A material performance data management method and system

Abstract

The present invention discloses a material performance data management method and system, which are used for material performance data management and shared use, and belong to the field of equipment comprehensive support and material science, and solve the problems of insufficient network sharing, insufficient data fusion and multidisciplinary support, insufficient intelligent services, insufficient standardization, and insufficient data security control of existing material performance databases. The present invention adopts a hierarchical access control strategy, and after the user logs into the system, the material performance data is standardized and stored; the stored standardized material performance data is controlled and shared according to the authority; the material performance data obtained by the controlled query sharing is compared and analyzed; and the obtained controlled query sharing results and the obtained comparative analysis results are visualized. The present invention is used for material performance data management and sharing services.

Description

Technical Field

[0001] A material performance data management method and system are used for material performance data management and shared use, belonging to the field of equipment comprehensive support and material science. Background Art

[0002] Material performance data is an important and indispensable basic information for national economic and infrastructure construction, national defense engineering construction, material science and technology innovation, and national defense construction. With the development of computer technology, the material information discipline formed by the cross-penetration between material science and information technology has become one of the important directions of material research. Material performance data plays an increasingly important role in the research, development, production, promotion, use of new products and the popularization of material science knowledge. The material performance database is an important part of the scientific database, and the material performance database system is an important pillar of CAD / CAE / CAM / CIMS and equipment comprehensive support. It has a wide range of applications in scientific research and engineering technology, and is one of the important ways to improve the level of machinery manufacturing, industrial production and material design. The application of the material performance database system as the basis of the expert system is a major trend in the development of the field of material science and high-performance numerical simulation. Some material performance data involved in the fields of aviation, aerospace, ships, and weapons and equipment are sensitive and related to national security. While sharing them in a network, it is necessary to provide authorized data use services for trusted users. Therefore, it is an urgent task for the field of material science and high-performance numerical simulation to establish a standard-based material performance database system and provide controllable data services.

[0003] In the past few decades, material design and selection based on effective data have developed rapidly, and developed countries and emerging industrialized countries are actively establishing various material performance databases. For example, the National Bureau of Standards of the United States has established a material mechanical properties database, an alloy phase diagram database, a ceramic phase diagram database, a material corrosion database, a material friction and wear database, etc. The U.S. Department of Defense, guided by the CALS (Computer Supported Equipment Integrated Support) program, has established a large number of engineering databases to serve the equipment of the Navy, Army, Air Force, and the research and development and construction of the Department of Defense, which involve multiple categories such as steel materials, non-ferrous metal materials, polymer materials, and composite materials, such as the MatWeb database. The metal database SOLMA of the German Technical Experiment Association contains more than 20,000 data on 3,000 ferrous and non-ferrous metals. France has built more than 40 material databases, covering most industrial materials. The French Physical Metallurgical Thermodynamics Laboratory has developed a zirconium alloy thermodynamic database Zireobase for the application of zirconium alloys in the nuclear industry. The High Temperature Materials Database (HT-DB) of the PETTER European Research Center in the Netherlands contains mechanical and thermodynamic data of metals, non-metals and composite materials. Finland's TVO and VTT Industrial Systems have jointly developed the TVO / VTT material database, which can simultaneously search for material fatigue curves, similar materials, etc. Some well-known professional analysis software comes with a material performance database, such as the metal material performance database that comes with the DEFORM finite element analysis software, and the NASGRO damage tolerance analysis software includes the NASMAT material database module. These databases must rely on professional software to run, and are difficult to update and trace. Under the complex international situation, foreign material performance databases are limited in their openness to my country's weapon equipment design and numerical simulation verification, and material performance data are difficult to use, and even more difficult to update and trace.

[0004] The development of databases in my country began in the 1970s, mainly distributed in scientific research, universities and factories in various industries. In 1979, the Institute of Chemical Metallurgy of the Chinese Academy of Sciences and the Shanghai Institute of Organic Chemistry jointly established a chemical database. The material database established by the Shenyang Institute of Metal Research of the Chinese Academy of Sciences includes 61,300 data and 7 sub-databases, covering superalloys, titanium alloys, nanomaterials, pipeline materials, material welding, material corrosion and material failure analysis. my country started late in the construction of material information systems. Some material database systems established in China have not been widely used. They are mainly manifested in: they are built in different department systems, using PC stand-alone mode, and have not formed real data sharing. The database is small in scale, disciplines and fields are specific, the process is slow, the service quality is poor, the degree of commercialization is not high, standardization work is not timely, the input is small and the output value is low, the discipline development is unbalanced, and the low-level duplication phenomenon is serious. The lagging and weakness of the construction of my country's scientific and technological infrastructure has seriously affected the improvement of my country's scientific and technological innovation capabilities and international competitiveness. The specific problems of the existing material performance data management method (i.e., material performance database system) are as follows:

[0005] 1. Insufficient network sharing. Currently, a large number of material performance databases are built in different department systems and run in offline PC stand-alone mode. The network sharing and external service capabilities are insufficient;

[0006] 2. Insufficient data fusion and multidisciplinary support. Weapon equipment engineering is a multidisciplinary and multi-field system engineering. Its design, development and maintenance process requires the use of multi-field and multi-disciplinary material performance data such as mechanical performance data, thermodynamic data, metal data and electromagnetic performance data. A single performance database cannot support the development of related work;

[0007] 3. Insufficient intelligent services. Existing material performance databases basically provide conditional retrieval services, and do not provide sufficient support for the customization of intelligent algorithms such as intelligent approximate material matching and existing advanced predictive analysis.

