CN113807100B - Protection device calculation model auditing method and device based on source end data - Google Patents
Protection device calculation model auditing method and device based on source end data Download PDFInfo
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Abstract
The application discloses a protection device calculation model auditing method and device based on source end data, wherein the method comprises the following steps: establishing a risk semantic identification library of a device calculation model; carrying out standardized identification and processing on source end data through risk semantic keywords, and forming an early warning result through checking analysis on device completeness, rationality, pre-calculation and version difference; and carrying out service semanteme description on the early warning result through the risk semantic identification library, and outputting defect and version difference information of the device calculation model for the user so as to assist the user in familiarizing with the characteristics of the new device model and realize model auditing. Therefore, the defect and rationality of the device model can be automatically analyzed, a protection device calculation model auditing method for natural semantic description risk is formed, the defects of automation and intellectualization of the current protection professional device level constant value calculation work are overcome, and a foundation is laid for informatization construction of the relay protection professional system.
Description
Technical Field
The application relates to the technical field of power system relay protection, in particular to a protection device calculation model auditing method and device based on source end data.
Background
Along with the extension of the power grid, the scale of the power grid is larger and larger, so that the task of setting calculation of a device-level fixed value and programming a fixed value list of a relay protection specialty is becoming heavy, and especially, the protection device model in currently applied setting calculation software is subjected to model sharing of the same manufacturer in a specific format, but the shared data has no effective checking and examining means, a certain error risk exists, and the working efficiency and quality of the protection device fixed value calculation are affected intangibly. In addition, the accurate and standard device fixed value and fixed value list thereof are the basis for guaranteeing the normal operation of the field device, and are also the basis for guaranteeing the stable operation of the power grid; abnormal or erroneous protector settings will affect the normal and reliable power supply of the grid to the user, even damage equipment, damage grid security, etc. Therefore, before the device sharing application, the normalization and rationality of the device model need to be checked and evaluated, and the fixed value security risk caused by the error of the basic calculation model is avoided.
In the related art, the current setting computing system aims at the acquired device computing model source end data, the accuracy of the device model imported by a manual checking and checking mode is adopted, and no clear auxiliary tool is used for improving the checking efficiency of the shared model. The human factor risk of the existing work is large, and when the equipment is put into production or the transformation range is large and the equipment is more, the device calculation model has insufficient time to carry out auditing and verification, so that the potential safety hazard of the application of the device calculation model is further enlarged. Especially for protecting professional replacement, new people are difficult to master the auditing points, so that the manual auditing and checking work is similar to the dummy.
Under the conditions of cloud computing, rapid development of big data and rapid application of the cloud computing and big data in a power grid dispatching informatization system, a large number of data model verification works are still developed manually so as not to accord with the development direction of each professional work and not to accord with the informatization and intelligent construction requirements of the dispatching system.
Content of the application
The application provides a protection device calculation model auditing method and device based on source end data, which can automatically analyze the defects and rationality of a device model, form a protection device calculation model auditing method for natural semantic description risk, make up for the defects of automation and intellectualization of the current protection professional device level constant value calculation work and lay a foundation for informatization construction of a relay protection professional system.
An embodiment of a first aspect of the present application provides a protection device calculation model auditing method based on source data, including the following steps:
establishing a risk semantic identification library of a device calculation model;
carrying out standardized identification and processing on source end data through risk semantic keywords, and forming an early warning result through checking analysis on device completeness, rationality, pre-calculation and version difference; and
And carrying out service semanteme description on the early warning result through the risk semantic identification library, and outputting defect and version difference information of the device calculation model for the user so as to assist the user to be familiar with the characteristics of the new device model and realize model auditing.
Optionally, the establishing device calculates a model risk semantic identifier library, including:
Converting risk identifications identified and analyzed by a computer language into business semantic identifications, and transversely dividing the business semantic identifications into a completeness examination semantic identification library, a rationality examination semantic identification library and a pre-calculation examination semantic identification library according to different examination dimensions, and/or longitudinally dividing the business semantic identification into device model basic information, constant value item basic attributes, constant value item and commonality quantity associated information, constant value item results and constant value item calculation principle results according to examination types;
and combining and splicing the examination element information to form business description semantics, wherein specific attributes corresponding to different examination items form basic business keywords of risk semantic identifications.
Optionally, the combining and splicing the audit element information to form service description semantics, where specific attributes corresponding to different audit items form basic service keywords of risk semantic identifiers, including:
and when the examination element information is combined and spliced into the business description semantics, identifying the keyword category of the examination item, the associated word among the keyword categories and the spacer to form readable risk semantic description information.
