault equipment; the fault prediction result comprises a fault prediction position of the power grid fault equipment and a fault solution of the fault prediction position;
and the scheme generating module is used for generating a fault solution of the power grid fault equipment according to the fault prediction result.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of:
acquiring equipment parameters of power grid fault equipment; the equipment parameter carries an equipment identifier of the power grid fault equipment;
according to the equipment identification, inquiring an operation logic diagram of the power grid fault equipment from an equipment database;
according to the operation logic diagram and the equipment parameters, performing fault prediction processing on the power grid fault equipment to obtain a fault prediction result of the power grid fault equipment; the fault prediction result comprises a fault prediction position of the power grid fault equipment and a fault solution of the fault prediction position;
And generating a fault solution of the power grid fault equipment according to the fault prediction result.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring equipment parameters of power grid fault equipment; the equipment parameter carries an equipment identifier of the power grid fault equipment;
according to the equipment identification, inquiring an operation logic diagram of the power grid fault equipment from an equipment database;
according to the operation logic diagram and the equipment parameters, performing fault prediction processing on the power grid fault equipment to obtain a fault prediction result of the power grid fault equipment; the fault prediction result comprises a fault prediction position of the power grid fault equipment and a fault solution of the fault prediction position;
and generating a fault solution of the power grid fault equipment according to the fault prediction result.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of:
Acquiring equipment parameters of power grid fault equipment; the equipment parameter carries an equipment identifier of the power grid fault equipment;
according to the equipment identification, inquiring an operation logic diagram of the power grid fault equipment from an equipment database;
according to the operation logic diagram and the equipment parameters, performing fault prediction processing on the power grid fault equipment to obtain a fault prediction result of the power grid fault equipment; the fault prediction result comprises a fault prediction position of the power grid fault equipment and a fault solution of the fault prediction position;
and generating a fault solution of the power grid fault equipment according to the fault prediction result.
The fault solution generating method, the device, the computer equipment, the storage medium and the computer program product based on the big data technology acquire equipment parameters of power grid fault equipment; the equipment parameter carries an equipment identifier of the power grid fault equipment; according to the equipment identification, inquiring an operation logic diagram of the power grid fault equipment from an equipment database; according to the operation logic diagram and the equipment parameters, performing fault prediction processing on the power grid fault equipment to obtain a fault prediction result of the power grid fault equipment; the fault prediction result comprises a fault prediction position of the power grid fault equipment and a fault solution of the fault prediction position; and generating a fault solution of the power grid fault equipment according to the fault prediction result. According to the scheme, equipment parameters of power grid fault equipment are obtained, wherein the equipment parameters carry equipment identifiers; inquiring an operation logic diagram of the power grid fault equipment from an equipment database according to the equipment identification; performing fault prediction processing on power grid fault equipment by combining an operation logic diagram and equipment parameters to obtain a fault prediction result, wherein the fault prediction result comprises a fault prediction position and a corresponding fault solution; generating a fault solution of the power grid fault equipment according to the fault prediction result; through the steps, the fault solution generating method based on the big data technology can efficiently and accurately conduct fault prediction and solution generation, and improves the efficiency and accuracy of fault processing, so that the efficiency and accuracy of fault solution generation are improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related art, the drawings that are required to be used in the embodiments or the related technical descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to the drawings without inventive effort for a person having ordinary skill in the art.
FIG. 1 is a flow diagram of a method of generating a fault solution based on big data technology in one embodiment;
FIG. 2 is a flowchart illustrating steps for determining a failure prediction result in one embodiment;
FIG. 3 is a flow diagram of the steps of a fault prediction process in one embodiment;
FIG. 4 is a block diagram of a failure solution generation apparatus based on big data technology in one embodiment;
fig. 5 is an internal structural diagram of a computer device in 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.
It should be noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use, and processing of the related data are required to meet the related regulations.
