CN117875698A - Distribution network risk intelligent assessment method and system based on multi-source data fusion - Google Patents
Distribution network risk intelligent assessment method and system based on multi-source data fusion Download PDFInfo
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Abstract
The application provides a distribution network risk intelligent assessment method and system based on multi-source data fusion, wherein the method comprises the following steps: acquiring acquisition data in an intelligent terminal, and preprocessing according to the acquisition data to generate target data; the intelligent terminal comprises a handheld terminal and a communicable terminal; generating a data source according to the target data, the on-line monitoring time sequence data and the business relation basic database, wherein the on-line monitoring time sequence data and the business relation basic database are obtained from a power grid management platform; and generating an evaluation analysis result through the data source and the evaluation data model. The risk assessment is embodied in a digital mode during field operation, and the assessment result is judged more simply and the data analysis is more comprehensive by the digital risk assessment.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a distribution network risk intelligent assessment method and system based on multi-source data fusion.
Background
With the development of power grid business, equipment relying on electric power service is increasingly widely applied in daily life, the requirements on-site operation risk management and control, comprehensive operation data application, intelligent evaluation and the like are increasingly higher, and in the existing power grid management system flow, if the operation risk manual evaluation is mostly dependent on the experience of staff, larger subjectivity and evaluation analysis missing places often exist, so that the high-efficiency operation of the whole system is seriously affected; in addition, the multi-source data of the cross-domain cannot be effectively integrated and utilized, so that the integrity of the system is further improved.
In the prior art, the problems of insufficient digital support force of on-site operation risk assessment, high difficulty in judging operation risk assessment results and incomplete post-hoc data analysis exist.
Disclosure of Invention
In view of the above problems, the present application is directed to providing a method and a system for intelligently evaluating a distribution network risk based on multi-source data fusion, which overcomes the above problems or at least partially solves the above problems, and includes:
an intelligent evaluation method for distribution network risk based on multi-source data fusion, comprising the following steps:
acquiring acquisition data in an intelligent terminal, and preprocessing according to the acquisition data to generate target data; the intelligent terminal comprises a handheld terminal and a communicable terminal;
Generating a data source according to the target data, the on-line monitoring time sequence data and the business relation basic database, wherein the on-line monitoring time sequence data and the business relation basic database are obtained from a power grid management platform;
and generating an evaluation analysis result through the data source and the evaluation data model.
Further, acquiring acquired data in the intelligent terminal; the intelligent terminal comprises a handheld terminal and a communicable terminal, and comprises the following steps:
when the intelligent terminal is the handheld terminal, fixed data are acquired in the handheld terminal, wherein the fixed data comprise fingerprint data, electronic signature data and face data; and/or the number of the groups of groups,
when the intelligent terminal is the communicable terminal, acquiring real-time data in the communicable terminal, wherein the real-time data comprises snapshot data, encryption data, coding data and analysis data;
and generating the acquisition data according to the fingerprint data, the electronic signature data, the face data, the snapshot data, the encryption data, the coding data and the analysis data.
Further, the preprocessing is performed according to the acquired data to generate target data, wherein the preprocessing includes steps of cleaning processing, analyzing processing, storing processing and uploading processing, and the preprocessing includes:
Determining an application scene of the acquired data;
determining a data type of the acquired data according to the application scene, wherein the data type comprises image data, video data and environmental sensor data;
determining target preprocessing of the acquired data according to the data type;
and generating target data by carrying out target preprocessing on the acquired data.
Further, the step of obtaining the online monitoring time sequence data and the business relation basic database in the power grid management platform comprises the following steps:
acquiring functional data in the power grid management platform, wherein the functional data comprises personnel information management data, accident event management data and violation management data;
generating convergence data according to the personnel information management data, the accident event management data and the violation management data;
performing image processing on the converged data to generate the on-line monitoring time sequence data;
and carrying out message transmission analysis on the converged data to generate the business relation basic database.
Further, the step of generating an evaluation analysis result by the data source and the evaluation data model includes:
Performing approximate processing on knowledge models in different fields through machine learning or data mining to obtain an evaluation data model;
model training is carried out in the evaluation data model according to the data source to obtain approximate parameters;
grid searching is carried out according to the approximate parameters to obtain a super-parameter position;
generating a self-evaluation algorithm according to the approximate parameters and the super-parameter bits;
and generating the evaluation analysis result according to the self-evaluation algorithm, the evaluation data model and the data source.