Optionally, the performing service semanteme description on the early warning result through the risk semantic identifier library, and calculating defect and version difference information existing in the model for the user output device to assist the user to familiarize with the characteristics of the new device model, so as to realize model auditing, including:
acquiring uploaded or submitted audit data;
Performing risk semantic keyword recognition matching on each attribute in the auditing data, and acquiring attribute values of each keyword by combining keywords to complete analysis and recognition of all devices to be audited;
Performing missing verification on the completeness of the attribute values according to the attribute values of the keywords, performing ICD standard data matching analysis on the rationality of the attribute values to judge whether the values are consistent or not, and judging whether the values are consistent or not until the last examination attribute item of the device model;
integrating and packaging the data of the next to-be-inspected device inspection item according to the data identified by the risk semantic keywords until all to-be-inspected devices are inspected;
Generating business risk semantic descriptions of all alarm information according to the examination alarm information and the risk semantic identification library;
After all the examination descriptions are generated, judging whether the examination conclusion descriptions in the business risk semantic descriptions of the examination results have missing records or not;
when the missing record exists, precalculating a device without a completeness defect by using a plurality of voltage class typical power grid models, judging the consistency of calculation results and the reasonability of variation trends of different voltage classes by combining business logic, performing difference correction analysis on the uploaded device calculation model and different versions of the same model uploaded historically, and recording difference information corresponding to semantic keywords of different risks, wherein the missing record does not exist;
And integrating the audit verification records to generate a protection device calculation model audit report based on the source data.
An embodiment of a second aspect of the present application provides a protection device calculation model auditing device based on source data, including:
the establishing module is used for establishing a risk semantic identification library of the device calculation model;
The analysis module is used for carrying out standardized identification and processing on the source data through the risk semantic keywords, and forming an early warning result through checking analysis on the completeness, rationality, pre-calculation and version difference of the device; and
And the auditing module is used for carrying out business semanteme description on the early warning result through the risk semantic identification library, and calculating defect and version difference information of the model for the user output device so as to assist the user to be familiar with the characteristics of the new device model and realize model auditing.
Optionally, the establishing module includes:
the conversion unit is used for converting the risk identification recognized and analyzed by the computer language into service semantic identification, so as to be transversely divided into a complete examination semantic identification library, a reasonable examination semantic identification library and a pre-calculation examination semantic identification library according to different examination dimensions, and/or longitudinally divided into device model basic information, fixed value item basic attribute, fixed value item and commonality quantity associated information, fixed value item result and fixed value item calculation principle result according to examination types;
and the combination splicing unit is used for carrying out combination splicing on the examination element information to form service description semantics, wherein specific attributes corresponding to different examination items form basic service keywords of risk semantic identifiers.
Optionally, the combined splicing unit includes:
And the identification subunit is used for identifying the keyword category of the examination item, the associated word among the keyword categories and the spacer when the examination element information is combined and spliced into the business description semantics so as to form readable risk semantic description information.
Optionally, the auditing module includes:
The acquisition unit is used for acquiring the uploaded or submitted audit data;
the matching unit is used for performing risk semantic keyword recognition matching on each attribute in the auditing data, and acquiring attribute values of each keyword by combining the keywords to complete the analysis and recognition of all the devices to be audited;
The verification unit is used for carrying out missing verification on the completeness of the attribute values according to the attribute values of the keywords, carrying out ICD standard data matching analysis on the rationality of the attribute values, and carrying out consistent verification on the numerical values until the last examination attribute item of the device model;
the packaging unit is used for integrating and packaging the data of the next to-be-inspected device inspection item according to the data identified by the risk semantic keywords until all to-be-inspected devices are inspected;
the first generation unit is used for generating business risk semantic descriptions of all the alarm information according to the examination alarm information and the risk semantic identification library;
The judging unit is used for judging whether the examination conclusion description exists a missing record in the business risk semantic description of the examination result after all the examination descriptions are generated;
The processing unit is used for pre-calculating a device without the defect of completeness by applying a plurality of voltage class typical power grid models when the missing record exists, judging the consistency of calculation results and the reasonability of variation trend of different voltage classes by combining service logic, performing difference correction analysis on the uploaded device calculation model and different versions of the same model uploaded in history, and recording difference information corresponding to different risk semantic keywords;
and the second generation unit is used for integrating the audit verification records and generating a protection device calculation model audit report based on the source end data.
An embodiment of a third aspect of the present application provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions configured to perform a source data based protection device computing model auditing method as described in the above embodiments.
A fourth aspect of the present application provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the protection device calculation model auditing method based on source data as described in the above embodiments.
Establishing a risk semantic identification library of a device calculation model, carrying out standardized identification and processing on source data through risk semantic keywords, forming an early warning result through checking analysis of device completeness, rationality, pre-calculation and version difference, carrying out business semantic description on the early warning result through the risk semantic identification library, and outputting defect and version difference information of the device calculation model for a user so as to assist the user to be familiar with the characteristics of a new device model, and realizing model auditing. Therefore, the defect and rationality of the device model can be automatically analyzed, a protection device calculation model auditing method for natural semantic description risk is formed, the defects of automation and intellectualization of the current protection professional device level constant value calculation work are overcome, and a foundation is laid for informatization construction of the relay protection professional system.
Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
Drawings
The foregoing and/or additional aspects and advantages of the application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a flowchart of a protection device calculation model auditing method based on source data according to an embodiment of the present application;
FIG. 2 is a flow chart of a method for auditing a computing model of a protection device based on source data according to an embodiment of the present application;
FIG. 3 is an exemplary diagram of a protection device calculation model auditing device based on source data according to an embodiment of the present application;
fig. 4 is an exemplary diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present application and should not be construed as limiting the application.
