Enterprise information management method and system based on cloud platform

Abstract

The invention discloses an enterprise information management method and system based on a cloud platform, and relates to the technical field of data migration. According to the method, enterprise performance index data and metadata are extracted in real time and preprocessed, and a management performance data set is obtained. Summarizing and calculating to obtain a data migration coefficient sqy according to the information data group, and performing migration evaluation on the data migration coefficient sqy and a preset data migration threshold A to determine whether the data needs to be migrated to a cold storage layer or not. A comprehensive migration risk assessment coefficient QYF (t) is calculated and compared with a preset migration risk threshold value B; and when the migration risk exists, constructing a migration risk regulation and control model through a Bayesian network model, calculating a risk control regulation coefficient fxt (t), and regulating the migration risk. And carrying out migration risk assessment on the adjusted risk coefficient QYT (t) and the migration risk threshold B to ensure that the data is safely migrated to the cold storage layer. According to the method, the security and efficiency of data migration are improved, and enterprise information management is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of data migration, and in particular to a cloud platform-based enterprise information management method and system. Background Art

[0002] With the rapid development of information technology and the acceleration of digital transformation, global enterprises are facing unprecedented data management challenges. Data has become the core asset of enterprise operations, and how to effectively manage and utilize this data, especially in the era of massive data, has become one of the important factors that determine the competitiveness of enterprises. Traditional enterprise information management methods have gradually exposed many shortcomings, such as poor data storage security, high storage costs, and easy loss or delays during data migration. In addition, with the widespread application of technologies such as cloud computing, big data, and artificial intelligence, enterprises' demand for data storage has become more complex and diversified, especially under the trend of globalization and remote office, the data distribution and storage environment of enterprises have become more decentralized. How to ensure data security and optimize storage structure, and ensure the efficiency and risk-free of data migration has become a problem that urgently needs to be solved.

[0003] Traditional data migration solutions often ignore the stability of data storage, the efficiency of migration, and possible risks, which may lead to unstable storage, migration failure, and long migration time during the data migration process. In addition, when dealing with large-scale data, existing management methods often rely on static migration rules and standards, lacking dynamic response and adjustment to risk changes. This makes migration decisions less precise during data migration and storage, which can easily lead to reduced data security and utilization efficiency. By comprehensively analyzing the storage risk data group and the time risk data group, the current solution can more comprehensively assess data storage risks, but there is still the challenge of how to more accurately and efficiently monitor risks in real time and make dynamic adjustments. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a cloud platform-based enterprise information management method and system, which solves the problems in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a cloud platform-based enterprise information management method, comprising the following steps: S1. Real-time extraction of enterprise performance indicator data and metadata of enterprise data through the enterprise information management platform EIMS and the cloud platform, and pre-processing of the enterprise performance indicator data and metadata to obtain a management performance data group, which includes an information data group, a storage risk data group, and a time risk data group; S2. According to the obtained information data group, the data migration coefficient sqy is obtained through summary calculation, and the data migration coefficient sqy is evaluated with the preset data migration threshold A to determine the data to be migrated to the cold storage layer; S3. When data needs to be migrated, a summary calculation is performed based on the acquired storage risk data group and time risk data group to obtain the migration storage risk coefficient qys(t) and the migration time risk coefficient sjf(t), and then a comprehensive calculation is performed to obtain the migration risk assessment coefficient QYF(t), and the migration risk assessment is performed with the preset migration risk threshold B; S4. When there is a risk in migration, a migration risk control model is constructed through the Bayesian network, and the migration risk assessment coefficient QYF(t) is input into the migration risk control model for calculation to obtain the risk control adjustment coefficient fxt(t); S5. Calculate based on the obtained migration risk assessment coefficient QYF(t) and risk control adjustment coefficient fxt(t) to obtain the adjusted risk coefficient QYT(t), and again perform an adjusted migration risk assessment with the preset migration risk threshold B, and generate an adjustment mechanism based on the assessment results.

[0006] Preferably, said S1 includes S11 and S12; S11. Real-time monitoring of enterprise performance indicator data through the enterprise's information management platform EIMS, and real-time transmission to the enterprise cloud platform through the wireless network, and then extracting metadata of the enterprise data by accessing the enterprise data stored in the enterprise cloud platform; S12. Perform data integration, data cleaning and dimensionless processing on enterprise performance indicator data and metadata through the cloud platform to obtain a management performance data group; The management performance data group includes an information data group, a storage risk data group and a time risk data group; The information data group includes data access frequency fa, data importance id, time from file creation to the present lc, data storage density ds and data sensitivity sa; The storage risk data group includes system stability pf(t), network delay nd(t), migration batch size bd(t), data integrity dc(t), bandwidth utilization nb(t) and migration delay yc(t); The time risk data group includes the time required for data migration tm(t), the migration window period tw(t), and the time from data creation to the last access dl(t).