In an exemplary embodiment, as shown in fig. 1, a fault solution generating method based on big data technology is provided, and this embodiment is applied to a terminal for illustration by the method; it will be appreciated that the method may also be applied to a server, and may also be applied to a system comprising a terminal and a server, and implemented by interaction between the terminal and the server. The terminal can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers and the like; the server may be implemented as a stand-alone server or as a server cluster composed of a plurality of servers. In this embodiment, the method includes the steps of:
step S101, obtaining equipment parameters of power grid fault equipment; the device parameter carries a device identification of the grid fault device.
The power grid fault device may be a device that fails in the power grid.
The device parameters may be various parameter information of the power grid fault device, such as information of model, state, temperature, voltage and the like of the power grid fault device.
The device identifier may be a unique identifier of the power grid fault device, may be a serial number or a name of the power grid fault device, and is used for uniquely identifying the device in a device database.
Optionally, the terminal acquires the relevant information of the power grid fault equipment by acquiring equipment parameters of the power grid fault equipment (such as excitation).
Step S102, according to the equipment identification, an operation logic diagram of the power grid fault equipment is queried from an equipment database.
The equipment database can be a database constructed based on a big data technology and is used for storing relevant information of power grid fault equipment, including an operation logic diagram, a fault history record, element specific information, drawing information and the like of the equipment.
Wherein the operational logic diagram may be an operational workflow diagram of the grid fault device describing the relationships and workflow between the individual components (or elements) within the grid fault device.
Optionally, the terminal is connected to the device database through a network, so that the data in the database can be accessed and queried; and sending a query request to the equipment database according to the equipment identifier, so that the equipment database is matched according to the equipment identifier, returning data (an operation logic diagram) related to the equipment identifier, and extracting the operation logic diagram of the power grid fault equipment from the data returned by the equipment database by the terminal.
Step S103, performing fault prediction processing on the power grid fault equipment according to the operation logic diagram and the equipment parameters to obtain a fault prediction result of the power grid fault equipment; the fault prediction result comprises a fault prediction position of the power grid fault equipment and a fault solution of the fault prediction position.
The fault prediction processing can be to analyze and process the equipment parameters and the operation logic diagram of the power grid fault equipment by utilizing a big data technology so as to predict the possible fault positions.
The fault prediction result may be a fault prediction position of the power grid fault equipment and a corresponding fault solution obtained according to a result of the fault prediction processing.
The fault prediction position may be a position predicted from the device parameters and the operation logic diagram, where a fault may occur, for example, the fault prediction position may be a position of a fault element in the power grid fault device.
The fault solution may be a solution provided for the fault prediction location, including specific maintenance steps, required spare parts and tools, etc., for example the fault solution of the fault prediction location may be a fault solution of a faulty element in a grid fault device.
Optionally, the terminal performs fault prediction processing on the power grid fault equipment according to the operation logic diagram and the equipment parameters, predicts the position where the fault is likely to occur, and provides a corresponding fault solution.
And step S104, generating a fault solution of the power grid fault equipment according to the fault prediction result.
The fault solution of the grid fault device may be an overall solution for solving a fault problem of the grid fault device, for example, a fault solution of the grid fault device may include one or more fault prediction positions or fault elements.
Optionally, the terminal determines all fault prediction positions of the power grid fault equipment and fault solutions corresponding to all fault prediction positions according to the fault prediction result; and combining all the fault prediction positions of the power grid fault equipment and fault solutions corresponding to all the fault prediction positions to generate an overall solution of the power grid fault equipment as a fault solution of the power grid fault equipment.