The following describes a protection device calculation model auditing method and device based on source end data according to an embodiment of the present application with reference to the accompanying drawings.
Before introducing the method for checking the computing model of the protection device based on the source data, the problems in the related art are briefly introduced.
Along with the extension of the power grid, the scale of the power grid is larger and larger, so that the task of setting calculation of a device-level fixed value and programming a fixed value list of a relay protection specialty is becoming heavy, and especially, the protection device model in the currently applied setting calculation software is subjected to model sharing of the same manufacturer in a specific format, but the shared data has no effective checking and examining means, a certain error risk exists, and the working efficiency and quality of the protection device fixed value calculation are affected intangibly.
In addition, the accurate and standard device fixed value and fixed value list thereof are the basis for guaranteeing the normal operation of the field device, and are also the basis for guaranteeing the stable operation of the power grid; abnormal or erroneous protector settings will affect the normal and reliable power supply of the grid to the user, even damage equipment, damage grid security, etc. Therefore, before the device sharing application is performed, the normalization and rationality of the acquired source data of the device calculation model need to be checked and evaluated, and the fixed value security risk caused by the error of the basic calculation model is avoided.
The application is based on the problems, and provides a protection device calculation model auditing method based on source data, which can establish a device calculation model risk semantic identification library, perform standardized identification and processing on the source data through risk semantic keywords, form an early warning result through checking analysis on device completeness, rationality, pre-calculation and version difference, perform business semantically description on the early warning result through the risk semantic identification library, and output defect and version difference information of a device calculation model for a user so as to assist the user to be familiar with the characteristics of a new device model and realize model auditing. Therefore, the defect and rationality of the device model can be automatically analyzed, a protection device calculation model auditing method for natural semantic description risk is formed, the defects of automation and intellectualization of the current protection professional device level constant value calculation work are overcome, and a foundation is laid for informatization construction of the relay protection professional system.
Specifically, fig. 1 is a flow chart of a method for auditing a computing model of a protection device based on source data according to an embodiment of the present application.
As shown in fig. 1, the method for auditing the computing model of the protection device based on the source data comprises the following steps:
in step S101, a device calculation model risk semantic identification library is established.
Optionally, in some embodiments, establishing the device computing model risk semantic identification library includes: converting risk identifications identified and analyzed by a computer language into business semantic identifications, and transversely dividing the business semantic identifications into a completeness examination semantic identification library, a rationality examination semantic identification library and a pre-calculation examination semantic identification library according to different examination dimensions, and/or longitudinally dividing the business semantic identification into device model basic information, constant value item basic attributes, constant value item and commonality quantity associated information, constant value item results and constant value item calculation principle results according to examination types; and combining and splicing the examination element information to form business description semantics, wherein specific attributes corresponding to different examination items form basic business keywords of risk semantic identifications.
In one embodiment of the present application, the audit element information is combined and spliced to form service description semantics, where specific attributes corresponding to different audit items form basic service keywords of risk semantic identifiers, and the method includes: when the combination and the splice of the examination element information are the service description semantics, the examination item keyword category, the association word among the keyword categories and the spacer are identified to form readable risk semantic description information.
It can be appreciated that when the device computing model risk semantic identifier library is built, the method mainly comprises the following steps: the method comprises a device calculation model risk semantic horizontal and vertical grading method, a device calculation model risk semantic keyword definition method and a device calculation model risk semantic association word and spacer definition method.
Specifically, the device calculates a model risk semantic horizontal and vertical grading method: in order to facilitate the readability of the auditing result of the device calculation model, the risk identification identified and analyzed by the computer language is converted into the business semantic identification, so that a user can intuitively know the business defect risk or difference of the auditing device model. The device model risk semantic identification library can be divided into a completeness inspection semantic identification library, a rationality inspection semantic identification library and a pre-calculation inspection semantic identification library according to different transverse directions of inspection dimensions; from different examination types, the risk semantic identifier is longitudinally divided into five types, namely, device model basic information, constant value item basic attribute, constant value item and commonality quantity related information, constant value item result, constant value item calculation principle result and the like;
The device calculates a model risk semantic keyword definition method: for computer program language, the examination results are both alarm or not, and in the actual output result, the risk of the examination item is required to be subjected to business semantic recognition, namely examination element information such as examination dimension, examination item, examination conclusion, processing mode and the like is combined and spliced to form business description semantics, and specific attributes corresponding to different examination items form basic business keywords of risk semantic identification.
The device calculates a model risk semantic association word and a spacer definition method: when the element keywords are combined and spliced into the business description semantics, the keyword category of the examination item, the related words among the keyword categories and the spacer are required to be clearly examined so as to form readable risk semantic description information. Related words such as completeness audit semantics are: the related word between the examination dimension and the examination item is "medium"; the related word between the examination item and the examination conclusion is information; adding a unique spacer between the examination conclusion and the processing mode as comma spacer ","; the related word between the examination conclusion and the processing mode is the suggestion.