[0007] Preferably, S2 includes S21 and S22; S21. Summarize and calculate the data based on the acquired information data group, obtain the data migration coefficient sqy, analyze the comprehensive utilization rate and importance of various types of enterprise data, and formulate strategies for enterprise data migration through multi-dimensional and interactive analysis; The data migration coefficient sqy is calculated by the following algorithm formula; ; In the formula, log represents the logarithmic function; S22. Relying on the elastic storage function of the cloud platform and the data tiered storage technology to automatically manage the data storage method, a cold storage layer is created; Based on the enterprise's standards for data security and data utilization, the data migration threshold A is set, and the data migration evaluation is performed with the obtained data migration coefficient sqy. The data is classified and the data to be migrated to the cold storage layer is determined. The specific evaluation plan is as follows; When the data migration coefficient sqy>data migration threshold A, the data needs to be migrated to the cold storage area for storage, and the migration risk assessment is triggered; When the data migration coefficient sqy ≤ the data migration threshold A, data does not need to be migrated.

[0008] Preferably, said S3 includes S31 and S32; S31. When the data migration assessment shows that the data needs to be migrated, further analyze the migration risk, perform summary calculation based on the storage risk data group, obtain the migration storage risk coefficient qys(t), and analyze the storage risk of the data migration and the impact of the migration process; The migration storage risk coefficient qys(t) is calculated by the following algorithm formula: ; In the formula, ln represents the logarithmic function, and a represents the adjustment factor of the broadband utilization rate; Based on the acquired time risk data group and combined with the impact of the migration batch size bd(t) on the migration time, a summary calculation is performed to obtain the migration time risk coefficient sjf(t) and analyze the impact of migration efficiency and time control; The migration time risk coefficient sjf(t) is calculated by the following algorithm formula; ; Where ln represents the logarithmic function, and sjf(t) represents the migration time risk coefficient at time t.

[0009] Preferably, S32, comprehensively analyzing migration risks and migrating data according to the analysis results, specifically including S321 and S322; S321, performing summary calculation based on the obtained migration storage risk coefficient qys(t) and migration time risk coefficient sjf(t), obtaining the migration risk assessment coefficient QYF(t), and performing a comprehensive analysis of the migration risk; The migration risk assessment coefficient QYF(t) is calculated by the following algorithm formula; ; Where QYF(t) represents the comprehensive migration risk assessment coefficient at time t, dt represents the symbol in the integral function, integrating the time variable t, k1 and k2 represent the adjustment factors of the migration storage risk coefficient qys(t) and the migration time risk coefficient sjf(t), respectively.

[0010] Preferably, S322, based on the enterprise data migration security standard, a preset migration risk threshold B is performed, and a migration risk assessment is performed with the obtained comprehensive migration risk assessment coefficient QYF(t), and an adjustment mechanism is triggered according to the assessment result. The specific assessment scheme is as follows; When the migration risk assessment coefficient QYF(t)> the migration risk threshold B, there is a risk in data migration, triggering the risk control mechanism; When the migration risk assessment coefficient QYF(t) ≤ the migration risk threshold B, there is no risk in data migration, and the data is migrated to the cold storage layer in accordance with regulations.

[0011] Preferably, S4, when the migration risk assessment is that there is a risk, a migration risk control model is constructed through a Bayesian network, and the obtained migration risk assessment coefficient QYF(t) is input into the migration risk control model for analysis to obtain a risk control adjustment coefficient fxt(t) to adjust the migration risk; The specific algorithm formula is as follows; ; Wherein, w1 represents the control coefficient of risk adjustment intensity, which controls the overall intensity of risk adjustment; w2 represents the control coefficient of risk adjustment response speed; r1 represents the control coefficient of risk adjustment amplitude; r2 represents the adjustment coefficient of the gradient speed of line adjustment; exp represents the exponential decay function; and B represents the migration risk threshold.

[0012] Preferably, said S5 includes S51 and S52; S51, performing calculations based on the obtained migration risk assessment coefficient QYF(t) and the risk control adjustment coefficient fxt(t), obtaining an adjusted risk coefficient QYT(t), and analyzing the adjusted migration risk; The adjusted risk coefficient QYT(t) is calculated by the following algorithm formula; ; Where exp represents the exponential decay function, and α represents the adjustment coefficient of the comprehensive migration risk assessment coefficient.

[0013] Preferably, S52, performing an adjusted migration risk assessment based on the obtained adjusted risk coefficient QYT(t) and the migration risk threshold B, and generating a corresponding adjustment mechanism based on the assessment result. The specific assessment scheme is as follows; When the adjusted risk coefficient QYT(t) is greater than the migration risk threshold B, data migration has not been adjusted to be safe. At this time, the migration risk control model is used to adjust the data again, and the adjusted data is iteratively analyzed and adjusted through S3 until there is no risk in data migration. When the adjusted risk coefficient QYT(t) ≤ the migration risk threshold B, data migration is adjusted to safety and data migration is carried out in accordance with regulations.

