CN113313475A - Intelligent skill review method based on big data - Google Patents
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Abstract
An intelligent technical review method based on big data comprises the steps of building a technical data base by data such as technical history data and electric power industry review specifications, then extracting data for technical review from a technical review actual service scene, building a technical review data pool, extracting the data in the built technical data base to build a data extraction model, dividing the data to be reviewed into structured data and unstructured data, finally forming one-to-one correspondence between the technical review data pool and the extracted data through the structured data, combining artificial adjustment to complete data matching and build a data matching model, simultaneously performing data matching comparison between the technical review data pool and the unstructured data to be reviewed, designing a review mode, further building an intelligent review model, then obtaining the data to be analyzed from the review data base, and realizing visual presentation after intelligent review, greatly improving the evaluation efficiency of the experts and better supporting the experts to finish evaluation and analysis work.
Description
Technical Field
The invention relates to the technical field of technical review, in particular to an intelligent technical review method based on big data.
Background
With the vigorous development of the electric power industry in China, the cost review is changing to the direction of intelligence, high efficiency and lean development, but the problems of isolated and dispersed data of historical skills, shortage of talents of review experts, saturated business volume, no formation of a systematic review mechanism and the like influence the development of the skill review.
The existing electric power industry still adopts the traditional manual mode to review, not only the workload is large, but also the defects of incomplete review and the like exist, although the improvement is carried out on the traditional manual review, only the management modes on the lines of review flow circulation, data collection and the like are emphasized, for example: the on-line informatization of the appraisers, the on-line online evaluation process, the on-line filling of the appraisers and the like can only meet the management functions of standardizing the appraising process and storing the appraising data, and cannot essentially reduce the current situation of the appraisers of the technology; huge historical skill data can not be reasonably utilized as data support for next evaluation, and the functions of intelligently analyzing the manufacturing cost file, intelligently generating analysis results, analyzing big data and the like can not be realized.
In order to promote the development of more scientific, more comprehensive and more deep investment cost analysis and cost examination of power grid projects, a technology based on big data acquisition and intelligent matching is urgently needed to be constructed for intelligent review so as to improve the quality and effect of the review.
Disclosure of Invention
The technical problem to be solved by the invention is to provide an intelligent skill review method based on big data to solve the problems in the background art.
The technical problem solved by the invention is realized by adopting the following technical scheme:
the intelligent technology review method based on big data comprises the following specific steps:
step 1) establishing a technical course database
Acquiring typical engineering data of nearly five years, preprocessing the data, importing the preprocessed data into a constructed technical data base, extracting technical indexes of key characteristics of the engineering from the engineering data of the technical data base through structural analysis to serve as identification codes, and performing report visual display on the technical data;
step 2) establishing a technical review data pool
Extracting data for technical review from an actual technical review service scene, and then building a technical review data pool for intelligent matching analysis;
step 3) extracting data
Extracting data in the technical data base built in the step 1), constructing a data extraction model, and dividing the data to be evaluated into structured data and unstructured data by a big data analysis method, wherein the structured data is used as a key factor for later stage matching, and the unstructured data is used as scanned content;
step 4) establishing a data matching model
Forming one-to-one correspondence between the technical review data pool established in the step 2) and the data extracted in the step 3) through structured data, and establishing a data matching model by combining artificial adjustment to complete data matching, and simultaneously performing data matching comparison on the technical review data pool and the non-structural data to be reviewed;
step 5) establishing an intelligent evaluation model
After the data continuously accumulated in the steps 1) to 4) are refined and upgraded, a review mode is designed, an intelligent review model is further established, data to be analyzed are obtained from a review database, and the complete review process and content are analyzed, so that the intelligent review is completed and the visual display is realized.
In the invention, the preprocessing in the step 1) comprises cleaning, statistics/analysis and classification of data.
In the invention, the data for technical review in the step 2) comprises compliance review data and industry normative data, the industry normative data is split, and an identification code is set for each split sub-item for classified storage.
In the invention, the structured data in the step 3) is identified and inquired through keyword fuzzy identification, and the unstructured data is analyzed and extracted to identify and inquire the key field.
In the invention, the data matching model in the step 4) realizes data matching through keyword identification, category matching and field splitting, recombining and matching.
In the invention, after the data to be analyzed is obtained from the review database in the step 5), the data to be analyzed is obtained through engineering key characteristic technical indexes, the intelligent review model adopts field recombination, forms byte splicing, outputs splicing results after completing comparison, and completes highlight visual display of data results according to the splicing results.
Has the advantages that: according to the invention, an intelligent technical review mode is constructed by extracting the data value, and the expert is assisted to finish the technical review work in a brand-new intelligent review mode, so that the review efficiency of the expert can be greatly improved, and the expert is better supported to finish the review analysis work; meanwhile, auxiliary work such as technical data analysis, cost control index measurement and updating, quota adjustment coefficient measurement and calculation and the like is provided, so that the potential of historical big data analysis is fully explored.
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FIG. 1 is a flow chart of the preferred embodiment of the present invention.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further explained below by combining the specific drawings.
Referring to fig. 1, the intelligent technology review method based on big data specifically comprises the following steps:
step 1) establishing a technical course database
Typical engineering data of nearly five years are collected, the data are cleaned, counted/analyzed, classified and then imported into a constructed technical data base, and then key engineering characteristic technical indexes (such as ' main transformer capacity, current stage number, long stage number, 220kV/110kV/20kV/10kV outgoing line side loop number, power distribution device and arrangement mode ' required to be extracted in power transformation engineering, voltage grade, wire section, loop number, path length, wire name, specification and laying mode ' required to be extracted in line engineering are extracted to serve as identification codes, and report visualization display is carried out on the technical data through structural analysis;
step 2) establishing a technical review data pool
The intelligent review can realize compliance review and normative review, such as: the method comprises the steps of splitting typical construction cost data of the south network with industrial normativity, setting identification codes for each split sub-item, and storing in a classified mode, so that a material data pool, a typical construction cost data pool, a fee-taking data pool and the like are built;
taking the typical cost of south net as an example:
table 1 south net typical cost data building part table building part
TABLE 2 typical cost data of south network and other fee combination table
The typical construction cost data of the south network is subjected to modularized splitting, then the data of each part are assigned to different parts, finally module matching is carried out, and modularized splicing calculation is completed, for example: typical manufacturing cost A total (static investment) ═ basic scheme (CSG-110B-G2a) -1 (110B-G1-1GIS2A) +1 (110B-G1-1GIS2B) +18 (G2-1GIS-MX1) +2 (G2-1GIS-BY) -40 (G2-1BYQ-MX2) -1 (G2-4KYN-FD2) +1 (G2-4KYN-FD1) -8 (G2-4KYN-MX2) -2 (G2-4 ZXD-002B) +2 (G2-4ZXD-001) + annual price difference + other cost adjustment + basic preparation cost;
step 3) extracting data
Extracting data in a skill database built in the step 1), analyzing and extracting key fields of unstructured data through keyword fuzzy recognition query of structured data, constructing a data extraction model, dividing data to be evaluated into structured data and unstructured data through a big data analysis method, taking the structured data as key factors for later stage matching, taking the unstructured data as scanned content, and extracting and classifying the data according to specific classification, such as: the construction cost files of the power transformation project and the line project are different in composition, and the acquired data table needs to be stored independently;
step 4) establishing a data matching model
Forming one-to-one correspondence between the technical review data pool established in the step 2) and the data extracted in the step 3) through structured data, and then combining with artificial adjustment to complete data matching and establishing a data matching model, and simultaneously matching and comparing the technical review data pool and the non-structural data to be reviewed, wherein the data matching model mainly realizes data matching through keyword identification, category matching and field splitting, recombining and matching;
step 5) establishing an intelligent evaluation model