It should be noted that, in the embodiment of the present application, the device may be designed according to the "completeness inspection semantic identifier library", "rationality inspection semantic identifier library", and "pre-calculation inspection semantic identifier library" by combining the above-mentioned classification method of risk semantic identifier library, and the following examples of the definition of keywords and related words from different types of inspected attributes and the risk semantic stitching combination are performed by combining different types of inspected attributes:
1. A completeness examination semantic identification;
a. Basic information of the device model;
Device model (audit dimension→device model basic information→audit item→device model, audit conclusion→absence/complete, processing mode→replenishment/no);
device version number (audit dimension→device model basic information, audit term→device version number, audit conclusion→missing/complete, processing mode→supplement/none);
Device check code (audit dimension→device model basic information, audit term→device check code, audit conclusion→missing/complete, processing mode→supplement/none).
B. A constant item base attribute;
Constant item name (audit dimension→constant item base attribute, audit item→constant item name, audit conclusion→missing/complete, processing mode→supplement/none);
a fixed value range (audit dimension→fixed value item base attribute, audit item→fixed value range, audit conclusion→absence/complete, processing mode→replenishment/none);
Fixed value step length (audit dimension→fixed value item base attribute, audit item→fixed value step length, audit conclusion→absence/complete, processing mode→replenishment/none);
Constant value type (audit dimension→constant value item base attribute, audit item→constant value type, audit conclusion→absence/complete, processing mode→replenishment/none);
Constant value item unit (audit dimension→constant value item base attribute, audit item→constant value item unit→audit conclusion→missing/complete, processing mode→replenishment/none).
C. The association information of the constant value item and the co-property quantity;
Constant item classification (audit dimension→constant item and co-attribute related information, audit item→constant item classification, audit conclusion→absence/complete, processing mode→replenishment/none);
a fixed value item and a correlation identifier (checking dimension, fixed value item and correlation information of the correlation, checking item, fixed value item and correlation identifier of the correlation, checking conclusion, deletion/complete, processing mode, supplement/no);
a fixed value item calculation principle (checking dimension, fixed value item and co-attribute related information, checking item, fixed value item calculation principle, checking conclusion, deletion/complete, processing mode, supplement/no);
2. Rationality censoring semantic identification
A. Basic information of the device model;
Device model (audit dimension→device model basic information, audit term→device model, audit conclusion→inconsistency/consistency, processing mode→correction/none);
device version number (audit dimension→device model basic information, audit term→device version number, audit conclusion→inconsistency/consistency, processing mode→correction/none);
Device check code (audit dimension→device model basic information, audit term→device check code, audit conclusion→inconsistency/consistency, processing mode→correction/none);
a constant item (audit dimension→device model basic information, audit item→constant item, audit conclusion→missing/redundant, processing mode→correction/none);
b. A constant item base attribute;
a fixed value range (audit dimension→fixed value item base attribute, audit item→fixed value range, audit conclusion→absence/inconsistency/consistency, processing mode→correction/none);
fixed value step length (audit dimension→fixed value item base attribute audit term→fixed value step length, audit conclusion→missing/inconsistent/consistent, processing mode→correction/none);
Constant value type (audit dimension→constant value item base attribute, audit term→constant value type, audit conclusion→absence/inconsistency/consistency, processing mode→correction/none);
a constant value item unit (audit dimension→constant value item base attribute, audit item→constant value item unit→audit conclusion→missing/inconsistent/consistent, processing mode→correction/none);
3. Pre-computed audit semantic identification
A. A constant value item result;
Constant item value (dimension of examination→result of constant item, examination term→value of constant item, examination conclusion→agreement of calculated value/agreement of calculated value with voltage class trend/disagreement of calculated value, processing mode→correction/no
B. Calculating a principle result by a constant value item;
the constant value item calculation principle value (checking dimension, constant value item result, checking item, constant value item value, checking conclusion, consistent calculated value/inconsistent calculated value according with voltage class trend/calculated value, processing mode, correction/no);
4. risk semantic stitching combination examples;
risk semantic combination stitching forms a final risk semantic description example: the device model basic information is missing in the device model information, and supplementation is suggested.
In step S102, standardized identification and processing are performed on source data through risk semantic keywords, and early warning results are formed through verification analysis of device completeness, rationality, pre-calculation and version differences.
Therefore, the problem that the device calculation models of different systems are difficult to identify and check through computer means is effectively solved by disassembling the device calculation models from different dimensions and combining the device calculation models with risk inspection dimensions.
In step S103, the pre-warning result is semantically described through the risk semantic identification library, and the defect and version difference information of the model are calculated for the user output device, so as to assist the user in familiarizing with the characteristics of the new device model, and realize model auditing.