[0014] An enterprise information management system based on a cloud platform, including a data extraction module, a migration analysis module, a preliminary risk assessment module, a risk optimization module, and a deep risk assessment module; The data extraction module extracts enterprise performance indicator data and enterprise data metadata in real time through the enterprise information management platform EIMS and the cloud platform, and pre-processes the enterprise performance indicator data and enterprise data metadata to obtain a management performance data group, which includes an information data group, a storage risk data group, and a time risk data group; The migration analysis module is used to obtain the data migration coefficient sqy based on the obtained information data group summary calculation, and perform data migration evaluation with the preset data migration threshold A to determine the data to be migrated to the cold storage layer; The preliminary risk assessment module is used to perform summary calculations based on the acquired storage risk data group and time risk data group when data needs to be migrated, obtain the migration storage risk coefficient qys(t) and the migration time risk coefficient sjf(t), and then perform comprehensive calculations to obtain the migration risk assessment coefficient QYF(t), and perform migration risk assessment with the preset migration risk threshold B; The risk optimization module is used to construct a migration risk control model through a Bayesian network when there is a risk in migration, input the migration risk assessment coefficient QYF(t) into the migration risk control model for calculation, and obtain the risk control adjustment coefficient fxt(t); The deep risk assessment module is used to calculate based on the obtained migration risk assessment coefficient QYF(t) and the risk control adjustment coefficient fxt(t), obtain the adjusted risk coefficient QYT(t), and again perform the adjusted migration risk assessment with the preset migration risk threshold B, and generate an adjustment mechanism based on the assessment results.

[0015] The present invention provides a cloud platform-based enterprise information management method and system, which has the following beneficial effects: (1) This method extracts and processes enterprise performance indicator data and enterprise data metadata in real time through the enterprise information management platform EIMS and the cloud platform, integrates, cleans and dimensionlessly processes the data to ensure the accuracy and consistency of the data, and provides a reliable basis for subsequent management and decision-making. Through the calculation and evaluation of the data migration coefficient sqy, it is possible to scientifically determine which data should be migrated to the cold storage layer, thereby optimizing the data storage structure, saving resources and improving storage efficiency. The setting of the data migration threshold A further ensures the accuracy of the migration decision. When the data migration coefficient sqy is greater than the data migration threshold A, the data will be migrated to the cold storage area for safer and longer-term storage.

[0016] (2) This method can monitor potential problems in data migration in real time and ensure the security and stability of data during the migration process by analyzing the storage risks during the migration process. The calculation of the migration storage risk coefficient qys(t) and the migration time risk coefficient sjf(t) provides a comprehensive risk assessment basis for migration decisions. By comprehensively calculating the migration risk assessment coefficient QYF(t) and performing migration risk assessment with the migration risk threshold B, the risk level of the migration process can be accurately assessed. When the migration risk assessment coefficient QYF(t) is greater than the preset migration risk threshold B, the risk control mechanism is triggered to reduce the probability of migration failure and ensure the smooth progress of data migration.

[0017] (3) This method constructs a migration risk control model through a Bayesian network, and inputs the migration risk assessment coefficient QYF(t) into the migration risk control model for analysis, calculates the risk control adjustment coefficient fxt(t), and dynamically adjusts the migration risk. Specifically, the intensity, response speed, and adjustment amplitude of the migration risk are controlled by the adjustment factors w1 and w2, the risk amplitude adjustment coefficient r1, the gradient speed adjustment coefficient r2, and the exponential decay function exp. After adjustment, the migration risk assessment coefficient QYF(t) and the risk control adjustment coefficient fxt(t) are calculated to obtain the adjusted risk coefficient QYT(t), which is further compared and evaluated with the migration risk threshold B according to the adjustment mechanism to ensure that the risk in the migration process is minimized. When the adjusted risk coefficient QYT(t) is greater than the migration risk threshold B, the migration risk adjustment will be performed again according to the new evaluation results until the data migration meets the security standards. Therefore, enterprises can carry out more efficient data migration, minimize risks, and optimize data storage and management while ensuring data security. Through a comprehensive risk assessment and control mechanism, enterprises can conduct real-time data risk assessment and adjustment based on actual needs, ensure effective control measures during data migration, and avoid resource waste and business interruption caused by improper risk control. By continuously optimizing the data migration process, enterprises can not only ensure data security, but also improve data utilization efficiency, thereby improving the level of enterprise information management. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a schematic diagram of the steps of the enterprise information management method based on the cloud platform of the present invention; Figure 2 It is a flow chart of the enterprise information management system based on cloud platform of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Example 1: Please refer to Figure 1 The present invention provides an enterprise information management method based on a cloud platform. To achieve the above purpose, the present invention is implemented through the following technical solutions: comprising the following steps: S1. Real-time extraction of enterprise performance indicator data and metadata of enterprise data through the enterprise information management platform EIMS and the cloud platform, and pre-processing of the enterprise performance indicator data and metadata to obtain a management performance data group, which includes an information data group, a storage risk data group, and a time risk data group; S2. According to the obtained information data group, the data migration coefficient sqy is obtained through summary calculation, and the data migration coefficient sqy is evaluated with the preset data migration threshold A to determine the data to be migrated to the cold storage layer; S3. When data needs to be migrated, a summary calculation is performed based on the acquired storage risk data group and time risk data group to obtain the migration storage risk coefficient qys(t) and the migration time risk coefficient sjf(t), and then a comprehensive calculation is performed to obtain the migration risk assessment coefficient QYF(t), and the migration risk assessment is performed with the preset migration risk threshold B; S4. When there is a risk in migration, a migration risk control model is constructed through the Bayesian network, and the migration risk assessment coefficient QYF(t) is input into the migration risk control model for calculation to obtain the risk control adjustment coefficient fxt(t); S5. Calculate based on the obtained migration risk assessment coefficient QYF(t) and risk control adjustment coefficient fxt(t) to obtain the adjusted risk coefficient QYT(t), and again perform an adjusted migration risk assessment with the preset migration risk threshold B, and generate an adjustment mechanism based on the assessment results.