In the fault solution generating method based on the big data technology, equipment parameters of power grid fault equipment are acquired; the equipment parameters carry equipment identifiers of power grid fault equipment; inquiring an operation logic diagram of power grid fault equipment from an equipment database according to the equipment identification; according to the operation logic diagram and the equipment parameters, performing fault prediction processing on the power grid fault equipment to obtain a fault prediction result of the power grid fault equipment; the fault prediction result comprises a fault prediction position of power grid fault equipment and a fault solution of the fault prediction position; and generating a fault solution of the power grid fault equipment according to the fault prediction result. According to the scheme, equipment parameters of power grid fault equipment are obtained, wherein the equipment parameters carry equipment identifiers; inquiring an operation logic diagram of the power grid fault equipment from an equipment database according to the equipment identification; performing fault prediction processing on power grid fault equipment by combining an operation logic diagram and equipment parameters to obtain a fault prediction result, wherein the fault prediction result comprises a fault prediction position and a corresponding fault solution; generating a fault solution of the power grid fault equipment according to the fault prediction result; through the steps, the fault solution generating method based on the big data technology can efficiently and accurately conduct fault prediction and solution generation, and improves the efficiency and accuracy of fault processing, so that the efficiency and accuracy of fault solution generation are improved.
In an exemplary embodiment, as shown in fig. 2, in step S103, according to the operation logic diagram and the device parameters, fault prediction processing is performed on the power grid fault device, so as to obtain a fault prediction result of the power grid fault device, which specifically includes the following contents:
step S201, inputting an operation logic diagram and equipment parameters into a pre-trained fault prediction model for fault prediction processing to obtain a fault prediction position of power grid fault equipment, and a fault solution and a standby solution of the fault prediction position;
step S202, the fault prediction position, the fault solution of the fault prediction position and the standby solution are used as the fault prediction result of the power grid fault equipment.
The fault prediction model can be a model constructed based on a big data technology, and the fault position and the corresponding solution of the power grid fault equipment can be predicted by learning historical fault data and equipment parameters.
Wherein the fault prediction location of the grid fault device may be a certain component (or element), part or connection point of the grid fault device.
Wherein the fault solution may be based on the location of the fault prediction, the fault prediction model may give a specific method of operation to address the fault, which may include repair, replacement, adjustment or other corresponding operations.
Wherein the backup solution may be one that provides an alternative solution when the primary solution (failure solution) is not practical or applicable, the failure prediction model may be an alternative repair method or other viable operation.
The fault prediction result can be formed by taking the fault prediction position, the corresponding fault solution and the standby solution as a whole, and the result can be used for guiding subsequent fault diagnosis and elimination work.
Optionally, the terminal inputs the collected operation logic diagram and equipment parameters as input data to a pre-trained fault prediction model to perform fault prediction processing, so that the pre-trained fault prediction model determines a fault prediction position where the power grid fault equipment is most likely to generate a fault according to the operation logic diagram and the equipment parameters, generates a specific operation method for solving the fault according to the fault prediction position, outputs a fault prediction position of the power grid fault equipment, and a fault solution and a standby solution of the fault prediction position, so that the terminal obtains the fault prediction position of the power grid fault equipment, and a fault solution and a standby solution of the fault prediction position, which are output by the pre-trained fault prediction model; and the terminal performs combined processing on the fault prediction position, the fault solution of the fault prediction position and the standby solution to obtain a fault prediction result of the power grid fault equipment.
According to the technical scheme provided by the embodiment, the power grid fault equipment is subjected to fault prediction processing by utilizing the pre-trained fault prediction model, so that the fault prediction result of the power grid fault equipment can be obtained more efficiently and accurately, and the generation efficiency and the accuracy of the fault solution are improved.
In an exemplary embodiment, as shown in fig. 3, in the above steps, the operation logic diagram and the device parameters are input into a pre-trained fault prediction model to perform fault prediction processing, so as to obtain a fault prediction position of the power grid fault device, and a fault solution and a standby solution of the fault prediction position, which specifically include the following contents:
step S301, identifying an operation logic diagram to obtain the execution sequence, the historical fault information and the element information of the operation logic points;
and step S302, inputting the equipment parameters, the execution sequence of the operation logic points, the historical fault information and the element information into a pre-trained fault prediction model to perform fault prediction processing, and obtaining a fault prediction position of the power grid fault equipment, and a fault solution and a standby solution of the fault prediction position.