[0008] 4. Insufficient standardization. The material performance databases of different countries, regions and units use different data standards, units and structures, which makes data sharing and use difficult;

[0009] 5. Insufficient data security control. The existing material performance database is limited to professional data in a certain field. It is built by different countries or departments and used internally. The hierarchical protection and security control of material performance data are basically not considered. Summary of the invention

[0010] In response to the above-mentioned research problems, the purpose of the present invention is to provide a material performance data management method and system to solve the problems of insufficient network sharing of existing material performance databases, insufficient data fusion and multidisciplinary support, insufficient intelligent services, insufficient standardization and insufficient data security control.

[0011] In order to achieve the above object, the present invention adopts the following technical solution:

[0012] A material performance data management method comprises the following steps:

[0013] Step 1. Based on the hierarchical access control strategy, after the user logs into the system, the material performance data is standardized and stored;

[0014] Step 2. Perform controlled query and sharing of the stored standardized material performance data according to the permissions;

[0015] Step 3. Compare and analyze the material performance data obtained through controlled query sharing;

[0016] Step 4. Visualize the controlled query sharing results obtained in step 2 and the comparative analysis results obtained in step 3.

[0017] Further, the specific steps of step 1 are:

[0018] Step 1.1. Material performance data standards

[0019] Based on the attributes of the material performance data standard, the material performance data of the standard is obtained by accessing the material performance data standard collection interface through a browser and stored in a structured manner. The attributes include the standard number, Chinese standard classification number CCS, international standard number ICS, name, release date, implementation date, abolition date, version number, issuing unit and sensitivity level. The browser is IE, Chrome or Firefox;

[0020] Step 1.2. Classification of material performance data

[0021] Based on the tree structure, the standard material performance data is classified and constructed to obtain a classification structure tree of the material performance data, and the material performance data in the classification structure tree is stored in a structured or file manner.

[0022] Step 1.3. Metadata

[0023] Metadata is abstracted from the material performance data in the classification structure tree, and the metadata is collected through the metadata collection interface used by the browser and stored in a structured manner. The attributes of the metadata include metadata identifier, parameter name, English name, definition, data type, value range, constraint conditions, maximum number of occurrences and unit.

[0024] Step 1.4. Templated import of material properties data

[0025] Generate a material performance data template in EXECL file or XML file format based on the material performance data and its metadata, and convert the collected material performance data into data that conforms to the material performance data template format based on the material performance data template. After conversion, perform data obfuscation protection, encryption or plain text storage according to the data sensitivity. If the sensitivity level is "public", it is stored in plain text; if the sensitivity level is "internal", it is stored in obfuscation protection; if the sensitivity level is higher than "internal", it is stored in encryption; use MD5 or HASH algorithm for obfuscation protection, and use integrator encryption, general encryption or national encryption products for encryption.

[0026] Step 1.5. Parameter modular splicing

[0027] The material performance data obtained in step 1.4 are spliced ​​based on the splicing order of the attributes of each given row of material performance data to obtain the spliced ​​material performance data, and data obfuscation protection or encryption or plain text storage is performed according to the sensitivity of the spliced ​​material performance data.

[0028] Furthermore, the specific steps of step 1 also include:

[0029] Step 1.6. Material performance data version update

[0030] When the material performance database data is modified or re-imported, the version of the material performance data is updated. For each minor revision, the last digit of the version number is increased by 1. For major revisions under the same standard, the middle digit of the version number is increased by 1. When the standard version of the material performance data changes, the version number of the new material performance data is the first digit of the original version number plus 1. The version number is three digits with dots, and the initial version is 1.0.0. Minor revisions refer to revisions to individual parameters and parameter values ​​or information improvements. Major revisions refer to large-scale modifications to material performance data of a certain standard and batch re-imports.

[0031] Further, the specific steps of step 2 are:

[0032] Step 2.1. Conditional classification query

[0033] Based on the classification query interface and permission information, query the input query conditions for the spliced ​​material performance data obtained in step 1.5. If it is sensitive level data, convert the corresponding query result into plain text through the deobfuscation protection method or decryption method. Otherwise, read the plain text data directly. If the plain text is not in the specified unit, perform unit conversion, otherwise do not convert. The query conditions include standards, versions, classifications and parameters. Use MD5 or HASH algorithm for deobfuscation protection, and use integrated commercial encryption, general encryption or national encryption products for decryption.

[0034] Step 2.2: CAE calculation data query service

[0035] Based on the classification query interface and permission information, the query conditions entered by the user in the CAE software are queried for the spliced ​​material performance data obtained in step 1.5. If the data is sensitive level data, the corresponding query result is converted into plain text through the deobfuscation protection method or decryption method. Otherwise, the plain text data is directly read. If the plain text is not in the specified unit, the unit conversion is performed, otherwise no conversion is performed, and the query result is pushed to the CAE calculation platform for calculation;

[0036] Step 2.3. Data update record query

[0037] According to the properties of specific material performance data, query the data corresponding to all historical version numbers of the performance data of the property. If the sensitivity level is higher than "internal", the corresponding query result is converted into plain text through a deobfuscation protection method or a decryption method. Otherwise, it is not decrypted.

[0038] Further, the specific steps of step 3 are:

[0039] Step 3.1. Analyze the material performance data obtained by the query based on the analysis algorithm, including material similarity, performance change and substitutability analysis of the material performance data. Among them, weighted evaluation method, parameter index data matching method, statistical quantitative synthesis method, cost analysis method, value engineering method, objective function method and margin comparison method are statistical analysis methods, BP neural network method, random forest method, LSTM, Bayesian network method and fuzzy comprehensive evaluation method are new machine learning algorithms. The new machine learning algorithm needs to use an integrated CAE computing platform or a deep learning framework to analyze the material performance data;

[0040] Step 3.2: Generate a standard format file with the analysis results. The standard format file is XML, TXT, CSV or Excel format.

