CN117787745A - Aviation equipment industry chain business cooperation intelligent decision method based on big data - Google Patents
Aviation equipment industry chain business cooperation intelligent decision method based on big data Download PDFInfo
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- CN117787745A CN117787745A CN202311839455.8A CN202311839455A CN117787745A CN 117787745 A CN117787745 A CN 117787745A CN 202311839455 A CN202311839455 A CN 202311839455A CN 117787745 A CN117787745 A CN 117787745A
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Abstract
The invention relates to the technical field of aerospace equipment industry, in particular to an aerospace equipment industry chain business cooperation intelligent decision-making method based on big data. The method comprises the following steps: s100: acquiring original data in an aviation equipment industrial chain; s200: the method comprises the steps of preprocessing raw data to form knowledge data, and storing the knowledge data in a data space; s300: carrying out evolution analysis on knowledge data in a data space to obtain various decision results; s400: and displaying various decision results through the information panel. Can help the enterprise to make more correct and effective decisions, promote enterprise production efficiency.
Description
Technical Field
The invention relates to the technical field of aerospace equipment industry, in particular to an aerospace equipment industry chain business cooperation intelligent decision-making method based on big data.
Background
The aviation equipment mainly comprises an airplane complete machine, an aeroengine, recording equipment, a system and the like, and relates to a plurality of knowledge fields such as mechanical design, circuit design, a hydraulic system, automatic control and the like. Compared with the general industry, the aviation equipment industry chain has the characteristics of large data volume, large scale, very strict technical requirements and the like, so that the analysis decision is very necessary to integrate the data of the industry chain, the resources of upstream and downstream enterprises in the aviation equipment industry chain can be further optimally configured while the core competitiveness of the enterprises is improved, and the maximum production value of the aviation equipment enterprises is exerted. The aircraft provides a very important tool for the economic development of China, the social progress and the daily traffic of people, so that the construction of more complete and intelligent aviation equipment manufacturing industry chain business cooperation is quickened, and the aircraft plays a very important role in maintaining national safety of China, improving economic activity, improving the competitiveness of aviation equipment enterprises, improving the daily life level of people and the like. The current method for intelligent decision-making of the aviation equipment industry chain mainly comprises an aviation equipment collaborative manufacturing industry chain collaborative intelligent decision-making method, but the method mainly relates to the collaborative manufacturing industry chain, has certain defects for the whole aviation equipment industry chain, has insufficient relating range, and needs a new method for further expanding the aviation equipment industry chain.
The existing aviation equipment industry chain relates to more enterprises, but the enterprises do not have unified standard construction and unified data systems, meanwhile, the integrity, accuracy, timeliness and the like of data have large gaps, the data size of the aviation equipment is more and more along with the development of the aviation equipment, and the aviation equipment industry chain business collaboration intelligent decision-making method is very challenging for data analysts and then influences decision-making, so that the design of the aviation equipment industry chain business collaboration intelligent decision-making method is very necessary for the development of the enterprises and the help of the enterprise decision-making.
Disclosure of Invention
The technical problem solved by the invention is to provide the aviation equipment industry chain business cooperation intelligent decision-making method based on big data, which can help enterprises to make more correct and effective decisions and improve the production efficiency of the enterprises.
The basic scheme provided by the invention is as follows: an aviation equipment industry chain business cooperation intelligent decision-making method based on big data comprises the following steps:
s100: acquiring original data in an aviation equipment industrial chain;
s200: the method comprises the steps of preprocessing raw data to form knowledge data, and storing the knowledge data in a data space;
s300: carrying out evolution analysis on knowledge data in a data space to obtain various decision results;
s400: and displaying various decision results through the information panel.
Further, the step S300 includes the steps of:
s301: trend evolution analysis, correlation evolution analysis and abnormal evolution analysis are carried out on the research and development data, the production data and the service data, and correlation among the evolution research and development data, the evolution production data, the evolution service data and various data is obtained;
s302: and carrying out problem evaluation analysis on the evolution research and development data, the evolution production data and the evolution service data to obtain problem fault analysis data.