Further, the step of generating the evaluation analysis result according to the self-evaluation algorithm, the evaluation data model and the data source includes:
generating a self-evaluation result according to the self-evaluation algorithm and the data source;
acquiring an actual risk result in the evaluation data model;
and generating the evaluation analysis result according to the self-evaluation result, the actual risk result and the model parameter.
Further, the method further comprises the following steps:
and carrying out visual analysis on the evaluation analysis result and the multi-source heterogeneous data to generate analysis data, wherein the multi-source heterogeneous data comprises multi-dimensional video data, monitoring data and business data, and the analysis data comprises operation planning professional comparison analysis data, operation planning risk and actual risk comparison analysis data, video access condition analysis data, liability associated punishment analysis data and supervision problem tracking and efficiency analysis data.
The embodiment of the application also discloses a distribution network risk intelligent evaluation system based on multi-source data fusion, which comprises:
the acquisition module is used for acquiring acquisition data in the intelligent terminal and preprocessing the acquisition data to generate target data; the intelligent terminal comprises a handheld terminal and a communicable terminal;
the first generation module is used for generating a data source according to the target data, the on-line monitoring time sequence data and the business relation basic database, wherein the on-line monitoring time sequence data and the business relation basic database are obtained from a power grid management platform;
and the second generation module is used for generating an evaluation analysis result through the data source and the evaluation data model.
An embodiment of the application further discloses a computer device, which comprises a processor, a memory and a computer program stored on the memory and capable of running on the processor, wherein the computer program realizes the steps of the intelligent evaluation method for the distribution network risk based on the multi-source data fusion when being executed by the processor.
An embodiment of the application further discloses a computer readable storage medium, where a computer program is stored, and the computer program when executed by a processor implements the steps of the intelligent evaluation method for risk of a distribution network based on multi-source data fusion as described above.
The application has the following advantages:
in the embodiment of the application, compared with the problems of insufficient digital support force, large judgment difficulty of operation risk assessment results and incomplete post-hoc data analysis in the field operation risk assessment in the prior art, the application provides a solution of a distribution network risk intelligent assessment method and system based on multi-source data fusion, which specifically comprises the following steps: acquiring acquisition data in the intelligent terminal, and preprocessing according to the acquisition data to generate target data; the intelligent terminal comprises a handheld terminal and a communicable terminal; generating a data source according to the target data, the on-line monitoring time sequence data and the business relation basic database, wherein the on-line monitoring time sequence data and the business relation basic database are obtained from a power grid management platform; and generating an evaluation analysis result through the data source and the evaluation data model. Generating a data source according to the target data, the on-line monitoring time sequence data and the business relation basic database, wherein the on-line monitoring time sequence data and the business relation basic database are obtained from a power grid management platform; the problems of insufficient digital support force, great difficulty in judging the operation risk assessment result and incomplete post-analysis of the operation risk assessment result are solved by the data source and the assessment data model to generate the assessment analysis result, and the effects of embodying the risk assessment in a digital mode during the operation on site, enabling the assessment result judgment to be simpler and the data analysis to be more comprehensive are achieved by the digital risk assessment.
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In order to more clearly illustrate the technical solutions of the present application, the drawings that are needed in the description of the present application will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort to a person skilled in the art.
Fig. 1 is a flowchart of steps of a method for intelligently evaluating risk of a distribution network based on multi-source data fusion according to an embodiment of the present application;
FIG. 2 is a block diagram of a distribution network risk intelligent assessment system based on multi-source data fusion according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present application more comprehensible, the present application is described in further detail below with reference to the accompanying drawings and detailed description. It will be apparent that the embodiments described are some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
The inventors found by analyzing the prior art that: with the development of power grid business, equipment relying on electric power service is increasingly widely applied in daily life, the requirements on-site operation risk management and control, comprehensive operation data application, intelligent evaluation and the like are increasingly higher, and in the existing power grid management system flow, if the operation risk manual evaluation is mostly dependent on the experience of staff, larger subjectivity and evaluation analysis missing places often exist, so that the high-efficiency operation of the whole system is seriously affected; in addition, the multi-source data of the cross-domain cannot be effectively integrated and utilized, so that the integrity of the system is further improved.
