CN115630104A - Railway engineering project plan data processing method and platform - Google Patents

Railway engineering project plan data processing method and platform Download PDF

Info

Publication number
CN115630104A
CN115630104A CN202211148502.XA CN202211148502A CN115630104A CN 115630104 A CN115630104 A CN 115630104A CN 202211148502 A CN202211148502 A CN 202211148502A CN 115630104 A CN115630104 A CN 115630104A
Authority
CN
China
Prior art keywords
data
engineering project
project plan
railway engineering
metadata
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202211148502.XA
Other languages
Chinese (zh)
Inventor
王超
王万齐
解亚龙
尹逊霄
李慧
刘北胜
吕向茹
王学强
王坤
张敬涵
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Institute of Computing Technologies of CARS
Beijing Jingwei Information Technology Co Ltd
Original Assignee
Institute of Computing Technologies of CARS
Beijing Jingwei Information Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Institute of Computing Technologies of CARS, Beijing Jingwei Information Technology Co Ltd filed Critical Institute of Computing Technologies of CARS
Priority to CN202211148502.XA priority Critical patent/CN115630104A/en
Publication of CN115630104A publication Critical patent/CN115630104A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/25—Integrating or interfacing systems involving database management systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/21—Design, administration or maintenance of databases
    • G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2455—Query execution
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2455—Query execution
    • G06F16/24564—Applying rules; Deductive queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28—Databases characterised by their database models, e.g. relational or object models
    • G06F16/284—Relational databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G06Q10/103—Workflow collaboration or project management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/08—Construction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/40—Business processes related to the transportation industry

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Databases & Information Systems (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Data Mining & Analysis (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Business, Economics & Management (AREA)
  • Tourism & Hospitality (AREA)
  • General Engineering & Computer Science (AREA)
  • Marketing (AREA)
  • Quality & Reliability (AREA)
  • Operations Research (AREA)
  • Computational Linguistics (AREA)
  • Development Economics (AREA)
  • Game Theory and Decision Science (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Educational Administration (AREA)
  • Train Traffic Observation, Control, And Security (AREA)

Abstract

The application provides a railway engineering project plan data processing method and a platform, wherein the method comprises the following steps: automatically extracting each metadata in currently received railway engineering project plan data, and importing each metadata into a multi-source query database; and according to a preset timing generation rule or a currently received query instruction, automatically searching corresponding target data in the multi-source query database by using a data engine, and outputting result data corresponding to the target data. According to the method and the device, the automation degree and efficiency of online unified summarization and storage of the railway engineering project plan data can be effectively improved, the automation degree and efficiency of searching and extracting the multi-source railway engineering project plan data can be improved, and the accuracy and reliability of data query results in the multi-source railway engineering project plan data can be guaranteed.

