Data management method and system based on optical-magnetic fusion storage

Abstract

This application proposes a data management method and system based on optical-magnetic fusion storage, which belongs to the field of data management. The method includes: obtaining target data and collecting data feature information, and performing data heat analysis; determining the basic storage plan based on data heat, and realizing intelligent decision-making of optical storage, magnetic storage, or fusion storage through data criticality analysis and heat critical coefficient evaluation; predicting storage loss parameters based on data heat and storage plan, and optimizing error correction coding or erasure code parameters in combination with data criticality; and finally implementing data management according to the determined storage plan and optimized parameters. The present invention solves the technical problems that traditional storage systems cannot intelligently allocate storage media according to data characteristics and have difficulty balancing access efficiency and storage security. It realizes adaptive selection of storage plans and optimization of storage quality, improving the efficiency and reliability of data management.

Description

Technical Field

[0001] The present invention relates to the field of data management, and in particular to a data management method and system based on optical-magnetic fusion storage. Background Art

[0002] With the rapid development of information technology, the demand for data storage has exploded, placing higher demands on data security, reliability, and access efficiency. Traditional storage methods are primarily divided into two categories: optical and magnetic. Optical storage (such as optical discs and Blu-ray discs) offers advantages such as stable media, long storage life, and low cost, making it suitable for long-term archival storage. Magnetic storage (such as hard drives and magnetic tapes) offers fast read and write speeds and strong random access capabilities, making it suitable for frequently accessed, hot data.

[0003] Currently, most data storage uses a single storage medium or a simple tiered storage architecture, which presents significant deficiencies in data management. On the one hand, a single storage medium cannot simultaneously meet the requirements of data access efficiency and long-term preservation. On the other hand, traditional tiered storage often relies on simple time rules or manual intervention for data migration, lacking the ability to intelligently analyze and judge data characteristics. Furthermore, existing technologies for predicting and compensating for data loss often employ a unified redundancy strategy, failing to differentiate data based on its importance and access patterns, resulting in wasted storage resources and insufficient data security. Consequently, existing technologies suffer from the inability to intelligently allocate storage media based on data characteristics and struggle to balance data access efficiency with long-term preservation security. Summary of the Invention

[0004] The present invention addresses the technical problems in the prior art that data storage cannot intelligently allocate storage media according to data characteristics and is difficult to balance data access efficiency and long-term storage security. It provides a data management method and system based on optical-magnetic fusion storage to solve the problem.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] In the first aspect, the present invention provides a data management method based on optical-magnetic fusion storage, including: acquiring target data to be stored, collecting data feature information of the target data, performing data heat analysis, and obtaining data heat; making a decision based on the data heat to obtain a basic storage plan for optical storage or magnetic storage, and performing data criticality analysis based on the data feature information to obtain data criticality, combining the data heat analysis heat critical coefficient, making a fusion storage decision on the target data, and obtaining a storage plan; based on the data heat and the storage plan, predicting data storage access loss, obtaining at least one of optical storage loss parameters and magnetic storage loss parameters, combining the data criticality, performing storage loss compensation optimization, and obtaining at least one of error correction coding parameters and erasure code parameters; performing optical-magnetic storage data management on the target data according to the storage plan combined with at least one of error correction coding parameters and erasure code parameters.

[0007] In a second aspect, the present invention provides a data management system based on optical-magnetic fusion storage, including: a data acquisition and analysis module, used to acquire target data to be stored, collect data feature information of the target data, perform data heat analysis, and obtain data heat; a storage decision optimization module, used to make decisions based on the data heat, obtain a basic storage plan for optical storage or magnetic storage, and perform data criticality analysis based on the data feature information to obtain data criticality, combine the data heat analysis heat critical coefficient, make a fusion storage decision on the target data, and obtain a storage plan; a loss prediction and compensation module, used to predict data storage access loss based on the data heat and storage plan, obtain at least one of optical storage loss parameters and magnetic storage loss parameters, combine the data criticality to perform storage loss compensation optimization, and obtain at least one of error correction coding parameters and erasure code parameters; an optical-magnetic data management module, used to perform optical-magnetic storage data management on the target data according to the storage plan in combination with at least one of error correction coding parameters and erasure code parameters.

[0008] The beneficial effects of the present invention are:

[0009] Acquire the target data to be stored, collect its data characteristics, and perform data heat analysis to determine its heat. This will serve as the basis for subsequent storage decisions and lay the foundation for intelligent storage media allocation. Decisions are made based on data heat to determine a basic storage solution for optical or magnetic storage. Data criticality analysis is then performed based on the data characteristics to determine its criticality. Combined with the heat criticality coefficient from the data heat analysis, a fused storage decision is made for the target data to determine a storage solution. By determining the initial storage media selection based on data heat (hot data tends to use magnetic storage for frequent access, while cold data tends to use optical storage for long-term archiving), and then further considering the data's criticality and heat criticality, fused storage decisions are made for critical data with high criticality, enabling refined adjustments to the storage solution.

[0010] Based on data popularity and storage solutions, data storage access loss is predicted to obtain at least one of optical and magnetic storage loss parameters. Combined with data criticality, storage loss compensation is optimized to obtain at least one of error correction coding parameters and erasure coding parameters. This ensures data integrity while avoiding storage waste caused by excessive redundancy. Optical and magnetic storage data management is performed on the target data based on the storage solution and at least one of the error correction coding parameters and erasure coding parameters. The determined storage solution and optimized error correction / erasure correction parameters are then applied to the target data during storage, completing intelligent data management.

[0011] Through the above technical solution, this application realizes intelligent analysis of data characteristics and adaptive allocation of storage media, which improves the security of long-term storage while ensuring data access efficiency, and improves the overall storage reliability and resource utilization efficiency through targeted loss prediction and compensation optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A schematic flow chart of the data management method based on optical-magnetic fusion storage provided by the present invention;

[0013] Figure 2 This is a structural diagram of the data management system based on optical-magnetic fusion storage provided by the present invention.

[0014] In the accompanying drawings, the components represented by the reference numerals are as follows:

[0015] Data acquisition and analysis module 11, storage decision optimization module 12, loss prediction and compensation module 13, optical and magnetic data management module 14. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0017] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the specified features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0018] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or illustration". Any embodiment of the present invention described as "for example" is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed herein.

