Material database construction method and device based on cloud computing

Abstract

The invention provides a material database construction method and device based on cloud computing, and relates to the technical field of databases. A cloud computing platform is configured, multi-source material data is acquired, and a basic material data set is established; performing data preprocessing by using the cloud computing platform; activating a data authentication network stored in the cloud computing platform, performing joint authentication of the preprocessing result through an embedded data pedigree authentication sub-network and a trust conflict authentication sub-network, and generating an update data set according to the joint authentication result; the method comprises the steps of performing correlation analysis on material attributes, extracting associated features, constructing a relational network, and performing structural storage on an updated data set to establish a material database, thereby solving the technical problems of low updating access speed and poor accuracy and adaptability of the material database, and achieving the purposes of improving data integration efficiency, system expandability, security and processing efficiency, and improving system reliability. And the instantaneity and accuracy of data updating access are ensured.

Description

Technical Field

[0001] The present invention relates to the field of database technology, and in particular to a material database construction method and device based on cloud computing. Background Art

[0002] In modern industry, with the development of technology and the increase in production needs, the demand for accurate management and real-time access to material property data is growing. Traditional material database systems are often limited to local servers or decentralized data management methods, which are insufficient in data integration, update speed and accessibility. Especially in cross-regional companies and large-scale research projects, material data needs to be quickly and accurately integrated and updated from various sources to support complex process flows and innovation activities. In addition, with the complexity of process flows, the requirements for data processing and storage are becoming increasingly stringent, requiring not only the ability to store large amounts of data, but more importantly, efficient data processing and security management. However, existing systems often respond slowly when processing large amounts of data, and there are significant risks in data security and integrity, making it difficult to meet the high standards of modern industry for data immediacy and reliability.

[0003] In summary, existing material databases often have significant limitations in data integration, scalability, security and data processing efficiency, which in turn leads to slow data update access, poor accuracy and adaptability. Summary of the invention

[0004] The present application provides a cloud computing-based material database construction method and device, which is used to solve the technical problem that the material database has significant limitations in data integration, scalability, security and data processing efficiency, which leads to slow data update access speed, poor accuracy and adaptability.

[0005] In view of the above problems, the present application provides a material database construction method and device based on cloud computing.

[0006] In a first aspect, the present application provides a method for constructing a material database based on cloud computing, the method comprising:

[0007] A cloud computing platform is configured, and material data from multiple sources are obtained to establish a basic material data set, wherein the material data from multiple sources include laboratory measurement data, literature data, and industry standard data; after uploading the basic material data set to the cloud computing platform, the cloud computing platform is used to perform data preprocessing to establish a preprocessing result, wherein the data preprocessing includes data integrity detection and supplementation; a data authentication network stored in the cloud computing platform is activated, and a joint authentication of the preprocessing result is performed through a data lineage authentication subnetwork and a trust conflict authentication subnetwork embedded in the data authentication network, and an updated data set is generated according to the joint authentication result; a correlation analysis of material properties is performed on the updated data set, and associated features are extracted according to the correlation analysis results, a relationship network is constructed using the associated features, and a structured storage of the updated data set is performed based on the relationship network to establish a material database.

[0008] In a second aspect, the present application provides a material database construction device based on cloud computing, the device comprising:

[0009] A data acquisition module is used to configure the cloud computing platform and acquire multi-source material data to establish a basic material data set, wherein the multi-source material data includes laboratory measurement data, literature data and industry standard data; a data preprocessing module is used to upload the basic material data set to the cloud computing platform, perform data preprocessing using the cloud computing platform, and establish a preprocessing result, wherein the data preprocessing includes data integrity detection and supplementation; a joint authentication module is used to activate the data authentication network stored in the cloud computing platform, perform joint authentication of the preprocessing results through the data lineage authentication subnetwork and trust conflict authentication subnetwork embedded in the data authentication network, and generate an updated data set based on the joint authentication results; a database construction module is used to perform correlation analysis of material properties on the updated data set, extract associated features based on the correlation analysis results, construct a relationship network using the associated features, and perform structural storage of the updated data set based on the relationship network to establish a material database.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] The cloud computing-based material database construction method provided in the present application configures a cloud computing platform and obtains multi-source material data to establish a basic material data set, wherein the multi-source material data includes laboratory measurement data, literature data and industry standard data; after uploading the basic material data set to the cloud computing platform, the cloud computing platform is used to perform data preprocessing to establish a preprocessing result, wherein the data preprocessing includes data integrity detection and supplementation; a data authentication network stored in the cloud computing platform is activated, and the preprocessing result is jointly authenticated by a data lineage authentication subnetwork and a trust conflict authentication subnetwork embedded in the data authentication network, and an updated data set is generated according to the joint authentication result; a correlation analysis of material properties is performed on the updated data set, and associated features are extracted according to the correlation analysis results, a relationship network is constructed using the associated features, and the updated data set is structurally stored based on the relationship network to establish a material database, thereby solving the technical problem that the material database has significant limitations in data integration, scalability, security and data processing efficiency, which leads to slow data update access speed, poor accuracy and adaptability, and achieving the technical effect of improving data integration efficiency, system scalability, security and processing efficiency, thereby ensuring the immediacy and accuracy of data update access. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A schematic flow chart of a cloud computing-based material database construction method is provided for this application.

[0013] Figure 2 A schematic diagram of the structure of a material database construction device based on cloud computing is provided for this application.

[0014] Explanation of the reference numerals: data acquisition module 11, data preprocessing module 12, joint authentication module 13, database construction module 14. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0016] Embodiment 1, as Figure 1 As shown, the present application provides a material database construction method based on cloud computing, the method comprising:

[0017] A cloud computing platform is configured, and multi-source material data is acquired to establish a basic material data set, wherein the multi-source material data includes laboratory measurement data, literature data, and industry standard data.

[0018] Specifically, first configure the cloud computing platform to support the processing and storage of large-scale data. The cloud computing platform provides the necessary hardware and software resources, including servers, storage devices, and database management systems, ensuring the efficiency of data processing and the scalability of the system. By configuring the cloud computing platform, it is possible to quickly process and flexibly manage a large amount of data to meet the needs of modern materials science research and industrial applications. Next, obtain multi-source material data and collect material-related data from different data sources, including laboratory measurement data, literature data, and industry standard data. Among them, laboratory measurement data is obtained through various scientific experiments in a laboratory environment, which directly reflects the basic properties and performance of materials; literature data comes from scientific papers, technical reports, etc., providing detailed descriptions and research results of material properties; and industry standard data comes from industry specifications and standards, including standard use conditions and performance requirements of materials. The collection of these data provides the basis for building a comprehensive and accurate material database. Finally, establish a basic material data set. Integrate the collected multi-source data to form a unified data set. In this process, the data may need to be formatted to ensure that data from different sources can be compatible and effectively used on the same platform. For example, the experimental conditions, material composition, performance test results and other information in the laboratory measurement data can be standardized according to the predetermined data template to ensure the consistency and queryability of the data. In this way, the basic material data set not only includes rich material data, but also has good correlation and traceability between the data, which is convenient for subsequent data processing and analysis.

[0019] Through the above steps, a database containing extensive material data can be successfully configured and managed on the cloud computing platform, providing solid data support for material research and development. In addition, the configuration of the cloud computing platform and the establishment of the data set lay the foundation for efficient and accurate material data management and application.

[0020] After uploading the basic material data set to the cloud computing platform, the cloud computing platform is used to perform data preprocessing to establish preprocessing results, wherein the data preprocessing includes data integrity detection and supplementation.

[0021] Optionally, after the basic material data set is successfully constructed and integrated with various types of data, the data set is then uploaded to a configured cloud computing platform. This operation is completed through a high-speed network connection to ensure that the data is quickly and securely transmitted to the cloud server, and the powerful computing power and storage capacity of the cloud platform are used to further process the data. After the data is uploaded, data preprocessing is performed. Data preprocessing is a key process to ensure the availability and accuracy of the data set, which mainly includes two aspects: data integrity detection and data supplementation. The data integrity detection refers to a systematic check of the uploaded data set to identify any missing or erroneous information in the data. For example, check whether each data point in the data set contains all necessary fields, such as material name, physical properties, chemical composition, etc., to ensure that there is no missing key information. This detection process may involve automated scripts that can quickly traverse the data set and identify incomplete or incorrectly formatted records. Once incomplete or erroneous data is identified, data supplementation is required. The data supplementation process involves using predefined logic or algorithms to complete the missing information based on existing data. For example, if the density information of a material is missing, the system can find the average density value of materials of the same category to fill the missing value. Alternatively, more sophisticated complementary approaches involve predicting missing values ​​using machine learning models that estimate missing physical or chemical properties based on other properties of the material (e.g. hardness, melting point, etc.).

[0022] Through data integrity detection and supplementation, a more complete and accurate preprocessing result is established, which provides a solid foundation for subsequent data authentication, analysis and application, ensuring the high quality and reliability of the material database. The entire preprocessing process not only enhances the practical value of the data, but also strengthens the security and integrity of the data, making it a powerful tool to support scientific research and industrial decision-making.

[0023] Activate the data authentication network stored in the cloud computing platform, perform joint authentication of the preprocessing results through the data lineage authentication sub-network and trust conflict authentication sub-network embedded in the data authentication network, and generate an updated data set based on the joint authentication results.

[0024] Further, after completing the data preprocessing, the data authentication network stored on the cloud computing platform is activated. The data authentication network is an advanced technical framework that aims to ensure the authenticity and reliability of data through a series of sub-networks. In this embodiment, the data authentication network specifically includes a data pedigree authentication sub-network and a trust conflict authentication sub-network. The two sub-networks work together to perform detailed joint authentication on the pre-processed data results. First, the data pedigree authentication sub-network is responsible for tracking and verifying the source and processing history of the data. In this process, the sub-network ensures the consistency of the data and the traceability of the source by analyzing the data generation, transmission and processing chain. For example, data pedigree authentication can verify whether the data has been unauthorizedly changed or tampered with during the entire processing process by checking the timestamp of the data modification and the identity information of the operator. In addition, pedigree authentication also includes authenticating the similarity of each link in the data chain to ensure the consistency of the data at different processing stages. Secondly, the trust conflict authentication sub-network focuses on evaluating the credibility of the data and potential conflicts in the processing process. This subnetwork is implemented by calculating various scores, including data source score, data integrity score, historical performance score and data timeliness score. Each score is weighted according to a preset weight coefficient to form a comprehensive credibility score. For example, if the source of a data point is a highly reputable scientific research institution and the data has been stable in historical records, its credibility score will be increased accordingly. In addition, trust conflict certification also involves evaluating and handling possible information conflicts in preprocessing results to ensure that the information in the data set is consistent and has no internal contradictions.

[0025] Through the joint certification of the above two sub-networks, a strictly verified updated dataset can be generated, which is not only strengthened in terms of data integrity and accuracy, but also significantly guaranteed in terms of data authenticity and trust. Ultimately, the updated dataset will be used to build a more accurate and reliable materials database, providing solid data support for scientific research and industrial applications.

[0026] The updated data set is subjected to correlation analysis of material properties, correlation features are extracted according to the correlation analysis results, a relationship network is constructed using the correlation features, and the updated data set is structurally stored based on the relationship network to establish a material database.

[0027] Specifically, after completing the data authentication, the updated data set is subjected to a correlation analysis of the material properties. Correlation analysis is a statistical method for evaluating the strength and direction of the relationship between different material properties. In this embodiment, the main purpose of this analysis is to identify which properties have a significant impact on the material properties and how these properties interact with each other. First, appropriate statistical tools and algorithms (such as Pearson correlation coefficient, principal component analysis, etc.) are used to process various material properties in the data set, such as hardness, melting point, conductivity, etc. Through these analyses, it can be revealed which properties have significant positive or negative correlations. Then, based on the results of the correlation analysis, correlation features are extracted, which represent the key connections between material properties and are the basis for building a relationship network. For example, all pairs of properties that show strong correlations can be composed of nodes and edges of a network, where nodes represent individual properties and edges represent the correlation between properties. Afterwards, the extracted correlation features are used to construct a relationship network. A relationship network is a graphical representation method for visualizing and analyzing complex relationships in data, in which the connection strength between nodes reflects the strength of the correlation between the corresponding properties, helping researchers understand how different properties jointly affect the overall performance of the material. Finally, the updated data set is structured and stored based on the constructed relationship network, and the data is organized according to the network structure to optimize data access efficiency and query response time. For example, the data table structure in the cloud database can be adjusted to reflect the node and edge relationships in the network, making queries based on associated features more intuitive and faster.

[0028] Through the above steps, a structured materials database is finally established. It not only contains rich and verified materials data, but also provides in-depth insights into the complex relationships between data through a relationship network. This structured storage method greatly improves the availability of data and the efficiency of scientific researchers in using these data for materials research and development.

[0029] Furthermore, the joint authentication of the preprocessing results by the data lineage authentication sub-network and the trust conflict authentication sub-network embedded in the data authentication network also includes:

[0030] The source chain in the preprocessing result is called through the data lineage authentication sub-network, and the generation-transmission-processing chain data consistency authentication is performed through the call result of the source chain to establish the first lineage authentication result.

[0031] The data pedigree authentication subnetwork is used to perform similarity authentication between multiple chain pedigrees in the preprocessing result, and a second pedigree authentication result is established based on the similarity authentication result.

[0032] Joint authentication is performed based on the first pedigree authentication result, the second pedigree authentication result, and the authentication result of the trust conflict authentication subnetwork.

[0033] Exemplarily, the data lineage authentication subnetwork and the trust conflict authentication subnetwork work together in the joint authentication process of preprocessed data to ensure the reliability and consistency of the data, and to enhance the overall trust of the data through detailed data source and processing chain analysis. The data lineage authentication subnetwork is responsible for the source chain call within the preprocessing result. The source chain call refers to tracking the entire process of data from generation to transmission to processing, ensuring that the data at each step is traceable and has not been tampered with. For example, a data point may originate from a laboratory test result, and is finally entered into the system after multiple data processing steps. The data lineage authentication subnetwork will verify whether this series of data operations is consistent, without errors or unauthorized modifications in the middle. The result of this step is the first lineage authentication result, which provides a verification report on data consistency. Immediately afterwards, the data lineage authentication subnetwork continues to perform similarity authentication between multiple chain lineages within the preprocessing result. Similarity authentication refers to analyzing the similarity between different data chains to confirm standardization and consistency in the data processing process. If multiple data sources produce consistent results after similar processing processes, this consistency will further verify the reliability of the data. Based on the results of similarity authentication, the second pedigree authentication results are established to further strengthen the trust of the data. Finally, joint authentication is performed based on the first pedigree authentication results, the second pedigree authentication results, and the authentication results of the trust conflict authentication sub-network. The trust conflict authentication sub-network is responsible for analyzing the credibility of the data, evaluating any possible trust conflicts, and assigning a trust score to each data point. This score reflects a comprehensive evaluation of factors such as the source reliability, integrity, historical performance, and temporal characteristics of the data.

[0034] Through the above-mentioned joint certification process, it is ensured that the data set meets high standards in accuracy, consistency and trustworthiness. This systematic certification process provides a solid foundation for building a high-quality materials database and ensures the reliability and effectiveness of the data in scientific research and industrial applications.

[0035] Furthermore, the performing joint authentication according to the first pedigree authentication result, the second pedigree authentication result and the authentication result of the trust conflict authentication sub-network further includes:

[0036] The credibility score of the preprocessing result is calculated through the trust conflict authentication sub-network as follows:

[0037] T=αS1+βS2+γS3+δS4.

[0038] Among them, T is the credibility score, S1 is the data source score, S2 is the data integrity score, which is constructed through complete and supplementary trust, S3 is the historical performance score, S4 is the data timeliness score, and α, β, γ, and δ are the weight coefficients of data source, data integrity, historical performance, and timeliness characteristics, respectively.

[0039] The trust conflict authentication subnetwork is used to perform conflict authentication on the preprocessing results and calculate the conflict score.

[0040] The credibility score and conflict score are used as the authentication result of the trust conflict authentication subnetwork to perform joint authentication.

[0041] The trust conflict authentication subnetwork is constructed to ensure the data credibility of the preprocessing results, and this goal is achieved by comprehensively evaluating multiple important dimensions of the data. Specifically, the trust conflict authentication subnetwork first calculates the credibility score T of the preprocessing results, which is obtained by weighted summation of four key dimensions. The calculation formula of the credibility score T is T = αS1 + βS2 + γS3 + δS4; where S1 represents the data source score, which reflects the reliability of the original source of the data; S2 represents the data integrity score, which reflects the integrity of the data after supplementation and verification; S3 is the historical performance score, which describes the performance record of the data in previous use; S4 is the data timeliness score, which considers the timeliness and update frequency of the data. Each dimension has a corresponding weight coefficient, namely α, β, γ, and δ, which are preset and adjusted according to the specific application scenario and importance of the data. In this way, the trust conflict authentication subnetwork not only evaluates each key attribute of the data separately, but also effectively integrates the influence of each attribute on the overall data credibility through the setting of weight coefficients, making the score more specific and application-relevant. Such a scoring mechanism ensures that the reliability and accuracy of the data are fully evaluated and confirmed before entering the material database. Finally, a final joint certification is performed based on the calculated credibility score and the conflict certification results conducted through the same network. Conflict certification further checks possible contradictions or errors between data to ensure that all data entered into the database are logically consistent and free of internal conflicts. This comprehensive certification process not only improves the transparency of data processing, but also improves the overall quality of the data set, providing a solid foundation for subsequent data applications and research.

[0042] Furthermore, the method of using the cloud computing platform to preprocess data and establish preprocessing results also includes:

[0043] A complete identification rule of the cloud computing platform is configured, wherein the complete identification rule is a setting rule for required fields and optional fields.

[0044] The complete identification rule is used to perform data integrity identification of the basic material data set and establish an identification result.

[0045] The data marked as incomplete in the recognition result is verified and filled under an adaptive window, and a preprocessing result is established based on the verification and filling result and the data marked as complete in the recognition result.

[0046] Optionally, the cloud computing platform performs preprocessing of the material data set, including configuring complete identification rules, performing data integrity identification, and performing verification and filling of incomplete data. Specifically, first configure the complete identification rules of the cloud computing platform, which define the attributes of each field in the data set, and divide them into mandatory fields and optional fields. Among them, mandatory fields are indispensable elements in the data set, which are crucial for subsequent data processing and analysis, such as the chemical composition and physical properties of the material. Optional fields are data points that can provide additional information but are not absolutely necessary, such as suppliers, prices, etc. By clarifying these rules, the integrity of each data record can be systematically checked to ensure the completeness of key information.

[0047] Next, the configured complete identification rules are used to identify the data integrity of the basic material data set. Specifically, an automated script or program scans the entire data set to identify which records are missing information in the required fields, and outputs an identification result that lists the integrity status of each record in the data set in detail. For data identified as incomplete during the identification process, the system will perform validation filling under an adaptive window. This process involves analyzing the contextual information around the incomplete data and using the statistical characteristics of adjacent data to infer and fill in the missing values. For example, if a certain type of material lacks melting point data, the system may refer to the average melting point of materials in the same category to fill it. The setting of the adaptive window makes the filling operation more accurate, and the window size and filling strategy are dynamically adjusted according to the specific situation of the data. Finally, a preprocessing result is established based on the validation filling result and the data records that have been identified as complete. This preprocessing result will serve as the basis for subsequent steps, such as data authentication and analysis, to ensure that the data input into the database and analysis model is reliable and comprehensive. Through the above steps, the cloud computing platform significantly improves the quality and availability of material data.

[0048] Furthermore, the verification and filling of the data identified as incomplete in the recognition result under the adaptive window also includes:

[0049] Verify and fill adjacent data for data identified as incomplete, and establish an adjacent filling range.

[0050] Perform feature fluctuation evaluation on the neighborhood data of the missing data, configure an adaptive extraction window according to the feature fluctuation evaluation result, extract data with the adaptive extraction window, and establish an adaptive filling range according to the data extraction result.

[0051] Perform global data filling identification on the data marked as incomplete and establish a global filling range.

[0052] Perform fill verification on adjacent fill ranges, adaptive fill ranges, and global fill ranges, and establish verification fill results.

[0053] Furthermore, for data identified as incomplete, data integrity is ensured through a multi-stage adaptive validation filling process, which includes adjacent data filling, feature fluctuation evaluation, and global data filling steps. First, adjacent data validation filling is performed for data identified as incomplete, and data records adjacent to the missing data points are analyzed to find possible similarities or regularities between data points that are close in physical location or logical sequence. By setting the adjacent filling range, statistical methods such as the mean or median of the surrounding data can be used for preliminary filling to provide a basic estimate for more accurate filling. Subsequently, the feature fluctuation evaluation of the neighborhood data of the missing data is performed. In this process, the variability or consistency of the data around the missing value is analyzed to evaluate the fluctuation pattern of the data, and an adaptive extraction window is configured based on the results of the feature fluctuation evaluation, which means adjusting the window size according to the volatility of the data, thereby optimizing the data extraction process. This adaptive method allows the system to more accurately extract and infer missing values ​​from neighboring data, and then establish an adaptive filling range based on the data extraction results. In addition, the system also performs global data filling identification. For those missing values ​​that may be difficult to accurately estimate in a local range, the system will expand the search range and perform global data analysis to find similar patterns or trends in the entire data set, thereby providing filling candidates for these missing values. Finally, all filling results within the adjacent filling range, adaptive filling range, and global filling range will be comprehensively verified. This verification step ensures that the filled data is logically consistent with the original data and that the filling results meet statistical reliability requirements. Through this comprehensive verification, a verification filling result is established to ensure that all data meets the predetermined quality standards.

[0054] Through the above data integrity processing and verification process, the accuracy and availability of the data are significantly improved, providing a solid foundation for subsequent data analysis and application. This systematic data preprocessing method not only improves the efficiency of data processing, but also improves the overall quality of the data set, providing reliable data support for scientific research and industrial applications.

[0055] Furthermore, the method further comprises:

[0056] Establish a mapping verification database between the access data of the material database and the access subject.

[0057] The mapping verification database is used to perform real-time cumulative verification of the access data of the access subject. When the mapping verification database is triggered, an abnormal warning is reported and the subject of the corresponding access subject is frozen.

[0058] Exemplarily, in order to enhance the security of the material database and the controllability of data access, a mapping verification database for access data and access subjects is introduced. The main function of this database is to monitor and verify the data access behavior of all access subjects, and to take corresponding security measures when abnormal behavior is detected. First, a mapping verification database is established, which records in detail the access details of each access subject (such as researchers, engineers or data analysts) to the data in the material database, including information such as access time, access data type, access frequency, and the type of operation performed. The mapping verification database is designed to create a direct association between an access subject and its access behavior, so that each data access can be tracked and audited. Then, the mapping verification database is used to perform real-time cumulative verification of the access data of the access subject, which means continuously collecting and analyzing the access behavior of the access subject, and performing pattern recognition and behavior analysis on these behaviors. By defining normal access behavior patterns, any abnormal or suspicious behavior that does not conform to these patterns can be detected instantly. For example, if an access subject attempts to access a large amount of data that is usually not within its access scope in a short period of time, the system may identify this behavior as a potential data leakage risk.

[0059] When the mapping verification database triggers an abnormal warning, security measures will be automatically executed, including issuing an alarm and freezing the corresponding access subject. The abnormal warning mechanism is configured based on the preset security protocol and abnormal behavior thresholds. Once a violation of these thresholds is detected, the system will immediately lock the access rights of the access subject and prevent further data access until further review. In addition, the frozen access subject needs to undergo a security audit and confirm the legality of its behavior before it can be unfrozen and its access rights restored. This process ensures the security of the material database, prevents data abuse and unauthorized access, and protects the integrity and confidentiality of the data.

[0060] The mapping verification database not only enhances the monitoring and control of material database access, but also improves the overall system's responsiveness to security threats, ensuring the stability and reliability of the data environment.

[0061] Furthermore, the method further comprises:

[0062] The access data of the access subject is obtained, and after data features are extracted from the access data, an access index of the access subject is established based on the access data feature extraction results and the relationship network.

[0063] Access management of the corresponding access subject is performed according to the access index.

[0064] Specifically, the access data of the access subject is obtained. These data record in detail all the activities of the access subject when using the material database, including access time, access data type, data volume, access frequency, and specific operations. This step is to ensure that each operation can be accurately recorded and tracked, providing a basis for subsequent data analysis and security audits. Subsequently, data feature extraction is performed on these access data. Feature extraction mainly involves analyzing the patterns and rules of access behavior, such as frequently accessed data types, peak access frequency, preferred data operations, etc. By using data mining and machine learning techniques, the system can extract valuable information from complex access data, which will be used to describe the behavioral characteristics of the access subject. The extracted features are then used to construct the access index of the access subject. The access index is a comprehensive information body that is defined based on the behavioral characteristics of the access subject and the relationship network with other access subjects. The relationship network refers to the connection map of possible collaboration or data sharing behaviors between access subjects, which helps to understand the socialization and organizational background of access behavior. Finally, access management of the corresponding access subject is performed according to the established access index, including controlling access rights, customizing access rules, and real-time monitoring of access behavior. For example, if the behavior of an access subject suddenly deviates from its normal pattern, the system can automatically adjust its access rights, restrict its access to sensitive data, or trigger a security audit. This dynamic access control based on behavioral characteristics and relationship networks not only enhances data security, but also improves the system's response speed and accuracy to abnormal behavior.

[0065] Through data processing and management steps, the material database can be effectively protected from unauthorized access and potential security threats, while providing necessary data access services to legitimate users.

[0066] Through the technical solutions of the above-mentioned embodiments, the cloud computing-based material database construction method provided by the present application solves the technical problems that the material database has significant limitations in data integration, scalability, security and data processing efficiency, which in turn leads to slow data update access speed, poor accuracy and adaptability, and achieves the technical effect of improving data integration efficiency, system scalability, security and processing efficiency, thereby ensuring the immediacy and accuracy of data update access.

[0067] Embodiment 2 is based on the same inventive concept as the cloud computing-based material database construction method in the previous embodiment. Figure 2As shown, the present application provides a material database construction device based on cloud computing, the device comprising:

[0068] The data acquisition module 11 is used to configure the cloud computing platform and acquire multi-source material data to establish a basic material data set. The multi-source material data includes laboratory measurement data, literature data and industry standard data.

[0069] The data preprocessing module 12 is used to upload the basic material data set to the cloud computing platform, perform data preprocessing using the cloud computing platform, and establish preprocessing results. The data preprocessing includes data integrity detection and supplementation.

[0070] The joint authentication module 13 is used to activate the data authentication network stored in the cloud computing platform, perform joint authentication of the preprocessing results through the data lineage authentication sub-network and the trust conflict authentication sub-network embedded in the data authentication network, and generate an updated data set according to the joint authentication results.

[0071] The database construction module 14 is used to perform correlation analysis of material properties on the updated data set, extract correlation features according to the correlation analysis results, construct a relationship network using the correlation features, and structurally store the updated data set based on the relationship network to establish a material database.

[0072] Furthermore, the joint authentication module 13 is further configured to perform the following steps:

[0073] The source chain in the preprocessing result is called through the data lineage authentication sub-network, and the generation-transmission-processing chain data consistency authentication is performed through the call result of the source chain to establish the first lineage authentication result.

[0074] The data pedigree authentication subnetwork is used to perform similarity authentication between multiple chain pedigrees in the preprocessing result, and a second pedigree authentication result is established based on the similarity authentication result.

[0075] Joint authentication is performed based on the first pedigree authentication result, the second pedigree authentication result, and the authentication result of the trust conflict authentication subnetwork.

[0076] Furthermore, the joint authentication module 13 is further configured to perform the following steps:

[0077] The credibility score of the preprocessing result is calculated through the trust conflict authentication sub-network as follows:

[0078] T=αS1+βS2+γS3+δS4.

[0079] Among them, T is the credibility score, S1 is the data source score, S2 is the data integrity score, which is constructed through complete and supplementary trust, S3 is the historical performance score, S4 is the data timeliness score, and α, β, γ, and δ are the weight coefficients of data source, data integrity, historical performance, and timeliness characteristics, respectively.

[0080] The trust conflict authentication subnetwork is used to perform conflict authentication on the preprocessing results and calculate the conflict score.

[0081] The credibility score and conflict score are used as the authentication result of the trust conflict authentication subnetwork to perform joint authentication.

[0082] Furthermore, the data preprocessing module 12 is further configured to perform the following steps:

[0083] A complete identification rule of the cloud computing platform is configured, wherein the complete identification rule is a setting rule for required fields and optional fields.

[0084] The complete identification rule is used to perform data integrity identification of the basic material data set and establish an identification result.

[0085] The data marked as incomplete in the recognition result is verified and filled under an adaptive window, and a preprocessing result is established based on the verification and filling result and the data marked as complete in the recognition result.

[0086] Furthermore, the data preprocessing module 12 is further configured to perform the following steps:

[0087] Verify and fill adjacent data for data identified as incomplete, and establish an adjacent filling range.

[0088] Perform feature fluctuation evaluation on the neighborhood data of the missing data, configure an adaptive extraction window according to the feature fluctuation evaluation result, extract data with the adaptive extraction window, and establish an adaptive filling range according to the data extraction result.

[0089] Perform global data filling identification on the data marked as incomplete and establish a global filling range.

[0090] Perform fill verification on adjacent fill ranges, adaptive fill ranges, and global fill ranges, and establish verification fill results.

[0091] Furthermore, the device also includes:

[0092] The database mapping module is used to establish a mapping verification database between the access data of the material database and the access subject.

[0093] The abnormal warning module is used to use the mapping verification database to perform real-time cumulative verification of the access data of the access subject. When the mapping verification database is triggered, an abnormal warning is reported and the subject of the corresponding access subject is frozen.

[0094] Furthermore, the device also includes:

[0095] The access index module is used to obtain the access data of the access subject, extract data features from the access data, and then establish the access index of the access subject based on the access data feature extraction results and the relationship network.

[0096] The access management module is used to perform access management of the corresponding access subject according to the access index.

[0097] Through the above-mentioned detailed description of the cloud computing-based material database construction method in this specification, those skilled in the art can clearly know the cloud computing-based material database construction device in this embodiment. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0098] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A material database construction method based on cloud computing, characterized in that: The method comprises: Configure a cloud computing platform and acquire multi-source material data to establish a basic material data set, wherein the multi-source material data includes laboratory measurement data, literature data, and industry standard data; After uploading the basic material data set to the cloud computing platform, using the cloud computing platform to perform data preprocessing and establish preprocessing results, wherein the data preprocessing includes data integrity detection and supplementation; Activate the data authentication network stored in the cloud computing platform, perform joint authentication of the preprocessing results through the data lineage authentication sub-network and trust conflict authentication sub-network embedded in the data authentication network, and generate an updated data set based on the joint authentication results; The updated data set is subjected to correlation analysis of material properties, correlation features are extracted according to the correlation analysis results, a relationship network is constructed using the correlation features, and the updated data set is structurally stored based on the relationship network to establish a material database.

2. The cloud computing-based material database construction method according to claim 1, characterized in that: The joint authentication of the preprocessing results by the data lineage authentication sub-network and the trust conflict authentication sub-network embedded in the data authentication network also includes: The source chain in the preprocessing result is called through the data pedigree authentication sub-network, and the generation-transmission-processing chain data consistency authentication is performed through the call result of the source chain to establish the first pedigree authentication result; Performing similarity authentication between multiple chain pedigrees in the preprocessing result through the data pedigree authentication subnetwork, and establishing a second pedigree authentication result according to the similarity authentication result; Joint authentication is performed based on the first pedigree authentication result, the second pedigree authentication result, and the authentication result of the trust conflict authentication subnetwork.

3. The cloud computing-based material database construction method according to claim 2, characterized in that: The performing joint authentication according to the first pedigree authentication result, the second pedigree authentication result and the authentication result of the trust conflict authentication sub-network also includes: The credibility score of the preprocessing result is calculated through the trust conflict authentication sub-network as follows: T = αS1 + βS2 + γS3 + δS4; Among them, T is the credibility score, S1 is the data source score, S2 is the data integrity score, which is constructed through complete and supplementary trust, S3 is the historical performance score, S4 is the data timeliness score, and α, β, γ, and δ are the weight coefficients of data source, data integrity, historical performance, and timeliness characteristics respectively; Use the trust conflict authentication sub-network to perform conflict authentication on the preprocessing results and calculate the conflict score; The credibility score and conflict score are used as the authentication result of the trust conflict authentication subnetwork to perform joint authentication.

4. The cloud computing-based material database construction method according to claim 1, characterized in that: The method of using the cloud computing platform to preprocess data and establish preprocessing results also includes: Configure a complete identification rule for the cloud computing platform, wherein the complete identification rule is a setting rule for required fields and optional fields; Using the complete identification rule to identify the data integrity of the basic material data set, and establishing an identification result; The data marked as incomplete in the recognition result is verified and filled under an adaptive window, and a preprocessing result is established based on the verification and filling result and the data marked as complete in the recognition result.

5. The cloud computing-based material database construction method according to claim 4, characterized in that: The verification filling of the data identified as incomplete in the recognition result under the adaptive window also includes: Verify and fill adjacent data for data marked as incomplete, and establish adjacent filling range; Perform characteristic fluctuation evaluation on the neighboring data of the missing data, configure an adaptive extraction window according to the characteristic fluctuation evaluation result, extract data with the adaptive extraction window, and establish an adaptive filling range according to the data extraction result; Perform global data filling identification on the data marked as incomplete and establish a global filling range; Perform fill verification on adjacent fill ranges, adaptive fill ranges and global fill ranges, and establish verification fill results.

6. The cloud computing-based material database construction method according to claim 1, characterized in that: The method further comprises: Establish a mapping verification database between the access data of the material database and the access subject; The mapping verification database is used to perform real-time cumulative verification of the access data of the access subject. When the mapping verification database is triggered, an abnormal warning is reported and the subject of the corresponding access subject is frozen.

7. The cloud computing-based material database construction method according to claim 1, characterized in that: The method further comprises: Obtaining access data of the access subject, extracting data features from the access data, and establishing an access index of the access subject based on the access data feature extraction results and the relationship network; Access management of the corresponding access subject is performed according to the access index.

8. A material database construction device based on cloud computing, characterized in that: For implementing the cloud computing-based material database construction method according to any one of claims 1 to 7, the device comprises: A data acquisition module, used to configure the cloud computing platform and acquire multi-source material data to establish a basic material data set, wherein the multi-source material data includes laboratory measurement data, literature data, and industry standard data; A data preprocessing module, used to upload the basic material data set to the cloud computing platform, perform data preprocessing using the cloud computing platform, and establish preprocessing results, wherein the data preprocessing includes data integrity detection and supplementation; The joint authentication module is used to activate the data authentication network stored in the cloud computing platform, perform joint authentication of the preprocessing results through the data lineage authentication sub-network and the trust conflict authentication sub-network embedded in the data authentication network, and generate an updated data set based on the joint authentication results; The database construction module is used to perform correlation analysis of material properties on the updated data set, extract correlation features according to the correlation analysis results, construct a relationship network using the correlation features, and structurally store the updated data set based on the relationship network to establish a material database.

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