Further, the step S300 includes the steps of:
s301: trend evolution analysis, correlation evolution analysis and abnormal evolution analysis are carried out on the research and development data, the production data and the service data, and correlation among the evolution research and development data, the evolution production data, the evolution service data and various data is obtained;
s302: and carrying out problem evaluation analysis on the evolution research and development data, the evolution production data and the evolution service data to obtain problem fault analysis data.
Further, the step S300 further includes the steps of:
s311: and obtaining development analysis data through efficiency analysis, cost analysis, risk analysis and design analysis on the evolution development data, the evolution production associated development data, the evolution service associated development data and the problem fault analysis data.
Further, the step S300 further includes the steps of:
s321: and analyzing and sorting the evolution production data, the evolution service data, the problem fault analysis data and the research and development analysis data to generate future planning data.
Further, the step S300 further includes the steps of:
s331: the fault analysis data, the research and development analysis data and the future planning data are arranged through a visualization tool to obtain visualization data;
the step S400 includes the steps of:
s401: and displaying the visual data of the fault analysis data, the research and development analysis data and the future planning data through different signboards.
The principle and the advantages of the invention are as follows: the method comprises the steps of obtaining original data in an aviation equipment industrial chain, wherein the aviation equipment industrial chain comprises design research and development, production and manufacturing and guarantee service, the original data of the design research and development comprise drawing data, design data and material data, and the original data of the production and manufacturing comprise manufacturer data, production data, equipment data and the like. The business support service data comprises maintenance data, service data and after-sales data.
All work of the existing aviation equipment industrial chain is generally carried out separately, unified observation of the working process of the industrial chain is not easy, a user can know the condition of the whole industrial chain to a certain extent, so that better decisions can be conveniently made, meanwhile, quality fault conditions in the industrial chain can be found and predicted in time, and the user can adjust all work of the industrial chain in time, so that risks are prevented.
The aviation equipment industry chain is relatively long, the aviation equipment industry chain can be integrated through the method, the method is not limited to a certain link of the industry chain, the efficiency and decision accuracy of the decision method for data analysis are high and the universality is wider compared with the traditional manual analysis, meanwhile, the method relates to business collaboration of the aviation equipment industry chain, the close collaboration of each link can be promoted, through the decision analysis of the method, a user can optimize resource allocation and the like, and the cost of resources, manpower and the like is reduced.
The method can help enterprises to realize industrial data, and can help the enterprises to quickly adapt to artificial intelligence industry markets along with the high-speed development of aviation industry in China.
Drawings
FIG. 1 is a schematic flow chart of an embodiment of an intelligent decision making method for business collaboration of an aviation equipment industrial chain based on big data;
FIG. 2 is a schematic diagram of data evolution in an embodiment of an intelligent decision method for business collaboration of an aviation equipment industrial chain based on big data;
fig. 3 is a schematic diagram of decision analysis in an embodiment of an intelligent decision method for business collaboration of an aviation equipment industrial chain based on big data.
Detailed Description
The following is a further detailed description of the embodiments:
an example is substantially as shown in figure 1:
an aviation equipment industry chain business cooperation intelligent decision-making method based on big data comprises the following steps:
s100: raw data in an avionics industry chain is acquired. Specifically, in this embodiment, the method mainly includes designing and developing an aviation equipment service, producing and manufacturing the aviation equipment service, guaranteeing service of the aviation equipment service, and collecting various industrial chain data such as drawing data, design data, material data, manufacturer data, production data, equipment data, maintenance data, service data, after-sales data, and the like generated in an aviation equipment industrial chain.
S200: the method is used for preprocessing the original data to form knowledge data, and storing the knowledge data in a data space.
The step S200 includes the steps of:
s201: and carrying out data extraction, data cleaning, data screening, data conversion and data loading on the original data in sequence to obtain knowledge data. Specifically, the collected information is subjected to data extraction, then the data is cleaned to remove impurity information, the impurity-removed information is screened to obtain corresponding required information, the screened data format is converted into a data format which can be stored in a data space, and finally the data is loaded into the data space.