[0021] In this embodiment, by extracting enterprise performance indicator data and metadata of enterprise data in real time and preprocessing these data, a management performance data group is obtained, covering information data group, storage risk data group and time risk data group. This process effectively integrates various types of enterprise data and provides a comprehensive data basis for subsequent data migration decisions and risk assessments. The calculation of the data migration coefficient sqy and the comparative evaluation of the preset data migration threshold A ensure the reasonable classification of data and scientifically determine which data should be migrated to the cold storage layer, thereby optimizing storage resources and reducing costs. By calculating the migration storage risk coefficient qys(t) and the migration time risk coefficient sjf(t), the potential risks in the migration process can be comprehensively evaluated, and the migration risk assessment coefficient QYF(t) can be obtained by summarizing and calculating to comprehensively analyze the risks. When there is a potential risk in migration, the risk control model constructed by the Bayesian network is used for dynamic adjustment, so as to accurately adjust the risk control strategy in the migration process. The calculation and application of the risk control adjustment coefficient fxt(t) further enhances the adaptability and flexibility of the migration process and greatly improves the security and success rate of data migration. After adjustment, the migration risk assessment coefficient QYF(t) and the risk control adjustment coefficient fxt(t) are calculated to obtain the adjusted risk coefficient QYT(t), which is further compared and evaluated with the migration risk threshold B according to the adjustment mechanism to ensure that the risk in the migration process is minimized. Compared with the existing traditional data migration methods, this cloud platform-based risk assessment and regulation mechanism can dynamically optimize the migration strategy in real time, effectively reducing the risks caused by human misjudgment or static strategies. Through comprehensive migration risk assessment and dynamic adjustment, it is ensured that data migration can be carried out in accordance with safe and efficient standards under any circumstances. The present invention greatly improves the reliability and efficiency of data migration through accurate risk assessment, intelligent regulation model and real-time risk control. Enterprises can optimize storage resources and improve data management efficiency while reducing risks, and achieve higher flexibility and scalability in the process of digital transformation, helping enterprises maintain their leading position in the fierce market competition.

[0022] Example 2: This example is an explanation of Example 1. Figure 1 , specifically: the S1 includes S11 and S12; S11. Real-time monitoring of enterprise performance indicator data through the enterprise's information management platform EIMS, and real-time transmission to the enterprise cloud platform through the wireless network, and then extracting metadata of the enterprise data by accessing the enterprise data stored in the enterprise cloud platform; S12. Perform data integration, data cleaning and dimensionless processing on enterprise performance indicator data and metadata through the cloud platform to obtain a management performance data group; The management performance data group includes an information data group, a storage risk data group and a time risk data group; The information data group includes data access frequency fa, data importance id, time from file creation to the present lc, data storage density ds and data sensitivity sa; The storage risk data group includes system stability pf(t), network delay nd(t), migration batch size bd(t), data integrity dc(t), bandwidth utilization nb(t) and migration delay yc(t); The time risk data group includes the time required for data migration tm(t), the migration window period tw(t), and the time from data creation to the last access dl(t).

[0023] In this embodiment, the enterprise performance indicator data is monitored in real time through the enterprise information management platform EIMS, and the data is transmitted to the cloud platform through a wireless network. Then, the metadata is extracted by accessing the data stored in the cloud platform, so as to achieve seamless acquisition and integration of data. The powerful computing and processing capabilities of the cloud platform are used to integrate, clean and dimensionlessly process the enterprise performance indicator data and metadata, further improving the data quality and consistency. As a result, the enterprise can obtain a high-quality management performance data group, which includes an information data group, a storage risk data group and a time risk data group, providing a comprehensive and reliable foundation for subsequent data migration and risk assessment. This integration and processing method not only improves the management efficiency of data, but also ensures that the data achieves the optimal decision and risk control during the migration and storage process, and improves the intelligence and automation level of enterprise information management. In this way, enterprises can more accurately evaluate the frequency of use, importance, storage risk and migration timeliness of data, thereby optimizing resource allocation, reducing potential risks, and ensuring the efficiency and security of data management decisions.

