CN-121010216-B - Science and technology project information management method and system based on big data

CN121010216BCN 121010216 BCN121010216 BCN 121010216BCN-121010216-B

Abstract

The invention relates to the technical field of information management, and particularly discloses a technological project information management method and system based on big data, wherein the method comprises the following steps of S1, obtaining technological project information to be processed, S2, analyzing project information association degree deviation, S3, constructing a technological project knowledge graph, S4, analyzing technological project information management risk, S5, analyzing technological project information matching, and S6, outputting a project information recommendation list; the invention realizes accurate management and efficient utilization of the technical project information by means of big data and knowledge graph technology, improves the scientificity of project information management decision, reduces project risk and provides powerful support for technological innovation development.

Inventors

ZHANG YANG
YANG XIUCAI
HOU JIANHUA
WANG YUANYUAN
GAO YUNYUN

Assignees

中山大学

Dates

Publication Date: 20260512
Application Date: 20250812

Claims (4)

1. The technological project information management method based on big data is characterized by comprising the following steps: S1, acquiring technological project information to be processed, namely acquiring newly added technological project information, marking the newly added technological project information as each piece of technological project information to be processed, and marking the verified historical technological project benchmark information in a technological project information management database as benchmark project information; S2, project information association degree deviation analysis, namely calculating association degree deviation coefficients of each piece of to-be-processed technological project information and the standard project information, carrying out association degree deviation judgment, carrying out optimization processing on the technological project information based on association degree deviation judgment results, and storing the technological project information in a technological project information database; S3, constructing a science and technology project knowledge graph, namely acquiring science and technology project information in a science and technology project information database, preprocessing to generate a feature vector set, and constructing the science and technology project knowledge graph based on the feature vector set; S4, analyzing the management risk of the science and technology project information, namely extracting project progress deviation, project cost deviation and project resource matching deviation coefficients in a knowledge graph of the science and technology project, and analyzing the management risk of the science and technology project information to generate an information management risk analysis result corresponding to the science and technology project; S5, carrying out matching analysis on the technological project information, namely extracting a retrieval keyword in a retrieval request of a user project, and carrying out matching analysis on a feature vector of the retrieval keyword and a feature vector of the keyword in a technological project knowledge graph to obtain a matching degree coefficient of the retrieval keyword of the user; s6, outputting a project information recommendation list, namely outputting a technological project information recommendation result corresponding to the user search keyword based on the matching degree coefficient of the user search keyword, wherein the technological project information recommendation result comprises an information management risk analysis result corresponding to the technological project; the implementation mode for acquiring the information of the technical project to be processed is specifically as follows: acquiring newly-added technological project information according to a preset acquisition period, marking the acquired newly-added technological project information as each piece of technological project information to be processed, numbering each piece of technological project information to be processed, wherein the serial numbers are 1,2, i, n, i represent the serial numbers of each piece of technological project information to be processed; The execution steps of the project information association degree deviation analysis are specifically as follows: s21, extracting characteristic parameters in the basic project information, constructing a characteristic parameter vector A of the basic project information, , wherein, A reference value indicating a kth characteristic parameter in the reference item information, k indicating a number of each characteristic parameter, k=1, 2,3,..m, m indicating a total number of characteristic parameters; S22, extracting characteristic parameters in the information of each technical project to be processed, constructing a characteristic parameter vector B i of the information of each technical project to be processed, , wherein, Representing the actual value of the kth characteristic parameter in the ith to-be-processed technical project information, wherein i represents the number of each to-be-processed technical project information; S23, based on the characteristic parameter vector A of the basic project information and the characteristic parameter vector B i of each piece of technical project information to be processed, constructing a relevance deviation analysis model, calculating a relevance deviation coefficient of each piece of technical project information to be processed and the basic project information, marking the relevance deviation coefficient as a relevance deviation coefficient DC i of each piece of technical project information to be processed, carrying out relevance deviation judgment on each piece of technical project information to be processed based on the relevance deviation coefficient, carrying out optimization processing on the technical project information according to a relevance deviation judgment result, and storing the technical project information in a technical project information database; The specific content of carrying out relevance deviation judgment on the information of each item to be processed based on the relevance deviation coefficient is as follows: Reading a correlation degree deviation coefficient DC i of each piece of technical project information to be processed, comparing the correlation degree deviation coefficient DC i of each piece of technical project information to be processed with a preset correlation degree deviation coefficient threshold value, judging that the correlation degree deviation of the technical project information to be processed and the reference project information is in a reasonable range if the correlation degree