In the prior art, patent application number CN202311179076.0 of a method, a device, equipment and a storage medium for analyzing the data quality of a smart grid discloses a method for analyzing the data quality of the smart grid, and the association and evaluation of the data are realized through a graph neural network so as to improve the efficiency and the accuracy of the data quality analysis of the smart grid; the intelligent power grid data aggregation method and system based on the security mask uses the secret key to generate the security mask by the patent application number CN202311223364.1, so that the security and the efficiency of the intelligent power grid data aggregation are improved; the patent application number CN202310734591.4 of the power supply and distribution security risk assessment system based on data analysis realizes power supply and distribution security risk assessment through fusion monitoring and load analysis.
The prior art has the following problems:
1. digital support of on-site operation risk assessment is insufficient
According to the management requirements of the '1+N' field operation risk management and control service instruction, an operation responsible person and a supervisory person should comprehensively grasp risks such as operation personnel, equipment, environment and the like, rapidly identify personnel qualification, identify and process illegal behaviors, conveniently acquire field data, provide data support for operation process management and control, and currently lack comprehensive support of digital means such as intelligent terminals, mobile applications and the like.
2. The judgment difficulty of the operation risk assessment result is large
The operation risk assessment process excessively depends on manual judgment and depends on manual calculation, part of personnel is insufficient in knowledge and experience, risk assessment randomness is large, risk assessment steps are complicated and intelligent, the risk result and the field actual deviation are large, the phenomenon that the risk level is artificially reduced is common, and the difficulty of judging the actual risk of operation by safety supervision personnel is large.
3. Post hoc analysis of data
At present, the operation risk focuses on risk distribution display, lacks tracking analysis on supervision problems, planning risk and current-day risk comparison analysis, video access condition analysis and the like, lacks means for intuitively displaying rules, risk states, risk trends, risk features and the like of risks, and lacks statistical analysis of risk rules and risk trends.
Referring to fig. 1, a step flowchart of a method for intelligently evaluating risk of a distribution network based on multi-source data fusion according to an embodiment of the present application is shown;
an intelligent evaluation method for distribution network risk based on multi-source data fusion, comprising the following steps:
s110, acquiring acquisition data in the intelligent terminal, and preprocessing according to the acquisition data to generate target data; the intelligent terminal comprises a handheld terminal and a communicable terminal;
s120, generating a data source according to the target data, the on-line monitoring time sequence data and a business relation basic database, wherein the on-line monitoring time sequence data and the business relation basic database are obtained from a power grid management platform;
s130, generating an evaluation analysis result through the data source and the evaluation data model.
In the embodiment of the application, compared with the problems of insufficient digital support force, large judgment difficulty of operation risk assessment results and incomplete post-hoc data analysis in the field operation risk assessment in the prior art, the application provides a solution of a distribution network risk intelligent assessment method and system based on multi-source data fusion, which specifically comprises the following steps: acquiring acquisition data in the intelligent terminal, and preprocessing according to the acquisition data to generate target data; the intelligent terminal comprises a handheld terminal and a communicable terminal; generating a data source according to the target data, the on-line monitoring time sequence data and the business relation basic database, wherein the on-line monitoring time sequence data and the business relation basic database are obtained from a power grid management platform; and generating an evaluation analysis result through the data source and the evaluation data model. Generating a data source according to the target data, the on-line monitoring time sequence data and the business relation basic database, wherein the on-line monitoring time sequence data and the business relation basic database are obtained from a power grid management platform; the problems of insufficient digital support force, great difficulty in judging the operation risk assessment result and incomplete post-analysis of the operation risk assessment result are solved by the data source and the assessment data model to generate the assessment analysis result, and the effects of embodying the risk assessment in a digital mode during the operation on site, enabling the assessment result judgment to be simpler and the data analysis to be more comprehensive are achieved by the digital risk assessment.
Next, a method for intelligently evaluating a distribution network risk based on multi-source data fusion in this exemplary embodiment will be further described.
Acquiring acquisition data in the intelligent terminal, and preprocessing the acquisition data to generate target data; the intelligent terminal comprises a handheld terminal and a communicable terminal.
In an embodiment of the present invention, the step S110 of "acquiring the acquired data in the intelligent terminal" may be further described in conjunction with the following description; the intelligent terminal comprises a handheld terminal and a communicable terminal.