The operation logic diagram comprises a plurality of operation logic points.
The operation logic point may be a key node or a stage in the operation process of the device in the operation logic diagram.
The execution sequence may refer to a sequence in which each operation logic point is triggered or executed according to a preset sequence in the operation process of the device.
The historical fault information may be a fault history of each operating logical point, such as various fault types, reasons and solutions that the operating logical point has occurred in the past.
The element information may be an element involved in the operation logic points, for example, information of a specific element associated with each operation logic point.
Optionally, the terminal identifies and analyzes the operation logic diagram to determine the execution sequence, the historical fault information and the element information of the operation logic points; the method comprises the steps of inputting equipment parameters, execution sequences of operation logic points, historical fault information and element information into a pre-trained fault prediction model to conduct fault prediction processing, enabling the pre-trained fault prediction model to determine the execution sequences of the operation logic points, fault characteristics and historical solutions of each operation logic point, characteristics and fault possibility of specific elements related to each operation logic point when faults occur, outputting the fault prediction position of power grid fault equipment, the fault solutions and standby solutions of the fault prediction position according to the execution sequences of the operation logic points, the fault characteristics, the historical solutions, the characteristics and the fault possibility of the specific elements related to, and obtaining the fault prediction position of the power grid fault equipment, the fault solutions and the standby solutions of the fault prediction position by a terminal.
According to the technical scheme provided by the embodiment, the equipment parameters, the execution sequence, the historical fault information and the element information are input into the pre-trained fault prediction model, so that the fault prediction position of the power grid fault equipment, the fault solution and the standby solution of the fault prediction position are obtained more efficiently and accurately, and the generation efficiency and the accuracy of the fault solution are improved.
In an exemplary embodiment, the step S104 further includes a step of sending a fault resolution worksheet after generating a fault solution of the power grid fault device according to the fault prediction result, and specifically includes the following contents: generating a fault solution work order of the power grid fault equipment according to the fault solution of the power grid fault equipment and the geographical position information of the power grid fault equipment; and sending the fault resolution worksheet to the target maintenance terminal.
The geographical location information of the grid fault device may be an actual geographical location of the grid fault device.
The fault resolution work order can be a work order containing information such as a fault solution and the geographical position of power grid fault equipment and is used for guiding maintenance personnel to carry out fault maintenance.
The target maintenance terminal can be a terminal device for receiving and processing the fault resolution work order, and can be a mobile device of maintenance personnel or a terminal device of a maintenance center.
Optionally, after generating the fault solution, the terminal generates a fault solution work order according to the fault solution and the geographical position information of the power grid fault equipment; and sending the generated fault resolution worksheet to a target maintenance terminal, so that the target maintenance terminal instructs maintenance personnel to go to the position of the power grid fault equipment for fault maintenance according to the fault resolution scheme and the equipment geographical position information in the worksheet after receiving the fault resolution worksheet.
According to the technical scheme provided by the embodiment, the fault resolution work order is generated and sent to the target maintenance terminal, so that the efficiency and the accuracy of solving the fault problem of the power grid fault equipment are improved.
In one exemplary embodiment, before the failure resolution worksheet is sent to the target maintenance terminal, the following is further included: and selecting a maintenance terminal with corresponding geographic position information matched with the geographic position information of the power grid fault equipment from the plurality of maintenance terminals as a target maintenance terminal, or selecting a maintenance terminal with corresponding terminal identification matched with the equipment identification of the power grid fault equipment from the plurality of maintenance terminals as a target maintenance terminal.
The terminal identification may be an identifier, such as a serial number, for uniquely identifying the maintenance terminal.