deviation coefficient of the technical project information to be processed is smaller than or equal to the preset correlation degree deviation coefficient threshold value, and storing the technical project information to be processed in a technical project information database; The executing steps of the technological project information management risk analysis are specifically as follows: S41, extracting project progress deviation, extracting a planning time node vector T of the technical project from a technical project knowledge graph, , Representing the planned completion date of the jth time node, while extracting the actual time node vector P, , Indicating the actual completion date of the jth time node, where j indicates the number of each time node, j=1, 2,3,., c, c represents the total number of time nodes; Combining the planning time node vector and the actual time node vector, and calculating project progress deviation Pd; s42, extracting project cost deviation, extracting budget cost vector S of the science and technology project from the knowledge graph of the science and technology project, , Representing the budget amount for the h cost category, while extracting the actual cost vector F, , Represents the actual payout amount of the h-th cost category, where h represents the number of each cost category, h=1, 2,3, v, v represents the total number of cost categories; Combining the budget cost vector and the actual cost vector, and calculating project cost deviation Cd; s43, reading project progress deviation Pd and project cost deviation Cd, extracting project resource matching deviation coefficient Mdc, constructing a management risk analysis model of the technological project information, analyzing to obtain a management risk index MRI of the technological project information, analyzing management risk of the technological project information based on the management risk index, and generating an information management risk analysis result corresponding to the technological project.
2. The method for managing information of a scientific and technological project based on big data of claim 1, wherein the content of the extracted project resource matching deviation coefficient Mdc is that a resource demand vector R of the scientific and technological project is extracted from a knowledge graph of the scientific and technological project, , Representing the planned demand of class g resources, while extracting the actual allocation vector X of the resources, , Represents the actual allocation of class g resources, where g represents the number of each class of resources, g=1, 2,3,..y, y represents the total number of resource classes; And combining the resource demand vector with the actual allocation vector, calculating project resource matching deviation Md, and analyzing the project resource matching deviation to obtain a project resource matching deviation coefficient Mdc.
3. The method for managing information of a scientific and technological project based on big data according to claim 1, wherein the step of performing the matching analysis of the information of the scientific and technological project is as follows: S51, carrying out text processing on item retrieval request information input by a user, extracting retrieval keywords, and generating a retrieval keyword feature vector L based on the retrieval keywords; S52, traversing each item u in the science and technology item knowledge graph, extracting the keyword of each item u, and generating a keyword feature vector C u of each item u based on the keyword of each item u; S53, calculating a matching degree coefficient DC u of the user search keyword and each item u in the technological item knowledge graph based on the search keyword feature vector L and the keyword feature vector C u of each item u, and marking the matching degree coefficient DC u as a first matching degree coefficient DC u of the user search keyword; S54, correcting the first matching degree coefficient by introducing the keyword position weight to obtain the matching degree coefficient of each item u in the corrected user search keyword and technological item knowledge graph Marking the second matching degree coefficient as the user search keyword 。
4. A big data based technology project information management system for implementing the big data based technology project information management method according to any one of the claims 1-3, comprising: The to-be-processed technological project information acquisition module is used for acquiring newly added technological project information, marking the newly added technological project information as each piece of to-be-processed technological project information, and marking the verified historical technological project benchmark information in the technological project information management database as benchmark project information; The project information association degree deviation analysis module is used for calculating association degree deviation coefficients of each piece of technological project information to be processed and the standard project information, carrying out association degree deviation judgment, carrying out optimization processing on the technological project information based on the association degree deviation judgment result, and storing the technological project information in the technological project information database; The science and technology project knowledge graph constructing module is used for acquiring the science and technology project information in the science and technology project information database, preprocessing the science and technology project information to generate a feature vector set, and constructing a science and technology project knowledge graph based on the feature vector set; The technological project information management risk analysis module is used for extracting project progress deviation, project cost deviation and project resource matching deviation coefficients in a technological project knowledge graph, analyzing management risk of technological project information and generating an information management risk analysis result corresponding to the technological project; The technological project information matching analysis module is used for extracting the search keywords in the user project search request, and carrying out matching analysis on the feature vectors of the search keywords and the feature vectors of the keywords in the technological project knowledge graph to obtain the matching degree coefficient of the user search keywords; and the output project information recommendation list module is used for outputting a technological project information recommendation result corresponding to the user search keyword based on the matching degree coefficient of the user search keyword, wherein the technological project information recommendation result comprises an information management risk analysis result corresponding to the technological project.