S202: and carrying out data classification on the knowledge data to obtain research and development data, production data and service data, and forming a database corresponding to the data types in a data space for storage. The obtained information is classified and respectively loaded into a research and development database, a production database and a service database, and meanwhile, the required information can be searched and checked through information retrieval.
S300: and carrying out evolution analysis on knowledge data in the data space to obtain various decision results. And carrying out data trend evolution analysis, data correlation evolution analysis, data abnormal evolution analysis and other analyses on the data in the research and development database, the production database and the service database by using the data in the data space by using the technologies such as data mining, machine learning technology and the like.
S300 includes the steps of:
s301: as shown in fig. 2 and 3, trend evolution analysis, correlation evolution analysis and abnormal evolution analysis are performed on the research and development data, the production data and the service data, so as to obtain correlation among the evolution research and development data, the evolution production data, the evolution service data and various data. Specifically, after industrial chain data are collected in a data space and stored, analysis methods such as nonlinear regression analysis, linear regression analysis and time series analysis are utilized to analyze the possible trend directions of the data so as to facilitate the utilization of a later decision system, meanwhile, problem fault correlation, research and development correlation, service correlation and data related to each other among three groups of service data and among single groups of data are produced, variables are analyzed through correlation analysis to construct corresponding correlation, and abnormal data in the evolution process is analyzed to judge whether the abnormal data has influence on the evolution analysis. After the evolution analysis is carried out on the data, a problem fault analysis decision system is adopted, fault diagnosis is carried out on the evolved data in the system, quality of the problem and the like are analyzed, corresponding analysis reports and charts are obtained, meanwhile, the data are arranged and sent to a research and development analysis intelligent decision system, and because faults, quality problems, service problems and the like can help to improve research and development effects, in the research and development analysis intelligent decision system, design data in the evolved data and data provided by the problem fault analysis decision system are provided for analysis by a research and development analysis technology, and corresponding data such as reports and the like are obtained.
S302: and carrying out problem evaluation analysis on the evolution research and development data, the evolution production data and the evolution service data to obtain problem fault analysis data. In this step, the evolution development data, evolution production manufacturing data, evolution service data are analyzed by a problem fault analysis method such as FTA, RCA, LTA and fault diagnosis to obtain problem quality analysis reports (these reports mainly contain problem quality descriptions, time environments, possible reasons, relate to units and equipment, mainly include production manufacturing quality, service quality problems: before sales, maintenance, development quality problems, etc.), fault diagnosis reports (three parts relate to fault parts, the report content is the same as the problem quality analysis report), fault trees, fault problem trend comprehensive graphs (fault diagnosis graphs, development, service and production quality problems thereof), and fault problem comprehensive tables. And analyzing the fault analysis data of the problems, developing and analyzing the fault analysis data of the problems and the problems in an intelligent decision system and a future planning analysis decision system.
S311: and obtaining development analysis data through efficiency analysis, cost analysis, risk analysis and design analysis on the evolution development data, the evolution production associated development data, the evolution service associated development data and the problem fault analysis data.
In the steps, evolution research and development data, evolution production manufacturing related research and development data, evolution service related research and development data and problem fault analysis data are analyzed through efficiency analysis, cost analysis, risk analysis and design analysis technology to obtain comprehensive analysis reports (mainly including research and development cost analysis reports, research and development efficiency analysis reports, research and development risk analysis reports, research and development design feature defect advantage reports, research and development design optimization reports and the like), research and development comprehensive charts (research and development information (Innography) charts, cost analysis, risk analysis and efficiency analysis charts, research and development design feature defect advantage scatter charts represent different defects and advantages by different colors, color depth representation degree, research and development design advantage optimization histogram and the like).
S321: and analyzing and sorting the evolution production data, the evolution service data, the problem fault analysis data and the research and development analysis data to generate future planning data.
In the step, research and development analysis data, evolution production manufacturing data, evolution service data and problem fault analysis data are analyzed and tidied through SWOT and an automatic script tool, advantages and disadvantages of all parts in an analysis industry chain are reflected in a chart and report form, a possible research and production service research and development plan is generated, a production manufacturing plan is produced, and a guarantee service plan helps a user to well plan in the future.