[0041] Furthermore, the step 4 is based on the visualization tool component, assembling the controlled query sharing results obtained in step 2 or the comparative analysis results obtained in step 3 for visualization, that is, according to the display method, Html5 technology is used to draw a visualization graph of material performance data, wherein the visualization tool component includes MXgraph, ChartDirector or ECharts, and the display method includes a table, a curve, a scatter plot, a pie chart, a binomial fitting graph, a bar chart, a surface graph or a radar chart.

[0042] Furthermore, step 5 is also included:

[0043] Audit records of user operations and data query usage behaviors and contents in steps 1 to 4 can be used to trace illegal behaviors such as illegal users logging into the system or legal users using unauthorized functions or data, ensuring the safe and controllable use of system data.

[0044] A material performance data management system comprises the following steps:

[0045] Material performance data collection and storage module: Based on the hierarchical access control strategy, the material performance data is standardized and stored after the user logs into the system;

[0046] Material performance data authorization query module: the stored standardized material performance data is queried and shared in a controlled manner according to the permissions;

[0047] Material performance data analysis service module: compare and analyze the material performance data obtained through controlled query sharing;

[0048] Data visualization module: Visualize the obtained controlled query sharing results and the obtained comparative analysis results.

[0049] Furthermore, the material performance data collection and storage module is specifically implemented as follows:

[0050] Step 1.1. Material performance data standards

[0051] Based on the attributes of the material performance data standard, the material performance data of the standard is obtained by accessing the material performance data standard collection interface through a browser and stored in a structured manner. The attributes include the standard number, Chinese standard classification number CCS, international standard number ICS, name, release date, implementation date, abolition date, version number, issuing unit and sensitivity level. The browser is IE, Chrome or Firefox;

[0052] Step 1.2. Classification of material performance data

[0053] Based on the tree structure, the standard material performance data is classified and constructed to obtain a classification structure tree of the material performance data, and the material performance data in the classification structure tree is stored in a structured or file manner;

[0054] Step 1.3. Metadata

[0055] Abstract metadata from the material performance data in the classification structure tree, collect metadata through the metadata collection interface used by the browser, and store them in a structured manner. The metadata attributes include metadata identifier, parameter name, English name, definition, data type, value range, constraint condition, maximum number of occurrences, and unit.

[0056] Step 1.4. Templated import of material properties data

[0057] Generate a material performance data template in the format of an EXECL file or XML file based on the material performance data and its metadata, and convert the collected material performance data into data that conforms to the material performance data template format based on the material performance data template. After conversion, perform data obfuscation protection, encryption, or plain text storage according to the sensitivity of the data. If the sensitivity level is "public", it is stored in plain text; if the sensitivity level is "internal", it is stored in obfuscation protection; if the sensitivity level is higher than "internal", it is stored in encryption; use MD5 or HASH algorithm for obfuscation protection, and use integrated commercial encryption, general encryption, or national encryption products for encryption;

[0058] Step 1.5. Parameter modular splicing

[0059] Based on the given splicing order of the attributes of each row of material performance data, the material performance data obtained in step 1.4 are spliced ​​to obtain spliced ​​material performance data, and data obfuscation protection or encryption or plain text storage is performed according to the sensitivity of the spliced ​​material performance data;

[0060] Step 1.6. Material performance data version update

[0061] When the material performance database data is modified or re-imported, the version of the material performance data is updated. For each minor revision, the last digit of the version number is increased by 1. For major revisions under the same standard, the middle digit of the version number is increased by 1. When the standard version of the material performance data changes, the version number of the corresponding new material performance data is the first digit of the original version number plus 1. The version number is three digits with dots, and the initial version is 1.0.0. Minor revisions refer to the revision of errors in individual parameters and parameter values ​​or the improvement of information. Major revisions refer to large-scale modifications and batch re-imports of material performance data of a certain standard.

[0062] Furthermore, the material performance data authorization query module is specifically implemented as follows:

[0063] Step 2.1. Conditional classification query

[0064] Based on the classification query interface and permission information, query the input query conditions for the spliced ​​material performance data obtained in step 1.5. If it is sensitive level data, convert the corresponding query result into plain text through the deobfuscation protection method or decryption method. Otherwise, read the plain text data directly. If the plain text is not in the specified unit, perform unit conversion, otherwise do not convert. The query conditions include standards, versions, classifications and parameters. Use MD5 or HASH algorithm for deobfuscation protection, and use integrated commercial encryption, general encryption or national encryption products for decryption.

[0065] Step 2.2: CAE calculation data query service

[0066] Based on the classification query interface and permission information, the query conditions entered by the user in the CAE software are queried for the spliced ​​material performance data obtained in step 1.5. If the data is sensitive level data, the corresponding query result is converted into plain text through the deobfuscation protection method or decryption method. Otherwise, the plain text data is directly read. If the plain text is not in the specified unit, the unit conversion is performed, otherwise no conversion is performed, and the query result is pushed to the CAE calculation platform for calculation;

[0067] Step 2.3. Data update record query

[0068] According to the properties of specific material performance data, query the data corresponding to all historical version numbers of the performance data of the property. If the sensitivity level is higher than "internal", convert the corresponding query result into plain text through the deobfuscation protection method or decryption method, otherwise, do not decrypt;

[0069] The specific steps of the material performance data analysis service module are:

[0070] Step 3.1. Analyze the material performance data obtained by the query based on the analysis algorithm, including material similarity, performance change and substitutability analysis of the material performance data. Among them, weighted evaluation method, parameter index data matching method, statistical quantitative synthesis method, cost analysis method, value engineering method, objective function method and margin comparison method are statistical analysis methods, BP neural network method, random forest method, LSTM, Bayesian network method and fuzzy comprehensive evaluation method are new machine learning algorithms. The new machine learning algorithm needs to use an integrated CAE computing platform or a deep learning framework to analyze the material performance data;

[0071] Step 3.2, generate the analysis results into a standard format file, which is in XML, TXT, CSV or Excel format;

[0072] The data visualization module is based on the visualization tool component assembly to obtain the controlled query sharing results or the comparative analysis results for visualization display, that is, according to the display method, the Html5 technology in the visualization tool component is used to draw the material performance data, wherein the visualization tool component includes MXgraph, ChartDirector or ECharts, and the display method includes table, curve, scatter plot, pie chart, binomial fitting chart, bar chart, surface chart or radar chart.