Optionally, the terminal compares the geographical position information of each maintenance terminal with the geographical position information of the power grid fault device, selects, from among the plurality of maintenance terminals, the maintenance terminal closest to the power grid fault device as the target maintenance terminal, or compares the terminal identifier of each maintenance terminal with the device identifier of the power grid fault device, and selects, from among the plurality of maintenance terminals, the maintenance terminal whose terminal identifier matches with the device identifier as the target maintenance terminal.
According to the technical scheme provided by the embodiment, the target maintenance terminal is determined according to the geographic position information or the identification, so that the target maintenance terminal can be determined more efficiently and accurately, and the efficiency and the accuracy of solving the fault problem of the power grid fault equipment can be improved.
In one exemplary embodiment, after the failure resolution worksheet is sent to the target maintenance terminal, the following is further included: receiving a fault detection result sent by a target maintenance terminal; generating a corresponding replacement element work order according to the fault detection result under the condition that the fault detection result indicates the replacement element; the replacement element worksheet is used for assisting corresponding personnel in carrying out replacement element treatment on the power grid fault equipment.
Wherein the element may be a component in a grid fault device.
The fault detection result may be a result obtained by the maintenance terminal after performing fault detection on the power grid fault equipment, and includes fault information, fault codes, fault phenomena and the like of the power grid fault equipment.
The replacement component worksheet may be a worksheet for guiding a maintenance person to perform a component replacement operation, and includes detailed component information, such as a component name, a model number, a specification, and the like.
Optionally, the terminal receives the fault detection result sent by the maintenance terminal by establishing communication connection with the target maintenance terminal; judging whether the element needs to be replaced according to the received fault detection result, if the fault detection result indicates that the element needs to be replaced, generating a corresponding replacement element work order according to the information such as the name, the model and the like of the element in the fault detection result, wherein the replacement element work order is distributed to corresponding maintenance personnel to assist the maintenance personnel in the processing of replacing the element of the power grid fault equipment, so that the maintenance personnel can prepare the required element according to the information on the work order and perform replacement operation according to the work order instruction.
According to the technical scheme provided by the embodiment, the corresponding replacement element work order is generated according to the fault detection result, so that the efficiency and the accuracy of solving the fault problem of the power grid fault equipment are improved.
In an exemplary embodiment, the step S104 further includes a step of receiving a first fault solution before generating the fault solution of the grid fault device according to the fault prediction result, and specifically includes the following: transmitting the equipment parameters to a processing terminal associated with the power grid fault equipment; the processing terminal is used for carrying out fault prediction processing on the power grid fault equipment according to the equipment parameters to obtain a first fault solution of the power grid fault equipment; receiving a first fault solution sent by a processing terminal; in step S104, according to the result of the fault prediction, a fault solution of the grid fault device is generated, which specifically includes the following contents: generating a second fault solution of the power grid fault equipment according to the fault prediction result; and determining the fault solution of the power grid fault equipment according to the first fault solution and the second fault solution.
The processing terminal can be a terminal device for performing fault prediction processing on the power grid fault device.
The first fault solution may be a fault solution generated according to information such as equipment parameters and historical fault data.
Wherein the second fault solution may be an alternative fault solution generated from the fault prediction result for providing further fault resolution options.
Optionally, the terminal sends the device parameters of the grid fault device to a processing terminal associated with the device; the processing terminal carries out fault prediction processing on the power grid fault equipment according to the received equipment parameters, and can generate a first fault solution of the power grid fault equipment by analyzing the equipment parameters, the historical fault data and other information; the terminal receives a first fault solution sent by the processing terminal, wherein the solution can comprise a fault solution step, a fault solution method, required spare parts and other information; the terminal generates a second fault solution of the power grid fault device according to the fault prediction result, wherein the second fault solution can be an alternative solution based on the fault prediction result and is used for providing more fault solution options; the terminal determines a fault solution of the final power grid fault device according to the first fault solution and the second fault solution, and the solution can comprehensively consider a plurality of factors, such as a fault prediction result, spare part availability, skills of maintenance personnel and the like.