As will be described in the following steps,
s210, when the intelligent terminal is the handheld terminal, fixed data are acquired in the handheld terminal, wherein the fixed data comprise fingerprint data, electronic signature data and face data; and/or the number of the groups of groups,
s220, when the intelligent terminal is the communicable terminal, acquiring real-time data in the communicable terminal, wherein the real-time data comprises snapshot data, encryption data, coding data and analysis data;
s230, generating the acquisition data according to the fingerprint data, the electronic signature data, the face data, the snapshot data, the encryption data, the coding data and the analysis data.
It should be noted that, the handheld terminal is an electronic equipment terminal of the user; the communication terminal is a camera and a distributed control ball; the other device may also be a safety helmet.
As an example, fixed data about fingerprints, electronic signatures, faces, etc. of the handheld intelligent terminal are collected.
As an example, real-time data including snapshot, encryption, encoding and analysis of the communication equipment is collected through intelligent terminal equipment recording with law enforcement recording, identity card reading and intercom conversation.
In a specific implementation, the collected data may be video, picture, text, or structured data.
Acquiring acquisition data in the intelligent terminal, and preprocessing the acquisition data to generate target data; the intelligent terminal comprises a handheld terminal and a communicable terminal.
In one embodiment of the present invention, the following description may be combined to further describe "preprocessing to generate target data according to the acquired data" in step S110; wherein the pretreatment includes specific processes of a cleaning process, an analysis process, a storage process and an uploading process.
As will be described in the following steps,
s310, determining an application scene of the acquired data;
S320, determining the data type of the acquired data according to the application scene, wherein the data type comprises image data, video data and environment sensor data;
s330, determining target preprocessing of the acquired data according to the data type;
s340, generating target data by carrying out target preprocessing on the acquired data.
The preprocessing includes a cleaning process, an analysis process, a storage process, and an uploading process.
As an example, for a specific application scenario, the type of data collected by the intelligent terminal, such as images, videos, environmental sensor data, etc., is determined, and a reasonable data processing flow is designed, including data cleaning, analysis, and intelligent processing, to record useful information about job risk.
In a specific implementation, a TF card supporting the pluggable of the intelligent terminal device stores the evidence-taking data locally or uploads the data to the command center by using a mobile network. The image recognition algorithm is supported to rapidly recognize the operation risk, and the face recognition is used for rapidly monitoring the job performance of security management personnel of each level.
In one specific implementation, preprocessing may be implemented by face recognition, multi-target tracking, OCR recognition (i.e., optical character recognition), or text entity recognition.
And step S120, generating a data source according to the target data, the on-line monitoring time sequence data and the business relation basic database, wherein the on-line monitoring time sequence data and the business relation basic database are obtained from a power grid management platform.
In an embodiment of the present invention, the specific process of "the online monitoring time series data and the business relation base database are both obtained in the grid management platform" in step S120 may be further described in conjunction with the following description.
As will be described in the following steps,
s410, acquiring functional data in the power grid management platform, wherein the functional data comprises personnel information management data, accident event management data and violation management data;
s420, generating convergence data according to the personnel information management data, the accident event management data and the violation management data;
s430, performing image processing on the converged data to generate the on-line monitoring time sequence data;
s440, carrying out message transmission analysis on the converged data to generate the business relation basic database.
It should be noted that, the personnel information management module corresponds to personnel information management data, the accident event management module corresponds to accident event management data, and the violation management module corresponds to violation management data; the personnel information management module, the accident event management module and the violation management module are all functional modules in the power grid management platform.
As an example, a multi-domain data fusion channel is constructed based on a power grid management platform, data convergence with functional templates such as personnel information management, accident event management, violation management and the like is realized, an on-line monitoring time sequence data and a business relation basic database are obtained through an image processing technology and a message transmission analysis technology, and a real-time comprehensive fusion data source is constructed.
In a specific implementation, the data collection may be implemented by HIVE, knowledge graph, minio, or kafka;
hive is a data warehouse tool based on Hadoop for data extraction, transformation, and loading, which is a mechanism that can store, query, and analyze large-scale data stored in Hadoop. The hive data warehouse tool can map a structured data file into a database table, provide SQL query functions, and convert SQL sentences into MapReduce tasks for execution. Hive has the advantages that learning cost is low, rapid MapReduce statistics can be realized through SQL-like sentences, mapReduce is simpler, and a special MapReduce application program does not need to be developed. hive is well suited for statistical analysis of data warehouses.