[0073] Compared with the prior art, the present invention has the following beneficial effects:

[0074] 1. The present invention can realize networked efficient sharing services. The material data management method is implemented by adopting the MVC architecture and Java language based on B / S (client / server) cross-platform J2EE, and finally forms a material performance data management system. The system directly analyzes the material performance data through the statistical analysis method in the material performance data management system, or queries and pushes the material performance data to be calculated and analyzed from the material performance data management system to the CAE computing platform (for example: AnsysFluent, CFX, Star ccm+, Ls-Dyna, Abaqus, Mechanicial, Feko, MatLab, etc.) or deep learning framework (for example: Pytorch, Tensorflow, Keras, Caffe) for analysis and calculation. The system integrates and calls the API interface of the CAE computing platform or deep learning framework, loads the data analysis algorithm (new machine learning method) of the data management system into the CAE computing platform or deep learning framework, runs the algorithm through the CAE computing platform or deep learning framework to process and calculate the material performance data, and forms the analysis results. The system reads the analysis results of the third-party platform for visual display, realizes the management of material performance data and intelligent sharing, and users can conveniently use material performance data and related services through the browser and the network; the database for storing data adopts MongoDB, which supports distributed storage and efficient query.

[0075] 2. The present invention can realize data fusion and multi-disciplinary support, that is, through standard data material performance data management, standard-based metadata and template management, and template-based material performance data import functions, it can realize the collection, update and tracing of multi-field, multi-standard, and multi-disciplinary material performance data; through parameter splicing, unit conversion and other functions, it can realize the fusion of material performance data of different standards, different disciplines, and different fields, which is convenient for users.

[0076] 3. The present invention has a high degree of intelligence, that is, it supports the import of intelligent customized analysis algorithms to form an algorithm library; direct calculation of conventional analysis algorithms and visualization of results; and integration of a third-party intelligent analysis tool platform to realize the operation of intelligent customized algorithms and visualization of results.

[0077] 4. The present invention realizes the standardization of material performance data and supports the standard and version classification collection, storage, query and comparative analysis of material performance data.

[0078] 5. The present invention can realize data security management and control, perform hierarchical protection (plain text, obfuscation protection, encryption protection) on material performance data, and realize secure transmission-level storage; realize data query and analysis management through data authorization, and realize illegal user login and user illegal acquisition of material performance data tracing through audit records. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 It is a schematic diagram of the system framework structure of the present invention;

[0080] Figure 2 It is a schematic diagram of the functional framework structure of each module in the present invention;

[0081] Figure 3 A schematic diagram of a classification structure tree after the material performance data of the present invention is classified;

[0082] Figure 4 This is a schematic diagram of hierarchical access control permissions in the present invention. DETAILED DESCRIPTION

[0083] The present invention will be further described below in conjunction with the accompanying drawings and specific implementation methods.

[0084] Example

[0085] A material performance data management method, for metal material performance data, includes the following steps:

[0086] 1. System login identity authentication, using PKI certificate or user name and password to authenticate the identity of the logged-in user. The user logs in to the system through a browser (IE, Chrome, Firefox), enters the user name and password or selects a user certificate and enters the password. The system reads the "user name + password" or "user certificate + password" on the login page and matches it with the "user name + password" or "user certificate + password" configured in the material performance data system. If the match is successful, the system will consider the logged-in user to be a legitimate user, otherwise it is an illegal user. Legitimate users are allowed to log in to the system, while illegal users are not allowed to log in to the system;

[0087] 2. Access control: Functional role access control adopts RBAC access control model to realize user-role-function and operation access control, including user management, role management, functional operation management, user role authorization, role function authorization and role function operation authorization. System administrators use user management, role management and work operation management functions to establish users, roles and system functions and operation information. Security administrators use user role authorization, role function authorization and role function operation authorization to configure user and role permissions. The system stores user role permission information, role function permission information and role function operation permission information. When a user logs in to the system, the system loads authorized work and operation buttons according to user role permission information, role function permission information and role function operation permission information to realize hierarchical access control.

[0088] Data access control is achieved through data authorization function and query and analysis permission filtering. The data administrator authorizes users to material performance data standards and classifications, and the system records user data permission information. When users use the system to query and analyze data, the system filters the user query and analysis data according to the data permission information, and only obtains material performance data within the data permission range from the conditional query results, ensuring that users use data under control, such as Figure 4 shown.

[0089] 1) User-role-function and operation access control process example:

[0090] a. The system administrator configures system users and passwords in the system (e.g., Zhang San + Zhang San password, Li Si + Li Si password, etc.), configures system roles (e.g., data collector, data query personnel, data analyst, system administrator, security administrator, audit administrator, etc.), configures software functions and operations (e.g., material performance data query function (query, export, reset, etc.), and the system stores user password information, role information, software functions and operation information in the database;

[0091] b. The security administrator configures user role permission information (for example, Zhang San - data query personnel), role software function permission information (data query personnel - material performance data query) and role software operation information (data query personnel - material performance data query - query operation). The system stores the permission information of Zhang San - data query personnel, data query personnel - material performance data query, and data query personnel - material performance data query - query operation in the database;

[0092] c. After user Zhang San logs in to the system using PKI certificate authentication or username and password, the system queries the function and operation permission information of the user "Zhang San" and obtains Zhang San's permission information as "Zhang San-Material Performance Data Query-Query Operation Permission". The system loads the software functions and operation buttons within the permission range according to Zhang San's permission information (functions and operation buttons outside the permission range are not loaded and displayed). When Zhang San uses the material performance data system, he can only see and operate the material data query function and query button. Other functions and operations (such as: standard data collection, metadata collection, material performance data template import, and data export operations of the material data query function) are not displayed.