[0019] Example 1, as Figure 1 As shown, an embodiment of the present invention provides a data management method based on optical-magnetic fusion storage, including:

[0020] S1. Acquire target data to be stored, collect data feature information of the target data, perform data heat analysis, and obtain data heat.

[0021] Specifically, first, the target data to be stored is obtained. The target data can be any form of digital information, including but not limited to text files, images, videos, audios, or database records.

[0022] After acquiring the target data, data feature information is collected. This data feature information includes key attributes such as the target data type, data size, and ownership. Data types include categories such as documents, media, or structured data; data size is recorded in units such as bytes, kilobytes, and megabytes; and ownership identifies the owner or creator of the target data. This data feature information forms the basic parameters for data popularity analysis.

[0023] Then, based on the collected data characteristics, data heat analysis is performed to determine the likely frequency of future access to the target data, which is used as the data heat. Data heat is a quantitative indicator reflecting the activity level of the target data. By analyzing the target data characteristics and historical access patterns, the future frequency of target data use can be determined, providing a basis for storage media selection.

[0024] By obtaining the data popularity of the target data, a basis is provided for subsequent storage decisions. The higher the data popularity, the more likely the target data will be frequently accessed in the future. Conversely, the data is more suitable for long-term archival storage.

[0025] S2. Make a decision based on the data heat to obtain a basic storage solution for optical storage or magnetic storage, and perform data criticality analysis based on the data feature information to obtain data criticality. Combined with the data heat analysis heat critical coefficient, make a fusion storage decision on the target data to obtain a storage solution.

[0026] Specifically, first, a preliminary storage decision is made based on the obtained data heat. For example, the data heat of the target data is compared with a preset data heat threshold. If the data heat is greater than or equal to the data heat threshold, magnetic storage is determined to be the basic storage solution; if the data heat is less than the data heat threshold, optical storage is determined to be the basic storage solution. This decision-making mechanism is based on the fact that high-heat data generally requires more frequent access, while magnetic storage has faster read and write speeds; low-heat data is suitable for long-term archiving, while optical storage has longer data retention periods and more stable storage characteristics.

[0027] After obtaining the basic storage solution, perform a data criticality analysis based on the target data's data characteristics. This data characteristic information is entered into a pre-established data criticality classification table, and the target data's data criticality is determined through index matching. Data criticality indicates the importance of the target data. A higher criticality indicates more important data, and data loss would have more severe consequences, thus requiring a higher level of data protection measures.

[0028] At the same time, the relationship between data heat and the data heat threshold is calculated to determine the critical heat coefficient. Specifically, the absolute difference between the data heat and the data heat threshold is first calculated, then the ratio of this absolute difference to the data heat threshold is calculated, and finally, this ratio is subtracted from 1 to obtain the critical heat coefficient. A larger critical heat coefficient indicates that the target data's heat is closer to the critical threshold and that it has higher access frequency and long-term storage requirements.

[0029] Based on data criticality and the heat threshold coefficient, a corrected data criticality is calculated. This corrected data criticality takes both factors into account, allowing the importance of the data and its access characteristics to jointly influence the final storage decision. The corrected data criticality is compared with a preset data criticality threshold. If the corrected data criticality is greater than or equal to the threshold, a fusion storage solution is adopted for the target data, i.e., simultaneous optical and magnetic storage, to provide a higher level of data security. If the corrected data criticality is less than the threshold, the previously determined basic storage solution is directly adopted.

[0030] Through a multi-factor comprehensive decision-making mechanism, the most appropriate storage solution can be adaptively determined based on the characteristics and importance of the target data, which not only meets the needs of data access efficiency, but also ensures the security of important data and realizes intelligent data management.

[0031] S3. According to the data heat and storage scheme, data storage access loss is predicted to obtain at least one of optical storage loss parameters and magnetic storage loss parameters. In combination with the data criticality, storage loss compensation optimization is performed to obtain at least one of error correction coding parameters and erasure code parameters.

[0032] Specifically, first, based on the determined data popularity and storage solution, data storage access loss prediction analysis is performed. In the actual storage process, different storage media will cause data loss due to various factors, affecting data integrity and reliability.

[0033] Loss predictions are performed separately for optical and magnetic storage, taking into account their different characteristics. When the storage solution includes optical storage, a mapping relationship between data heat and data access time is established based on the data heat of the target data. This mapping relationship is used to predict the access time distribution of the target data on the optical storage medium. Since optical storage media are affected by physical environmental factors such as dust and scratches during long-term storage, the longer the access time, the greater the possibility of accumulated physical damage on the medium surface. Based on this, optical storage loss mapping is performed to obtain optical storage loss parameters. The key indicator is the optical storage data loss ratio, which represents the proportion of data that may be lost during the optical storage process.

[0034] When the storage solution includes magnetic storage, a mapping relationship is established between the target data's popularity and the number of data accesses within a preset time period. Data loss in magnetic storage media primarily stems from magnetic attenuation and frequent read and write operations. The higher the access frequency, the greater the likelihood of magnetic material demagnetization. Based on this, magnetic storage loss mapping is performed to obtain magnetic storage loss parameters. A key metric is the magnetic storage data loss ratio, which represents the proportion of data that may be lost during magnetic storage.

[0035] After obtaining the loss parameters, combined with the previously determined data criticality, storage loss compensation optimization is performed to determine the corresponding error correction strategy parameters. Different data redundancy protection mechanisms are employed based on the characteristics of different storage media: error correction coding for optical storage and erasure coding for magnetic storage. An optimization algorithm is used to determine the optimal error correction coding or erasure coding parameters, including key metrics such as the redundancy ratio. These parameters directly determine the ability to recover from data loss, impacting data integrity and storage space utilization efficiency. The optimization process comprehensively considers data criticality, loss parameters, and storage resource constraints to achieve a balance between data security and storage efficiency.

[0036] Through the above-mentioned predictive optimization mechanism, it is possible to accurately assess the possible loss risks before data storage based on the characteristics and importance of the target data, and formulate corresponding compensation strategies, thereby significantly improving the reliability and security of long-term data storage.