The Knowledge map (knowledgegraph), called Knowledge domain visualization or Knowledge domain mapping map in book condition report, is a series of various graphs showing Knowledge development process and structural relationship, and uses visualization technology to describe Knowledge resources and their carriers, and excavate, analyze, construct, draw and display Knowledge and their interrelationships.
Minio is an object storage item based on Apache License v2.0 open source protocol, and is realized by Golang, and a client supports Java, python, javanipt and Golang languages. The main goal of its design is a standard solution stored as a private cloud object. The method is mainly used for storing massive pictures, videos, documents and the like. It is well suited for storing large volumes of unstructured data, such as pictures, videos, log files, backup data, and container/virtual machine images, etc., while an object file may be any size, varying from a few kb up to a maximum of 5T.
Kafka is a high-throughput distributed publish-subscribe messaging system that can handle all action flow data for consumers in a web site. Such actions (web browsing, searching and other user actions) are a key factor in many social functions on modern networks. These data are typically addressed by processing logs and log aggregations due to throughput requirements. This is a viable solution for log data and offline analysis systems like Hadoop, but with the limitation of requiring real-time processing. The purpose of Kafka is to unify on-line and off-line message processing through the Hadoop parallel loading mechanism, and also to provide real-time messages through the clusters.
An evaluation analysis result is generated by the data source and the evaluation data model, as described in the step S130.
In one embodiment of the present invention, the specific process of "generating an evaluation analysis result by the data source and the evaluation data model" described in step S130 may be further described in conjunction with the following description.
As will be described in the following steps,
s510, performing approximate processing on knowledge models in different fields through machine learning or data mining to obtain an evaluation data model;
s520, performing model training in the estimated data model according to the data source to obtain approximate parameters;
s530, grid searching is carried out according to the approximate parameters to obtain a super-parameter position;
s540, generating a self-evaluation algorithm according to the approximate parameters and the super-parameter bits;
s550, generating the evaluation analysis result according to the self-evaluation algorithm, the evaluation data model and the data source.
As an example, knowledge models in different fields are approximated by means of machine learning, data mining and the like, model training is performed by using data to solve approximation parameters, and super-parametric positioning is realized by combining grid search, so that a self-evaluation algorithm and a data model are continuously optimized.
The evaluation analysis results are generated according to the self-evaluation algorithm, the evaluation data model and the data source as described in the step S550.
In one embodiment of the present invention, the specific process of "generating the evaluation analysis result according to the self-evaluation algorithm, the evaluation data model and the data source" in step S550 may be further described in conjunction with the following description.
As will be described in the following steps,
s610, generating a self-evaluation result according to the self-evaluation algorithm and the data source;
s620, acquiring an actual risk result in the evaluation data model;
s630, generating the evaluation analysis result according to the self-evaluation result, the actual risk result and the model parameter.
As an example, the obtained self-evaluation result and the actual risk result are fed back, differential control is achieved, model parameters are reasonably adjusted according to actual conditions, an effective evaluation result intelligent verification closed loop is formed, and an actual business application is supported through system solidification research results.
In one embodiment, the evaluation analysis result may be qualification checking, two-ticket irregular identification, unworn safety helmet or overall job risk evaluation.
An evaluation analysis result is generated by the data source and the evaluation data model, as described in the step S130.
In one embodiment of the present invention, the specific procedure following the "generating an evaluation analysis result by the data source and the evaluation data model" described in step S130 may be further described in connection with the following description.
As will be described in the following steps,
and S140, carrying out visual analysis on the evaluation analysis result and the multi-source heterogeneous data to generate analysis data, wherein the multi-source heterogeneous data comprises multi-dimensional video data, monitoring data and business data, and the analysis data comprises operation plan division professional comparison analysis data, operation plan risk and actual risk comparison analysis data, video access condition analysis data, liability associated punishment analysis data, supervision problem tracking and efficiency analysis data.
As an example, based on big data analysis technology, multi-source heterogeneous data calculation technology and intelligent analysis algorithm are utilized to fuse multi-dimensional video data, monitoring data and business data, and application scenes such as professional comparison analysis of job planning division, comparison analysis of job planning risks and actual risks, video access condition analysis, liability associated punishment analysis, supervision problem tracking and efficiency analysis are presented in a data visualization mode, so that data such as risk rules, risk trends, risk assessment and the like in a period of time are intuitively displayed.
In one embodiment, the analysis data is returned to the handheld terminal or communicable terminal in visual form.