Description

Railway engineering project plan data processing method and platform
Technical Field
The application relates to the technical field of data processing, in particular to a railway engineering project plan data processing method and platform.
Background
A large amount of engineering project data can be related in the railway construction process, and particularly in the early engineering planning stage, the data includes various kinds of data with large information, such as required materials, equipment, capital investment, construction units, engineering planning periods and the like. The processing of the data of the railway engineering project plan is also one of the important contents in the field of railway key engineering control because a great deal of manpower is required to be invested to process the complex engineering project data.
At present, the existing railway engineering project plan data processing mode generally needs to manually upload and arrange railway engineering project plan data into storage carriers such as tables, and then locally call the tables for data search when the requirements such as information extraction or risk estimation exist.
However, because the data uploaded by different persons lacks a uniform format and a uniform carrier, the conventional railway engineering project plan data processing method is only suitable for local data search, and in the scenes of multi-source data summarization and transmission, a large amount of labor and time cost are consumed to perform uniform processing on the multi-source data, and the method is easy to miss, so that the accuracy of subsequent processing such as information extraction or risk estimation performed according to the online summarized multi-source data is poor.
Disclosure of Invention
In view of the above, embodiments of the present application provide a method and a platform for processing data of a railway engineering project plan, so as to obviate or mitigate one or more of the disadvantages in the prior art.
One aspect of the present application provides a method for processing data of a railway engineering project plan, comprising:
automatically extracting each metadata in currently received railway engineering project plan data, and importing each metadata into a multi-source query database;
and according to a preset timing generation rule or a currently received query instruction, automatically searching corresponding target data in the multi-source query database by using a data engine, and outputting result data corresponding to the target data.
In some embodiments of the present application, the automatically extracting respective metadata in the currently received railroad engineering project plan data comprises:
receiving railroad engineering project plan data, wherein the railroad engineering project plan data comprises: single-source data or multi-source data;
and preprocessing the railway engineering project plan data based on a DPS data processing system to obtain each metadata corresponding to the railway engineering project plan data, wherein each metadata corresponds to a preset data type.
In some embodiments of the present application, the importing each of the metadata into a multi-source query database includes:
initializing and cleaning each metadata to obtain corresponding formatted data and unformatted data;
loading and processing the formatted data based on a DPS data processing system, introducing the formatted data into a multi-source query database by applying a relational database system (RDBS), and establishing an index and a query data set of the multi-source query database, wherein the multi-source query database is a relational database and is used for storing the corresponding relation between each piece of formatted metadata and each preset railway engineering project original classification;
and screening out the unformatted data.
In some embodiments of the present application, the applying a data engine to automatically search for corresponding target data in the multi-source query database according to a preset timing generation rule or a currently received query instruction includes:
receiving a query instruction aiming at the railway engineering project plan data, automatically searching corresponding target data in the multi-source query database based on a retrieval keyword in the query instruction, and generating result data corresponding to the target data;
or automatically searching corresponding target data from the multi-source query database at regular time according to a preset timing generation rule, and generating corresponding result data according to the target data.
In some embodiments of the application, the outputting result data corresponding to the target data includes:
and sending the result data corresponding to the target data to corresponding client equipment, and/or sending the result data corresponding to the target data to a preset display screen for visual display.
In some embodiments of the present application, further comprising:
extracting each metadata belonging to the same target railway engineering project plan from the multi-source query database to obtain corresponding data to be predicted;
inputting the data to be predicted into a preset machine learning model for predicting the completion degree of the railway engineering project plan so that the machine learning model outputs the completion degree prediction result data corresponding to the target railway engineering project plan; wherein the completion prediction result data comprises: the system comprises a deviation prediction result between the planned amount of equipment and the predicted actual input amount of equipment, a deviation prediction result between the planned input amount of funds and the predicted actual input amount of funds, and a deviation prediction result between the project planning period and the predicted actual completion period of the project.
In some embodiments of the present application, the machine learning model comprises: and training a deep neural network DNN obtained in advance based on historical railway engineering project plan data with a finish degree label.
Another aspect of the present application provides a railway engineering project plan data processing platform, comprising:
the automatic data import module is used for automatically extracting each metadata in the currently received railway engineering project plan data and importing each metadata into a multi-source query database;
and the data automatic extraction module is used for automatically searching corresponding target data in the multi-source query database by applying a data engine according to a preset timing generation rule or a currently received query instruction, and outputting result data corresponding to the target data.
Another aspect of the present application provides an electronic device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method for processing data of a railway engineering project plan when executing the computer program.
Another aspect of the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of railroad engineering project plan data processing.
According to the railway engineering project plan data processing method, by automatically extracting each metadata in currently received target railway engineering project plan data and leading each metadata into the same multi-source query database, unified online summarization and storage can be realized for the railway engineering project plan data from different sources; the application data engine automatically calls corresponding target data from the query database according to the received query instruction or data generation rule and outputs result data corresponding to the target data, so that unified query and calling of railway engineering project plan data from different sources can be effectively realized, further, the automation degree and efficiency of unified storage of the railway engineering project plan data can be effectively improved, a large amount of manpower and time cost can be saved, the automation degree and efficiency of searching and extracting the railway engineering project plan data can be improved, the accuracy and application reliability of data query results in the multisource railway engineering project plan data can be ensured, further, the accuracy and reliability of subsequent processing such as risk estimation can be effectively improved, and further, the user experience of railway engineering project plan data managers can be effectively improved.
Additional advantages, objects, and features of the application will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and drawings.
It will be appreciated by those skilled in the art that the objects and advantages that can be achieved with the present application are not limited to the specific details set forth above, and that these and other objects that can be achieved with the present application will be more clearly understood from the detailed description that follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application, are incorporated in and constitute a part of this application, and are not intended to limit the application. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the application. For purposes of illustrating and describing certain portions of the present application, the drawings may have been enlarged, i.e., may be larger, relative to other features of the exemplary devices actually made in accordance with the present application. In the drawings:
fig. 1 is a general flowchart of a railway engineering project plan data processing method according to an embodiment of the present application.
Fig. 2 is a schematic specific flowchart of a railway engineering project plan data processing method according to an embodiment of the present application.
Fig. 3 is a schematic structural diagram of a railway engineering project plan data processing platform according to an embodiment of the present application.
Fig. 4 is a schematic flow chart of a method for processing data of a railway engineering project plan for contract data provided in an application example of the present application.
Fig. 5 is a schematic diagram illustrating an example of logic of a deep neural network learning algorithm provided in an application example of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below with reference to the following embodiments and the accompanying drawings. The exemplary embodiments and descriptions of the present application are provided to explain the present application and not to limit the present application.
Here, it should be further noted that, in order to avoid obscuring the present application with unnecessary details, only the structures and/or processing steps closely related to the scheme according to the present application are shown in the drawings, and other details not so related to the present application are omitted.
It should be emphasized that the term "comprises/comprising" when used herein, is taken to specify the presence of stated features, elements, steps or components, but does not preclude the presence or addition of one or more other features, elements, steps or components.
It is also noted herein that the term "coupled," if not specifically stated, may refer herein to not only a direct connection, but also an indirect connection in which an intermediate is present.
Hereinafter, embodiments of the present application will be described with reference to the drawings. In the drawings, the same reference numerals denote the same or similar parts, or the same or similar steps.
The method aims at solving the problems that the existing railway engineering project plan data processing method relates to an offline operation process of business and lacks of assistance of an information platform. In addition, in the data transmission and the later data processing, a large amount of manpower is consumed for data processing, and the accuracy of the result is not high; the method comprises the steps of processing all railway engineering project plan data management flows on line through a data processing engine, processing all three links of data input, middle-stage processing and data output by adopting an informatization means, reducing the complexity of manual operation processing, generating visual display contents of data which accord with business, simultaneously meeting the overall compatibility of a system under the condition of frequent business change, and improving the usability.
The details are explained by the following examples.
Based on this, an embodiment of the present application provides a method for processing data of a railway engineering project plan, which can be implemented by a platform for processing data of a railway engineering project plan, and referring to fig. 1, the method for processing data of a railway engineering project plan specifically includes the following contents:
step 100: automatically extracting each metadata in currently received railway engineering project plan data, and importing each metadata into a multi-source query database;
in step 100, the currently received railway engineering project plan data may refer to single-source or multi-source railway engineering project plan data currently received by the railway engineering project plan data processing platform, where the single-source railway engineering project plan data may also include plan data corresponding to multiple railway engineering project plans respectively. That is to say, in the present application, no matter how much data is received by the railway engineering project plan data processing platform in a single time or within a preset time period, metadata extraction and storage can be performed on the data based on the step 100 provided by the present application, so that the applicability and convenience of the railway engineering project plan data processing method can be effectively improved, and simultaneous online processing of multiple data is realized.
In one or more embodiments of the present application, the Metadata (Metadata) refers to information for describing the classification attribute of each data, that is, each Metadata corresponds to only one data type, for example: for a railway engineering project plan A1, if the corresponding railway engineering project plan data records: "amount contracted: and if the contract amount in the content is recognized as belonging to the data type capital investment according to a preset data type table, and the thirty million dollars in the content are recognized as the metadata corresponding to the data type capital investment.
The data type table is used for recording each data type related to the railway engineering project plan, and can be specifically set according to a contract template common to the railway engineering project.
And in order to further adapt to the difference of multi-source data, the data type table is also used for recording similar expressions corresponding to each data type, for example, the above-mentioned "amount contracted" can be queried in the data type table for the similar expression of "fund investment" of the data type.
Step 200: and according to a preset timing generation rule or a currently received query instruction, automatically searching corresponding target data in the multi-source query database by using a data engine, and outputting result data corresponding to the target data.
In step 200, the data engine mainly performs comprehensive processing on data of each link in the whole life cycle of the railway engineering project plan to generate an intelligent report and a data set.
As can be seen from the above description, the method for processing the railway engineering project plan data provided in the embodiment of the present application can implement unified online summarization and storage for the railway engineering project plan data from different sources by automatically extracting each metadata in the currently received target railway engineering project plan data and importing each metadata into the same multi-source query database; the application data engine automatically calls corresponding target data from the query database according to the received query instruction or data generation rule and outputs result data corresponding to the target data, so that unified query and calling of railway engineering project plan data from different sources can be effectively realized, further, the automation degree and efficiency of unified storage of the railway engineering project plan data can be effectively improved, a large amount of manpower and time cost can be saved, the automation degree and efficiency of searching and extracting the railway engineering project plan data can be improved, the accuracy and application reliability of data query results in the multisource railway engineering project plan data can be ensured, further, the accuracy and reliability of subsequent processing such as risk estimation can be effectively improved, and further, the user experience of railway engineering project plan data managers can be effectively improved.
In order to further improve the automation degree and reliability of the railway engineering project plan data processing, in a railway engineering project plan data processing method provided in the embodiment of the present application, referring to fig. 2, step 100 in the railway engineering project plan data processing method may include the following steps:
step 110: receiving railroad engineering project plan data, wherein the railroad engineering project plan data comprises: single-source data or multi-source data;
step 120: preprocessing the railway engineering project plan data based on a DPS data processing system to obtain each metadata corresponding to the railway engineering project plan data, wherein each metadata corresponds to a preset data type.
In one or more embodiments of the present application, the data types at least include: equipment, capital investment, construction units, and project planning cycles.
In a specific example, the data types include: equipment information, supplier information, construction unit information, planned amount of capital investment (e.g., contract amount), time of project validation (e.g., time of contract), project planning cycle, project planning classification (e.g., contract classification), material cost, supplemental information (e.g., supplemental contract information), and discharge from validation classification, among others.
Specifically, the method inputs full life cycle Data in a railway engineering project plan, and comprises structured Data such as supplier management, first supply and material collection, early bid and bid supervision, investment control, contract management, and verification and pricing, and adopts a DPS (Data Processing System) Data engine to support the introduction of various Data formats and automatically complete front-end Data input.
