Electric power engineering cost data management method and system

Abstract

The invention provides an electric power engineering cost data management method and system, and the method comprises the steps: carrying out the data optimization of cost data in a cost database, obtaining optimized data, and carrying out the business abstraction and business modeling analysis based on the optimized data, performing entity definition, relationship definition and attribute definition analysis on a cost data model of the cost database according to the analysis result to obtain the analysis result; constructing a statistical rule according to different cost indexes in different cost systems, extracting a data source from cost data in the cost database, realizing business index analysis of the cost data through combined extraction, and constructing a data extraction rule according to an analysis result and the statistical rule; basic parameter indexes of cost analysis are processed based on the template data, and a general scheme library and a module database are constructed based on a processing result and historical engineering data; and performing project cost analysis on the to-be-processed data according to the to-be-processed data, the data extraction rule and the analysis result to form a cost analysis table.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and system for managing power engineering cost data. Background Art

[0002] With the rapid development of science and technology and the improvement of people's living standards, the number of engineering construction projects is gradually increasing. Therefore, surveying in the process of engineering construction is also a top priority.

[0003] In the field of surveying, the extraction, integration and matching of pipeline information is a complex and time-consuming task. The traditional processing method is for staff to manually collect data and manually process the collected data to achieve the conversion and processing of pipeline information. However, manual collection and manual processing methods are inefficient and prone to errors, which leads to abnormal processing of pipeline information and affects the progress of engineering construction. Summary of the invention

[0004] The embodiments of the present application provide a method and system for managing power engineering cost data to at least address the deficiencies in the above-mentioned related technologies.

[0005] In a first aspect, an embodiment of the present application provides a method for managing power engineering cost data, comprising the following steps: Step 1: Optimize the cost data in the cost database to obtain corresponding optimized data, perform business abstraction and business modeling analysis based on the optimized data, and perform entity definition, relationship definition and attribute definition analysis on the cost data model of the cost database according to the analysis results to obtain corresponding analysis results; Step 2: Construct statistical rules according to different cost indicators in different cost systems, extract data sources from the cost data in the cost database, implement business indicator analysis of the cost data through combined extraction, and construct corresponding data extraction rules according to the analysis results and the statistical rules; Step 3: Process the basic parameter indicators of cost analysis based on the template data, and build a general solution library and module database based on the processing results and historical engineering data; Step 4: Acquire the data to be processed, and perform engineering cost analysis on the data to be processed according to the data to be processed, the data extraction rules and the analysis results to form a cost analysis table.

[0006] Furthermore, the step 1 includes: Classifying the cost data in the cost database to classify the cost data into a plurality of classified data according to the data type; The classified data of the same data class are normalized, and the normalized data are de-discretized to obtain the corresponding optimized data.

[0007] Furthermore, the step 1 also includes: Performing business abstraction and business modeling analysis based on the optimized data to obtain structured data and semi-structured data; The data source attribution of the structured data and the semi-structured data is analyzed to determine the indicator data in the structured data and the semi-structured data, wherein the indicator data is directly applied to indicator extraction.

[0008] Furthermore, the step 2 includes: Constructing statistical rules based on different cost indicators in different cost systems, wherein the cost indicators include project scale, project quantity indicators and cost indicators; The source data is extracted from the cost data in the cost database, and the business indicators are analyzed by combined extraction calculation, and corresponding data extraction rules are constructed according to the analysis results and the statistical rules.

[0009] Furthermore, the step three includes: Summarize and integrate the basic parameter indicators of cost analysis based on template data, extract and improve the statistics and sorting of cost indicators to obtain the corresponding general cost data; Historical engineering data is obtained, and a difference comparison analysis is performed based on the historical engineering data and the general cost data to build a general solution library and a module database.

[0010] In a second aspect, an embodiment of the present application provides a power engineering cost data management system, including: A data optimization module is used to optimize the cost data in the cost database to obtain corresponding optimized data, and perform business abstraction and business modeling analysis based on the optimized data, and perform entity definition, relationship definition and attribute definition analysis on the cost data model of the cost database according to the analysis results to obtain corresponding analysis results; A rule construction module is used to construct statistical rules according to different cost indicators in different cost systems, extract data sources from the cost data in the cost database, implement business indicator analysis of the cost data through combined extraction, and construct corresponding data extraction rules according to the analysis results and the statistical rules; The data processing module is used to process the basic parameter indicators of cost analysis based on the template data, and to build a general solution library and module database based on the processing results and historical engineering data; The cost analysis module is used to obtain the data to be processed, and perform engineering cost analysis on the data to be processed according to the data to be processed, the data extraction rules and the analysis results to form a cost analysis table.