For system embodiments, the description is relatively simple as it is substantially similar to method embodiments, and reference is made to the description of method embodiments for relevant points.
Referring to fig. 2, a block diagram of a distribution network risk intelligent assessment system based on multi-source data fusion according to an embodiment of the present application is shown;
a distribution network risk intelligent assessment system based on multi-source data fusion, the system comprising:
the acquisition module 310 is configured to acquire acquired data in the intelligent terminal, and perform preprocessing according to the acquired data to generate target data; the intelligent terminal comprises a handheld terminal and a communicable terminal;
a first generation module 320, configured to generate a data source according to the target data, the online monitoring time sequence data, and a business relationship basic database, where the online monitoring time sequence data and the business relationship basic database are obtained from a power grid management platform;
and a second generation module 330, configured to generate an evaluation analysis result through the data source and the evaluation data model.
In an embodiment of the present invention, the obtaining module 310 includes:
the first acquisition sub-module is used for acquiring fixed data in the handheld terminal when the intelligent terminal is the handheld terminal, wherein the fixed data comprises fingerprint data, electronic signature data and face data; and/or the number of the groups of groups,
The second acquisition sub-module is used for acquiring real-time data in the communicable terminal when the intelligent terminal is the communicable terminal, wherein the real-time data comprises snapshot data, encryption data, coding data and analysis data;
the first generation sub-module is used for generating the acquisition data according to the fingerprint data, the electronic signature data, the face data, the snapshot data, the encryption data, the coding data and the analysis data.
In an embodiment of the present invention, the obtaining module 310 includes:
the first determining submodule is used for determining the application scene of the acquired data;
a second determining submodule, configured to determine a data type of the acquired data according to the application scenario, where the data type includes image data, video data, and environmental sensor data;
a third determining sub-module, configured to determine a target preprocessing of the collected data according to the data type;
and the second generation sub-module is used for generating target data by carrying out the target preprocessing on the acquired data.
In an embodiment of the present invention, the first generating module 320 includes:
The third acquisition sub-module is used for acquiring functional data in the power grid management platform, wherein the functional data comprises personnel information management data, accident event management data and violation management data;
the third generation sub-module is used for generating convergence data according to the personnel information management data, the accident event management data and the violation management data;
a fourth generation sub-module, configured to perform image processing on the aggregate data to generate the online monitoring time sequence data;
and the fifth generation sub-module is used for carrying out message transmission analysis on the converged data to generate the business relation basic database.
In an embodiment of the present invention, the second generating module 330 includes:
the processing sub-module is used for performing approximate processing on knowledge models in different fields through machine learning or data mining to obtain an evaluation data model;
the training sub-module is used for carrying out model training in the evaluation data model according to the data source to obtain approximate parameters;
the searching sub-module is used for carrying out grid searching according to the approximate parameters to obtain a super-parameter position;
a sixth generation sub-module, configured to generate a self-evaluation algorithm according to the approximation parameter and the super-parameter bit;
And a seventh generation sub-module, configured to generate the evaluation analysis result according to the self-evaluation algorithm, the evaluation data model and the data source.
In an embodiment of the present invention, the seventh generating sub-module includes:
the first generation unit is used for generating a self-evaluation result according to the self-evaluation algorithm and the data source;
the acquisition unit is used for acquiring an actual risk result in the evaluation data model;
and the second generation unit is used for generating the evaluation analysis result according to the self-evaluation result, the actual risk result and the model parameter.
In an embodiment of the present invention, after the second generating module 330, the method includes:
the third generating module 340 is configured to perform visual analysis on the evaluation analysis result in combination with multi-source heterogeneous data to generate analysis data, where the multi-source heterogeneous data includes multi-dimensional video data, monitoring data and business data, and the analysis data includes operation plan division professional comparison analysis data, operation plan risk and actual risk comparison analysis data, video access condition analysis data, liability associated punishment analysis data, supervision problem tracking and efficiency analysis data.