[0037] S4. Perform optical-magnetic storage data management on the target data according to the storage scheme in combination with at least one of error correction coding parameters and erasure correction coding parameters.

[0038] In a preferred embodiment, first, based on the determined storage scheme, it is determined whether the target data should be stored using optical, magnetic, or fusion storage. When optical storage is used, the target data is written to an optical storage medium, such as an optical disc or Blu-ray disc; when magnetic storage is used, the target data is written to a magnetic storage medium, such as a hard drive or magnetic tape; and when fusion storage is used, the target data is written to both the optical and magnetic storage media, achieving dual data protection.

[0039] During the data writing process, optimized error correction coding parameters or erasure coding parameters are applied to encode the target data. For data written to optical storage media, error correction coding parameters, such as redundancy rate, are applied to generate appropriate redundant data and store it together with the original data. This encoding mechanism can restore the original information by calculating the remaining data and redundant data when the optical storage medium is partially lost due to physical damage. For data written to magnetic storage media, erasure coding parameters are applied to split the target data into multiple data blocks and generate corresponding check blocks, which together constitute a complete encoded data set. This encoding mechanism can reconstruct the original data using the remaining data blocks and check blocks when some data blocks are lost due to demagnetization or other reasons on the magnetic storage medium, effectively preventing permanent data loss.

[0040] Preferably, a metadata index for the target data is established to record the data storage location, storage method, encoding parameters, and other key information to facilitate subsequent data access and management. When data needs to be read, it is retrieved from the corresponding storage medium based on the storage scheme and metadata index, and the corresponding decoding algorithm is applied to restore the original data. In addition, dynamic data management strategies can be implemented based on the data popularity of the target data. Specifically, for high-hot data, priority is given to reading from magnetic storage media to ensure access efficiency; for data whose popularity decreases over time, it may be migrated from magnetic storage media to optical storage media to optimize storage resource allocation.

[0041] Through intelligent optical-magnetic fusion storage data management, efficient data storage and reliable protection can be achieved based on the characteristics, importance and access patterns of the target data, thereby improving the overall performance and security of data management.

[0042] Furthermore, acquiring target data to be stored, collecting data feature information of the target data, performing data heat analysis, and obtaining data heat include:

[0043] S11. Collect data characteristic information of the target data, wherein the data characteristic information includes data type, data size, and ownership entity;

[0044] S12. Based on the data feature information, machine learning is used to perform data heat analysis on the target data to obtain data heat.

[0045] In one feasible implementation, data feature information of the target data is first collected. Data feature information is a set of parameters describing the attributes of the target data, including but not limited to data type, data size, and ownership. The data type represents the content format of the target data and can be text documents, tabular data, images, videos, audio, database files, or other specialized formats; the data size represents the storage space occupied by the target data and can be a byte-accurate numerical value; and the ownership represents the owner or creator of the target data and can be an individual user, department, unit, or application. Together, these feature information constitute a multidimensional feature that describes the characteristics of the target data.

[0046] After acquiring data feature information, machine learning techniques are used to analyze data popularity. Machine learning methods can learn the inherent correlation between data features and popularity from historical data access patterns, thereby predicting the popularity of new target data. The collected data feature information is used as input and processed by a pre-trained machine learning model. This machine learning model can take the form of a multi-layer perceptron, support vector machine, decision tree, random forest, or deep neural network, capable of capturing the nonlinear relationship between feature information and data popularity. By analyzing and training historical data storage and access records, this machine learning model learns the patterns of popularity under different data feature combinations. For example, small documents of a certain type are frequently accessed by a specific entity, while large media files may exhibit different access patterns under different entities. This data-driven learning process enables accurate prediction of the popularity level of new target data. After processing by the machine learning model, it outputs a quantitative indicator representing the popularity of the target data, namely data popularity. This indicator reflects the expected frequency of access to the target data over a period of time, providing a foundation for intelligent data storage management.

[0047] By combining the above-mentioned feature collection with machine learning, we can adaptively analyze the access characteristics of different types of data, accurately predict data popularity, and lay the foundation for subsequent storage solution selection.

[0048] Furthermore, based on the data feature information, machine learning is used to perform data heat analysis on the target data to obtain data heat, including:

[0049] S111: Perform data heat analysis using a machine learning data heat analysis unit. The training steps of the data heat analysis unit include:

[0050] S1111. Collect a set of sample data feature information based on data storage management history log data, and collect the ratio of the access frequency after data storage to the preset access frequency under different sample data feature information, and mark and obtain a sample data heat set;

[0051] S1112. Using machine learning, the data feature information and data heat are used as input features and output features respectively to construct a data heat analysis unit;

[0052] S1113. Use the sample data feature information set and the sample data heat set to iteratively train and verify the data heat analysis unit, and complete the training after convergence.

[0053] In a preferred embodiment, a data heat analysis unit is constructed using machine learning technology, and data heat analysis is performed by the data heat analysis unit. The data heat analysis unit outputs a predicted data heat index based on the input data feature information. The training steps of the data heat analysis unit are as follows:

[0054] First, historical log data is extracted from the data storage management system. The historical log data records detailed information about past data storage and access. Based on these historical log data, a set of sample data feature information constituting the training set is collected. At the same time, the ratio between the actual access frequency of each sample data after storage and the preset access frequency is calculated, and this ratio is used as the heat index of the sample data to form a sample data heat set. This ratio representation method can standardize the access frequency of different types of data, facilitating cross-type comparison and analysis. Among them, the sample data feature information set contains feature information of a large amount of historical data, and each feature information includes dimensions such as data type, data size, and ownership; the corresponding sample data heat set contains heat index values that correspond one-to-one to the sample data feature information. These two sets together constitute the training data set for machine learning, providing a data basis for the subsequent training of the data heat analysis unit.