S331: and sorting the fault analysis data, the research and development analysis data and the future planning data through a visualization tool to obtain the visualization data. Specifically, the collected data is collated using visualization tools such as charts, digital twinning, and the like.
S400: and displaying various decision results through the information panel.
S401: and displaying the visual data of the fault analysis data, the research and development analysis data and the future planning data through different signboards.
The analysis results (charts, fault trees and the like) and reports of the three systems are mainly reflected through three signboards. Although three signboards are related to each other, all corresponding chart reports related to another system can be checked and opened in one system, each signboard is provided with an information inquiry and information feedback window, and a user can feed back the information inquiry and information feedback window to decision systems of three corresponding parts through a real-time information tracking feedback system according to the checked problems found by the checked data, and perform new analysis.
The foregoing is merely exemplary of the present invention, and the specific structures and features well known in the art are not described in any way herein, so that those skilled in the art will be able to ascertain all prior art in the field, and will not be able to ascertain any prior art to which this invention pertains, without the general knowledge of the skilled person in the field, before the application date or the priority date, to practice the present invention, with the ability of these skilled persons to perfect and practice this invention, with the help of the teachings of this application, with some typical known structures or methods not being the obstacle to the practice of this application by those skilled in the art. It should be noted that modifications and improvements can be made by those skilled in the art without departing from the structure of the present invention, and these should also be considered as the scope of the present invention, which does not affect the effect of the implementation of the present invention and the utility of the patent. The protection scope of the present application shall be subject to the content of the claims, and the description of the specific embodiments and the like in the specification can be used for explaining the content of the claims.
Claims (6)
1. The intelligent decision method for the business collaboration of the industry chain of the aviation equipment based on big data is characterized by comprising the following steps of: the method comprises the following steps:
s100: acquiring original data in an aviation equipment industrial chain;
s200: the method comprises the steps of preprocessing raw data to form knowledge data, and storing the knowledge data in a data space;
s300: carrying out evolution analysis on knowledge data in a data space to obtain various decision results;
s400: and displaying various decision results through the information panel.
2. The big data-based aviation equipment industry chain business collaborative intelligent decision-making method is characterized by comprising the following steps of: the step S200 includes the steps of:
s201: sequentially carrying out data extraction, data cleaning, data screening, data conversion and data loading on the original data to obtain knowledge data;
s202: and carrying out data classification on the knowledge data to obtain research and development data, production data and service data, and forming a database corresponding to the data types in a data space for storage.
3. The big data-based aviation equipment industry chain business collaborative intelligent decision-making method is characterized by comprising the following steps of: the step S300 includes the steps of:
s301: trend evolution analysis, correlation evolution analysis and abnormal evolution analysis are carried out on the research and development data, the production data and the service data, and correlation among the evolution research and development data, the evolution production data, the evolution service data and various data is obtained;
s302: and carrying out problem evaluation analysis on the evolution research and development data, the evolution production data and the evolution service data to obtain problem fault analysis data.
4. The big data-based aviation equipment industry chain business collaborative intelligent decision-making method according to claim 3, wherein the method comprises the following steps: the step S300 further includes the steps of:
s311: and obtaining development analysis data through efficiency analysis, cost analysis, risk analysis and design analysis on the evolution development data, the evolution production associated development data, the evolution service associated development data and the problem fault analysis data.
5. The big data-based aviation equipment industry chain business collaborative intelligent decision-making method is characterized by comprising the following steps of: the step S300 further includes the steps of:
s321: and analyzing and sorting the evolution production data, the evolution service data, the problem fault analysis data and the research and development analysis data to generate future planning data.
6. The big data-based aviation equipment industry chain business collaborative intelligent decision-making method is characterized by comprising the following steps of: the step S300 further includes the steps of:
s331: the fault analysis data, the research and development analysis data and the future planning data are arranged through a visualization tool to obtain visualization data;
the step S400 includes the steps of:
s401: and displaying the visual data of the fault analysis data, the research and development analysis data and the future planning data through different signboards.
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| CN202311839455.8A CN117787745A (en) | 2023-12-27 | 2023-12-27 | Aviation equipment industry chain business cooperation intelligent decision method based on big data |
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