Description

Science and technology project information management method and system based on big data Technical Field The invention relates to the technical field of information management, in particular to a technological project information management method and system based on big data. Background In the digital process of technical project management, along with the proliferation of the number of projects and the improvement of information complexity, the realization of efficient management by means of big data becomes a necessary trend. From the integrated analysis of project information to risk prejudgment and accurate retrieval recommendation, an intelligent management system is constructed, and the method is important for improving scientific research efficiency and guaranteeing project promotion. Traditional science and technology project information management methods mainly rely on manual operation combined with simple informatization tools. There are a number of limitations in that, first, the conventional method has a significant disadvantage in information association analysis. Because of lack of deep analysis on the association degree between the technological project information and the historical reference information, the similarity and the difference between the new project and the previous project are difficult to accurately judge, and the new project cannot be subjected to targeted optimization processing based on the historical experience, so that the scientificity and the accuracy of project information management are greatly compromised. Secondly, in the aspect of risk analysis, the traditional method mainly depends on manual experience, lacks comprehensive analysis of project progress, cost and resource multidimensional data, is difficult to comprehensively and accurately identify potential management risks of projects, and cannot provide powerful data support for project decision. In addition, in the information retrieval and matching link, the traditional simple retrieval mode based on the keywords cannot fully consider the semantic relation and the context information among the keywords, the retrieval result is often inaccurate and comprehensive, the diversified retrieval requirements of users are difficult to meet, the project recommendation containing the information management risk analysis result cannot be provided for the users, and the efficiency of the users for acquiring effective information is reduced. Disclosure of Invention In order to overcome the above-mentioned drawbacks of the prior art, embodiments of the present invention provide a method and a system for managing information of a scientific and technological project based on big data, so as to solve the above-mentioned problems in the background art. In order to achieve the purpose, the technical scheme provided by the invention is that the technological project information management method based on big data comprises the following steps: S1, acquiring technological project information to be processed, namely acquiring newly added technological project information, marking the newly added technological project information as each piece of technological project information to be processed, and marking the verified historical technological project benchmark information in a technological project information management database as benchmark project information; S2, project information association degree deviation analysis, namely calculating association degree deviation coefficients of each piece of to-be-processed technological project information and the standard project information, carrying out association degree deviation judgment, carrying out optimization processing on the technological project information based on association degree deviation judgment results, and storing the technological project information in a technological project information database; S3, constructing a science and technology project knowledge graph, namely acquiring science and technology project information in a science and technology project information database, preprocessing to generate a feature vector set, and constructing the science and technology project knowledge graph based on the feature vector set; S4, analyzing the management risk of the science and technology project information, namely extracting project progress deviation, project cost deviation and project resource matching deviation coefficients in a knowledge graph of the science and technology project, and analyzing the management risk of the science and technology project information to generate an information management risk analysis result corresponding to the science and technology project; S5, carrying out matching analysis on the technological project information, namely extracting a retrieval keyword in a retrieval request of a user project, and carrying out matching analysis on a feature vector of the retrieval keyword and a feature vector of the keyword in a techn