The beneficial effects of this application: in order to solve the problems of the existing power management system in links such as data resource fusion, risk assessment and intelligent analysis, the application provides a distribution network risk intelligent assessment method and system based on multi-source data fusion, firstly, application scenes of intelligent equipment such as handheld terminals, law enforcement instruments, distribution control balls and safety helmets in on-site operation risk management are researched, the characteristics of convenience, timeliness and intelligence of the intelligent terminals are brought into play, rapid data acquisition and intelligent processing are realized, and a data basis is provided for operation risk intelligent assessment and analysis; secondly, developing mobile application support paperless evidence obtaining operation and operation risk process assessment, simplifying management, reducing workload and improving work efficiency of distribution network operation; thirdly, aiming at original structure, time sequence, unstructured and other multi-source data, data processing is carried out through a fusion framework, different structure data types are output, and integration and convergence of resources are achieved; and finally, fusing each service data to carry out quantization and evaluation, constructing an omnibearing risk assessment label system, adopting a cross-domain knowledge fusion method based on machine learning, and utilizing a data model to train and solve approximate parameters, thereby effectively improving the accuracy of an operation risk automatic assessment algorithm.
Referring to fig. 3, a computer device of the present invention for intelligently evaluating a distribution network risk based on multi-source data fusion may specifically include the following:
the computer device 12 described above is embodied in the form of a general purpose computing device, and the components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, a bus 18 that connects the various system components, including the system memory 28 and the processing units 16.
Bus 18 represents one or more of several types of bus 18 structures, including a memory bus 18 or memory controller, a peripheral bus 18, an accelerated graphics port, a processor, or a local bus 18 using any of a variety of bus 18 architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus 18, micro channel architecture (MAC) bus 18, enhanced ISA bus 18, video Electronics Standards Association (VESA) local bus 18, and Peripheral Component Interconnect (PCI) bus 18.
Computer device 12 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by computer device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32. The computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer storage media. By way of example only, storage system 34 may be used to read from or write to non-removable, nonvolatile magnetic media (commonly referred to as a "hard disk drive"). Although not shown in fig. 3, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk such as a CD-ROM, DVD-ROM, or other optical media may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. The memory may include at least one program product having a set (e.g., at least one) of program modules 42, the program modules 42 being configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored in, for example, a memory, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules 42, and program data, each or some combination of which may include an implementation of a network environment. Program modules 42 generally perform the functions and/or methods of the embodiments described herein.
The computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, camera, etc.), one or more devices that enable a user to interact with the computer device 12, and/or any devices (e.g., network card, modem, etc.) that enable the computer device 12 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 22. Moreover, computer device 12 may also communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet, through network adapter 20. As shown, network adapter 20 communicates with other modules of computer device 12 via bus 18. It should be appreciated that although not shown in fig. 3, other hardware and/or software modules may be used in connection with computer device 12, including, but not limited to: microcode, device drivers, redundant processing units 16, external disk drive arrays, RAID systems, tape drives, data backup storage systems 34, and the like.
The processing unit 16 executes programs stored in the system memory 28 to perform various functional applications and data processing, for example, to implement a network risk intelligent assessment method based on multi-source data fusion according to an embodiment of the present invention.
That is, the processing unit 16 realizes when executing the program: acquiring acquisition data in an intelligent terminal, and preprocessing according to the acquisition data to generate target data; the intelligent terminal comprises a handheld terminal and a communicable terminal; generating a data source according to the target data, the on-line monitoring time sequence data and the business relation basic database, wherein the on-line monitoring time sequence data and the business relation basic database are obtained from a power grid management platform; and generating an evaluation analysis result through the data source and the evaluation data model.
In the embodiment of the present invention, the present invention further provides a computer readable storage medium, where a computer program is stored, where the program when executed by a processor implements a distribution network risk intelligent assessment method based on multi-source data fusion as provided in all embodiments of the present application:
that is, the program is implemented when executed by a processor: acquiring acquisition data in an intelligent terminal, and preprocessing according to the acquisition data to generate target data; the intelligent terminal comprises a handheld terminal and a communicable terminal; generating a data source according to the target data, the on-line monitoring time sequence data and the business relation basic database, wherein the on-line monitoring time sequence data and the business relation basic database are obtained from a power grid management platform; and generating an evaluation analysis result through the data source and the evaluation data model.
Any combination of one or more computer readable media may be employed. The computer readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Computer program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described by differences from other embodiments, and identical and similar parts between the embodiments are all enough to be referred to each other.
While preferred embodiments of the present embodiments have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the present application.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or terminal device comprising the element.