[0011] Furthermore, the data optimization module includes: A data classification unit is used to classify the cost data in the cost database so as to classify the cost data into a plurality of classified data according to the data type; The data optimization unit is used to normalize the classified data of the same data class and de-discretize the normalized data to obtain corresponding optimized data.

[0012] Furthermore, the data optimization module also includes: A modeling and analysis unit, used for performing business abstraction and business modeling analysis based on the optimized data to obtain structured data and semi-structured data; The indicator determination unit is used to analyze the data source attribution of the structured data and the semi-structured data to determine the indicator data in the structured data and the semi-structured data, wherein the indicator data is directly applied to indicator extraction.

[0013] Furthermore, the rule building module includes: A rule building unit, used to build statistical rules according to different cost indicators in different cost systems, wherein the cost indicators include project scale, project quantity indicators and cost indicators; The data extraction unit is used to extract source data from the cost data in the cost database, and use combined extraction calculation to analyze business indicators, and construct corresponding data extraction rules based on the analysis results and the statistical rules.

[0014] Furthermore, the data processing module includes: The data aggregation unit is used to aggregate and integrate the basic parameter indicators of cost analysis based on the template data, extract and improve the statistics and sorting of cost indicators to obtain the corresponding general cost data; The database construction unit is used to obtain historical engineering data and perform difference comparison and analysis based on the historical engineering data and the general cost data to construct a general solution library and a module database.

[0015] Compared with the related art, the embodiment of the present application provides a power engineering cost data management method and system thereof, which optimizes the cost data to improve the efficiency and accuracy of data processing, performs business abstraction and business modeling analysis on the optimized data, and quickly determines the data model composition of structured data and semi-structured data, thereby improving data processing efficiency; constructs statistical rules according to different cost indicators in different cost systems, extracts data sources from the cost data in the cost database, constructs data extraction rules through combined extraction, effectively cleans and verifies the data, eliminates data errors and inconsistencies, and improves data accuracy; conducts real-time analysis of cost indicator data, reflects changes in cost data in real time, and provides timely and accurate data support for decision makers.

[0016] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 It is a flow chart of the power engineering cost data management method in the first embodiment of the present invention; Figure 2 for Figure 1 Detailed flow chart of step S101; Figure 3 for Figure 1 Detailed flow chart of step S102; Figure 4 for Figure 1 Detailed flow chart of step S103; Figure 5 This is a structural block diagram of a power engineering cost data management system in the second embodiment of the present invention.

[0018] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0020] Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. In addition, it can also be understood that although the efforts made in this development process may be complicated and lengthy, for ordinary technicians in this field related to the content disclosed in this application, some changes in design, manufacturing or production based on the technical content disclosed in this application are just conventional technical means, and should not be understood as insufficient content disclosed in this application.

[0021] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0022] Unless otherwise defined, the technical terms or scientific terms involved in this application should be understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the" and the like involved in this application do not indicate a quantitative limitation and may represent the singular or plural. The terms "include", "comprise", "have" and any of their variations involved in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. Embodiment 1

[0023] See also Figure 1 , which is a method for managing power engineering cost data in a first embodiment of the present invention, and the method specifically includes steps S101 to S104: S101, optimizing the cost data in the cost database to obtain corresponding optimized data, performing business abstraction and business modeling analysis based on the optimized data, and performing entity definition, relationship definition, and attribute definition analysis on the cost data model of the cost database according to the analysis results to obtain corresponding analysis results; For further information, see Figure 2, the step S101 specifically includes steps S1011~S1012: S1011, classifying the cost data in the cost database to classify the cost data into a plurality of classified data according to data types; S1012, normalizing the classified data of the same data class, and de-discretizing the normalized data to obtain corresponding optimized data.

[0024] In the specific implementation, the cost data in the cost database are analyzed, wherein the cost data includes relevant attributes such as basic engineering characteristics, design parameters, and technical and economic indicators, and are preliminarily screened to obtain the data type of the cost data, and the cost data in the above-mentioned cost database are divided into a variety of classified data according to the data type. It should be noted that the classified data is in the form of numerical attributes, so it is necessary to first normalize the continuous attribute values ​​to reduce the noise between the data, and discretize the normalized data to achieve the data optimization effect and obtain the corresponding optimized data: ; In the formula, represents a Gaussian density function with a mean of 0 and a variance of 1. Indicates the category The number of samples, Indicates the number of categorical data; In some optional embodiments, the step S101 further includes steps S1111-S1112: S1111, performing business abstraction and business modeling analysis based on the optimized data to obtain structured data and semi-structured data; S1112, analyzing the data source attribution of the structured data and the semi-structured data to determine the indicator data in the structured data and the semi-structured data, wherein the indicator data is directly used for indicator extraction.