Importing a rule:
a. similar data automatically completes screening and sorting;
b. the standard specified xml and json format data can be directly identified and the header can be automatically generated;
c. and completing automatic filtering of redundant data.
In order to further improve the automation degree and reliability of the railway engineering project plan data processing, in the railway engineering project plan data processing method provided in the embodiment of the present application, referring to fig. 2, the following contents may be further included after step 120 in the railway engineering project plan data processing method:
step 130: initializing and cleaning each metadata to obtain corresponding formatted data and unformatted data;
step 140: loading the formatted data based on a DPS data processing system, introducing the formatted data into a multi-source query database by applying a relational database system (RDBS), and establishing an index and a query data set of the multi-source query database, wherein the multi-source query database is a relational database and is used for storing the corresponding relation between each piece of formatted metadata and each preset railway engineering project original classification;
step 150: and screening out the unformatted data.
Specifically, the integrated metadata is processed, and initialization and cleansing of the data is performed before entering the data engine for processing. And loading the formatted data through a DPS data initialization technology, importing the data into a relational database through an RDBS technology, completing index establishment and set calculation of a data set, and screening unformatted data to ensure the operability of the data.
And (3) initializing a result:
a. completely importing metadata into a relational database;
b. establishing data query conditions and establishing an intelligent query data set.
In order to further improve the convenience and accuracy of querying the result of the railway engineering project plan data, in the method for processing the railway engineering project plan data provided in the embodiment of the present application, referring to fig. 2, step 200 in the method for processing the railway engineering project plan data may include the following steps:
step 210: receiving a query instruction aiming at the railway engineering project plan data, automatically searching corresponding target data in the multi-source query database based on a retrieval keyword in the query instruction, and generating result data corresponding to the target data;
alternatively, step 220: and automatically searching corresponding target data from the multi-source query database at regular time according to a preset timing generation rule, and generating corresponding result data according to the target data.
In order to further improve the user experience and convenience of querying the railway engineering project plan data, in the railway engineering project plan data processing method provided in the embodiment of the present application, referring to fig. 2, the following contents may be further included after step 210 or step 220 in the railway engineering project plan data processing method:
step 230: and sending the result data corresponding to the target data to corresponding client equipment, and/or sending the result data corresponding to the target data to a preset display screen for visual display.
Specifically, the data engine mainly carries out comprehensive processing on data of all links in the whole life cycle of the railway engineering project plan to generate an intelligent report and a data set. The method mainly comprises the following steps:
a. the generation data can automatically form the generation and display of the annual report by independently collecting the data of the segment time and the region.
b. The intelligent data of units, time, amount and the like can be intelligently inquired according to the railway engineering project plan, and an intelligent report can be generated.
c. And (3) butting a plurality of railway engineering project plan management services and integrating data to generate data visualization contents such as an intelligent large screen and a flow chart.
In order to further realize the automated risk prediction of the railway engineering project plan completion degree on the basis of improving the automation degree of the railway engineering project plan data processing, in the railway engineering project plan data processing method provided in the embodiment of the present application, referring to fig. 2, after step 200 in the railway engineering project plan data processing method, the following contents may be included:
step 300: extracting each metadata belonging to the same target railway engineering project plan from the multi-source query database to obtain corresponding data to be predicted;
step 400: inputting the data to be predicted into a preset machine learning model for predicting the completion degree of the railway engineering project plan so that the machine learning model outputs the completion degree prediction result data corresponding to the target railway engineering project plan; wherein the completion prediction result data comprises: the system comprises a deviation prediction result between the planned amount of equipment and the predicted actual input amount of equipment, a deviation prediction result between the planned input amount of funds and the predicted actual input amount of funds, and a deviation prediction result between the project planning period and the predicted actual completion period of the project.
Specifically, a learning model is established for the contract management process through a machine learning algorithm. The learning algorithm is mainly established by inputting key index information, and the model is established by continuously learning and calculating a large amount of historical data and verifying and comparing the existing data results. The key information comprises signing unit information, contract amount, supplementary contract signing condition, verified charge, corresponding data of investment completion and fund in place and the like. And comparing the project structure decomposition with the project cost in the process of checking the project price, and deducing a learning algorithm by considering the influence factor of the project change to generate a verification result of the capital investment and the actual in-place condition in the bid inviting stage.
In order to further improve reliability and accuracy of the automated risk prediction on the railway engineering project plan completion degree, in the railway engineering project plan data processing method provided by the embodiment of the present application, the machine learning model includes: and training a deep neural network DNN obtained in advance based on historical railway engineering project plan data with a finish degree label.
Specifically, the main implementation method of machine learning adopts a deep neural network learning algorithm, generates forward and reverse results by randomly sampling input results and output results, and generates a correlation result according with actual conditions by using hardware basic computing power, thereby discovering a repeatedly verified learning rule.
From the aspect of software, the present application further provides a railway engineering project plan data processing platform for implementing all or part of the method for processing railway engineering project plan data, referring to fig. 3, where the railway engineering project plan data processing platform specifically includes the following contents:
the automatic data importing module 10 is configured to automatically extract each metadata in currently received railway engineering project plan data, and import each metadata into a multi-source query database;
and the data automatic extraction module 20 is configured to generate a rule according to a preset timing or a currently received query instruction, automatically search corresponding target data in the multi-source query database by using a data engine, and output result data corresponding to the target data.
The embodiment of the railway engineering project plan data processing platform provided in the present application may be specifically configured to execute the processing flow of the embodiment of the railway engineering project plan data processing method in the foregoing embodiment, and the functions thereof are not described herein again, and reference may be made to the detailed description of the embodiment of the railway engineering project plan data processing method.
The railway engineering project plan data processing platform can be used for carrying out the part of railway engineering project plan data processing in a server, and in another practical application situation, all the operations can be completed in a client device. The selection may be specifically performed according to the processing capability of the client device, the limitation of the user usage scenario, and the like. This is not a limitation of the present application. If all operations are completed in the client device, the client device may further include a processor for specific processing of railway engineering project plan data processing.