Optionally, in an embodiment of the present application, the service semantically describes the early warning result through a risk semantic identifier library, and calculates defect and version difference information existing in the model for the user output device, so as to assist the user in familiarizing with the characteristics of the new device model, and implement model auditing, including: acquiring uploaded or submitted audit data; performing risk semantic keyword recognition matching on each attribute in the auditing data, and acquiring attribute values of each keyword by combining keywords to complete analysis and recognition of all devices to be audited; performing missing verification on the completeness of the attribute values according to the attribute values of the keywords, performing ICD standard data matching analysis on the rationality of the attribute values to judge whether the values are consistent or not, and until the last examination attribute item of the device model; integrating and packaging the data of the next to-be-inspected device inspection item according to the data identified by the risk semantic keywords until all to-be-inspected devices are inspected; generating business risk semantic descriptions of each alarm information according to the examination alarm information and the risk semantic identification library; after all the examination descriptions are generated, judging whether the examination conclusion descriptions in the business risk semantic descriptions of the examination results have missing records or not; when the missing record exists, precalculating a device without the defect of completeness by applying a plurality of voltage class typical power grid models, judging the consistency of calculation results and the reasonability of variation trends of different voltage classes by combining business logic, carrying out difference correction analysis on the uploaded device calculation model and different versions of the same model uploaded historically and recording difference information corresponding to semantic keywords of different risks, wherein the difference correction analysis is the same as that of the device calculation model without the missing record; and integrating the audit verification records to generate a protection device calculation model audit report based on the source data.
It can be understood that the source end of the device calculation model in the embodiment of the application is a setting calculation system, and standardized identification and processing are carried out on source end data through risk semantic keywords, so that the problem that the device calculation model of different manufacturers and different versions of setting systems is difficult to identify and apply at present is solved; forming early warning results through checking and analyzing the completeness, rationality, pre-calculation and version difference of the device, and carrying out service semanteme description on each early warning through a risk semanteme identification library; and (3) calculating defect and version difference information of the model for the user output device, and assisting the user in quickly familiarizing with the new device model characteristics.
As shown in fig. 2, fig. 2 is a flowchart of a device computing model auditing implementation based on source data, which provides a technical basis for development and design of a program, and specifically includes the following steps:
(1) Device upload or submit audit: uploading a device calculation model through a folder retrieval or manual interface, enabling the system to enter an auditing logic, starting data identification and auditing, and entering a step (2) after the data acquisition is successful;
(2) Risk semantic keyword recognition: performing risk semantic keyword recognition matching on each attribute in the to-be-analyzed auditing device, and acquiring attribute values of each keyword by combining the keywords, and entering the step (3) after the analysis and recognition of all to-be-analyzed devices are completed;
(3) Executing completeness and rationality automatic examination according to the keywords: according to the attribute values of the keywords, checking whether the completeness of the attribute values is missing or not, and carrying out ICD standard data matching analysis on the rationality of the attribute values to judge whether the values are consistent or not; enter step (4) after completing the automatic examination of the current examination item
(4) Whether the last censored item: judging whether the last item of the examination attribute of the device is the last item, if not, entering the step (5), and if so, entering the step (6);
(5) Executing the next censoring item censoring logic: executing the examination logic of the next examination item, completing examination and recording the alarm identification, and then entering step (4);
(6) Whether the last device to be examined: judging whether the device is the last device to be audited in this time, if not, entering the step 7, if so, entering the step 8;
(7) Acquiring the information of the next device to be checked: integrating and packaging the next to-be-inspected device inspection item data according to the data identified by the risk semantic keywords, and entering the step (3) after the completion;
(8) Generating completeness and rationality examination results: generating business risk semantic descriptions of all the alarm information according to the examination alarm information and combining a risk semantic identification library; step (9) is carried out after all the examination descriptions are generated;
(9) Judging whether the examination result has a completeness alarm or not: judging whether a 'missing' record exists in the business risk semantic description of the examination result; if not, the device can perform pre-calculation self-examination, and the step (10) is performed, if yes, defect early warning exists for proving the completeness of the examination result, and the step (11) is performed;
(10) Pre-computing and checking: pre-calculating a device without a completeness defect by applying a plurality of voltage class typical power grid models, and judging the consistency of calculation results and the reasonability of variation trends of different voltage classes by combining business logic; after completion, step (11) is entered;
(11) Device calculation model version difference analysis: performing difference correction analysis on the uploaded device calculation model and different versions of the same model uploaded in history, recording difference information corresponding to semantic keywords of different risks, and entering a step (12) after the completion;
(12) Ending the examination to generate a risk result: and finishing the examination flow of the secondary device calculation model, integrating examination and verification records such as completeness, rationality, pre-calculation, version difference and the like, and generating a protection device calculation model examination report based on the source data.
In summary, the embodiment of the application provides a good examination tool for the sharing application of the device calculation model, greatly improves the sharing application efficiency of the device calculation model, improves the application efficiency of the device setting calculation function, further greatly improves the overall practicality level of the setting calculation system and the protection sharing platform, effectively solves the problem of missed examination risk existing in the manual examination of the device calculation model, can carry out batch examination, greatly reduces the workload of manual examination, effectively solves the problem of the examination of the device calculation model which is carried out by a large amount of manual work at present, and overcomes the defect of informationized construction of the setting calculation system and the protection platform in the examination direction of the device calculation model.