[0024] Example 3: This example is an explanation of Example 2. Figure 1 , specifically: S2 includes S21 and S22; S21. Summarize and calculate the data based on the acquired information data group, obtain the data migration coefficient sqy, analyze the comprehensive utilization rate and importance of various types of enterprise data, and formulate strategies for enterprise data migration through multi-dimensional and interactive analysis; The data migration coefficient sqy is calculated by the following algorithm formula; ; In the formula, log represents the logarithmic function; S22. Relying on the elastic storage function and data tiered storage technology of the cloud platform to automatically manage data storage, optimize resource usage and improve storage efficiency, create a cold storage layer to store infrequently accessed data; Based on the enterprise's standards for data security and data utilization, the data migration threshold A is set, and the data migration is evaluated with the obtained data migration coefficient sqy. The data is classified and the data to be migrated to the cold storage layer is determined to store the data more securely and for a long time. The data migration priority decision is made based on the size of the data migration coefficient sqy. The specific evaluation plan is as follows; When the data migration coefficient sqy>data migration threshold A, the data needs to be migrated to the cold storage area for storage, and the migration risk assessment is triggered; When the data migration coefficient sqy ≤ the data migration threshold A, data does not need to be migrated.

[0025] In this embodiment, by summarizing and calculating the acquired information data group, a data migration coefficient sqy is generated. This coefficient can comprehensively evaluate the utilization and importance of various types of enterprise data, and formulate a scientific priority decision-making mechanism for data migration. This strategy based on multi-dimensional and interactive analysis not only improves the accuracy and efficiency of data migration, but also helps enterprises to reasonably allocate storage resources according to actual data characteristics. Secondly, relying on the elastic storage function and data tiered storage technology of the cloud platform, data storage is automatically managed to ensure the flexibility of data migration and the maximum utilization of storage resources. By setting the data migration threshold A, enterprises can accurately evaluate which data should be migrated to the cold storage layer and ensure the long-term and safe storage of data in the cold storage area. Finally, this priority decision based on the data migration coefficient not only optimizes the use of storage resources and improves storage efficiency, but also reduces the risk of data migration, ensuring the intelligence, automation and efficiency of the data management process.

[0026] Example 4: This example is an explanation of Example 3. Figure 1 , specifically: S3 includes S31 and S32; S31. When the data migration assessment shows that the data needs to be migrated, further analyze the migration risk, perform summary calculation based on the storage risk data group, obtain the migration storage risk coefficient qys(t), and analyze the storage risk of the data migration and the impact of the migration process; The migration storage risk coefficient qys(t) is calculated by the following algorithm formula: ; In the formula, ln represents the logarithmic function, and a represents the adjustment factor of the broadband utilization rate; Based on the acquired time risk data group and combined with the impact of the migration batch size bd(t) on the migration time, a summary calculation is performed to obtain the migration time risk coefficient sjf(t) and analyze the impact of migration efficiency and time control; The migration time risk coefficient sjf(t) is calculated by the following algorithm formula; ; Where ln represents the logarithmic function, and sjf(t) represents the migration time risk coefficient at time t.

[0027] S32. Conduct a comprehensive analysis of migration risks and migrate data based on the analysis results, including S321 and S322; S321. Summarize and calculate the obtained migration storage risk coefficient qys(t) and migration time risk coefficient sjf(t) to obtain the migration risk assessment coefficient QYF(t), and conduct a comprehensive analysis of the migration risk. By analyzing the migration risk in advance, potential risks can be prevented and measures can be taken in time to reduce the probability of migration failure. The migration risk assessment coefficient QYF(t) is calculated by the following algorithm formula; ; Where QYF(t) represents the comprehensive migration risk assessment coefficient at time t, dt represents the symbol in the integral function, integrating the time variable t, k1 and k2 represent the adjustment factors of the migration storage risk coefficient qys(t) and the migration time risk coefficient sjf(t), respectively.

[0028] S322. Based on the enterprise data migration security standard, a migration risk threshold B is preset, and a migration risk assessment is performed with the obtained comprehensive migration risk assessment coefficient QYF(t). Based on the assessment result, an adjustment mechanism is triggered. The specific assessment plan is as follows; When the migration risk assessment coefficient QYF(t)> the migration risk threshold B, there is a risk in data migration, triggering the risk control mechanism; When the migration risk assessment coefficient QYF(t) ≤ the migration risk threshold B, there is no risk in data migration, and the data is migrated to the cold storage layer in accordance with regulations.

[0029] In this embodiment, based on the analysis of storage risk data and time risk data, the migration storage risk coefficient qys(t) and the migration time risk coefficient sjf(t) can be accurately calculated, potential risk factors can be effectively identified, and the stability and efficiency of the data migration process can be ensured. The calculation of the migration risk assessment coefficient QYF(t) further improves the scientific nature of the migration decision, allowing enterprises to predict possible problems before data migration and take measures in advance, thereby significantly reducing the probability of migration failure. At the same time, with the help of the enterprise data migration security standard to preset the migration risk threshold B, the migration strategy can be dynamically adjusted, the risk control mechanism can be triggered in time, and the security of the data migration process can be guaranteed. When the migration risk assessment coefficient QYF(t) is lower than the migration risk threshold B, the migration can proceed smoothly, and when the migration risk assessment coefficient QYF(t) exceeds the migration risk threshold B, the migration strategy will be automatically adjusted to ensure that the risk is effectively controlled. In general, this method not only improves the security and success rate of the data migration process, but also optimizes resource utilization through an intelligent risk adjustment mechanism, reducing the business interruption and resource waste risks that enterprises may face during the migration process.

