CN112580258B - Data-driven-based engine design simulation artificial intelligence analysis method and device - Google Patents

Data-driven-based engine design simulation artificial intelligence analysis method and device Download PDF

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CN112580258B
CN112580258B CN202011483801.XA CN202011483801A CN112580258B CN 112580258 B CN112580258 B CN 112580258B CN 202011483801 A CN202011483801 A CN 202011483801A CN 112580258 B CN112580258 B CN 112580258B
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孔祥龙
王西雁
林艺斌
王怀斌
冯夏芸
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Beijing Power Machinery Institute
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Abstract

The invention discloses a data-driven-based engine design simulation artificial intelligence analysis method and a device, wherein the method comprises the following steps: initial simulation data of the transmitter design in a data simulation process are collected; processing modeling treatment is carried out on the initial simulation data to obtain final simulation data; and carrying out data analysis on the final simulation data, and providing services corresponding to the analysis results according to the content showing the corresponding analysis results. The method improves the flexibility and the intelligent level of the design simulation, and enables the design simulation process to be changed from a primary process to an intelligent process, thereby greatly improving the efficiency and the performance.

Description

Data-driven-based engine design simulation artificial intelligence analysis method and device
Technical Field
The invention relates to the technical field of design simulation, in particular to an engine design simulation artificial intelligent analysis method and device based on data driving.
Background
In the related art, design simulation is generally based on design simulation of a flow, a design simulation flow is built by a master, and then each component designer calls a design simulation tool to develop design activities according to the flow.
However, the related art still needs a process-driven design simulation activity, which greatly reduces the intelligence and efficiency. There is a need for a solution.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems in the related art to some extent.
Therefore, an object of the present invention is to provide an artificial intelligence analysis method for engine design simulation based on data driving, which improves flexibility and intelligence level of design simulation, and enables a design simulation process to be changed from a primary process to an intelligent process, thereby greatly improving efficiency and performance.
Another object of the present invention is to provide a data-driven based engine design simulation artificial intelligence analysis device.
It is a further object of the invention to propose an electronic device.
It is yet another object of the present invention to propose a computer readable storage medium.
In order to achieve the above objective, an embodiment of the present invention provides a data-driven engine design simulation artificial intelligence analysis method, which includes the following steps: initial simulation data of the transmitter design in a data simulation process are collected; processing modeling treatment is carried out on the initial simulation data to obtain final simulation data; and carrying out data analysis on the final simulation data, and providing services corresponding to the analysis results according to the content showing the corresponding analysis results.
According to the data-driven engine design simulation artificial intelligence analysis method, design simulation data are mined through means of big data, machine learning, artificial intelligence analysis and the like, a design flow is refined, a design simulation flow is automatically constructed, a design simulation model is driven, and design simulation activities based on the model are developed, so that the traditional flow is eliminated from driving the design simulation activities, the flexibility and the intelligence level of the design simulation are improved, the design simulation process is converted from a primary process to an intelligent process, and the efficiency and the performance are greatly improved.
In addition, the data-driven engine design simulation artificial intelligence analysis method according to the embodiment of the invention can also have the following additional technical characteristics:
further, in one embodiment of the invention, the simulation data includes one or more of designer behavior data, design process data, and simulation result data.
Further, in an embodiment of the present invention, before the processing modeling processing is performed on the initial simulation data, the method further includes: generating a service model for resolving and programming the service layer; and/or generating a domain model for abstracting the business model; and/or generating a logical model for logically layering the conceptual entities of the domain model and the relationships between the entities in a database; and/or generating a physical model that addresses the physical chemistry and performance of the logical model against different relational databases.
Further, in an embodiment of the present invention, the processing modeling the initial simulation data includes: and inputting the initial simulation data into the business model, the domain model, the logic model and/or the physical model to obtain the final simulation data.