According to the technical scheme provided by the embodiment, the fault solution of the power grid fault equipment is determined according to the first fault solution and the second fault solution, so that more accurate fault solution can be obtained, and the generation accuracy of the fault solution can be improved.
The following describes a fault solution generating method based on big data technology provided by the application example, and the application example is applied to a terminal for illustration by the method, and the main steps include:
firstly, a terminal collects equipment parameters of power grid fault equipment (such as excitation); the device parameter carries the device name.
And secondly, the terminal inputs equipment parameters of the power grid fault equipment into a query interface of the terminal, and triggers the generation of a query request.
Thirdly, the terminal queries a database (namely, a database constructed based on a big data technology, such as a fault library) according to the query request to obtain an operation logic diagram (such as A, B, C, D and E) of the power grid fault equipment, wherein A, B, C, D, E can be expressed as different operation logic points; the operation logic diagram comprises a plurality of operation logic points (such as A, B, C and the like), and each operation logic point can be associated with a test method, a test tool, a fault history record, element specific information, drawing information, specific spare parts and the like.
Fourthly, the terminal generates a fault resolution work order according to the operation logic diagram of the power grid fault equipment and equipment parameters of the power grid fault equipment; the fault resolution work order comprises possible fault positions judged by the background based on the equipment parameters and the operation logic diagram, fault solutions recommended based on the possible fault positions (such as execution sequences of the operation logic points, fault histories of the operation logic points, element specific information and equipment parameters of power grid fault equipment, input into a fault prediction model, and the possible fault positions are predicted through the fault prediction model), standby solutions, power grid fault equipment positions and the like.
Fifthly, the terminal distributes the fault resolution worksheet to maintenance personnel nearby the position of the power grid fault equipment, and the maintenance personnel go to the position of the power grid fault equipment to perform fault maintenance; or, remotely calling a maintainer to go to the position of the power grid fault equipment to perform fault maintenance through a fault resolution work order. In addition, the terminal can also remotely find the producer or the rear professional to consult on the inquiry interface, generate a fault solution according to the consultation result, and dispatch the fault solution to the corresponding maintenance personnel in the form of a work order.
And sixthly, the terminal can upload the fault detection result to the background when confirming that the instrument needs to be replaced according to the fault detection result of the maintenance personnel, directly generate purchase orders (such as instrument orders), transportation orders, dispatcher orders and the like based on the fault detection result through the background, and finally distribute corresponding personnel to the position of the power grid fault equipment for instrument replacement.
The technical scheme provided by the application example realizes high-efficiency and accurate fault prediction and solution generation, and improves the efficiency and accuracy of fault processing, thereby being beneficial to improving the efficiency and accuracy of fault solution generation.
It should be understood that, although the steps in the flowcharts related to the embodiments described above 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 solution generating device based on big data technology for implementing the fault solution generating method based on big data technology. The implementation of the solution provided by the apparatus is similar to the implementation described in the above method, so the specific limitation in the embodiments of the fault solution generating apparatus based on the big data technology provided below may refer to the limitation of the fault solution generating method based on the big data technology hereinabove, and will not be repeated herein.
In an exemplary embodiment, as shown in fig. 4, a fault solution generating apparatus based on big data technology is provided, and the apparatus 400 may include:
a parameter obtaining module 401, configured to obtain device parameters of power grid fault devices; the equipment parameters carry equipment identifiers of power grid fault equipment;
the logic query module 402 is configured to query an operation logic diagram of the power grid fault device from the device database according to the device identifier;
the fault prediction module 403 is configured to perform fault prediction processing on the power grid fault device according to the operation logic diagram and the device parameter, so as to obtain a fault prediction result of the power grid fault device; the fault prediction result comprises a fault prediction position of power grid fault equipment and a fault solution of the fault prediction position;
And the scheme generating module 404 is configured to generate a fault solution of the power grid fault device according to the fault prediction result.