The client device may have a communication module (i.e., a communication unit), and may be communicatively connected to a remote server to implement data transmission with the server. The server may include a server on the task scheduling center side, and in other implementation scenarios, the server may also include a server on an intermediate platform, for example, a server on a third-party server platform that is communicatively linked to the task scheduling center server. The server may include a single computer device, or may include a server cluster formed by a plurality of servers, or a server structure of a distributed apparatus.
The server and the client device may communicate using any suitable network protocol, including a network protocol that has not been developed at the filing date of the present application. The network protocol may include, for example, a TCP/IP protocol, a UDP/IP protocol, an HTTP protocol, an HTTPS protocol, or the like. Of course, the network Protocol may also include, for example, an RPC Protocol (Remote Procedure Call Protocol), a REST Protocol (Representational State Transfer Protocol), and the like used above the above Protocol.
As can be seen from the above description, the railway engineering project plan data processing platform provided in the embodiment of the present application can implement unified online summarization and storage for railway engineering project plan data from different sources by automatically extracting each metadata in currently received target railway engineering project plan data and importing each metadata into the same multi-source query database; the application data engine automatically calls corresponding target data from the query database according to the received query instruction or data generation rule and outputs result data corresponding to the target data, so that unified query and calling of railway engineering project plan data from different sources can be effectively realized, further, the automation degree and efficiency of unified storage of the railway engineering project plan data can be effectively improved, a large amount of manpower and time cost can be saved, the automation degree and efficiency of searching and extracting the railway engineering project plan data can be improved, the accuracy and application reliability of data query results in the multisource railway engineering project plan data can be ensured, further, the accuracy and reliability of subsequent processing such as risk estimation can be effectively improved, and further, the user experience of railway engineering project plan data managers can be effectively improved.
In order to further explain the scheme, contract data of the railway engineering project is taken as a specific example of the plan data of the railway engineering project, and a construction unit in the railway construction process relates to a large number of contract signing processes and mainly carries out offline management on a professional person. The management process relates to the circulation, settlement and statistics of a large amount of funds, and related derivative services comprise investment control, in-place funds and other links, and are important contents in the field of railway key engineering control.
The railway engineering suppliers are numerous, the project is changed frequently, and the supplementary contract signing condition is complex. The standard management method for railway construction cost released in 2020 unifies and standardizes the railway construction cost, but does not perform detailed management aiming at contract management conditions. At present, a technical means adopted by a contract management process is a technical support for an information platform, data uploading is realized mainly by using computing resources of a cloud platform, the current process is in a transition stage of on-line operation and on-line operation, data on-line circulation is basically realized by importing data files such as Excel and the like, but an integral process engine and a data processing engine are not used for supporting, the process is only limited to on-line display of data, the process of a basic business process is met, along with continuous improvement of business complexity, the compatibility of a system is insufficient due to change of process contents, a large number of functions need to be researched and developed again, and the on-line time needed to be modified and deployed is longer. Specifically, the method comprises the following steps:
1. at present, the related business still has an offline operation process and lacks the assistance of an information platform. In addition, in the data transmission and the post-processing, a large amount of manpower is consumed for data processing, and the accuracy of the result is not high.
2. The contract management flow in the railway industry is complex, the project change conditions are many, the service requirements are constantly changed, related intelligent technical support is lacked, and the current technical scheme is difficult to meet the flexible and changeable service requirements.
3. The support of a machine algorithm is lacked, the accuracy and the effectiveness of result verification are lacked in a general information platform, the problem of contract signing is difficult to find, and an early warning mechanism of contract service is not established in the current technical means.
Therefore, based on the above problems, the present application provides a specific application example of a method for implementing railway engineering project plan data processing for contract data by using a railway engineering project plan data processing platform, and a dynamic report data engine is adopted to automatically complete storage, calculation and statistics of metadata in a contract, so as to generate an intelligent data report; an AI calculation algorithm is integrated in the system for the first time in the field to intervene in a business deduction process, the contract signing process is calculated through parameter input in the processes of contract amount, unit, time, labor checking, pricing and the like, and the contract signing problem is found through machine learning. The concrete advantages are as follows:
1. the data processing engine established for the railway contract management service can support data entry in various formats and change of service requirements caused by project change, can adapt to the service requirements under the condition of not changing codes, and achieves the purpose of agile development.
2. An AI algorithm library applicable to the field of railway contract business for the first time generates a verification result model by learning and deduction of internal data signed by a railway contract, improves the accuracy of solving problems in the contract signing process, and generates a prediction model for managing the reduced cost and the residual contract amount.
Specifically, the railway engineering project plan data processing method for contract data, which is implemented by applying a railway engineering project plan data processing platform and provided by the application example, can be used for the whole life cycle of railway industry contract management, and mainly comprises basic information entry, metadata initialization, a data processing engine, an AI algorithm library and data result generation, all contract management processes are processed on line through the data processing engine, and all three links of data input, middle-stage processing and data output are processed by an informatization means, so that the complexity of manual operation processing is reduced, visual display contents of data conforming to services are generated, the overall compatibility of a system under the condition of frequent service change can be met, and the usability is improved. And establishing a data processing engine through an algorithm library to realize data calculation and statistics. The AI intelligent learning algorithm is used for calculating key information during contract signing, so that the deduction of results is realized, the problems are found and early warning is realized in the contract signing process, meanwhile, the in-place fund and investment condition are pre-judged, and the application and demonstration of the machine learning algorithm in railway contract management business are realized.
Referring to fig. 4, the method for processing data of a railway engineering project plan for contract data provided by the application example of the present application includes the following steps:
(1) Basic information input: the method is characterized in that full life cycle Data in railway contract management is input, and the full life cycle Data comprises structured Data such as supplier management, first supply and collection of materials, early bid and tender supervision, investment control, contract management, verification and pricing and the like, and the Data Processing System (DPS) Data engine can support introduction of various Data formats and automatically complete front-end Data input.
Importing a rule:
a. similar data automatically completes screening and sorting;