[0030] Example 5: This example is an explanation of Example 4. Figure 1 Specifically: S4. When the migration risk assessment is that there is a risk, a migration risk control model is constructed through a Bayesian network, and the obtained migration risk assessment coefficient QYF(t) is input into the migration risk control model for analysis to obtain the risk control adjustment coefficient fxt(t) to adjust the migration risk; The specific algorithm formula is as follows; ; Wherein, w1 represents the control coefficient of risk adjustment intensity, which controls the overall intensity of risk adjustment; w2 represents the control coefficient of risk adjustment response speed; r1 represents the control coefficient of risk adjustment amplitude; r2 represents the adjustment coefficient of the gradient speed of line adjustment; exp represents the exponential decay function; and B represents the migration risk threshold.

[0031] In this embodiment, by introducing the migration risk control model constructed by the Bayesian network, the risk in the migration process can be accurately adjusted, and the security and reliability of data migration can be significantly improved. When the migration risk assessment results show that there is a potential risk, the migration risk assessment coefficient QYF (t) is input into the migration risk control model for analysis, and the risk control adjustment coefficient fxt (t) is calculated, and then the migration risk is dynamically adjusted. By adjusting the intensity and response speed of risk adjustment by adjusting factors w1 and w2, and adjusting the amplitude and gradual change speed by r1 and r2, combined with the introduction of the exponential decay function, the migration strategy can be adjusted flexibly and in real time to optimize risk management. This process not only improves the stability of migration, reduces the waste of resources caused by misjudgment of risks, but also can make immediate adjustments according to different situations during the data migration process, thereby ensuring the smooth progress of enterprise data migration. Compared with the traditional static migration strategy, the dynamic control mechanism using the Bayesian network can more effectively adapt to the ever-changing migration environment, ensure data security and reduce the risk of migration failure, and greatly improve the intelligence and reliability of enterprise data management.

[0032] Example 6: This example is an explanation of Example 5. Please refer to Figure 1 , specifically: the S5 includes S51 and S52; S51, performing calculations based on the obtained migration risk assessment coefficient QYF(t) and the risk control adjustment coefficient fxt(t), obtaining an adjusted risk coefficient QYT(t), and analyzing the adjusted migration risk; The adjusted risk coefficient QYT(t) is calculated by the following algorithm formula; ; Where exp represents the exponential decay function, and α represents the adjustment coefficient of the comprehensive migration risk assessment coefficient.

[0033] S52, performing an adjusted migration risk assessment based on the obtained adjusted risk coefficient QYT(t) and the migration risk threshold B, and generating a corresponding adjustment mechanism based on the assessment result. The specific assessment scheme is as follows; When the adjusted risk coefficient QYT(t) is greater than the migration risk threshold B, data migration is not adjusted to safety. At this time, the migration risk control model is used to adjust the data again, and the adjusted data is iteratively analyzed and adjusted through S3 until there is no risk in data migration. When the adjusted risk coefficient QYT(t) ≤ the migration risk threshold B, data migration is adjusted to safety and data migration is carried out in accordance with regulations.

[0034] In this embodiment, by calculating the comparative evaluation of the adjusted risk coefficient QYT(t) and the migration risk threshold B, potential risks can be identified and adjusted in real time to ensure that the data migration process is always in a safe and controllable state. When the adjusted risk coefficient QYT(t) exceeds the migration risk threshold B, the re-adjustment mechanism is immediately triggered, and iterative optimization is performed through the Bayesian network and risk control model to ensure that data migration is not affected by unforeseen risks and to avoid migration failure or data loss. Unlike traditional static risk assessment methods, the present invention can respond to risk changes during the migration process in real time, and ensure that each step in the migration process is in an optimal control state through dynamic adjustment. This intelligent and real-time risk management mechanism greatly improves the reliability, accuracy and security of data migration, while improving resource utilization efficiency, ensuring that enterprises can quickly respond to changes and reduce potential risks when performing data migration, and ultimately achieve safe data migration and optimal utilization of storage resources.