According to the method for auditing the protection device calculation model based on the source data, disclosed by the embodiment of the application, a device calculation model risk semantic identification library can be established, the source data is identified and processed in a standardized manner through the risk semantic keywords, an early warning result is formed through checking analysis of device completeness, rationality, pre-calculation and version difference, business semantic description is carried out on the early warning result through the risk semantic identification library, defect and version difference information of the device calculation model are output for a user, so that the user is assisted in familiarity with the characteristics of a new device model, and model auditing is realized. Therefore, the defect and rationality of the device model can be automatically analyzed, a protection device calculation model auditing method for natural semantic description risk is formed, the defects of automation and intellectualization of the current protection professional device level constant value calculation work are overcome, and a foundation is laid for informatization construction of the relay protection professional system.
Next, a protection device calculation model auditing device based on source end data according to an embodiment of the present application is described with reference to the accompanying drawings.
Fig. 3 is a block diagram of a protection device calculation model auditing device based on source data according to an embodiment of the present application.
As shown in fig. 3, the protection device calculation model auditing device 10 based on source data includes: the module 100, the analysis module 200 and the auditing module 300 are established.
The establishing module 100 is used for establishing a risk semantic identification library of the device computing model;
The analysis module 200 is used for carrying out standardized identification and processing on source end data through risk semantic keywords, and forming an early warning result through verification analysis on device completeness, rationality, pre-calculation and version difference; and
The auditing module 300 is used for carrying out business semanteme description on the early warning result through the risk semanteme identification library, and calculating defect and version difference information of the model for the user output device so as to assist the user to be familiar with the characteristics of the new device model and realize model auditing
Optionally, in some embodiments, the setup module 100 includes: a conversion unit and a combination splicing unit.
The conversion unit is used for converting risk identifications recognized and analyzed by a computer language into business semantic identifications, so as to be transversely divided into a complete examination semantic identification library, a reasonable examination semantic identification library and a pre-calculation examination semantic identification library according to different examination dimensions, and/or longitudinally divided into device model basic information, fixed value item basic attributes, fixed value item and common quantity associated information, fixed value item results and fixed value item calculation principle results according to examination types;
The combination and splicing unit is used for carrying out combination and splicing on the examination element information to form service description semantics, wherein specific attributes corresponding to different examination items form basic service keywords of risk semantic identifications.
Optionally, in some embodiments, the combined splice unit comprises: the subunit is identified.
The identification subunit is used for identifying keyword categories of the examination items, associated words among the keyword categories and spacers when the examination element information is combined and spliced into the service description semantics, so as to form readable risk semantic description information.
Optionally, in some embodiments, the auditing module 300 includes: the device comprises an acquisition unit, a matching unit, a verification unit, a packaging unit, a first generation unit, a judgment unit, a processing unit and a second generation unit.
The system comprises an acquisition unit, a verification unit and a storage unit, wherein the acquisition unit is used for acquiring uploaded or submitted audit data;
The matching unit is used for performing risk semantic keyword recognition matching on each attribute in the auditing data, and acquiring attribute values of each keyword by combining the keywords to finish the analysis and recognition of all the devices to be audited;
the verification unit is used for carrying out missing verification on the completeness of the attribute values according to the attribute values of the keywords, carrying out ICD standard data matching analysis on the rationality of the attribute values, and verifying whether the values are consistent or not until the last examination attribute item of the device model;
the packaging unit is used for integrating and packaging the data of the next to-be-inspected device inspection item according to the data identified by the risk semantic keywords until all to-be-inspected devices are inspected;
the first generation unit is used for generating business risk semantic descriptions of the alarm information according to the examination alarm information and the risk semantic identification library;
The judging unit is used for judging whether the examination conclusion description exists a missing record in the business risk semantic description of the examination result after all the examination descriptions are generated;
The processing unit is used for pre-calculating a device without the defect of completeness by applying a plurality of voltage class typical power grid models when the missing record exists, judging the consistency of calculation results and the reasonability of the change trend of different voltage classes by combining service logic, performing difference correction analysis on the uploaded device calculation model and different versions of the same model uploaded historically and recording difference information corresponding to different risk semantic keywords as the missing record does not exist;
and the second generation unit is used for integrating the audit verification records and generating a protection device calculation model audit report based on the source end data.
It should be noted that the foregoing explanation of the embodiment of the protection device calculation model auditing method based on source data is also applicable to the protection device calculation model auditing device based on source data in this embodiment, and will not be repeated here.
According to the protection device calculation model auditing device based on the source data, a device calculation model risk semantic identification library can be established, the source data is identified and processed in a standardized mode through the risk semantic keywords, an early warning result is formed through checking analysis of device completeness, rationality, pre-calculation and version difference, business semantic description is carried out on the early warning result through the risk semantic identification library, defect and version difference information of a device calculation model are output for a user, so that the user is assisted in familiarity with characteristics of a new device model, and model auditing is achieved. Therefore, the defect and rationality of the device model can be automatically analyzed, a protection device calculation model auditing method for natural semantic description risk is formed, the defects of automation and intellectualization of the current protection professional device level constant value calculation work are overcome, and a foundation is laid for informatization construction of the relay protection professional system.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device may include:
memory 1201, processor 1202, and computer program stored on memory 1201 and executable on processor 1202.