[0093] 2) Data access control process instance:

[0094] a. The security administrator selects the material classification (metal materials - nonferrous metals - heavy metals - metal forgings) in the data authorization function and assigns the classification data permissions (read and write) to user Zhang San. The system stores the data read and write permission information of "Zhang San - Metal Forgings" in the database;

[0095] b. After Zhang San logs into the system, he uses the material performance data query function according to the software functions and operation permissions. When using the conditional query operation, no matter what query conditions are used, the system will filter the material performance data according to the query conditions and Zhang San's data permission information (Zhang San-metal forging data). The system only returns the material performance data in the metal forging data that meets the query conditions. Zhang San can only see the data in the metal forgings that meets the query conditions, and other data cannot be queried. Among them, system login identity authentication, access control and audit records are all Figure 1 The content of the data security management and control module, which includes a hierarchical access control strategy.

[0096] 3. Collect standard data. Based on the attributes of material performance data standards (for example, national standards are divided into mandatory national standards (GB) and recommended national standards (GB / T), local standards (DB), enterprise standards (Q / ), ANSI American national standards, BS British standards, EN European standards, NF French standards, JIS Japanese industrial standards, ISO international standards, ASTM American materials and testing standards, etc.), access the material performance data standard collection interface through the browser to obtain standard material performance data, and store it in a structured manner (stored in the MongoDB database). The attributes include standard number, Chinese standard classification number CCS, international standard number ICS, name, release date, implementation date, abolition date, version number, issuing unit and sensitivity level. The browser is IE, Chrome or Firefox;

[0097] Examples of material performance data standards are:

[0098]

[0099]

[0100]

[0101] 4. Establish a tree structure classification of material performance data, that is, based on the tree structure, classify the standard material performance data by type, obtain a classification structure tree of material performance data, and store the material performance data in the classification structure tree in a structured or file manner (stored in a MongoDB database), as follows: Figure 3 As shown;

[0102] An example of the tree-structured material performance data classification XML file storage structure is as follows:

[0103]

[0104]

[0105] 5. Metadata collection: abstract metadata from the material performance data in the classification structure tree, collect metadata through the metadata collection interface used by the browser, and store them in a structured manner (stored in the MongoDB database). The data attributes of the metadata (i.e., the attributes of the metadata) include metadata identifier, parameter name, English name, constraint definition, data type, value range, unit, etc. for conversion. The metadata is used to generate a collection template for specific material performance data; some metadata samples of metal forgings are shown in the following table:

[0106]

[0107]

[0108]

[0109] 6. Templated import of material performance data, that is, generating a material performance data template in the format of an EXECL file or XML file based on the material performance data and its metadata, that is, selecting and generating from the data attributes of the metadata corresponding to the material performance data according to needs, collecting material performance data based on the material performance data template, and parsing the templated material performance data, performing data obfuscation protection or encryption or storing in plain text according to the sensitivity of the data. Among them, if the sensitivity level is "public", it is stored in plain text; if the sensitivity level is "internal", it is stored in obfuscation protection; if the sensitivity level is higher than "internal", it is stored in encryption. For example, the data collection template generated based on the metadata of metal forgings is shown in the following table, including the implementation standard, material brand, density, weight, price, chemical composition, mechanical properties, metallography, non-destructive testing, sensitivity level and other attributes, generally in EXECL file or XML file format; users collect material performance data through the template to form a standardized material performance data file or XML file; import the data file in the given material performance data template format, the system uses DOM or SAX method to parse the read-in data file, and performs data verification based on the value range, data type, number of occurrences, and constraints of the metadata to verify the legitimacy of the data. If it is illegal, it throws a data non-standardization exception. If it is legal, it uses MD5 or HASH algorithms for obfuscation protection according to the sensitivity level of the data, and integrates commercial, general or national encryption products for encryption and stores it in the MongoDB database. The data collection template and some data samples of metal forgings are shown in the following table, which are stored in the database in three XML files.

[0110] Metal forging basic information template and data sample

[0111]

[0112] Titanium alloy forgings 1 chemical composition data template and data sample

[0113]

[0114]

[0115] Mechanical properties data template and data sample of titanium alloy forging 1

[0116]

[0117] An example of the XML file structure for storing a single piece of material performance data is shown below:

[0118]

[0119] 7. Parameter modular splicing, select the material performance data imported and stored in the MongoDB database in the previous step to form a list A of material performance data to be spliced; configure the splicing order of the attributes of the specific material performance data in each row of the material performance list; then splice the material performance data files according to the configured splicing order to obtain a spliced ​​material performance data set B. The spliced ​​material performance data are used for subsequent condition classification queries and data analysis. The sensitivity level of the spliced ​​material performance data set is the highest sensitivity level of the data to be spliced, and the data is obfuscated or encrypted or saved in plain text according to the sensitivity level of the spliced ​​material performance data (read the "sensitivity level" attribute value of the material data, and determine whether to use plain text, obfuscation protection or encryption for storage according to the attribute value. The specific judgment method is: if the sensitivity level is "public", it is saved in plain text; if the sensitivity level is "internal", it is saved in obfuscation protection; if the sensitivity level is higher than "internal", it is saved in encryption).