[0035] Example 7: Please refer to Figure 2 , an enterprise information management system based on a cloud platform, including a data extraction module, a migration analysis module, a preliminary risk assessment module, a risk optimization module, and a deep risk assessment module; The data extraction module extracts enterprise performance indicator data and enterprise data metadata in real time through the enterprise information management platform EIMS and the cloud platform, and pre-processes the enterprise performance indicator data and enterprise data metadata to obtain a management performance data group, which includes an information data group, a storage risk data group, and a time risk data group; The migration analysis module is used to obtain the data migration coefficient sqy based on the obtained information data group summary calculation, and perform data migration evaluation with the preset data migration threshold A to determine the data to be migrated to the cold storage layer; The preliminary risk assessment module is used to perform summary calculations based on the acquired storage risk data group and time risk data group when data needs to be migrated, obtain the migration storage risk coefficient qys(t) and the migration time risk coefficient sjf(t), and then perform comprehensive calculations to obtain the migration risk assessment coefficient QYF(t), and perform migration risk assessment with the preset migration risk threshold B; The risk optimization module is used to construct a migration risk control model through a Bayesian network when there is a risk in migration, input the migration risk assessment coefficient QYF(t) into the migration risk control model for calculation, and obtain the risk control adjustment coefficient fxt(t); The deep risk assessment module is used to calculate based on the obtained migration risk assessment coefficient QYF(t) and the risk control adjustment coefficient fxt(t), obtain the adjusted risk coefficient QYT(t), and again perform the adjusted migration risk assessment with the preset migration risk threshold B, and generate an adjustment mechanism based on the assessment results.

[0036] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An enterprise information management method based on a cloud platform, characterized in that: The following steps are involved: S1. Real-time extraction of enterprise performance indicator data and metadata of enterprise data through the enterprise information management platform EIMS and the cloud platform, and pre-processing of the enterprise performance indicator data and metadata to obtain a management performance data group, which includes an information data group, a storage risk data group, and a time risk data group; S2. According to the obtained information data group, the data migration coefficient sqy is obtained through summary calculation, and the data migration coefficient sqy is evaluated with the preset data migration threshold A to determine the data to be migrated to the cold storage layer; S3. When data needs to be migrated, a summary calculation is performed based on the acquired storage risk data group and time risk data group to obtain the migration storage risk coefficient qys(t) and the migration time risk coefficient sjf(t), and then a comprehensive calculation is performed to obtain the migration risk assessment coefficient QYF(t), and the migration risk assessment is performed with the preset migration risk threshold B; S4. When there is a risk in migration, a migration risk control model is constructed through the Bayesian network, and the migration risk assessment coefficient QYF(t) is input into the migration risk control model for calculation to obtain the risk control adjustment coefficient fxt(t); S5. Calculate based on the obtained migration risk assessment coefficient QYF(t) and risk control adjustment coefficient fxt(t) to obtain the adjusted risk coefficient QYT(t), and again perform an adjusted migration risk assessment with the preset migration risk threshold B, and generate an adjustment mechanism based on the assessment results.

2. The cloud platform-based enterprise information management method according to claim 1, characterized in that: Said S1 includes S11 and S12; S11. Real-time monitoring of enterprise performance indicator data through the enterprise's information management platform EIMS, and real-time transmission to the enterprise cloud platform through the wireless network, and then extracting metadata of the enterprise data by accessing the enterprise data stored in the enterprise cloud platform; S12. Perform data integration, data cleaning and dimensionless processing on enterprise performance indicator data and metadata through the cloud platform to obtain a management performance data group; The management performance data group includes an information data group, a storage risk data group and a time risk data group; The information data group includes data access frequency fa, data importance id, time from file creation to the present lc, data storage density ds and data sensitivity sa; The storage risk data group includes system stability pf(t), network delay nd(t), migration batch size bd(t), data integrity dc(t), bandwidth utilization nb(t) and migration delay yc(t); The time risk data group includes the time required for data migration tm(t), the migration window period tw(t), and the time from data creation to the last access dl(t).

3. The cloud platform-based enterprise information management method according to claim 2, characterized in that: The S2 includes S21 and S22; S21. Summarize and calculate the data based on the acquired information data group, obtain the data migration coefficient sqy, analyze the comprehensive utilization rate and importance of various types of enterprise data, and formulate strategies for enterprise data migration through multi-dimensional and interactive analysis; The data migration coefficient sqy is calculated by the following algorithm formula; ； In the formula, log represents the logarithmic function; S22. Relying on the elastic storage function of the cloud platform and the data tiered storage technology to automatically manage the data storage method, a cold storage layer is created; Based on the enterprise's standards for data security and data utilization, the data migration threshold A is set, and the data migration evaluation is performed with the obtained data migration coefficient sqy, the data is classified, and the data to be migrated to the cold storage layer is determined. The specific evaluation plan is as follows; When the data migration coefficient sqy>data migration threshold A, the data needs to be migrated to the cold storage area for storage, and the migration risk assessment is triggered; When the data migration coefficient sqy ≤ the data migration threshold A, data does not need to be migrated.