[0055] Next, a machine learning algorithm is used, taking the data feature information as input features and the corresponding data heat as output features, to construct the initial structure of the data heat analysis unit. This data heat analysis unit can be implemented using a variety of machine learning frameworks, such as multilayer perceptrons, support vector regression, random forest regression, or deep neural networks. The specific structural design of the data heat analysis unit is optimized based on feature dimensionality and complexity to capture the complex nonlinear relationship between features and heat. Next, the data heat analysis unit is iteratively trained using the constructed sample data feature information set and sample data heat set. During training, the sample data feature information is input into the data heat analysis unit and compared with the actual sample data heat in the sample data heat set to calculate the error. Backpropagation or other optimization algorithms are then used to adjust the parameters of the data heat analysis unit to continuously reduce the prediction error. Furthermore, cross-validation and other techniques are used to evaluate the performance of the data heat analysis unit to prevent overfitting. Training is completed when the prediction error of the data heat analysis unit stabilizes and meets the preset convergence conditions. After a complete training process, the Data Heat Analysis Unit accurately captures the inherent correlation between data features and heat, achieving highly accurate heat predictions for new target data. In practical applications, simply inputting the feature information of new target data into the trained Data Heat Analysis Unit will yield its predicted heat value, which serves as the data heat and provides a basis for subsequent storage decisions.

[0056] Through a machine learning method driven by historical data, intelligent learning and prediction of data access patterns are achieved, the accuracy and adaptability of data heat analysis are improved, and the foundation is laid for intelligent decision-making in optical-magnetic fusion storage.

[0057] Furthermore, a decision is made based on the data heat to obtain a basic storage solution for optical storage or magnetic storage, and data criticality analysis is performed based on the data feature information to obtain data criticality. Combined with the heat criticality coefficient of the data heat analysis, a fusion storage decision is made for the target data to obtain a storage solution, including:

[0058] S21. Determine whether the data heat is greater than or equal to a data heat threshold. If so, decide to obtain a basic storage solution for magnetic storage; if not, decide to obtain a basic storage solution for optical storage.

[0059] S22. Input the data feature information into a data criticality classification table, and obtain data criticality through index classification, wherein the data criticality classification table includes an index relationship between sample data feature information and sample data criticality;

[0060] S23. Calculate the ratio of the absolute difference between the data heat and the data heat threshold to the data heat threshold, and calculate a heat critical coefficient of the data heat;

[0061] S24, calculating and obtaining the corrected data criticality of the target data based on the data criticality and the heat criticality coefficient;

[0062] S25. Determine whether the criticality of the correction data is greater than or equal to a data criticality threshold. If so, decide to obtain a storage solution for fusion storage of the target data. If not, use the basic storage solution as the storage solution.

[0063] In a preferred embodiment, first, a preliminary storage decision is made based on the obtained data heat. Specifically, a data heat threshold is set, which serves as the dividing point between high-heat data and low-heat data. This data heat threshold is determined by statistically analyzing the access frequency distribution curve of historical data, combining the experience of domain experts and business needs assessment, and selecting its inflection point or characteristic value. The data heat of the target data is compared with the data heat threshold: if the data heat is greater than or equal to the data heat threshold, it indicates that the target data has a high access frequency requirement, and the decision is made to use magnetic storage as the basic storage solution to provide faster data access speed; if the data heat is less than the data heat threshold, it indicates that the target data has a low access frequency and is suitable for long-term archiving, and the decision is made to use optical storage as the basic storage solution to provide longer-term data preservation capabilities. Then, a data criticality analysis is performed on the target data to assess its importance level. Specifically, first, a data criticality classification table is established, which contains an index relationship between sample data feature information and the corresponding sample data criticality. The target data's data characteristics are entered into the data criticality classification table. Through index matching, the criticality of the most similar sample data is obtained as the target data's data criticality indicator. Data criticality is a quantitative indicator reflecting the importance of the target data. A higher data criticality indicates more important data and a more severe impact of data loss.

[0064] Subsequently, the heat critical coefficient is calculated to assess the heat characteristics of the target data. The heat critical coefficient indicates how close the heat of the target data is to the data heat threshold. The calculation formula is: Heat critical coefficient = 1 - (|data heat - data heat threshold| / data heat threshold). Here, |data heat - data heat threshold| represents the absolute difference between the data heat and the heat threshold. According to this formula, when the data heat of the target data is close to the data heat threshold, the difference is small, and the heat critical coefficient is close to 1; when the heat is far from the threshold, the difference is large, and the heat critical coefficient is close to 0. A larger heat critical coefficient indicates more ambiguous access characteristics of the target data and a greater likelihood of simultaneous high-frequency access and long-term storage requirements. Next, based on the data criticality and the heat critical coefficient, the corrected data criticality is calculated. The corrected data criticality is calculated by multiplying the data criticality by the heat critical coefficient: Corrected data criticality = data criticality × heat critical coefficient. This calculation method takes into account the importance and access characteristics of the data. When the data is both important and in a critical state, the correction criticality will reach the highest value, indicating that the data requires a higher level of protection.

[0065] Afterwards, the final storage solution decision is made based on the corrected data criticality. Specifically, a predetermined data criticality threshold is obtained. The data criticality threshold is set based on storage resources and security level requirements, combined with expert experience and historical data analysis results. The calculated corrected data criticality is compared with the data criticality threshold. If the corrected data criticality is greater than or equal to the data criticality threshold, it indicates that the target data has a high importance and protection requirements, and the decision is made to adopt a fusion storage solution, that is, optical storage and magnetic storage are performed simultaneously to provide a dual data protection mechanism; if the corrected data criticality is less than the data criticality threshold, it indicates that the basic storage solution can already meet the data protection requirements, and the determined basic storage solution is directly adopted as the final storage solution.

[0066] Through a multi-level, multi-factor decision-making mechanism, it is possible to comprehensively consider data access requirements, importance levels, and characteristic ambiguity, and adaptively determine the most appropriate storage solution, which not only ensures the security of important data, but also optimizes the allocation efficiency of storage resources and realizes intelligent data storage management.

[0067] Furthermore, according to the data popularity and storage scheme, data storage access loss prediction is performed to obtain at least one of an optical storage loss parameter and a magnetic storage loss parameter, including:

[0068] S31. When the storage solution includes optical storage, obtain data access time by mapping according to the data heat;

[0069] S32. Perform optical storage loss mapping according to the data access time to obtain an optical storage loss parameter, wherein the optical storage loss parameter includes an optical storage data loss ratio;

[0070] S33. When the storage solution includes magnetic storage, obtain the number of data accesses within a preset time period by mapping according to the data heat;

[0071] S34. Perform magnetic storage loss mapping according to the number of data accesses to obtain magnetic storage loss parameters, wherein the magnetic storage loss parameters include a magnetic storage data loss ratio.