The processor 1202, when executing the program, implements the protection device calculation model auditing method based on source data provided in the above embodiment.
Further, the electronic device further includes:
a communication interface 1203 for communication between the memory 1201 and the processor 1202.
A memory 1201 for storing a computer program executable on the processor 1202.
Memory 1201 may comprise high speed RAM memory, and may also include non-volatile memory (non-volatilememory), such as at least one disk memory.
If the memory 1201, the processor 1202, and the communication interface 1203 are implemented independently, the communication interface 1203, the memory 1201, and the processor 1202 may be connected to each other through a bus and perform communication with each other. The bus may be an industry standard architecture (Industry Standard Architecture, abbreviated ISA) bus, an Peripheral Component Interconnect (PCI) bus, an extended industry standard architecture (Extended Industry StandardArchitecture, abbreviated EISA) bus, or the like. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, only one thick line is shown in fig. 4, but not only one bus or one type of bus.
Alternatively, in a specific implementation, if the memory 1201, the processor 1202 and the communication interface 1203 are integrated on a chip, the memory 1201, the processor 1202 and the communication interface 1203 may communicate with each other through internal interfaces.
The processor 1202 may be a central processing unit (Central Processing Unit, abbreviated as CPU), or an Application SPECIFIC INTEGRATED Circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present application.
The present embodiment also provides a computer-readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements the protection device calculation model auditing method based on source data as above.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, "N" means at least two, for example, two, three, etc., unless specifically defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more N executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or N wires, a portable computer cartridge (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. While embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.
Claims (8)
1. A protection device calculation model auditing method based on source end data is characterized by comprising the following steps:
establishing a risk semantic identification library of a device calculation model;
carrying out standardized identification and processing on source end data through risk semantic keywords, and forming an early warning result through checking analysis on device completeness, rationality, pre-calculation and version difference; and
Carrying out service semanteme description on the early warning result through the risk semantic identification library, and outputting defect and version difference information of the device calculation model for the user so as to assist the user to be familiar with the characteristics of a new device model and realize model auditing;
The step of carrying out business semantic description on the early warning result through the risk semantic identification library and calculating defect and version difference information of the model for the user output device so as to assist the user to be familiar with the characteristics of the new device model and realize model auditing comprises the following steps:
acquiring uploaded or submitted audit data;
Performing risk semantic keyword recognition matching on each attribute in the auditing data, and acquiring attribute values of each keyword by combining keywords to complete analysis and recognition of all devices to be audited;
Performing missing verification on the completeness of the attribute values according to the attribute values of the keywords, performing ICD standard data matching analysis on the rationality of the attribute values to judge whether the values are consistent or not, and judging whether the values are consistent or not until the last examination attribute item of the device model;
integrating and packaging the data of the next to-be-inspected device inspection item according to the data identified by the risk semantic keywords until all to-be-inspected devices are inspected;
Generating business risk semantic descriptions of all alarm information according to the examination alarm information and the risk semantic identification library;
After all the examination descriptions are generated, judging whether the examination conclusion descriptions in the business risk semantic descriptions of the examination results have missing records or not;
when the missing record exists, precalculating a device without a completeness defect by using a plurality of voltage class typical power grid models, judging the consistency of calculation results and the reasonability of variation trends of different voltage classes by combining business logic, performing difference correction analysis on the uploaded device calculation model and different versions of the same model uploaded historically, and recording difference information corresponding to semantic keywords of different risks, wherein the missing record does not exist;
And integrating the audit verification records to generate a protection device calculation model audit report based on the source data.
2. The method of claim 1, wherein the building means calculates a model risk semantic identity library comprising:
Converting risk identifications identified and analyzed by a computer language into business semantic identifications, and transversely dividing the business semantic identifications into a completeness examination semantic identification library, a rationality examination semantic identification library and a pre-calculation examination semantic identification library according to different examination dimensions, and/or longitudinally dividing the business semantic identification into device model basic information, constant value item basic attributes, constant value item and commonality quantity associated information, constant value item results and constant value item calculation principle results according to examination types;
and combining and splicing the examination element information to form business description semantics, wherein specific attributes corresponding to different examination items form basic business keywords of risk semantic identifications.
3. The method according to claim 2, wherein the assembling and splicing the audit element information to form a business description semantic, wherein specific attributes corresponding to different audit items form a basic business keyword of risk semantic identification, includes:
and when the examination element information is combined and spliced into the business description semantics, identifying the keyword category of the examination item, the associated word among the keyword categories and the spacer to form readable risk semantic description information.