[0120] For example, the material performance data files of TA7 and TA12 titanium alloy forgings in parameter modular splicing include three XML files: basic information, chemical composition data, and mechanical performance data. A total of six files are stored in the MongoDB database. When the amount of data increases significantly, there will be a large number of traversal files and library table operations when doing query comparison and analysis (for example, when the amount of data increases to 1,000,000, there will be 3,000,000 XML files stored in the database). Opening files, traversing and parsing files, and closing files will take a lot of time, seriously affecting query efficiency. Therefore, through parameter modular splicing, commonly used data is spliced ​​to form a complex structure of spliced ​​XML files, which are stored in MongoDB for efficient query and analysis. The spliced ​​material performance data XML file structure is shown below:

[0121]

[0122]

[0123]

[0124] 8. Material performance data version update, that is, used for version tracing and query of material performance data, represented by three dotted data, and the initial version number is 1.0.0. When a single piece of data in the material performance database is modified or re-imported to save data, the system automatically updates the version number of the material performance data. For each minor revision, the last digit of the version number increases by 1, such as the version update to 1.0.1 or 1.0.2, etc. For batch major revisions under the same standard (when the amount of imported data is multiple), the middle digit of the version number increases by 1, such as the version update to 1.1.1 or 1.2.1, etc. When the standard version of material performance data changes, the first digit of the corresponding new material performance data increases by 1, such as 2.0.0 or 3.0.0, and the system saves the updated material performance data. Minor revision refers to the revision of individual parameters and parameter values ​​or the improvement of information. Major revision refers to the large-scale modification and batch re-import of material performance data of a certain standard. Standard version change refers to the upgrade and revision of the standard version, and the release and implementation of the new version of the standard. For example, the S1000D standard version includes V1.0 (released and implemented in 1989), V2.0 (released and implemented in 2003), V3.0 (released and implemented in 2007), V4.0 (released and implemented in 2008), V4.1 (released and implemented in 2012), V4.2 (released and implemented in 2016), V5.0 (released and implemented in 2019), etc. The upgrade from V1.0 to V5.0 is also called a standard version change.

[0125] 9. Conditional classification query, that is, query the spliced ​​material performance data obtained by inputting query conditions based on the classification query interface and permission information. If it is sensitive level data, the corresponding query result is converted into plain text through the deobfuscation protection method or decryption method. Otherwise, the plain text data is directly read. If the plain text is not in the specified unit, the unit conversion is performed, otherwise no conversion is performed. The query conditions include standards, versions, classifications and parameters. (The default query is the latest material performance data of the corresponding standard and version. If you need to trace the update record of a certain material performance data, call the material performance data classification result to query the result).

[0126] 10. CAE calculation data query service, that is, based on the classification query interface and permission information, the query conditions entered by the user in the CAE software (such as: Ansys Fluent, CFX, Star ccm+, Ls-Dyna, Abaqus, Mechanicial, Feko, etc.) are queried for the spliced ​​material performance data. If it is sensitive level data, the corresponding query result is converted into plain text through the deobfuscation protection method or decryption method. Otherwise, the plain text data is directly read. If the plain text is not in the specified unit, the unit conversion is performed, otherwise no conversion is performed, and the query result is pushed to the CAE calculation platform for calculation;

[0127] 11. According to the properties of the specific material performance data, query the data corresponding to all historical version numbers of the performance data of the property. If the sensitivity level is higher than "internal", the corresponding query result is converted into plain text through the deobfuscation protection method or the decryption method, otherwise, it is not decrypted.

[0128] 12. Material performance data analysis: material similarity, performance change, substitutability, etc. of the queried material performance data are analyzed based on the analysis algorithm, wherein the analysis algorithm is one or a combination of weighted evaluation method, BP neural network method, seven-point star diagram material selection method, parameter index data matching method, star profile model method, statistical quantitative comprehensive method, cost analysis method, value engineering method and objective function method, margin comparison method, BP neural network, LSTM, Bayesian network method or fuzzy comprehensive evaluation method; weighted evaluation method, parameter index data matching method, statistical quantitative comprehensive method, cost analysis method, value engineering method and objective function method, margin comparison method, etc. are statistical analysis methods, and the analysis is directly realized through the material performance data system; BP neural network method, random forest method, LSTM, Bayesian network method and fuzzy comprehensive evaluation method, etc. are new machine learning algorithms, and the new machine learning algorithms use integrated CAE computing platforms (for example: Pytorch, Tensorflow, Keras, MatLab, Ansys Fluent, CFX, etc.) or deep learning framework (for example: Pytorch, Tensorflow, Keras, Caffe), push the material performance data to be analyzed to achieve analysis such as material similarity, performance change, and material substitutability.

[0129] 13. Convert the calculation results obtained from the material performance data analysis into a standard format file that meets the requirements of visual display. The standard format file is XML, TXT, CSV or Excel format.

[0130] 14. Visual display of analysis results, i.e., visual display of controlled query sharing results or comparative analysis results assembled based on visualization tool components, i.e., drawing material performance data according to the display method using the Html5 technology in the visualization tool components, wherein the visualization tool components include MXgraph, ChartDirector or ECharts, and the display methods include tables, curves, scatter plots, pie charts, binomial fitting graphs, bar charts, surface graphs or radar graphs, etc.

[0131] This step is implemented in the material performance data management system. The specific implementation process is as follows:

[0132] 1) Introduce the running program of the visualization tool component as a Jar package into the code engineering of the system software;

[0133] 2) Convert the formatted material performance data analysis results into a display array;

[0134] 3) Take the display array, drawing area and visualization page jsp file name as input parameters, and call the drawing method of visualization tool components such as MXgraph, ChartDirector or ECharts;

[0135] 4) Visualization tool components such as MXgraph, ChartDirector or ECharts draw material performance data visualization graphics such as tables, curves, scatter plots, pie charts, binomial fit graphs, bar charts, surface graphs or radar charts in the designated area of ​​the jsp page.

[0136] 15. Audit records record user operations and data query usage behaviors and content, so as to trace illegal behaviors such as illegal users logging into the system or legal users using unauthorized functions or data, and ensure the safe and controllable use of system data. When a user logs into the system and uses software functions, the audit configuration information of the functions and operations is queried first. If the current audit configuration information of the function and operation is "No", no audit will be performed. If the current audit configuration information of the function and operation is "Yes", the system automatically obtains information such as the current user, operation data object, data ID, current operation name, operation time, operation machine, machine IP, etc., and records it in the audit record table; the audit records can be queried on schedule or at any time as needed to generate an audit report. The audit record search conditions are: operation time, operator, operation name, machine IP, etc. The following is an example of an audit record:

[0137]

[0138] The above are only representative embodiments of the present invention in many specific application scopes, and do not constitute any limitation on the protection scope of the present invention. Any technical solutions formed by transformation or equivalent replacement fall within the protection scope of the present invention.

Claims

1. A material performance data management method, characterized in that: The steps include: Step 1. Based on the hierarchical access control strategy, after the user logs into the system, the material performance data is standardized and stored; Step 2. Perform controlled query and sharing of the stored standardized material performance data according to the permissions; Step 3. Compare and analyze the material performance data obtained through controlled query sharing; Step 4. Visually display the controlled query sharing results obtained in step 2 and the comparative analysis results obtained in step 3; Step 1.

1. Material performance data standards Based on the attributes of the material performance data standard, the material performance data of the standard is obtained by accessing the material performance data standard collection interface through a browser and stored in a structured manner. The attributes include the standard number, Chinese standard classification number CCS, international standard number ICS, name, release date, implementation date, abolition date, version number, issuing unit and sensitivity level. The browser is IE, Chrome or Firefox; Step 1.

2. Classification of material performance data Based on the tree structure, the standard material performance data is classified and constructed to obtain a classification structure tree of the material performance data, and the material performance data in the classification structure tree is stored in a structured or file manner; Step 1.

3. Metadata Abstract metadata from the material performance data in the classification structure tree, collect metadata through the metadata collection interface used by the browser, and store them in a structured manner. The metadata attributes include metadata identifier, parameter name, English name, definition, data type, value range, constraint condition, maximum number of occurrences, and unit. Step 1.

4. Templated import of material properties data Generate a material performance data template in the format of an EXECL file or XML file based on the material performance data and its metadata, and convert the collected material performance data into data that conforms to the material performance data template format based on the material performance data template. After conversion, perform data obfuscation protection, encryption, or plain text storage according to the data sensitivity. If the sensitivity level is "public", it is stored in plain text; if the sensitivity level is "internal", it is stored in obfuscation protection; if the sensitivity level is higher than "internal", it is stored in encryption; use MD5 or HASH algorithm for obfuscation protection, and use integrated commercial encryption, general encryption, or national encryption products for encryption; Step 1.

5. Parameter modularization Based on the given splicing order of the attributes of each row of material performance data, the material performance data obtained in step 1.4 are spliced ​​to obtain spliced ​​material performance data, and data obfuscation protection or encryption or plain text storage is performed according to the sensitivity of the spliced ​​material performance data; Step 1.

6. Material performance data version update When the material performance database data is modified or re-imported, the version of the material performance data is updated. For each minor revision, the last digit of the version number is increased by 1. For major revisions under the same standard, the middle digit of the version number is increased by 1. When the standard version of the material performance data changes, the version number of the new material performance data is the first digit of the original version number plus 1. The version number is three digits with dots, and the initial version is 1.0.

0. Minor revisions refer to revisions to individual parameters and parameter values ​​or information improvements. Major revisions refer to large-scale modifications to material performance data of a certain standard and batch re-imports.

2. A material performance data management method according to claim 1, characterized in that: The specific steps of step 2 are: Step 2.

1. Conditional classification query Based on the classification query interface and permission information, query the input query conditions for the spliced ​​material performance data obtained in step 1.

5. If it is sensitive level data, convert the corresponding query result into plain text through the deobfuscation protection method or decryption method. Otherwise, read the plain text data directly. If the plain text is not in the specified unit, perform unit conversion, otherwise do not convert. The query conditions include standards, versions, classifications and parameters. Use MD5 or HASH algorithm for deobfuscation protection, and use integrated commercial encryption, general encryption or national encryption products for decryption. Step 2.2: CAE calculation data query service Based on the classification query interface and permission information, the query conditions entered by the user in the CAE software are queried for the spliced ​​material performance data obtained in step 1.

5. If the data is sensitive level data, the corresponding query result is converted into plain text through the deobfuscation protection method or decryption method. Otherwise, the plain text data is directly read. If the plain text is not in the specified unit, the unit conversion is performed, otherwise no conversion is performed, and the query result is pushed to the CAE calculation platform for calculation; Step 2.

3. Data update record query According to the properties of specific material performance data, query the data corresponding to all historical version numbers of the performance data of the property. If the sensitivity level is higher than "internal", the corresponding query result is converted into plain text through a deobfuscation protection method or a decryption method. Otherwise, it is not decrypted.

3. A material performance data management method according to claim 2, characterized in that: The specific steps of step 3 are: Step 3.

1. Analyze the material performance data obtained by the query based on the analysis algorithm, including material similarity, performance change and substitutability analysis of the material performance data. Among them, weighted evaluation method, parameter index data matching method, statistical quantitative synthesis method, cost analysis method, value engineering method, objective function method and margin comparison method are statistical analysis methods, BP neural network method, random forest method, LSTM, Bayesian network method and fuzzy comprehensive evaluation method are new machine learning algorithms. The new machine learning algorithm needs to use an integrated CAE computing platform or a deep learning framework to analyze the material performance data; Step 3.2: Generate a standard format file with the analysis results. The standard format file is XML, TXT, CSV or Excel format.

4. A material performance data management method according to claim 3, characterized in that: The step 4 is to assemble the controlled query sharing results obtained in step 2 or the comparative analysis results obtained in step 3 for visual display based on the visualization tool component, that is, to draw the material performance data visualization graph using Html5 technology according to the display method, wherein the visualization tool component includes MXgraph, ChartDirector or ECharts, and the display method includes table, curve, scatter plot, pie chart, binomial fitting graph, bar chart, surface graph or radar chart.