Further, in an embodiment of the present invention, the performing data analysis on the final simulation data includes: visual presentation is carried out on the final simulation data; and/or performing index calculation on the final simulation data; and/or using the final simulation data for training a preset model; and/or depicting a user representation based on the simulation data.
In order to achieve the above object, another embodiment of the present invention provides a data-driven engine design simulation artificial intelligence analysis device, including: the acquisition module is used for acquiring initial simulation data of the transmitter design in the data simulation process; the processing module is used for processing and modeling the initial simulation data to obtain final simulation data; and the analysis module is used for carrying out data analysis on the final simulation data, displaying the content of the corresponding analysis result and providing service corresponding to the analysis result.
According to the engine design simulation artificial intelligence analysis device based on data driving, design simulation data are mined through means of big data, machine learning, artificial intelligence analysis and the like, a design flow is refined, a design simulation flow is automatically constructed, a design simulation model is driven, and design simulation activities based on the model are developed, so that the situation that the traditional flow drives the design simulation activities is eliminated, the flexibility and the intelligence level of the design simulation are improved, the design simulation process is converted from a primary process to an intelligent process, and the efficiency and the performance are greatly improved.
In addition, the data-driven engine design simulation artificial intelligence analysis device according to the embodiment of the invention can also have the following additional technical features:
further, in one embodiment of the invention, the simulation data includes one or more of designer behavior data, design process data, and simulation result data.
Further, in an embodiment of the present invention, before the processing modeling the initial simulation data, the processing module is further configured to: generating a service model for resolving and programming the service layer; and/or generating a domain model for abstracting the business model; and/or generating a logical model for logically layering the conceptual entities of the domain model and the relationships between the entities in a database; and/or generating a physical model that addresses the physical chemistry and performance of the logical model against different relational databases.
Further, in an embodiment of the present invention, the processing module is specifically configured to: and inputting the initial simulation data into the business model, the domain model, the logic model and/or the physical model to obtain the final simulation data.
Further, in an embodiment of the present invention, the analysis module is specifically configured to: visual presentation is carried out on the final simulation data; and/or performing index calculation on the final simulation data; and/or using the final simulation data for training a preset model; and/or depicting a user representation based on the simulation data.
To achieve the above object, an embodiment of the present invention provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions configured to perform the data-driven based engine design simulation artificial intelligence analysis method described above.
To achieve the above object, an embodiment of the present invention provides a computer readable storage medium having a computer program stored thereon, the program being executed by a processor for implementing the above-described data-driven engine design simulation artificial intelligence analysis method.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The foregoing and/or additional aspects and advantages of the invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a flow chart of a data-driven based engine design simulation artificial intelligence analysis method in accordance with an embodiment of the present invention;
FIG. 2 is a block schematic diagram of a data-driven based engine design simulation artificial intelligence analysis apparatus in accordance with an embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
The method and the device for analyzing the simulated artificial intelligence of the engine design based on the data driving according to the embodiment of the invention are described below with reference to the accompanying drawings.
FIG. 1 is a flow chart of a data-driven engine design simulation artificial intelligence analysis method in accordance with an embodiment of the present invention.
As shown in fig. 1, the data-driven engine design simulation artificial intelligence analysis method comprises the following steps:
in step S101, initial simulation data of the transmitter design in the data simulation process is acquired.
Wherein in one embodiment of the invention, the simulation data includes one or more of designer behavior data, design process data, and simulation result data.