b. the data in the format of xml and json with standard specifications can be directly identified and a header can be automatically generated;
c. and completing automatic filtering of redundant data.
(2) Metadata initialization: and (2) processing the metadata input in the integration step (1), and initializing and cleaning the data before entering the data engine for processing. And loading the formatted data through a DPS data initialization technology, importing the data into a relational database through an RDBS technology, completing index establishment and set calculation data set, and screening the unformatted data to ensure the operability of the data.
And (3) initializing a result:
a. completely importing metadata into a relational database;
b. and establishing data query conditions and establishing an intelligent query data set.
(3) Contract management data engine: the data engine is mainly used for comprehensively processing data of all links in the full life cycle of the contract to generate an intelligent report and a data set. The method mainly comprises the following steps:
a. the generated data can automatically form the generation and display of the annual report by independently collecting the data of the segment time and the region.
b. The intelligent inquiry can be carried out according to key data such as contract signing units, contract signing time, contract amount and the like, and an intelligent report is generated.
c. And (4) butting a plurality of contract management services and integrating data to generate data visualization contents such as an intelligent large screen and a flow chart.
(4) AI algorithm library: and establishing a learning model aiming at the contract management process through a machine learning algorithm. The learning algorithm is mainly established on the input of key index information, and the model is built by continuously learning and calculating a large amount of historical data and verifying and comparing the existing data results. The key information comprises signing unit information, contract amount, supplementary contract signing condition, checking and pricing expense, corresponding data of investment completion, fund in place and the like. And comparing the engineering structure decomposition with the engineering cost in the process of checking and pricing, and performing learning algorithm deduction by combining the consideration of influence factors of project change to generate a verification result of the capital investment and the actual in-place condition in the bidding stage. The main implementation method of machine learning adopts a deep neural network learning algorithm, referring to fig. 5, by randomly sampling an input result and an output result, a forward result and a reverse result are generated, and a correlation result conforming to an actual situation is generated by using hardware basic computing power, so that a repeatedly verified learning rule is found.
Substituting the input parameters, and verifying and prejudging the current input by using the generated verification result model, wherein the output result of the machine learning algorithm comprises the following steps:
a. the reduced cost can be pre-judged through a machine learning result, and the condition of over-shortage and under-shortage is avoided. The contract signing conditions can be pre-judged in advance through the algorithm library in the data set, and the contract signing completion time and the signing conditions are controlled.
b. Machine learning can count the residual contract amount in the valuation of the tester, verify the residual contract amount and the data of investment completion, determine whether the condition of signing a supplementary contract is met, and perform recursive calculation on the residual contract amount included in the data set.
(5) Data statistics and process verification: and generating a data statistic and process verification result by the data engine and the AI algorithm together.
Compared with the prior art, the application example has the beneficial effects that:
1. the data processing engine for contract management of the application example comprises a multi-format document import plug-in, a dynamic report engine (DPS), an RDBS relational database, a large-screen display system for data output and a data visualization plug-in.
2. The application example is established in the learning processing of a large amount of internal data aiming at an AI algorithm of contract management, wherein the internal data comprises key data sets of contract signing amount, signing time, verification pricing, engineering structure decomposition and the like, and a deep network neural learning algorithm.
The present application further provides an electronic device (that is, an electronic device), where the electronic device may include a processor, a memory, a receiver, and a transmitter, and the processor is configured to execute the method for processing data of a railway engineering project plan mentioned in the foregoing embodiments, where the processor and the memory may be connected through a bus or in another manner, for example, by being connected through a bus. The receiver can be connected with the processor and the memory in a wired or wireless mode. The electronic device may receive real-time motion data from sensors in the wireless multimedia sensor network and receive an original video sequence from the video capture device.
The processor may be a Central Processing Unit (CPU). The Processor may also be other general purpose Processor, digital Signal Processor (DSP), application Specific Integrated Circuit (ASIC), field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or a combination thereof.
The memory, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the railroad engineering project plan data processing method in the embodiments of the present application. The processor executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions and modules stored in the memory, namely, the method for processing the railway engineering project plan data in the above method embodiment is realized.
The memory may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor, and the like. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and such remote memory may be coupled to the processor via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more modules are stored in the memory and, when executed by the processor, perform a railroad engineering project plan data processing method in an embodiment.
In some embodiments of the present application, the user equipment may include a processor, a memory, and a transceiver unit, the transceiver unit may include a receiver and a transmitter, the processor, the memory, the receiver, and the transmitter may be connected by a bus system, the memory is configured to store computer instructions, and the processor is configured to execute the computer instructions stored in the memory to control the transceiver unit to transceive signals.
As an implementation manner, the functions of the receiver and the transmitter in this application may be considered to be implemented by a transceiving circuit or a transceiving dedicated chip, and the processor may be considered to be implemented by a dedicated processing chip, a processing circuit or a general-purpose chip.
As another implementation manner, a manner of using a general-purpose computer to implement the server provided in the embodiment of the present application may be considered. That is, program code that implements the functions of the processor, receiver and transmitter is stored in the memory, and a general-purpose processor implements the functions of the processor, receiver and transmitter by executing the code in the memory.
The embodiment of the application also provides a computer readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to implement the steps of the railway engineering project plan data processing method. The computer readable storage medium may be a tangible storage medium such as Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, floppy disks, hard disks, removable storage disks, CD-ROMs, or any other form of storage medium known in the art.
Those of ordinary skill in the art will appreciate that the various illustrative components, systems, and methods described in connection with the embodiments disclosed herein may be implemented as hardware, software, or combinations thereof. Whether this is done in hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the present application are the programs or code segments used to perform the required tasks. The program or code segments can be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link.
It is to be understood that the present application is not limited to the particular arrangements and instrumentalities described above and shown in the attached drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications, and additions or change the order between the steps after comprehending the spirit of the present application.
Features that are described and/or illustrated with respect to one embodiment may be used in the same way or in a similar way in one or more other embodiments and/or in combination with or instead of the features of the other embodiments.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made to the embodiment of the present application for those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims (10)