In an exemplary embodiment, the fault prediction module 403 is further configured to input the operation logic diagram and the device parameter into a pre-trained fault prediction model to perform fault prediction processing, so as to obtain a fault prediction position of the power grid fault device, and a fault solution and a standby solution of the fault prediction position; and taking the fault prediction position, the fault solution and the standby solution of the fault prediction position as the fault prediction result of the power grid fault equipment.
In one exemplary embodiment, the operational logic diagram includes a plurality of operational logic points; the fault prediction module 403 is further configured to identify an operation logic diagram, so as to obtain an execution sequence of the operation logic point, historical fault information and element information; and inputting the equipment parameters, the execution sequence of the operation logic points, the historical fault information and the element information into a pre-trained fault prediction model to perform fault prediction processing, so as to obtain a fault prediction position of the power grid fault equipment, and a fault solution and a standby solution of the fault prediction position.
In an exemplary embodiment, the apparatus 400 further includes: the work order sending module is used for generating a fault solution work order of the power grid fault equipment according to the fault solution of the power grid fault equipment and the geographical position information of the power grid fault equipment; and sending the fault resolution worksheet to the target maintenance terminal.
In an exemplary embodiment, the apparatus 400 further includes: the terminal selection module is used for selecting a maintenance terminal with corresponding geographic position information matched with the geographic position information of the power grid fault equipment from a plurality of maintenance terminals to serve as a target maintenance terminal, or selecting a maintenance terminal with corresponding terminal identification matched with the equipment identification of the power grid fault equipment from a plurality of maintenance terminals to serve as a target maintenance terminal.
In an exemplary embodiment, the apparatus 400 further includes: the work order generation module is used for receiving the fault detection result sent by the target maintenance terminal; generating a corresponding replacement element work order according to the fault detection result under the condition that the fault detection result indicates the replacement element; the replacement element worksheet is used for assisting corresponding personnel in carrying out replacement element treatment on the power grid fault equipment.
In an exemplary embodiment, the apparatus 400 further includes: the scheme receiving module is used for sending the equipment parameters to a processing terminal associated with the power grid fault equipment; the processing terminal is used for carrying out fault prediction processing on the power grid fault equipment according to the equipment parameters to obtain a first fault solution of the power grid fault equipment; receiving a first fault solution sent by a processing terminal; the solution generating module 404 is further configured to generate a second fault solution of the power grid fault device according to the fault prediction result; and determining the fault solution of the power grid fault equipment according to the first fault solution and the second fault solution.
The respective modules in the above-described failure solution generation apparatus based on big data technology may be implemented in whole or in part by software, hardware, and combinations 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 exemplary embodiment, a computer device is provided, which may be a terminal, and an internal structure diagram thereof may be as shown in fig. 5. The computer device includes a processor, a memory, an input/output interface, a communication interface, a display unit, and an input means. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input/output interface. 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 and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program, when executed by a processor, implements a fault solution generation method based on big data technology. The display unit of the computer device is used for forming a visual picture, and can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be a key, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 5 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In an exemplary embodiment, a computer device is also provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In one exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method embodiments described above.
In an exemplary embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
Those skilled in the art will appreciate that implementing all or part of the above described methods 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 above examples only 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 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. A method for generating a fault solution based on big data technology, the method comprising:
acquiring equipment parameters of power grid fault equipment; the equipment parameter carries an equipment identifier of the power grid fault equipment;
according to the equipment identification, inquiring an operation logic diagram of the power grid fault equipment from an equipment database;
according to the operation logic diagram and the equipment parameters, performing fault prediction processing on the power grid fault equipment to obtain a fault prediction result of the power grid fault equipment; the fault prediction result comprises a fault prediction position of the power grid fault equipment and a fault solution of the fault prediction position;
And generating a fault solution of the power grid fault equipment according to the fault prediction result.