Generally, epidemic situation is high in outburst, strong in infectivity, wide in spread and high in risk, and the task of prevention and control is hard, time is urgent and situation is severe. In the epidemic situation fight war, the new generation information communication technology of rapid development such as big data, cloud computing, artificial intelligence and the like is accelerated to be deeply fused with the fields of traffic, medical treatment, education and the like, so that the organization and execution of epidemic situation prevention and control are more efficient, and the data driving also becomes a powerful weapon for war epidemic.
The three elements of the data driving mode are the internet, big data and data mining. On battlefield of the battle COVID-19, the data battle epidemic is all-line hit. Ice-cold data is continually sublimating its value: the method has great significance from global monitoring of epidemic situation, grid isolation prevention and control, even guidance on epidemic situation research and analysis of public opinion.
Therefore, the embodiment of the invention can be applied to the intelligent design of the aerospace power engine by applying big data and artificial intelligent analysis.
In particular, the data driver may include four links, data acquisition, data modeling, data analysis, and data decision making, each of which is part of the data driver. When data acquisition is carried out, the embodiment of the invention can record the data of the design simulation process through a certain data acquisition means, and the table structure of the design science is stored in a structuring way. The simulation data may include: designer behavior data, design process data, simulation result data, and the like.
It should be noted that the process of data acquisition may include two points of interest: one is that the data is to be collected comprehensively and carefully; the other is to store scientifically, and the structured data has utility value. Structured data, also called row data, is data logically expressed and implemented by a two-dimensional table structure, strictly following data format and length specifications, and is stored and managed mainly by relational databases. If this stage is analogous to the acquisition society, humans rely on the most primitive nature resources for nutrient intake and survival, what nature has, what humans can get, and can only meet the basic needs of survival.
In step S102, processing modeling is performed on the initial simulation data to obtain final simulation data.
Further, in one embodiment of the present invention, before performing the process modeling processing on the initial simulation data, the method further includes: generating a service model for resolving and programming the service layer; and/or generating a domain model for abstracting the business model; and/or generating a logical model that performs database-level logically on the conceptual entities of the domain model and the relationships between the entities; and/or generating a physical model that addresses the physical and chemical properties of the logical model for the different relational databases.
Further, in one embodiment of the present invention, the processing modeling process is performed on the initial simulation data, including: and inputting the initial simulation data into a business model, a domain model, a logic model and/or a physical model to obtain final simulation data.
It can be understood that the processing modeling processing of the initial simulation data can also be called data processing, generally, the original data source is cleaned, processed and calculated to meet a certain purpose, and finally the needed data is generated, wherein the original data is not added with any logic and has low utilization value.
For example, for an order form, the basic information that each data line is a record is that a certain sku is ordered by a certain user at a certain price at a certain time, but the performance of a single sku is usually not of analytical value and utilization value, and more valuable is that a certain category of sku is ordered by a certain batch of users for a certain period of time, which is more beneficial to subsequent decisions. This is one of the processes for performing the process modeling.
Further, data modeling may generally include the following four modeling classifications: business modeling, domain modeling, logical modeling, and physical modeling. Specifically, the service modeling can generate a service model, and mainly solves the decomposition and programming of a service layer; the domain modeling can generate a domain model, mainly abstract the business model to generate a domain concept model; the logic modeling can generate a logic model, and mainly carries out database-level logic on concept entities and relations among the entities of the field model; the physical modeling can generate a physical model, and mainly solves specific technical problems of physical chemistry, performance and the like of a logic model aiming at different relational databases. Therefore, according to the embodiment of the invention, the four models complement each other or are mutually independent according to the application scene of the models.
In step S103, data analysis is performed on the final simulation data, and services corresponding to the analysis result are provided according to the content of the display corresponding to the analysis result.
Further, in one embodiment of the present invention, performing data analysis on the final simulation data includes: visual presentation is carried out on the final simulation data; and/or performing index calculation on the final simulation data; and/or using the final simulation data for training a preset model; and/or depicting the user representation based on the simulation data.