[0072] In a preferred embodiment, when the storage solution includes optical storage, the data access time (e.g., 100 days) is first determined based on the target data's popularity through a mapping relationship. Specifically, a mapping table or function is established between data popularity and data access time. Target data with lower data popularity typically indicates a longer access interval, meaning a longer period between storage and next access; target data with higher data popularity indicates a shorter access interval. This mapping process can be implemented through a table lookup, function calculation, or other mathematical model, outputting a numerical value representing the expected data access time. After obtaining the expected data access time, optical storage loss mapping is performed to calculate the optical storage loss parameter. During long-term storage, optical storage media are subject to numerous physical environmental factors, such as dust accumulation, surface scratches, and material aging. These factors intensify over time, increasing the risk of data loss. By establishing a mapping relationship between data access time and optical storage loss parameters, the optical storage loss parameters that may occur under the expected data access time are assessed. The core indicator of the optical storage loss parameter is the optical storage data loss ratio, which represents the proportion of data stored on the optical medium that may be lost within the expected time period. The higher the loss ratio, the more difficult it is to recover the data and the stronger the error correction capability is required.

[0073] When the storage solution includes magnetic storage, the expected number of data accesses within a preset time period (e.g., once per day) is first determined based on the target data's data popularity through a mapping relationship. Specifically, a mapping table or mapping function is established between data popularity and access frequency. Target data with higher data popularity typically indicates a higher access frequency, meaning it is accessed more frequently within the preset time period; target data with lower data popularity indicates a lower access frequency. This mapping process can also be implemented using a table lookup, function calculation, or other mathematical model, with the output representing the expected number of data accesses. After obtaining the expected number of data accesses, magnetic storage loss mapping is performed to calculate the magnetic storage loss parameter. Magnetic storage media are subject to factors such as magnetic attenuation and mechanical wear during frequent read and write operations. These factors intensify with increasing access times, leading to an increased risk of data loss. By establishing a mapping relationship between access times and magnetic storage loss parameters, the magnetic storage loss parameters that may be expected given the expected number of data accesses are assessed. The core metric of the magnetic storage loss parameter is the magnetic storage data loss ratio, which represents the proportion of data stored on the magnetic media that is likely to be lost given the expected access frequency. Similarly, a higher loss ratio indicates that data recovery is more difficult and requires stronger error correction capabilities.

[0074] Through the storage loss prediction mechanism based on data heat, it is possible to accurately assess the loss risk of target data under different storage solutions based on the physical characteristics of different storage media, provide a basis for the subsequent optimization of error correction coding and erasure coding strategies, and improve the reliability and security of long-term data storage.

[0075] Furthermore, combining the data criticality, performing storage loss compensation optimization to obtain at least one of error correction coding parameters and erasure correction coding parameters includes:

[0076] S31. When the storage solution includes optical storage, randomly configure a first error correction coding parameter, wherein the first error correction coding parameter includes a redundancy rate of error correction coding in the optical storage;

[0077] S32. Calculate a first error correction adaptability of the first error correction coding parameter based on the data criticality and the optical storage loss parameter;

[0078] S33, continue to randomly configure error correction coding parameters and calculate error correction fitness, optimize the error correction coding parameters until convergence, and retain the error correction coding parameters with the largest error correction fitness;

[0079] S34. When the storage solution includes magnetic storage, randomly configure a first erasure code parameter, wherein the first erasure code parameter includes a redundancy rate of the erasure code in the magnetic storage;

[0080] S35. Calculate a first erasure fitness of the first erasure code parameter based on the data criticality and the magnetic storage loss parameter.

[0081] S36. Continue to randomly configure erasure code parameters and calculate erasure fitness, optimize the erasure code parameters until convergence, and retain the erasure code parameters with the largest erasure fitness.

[0082] In a preferred embodiment, when determining the optimal error correction coding parameters for an optical storage solution, the initial error correction coding parameters, i.e., the first error correction coding parameters, are randomly configured. The core metric for error correction coding parameters is the redundancy ratio of the error correction coding in optical storage, representing the ratio of redundant data stored to the original data to ensure data integrity, e.g., 15%. A higher redundancy ratio increases the ability to resist data loss, but also occupies more storage space. Initial parameter configuration is performed randomly to cover a wider parameter space and avoid falling into local optimal solutions. Next, based on the obtained data criticality and optical storage loss parameter, the fitness value of the first error correction coding parameters, i.e., the first error correction fitness, is calculated. The fitness calculation comprehensively considers both data protection capabilities and storage efficiency. Higher data criticality requires greater data protection; higher optical storage loss parameters increase the difficulty of data recovery. By combining these factors with the error correction coding parameters, a fitness value is calculated; a higher value indicates a more reasonable parameter configuration. An iterative optimization process then begins. The algorithm then randomly configures new error correction coding parameters, calculates their fitness, compares them with the currently optimal error correction coding parameters, and retains the error correction coding parameter configuration with the highest fitness. This process is repeated until a preset convergence condition is met, such as the fitness change being less than a threshold or the maximum number of iterations being reached. Finally, the error correction coding parameter with the highest fitness across all iterations is retained as the final result.

[0083] To determine the optimal erasure code parameters for a magnetic storage solution, the initial erasure code parameters, known as the first erasure code parameters, are randomly configured. The core of erasure code parameters is the redundancy rate of the erasure code in magnetic storage. Unlike error-correcting codes, their implementation mechanism primarily achieves data protection through data block segmentation and parity block generation. This initial parameter configuration is also randomized to increase the diversity of parameter search. Next, based on the obtained data criticality and magnetic storage loss parameters, the fitness value of the first erasure code parameters, known as the first erasure fitness, is calculated. This fitness calculation also comprehensively considers data protection and storage efficiency, but is optimized for magnetic storage characteristics. Then, an iterative optimization process begins, where new erasure code parameters are randomly configured, their fitness values are calculated, and compared with the currently optimal erasure code parameters. The parameter configuration with the higher fitness is retained. This process is repeated until the preset convergence criteria are met. Finally, the erasure code parameters with the highest fitness across all iterations are retained as the final result.