1. A railway engineering project plan data processing method is characterized by comprising the following steps:
automatically extracting each metadata in the currently received railway engineering project plan data, and importing each metadata into a multi-source query database;
and according to a preset timing generation rule or a currently received query instruction, automatically searching corresponding target data in the multi-source query database by using a data engine, and outputting result data corresponding to the target data.
2. The railroad engineering project plan data processing method of claim 1, wherein the automatically extracting respective metadata in the currently received railroad engineering project plan data comprises:
receiving railroad engineering project plan data, wherein the railroad engineering project plan data comprises: single-source data or multi-source data;
preprocessing the railway engineering project plan data based on a DPS data processing system to obtain each metadata corresponding to the railway engineering project plan data, wherein each metadata corresponds to a preset data type.
3. The method of claim 1, wherein said importing each of said metadata into a multi-source query database comprises:
initializing and cleaning each metadata to obtain corresponding formatted data and unformatted data;
loading the formatted data based on a DPS data processing system, introducing the formatted data into a multi-source query database by applying a relational database system (RDBS), and establishing an index and a query data set of the multi-source query database, wherein the multi-source query database is a relational database and is used for storing the corresponding relation between each piece of formatted metadata and each preset railway engineering project original classification;
and screening out the unformatted data.
4. The method for processing the data of the railway engineering project plan according to claim 1, wherein the step of automatically searching the corresponding target data in the multi-source query database by applying a data engine according to a preset timing generation rule or a currently received query instruction comprises the steps of:
receiving a query instruction aiming at the railway engineering project plan data, automatically searching corresponding target data in the multi-source query database based on a retrieval keyword in the query instruction, and generating result data corresponding to the target data;
or automatically searching corresponding target data from the multi-source query database at regular time according to a preset timing generation rule, and generating corresponding result data according to the target data.
5. The method for processing the railway engineering project plan data according to claim 1, wherein the outputting the result data corresponding to the target data comprises:
and sending the result data corresponding to the target data to corresponding client equipment, and/or sending the result data corresponding to the target data to a preset display screen for visual display.
6. The railroad engineering project plan data processing method of any one of claims 1 to 5, further comprising:
extracting each metadata belonging to the same target railway engineering project plan from the multi-source query database to obtain corresponding data to be predicted;
inputting the data to be predicted into a preset machine learning model for predicting the completion degree of the railway engineering project plan so that the machine learning model outputs the completion degree prediction result data corresponding to the target railway engineering project plan; wherein the completion prediction result data comprises: the system comprises a deviation prediction result between the planned amount of equipment and the predicted actual input amount of equipment, a deviation prediction result between the planned input amount of funds and the predicted actual input amount of funds, and a deviation prediction result between the project planning period and the predicted actual completion period of the project.
7. The railroad engineering project plan data processing method of claim 6, wherein the machine learning model comprises: and training a deep neural network DNN obtained in advance based on historical railway engineering project plan data with a finish degree label.
8. A railroad engineering project plan data processing platform, comprising:
the automatic data importing module is used for automatically extracting each metadata in the currently received railway engineering project plan data and importing each metadata into a multi-source query database;
and the data automatic extraction module is used for automatically searching corresponding target data in the multi-source query database by applying a data engine according to a preset timing generation rule or a currently received query instruction, and outputting result data corresponding to the target data.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of railroad engineering project plan data processing according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method of processing railroad engineering project plan data according to any one of claims 1 to 7.
CN202211148502.XA 2022-09-21 2022-09-21 Railway engineering project plan data processing method and platform Pending CN115630104A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202211148502.XA CN115630104A (en) 2022-09-21 2022-09-21 Railway engineering project plan data processing method and platform