[0025] In the specific implementation, by performing business abstraction and business modeling analysis on the above-mentioned optimization data, the entity definition, relationship definition, and attribute definition analysis of the cost data model are performed, the relationship between the cost data is explained, and finally the data model composition of the structured data and semi-structured data in the optimization data is understood.

[0026] Specifically, we analyze the data source and whether different indicators have the basic conditions for intelligent identification and extraction. By analyzing the data structure and data relationship of the cost software background, we can clarify what kind of data can be directly used in indicator extraction and data analysis.

[0027] S102, constructing statistical rules according to different cost indicators in different cost systems, extracting data sources from the cost data in the cost database, analyzing the business indicators of the cost data through combined extraction, and constructing corresponding data extraction rules according to the analysis results and the statistical rules; For further information, see Figure 3 , the step S102 specifically includes steps S1021~S1022: S1021, constructing statistical rules according to different cost indicators in different cost systems, wherein the cost indicators include project scale, project quantity indicator and cost indicator; S1022, extracting source data from the cost data in the cost database, and analyzing business indicators using combined extraction calculations, and constructing corresponding data extraction rules based on the analysis results and the statistical rules.

[0028] In the specific implementation, based on the cost indicators such as different project scales, engineering quantity indicators, and cost indicators in different cost systems, appropriate automatic and semi-automatic calculation methods and statistical rules are established to extract effective data sources from massive amounts of raw data, and to achieve the analysis of business indicators through combined extraction and calculation. All extraction rules try to avoid calculation rules with large amounts of data or complex algorithms.

[0029] Specifically, according to the analysis dimension of the cost index system, the extraction rules of the cost index system and the basic technical parameter information of a single project are clarified, and based on the cost software, the interface is developed to communicate with the cost software. The auxiliary tools can be used for engineering parameter information and capital collection index parameter information.

[0030] S103, processing basic parameter indicators of cost analysis based on template data, and building a general solution library and module database based on processing results and historical engineering data; For further information, see Figure 4 , the step S103 specifically includes steps S1031~S1032: S1031, summarizing and integrating basic parameter indicators of cost analysis based on template data, extracting and improving statistics and sorting of cost indicators to obtain corresponding general cost data; S1032, acquiring historical engineering data, and performing a difference comparison analysis based on the historical engineering data and the general cost data to construct a general solution library and a module database.

[0031] In the specific implementation, the analysis of technical and economic indicators is based on the filling report template (i.e. template data) to achieve the summary and integration of basic parameter indicators of cost analysis. The statistics and sorting of indicators are mainly improved through two statistical modes: automatic extraction and manual improvement. The system provides manual configuration function of indicator parameters to meet the continuous in-depth development of cost analysis work.

[0032] Specifically, historical engineering data is obtained. The general cost analysis mainly realizes the comparative analysis of the differences between historical engineering data and general costs, and quickly assists in the output of the general cost difference analysis table. The historical version can be selected for general cost analysis and the analysis records can be saved.

[0033] S104, obtaining data to be processed, and performing engineering cost analysis on the data to be processed according to the data to be processed, the data extraction rule and the analysis result to form a cost analysis table.

[0034] During the specific implementation, by selecting schemes and modules, a general scheme and module database is established; according to the general cost content, a general cost analysis table, a module summary table, a building table three comparison table, an installation table three comparison table, other costs, and a technical and economic indicator table are generated; based on the actual project and general cost data difference analysis table generated by the system, the reasons for the differences are analyzed, and the difference items that need to be analyzed include: equipment costs, installation costs, construction costs, other costs, and basic reserve funds.

[0035] In this embodiment, the cost influencing factors are analyzed. There are many factors that affect the project cost. By systematically combing and identifying these factors, the mechanism and degree of influence of each factor on the project cost are clarified, and the intelligent identification, classification and impact measurement analysis of the factors affecting the project cost are realized. Under the premise of losing little information, multiple related indicators are linearly combined and converted into several principal component comprehensive indicators. Then, the linear (non-linear) regression analysis method is used to analyze the quantitative change law of each variable, and the relationship is reflected and described in the form of a regression equation, and finally several types of indicators with the greatest correlation affecting the project cost are obtained.