It will be appreciated that the data analysis of the final simulation data may be a preliminary stage in which the data actually takes its value, by visually rendering the data, index calculations are performed on the data, the data is used to train a model algorithm, the data is used to characterize a user representation, and so on.
It should be noted that the actual data driver should be a more intelligent and automated driver, that is, the user will come in at that moment and the entire data link will start to operate to determine what content is presented to the designer and what services are provided.
According to the data-driven engine design simulation artificial intelligence analysis method provided by the embodiment of the invention, design simulation data are mined through means such as big data, machine learning, artificial intelligence analysis and the like, a design flow is refined, a design simulation flow is automatically constructed, a design simulation model is driven, and design simulation activities based on the model are developed, so that the situation that the traditional flow is used for driving the design simulation activities is eliminated, the flexibility and the intelligence level of the design simulation are improved, the design simulation process is converted from a primary process to an intelligent process, and the efficiency and the performance are greatly improved.
Next, a data-driven-based engine design simulation artificial intelligence analysis device according to an embodiment of the present invention will be described with reference to the accompanying drawings.
FIG. 2 is a block schematic diagram of a data-driven engine design simulation artificial intelligence analysis device in accordance with an embodiment of the present invention.
As shown in fig. 2, the data-driven engine design simulation artificial intelligence analysis apparatus 10 includes: an acquisition module 100, a processing module 200 and an analysis module 300.
The acquisition module 100 is used for acquiring initial simulation data of the transmitter design in a data simulation process. The processing module 200 is configured to perform processing modeling on the initial simulation data to obtain final simulation data. The analysis module 300 is used for performing data analysis on the final simulation data, and providing services corresponding to the analysis results according to the content of the display corresponding to the analysis results.
Further, in one embodiment of the invention, the simulation data includes one or more of designer behavior data, design process data, and simulation result data.
Further, in one embodiment of the present invention, the processing module 200 is further configured to, prior to performing the process modeling process on the initial simulation data: generating a service model for resolving and programming the service layer; and/or generating a domain model for abstracting the business model; and/or generating a logical model that performs database-level logically on the conceptual entities of the domain model and the relationships between the entities; and/or generating a physical model that addresses the physical and chemical properties of the logical model for the different relational databases.
Further, in one embodiment of the present invention, the processing module 200 is specifically configured to: and inputting the initial simulation data into a business model, a domain model, a logic model and/or a physical model to obtain final simulation data.
Further, in one embodiment of the present invention, the analysis module 300 is specifically configured to: visual presentation is carried out on the final simulation data; and/or performing index calculation on the final simulation data; and/or using the final simulation data for training a preset model; and/or depicting the user representation based on the simulation data.
It should be noted that the foregoing explanation of the embodiment of the data-driven engine design simulation artificial intelligence analysis method is also applicable to the data-driven engine design simulation artificial intelligence analysis device of the embodiment, and will not be repeated herein.
According to the engine design simulation artificial intelligence analysis device based on data driving, which is provided by the embodiment of the invention, design simulation data are mined through means such as big data, machine learning, artificial intelligence analysis and the like, a design flow is refined, a design simulation flow is automatically constructed, a design simulation model is driven, and design simulation activities based on the model are developed, so that the situation that the traditional flow drives the design simulation activities is eliminated, the flexibility and the intelligence level of the design simulation are improved, the design simulation process is converted from a primary process to an intelligent process, and the efficiency and the performance are greatly improved.
An embodiment of the present invention provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions configured to perform the data-driven based engine design simulation artificial intelligence analysis method described above.
The embodiment of the invention provides a computer readable storage medium, on which a computer program is stored, the program being executed by a processor for implementing the data-driven engine design simulation artificial intelligence analysis method.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.

Claims (7)

1. The engine design simulation artificial intelligence analysis method based on data driving is characterized by comprising the following steps of:
Initial simulation data of the transmitter design in a data simulation process are collected;
processing modeling treatment is carried out on the initial simulation data to obtain final simulation data; and
Carrying out data analysis on the final simulation data, and providing services corresponding to the analysis results according to the content showing the corresponding analysis results;
before the processing modeling processing is performed on the initial simulation data, the method further comprises the following steps:
Generating a service model for resolving and programming the service layer; and/or
Generating a domain model for carrying out abstract processing on the business model; and/or
Generating a logic model for carrying out database-level logic on concept entities of the domain model and relations among the entities; and/or
Generating a physical model for solving physical and chemical properties and performances of the logic model aiming at different relational databases;
The processing modeling processing of the initial simulation data comprises the following steps:
And inputting the initial simulation data into the business model, the domain model, the logic model and/or the physical model to obtain the final simulation data.
2. The method of claim 1, wherein the simulation data comprises one or more of designer behavior data, design process data, and simulation result data.
3. The method of claim 1, wherein the performing data analysis on the final simulation data comprises:
Visual presentation is carried out on the final simulation data; and/or
Performing index calculation on the final simulation data; and/or
The final simulation data are used for training a preset model; and/or
And describing the user portrait according to the simulation data.
4. A data-driven based engine design simulation artificial intelligence analysis device, comprising:
the acquisition module is used for acquiring initial simulation data of the transmitter design in the data simulation process;
the processing module is used for processing and modeling the initial simulation data to obtain final simulation data; and
The analysis module is used for carrying out data analysis on the final simulation data, displaying the content of the corresponding analysis result and providing service corresponding to the analysis result;
Before the processing modeling processing is performed on the initial simulation data, the processing module is further configured to:
Generating a service model for resolving and programming the service layer; and/or
Generating a domain model for carrying out abstract processing on the business model; and/or
Generating a logic model for carrying out database-level logic on concept entities of the domain model and relations among the entities; and/or
Generating a physical model for solving physical and chemical properties and performances of the logic model aiming at different relational databases;
The processing module is further configured to input the initial simulation data into the service model, the domain model, the logic model and/or the physical model, to obtain the final simulation data.
5. The apparatus according to claim 4, wherein the analysis module is specifically configured to:
Visual presentation is carried out on the final simulation data; and/or
Performing index calculation on the final simulation data; and/or
The final simulation data are used for training a preset model; and/or
And describing the user portrait according to the simulation data.
6. An electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions configured to perform the data-driven based engine design simulation artificial intelligence analysis method of any one of claims 1-3.
7. A computer readable storage medium having stored thereon a computer program, the program being executable by a processor for implementing a data driven based engine design simulation artificial intelligence analysis method as claimed in any of claims 1-3.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004185539A (en) * 2002-12-06 2004-07-02 Yunitekku:Kk Trading area analyzing system, method, program, and record medium
EP1659468A2 (en) * 2004-11-16 2006-05-24 Rockwell Automation Technologies, Inc. Universal run-time interface for agent-based simulation and control systems
WO2019113508A1 (en) * 2017-12-07 2019-06-13 Fractal Industries, Inc. A system and methods for multi-language abstract model creation for digital environment simulations
CN110557385A (en) * 2019-08-22 2019-12-10 西安电子科技大学 information hiding access method and system based on behavior confusion, and server
CN111597176A (en) * 2020-05-09 2020-08-28 山东财经大学 Teaching simulation training method and system for delaying supply chain generation

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150178425A1 (en) * 2013-12-20 2015-06-25 The Procter & Gamble Company Method for modeling graphics on a flexible form

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004185539A (en) * 2002-12-06 2004-07-02 Yunitekku:Kk Trading area analyzing system, method, program, and record medium
EP1659468A2 (en) * 2004-11-16 2006-05-24 Rockwell Automation Technologies, Inc. Universal run-time interface for agent-based simulation and control systems
WO2019113508A1 (en) * 2017-12-07 2019-06-13 Fractal Industries, Inc. A system and methods for multi-language abstract model creation for digital environment simulations
CN110557385A (en) * 2019-08-22 2019-12-10 西安电子科技大学 information hiding access method and system based on behavior confusion, and server
CN111597176A (en) * 2020-05-09 2020-08-28 山东财经大学 Teaching simulation training method and system for delaying supply chain generation

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
支持模型复用的通用大数据分析平台;崔晓龙;郭茜;边胜琴;张德政;;实验室研究与探索(07);全文 *
智慧信息服务大数据分析框架;吴丹;陆柳杏;;图书与情报(02);全文 *
智能情报分析系统的架构设计与关键技术研究;化柏林;李广建;;图书与情报(06);全文 *
构建虚拟仿真实验教学多元评价体系――以《水电站》课程虚拟仿真实验为例;龚成勇;韩伟;王之君;何香如;;中国教育信息化(06);全文 *
面向大数据领域的事理认知图谱构建与推断分析;王军平;张文生;王勇飞;孙正雅;;中国科学:信息科学(07);全文 *

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