[0084] This iterative optimization method, based on random search and fitness evaluation, finds the optimal balance between data security and storage efficiency, providing differentiated protection strategies for data of varying importance and improving the data management efficiency of optical-magnetic hybrid storage. The method's convergence characteristics ensure that the optimization process can be completed within a limited timeframe, making it suitable for practical storage applications.

[0085] Furthermore, calculating and obtaining a first error correction fitness of the first error correction coding parameter according to the data criticality and the optical storage loss parameter includes:

[0086] S321: Calculate the ratio of the first error correction coding parameter to the optical storage loss parameter, and perform correction calculation on the ratio in combination with the data criticality to obtain a first error correction data fitness.

[0087] S322. Calculate a ratio of a preset error correction coding parameter to the first error correction coding parameter to obtain a first error correction redundancy fitness;

[0088] S323: Calculate and obtain a first error correction fitness according to the first error correction data fitness and the first error correction redundancy fitness.

[0089] In a preferred embodiment, first, the first error correction data fitness is calculated. This indicator measures the adaptability of the error correction coding parameters to data protection. Specifically, first, the ratio of the first error correction coding parameter to the optical storage loss parameter is calculated. This ratio represents the degree of matching between the error correction capability and the expected loss risk. The larger the ratio, the higher the error correction redundancy is than the expected loss risk, and the stronger the data protection capability is. Then, the ratio is combined with the data criticality to perform a correction calculation. The correction method uses a product operation of the ratio and the data criticality, so that the data criticality directly affects the fitness result. Through the product relationship, when the data criticality increases, the first error correction data fitness also increases accordingly, so that in subsequent optimization, a higher error correction redundancy rate tends to be selected for important data, providing stronger data protection capabilities. By combining the correction calculation with the criticality, the first error correction data fitness that characterizes the adaptability of the error correction parameters to data protection is obtained.

[0090] Secondly, the first error correction redundancy fitness is calculated. This indicator measures the storage efficiency of the error correction coding parameters. Specifically, the ratio of the preset error correction coding parameters to the first error correction coding parameters is calculated as the first error correction redundancy fitness. Among them, the preset error correction coding parameters are predetermined benchmark parameters, representing a reasonable redundancy level. This ratio represents the redundancy level of the current error correction coding parameters relative to the benchmark parameters. The larger the ratio, the lower the redundancy level of the current error correction coding parameters is below the benchmark level, and the higher the storage efficiency. Afterwards, the first error correction data fitness and the first error correction redundancy fitness are obtained by combining them to obtain the final first error correction fitness. The comprehensive calculation can adopt weighted average, geometric average or other functional relationships. By adjusting the weight coefficient, the importance of data protection capability and storage efficiency can be flexibly balanced, and the error correction strategy can be optimized for different application scenarios.

[0091] Through multi-level error correction fitness calculations, the performance of error correction coding parameters can be comprehensively evaluated, and the optimal parameter configuration can be selected during an iterative optimization process. This approach not only considers data protection requirements but also the efficient utilization of storage resources, achieving comprehensive optimization of storage performance. Furthermore, this fitness-based evaluation mechanism is highly scalable, allowing the calculation model to be adjusted according to specific application requirements and adapt to the specific requirements of different storage systems.

[0092] Furthermore, calculating and obtaining a first erasure fitness of the first erasure code parameter according to the data criticality and the magnetic storage loss parameter includes:

[0093] S351: Calculate the ratio of the first erasure code parameter to the magnetic storage loss parameter, and perform a correction calculation on the ratio in combination with the data criticality to obtain a first erasure data fitness.

[0094] S352: Calculate a ratio of a preset erasure code parameter to the first erasure code parameter to obtain a first erasure redundancy fitness.

[0095] S353: Calculate and obtain a first erasure fitness according to the first erasure data fitness and the first erasure redundancy fitness.

[0096] In a preferred embodiment, first, the first erasure data fitness is calculated. This indicator measures the adaptability of the erasure code parameters to the protection of magnetic storage data. Specifically, the ratio of the first erasure code parameters to the magnetic storage loss parameters is first calculated. This ratio represents the degree of matching between the erasure capability and the expected magnetic storage loss risk. The larger the ratio, the higher the erasure redundancy is than the expected loss risk, and the stronger the data recovery capability is. Then, the ratio is combined with the data criticality for correction calculation. The correction method adopts the product operation of the ratio and the data criticality, so that the data criticality directly affects the fitness evaluation result. Through the product relationship, when the data criticality increases, the first erasure data fitness also increases accordingly, so that in the subsequent optimization, a higher erasure redundancy rate tends to be selected for important data, providing stronger data recovery capability.

[0097] Next, the first erasure redundancy fitness is calculated. This metric measures the storage efficiency of the erasure code parameters. Specifically, the ratio of the preset erasure code parameters to the first erasure code parameters is calculated as the first erasure redundancy fitness. The preset erasure code parameters are predetermined baseline parameters that represent a reasonable level of redundancy for magnetic storage media. This ratio indicates the degree of redundancy of the current erasure code parameters relative to the baseline erasure code parameters. A larger ratio indicates that the redundancy of the current parameters is lower than the baseline, resulting in higher storage efficiency and avoiding excessive redundant storage that wastes storage space. The first erasure data fitness and the first erasure redundancy fitness are then combined to obtain the final first erasure fitness. This combined calculation can utilize a weighted average method, adjusting the weights of different indicators to flexibly balance the importance of data protection and storage efficiency. Dynamically adjust the weight configuration to optimize the erasure strategy for different application scenarios and different levels of magnetic storage systems.

[0098] Through multi-level erasure fitness calculations, the performance of erasure code parameters can be comprehensively evaluated, and the parameter configuration that best suits the characteristics of magnetic storage can be selected during an iterative optimization process. This approach takes into account the particularities of magnetic storage media and provides targeted optimization for the risk of demagnetization caused by frequent accesses, while also taking into account the efficient utilization of storage resources, achieving comprehensive optimization of magnetic storage system performance. Furthermore, this fitness assessment mechanism forms a unified evaluation system with the aforementioned optical storage error correction fitness calculation method, providing a consistent optimization framework for optical-magnetic fusion storage systems and enhancing overall coordination and manageability.