4. The cloud platform-based enterprise information management method according to claim 3, characterized in that: The S3 includes S31 and S32; S31. When the data migration assessment shows that the data needs to be migrated, further analyze the migration risk, perform summary calculation based on the storage risk data group, obtain the migration storage risk coefficient qys(t), and analyze the storage risk of the data migration and the impact of the migration process; The migration storage risk coefficient qys(t) is calculated by the following algorithm formula: ； In the formula, ln represents the logarithmic function, and a represents the adjustment factor of broadband utilization; Based on the acquired time risk data group and combined with the impact of the migration batch size bd(t) on the migration time, a summary calculation is performed to obtain the migration time risk coefficient sjf(t) and analyze the impact of migration efficiency and time control; The migration time risk coefficient sjf(t) is calculated by the following algorithm formula; ； In the formula, ln represents the logarithmic function; S32. Conduct a comprehensive analysis of migration risks and migrate data based on the analysis results, including S321 and S322; S321, performing summary calculation based on the obtained migration storage risk coefficient qys(t) and migration time risk coefficient sjf(t), obtaining the migration risk assessment coefficient QYF(t), and performing a comprehensive analysis of the migration risk; The migration risk assessment coefficient QYF(t) is calculated by the following algorithm formula; ； In the formula, dt represents the symbol in the integral function, integrating the time variable t, k1 and k2 represent the adjustment factors of the migration storage risk coefficient qys(t) and the migration time risk coefficient sjf(t), respectively; S322. Based on the enterprise data migration security standard, a migration risk threshold B is preset, and a migration risk assessment is performed with the obtained comprehensive migration risk assessment coefficient QYF(t). Based on the assessment result, an adjustment mechanism is triggered. The specific assessment plan is as follows; When the migration risk assessment coefficient QYF(t)> the migration risk threshold B, there is a risk in data migration, triggering the risk control mechanism; When the migration risk assessment coefficient QYF(t) ≤ the migration risk threshold B, there is no risk in data migration, and the data is migrated to the cold storage layer in accordance with regulations.

5. The cloud platform-based enterprise information management method according to claim 4, characterized in that: S4. When the migration risk assessment shows that there is a risk, a migration risk control model is constructed through the Bayesian network, and the obtained migration risk assessment coefficient QYF(t) is input into the migration risk control model for analysis to obtain the risk control adjustment coefficient fxt(t) and adjust the migration risk; The specific algorithm formula is as follows; ； Wherein, w1 represents the control coefficient of risk adjustment intensity, which controls the overall intensity of risk adjustment; w2 represents the control coefficient of risk adjustment response speed; r1 represents the control coefficient of risk adjustment amplitude; r2 represents the adjustment coefficient of the gradient speed of line adjustment; exp represents the exponential decay function; and B represents the migration risk threshold.

6. The cloud platform-based enterprise information management method according to claim 5, characterized in that: The S5 includes S51 and S52; S51, performing calculations based on the obtained migration risk assessment coefficient QYF(t) and the risk control adjustment coefficient fxt(t), obtaining an adjusted risk coefficient QYT(t), and analyzing the adjusted migration risk; The adjusted risk coefficient QYT(t) is calculated by the following algorithm formula; ； In the formula, exp represents the exponential decay function, and α represents the adjustment coefficient of the comprehensive migration risk assessment coefficient; S52, performing an adjusted migration risk assessment based on the obtained adjusted risk coefficient QYT(t) and the migration risk threshold B, and generating a corresponding adjustment mechanism based on the assessment result. The specific assessment scheme is as follows; When the adjusted risk coefficient QYT(t) is greater than the migration risk threshold B, data migration has not been adjusted to be safe. At this time, the migration risk control model is used to adjust the data again, and the adjusted data is iteratively analyzed and adjusted through S3 until there is no risk in data migration. When the adjusted risk coefficient QYT(t) ≤ the migration risk threshold B, data migration is adjusted to safety and data migration is carried out in accordance with regulations.

7. A cloud-based enterprise information management system, comprising the cloud-based enterprise information management method according to any one of claims 1 to 6, characterized in that: It includes data extraction module, migration analysis module, preliminary risk assessment module, risk optimization module and in-depth risk assessment module; The data extraction module extracts enterprise performance indicator data and enterprise data metadata in real time through the enterprise information management platform EIMS and the cloud platform, and pre-processes the enterprise performance indicator data and enterprise data metadata to obtain a management performance data group, which includes an information data group, a storage risk data group, and a time risk data group; The migration analysis module is used to obtain the data migration coefficient sqy based on the obtained information data group summary calculation, and perform data migration evaluation with the preset data migration threshold A to determine the data to be migrated to the cold storage layer; The preliminary risk assessment module is used to perform summary calculations based on the acquired storage risk data group and time risk data group when data needs to be migrated, obtain the migration storage risk coefficient qys(t) and the migration time risk coefficient sjf(t), and then perform comprehensive calculations to obtain the migration risk assessment coefficient QYF(t), and perform migration risk assessment with the preset migration risk threshold B; The risk optimization module is used to construct a migration risk control model through a Bayesian network when there is a risk in migration, input the migration risk assessment coefficient QYF(t) into the migration risk control model for calculation, and obtain the risk control adjustment coefficient fxt(t); The deep risk assessment module is used to calculate based on the obtained migration risk assessment coefficient QYF(t) and the risk control adjustment coefficient fxt(t), obtain the adjusted risk coefficient QYT(t), and again perform the adjusted migration risk assessment with the preset migration risk threshold B, and generate an adjustment mechanism based on the assessment results.

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