After the data continuously accumulated in the steps 1) to 4) are refined and upgraded, a review mode is designed, an intelligent review model is further established, and the complete review flow and content are analyzed to complete the related functions of cost analysis and investment decision, such as: building an engineering investment amount analysis model, and acquiring data to be analyzed from a review database by taking an engineering type and a voltage grade as dimensions; meanwhile, acquiring data to be analyzed through engineering key characteristic technical indexes, adopting field recombination and byte splicing by the intelligent review model, outputting a splicing result after completing comparison, and finishing highlight visual display of data results according to the splicing result;
step 6) application analysis
The intelligent review model is used for finishing the functions of index comparison, typical technical data comparison, normative file content comparison, key equipment material price comparison, key technical index comparison, historical data comparison, different stage comparison, different version comparison, index analysis statistical chart, technical review intelligent report and the like, wherein the functions comprise: historical engineering comparison table 3:
TABLE 3 History engineering comparison table
| Serial number | Project or expense name | Pending project (A) | History engineering (B) | Difference (%) (B-A)/A |
| - | Main and auxiliary production engineering | 521.3 | 600 | |
| (A) | Main production engineering | 521.3 | 600 | |
| (II) | Auxiliary production engineering | |||
| II | Single project relating to site | |||
| Small counter | 521.3 | 512.3 | ||
| III | Compiling reference time price difference | 154.4 | 154.4 | |
| Five of them | Other costs | 5401.2 | 5401.2 | |
| 1 | Wherein: construction site requisition and cleaning fee | 712.9 | 1000 | |
| Five of them | Basic reserve charge | 91 | 91 | |
| Six ingredients | Special projects |
The foregoing is only a preferred embodiment of the present invention and it should be noted that modifications and adaptations can be made by those skilled in the art without departing from the principle of the present invention and are intended to be included within the scope of the present invention.
Claims (6)
1. The intelligent technology review method based on big data is characterized by comprising the following specific steps:
step 1) establishing a technical course database
Typical engineering data are collected, the data are preprocessed and then are led into a constructed technical channel database, and then through structured analysis, engineering key characteristic technical indexes are extracted from the engineering data of the technical channel database and serve as identification codes;
step 2) establishing a technical review data pool
Extracting data for technical review from an actual technical review service scene, and then building a technical review data pool;
step 3) extracting data
Extracting data in the technical data base built in the step 1), constructing a data extraction model, and dividing the data to be evaluated into structured data and unstructured data by a big data analysis method, wherein the structured data is used as a key factor for later stage matching, and the unstructured data is used as scanned content;
step 4) establishing a data matching model
Forming one-to-one correspondence between the technical review data pool established in the step 2) and the data extracted in the step 3) through structured data, and establishing a data matching model by combining artificial adjustment to complete data matching, and simultaneously performing data matching comparison on the technical review data pool and the non-structural data to be reviewed;
step 5) establishing an intelligent evaluation model
After the data continuously accumulated in the steps 1) to 4) are refined and upgraded, a review mode is designed, an intelligent review model is further established, data to be analyzed are obtained from a review database, and the complete review process and content are analyzed, so that the intelligent review is completed and the visual display is realized.
2. The big data based intelligent technical review method according to claim 1, wherein in the step 1), the preprocessing comprises cleaning, counting/analyzing and classifying the data.
3. An intelligent technical review method based on big data according to claim 1, wherein in the step 2), the data for technical review includes compliance review data and industry normative data, the industry normative data is split, and an identification code is set for each split sub-item for classified storage.
4. The big data-based intelligent technical review method according to claim 1, wherein in the step 3), the structured data is identified and queried through keyword fuzzy identification, and the unstructured data is analyzed to extract a key field identification and query.
5. The big data-based intelligent technology review method according to claim 1, wherein in step 4), the data matching model realizes data matching through keyword recognition, category matching, and field splitting, recombining and matching.
6. The intelligent technical review method based on big data according to claim 1, wherein in step 5), after the data to be analyzed is obtained from the review database, the data to be analyzed is obtained through engineering key feature technical indexes, the intelligent review model adopts field recombination, forms byte splicing, completes comparison, outputs a splicing result, and completes highlight visual display of data results according to the splicing result.
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