[0099] Example 2, as Figure 2 As shown, based on the same inventive concept as the data management method based on optical-magnetic fusion storage provided in Example 1, an embodiment of the present invention also provides a data management system based on optical-magnetic fusion storage, including:

[0100] The data acquisition and analysis module 11 is used to acquire target data to be stored, collect data feature information of the target data, perform data heat analysis, and obtain data heat;

[0101] The storage decision optimization module 12 is configured to make a decision based on the data heat to obtain a basic storage solution for optical storage or magnetic storage, perform data criticality analysis based on the data feature information to obtain data criticality, analyze the heat criticality coefficient based on the data heat, make a fusion storage decision for the target data, and obtain a storage solution;

[0102] a loss prediction and compensation module 13 configured to predict data storage access loss based on the data popularity and storage scheme, obtain at least one of an optical storage loss parameter and a magnetic storage loss parameter, and optimize storage loss compensation based on the data criticality to obtain at least one of an error correction coding parameter and an erasure coding parameter;

[0103] The optical-magnetic data management module 14 is configured to manage the optical-magnetic storage data of the target data in accordance with the storage scheme in combination with at least one of error correction coding parameters and erasure code parameters.

[0104] Furthermore, the data acquisition and analysis module 11 includes the following execution steps:

[0105] Collecting data characteristic information of the target data, wherein the data characteristic information includes data type, data size, and ownership entity;

[0106] Based on the data feature information, machine learning is used to perform data heat analysis on the target data to obtain data heat.

[0107] Furthermore, the data acquisition and analysis module 11 includes the following execution steps:

[0108] A data heat analysis unit based on machine learning is used to perform data heat analysis. The training steps of the data heat analysis unit include:

[0109] According to the historical log data of data storage management, a set of sample data feature information is collected, and the ratio of the access frequency after data storage to the preset access frequency under different sample data feature information is collected, and the sample data heat set is obtained by annotation;

[0110] Using machine learning, data feature information and data heat are used as input features and output features respectively to build a data heat analysis unit;

[0111] The data heat analysis unit is iteratively trained and verified using the sample data feature information set and the sample data heat set, and the training is completed after convergence.

[0112] Furthermore, the storage decision optimization module 12 includes the following execution steps:

[0113] Determine whether the data heat is greater than or equal to a data heat threshold, and if so, decide to obtain a basic storage solution for magnetic storage; if not, decide to obtain a basic storage solution for optical storage;

[0114] Inputting the data feature information into a data criticality classification table, and obtaining data criticality through index classification, wherein the data criticality classification table includes an index relationship between sample data feature information and sample data criticality;

[0115] Calculating the ratio of the absolute difference between the data heat and the data heat threshold to the data heat threshold, and calculating a heat critical coefficient of the data heat;

[0116] Calculating the corrected data criticality of the target data according to the data criticality and the heat critical coefficient;

[0117] Determine whether the correction data criticality is greater than or equal to a data criticality threshold; if so, decide to obtain a storage solution for fusion storage of the target data; if not, use the basic storage solution as the storage solution.

[0118] Furthermore, the loss prediction and compensation module 13 includes the following execution steps:

[0119] When the storage solution includes optical storage, mapping to obtain data access time according to the data heat;

[0120] Performing optical storage loss mapping according to the data access time to obtain an optical storage loss parameter, wherein the optical storage loss parameter includes an optical storage data loss ratio;

[0121] When the storage solution includes magnetic storage, mapping and obtaining the number of data accesses within a preset time period according to the data heat;

[0122] Magnetic storage loss mapping is performed according to the data access times to obtain magnetic storage loss parameters, wherein the magnetic storage loss parameters include a magnetic storage data loss ratio.

[0123] Furthermore, the loss prediction and compensation module 13 includes the following execution steps:

[0124] When the storage scheme includes optical storage, randomly configuring a first error correction coding parameter, wherein the first error correction coding parameter includes a redundancy rate of error correction coding in the optical storage;

[0125] Calculating a first error correction adaptability of the first error correction coding parameter according to the data criticality and the optical storage loss parameter;

[0126] Continue to randomly configure error correction coding parameters and calculate error correction fitness, optimize error correction coding parameters until convergence, and retain the error correction coding parameters with the highest error correction fitness;

[0127] When the storage solution includes magnetic storage, randomly configuring a first erasure code parameter, wherein the first erasure code parameter includes a redundancy rate of the erasure code in the magnetic storage;

[0128] Calculating a first erasure fitness of the first erasure code parameter according to the data criticality and the magnetic storage loss parameter;

[0129] Continue to randomly configure erasure code parameters and calculate erasure fitness, optimize the erasure code parameters until convergence, and retain the erasure code parameters with the highest erasure fitness.

[0130] Furthermore, the loss prediction and compensation module 13 further includes the following execution steps:

[0131] Calculating a ratio of the first error correction coding parameter to the optical storage loss parameter, combining the data criticality, and performing a correction calculation on the ratio to obtain a first error correction data fitness;

[0132] Calculating a ratio of a preset error correction coding parameter to the first error correction coding parameter to obtain a first error correction redundancy fitness;

[0133] A first error correction fitness is obtained by calculation according to the first error correction data fitness and the first error correction redundancy fitness.

[0134] Furthermore, the loss prediction and compensation module 13 further includes the following execution steps:

[0135] Calculating a ratio of the first erasure code parameter to the magnetic storage loss parameter, and performing a correction calculation on the ratio in combination with the data criticality to obtain a first erasure data fitness;

[0136] Calculating a ratio of a preset erasure code parameter to the first erasure code parameter to obtain a first erasure redundancy fitness;

[0137] A first erasure fitness is obtained by calculation according to the first erasure data fitness and the first erasure redundancy fitness.

[0138] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0139] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0140] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0141] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0142] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0143] Although preferred embodiments of the present invention have been described, additional changes and modifications to these embodiments may occur to those skilled in the art once the basic inventive concepts become known.