[0036] Specifically, cost forecasting and analysis is based on the analysis of influencing factors, and according to data information such as historical engineering data information, engineering technology and design index information, and expert experience information, through the prediction method model provided by the system big data platform, to make scientific predictions on the engineering cost level and cost development trend, and use core technical capabilities to support the company's cost control and cost analysis capabilities.

[0037] In this embodiment, the system constructed by this method also has the function of customizing the index report, realizing the template management of the internal cost analysis and the construction unit cost analysis index report. The horizontal and vertical coordinates of the report can be flexibly set to customize a new index report. At the same time, the template of the selected cost analysis report can be adjusted to form a new cost analysis report. By setting the project reference, the project for reference analysis can be selected. The system reads the data value corresponding to the project, forms the basic data report of the cost analysis of the provincial power company, the economic research institute, the construction unit, etc., and realizes the export of its report data.

[0038] It provides independent management and sample elimination functions for cost analysis samples, supports real-time online preview of cost analysis reports and outputs WYSIWYG standard format results reports, and automatically outputs standard format reports required for engineering cost analysis work.

[0039] In summary, the power engineering cost data management method in the above-mentioned embodiment of the present invention optimizes the cost data to improve the efficiency and accuracy of data processing, and performs business abstraction and business modeling analysis on the optimized data to quickly determine the data model composition of structured data and semi-structured data, thereby improving data processing efficiency; constructs statistical rules according to different cost indicators in different cost systems, and extracts data sources from the cost data in the cost database, constructs data extraction rules through combined extraction, effectively cleans and verifies the data, eliminates data errors and inconsistencies, and improves data accuracy; analyzes cost indicator data in real time, reflects changes in cost data in real time, and provides timely and accurate data support for decision makers. Embodiment 2

[0040] Another aspect of the present invention is to provide a power engineering cost data management system, see Figure 5 , shown is a power engineering cost data management system in the second embodiment of the present invention, comprising: The data optimization module 11 is used to optimize the cost data in the cost database to obtain corresponding optimized data, and perform business abstraction and business modeling analysis based on the optimized data, and perform entity definition, relationship definition and attribute definition analysis on the cost data model of the cost database according to the analysis results to obtain corresponding analysis results; Furthermore, the data optimization module 11 includes: A data classification unit is used to classify the cost data in the cost database so as to classify the cost data into a plurality of classified data according to the data type; The data optimization unit is used to normalize the classified data of the same data class and de-discretize the normalized data to obtain corresponding optimized data.

[0041] Furthermore, the data optimization module 11 also includes: A modeling and analysis unit, used for performing business abstraction and business modeling analysis based on the optimized data to obtain structured data and semi-structured data; The indicator determination unit is used to analyze the data source attribution of the structured data and the semi-structured data to determine the indicator data in the structured data and the semi-structured data, wherein the indicator data is directly applied to indicator extraction.

[0042] A rule construction module 12 is used to construct statistical rules according to different cost indicators in different cost systems, extract data sources from the cost data in the cost database, implement business indicator analysis of the cost data through combined extraction, and construct corresponding data extraction rules according to the analysis results and the statistical rules; Furthermore, the rule construction module 12 includes: A rule building unit, used to build statistical rules according to different cost indicators in different cost systems, wherein the cost indicators include project scale, project quantity indicators and cost indicators; The data extraction unit is used to extract source data from the cost data in the cost database, and use combined extraction calculation to analyze business indicators, and construct corresponding data extraction rules based on the analysis results and the statistical rules.

[0043] The data processing module 13 is used to process the basic parameter indicators of the cost analysis based on the template data, and to build a general solution library and a module database based on the processing results and historical engineering data; Furthermore, the data processing module 13 includes: The data aggregation unit is used to aggregate and integrate the basic parameter indicators of cost analysis based on the template data, extract and improve the statistics and sorting of cost indicators to obtain the corresponding general cost data; The database construction unit is used to obtain historical engineering data and perform difference comparison and analysis based on the historical engineering data and the general cost data to construct a general solution library and a module database.

[0044] The cost analysis module 14 is used to obtain the data to be processed, and perform engineering cost analysis on the data to be processed according to the data to be processed, the data extraction rules and the analysis results to form a cost analysis table.

[0045] The functions or operation steps implemented when the above modules are executed are substantially the same as those in the above method embodiments, and will not be repeated here.