The foregoing describes in detail a method and a system for intelligently evaluating a distribution network risk based on multi-source data fusion, and specific examples are applied to illustrate the principles and embodiments of the present application, and the description of the foregoing examples is only used to help understand the method and core ideas of the present application; meanwhile, as those skilled in the art will have modifications in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
Claims (10)
1. The intelligent evaluation method for the distribution network risk based on the multi-source data fusion is characterized by comprising the following steps:
acquiring acquisition data in an intelligent terminal, and preprocessing according to the acquisition data to generate target data; the intelligent terminal comprises a handheld terminal and a communicable terminal;
generating a data source according to the target data, the on-line monitoring time sequence data and the business relation basic database, wherein the on-line monitoring time sequence data and the business relation basic database are obtained from a power grid management platform;
and generating an evaluation analysis result through the data source and the evaluation data model.
2. The method of claim 1, wherein the acquiring the acquisition data in the intelligent terminal; the intelligent terminal comprises a handheld terminal and a communicable terminal, and comprises the following steps:
when the intelligent terminal is the handheld terminal, fixed data are acquired in the handheld terminal, wherein the fixed data comprise fingerprint data, electronic signature data and face data; and/or the number of the groups of groups,
when the intelligent terminal is the communicable terminal, acquiring real-time data in the communicable terminal, wherein the real-time data comprises snapshot data, encryption data, coding data and analysis data;
and generating the acquisition data according to the fingerprint data, the electronic signature data, the face data, the snapshot data, the encryption data, the coding data and the analysis data.
3. The method of claim 1, wherein the preprocessing to generate target data based on the acquired data includes the steps of cleaning, analyzing, storing, and uploading, including:
determining an application scene of the acquired data;
determining a data type of the acquired data according to the application scene, wherein the data type comprises image data, video data and environmental sensor data;
Determining target preprocessing of the acquired data according to the data type;
and generating target data by carrying out target preprocessing on the acquired data.
4. The method of claim 1, wherein the step of obtaining the online monitoring time series data and the business relationship base database from a grid management platform comprises:
acquiring functional data in the power grid management platform, wherein the functional data comprises personnel information management data, accident event management data and violation management data;
generating convergence data according to the personnel information management data, the accident event management data and the violation management data;
performing image processing on the converged data to generate the on-line monitoring time sequence data;
and carrying out message transmission analysis on the converged data to generate the business relation basic database.
5. The method of claim 1, wherein the step of generating an evaluation analysis result from the data source and an evaluation data model comprises:
performing approximate processing on knowledge models in different fields through machine learning or data mining to obtain an evaluation data model;
Model training is carried out in the evaluation data model according to the data source to obtain approximate parameters;
grid searching is carried out according to the approximate parameters to obtain a super-parameter position;
generating a self-evaluation algorithm according to the approximate parameters and the super-parameter bits;
and generating the evaluation analysis result according to the self-evaluation algorithm, the evaluation data model and the data source.
6. The method of claim 5, wherein the step of generating the evaluation analysis result from the self-evaluation algorithm, the evaluation data model, and the data source comprises:
generating a self-evaluation result according to the self-evaluation algorithm and the data source;
acquiring an actual risk result in the evaluation data model;
and generating the evaluation analysis result according to the self-evaluation result, the actual risk result and the model parameter.
7. The method as recited in claim 1, further comprising:
and carrying out visual analysis on the evaluation analysis result and the multi-source heterogeneous data to generate analysis data, wherein the multi-source heterogeneous data comprises multi-dimensional video data, monitoring data and business data, and the analysis data comprises operation planning professional comparison analysis data, operation planning risk and actual risk comparison analysis data, video access condition analysis data, liability associated punishment analysis data and supervision problem tracking and efficiency analysis data.
8. An intelligent evaluation system for distribution network risk based on multi-source data fusion, which is characterized by comprising:
the acquisition module is used for acquiring acquisition data in the intelligent terminal and preprocessing the acquisition data to generate target data; the intelligent terminal comprises a handheld terminal and a communicable terminal;
the first generation module is used for generating a data source according to the target data, the on-line monitoring time sequence data and the business relation basic database, wherein the on-line monitoring time sequence data and the business relation basic database are obtained from a power grid management platform;
and the second generation module is used for generating an evaluation analysis result through the data source and the evaluation data model.
9. A computer device comprising a processor, a memory and a computer program stored on the memory and capable of running on the processor, which computer program, when executed by the processor, implements the method of any one of claims 1 to 7.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the method according to any of claims 1 to 7.
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