[0144] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A data management method based on optical-magnetic fusion storage, characterized in that: The method comprises: Acquire target data to be stored, collect data feature information of the target data, perform data heat analysis, and obtain data heat; A decision is made based on the data heat to obtain a basic storage solution for optical storage or magnetic storage, and data criticality analysis is performed based on the data feature information to obtain data criticality. A heat criticality coefficient is analyzed based on the data heat to make a fusion storage decision for the target data to obtain a storage solution, including: Determine whether the data heat is greater than or equal to a data heat threshold, and if so, decide to obtain a basic storage solution for magnetic storage; if not, decide to obtain a basic storage solution for optical storage; Inputting the data feature information into a data criticality classification table, and obtaining data criticality through index classification, wherein the data criticality classification table includes an index relationship between sample data feature information and sample data criticality; Calculating the ratio of the absolute difference between the data heat and the data heat threshold to the data heat threshold, and calculating a heat critical coefficient of the data heat; Calculating the corrected data criticality of the target data according to the data criticality and the heat critical coefficient; Determine whether the criticality of the correction data is greater than or equal to a data criticality threshold; if so, decide to obtain a storage solution for fusion storage of the target data; if not, use the basic storage solution as the storage solution; According to the data popularity and storage scheme, data storage access loss is predicted to obtain at least one of an optical storage loss parameter and a magnetic storage loss parameter. In combination with the data criticality, storage loss compensation optimization is performed to obtain at least one of an error correction coding parameter and an erasure coding parameter, including: When the storage scheme includes optical storage, randomly configuring a first error correction coding parameter, wherein the first error correction coding parameter includes a redundancy rate of error correction coding in the optical storage; Calculating a first error correction adaptability of the first error correction coding parameter according to the data criticality and the optical storage loss parameter; Continue to randomly configure error correction coding parameters and calculate error correction fitness, optimize error correction coding parameters until convergence, and retain the error correction coding parameters with the highest error correction fitness; When the storage solution includes magnetic storage, randomly configuring a first erasure code parameter, wherein the first erasure code parameter includes a redundancy rate of the erasure code in the magnetic storage; Calculating a first erasure fitness of the first erasure code parameter according to the data criticality and the magnetic storage loss parameter; Continue to randomly configure erasure code parameters and calculate erasure fitness, optimize the erasure code parameters until convergence, and retain the erasure code parameters with the highest erasure fitness; According to the storage scheme and at least one of error correction coding parameters and erasure code parameters, optical-magnetic storage data management is performed on the target data.

2. The data management method based on optical-magnetic fusion storage according to claim 1, characterized in that: Acquiring target data to be stored, collecting data feature information of the target data, performing data heat analysis, and obtaining data heat, including: Collecting data characteristic information of the target data, wherein the data characteristic information includes data type, data size, and ownership entity; Based on the data feature information, machine learning is used to perform data heat analysis on the target data to obtain data heat.

3. The data management method based on optical-magnetic fusion storage according to claim 2, characterized in that: According to the data feature information, machine learning is used to perform data heat analysis on the target data to obtain data heat, including using a machine learning data heat analysis unit to perform data heat analysis, and the training steps of the data heat analysis unit include: According to the historical log data of data storage management, a set of sample data feature information is collected, and the ratio of the access frequency after data storage to the preset access frequency under different sample data feature information is collected, and the sample data heat set is obtained by annotation; Using machine learning, data feature information and data heat are used as input features and output features respectively to build a data heat analysis unit; The data heat analysis unit is iteratively trained and verified using the sample data feature information set and the sample data heat set, and the training is completed after convergence.

4. The data management method based on optical-magnetic fusion storage according to claim 1, characterized in that: According to the data popularity and storage scheme, data storage access loss prediction is performed to obtain at least one of an optical storage loss parameter and a magnetic storage loss parameter, including: When the storage solution includes optical storage, mapping to obtain data access time according to the data heat; Performing optical storage loss mapping according to the data access time to obtain an optical storage loss parameter, wherein the optical storage loss parameter includes an optical storage data loss ratio; When the storage solution includes magnetic storage, mapping and obtaining the number of data accesses within a preset time period according to the data heat; Magnetic storage loss mapping is performed according to the data access times to obtain magnetic storage loss parameters, wherein the magnetic storage loss parameters include a magnetic storage data loss ratio.

5. The data management method based on optical-magnetic fusion storage according to claim 1, characterized in that: Calculating a first error correction fitness of the first error correction coding parameter according to the data criticality and the optical storage loss parameter includes: Calculating a ratio of the first error correction coding parameter to the optical storage loss parameter, combining the data criticality, and performing a correction calculation on the ratio to obtain a first error correction data fitness; Calculating a ratio of a preset error correction coding parameter to the first error correction coding parameter to obtain a first error correction redundancy fitness; A first error correction fitness is obtained by calculation according to the first error correction data fitness and the first error correction redundancy fitness.

6. The data management method based on optical-magnetic fusion storage according to claim 1, characterized in that: Calculating a first erasure fitness of the first erasure code parameter according to the data criticality and the magnetic storage loss parameter includes: Calculating a ratio of the first erasure code parameter to the magnetic storage loss parameter, and performing a correction calculation on the ratio in combination with the data criticality to obtain a first erasure data fitness; Calculating a ratio of a preset erasure code parameter to the first erasure code parameter to obtain a first erasure redundancy fitness; A first erasure fitness is obtained by calculation according to the first erasure data fitness and the first erasure redundancy fitness.

7. A data management system based on optical-magnetic fusion storage, characterized in that: For implementing the data management method based on optical-magnetic fusion storage according to any one of claims 1 to 6, the system comprises: A data acquisition and analysis module is used to acquire target data to be stored, collect data feature information of the target data, perform data heat analysis, and obtain data heat; A storage decision optimization module is configured to make a decision based on the data heat to obtain a basic storage solution for optical storage or magnetic storage, perform data criticality analysis based on the data feature information to obtain data criticality, analyze the heat criticality coefficient based on the data heat, make a fusion storage decision for the target data, and obtain a storage solution; a loss prediction and compensation module, configured to predict data storage access loss based on the data popularity and storage scheme, obtain at least one of an optical storage loss parameter and a magnetic storage loss parameter, and optimize storage loss compensation based on the data criticality to obtain at least one of an error correction coding parameter and an erasure coding parameter; The optical-magnetic data management module is used to perform optical-magnetic storage data management on the target data in accordance with the storage scheme in combination with at least one of error correction coding parameters and erasure code parameters.

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