[0046] An embodiment of the present invention provides an electric power engineering cost data management system, whose implementation principle and technical effects are the same as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the system embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0047] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0048] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for managing power engineering cost data, It is characterized in that The following steps are involved: Step 1: Optimize the cost data in the cost database to obtain corresponding optimized data, perform business abstraction and business modeling analysis based on the optimized data, and perform entity definition, relationship definition and attribute definition analysis on the cost data model of the cost database according to the analysis results to obtain corresponding analysis results; Step 2: Construct statistical rules according to different cost indicators in different cost systems, extract data sources from the cost data in the cost database, implement business indicator analysis of the cost data through combined extraction, and construct corresponding data extraction rules according to the analysis results and the statistical rules; Step 3: Process the basic parameter indicators of cost analysis based on the template data, and build a general solution library and module database based on the processing results and historical engineering data; Step 4: Acquire the data to be processed, and perform engineering cost analysis on the data to be processed according to the data to be processed, the data extraction rules and the analysis results to form a cost analysis table.

2. The power engineering cost data management method according to claim 1, It is characterized in that The step one comprises: Classifying the cost data in the cost database to classify the cost data into a plurality of classified data according to the data type; The classified data of the same data class are normalized, and the normalized data are de-discretized to obtain the corresponding optimized data.

3. The power engineering cost data management method according to claim 1, It is characterized in that The step one also includes: Performing business abstraction and business modeling analysis based on the optimized data to obtain structured data and semi-structured data; The data source attribution of the structured data and the semi-structured data is analyzed to determine the indicator data in the structured data and the semi-structured data, wherein the indicator data is directly applied to indicator extraction.

4. The power engineering cost data management method according to claim 1, It is characterized in that The second step comprises: Constructing statistical rules based on different cost indicators in different cost systems, wherein the cost indicators include project scale, project quantity indicators and cost indicators; The source data is extracted from the cost data in the cost database, and the business indicators are analyzed by combined extraction calculation, and corresponding data extraction rules are constructed according to the analysis results and the statistical rules.

5. The power engineering cost data management method according to claim 1, It is characterized in that The step three comprises: Summarize and integrate the basic parameter indicators of cost analysis based on template data, extract and improve the statistics and sorting of cost indicators to obtain the corresponding general cost data; Historical engineering data is obtained, and a difference comparison analysis is performed based on the historical engineering data and the general cost data to build a general solution library and a module database.

6. A power engineering cost data management system, It is characterized in that include: A data optimization module is used to optimize the cost data in the cost database to obtain corresponding optimized data, and perform business abstraction and business modeling analysis based on the optimized data, and perform entity definition, relationship definition and attribute definition analysis on the cost data model of the cost database according to the analysis results to obtain corresponding analysis results; A rule construction module is used to construct statistical rules according to different cost indicators in different cost systems, extract data sources from the cost data in the cost database, implement business indicator analysis of the cost data through combined extraction, and construct corresponding data extraction rules according to the analysis results and the statistical rules; The data processing module is used to process the basic parameter indicators of cost analysis based on the template data, and to build a general solution library and module database based on the processing results and historical engineering data; The cost analysis module is used to obtain the data to be processed, and perform engineering cost analysis on the data to be processed according to the data to be processed, the data extraction rules and the analysis results to form a cost analysis table.

7. The power engineering cost data management system according to claim 6, It is characterized in that The data optimization module includes: A data classification unit is used to classify the cost data in the cost database so as to classify the cost data into a plurality of classified data according to the data type; The data optimization unit is used to normalize the classified data of the same data class and de-discretize the normalized data to obtain corresponding optimized data.

8. The power engineering cost data management system according to claim 6, It is characterized in that The data optimization module also includes: A modeling and analysis unit, used for performing business abstraction and business modeling analysis based on the optimized data to obtain structured data and semi-structured data; The indicator determination unit is used to analyze the data source attribution of the structured data and the semi-structured data to determine the indicator data in the structured data and the semi-structured data, wherein the indicator data is directly applied to indicator extraction.

9. The power engineering cost data management system according to claim 6, It is characterized in that The rule building module includes: A rule building unit, used to build statistical rules according to different cost indicators in different cost systems, wherein the cost indicators include project scale, project quantity indicators and cost indicators; The data extraction unit is used to extract source data from the cost data in the cost database, and use combined extraction calculation to analyze business indicators, and construct corresponding data extraction rules based on the analysis results and the statistical rules.

10. The power engineering cost data management system according to claim 6, It is characterized in that The data processing module comprises: The data aggregation unit is used to aggregate and integrate the basic parameter indicators of cost analysis based on the template data, extract and improve the statistics and sorting of cost indicators to obtain the corresponding general cost data; The database construction unit is used to obtain historical engineering data and perform difference comparison and analysis based on the historical engineering data and the general cost data to construct a general solution library and a module database.

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