2. The method according to claim 1, wherein the performing fault prediction processing on the power grid fault device according to the operation logic diagram and the device parameter to obtain a fault prediction result of the power grid fault device includes:
inputting the operation logic diagram and the equipment parameters into a pre-trained fault prediction model to perform fault prediction processing to obtain a fault prediction position of the power grid fault equipment, and a fault solution and a standby solution of the fault prediction position;
and taking the fault prediction position, the fault solution and the standby solution of the fault prediction position as a fault prediction result of the power grid fault equipment.
3. The method of claim 2, wherein the operating logic diagram includes a plurality of operating logic points;
and inputting the operation logic diagram and the equipment parameters into a pre-trained fault prediction model to perform fault prediction processing to obtain a fault prediction position of the power grid fault equipment, and a fault solution and a standby solution of the fault prediction position, wherein the fault solution and the standby solution comprise the following steps:
Identifying the operation logic diagram to obtain the execution sequence, the historical fault information and the element information of the operation logic points;
and inputting the equipment parameters, the execution sequence of the operation logic points, the historical fault information and the element information into a pre-trained fault prediction model to perform fault prediction processing, so as to obtain a fault prediction position of the power grid fault equipment, and a fault solution and a standby solution of the fault prediction position.
4. The method of claim 1, further comprising, after generating a fault solution for the grid fault device based on the fault prediction result:
generating a fault resolution work order of the power grid fault equipment according to the fault resolution scheme of the power grid fault equipment and the geographical position information of the power grid fault equipment;
and sending the fault resolution worksheet to a target maintenance terminal.
5. The method of claim 4, further comprising, prior to sending the troubleshooting worksheet to a target service terminal:
selecting a maintenance terminal with corresponding geographic position information matched with the geographic position information of the power grid fault equipment from a plurality of maintenance terminals as a target maintenance terminal,
Or selecting a maintenance terminal with a corresponding terminal identifier matched with the equipment identifier of the power grid fault equipment from the plurality of maintenance terminals as the target maintenance terminal.
6. The method of claim 4, further comprising, after transmitting the troubleshooting worksheet to a target service terminal:
receiving a fault detection result sent by the target maintenance terminal;
generating a corresponding replacement element work order according to the fault detection result under the condition that the fault detection result indicates replacement elements; the replacement element worksheet is used for assisting corresponding personnel in carrying out replacement element processing on the power grid fault equipment.
7. The method of claim 1, further comprising, prior to generating a fault solution for the grid fault device based on the fault prediction result:
transmitting the equipment parameters to a processing terminal associated with the power grid fault equipment; the processing terminal is used for carrying out fault prediction processing on the power grid fault equipment according to the equipment parameters to obtain a first fault solution of the power grid fault equipment;
receiving the first fault solution sent by the processing terminal;
The generating a fault solution of the power grid fault equipment according to the fault prediction result comprises the following steps:
generating a second fault solution of the power grid fault equipment according to the fault prediction result;
and determining the fault solution of the power grid fault equipment according to the first fault solution and the second fault solution.
8. A big data technology based fault solution generating device, the device comprising:
the parameter acquisition module is used for acquiring equipment parameters of power grid fault equipment; the equipment parameter carries an equipment identifier of the power grid fault equipment;
the logic query module is used for querying an operation logic diagram of the power grid fault equipment from an equipment database according to the equipment identifier;
the fault prediction module is used for performing fault prediction processing on the power grid fault equipment according to the operation logic diagram and the equipment parameters to obtain a fault prediction result of the power grid fault equipment; the fault prediction result comprises a fault prediction position of the power grid fault equipment and a fault solution of the fault prediction position;
and the scheme generating module is used for generating a fault solution of the power grid fault equipment according to the fault prediction result.
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 to 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 to 7.
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