4. The utility model provides a protection device calculation model audit device based on source end data which characterized in that includes:
the establishing module is used for establishing a risk semantic identification library of the device calculation model;
The analysis module is used for carrying out standardized identification and processing on the source data through the risk semantic keywords, and forming an early warning result through checking analysis on the completeness, rationality, pre-calculation and version difference of the device; and
The auditing module is used for carrying out business semanteme description on the early warning result through the risk semantic identification library, and calculating defect and version difference information of the model for the user output device so as to assist the user to be familiar with the characteristics of the new device model and realize model auditing;
the auditing module comprises:
The acquisition unit is used for acquiring the uploaded or submitted audit data;
the matching unit is used for performing risk semantic keyword recognition matching on each attribute in the auditing data, and acquiring attribute values of each keyword by combining the keywords to complete the analysis and recognition of all the devices to be audited;
The verification unit is used for carrying out missing verification on the completeness of the attribute values according to the attribute values of the keywords, carrying out ICD standard data matching analysis on the rationality of the attribute values, and carrying out consistent verification on the numerical values until the last examination attribute item of the device model;
the packaging unit is used for integrating and packaging the data of the next to-be-inspected device inspection item according to the data identified by the risk semantic keywords until all to-be-inspected devices are inspected;
the first generation unit is used for generating business risk semantic descriptions of all the alarm information according to the examination alarm information and the risk semantic identification library;
The judging unit is used for judging whether the examination conclusion description exists a missing record in the business risk semantic description of the examination result after all the examination descriptions are generated;
The processing unit is used for pre-calculating a device without the defect of completeness by applying a plurality of voltage class typical power grid models when the missing record exists, judging the consistency of calculation results and the reasonability of variation trend of different voltage classes by combining service logic, performing difference correction analysis on the uploaded device calculation model and different versions of the same model uploaded in history, and recording difference information corresponding to different risk semantic keywords;
and the second generation unit is used for integrating the audit verification records and generating a protection device calculation model audit report based on the source end data.
5. The apparatus of claim 4, wherein the means for establishing comprises:
the conversion unit is used for converting the risk identification recognized and analyzed by the computer language into service semantic identification, so as to be transversely divided into a complete examination semantic identification library, a reasonable examination semantic identification library and a pre-calculation examination semantic identification library according to different examination dimensions, and/or longitudinally divided into device model basic information, fixed value item basic attribute, fixed value item and commonality quantity associated information, fixed value item result and fixed value item calculation principle result according to examination types;
and the combination splicing unit is used for carrying out combination splicing on the examination element information to form service description semantics, wherein specific attributes corresponding to different examination items form basic service keywords of risk semantic identifiers.
6. The apparatus of claim 5, wherein the combination splice unit comprises:
And the identification subunit is used for identifying the keyword category of the examination item, the associated word among the keyword categories and the spacer when the examination element information is combined and spliced into the business description semantics so as to form readable risk semantic description information.
7. An electronic device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the source data-based protection device calculation model auditing method of any of claims 1-3.
8. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the program is executed by a processor for implementing the source data-based protection device calculation model auditing method of any of claims 1-3.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102663530A (en) * | 2012-05-25 | 2012-09-12 | 中国南方电网有限责任公司超高压输电公司 | Safety early warning and evaluating system for high-voltage direct current transmission system |
| WO2018053934A1 (en) * | 2016-09-22 | 2018-03-29 | 北京国电通网络技术有限公司 | Early-warning method and early-warning apparatus for devices in power grid |
| CN110008311A (en) * | 2019-04-04 | 2019-07-12 | 北京邮电大学 | A kind of product information security risk monitoring method based on semantic analysis |
| CN110347719A (en) * | 2019-06-24 | 2019-10-18 | 华南农业大学 | A kind of enterprise's foreign trade method for prewarning risk and system based on big data |
| WO2020082579A1 (en) * | 2018-10-25 | 2020-04-30 | 深圳壹账通智能科技有限公司 | Risk review and approval method, device, storage medium, and server |
| CN111260223A (en) * | 2020-01-17 | 2020-06-09 | 山东省计算中心（国家超级计算济南中心） | Intelligent identification and early warning method, system, medium and equipment for trial and judgment risk |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110282710A1 (en) * | 2010-05-14 | 2011-11-17 | International Business Machines Corporation | Enterprise risk analysis system |
-
2020
- 2020-06-11 CN CN202010527419.8A patent/CN113807100B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102663530A (en) * | 2012-05-25 | 2012-09-12 | 中国南方电网有限责任公司超高压输电公司 | Safety early warning and evaluating system for high-voltage direct current transmission system |
| WO2018053934A1 (en) * | 2016-09-22 | 2018-03-29 | 北京国电通网络技术有限公司 | Early-warning method and early-warning apparatus for devices in power grid |
| WO2020082579A1 (en) * | 2018-10-25 | 2020-04-30 | 深圳壹账通智能科技有限公司 | Risk review and approval method, device, storage medium, and server |
| CN110008311A (en) * | 2019-04-04 | 2019-07-12 | 北京邮电大学 | A kind of product information security risk monitoring method based on semantic analysis |
| CN110347719A (en) * | 2019-06-24 | 2019-10-18 | 华南农业大学 | A kind of enterprise's foreign trade method for prewarning risk and system based on big data |
| CN111260223A (en) * | 2020-01-17 | 2020-06-09 | 山东省计算中心（国家超级计算济南中心） | Intelligent identification and early warning method, system, medium and equipment for trial and judgment risk |
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