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202211148502.XA CN115630104A (en) 2022-09-21 2022-09-21 Railway engineering project plan data processing method and platform

Publications (1)

Publication Number Publication Date
CN115630104A true CN115630104A (en) 2023-01-20

Family

ID=84902810

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202211148502.XA Pending CN115630104A (en) 2022-09-21 2022-09-21 Railway engineering project plan data processing method and platform

Country Status (1)

Country Link
CN (1) CN115630104A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116187588A (en) * 2023-04-24 2023-05-30 成都思威服供应链管理有限公司 Project task information extraction and cost optimization method and device and electronic equipment

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116187588A (en) * 2023-04-24 2023-05-30 成都思威服供应链管理有限公司 Project task information extraction and cost optimization method and device and electronic equipment
CN116187588B (en) * 2023-04-24 2023-06-27 成都思威服供应链管理有限公司 Project task information extraction and cost optimization method and device and electronic equipment

Similar Documents

Publication Publication Date Title
CN110443552B (en) Method and device for automatically transmitting product main data information
CN113191502A (en) Artificial intelligence model on-line training method and system
CN103942220A (en) Method used for intelligently linking work orders with knowledge of knowledge base and suitable for IT operation and maintenance system
US10642838B2 (en) Real-time or event-based analysis of process data
CN111949795A (en) Work order automatic classification method and device
CN112613627A (en) Vehicle maintenance information pushing method, device, system, equipment and storage medium
CN115630104A (en) Railway engineering project plan data processing method and platform
CN104123847A (en) ACP (artificial societies, computational experiments and parallel execution) method-based traffic signal recommendation system and corresponding method
KR20200064044A (en) Method And Apparatus for Managing Automatic Learning
CN104318459A (en) Automatic market analysis method based on multi-mode learning
CN113609393B (en) Digital platform based on data service and data management
CN116629606A (en) Industrial chain early warning method, device, equipment and medium based on power data
CN108446318A (en) A kind of mass data intelligent decision analysis system
CN111210321A (en) Risk early warning method and system based on contract management
CN114186944A (en) Digital collaborative tracing method and system for cold-chain logistics
CN116663519A (en) Automatic ticket forming method, device, equipment and storage medium
Kopp et al. A Method for Business Process Model Analysis and Improvement.
CN116051301A (en) Method and system for tax declaration
CN109739914A (en) Processing method, device, equipment and the computer readable storage medium of multi-data source
CN113535815B (en) Business operation behavior big data mining method and system suitable for electronic commerce
CN115330200A (en) Apparatus, system and method for operating artificial intelligence model
CN113743074A (en) Data report generation method and device based on robot process automation
CN114548840B (en) Production scheduling method and system based on artificial intelligence
CN115879753A (en) Point-of-sale work reporting method and system
CN110516195B (en) Financial time vector integrating method and time vector integrator

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination