Method and system for migrating data of credential system based on trusted data

Abstract

The invention relates to the technical field of data migration, in particular to a credential system data migration method and system based on trusted data. The method comprises the following steps: acquiring source information creation data; performing data preprocessing on the source credential data to generate quality-to-source credential data; acquiring real-time multi-dimensional monitoring data of the server; carrying out credibility evaluation model construction on the source credential data based on the quality, and generating a new credible data credential evaluation model; performing credibility evaluation on the real-time multi-dimensional monitoring data of the server by using the credible data credential evaluation model to generate a credential credential credibility comprehensive score; therefore, by constructing a comprehensive credible data evaluation and migration system, the problems of insufficient data credibility quantification, single migration strategy and relatively low execution reliability in a traditional credential system are solved, and the scientificity, efficiency and safety of data migration of the credential system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data migration, and in particular, to a data migration method and system for a Xinchuang system based on trusted data. Background Art

[0002] Existing Xinchuang system migration technologies still have some deficiencies in terms of data security, migration path optimization, multi-level strategy support, abnormal data processing, and intelligent processing. Data security fails to effectively guarantee the encryption, verification, and permission control of sensitive data, posing potential leakage risks. The design and optimization of migration paths are limited, unable to fully meet the requirements of efficient, low-latency, cross-platform data migration, especially the insufficient ability to process multi-type and multi-format data. In addition, the detection and processing mechanism for abnormal data is relatively single, making it difficult to effectively identify and process outliers, thus affecting the data quality and system stability after migration. The models and strategies lack flexibility and multi-level support, and cannot provide efficient diversified migration solutions. In terms of automation and intelligent processing capabilities, existing technologies have not fully achieved the adaptability and intelligent optimization of real-time data migration, easily causing delays and errors during the data migration process. Summary of the Invention

[0003] Based on this, it is necessary to provide a data migration method and system for a Xinchuang system based on trusted data to solve at least one of the above technical problems.

[0004] To achieve the above object, a data migration method for a Xinchuang system based on trusted data, the method includes the following steps:

[0005] Step S1: Obtain the source Xinchuang data; perform data preprocessing on the source Xinchuang data to generate quality source Xinchuang data;

[0006] Step S2: Obtain the server real-time multi-dimensional monitoring data; build a credibility evaluation model based on the quality source Xinchuang data to generate a new credibility evaluation model for trusted data; use the Xinchuang credibility evaluation model for trusted data to evaluate the credibility of the server real-time multi-dimensional monitoring data and generate a comprehensive Xinchuang credibility score;

[0007] Step S3: Perform priority division based on the comprehensive Xinchuang credibility score to generate Xinchuang priority division data; build a migration strategy for the Xinchuang priority division data to generate a Xinchuang migration strategy for trusted data;

[0008] Step S4: Perform integrity evaluation on the Xinchuang migration strategy for trusted data to generate Xinchuang migration integrity evaluation data; generate a migration report for the Xinchuang migration complete data, thereby completing the migration operation of the Xinchuang system for trusted data.

[0009] The beneficial effects of the present invention are as follows. By obtaining the source Xinchuang data and performing quality improvement processing on it, it ensures that the data used for subsequent analysis and evaluation has a basis of consistency, integrity, and high quality, laying a solid foundation for the credibility evaluation of the Xinchuang system. Secondly, by constructing a credible data Xinchuang evaluation model and using the server's real-time multi-dimensional monitoring data for credibility analysis, a comprehensive Xinchuang credibility score is generated, which can objectively quantify the reliability and importance of each data unit at the data level. This quantitative score provides a scientific basis for priority division, avoiding the deficiencies of relying on subjective experience or simple rules in data classification in traditional methods. Based on the comprehensive credibility score, the refined division of Xinchuang data priorities is further realized, and a migration strategy adapted to different priority requirements is designed. During the data migration process, the importance and dependency relationships of different data are fully considered, thereby optimizing the migration path and execution order. Finally, by conducting an integrity evaluation of the migration strategy and generating a detailed report on the migration process, the traceability and reliability of the entire migration job are ensured, providing rich data support for system operation and maintenance and subsequent optimization. Therefore, the present invention solves the problems of insufficient quantification of data credibility, single migration strategy, and low execution reliability in traditional Xinchuang systems by constructing a comprehensive credible data evaluation and migration system, improving the scientificity, efficiency, and security of Xinchuang system data migration.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Obtain the source Xinchuang data;

[0012] Step S12: Perform data cleaning on the source Xinchuang data to generate the source Xinchuang cleaned data;

[0013] Step S13: Perform data standardization processing on the source Xinchuang cleaned data to generate the high-quality source Xinchuang data.

[0014] The present invention obtains source Xinchuang data, performs data cleaning and standardization processing on it, significantly improves the quality and usability of Xinchuang data at the data level, and meets the requirements of subsequent analysis and processing. During the process of obtaining source Xinchuang data, the integrity and coverage of the data are ensured, providing a rich source of original information for data processing. Through data cleaning operations, redundant information, inconsistent data, and abnormal data in the source data are effectively removed, thereby reducing the interference of data noise on the analysis results and improving the accuracy and reliability of the data. At the same time, data cleaning also optimizes the data structure to make it more compliant with the input requirements of the analysis model. Further, based on the cleaned data, standardization processing is carried out. By methods such as normalization, discretization, or format unification, the problems of dimensional differences and format inconsistencies caused by diverse data sources are solved, creating conditions for comparison, calculation, and comprehensive analysis between data. The whole process of this method from data acquisition to cleaning and then to standardization processing forms a scientific and efficient processing flow, providing high-quality data support for the subsequent credible evaluation and migration strategy design.

[0015] Preferably, step S2 includes the following steps:

[0016] Step S21: Obtain real-time multi-dimensional monitoring data of the server;

[0017] Step S22: Build a credibility evaluation model for source Xinchuang data based on quality, and generate a new credibility evaluation model for Xinchuang data;

[0018] Step S23: Use the Xinchuang credibility evaluation model for the server's real-time multi-dimensional monitoring data to conduct a credibility evaluation, generating Xinchuang credibility evaluation data; correct the scoring system for the Xinchuang credibility evaluation data to generate a comprehensive Xinchuang credibility score.

[0019] The present invention realizes the dynamic quantification and optimized management of the data quality of the information technology application innovation system at the data level by obtaining the real-time multi-dimensional monitoring data of the server, constructing a credibility evaluation model for the source information technology application innovation data in combination with quality, evaluating the credibility of the real-time data, and generating a comprehensive score. First, the acquisition of the real-time multi-dimensional monitoring data of the server ensures the timeliness and diversity of the data source, providing a comprehensive basis for model evaluation. Through the credibility evaluation model constructed based on high-quality information technology application innovation data, the characteristics of multi-dimensional data are effectively integrated, and the change characteristics of the data in terms of accuracy, consistency, and integrity can be comprehensively captured, realizing the scientific analysis of the real-time data. During the credibility evaluation process, the model accurately deduces the source data based on quality, comprehensively quantifies the reliability of the real-time monitoring data, and generates credibility evaluation data. This method can significantly improve the accuracy and credibility of the data evaluation results. At the same time, through the correction of the scoring system, the evaluation data is further optimized to minimize potential biases and errors, and finally a comprehensive score of the information technology application innovation credibility is generated. This scoring system provides a refined credibility grading mechanism at the data level, providing a solid support for data priority division, strategy design, and scientific decision-making in the migration process.

[0020] Preferably, step S22 includes the following steps:

[0021] Step S221: Obtain the initial evaluation weight data;

[0022] Step S222: Initialize the model of the preset neural data migration type through the initial evaluation weight data to generate an initial credibility migration model;

[0023] Step S223: Perform migration embedding matrix processing on the initial credibility migration model to generate data migration embedding matrix data;

[0024] Step S224: Customize the migration style feature layer of the initial credibility migration model and initialize the weights of the new layer to generate an initial data migration style layer; use the data migration embedding matrix data to construct a credible data migration learning model for the initial data migration style layer to generate a credible data new creation evaluation model.

[0025] The present invention ensures the scientificity and rationality of the weight distribution in the model initialization stage by obtaining the initial evaluation weight data as the basis for weight design. Based on this weight data, the model is initialized by presetting the neural data migration type to generate an initial credibility migration model, which lays the foundation for subsequent migration embedding matrix processing and style layer customization. In the process of migration embedding matrix processing, the embedding matrix is designed by combining multi-dimensional feature data, which strengthens the adaptability of data features and the rationality of the migration path. The generated data migration embedding matrix can effectively improve the accuracy of data transmission and mapping. In addition, by customizing the migration style feature layer of the initial credibility migration model and initializing the weights of the new layer, the specific scenario adaptation ability of the model is further enhanced, enabling the model to generate the optimal feature mapping according to different data migration requirements. Finally, the data migration embedding matrix is combined with the style feature layer to construct a trusted data migration learning model, completing the generation of a trusted data new creation evaluation model. From the data level, this invention can maximize the retention and enhancement of data features through steps such as weight design, embedding matrix optimization, and style feature customization, ensuring the generality and efficiency of the migration learning model in different Xinchuang scenarios.

[0026] Preferably, step S3 includes the following steps:

[0027] Step S31: Obtain the server load situation data; use the server load situation data to confirm the Xinchuang outlier judgment threshold, and obtain the Xinchuang outlier judgment threshold; based on the Xinchuang outlier judgment threshold, draw the abnormal fluctuations of the Xinchuang credibility comprehensive score, and eliminate the outliers to generate the Xinchuang outlier screening data;

[0028] Step S32: Divide the priority according to the Xinchuang outlier screening data to generate the Xinchuang priority division data;

[0029] Step S33: Construct a migration strategy for the Xinchuang priority division data to generate a trusted data Xinchuang migration strategy.

[0030] The present invention draws abnormal fluctuations of the comprehensive evaluation score of the credibility of information and communication technology (ICT) innovation based on the judgment threshold of ICT innovation outliers, eliminates the outliers, and generates ICT innovation outlier screening data. This process effectively eliminates the interference of abnormal data on the overall evaluation, ensures the stability and authenticity of the data, and comprehensively reflects the dynamic distribution characteristics of data anomalies through visual fluctuation drawing. According to the ICT innovation outlier screening data, priority division is carried out, and the screened data is classified to generate ICT innovation priority division data. This priority division process scientifically stratifies the data based on the screened high-quality data, laying a precise foundation for subsequent resource allocation and strategy design. Based on the priority division data, a migration strategy is constructed. By analyzing the characteristics and requirements of data with different priorities, a trustworthy data ICT innovation migration strategy is generated to ensure that the data migration process can specifically meet the data requirements of different priorities, avoiding the problems of low efficiency and resource waste caused by the "one-size-fits-all" approach in traditional migration strategies.

[0031] Preferably, step S33 includes the following steps:

[0032] Step S331: Construct a migration strategy for the ICT innovation priority division data to generate a trustworthy data ICT innovation migration strategy, where the trustworthy data ICT innovation migration strategy includes a multi-level migration strategy and a decentralized migration strategy;

[0033] Step S332: When divided into a multi-level migration strategy based on the ICT innovation priority division data, conduct a comprehensive evaluation of the ICT innovation outlier screening data to generate comprehensive evaluation score data of ICT innovation credibility, where the comprehensive evaluation score data of ICT innovation credibility includes ICT innovation sensitive data and ICT innovation regular data; perform data migration on the ICT innovation sensitive data and ICT innovation regular data to generate high-speed low-latency migration data and low-speed high-latency migration data; summarize the high-speed low-latency migration data and low-speed high-latency migration data to complete the multi-level migration strategy;

[0034] Step S333: When divided into a decentralized migration strategy based on the ICT innovation priority division data, generate a distributed network for data migration to generate a distributed device network for data migration; perform multi-node distributed transmission on the distributed device network for data migration based on a preset migration smart contract to generate a distributed result of ICT innovation data migration; conduct blockchain verification on the distributed result of ICT innovation data migration to complete the decentralized migration strategy.

[0035] Through the analysis of data classified by Xinchuang priority, this invention constructs two different data migration methods: a multi-level migration strategy and a decentralized migration strategy. In the multi-level migration strategy, by comprehensively scoring the data screened for Xinchuang outliers, Xinchuang credibility comprehensive scoring data containing Xinchuang sensitive data and Xinchuang regular data is generated, and hierarchical transmission is performed according to data characteristics, respectively generating high-speed low-latency migration data and low-speed high-latency migration data. Through this differential transmission mechanism, the priority and fast transmission of highly sensitive data and the resource-optimized transmission of regular data are achieved, thereby improving the migration efficiency and effectively avoiding data congestion problems. On the other hand, in the decentralized migration strategy, a distributed device network based on data screened for Xinchuang outliers is constructed through distributed network generation, and combined with a preset migration smart contract, distributed transmission of multiple nodes is achieved, generating a decentralized result of Xinchuang data migration. During this process, blockchain technology is used to verify the distributed result, ensuring the transparency and integrity of the data migration process, and enhancing the security and anti-tampering ability of data transmission. Through this dual-track strategy design, the optimal migration plan can be selected according to different scenarios and requirements, solving the problem that it is difficult to balance efficiency and security in traditional data migration modes.

[0036] Preferably, data migration for Xinchuang sensitive data and Xinchuang regular data includes the following steps:

[0037] Design a high-speed migration path according to Xinchuang sensitive data to generate a high-speed low-latency migration path;

[0038] Design a migration path according to Xinchuang regular data to generate a low-speed high-latency migration path;

[0039] Encrypt the data according to Xinchuang sensitive data and perform high-speed data migration through the high-speed low-latency migration path to generate high-speed low-latency migration data;

[0040] Send the Xinchuang regular data when the device is idle through the low-speed high-latency migration path to generate low-speed high-latency migration data.

[0041] The present invention significantly improves the security and efficiency of data migration by designing different migration paths for Xinchuang sensitive data and conventional data. In the design of the Xinchuang sensitive data migration path, according to the characteristics of Xinchuang sensitive data, the high-speed and low-latency migration path is optimized. This path ensures the privacy protection of sensitive data during transmission through an efficient data encryption mechanism, and significantly reduces the time and latency required during data transmission through a high-speed and low-latency transmission method, thereby improving the real-time performance and response speed of the data. In the design of the Xinchuang conventional data migration path, a low-speed and high-latency migration path is generated according to the characteristics of conventional data. Under this path, when the device is idle, resources are reasonably allocated through a low-speed and high-latency data transmission method, reducing the resource utilization rate. At the same time, the overall transmission stability is improved through data buffering and management during the transmission process. This differential path design enables sensitive data and conventional data to be efficiently and securely migrated and processed according to requirements respectively, solving the problems of low efficiency and security risks caused by the lack of targeted paths in traditional data migration. Through this strategy, the speed and security requirements of data migration can be effectively balanced to adapt to diverse application scenarios.

[0042] Preferably, step S4 includes the following steps:

[0043] Step S41: Obtain historical migration data; conduct an integrity assessment of the Xinchuang migration strategy for trusted data to generate complete Xinchuang migration assessment data;

[0044] Step S42: Conduct an integrity comparison and analysis of the historical migration data and the complete Xinchuang migration assessment data to generate comparative integrity data for Xinchuang migration;

[0045] Step S43: Generate a migration report based on the comparative integrity data for Xinchuang migration, thereby completing the Xinchuang system migration operation of trusted data.

[0046] The present invention obtains historical migration data and conducts an integrity assessment of the Xinchuang migration strategy for trusted data to generate complete Xinchuang migration assessment data. This link ensures the accuracy and adaptability of the migration strategy, avoiding data loss or inconsistency problems caused by strategy defects. Conduct an integrity comparison and analysis of the historical migration data and the complete Xinchuang migration assessment data to generate comparative integrity data for Xinchuang migration. This comparative analysis can accurately identify abnormal situations or deviations during the migration process, providing a scientific basis for subsequent data verification and correction. Finally, generate a migration report based on the comparative integrity data for Xinchuang migration, comprehensively recording every link in the migration process to ensure the transparency and traceability of data migration. Through this process of layer-by-layer verification and analysis, the risks during data migration can be effectively reduced, while improving the migration efficiency and data quality.

[0047] Preferably, step S42 includes the following steps:

[0048] Step S421: Perform a difference analysis on the historical migration data and the complete evaluation data of the Xinchuang migration to generate Xinchuang comparison difference data;

[0049] Step S422: Perform a trend comparison curve analysis on the Xinchuang comparison difference data to generate a Xinchuang trend comparison curve;

[0050] Step S423: Identify the frequency of abnormal points based on the Xinchuang trend comparison curve to generate Xinchuang migration identification data; perform an integrity comparison analysis on the Xinchuang migration identification data to generate Xinchuang migration comparison integrity data.

[0051] In the present invention, by performing a difference analysis on the historical migration data and the complete evaluation data of the Xinchuang migration, Xinchuang comparison difference data is generated. This analysis process can clearly reveal the change range and deviation of the data during the migration process, ensuring the consistency and stability of the data during migration. Perform a trend comparison curve analysis on the Xinchuang comparison difference data to generate a Xinchuang trend comparison curve. This trend analysis can dynamically monitor the overall trend of data changes during the migration process, helping to identify potential anomalies or trends deviating from expectations, and then making more accurate corrections and adjustments. Identify the frequency of abnormal points based on the Xinchuang trend comparison curve to generate Xinchuang migration identification data, and perform an integrity comparison analysis on it to generate Xinchuang migration comparison integrity data. This process ensures that the migrated data meets the expected effect in terms of integrity and consistency by identifying and excluding abnormal data points, thereby improving the reliability and security of data migration.

[0052] In this specification, a Xinchuang system data migration system based on trusted data is provided for executing the above-mentioned Xinchuang system data migration method based on trusted data. This Xinchuang system data migration system based on trusted data includes:

[0053] A Xinchuang data quality improvement module for obtaining source Xinchuang data; generating quality source Xinchuang data according to data preprocessing of the source Xinchuang data;

[0054] A credibility evaluation and priority division module for obtaining real-time multi-dimensional monitoring data of the server; constructing a credibility evaluation model based on the quality source Xinchuang data to generate a new evaluation model of trusted data for Xinchuang; using the Xinchuang evaluation model of trusted data to evaluate the credibility of the real-time multi-dimensional monitoring data of the server to generate a comprehensive Xinchuang credibility score;

[0055] A migration strategy construction module for performing priority division based on the comprehensive Xinchuang credibility score to generate Xinchuang priority division data; constructing a migration strategy for the Xinchuang priority division data to generate a Xinchuang migration strategy for trusted data;

[0056] The migration execution and report generation module is used to conduct integrity assessment on the Xinchuang migration strategy of trusted data, generate complete Xinchuang migration assessment data; and generate migration reports based on the complete Xinchuang migration data, thereby completing the Xinchuang system migration operation of trusted data.

[0057] The beneficial effects of the present invention are as follows. By obtaining the source Xinchuang data and performing quality improvement processing on it, it ensures that the data used for subsequent analysis and evaluation has a basis of consistency, integrity, and high quality, laying a solid foundation for the credibility assessment of the Xinchuang system. Secondly, by constructing a Xinchuang evaluation model for trusted data and using real-time multi-dimensional monitoring data of the server for credibility analysis, a comprehensive Xinchuang credibility score is generated, which can objectively quantify the reliability and importance of each data unit at the data level. This quantitative score provides a scientific basis for priority division, avoiding the deficiencies of relying on subjective experience or simple rules in traditional data classification methods. Based on the comprehensive credibility score, the refined division of Xinchuang data priorities is further realized, and migration strategies adapted to different priority requirements are designed. During the data migration process, the importance and dependency relationships of different data are fully considered, thereby optimizing the migration path and execution order. Finally, by conducting integrity assessment on the migration strategy and generating detailed reports on the migration process, the traceability and reliability of the entire migration operation are ensured, providing rich data support for system operation and maintenance and subsequent optimization. Therefore, by constructing a comprehensive trusted data evaluation and migration system, the present invention solves the problems of insufficient quantification of data credibility, single migration strategy, and low execution reliability in traditional Xinchuang systems, and improves the scientificity, efficiency, and security of Xinchuang system data migration. Brief Description of the Drawings

[0058] Figure 1 It is a schematic diagram of the step flow of a Xinchuang system data migration method based on trusted data;

[0059] Figure 2 is Figure 1 a schematic diagram of the detailed implementation step flow of step S2 in

[0060] Figure 3 is Figure 1 a schematic diagram of the detailed implementation step flow of step S3 in

[0061] Figure 4 is Figure 1 a schematic diagram of the detailed implementation step flow of step S4 in

[0062] The realization, functional characteristics, and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiments

[0063] The technical method of the present invention for a patent will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art within the scope of the present invention without creative work belong to the scope of protection of the present invention.

[0064] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0065] It should be understood that although terms such as "first", "second", etc. may be used here to describe each unit, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed associated items.

[0066] To achieve the above object, please refer to Figures 1 to 4 , a data migration method for a Xinchuang system based on trusted data, the method comprising the following steps:

[0067] Step S1: Obtain source Xinchuang data; perform data preprocessing on the source Xinchuang data to generate quality source Xinchuang data;

[0068] Step S2: Obtain real-time multi-dimensional monitoring data of the server; build a credibility evaluation model based on the quality source Xinchuang data to generate a new credibility evaluation model for trusted data; use the Xinchuang credibility evaluation model for trusted data to evaluate the credibility of the real-time multi-dimensional monitoring data of the server to generate a comprehensive Xinchuang credibility score;

[0069] Step S3: Perform priority division based on the comprehensive Xinchuang credibility score to generate Xinchuang priority division data; build a migration strategy for the Xinchuang priority division data to generate a Xinchuang migration strategy for trusted data;

[0070] Step S4: Perform integrity evaluation on the Xinchuang migration strategy for trusted data to generate Xinchuang migration integrity evaluation data; generate a migration report for the Xinchuang migration complete data, thereby completing the Xinchuang system migration operation of trusted data.

[0071] The beneficial effects of the present invention are as follows. By obtaining the source Xinchuang data and performing quality improvement processing on it, it ensures that the data used for subsequent analysis and evaluation has a basis of consistency, integrity, and high quality, laying a solid foundation for the credibility evaluation of the Xinchuang system. Secondly, by constructing a credible data Xinchuang evaluation model and using the server's real-time multi-dimensional monitoring data for credibility analysis, a comprehensive Xinchuang credibility score is generated, which can objectively quantify the reliability and importance of each data unit at the data level. This quantitative score provides a scientific basis for priority division, avoiding the deficiencies of data classification relying on subjective experience or simple rules in traditional methods. Based on the comprehensive credibility score, the refined division of Xinchuang data priorities is further realized, and a migration strategy adapted to different priority requirements is designed. During the data migration process, the importance and dependency relationships of different data are fully considered, thereby optimizing the migration path and execution order. Finally, through the integrity evaluation of the migration strategy and the generation of a detailed report on the migration process, the traceability and reliability of the entire migration job are ensured, providing rich data support for system operation and maintenance and subsequent optimization. Therefore, the present invention solves the problems of insufficient quantification of data credibility, single migration strategy, and low execution reliability in traditional Xinchuang systems by constructing a comprehensive credible data evaluation and migration system, improving the scientificity, efficiency, and security of Xinchuang system data migration.

[0072] In the embodiment of the present invention, refer to Figure 1 As shown, it is a schematic diagram of the step flow of a method for migrating Xinchuang system data based on credible data according to the present invention. In this example, the method for migrating Xinchuang system data based on credible data includes the following steps:

[0073] Step S1: Obtain the source Xinchuang data; perform data preprocessing on the source Xinchuang data to generate the quality source Xinchuang data;

[0074] In the embodiments of the present invention, the obtained source Xinchuang data usually contains various dimensions of information, including structured and unstructured data, and there are noise, redundancy, and inconsistencies in these data. Therefore, it is very necessary to perform data preprocessing. Data preprocessing includes operations such as data cleaning, data normalization, and data dimensionality reduction. The data cleaning stage mainly deals with missing values, duplicate data, outliers, and noise in the source Xinchuang data. By filling in missing values, removing duplicate data, and eliminating outliers based on rules or machine learning algorithms, the accuracy and consistency of the data can be effectively improved. Secondly, the data normalization process maps data information of different dimensions or scales to a unified standardized data format, making the data have better comparability and processing capabilities. For example, methods such as numerical normalization and label encoding can ensure that different types of data can be seamlessly docked during analysis and modeling. Finally, data dimensionality reduction techniques such as principal component analysis (PCA) and feature selection can reduce the dimensionality of the data while retaining key features and improving data processing efficiency. Through these data preprocessing steps, the generated data is not only more efficient and accurate for the source Xinchuang data, but also can provide a solid data foundation for subsequent analysis, modeling, and migration tasks.

[0075] Step S2: Obtain the server real-time multi-dimensional monitoring data; construct a credibility evaluation model based on the source Xinchuang data in terms of quality to generate a new Xinchuang evaluation model for credible data; use the Xinchuang credibility evaluation model for credible data to evaluate the credibility of the server real-time multi-dimensional monitoring data and generate a comprehensive Xinchuang credibility score;

[0076] In the embodiments of the present invention, the obtained server real-time multi-dimensional monitoring data usually contains data information in multiple dimensions such as time series, spatial dimensions, and environmental variables. These data need to be cleaned and normalized to remove outliers and noise and improve the accuracy of the data. In the data preprocessing stage, data dimensionality reduction, data standardization, and data structuring are carried out to ensure that data in different dimensions can be smoothly used in the model. Based on the source Xinchuang data in terms of quality, a credibility evaluation model is constructed. This evaluation model usually uses machine learning algorithms such as support vector machine (SVM), random forest, or deep learning models to learn the internal characteristics and relationships of the data. During the training process of the model, historical data and multi-dimensional monitoring data are used for joint training, enabling the model to automatically adapt to new data streams for dynamic evaluation. In the model application stage, by using the Xinchuang credibility evaluation model for credible data to evaluate the credibility of the server real-time multi-dimensional monitoring data, a comprehensive Xinchuang credibility score is generated. This comprehensive score not only reflects the reliability of the data itself but also provides a basis for comparative analysis. For example, for data streams within different time windows, the credibility score can change dynamically, thus capturing the changing trend of data quality in real time. Through data preprocessing, construction of the credibility evaluation model, and dynamic evaluation strategies, the credibility and quality of the server real-time multi-dimensional monitoring data are ensured.

[0077] Step S3: Based on the comprehensive evaluation score of the Xinchuang credibility, perform priority division to generate Xinchuang priority division data; construct a migration strategy for the Xinchuang priority division data to generate a Xinchuang migration strategy for trusted data.

[0078] In the embodiment of the present invention, in the stage of performing priority division based on the comprehensive evaluation score of the Xinchuang credibility, mainly data stratification and clustering techniques are used. These techniques classify the data set based on the multi-dimensional evaluation of Xinchuang data and sort the priorities according to the high and low of the evaluation values. Common stratification techniques include decision trees, clustering algorithms (such as K-Means or hierarchical clustering), and machine learning models such as support vector machines (SVM), etc. These techniques help to identify different levels of Xinchuang data, thus providing a basis for subsequent migration strategies. Then, in the stage of constructing the migration strategy, the data migration requirements of different priorities need to be considered. For example, high-credibility data requires a low-risk and low-latency migration strategy, while low-priority data can appropriately choose a longer migration path. In this process, the construction of the migration strategy usually involves a risk assessment model, a path optimization algorithm, and a data flow control model. These technical means can automatically generate a migration strategy that meets the requirements of security and efficiency by constructing model-based migration rules. The construction of the migration strategy also includes the screening and processing of abnormal data to ensure that the data is not damaged or leaked during the migration process. At the same time, based on the principle of data security, encryption technology and access control strategies for sensitive data also need to be considered to enhance the security of the migration process.

[0079] Step S4: Evaluate the integrity of the Xinchuang migration strategy for trusted data to generate complete Xinchuang migration evaluation data; generate a migration report for the complete Xinchuang migration data, thus completing the Xinchuang system migration operation of trusted data.

[0080] In the embodiments of the present invention, for the integrity evaluation of the trusted data Xinchuang migration strategy, data verification and consistency checking technologies are mainly adopted. These technologies include the verification of each step in the migration strategy to ensure that each step follows the predetermined rules and standards. At this stage, data flow analysis tools and rule engines are usually used to automatically verify the migration strategy. In addition, data consistency checking technologies such as hash algorithms and difference analysis are applied to compare the integrity of the data before and after migration to ensure that the data is not lost or tampered with during the migration process. For generating a migration report for the complete Xinchuang migration data, data analysis and generation technologies are used. In the process of generating the migration report, various data generated during the migration process need to be aggregated first, such as the usage of the migration strategy, the status information during data processing, and the final generated migration results. Then, based on data visualization technologies and statistical analysis methods, a detailed migration report is generated. These reports not only include basic migration data but also migration risk assessments, operation logs, and exception handling records, etc., so as to comprehensively reflect the status of the migration process. In addition, in order to improve the credibility and accuracy of the migration report, artificial intelligence and machine learning technologies can also be combined to automatically analyze data deviations and abnormal patterns. These technical means can identify potential problems in the data and suggest corresponding solutions while generating the report.

[0081] Preferably, step S1 includes the following steps:

[0082] Step S1: Obtain the source Xinchuang data;

[0083] Step S2: Clean the source Xinchuang data to generate the cleaned source Xinchuang data;

[0084] Step S3: Perform data standardization processing on the cleaned source Xinchuang data to generate the high-quality source Xinchuang data.

[0085] In the embodiments of the present invention, when obtaining source Xinchuang data, various data collection techniques are often used, such as data crawling, API calls, database queries, etc. Through these means, relevant data is extracted from various heterogeneous systems and platforms. These data have problems such as inconsistent formats, a large amount of noise, and missing data. Therefore, after obtaining the data, further preprocessing is required. Data cleaning is a key technology for processing incomplete or inconsistent data, including outlier detection, data standardization, duplicate record elimination, etc. In this stage, methods such as data filtering, filling missing values, and outlier detection algorithms based on statistical analysis and rule engines are usually used. Through these means, the noise in the data can be effectively removed, the missing values can be repaired, and the data quality can be improved. Standardization processing is a process to ensure that the data has a unified scale and range for subsequent analysis and processing. Specifically, this step adjusts the ratio between each data item through algorithms such as regularization, standardization, and min-max standardization to make the data consistent. In the process of data standardization, mathematical formulas and machine learning models are often used to automatically adjust the data to make it have a relatively stable numerical distribution. This processing can not only improve the comparability of the data in different analysis models but also provide a more stable input for subsequent machine learning and data mining tasks.

[0086] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0087] Step S21: Obtain the server real-time multi-dimensional monitoring data;

[0088] Step S22: Build a credibility evaluation model for the source Xinchuang data based on quality to generate a new credibility evaluation model for Xinchuang data;

[0089] Step S23: Use the Xinchuang credibility evaluation model for the server real-time multi-dimensional monitoring data to generate Xinchuang credibility evaluation data; correct the scoring system for the Xinchuang credibility evaluation data to generate a comprehensive Xinchuang credibility score.

[0090] In the embodiments of the present invention, the server's real-time multi-dimensional monitoring data is obtained through various sensors, devices, or network acquisition devices, covering various dimensions of data streams, such as time, space, status, etc. These data come from fields such as device operation, environmental monitoring, and business systems. Therefore, technical means such as network crawling, data interface calling, and data stream processing are used to obtain real-time data. To ensure the high quality and real-time nature of the data, asynchronous or distributed acquisition modes are usually adopted for data acquisition, supporting the efficient processing of large amounts of data. The credibility evaluation model constructs a variety of data processing and analysis techniques. For example, machine learning algorithms, statistical analysis techniques, and rule engines can be used for model training and verification. For the source Xinchuang data based on quality, data preprocessing is first required, including steps such as data cleaning, outlier detection, and standardization. These processes provide high-quality input data for model construction. In the construction stage, algorithms such as support vector machine (SVM), neural network, and random forest can be used to optimize the accuracy and robustness of the model, thereby generating a credible data Xinchuang evaluation model. Using the credible data Xinchuang evaluation model to evaluate the credibility of real-time data is a dynamic processing process. In this process, the model generates real-time evaluation results through the interactive analysis of historical data and real-time data. For the Xinchuang credibility evaluation data, a scoring system correction is usually required to make up for the deviation of the model in different data states. This process is based on the application of statistical methods, model correction techniques, and dynamic adjustment algorithms, making the evaluation results more accurate and capable of continuous optimization as the data changes, thereby generating a comprehensive Xinchuang credibility score.

[0091] Preferably, step S22 includes the following steps:

[0092] Step S221: Obtain initialization evaluation weight data;

[0093] Step S222: Initialize the model of the preset neural data migration type through the initialization evaluation weight data to generate an initial credibility migration model;

[0094] Step S223: Perform migration embedding matrix processing on the initial credibility migration model to generate data migration embedding matrix data;

[0095] Step S224: Customize the migration style feature layer of the initial credibility migration model and initialize the weights of the new layer to generate an initial data migration style layer; use the data migration embedding matrix data to construct a credible data migration learning model for the initial data migration style layer to generate a credible data Xinchuang evaluation model.

[0096] In the embodiments of the present invention, the initialized evaluation weight data usually comes from historical data, empirical models, or the results of preliminary statistical analysis. The acquisition of these weight data involves data preprocessing, feature extraction, and data screening techniques. Methods such as data sampling, data cleaning, and feature engineering can be used to extract relevant data to generate initial weight information. At this stage, the preset transfer model (such as a neural network or deep learning model) is initially initialized through the initialized weight data. Model initialization adopts parameter initialization, hyperparameter optimization, and network structure design to ensure that the model can quickly perform transfer learning on new tasks. At the data level, this leads to the model training, adjustment, and verification process based on the weight data. Transfer embedding matrix processing is a method of mapping high-dimensional data to a low-dimensional space to extract relevant features. By performing embedding processing on the output of the initial credibility transfer model, data transfer embedding matrix data can be generated. The data in the embedding matrix usually includes vector representation, feature mapping, and the structured processing of high-dimensional data, which can support subsequent transfer learning tasks. In the customization of the transfer style feature layer, through combining the characteristics of the existing data and the transfer model, the initialization of the new feature layer is carried out. This process leads to the extension or customization of the original model structure, adding new weight layers to adapt to more complex or specific transfer tasks. This customized style layer can significantly improve the data transfer ability of the model in different fields. Finally, based on the embedding matrix data and the transfer style layer, a transfer learning model is constructed. This model generates a credible data innovation evaluation model through repeated learning and adjustment. At the data level, this process leads to the dynamic adjustment of the model and the continuous optimization of the data flow to ensure the effect of the transfer task and the efficient use of data.

[0097] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0098] Step S31: Obtain the server load condition data; use the server load condition data to confirm the threshold for judging the innovation and creation outliers, and obtain the threshold for judging the innovation and creation outliers; based on the threshold for judging the innovation and creation outliers, plot the abnormal fluctuations of the comprehensive innovation and creation credibility score, and eliminate the outliers to generate the screened data of the innovation and creation outliers;

[0099] Step S32: Perform priority division according to the screened data of the innovation and creation outliers to generate the divided data of the innovation and creation priorities;

[0100] Step S33: Construct a transfer strategy for the divided data of the innovation and creation priorities to generate a credible data innovation and creation transfer strategy.

[0101] In the embodiments of the present invention, outlier detection is one of the key steps in data processing. Usually, methods based on mathematical statistics, machine learning algorithms, or time series analysis are used to identify abnormal fluctuations. At this stage, abnormal judgment is performed on the data of the comprehensive evaluation score of the credibility of information and communication technology (ICT) innovation. A preset threshold is used to plot abnormal fluctuations, and outliers are removed. This process involves data standardization, deviation analysis, and outlier identification based on upper and lower limits, so as to generate ICT innovation outlier screening data. According to the outlier screening data, the ICT innovation data is further classified by priority. The priority classification is usually based on multiple factors, including the sensitivity of the data, business requirements, and the importance of the data, etc. This stage involves data clustering, risk assessment, and the construction of a business priority model. Methods such as multivariate analysis, the analytic hierarchy process (AHP), and fuzzy clustering are used to generate ICT innovation priority classification data. Based on the ICT innovation priority classification data, a migration strategy is constructed. The migration strategy is designed based on the priority data to ensure the security, efficiency, and adaptability of the data during the migration process. This stage involves the construction of multi-dimensional strategies, including data encryption, flow control optimization, fault tolerance mechanisms, and distributed data processing technologies. These strategies generate a trusted data ICT innovation migration strategy by combining different migration algorithms and strategy models to ensure the quality and security of the data during the migration process.

[0102] Preferably, step S33 includes the following steps:

[0103] Step S331: Construct a migration strategy for the ICT innovation priority classification data to generate a trusted data ICT innovation migration strategy, where the trusted data ICT innovation migration strategy includes a multi-level migration strategy and a decentralized migration strategy;

[0104] Step S332: When it is classified as a multi-level migration strategy based on the ICT innovation priority classification data, perform a comprehensive evaluation on the ICT innovation outlier screening data to generate ICT innovation credibility comprehensive evaluation data, where the ICT innovation credibility comprehensive evaluation data includes ICT innovation sensitive data and ICT innovation regular data; perform data migration on the ICT innovation sensitive data and ICT innovation regular data to generate high-speed low-latency migration data and low-speed high-latency migration data; summarize the high-speed low-latency migration data and low-speed high-latency migration data to complete the multi-level migration strategy;

[0105] Step S333: When it is classified as a decentralized migration strategy based on the ICT innovation priority classification data, generate a distributed network for data migration to generate a data migration distributed device network; perform multi-node distributed transmission on the data migration distributed device network based on a preset migration smart contract to generate a distributed result of ICT innovation data migration; perform blockchain verification on the distributed result of ICT innovation data migration to complete the decentralized migration strategy.

[0106] In the embodiments of the present invention, the construction of the migration strategy involves multiple technical means, among which the multi-level migration strategy and the decentralized migration strategy are common processing methods. In the multi-level migration strategy, hierarchical analysis and decision model construction are carried out on the data divided by the Xinchuang priority, and data migration tasks at different levels are assigned. For example, high-priority data preferentially adopts a high-speed and low-latency migration path, while low-priority data adopts a low-speed and high-latency migration path. This process usually uses methods such as the Analytic Hierarchy Process (AHP) and game theory to construct the migration strategy, so as to ensure the security and efficiency of the migration of different types of data. The decentralized migration strategy is based on the distributed processing of Xinchuang data. Data is generated through a distributed device network and transmitted through multiple nodes using a preset migration smart contract. In this process, the data is split into small pieces and dispersed to each node for transmission, ensuring the data integrity and security of each node. In addition, the introduction of blockchain technology can provide the immutability and transparency of data, further enhancing the security of data migration. In the multi-level migration strategy, comprehensive scoring is carried out on the data screened for Xinchuang outliers, and the data is classified according to data sensitivity and business requirements. Xinchuang sensitive data and Xinchuang regular data adopt different migration paths and strategies during the migration process. In the high-speed and low-latency path, efficient algorithms are used to optimize the data transmission speed and reduce the latency time, thereby improving the migration efficiency. In the low-speed and high-latency path, data security is ensured through device idle or offline processing. In addition, the summary processing of the migrated data can ensure the integration and consistency of the data at the final stage. In the decentralized migration strategy, the data screened for Xinchuang outliers is generated through a distributed network and transmitted through multiple nodes based on the migration smart contract. This process controls the data migration of each node through the smart contract to ensure data consistency and security between nodes. The introduction of blockchain technology enables the transactions of each node to be recorded and verified, thereby realizing the decentralized storage and management of data. In each link of data migration, the security and traceability of the data are further ensured through the verification of the smart contract of the blockchain.

[0107] Preferably, the data migration of Xinchuang sensitive data and Xinchuang regular data includes the following steps:

[0108] Design a high-speed migration path according to Xinchuang sensitive data to generate a high-speed and low-latency migration path;

[0109] Design a migration path according to Xinchuang regular data to generate a low-speed and high-latency migration path;

[0110] Encrypt the Xinchuang sensitive data and perform high-speed data migration through the high-speed and low-latency migration path to generate high-speed and low-latency migration data;

[0111] Idle device sending of general X86-architecture replacement data through a low-speed and high-latency migration path to generate low-speed and high-latency migration data.

[0112] In the embodiments of the present invention, for the design of a high-speed and low-latency migration path based on X86-architecture replacement sensitive data, efficient data transmission algorithms and optimized data compression techniques are mainly used to reduce the latency and transmission time of data during migration. In this process, the data transmission traffic is optimized, and advanced network protocols (such as TCP / IP protocol optimization) and asynchronous transmission techniques are used to ensure that the data remains in a low-latency state during transmission. This method is more precise in packet scheduling and routing selection, which helps to achieve fast data transmission in a high-speed environment. For general X86-architecture replacement data, a different method is adopted for the low-speed and high-latency migration path. Through the data sending mechanism during device idle time, the data is migrated in a non-real-time state. The task scheduling mechanism in the device idle state uses a low-priority data processing strategy to avoid burdening the system during the transmission of real-time demand data. At the same time, data chunking and offline storage techniques are used to reduce the processing load in a high-latency environment. For X86-architecture replacement sensitive data, data encryption is a key technical means to achieve data security. Data encryption methods include processing the data with complex encryption algorithms, such as AES, RSA, etc. Algorithms, and the privacy of the data is protected by generating encryption keys and security certificates. On the high-speed and low-latency migration path, these encrypted data are processed through efficient transmission algorithms to further reduce the risk of data being cracked. During the migration process, the data stream passes through the encrypted transmission mechanism to ensure the security and integrity of the data. In the low-speed and high-latency migration path, by sending general X86-architecture replacement data in the device idle mode, data synthesis and aggregation techniques can be combined. Using data caching and batch processing mechanisms, multiple small pieces of data are integrated to reduce the number of data packets during migration and optimize the overall transmission efficiency. At the same time, through data asynchronous processing and offline scheduling techniques, the impact of the migration operation on the system performance is minimized.

[0113] As an example of the present invention, refer to Figure 4 As shown, in this example, step S4 includes:

[0114] Step S41: Obtain historical migration data; perform an integrity assessment on the X86-architecture replacement migration strategy for trusted data to generate X86-architecture replacement migration complete assessment data;

[0115] Step S42: Perform an integrity comparison and analysis on the historical migration data and the X86-architecture replacement migration complete assessment data to generate X86-architecture replacement migration comparison integrity data;

[0116] Step S43: Generate a migration report for the X86-architecture replacement migration comparison integrity data, thereby completing the X86-architecture replacement system migration operation for trusted data.

[0117] In the embodiments of the present invention, the technical means for obtaining historical migration data are data storage and extraction technologies. At the data level, this includes extracting the migrated data from the historical data repository, and ensuring the acquisition of all relevant migration records through database queries, log analysis, and data backup and recovery mechanisms. In addition, data format conversion and data cleaning technologies are also applied to process and integrate data from different sources to ensure data consistency and standardization. The technical means for performing integrity assessment on the migration strategy of trusted data involve multiple data analysis and processing steps. First, data verification and consistency check tools are used to verify the logical relationships and detect data redundancy in the data of the migration strategy, generating complete evaluation data for the migration of IT application innovation. Then, through a detailed comparative analysis of the historical migration data and the complete evaluation data for the migration of IT application innovation, techniques such as difference analysis and association analysis are used to generate comparative integrity data for the migration of IT application innovation. Data association algorithms such as union queries, cross-validation, and time series analysis are used to identify data differences and data integration problems at different migration stages, thereby improving the overall integrity of the data. The technical means for generating a migration report for the comparative integrity data of the migration of IT application innovation are data visualization and report generation tools. First, data modeling techniques are used to generate the basic structure of the migration report, including the migration process, data verification results, and strategy evaluation reports. During this process, advanced data processing tools, such as data mining tools, machine learning models, and data visualization tools, are used to verify the migration quality of the data by automatically generating and previewing the report. At the same time, the report generation system can also automatically generate relevant data verification reports based on techniques such as outlier detection and data quality scoring, thereby completing the migration operation of the trusted data to the IT application innovation system.

[0118] Preferably, step S42 includes the following steps:

[0119] Step S421: Perform difference analysis on the historical migration data and the complete evaluation data for the migration of IT application innovation to generate comparative difference data for the migration of IT application innovation;

[0120] Step S422: Perform trend comparison curve analysis on the comparative difference data for the migration of IT application innovation to generate a comparative trend curve for the migration of IT application innovation;

[0121] Step S423: Identify the frequency of outlier points based on the comparative trend curve for the migration of IT application innovation to generate identification data for the migration of IT application innovation; perform integrity comparative analysis on the identification data for the migration of IT application innovation to generate comparative integrity data for the migration of IT application innovation.

[0122] In the embodiments of the present invention, by comparing the differences between historical migration data and complete evaluation data of Xinchuang migration, Xinchuang comparison difference data is generated. The difference analysis technology includes data difference calculation, statistical analysis, and the application of data association algorithms. Data difference calculation methods such as subtraction operation, difference matrix generation, and data filling algorithms are used to extract data differences within different migration cycles from multiple data sources. At the same time, statistical tools and machine learning models are used to identify the variation laws between data and generate difference data with high accuracy. Trend comparison curve analysis aims to reveal the time series change trend of data during the Xinchuang migration process. Data processing technologies such as time series analysis, data fitting algorithms, and sliding window technology are widely applied. At the data level, through these technologies, Xinchuang trend comparison curves are generated to display the long-term trend and short-term fluctuation characteristics of data during the migration process. Trend comparison curve analysis uses machine learning models, such as support vector machines (SVM) and time series prediction models, to predict and evaluate the long-term evolution of data. The abnormal point frequency identification technology is combined with trend comparison curve analysis to generate Xinchuang migration identification data. During the data processing process, outlier detection algorithms, such as Z-score-based anomaly detection and outlier detection algorithms, are used to extract outliers from the data. Subsequently, integrity comparison analysis is performed on the Xinchuang migration identification data, and multidimensional data analysis tools, such as clustering analysis, heterogeneity detection, and difference model verification, are used to generate Xinchuang migration comparison integrity data.

[0123] In this specification, a Xinchuang system data migration system based on trusted data is provided for executing the above-mentioned Xinchuang system data migration method based on trusted data. The Xinchuang system data migration system based on trusted data includes:

[0124] The Xinchuang data quality improvement module is used to obtain the source Xinchuang data; perform data preprocessing on the source Xinchuang data to generate the quality source Xinchuang data;

[0125] The credibility evaluation and priority division module is used to obtain the server real-time multidimensional monitoring data; construct a credibility evaluation model based on the quality source Xinchuang data to generate a new evaluation model for trusted data in Xinchuang; use the Xinchuang evaluation model of trusted data to evaluate the credibility of the server real-time multidimensional monitoring data to generate a comprehensive Xinchuang credibility score;

[0126] The migration strategy construction module is used to perform priority division based on the comprehensive Xinchuang credibility score to generate Xinchuang priority division data; construct a migration strategy for the Xinchuang priority division data to generate a Xinchuang migration strategy for trusted data;

[0127] The migration execution and report generation module is used to conduct an integrity assessment on the Xinchuang migration strategy of trusted data, generate complete Xinchuang migration assessment data; generate a migration report based on the complete Xinchuang migration data, thereby completing the Xinchuang system migration operation of trusted data.

[0128] The beneficial effects of the present invention are as follows. By obtaining the source Xinchuang data and performing quality improvement processing on it, it ensures that the data used for subsequent analysis and evaluation has a basis of consistency, integrity, and high quality, laying a solid foundation for the credibility assessment of the Xinchuang system. Secondly, by constructing a Xinchuang assessment model for trusted data and using the real-time multi-dimensional monitoring data of the server for credibility analysis, a comprehensive Xinchuang credibility score is generated, which can objectively quantify the reliability and importance of each data unit at the data level. This quantitative score provides a scientific basis for priority division, avoiding the deficiencies of data classification relying on subjective experience or simple rules in traditional methods. Based on the comprehensive credibility score, the refined division of Xinchuang data priorities is further realized, and a migration strategy adapted to different priority requirements is designed. During the data migration process, the importance and dependency relationships of different data are fully considered, thereby optimizing the migration path and execution order. Finally, by conducting an integrity assessment on the migration strategy and generating a detailed report on the migration process, the traceability and reliability of the entire migration operation are ensured, providing rich data support for system operation and maintenance and subsequent optimization. Therefore, by constructing a comprehensive trusted data assessment and migration system, the present invention solves the problems of insufficient quantification of data credibility, single migration strategy, and low execution reliability in traditional Xinchuang systems, and improves the scientificity, efficiency, and security of Xinchuang system data migration.

[0129] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to cover all changes falling within the meaning and scope of the equivalent elements of the application documents within the present invention.

[0130] The above are only specific implementation manners of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A data migration method for a trusted data-based information innovation system, characterized in that: The following steps are involved: Step S1: Obtain source information creation data; According to data preprocessing of source information creation data, quality source information creation data is generated; Step S2: Obtain real-time multi-dimensional monitoring data of the server; Construct a credibility assessment model for source credible data based on quality, and generate a credible data credible assessment model; use the credible data credible assessment model to conduct credibility assessment on the server's real-time multi-dimensional monitoring data, and generate a comprehensive credible score for credible data; Step S3: Prioritize based on the comprehensive score of the trustworthiness of the information innovation, and generate information innovation priority classification data; Build a migration strategy for the prioritized data of trusted data and generate a trusted data trusted data migration strategy; Step S4: Conduct integrity assessment on the trusted data migration strategy and generate complete assessment data for the migration; Generate a migration report for the complete data of the trusted innovation migration, thereby completing the trusted innovation system migration task of trusted data.

2. According to the method for data migration of a trusted data-based information innovation system according to claim 1, it is characterized in that: Step S1 includes the following steps: Step S11: Obtain source information creation data; Step S12: Clean the source information creation data to generate source information creation cleansing data; Step S13: Generate quality source information creation data by performing data standardization processing on the source information creation cleansing data.

3. According to the method for data migration of a trusted data-based information innovation system according to claim 1, it is characterized in that: Step S2 includes the following steps: Step S21: Acquire real-time multi-dimensional monitoring data of the server; Step S22: construct a credibility assessment model for the source trusted data based on quality, and generate a trusted data creation assessment model; Step S23: Use the trusted data trust creation evaluation model to conduct credibility evaluation on the real-time multi-dimensional monitoring data of the server to generate trust creation credibility evaluation data; modify the scoring system of the trust creation credibility evaluation data to generate a comprehensive trust creation credibility score.

4. The method for data migration of a trusted data-based information innovation system according to claim 3 is characterized in that: Step S22 includes the following steps: Step S221: Obtaining initialization evaluation weight data; Step S222: Initializing the model of the preset neural data transfer type by initializing the evaluation weight data to generate an initial credibility transfer model; Step S223: performing migration embedding matrix processing on the initial credibility migration model to generate data migration embedding matrix data; Step S224: Customize the migration style feature layer of the initial credibility migration model, initialize the new layer weight, and generate an initial data migration style layer; construct a credible data migration learning model for the initial data migration style layer using the data migration embedding matrix data, and generate a credible data creation evaluation model.

5. According to the method for data migration of a trusted data-based information innovation system according to claim 1, it is characterized in that: Step S3 includes the following steps: Step S31: Obtain server load data; use the server load data to confirm the threshold for judging the abnormal value of the trust creation, and obtain the threshold for judging the abnormal value of the trust creation; draw abnormal fluctuations of the comprehensive score of the trust creation credibility based on the threshold for judging the abnormal value of the trust creation, and remove abnormal values ​​to generate the screening data for abnormal value of the trust creation; Step S32: Prioritize the data based on the outlier values ​​of the information creation, and generate information creation priority classification data; Step S33: Construct a migration strategy for the prioritized data of the trusted data and generate a trusted data trusted data migration strategy.

6. The method for data migration of a trusted data-based information innovation system according to claim 1 is characterized in that: Step S33 includes the following steps: Step S331: construct a migration strategy for the trusted data priority classification data to generate a trusted data trusted data migration strategy, wherein the trusted data trusted data migration strategy includes a multi-level migration strategy and a decentralized migration strategy; Step S332: When the data is divided into a multi-level migration strategy based on the priority classification of the credible innovation, the credible innovation outlier screening data is comprehensively scored to generate credible innovation credibility comprehensive scoring data, wherein the credible innovation credibility comprehensive scoring data includes credible innovation sensitive data and credible innovation conventional data; data migration is performed on the credible innovation sensitive data and the credible innovation conventional data to generate high-speed and low-latency migration data and low-speed and high-latency migration data; the migration data of the high-speed and low-latency migration data and the low-speed and high-latency migration data are aggregated to complete the multi-level migration strategy; Step S333: When the data is divided into a decentralized migration strategy based on the priority level of the trusted innovation, a distributed network is generated for the trusted innovation outlier screening data to generate a distributed device network for data migration; based on the preset migration smart contract, a multi-node distributed transmission is performed on the data migration distributed device network to generate a distributed result of the trusted innovation data migration; the distributed result of the trusted innovation data migration is verified by blockchain, thereby completing the decentralized migration strategy.

7. The method for data migration of a trusted data-based information innovation system according to claim 6 is characterized in that: The data migration of sensitive and routine data of Xinchuang includes the following steps: Design a high-speed migration path based on sensitive information and generate a high-speed, low-latency migration path; Design the migration path based on the conventional data of Xinchuang and generate a low-speed and high-latency migration path; Encrypt data based on sensitive information and migrate data at high speed through a high-speed, low-latency migration path to generate high-speed, low-latency migration data; The conventional data of the trusted computing platform is sent through a low-speed and high-latency migration path when the device is idle, generating low-speed and high-latency migration data.

8. The method for data migration of a trusted data-based information innovation system according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: Acquire historical migration data; perform integrity assessment on the trusted data trust creation migration strategy to generate trust creation migration complete assessment data; Step S42: Perform integrity comparison analysis on the historical migration data and the complete evaluation data of the ICT migration to generate ICT migration comparison integrity data; Step S43: Generate a migration report for the integrity data of the trusted data migration, thereby completing the trusted data migration operation of the trusted data system.

9. The method for data migration of a trusted data-based information innovation system according to claim 8 is characterized in that: Step S42 includes the following steps: Step S421: Perform difference analysis on historical migration data and complete assessment data of ICT migration to generate ICT comparison difference data; Step S422: Perform trend comparison curve analysis on the credential creation comparison difference data to generate a credential creation trend comparison curve; Step S423: Identify the frequency of abnormal points based on the credible innovation trend comparison curve to generate credible innovation migration identification data; perform integrity comparison analysis on the credible innovation migration identification data to generate credible innovation migration comparison integrity data.

10. A data migration system for a trusted data-based information innovation system, characterized in that: The method for migrating data of a trusted innovation system based on trusted data as claimed in claim 1 comprises: The credible data quality improvement module is used to obtain the source credible data; generate quality credible data based on the data preprocessing of the source credible data; The credibility assessment and priority division module is used to obtain real-time multi-dimensional monitoring data of the server; construct a credibility assessment model for the source credible data based on quality, and generate a credible data credible assessment model; use the credible data credible assessment model to conduct credibility assessment on the real-time multi-dimensional monitoring data of the server, and generate a comprehensive credible score; A migration strategy building module is used to prioritize based on the comprehensive score of the trustworthiness of the trusted innovation and generate trusted innovation priority data; to build a migration strategy for the trusted innovation priority data and generate a trusted data trusted innovation migration strategy; The migration execution and report generation module is used to conduct integrity assessment on the trusted data migration strategy and generate complete assessment data for the trusted data migration; it generates a migration report for the complete trusted data migration data, thereby completing the trusted data system migration operation.

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