5. A material performance data management method according to claim 4, characterized in that: Also includes step 5: Audit records of user operations and data query usage behaviors and contents in steps 1 to 4 can be used to trace illegal behaviors such as illegal users logging into the system or legal users using unauthorized functions or data, ensuring the safe and controllable use of system data.

6. A material performance data management system, characterized in that: include: Material performance data collection and storage module: Based on the hierarchical access control strategy, the material performance data is standardized and stored after the user logs into the system; Material performance data authorization query module: the stored standardized material performance data is queried and shared in a controlled manner according to the permissions; Material performance data analysis service module: compare and analyze the material performance data obtained through controlled query sharing; Data visualization module: Visualize the obtained controlled query sharing results and the obtained comparative analysis results; The specific implementation of the collection storage module is: Step 1.

1. Material performance data standards Based on the attributes of the material performance data standard, the material performance data of the standard is obtained by accessing the material performance data standard collection interface through a browser and stored in a structured manner. The attributes include the standard number, Chinese standard classification number CCS, international standard number ICS, name, release date, implementation date, abolition date, version number, issuing unit and sensitivity level. The browser is IE, Chrome or Firefox; Step 1.

2. Classification of material performance data Based on the tree structure, the standard material performance data is classified and constructed to obtain a classification structure tree of the material performance data, and the material performance data in the classification structure tree is stored in a structured or file manner; Step 1.

3. Metadata Abstract metadata from the material performance data in the classification structure tree, collect metadata through the metadata collection interface used by the browser, and store them in a structured manner. The metadata attributes include metadata identifier, parameter name, English name, definition, data type, value range, constraint condition, maximum number of occurrences, and unit. Step 1.

4. Templated import of material properties data Generate a material performance data template in the format of an EXECL file or XML file based on the material performance data and its metadata, and convert the collected material performance data into data that conforms to the material performance data template format based on the material performance data template. After conversion, perform data obfuscation protection, encryption, or plain text storage according to the data sensitivity. If the sensitivity level is "public", it is stored in plain text; if the sensitivity level is "internal", it is stored in obfuscation protection; if the sensitivity level is higher than "internal", it is stored in encryption; use MD5 or HASH algorithm for obfuscation protection, and use integrated commercial encryption, general encryption, or national encryption products for encryption; Step 1.

5. Parameter modularization Based on the given splicing order of the attributes of each row of material performance data, the material performance data obtained in step 1.4 are spliced ​​to obtain spliced ​​material performance data, and data obfuscation protection or encryption or plain text storage is performed according to the sensitivity of the spliced ​​material performance data; Step 1.

6. Material performance data version update When the material performance database data is modified or re-imported, the version of the material performance data is updated. For each minor revision, the last digit of the version number is increased by 1. For major revisions under the same standard, the middle digit of the version number is increased by 1. When the standard version of the material performance data changes, the version number of the corresponding new material performance data is the first digit of the original version number plus 1. The version number is three digits with dots, and the initial version is 1.0.

0. Minor revisions refer to the revision of errors in individual parameters and parameter values ​​or the improvement of information. Major revisions refer to large-scale modifications and batch re-imports of material performance data of a certain standard.

7. A material performance data management system according to claim 6, characterized in that: The specific implementation of the material performance data authorization query module is: Step 2.

1. Conditional classification query Based on the classification query interface and permission information, query the input query conditions for the spliced ​​material performance data obtained in step 1.

5. If it is sensitive level data, convert the corresponding query result into plain text through the deobfuscation protection method or decryption method. Otherwise, read the plain text data directly. If the plain text is not in the specified unit, perform unit conversion, otherwise do not convert. The query conditions include standards, versions, classifications and parameters. Use MD5 or HASH algorithm for deobfuscation protection, and use integrated commercial encryption, general encryption or national encryption products for decryption. Step 2.2: CAE calculation data query service Based on the classification query interface and permission information, the query conditions entered by the user in the CAE software are queried for the spliced ​​material performance data obtained in step 1.

5. If the data is sensitive level data, the corresponding query result is converted into plain text through the deobfuscation protection method or decryption method. Otherwise, the plain text data is directly read. If the plain text is not in the specified unit, the unit conversion is performed, otherwise no conversion is performed, and the query result is pushed to the CAE calculation platform for calculation; Step 2.

3. Data update record query According to the properties of specific material performance data, query the data corresponding to all historical version numbers of the performance data of the property. If the sensitivity level is higher than "internal", convert the corresponding query result into plain text through the deobfuscation protection method or decryption method, otherwise, do not decrypt; The specific steps of the material performance data analysis service module are: Step 3.

1. Analyze the material performance data obtained by the query based on the analysis algorithm, including material similarity, performance change and substitutability analysis of the material performance data, where: Weighted evaluation method, parameter index data matching method, statistical quantitative synthesis method, cost analysis method, value engineering method, objective function method and margin comparison method are statistical analysis methods, BP neural network method, random forest method, LSTM, Bayesian network method and fuzzy comprehensive evaluation method are new machine learning algorithms. New machine learning algorithms need to use integrated CAE computing platform or deep learning framework to analyze material performance data; Step 3.2, generate the analysis results into a standard format file, which is in XML, TXT, CSV or Excel format; The data visualization module is based on the visualization tool component assembly to obtain the controlled query sharing results or the comparative analysis results for visualization display, that is, according to the display method, the Html5 technology in the visualization tool component is used to draw the material performance data, wherein the visualization tool component includes MXgraph, ChartDirector or ECharts, and the display method includes table, curve, scatter plot, pie chart, binomial fitting chart, bar chart, surface chart or radar chart.

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