Optical and electromagnetic storage method and system based on hierarchical management

Abstract

The present invention relates to the field of tiered storage technology, specifically a method and system for optical and electromagnetic storage based on tiered management, comprising the following steps: collecting data on read and write speeds, error rates, durability, and costs of optical and electromagnetic storage media; sorting and grading the data based on read and write speeds and error rates; and calculating a score for each storage medium to obtain a medium performance score. In the present invention, by accurately scoring storage medium performance and combining it with the specific requirements of the data to be stored, data is effectively allocated to the most suitable storage medium, thereby improving data access speed and efficiency while reducing overall storage costs. In particular, when processing large amounts of data, data storage efficiency is further optimized and data redundancy is reduced by dynamically adjusting the data compression level and encoding format. These optimization measures significantly improve the overall performance of data storage and access, while also ensuring the long-term security and integrity of the data.

Description

Technical Field

[0001] The present invention relates to the technical field of hierarchical storage, and in particular to an optical and electromagnetic storage method and system based on hierarchical management. Background Art

[0002] The field of tiered storage technology aims to improve storage efficiency and data access speed. It enables efficient data management by allocating data to different storage media (such as SSDs, HDDs, or tapes) based on frequency of use, criticality, or standards. Each tier of storage media offers different access speeds and cost-effectiveness, allowing data center operators to tailor data storage to specific needs and budgets. Tiered storage is used in large-scale data centers, cloud storage services, and enterprise IT environments requiring efficient data backup and recovery. By automating data classification and migration, tiered storage optimizes data processing performance and reduces overall storage costs.

[0003] Among them, the optical-electromagnetic storage method based on hierarchical management is an advanced data storage solution that combines optical and electromagnetic storage technologies. It is suitable for application scenarios that require batch data processing, such as large data centers or high-performance computing environments. By using different storage technologies (optical storage and electromagnetic storage) at different storage levels, storage performance and cost efficiency can be optimized while providing high-speed data access and reliable data protection. Key uses include data backup, long-term data archiving, and supporting business operations with high-frequency data reads and writes, fully leveraging the characteristics of various storage media to meet diverse data storage needs.

[0004] While existing tiered storage technologies can optimize storage media usage based on data frequency and importance, they lack flexibility and automation, and are unable to adapt quickly to sudden bursts of bulk data or data requiring frequent updates. Existing systems fail to fully utilize the full potential of all storage media, particularly in terms of read and write speeds and error rates. This leads to data access bottlenecks, impacting data center response times and service quality. Due to the lack of highly automated and intelligent data management strategies, existing technologies exhibit limitations in large-scale data processing and addressing real-time data demands. The lack of effective data compression and encoding optimization mechanisms results in low data storage efficiency, leading to unnecessary increases in storage costs. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an optical and electromagnetic storage method and system based on hierarchical management.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an optical electromagnetic storage method based on hierarchical management, comprising the following steps:

[0007] S1: Collect data on the read / write speed, error rate, durability, and cost of optical and electromagnetic storage media. Sorting and grading the data based on read / write speed and error rate, and calculating a score for each storage medium to obtain a media performance score.

[0008] S2: Based on the media performance score, the size, access frequency, and urgency of the data to be stored are evaluated. By sorting and prioritizing the parameters, the data is divided into differentiated storage requirement categories to obtain a data performance requirement table.

[0009] S3: Based on the data performance requirement table, the data category and medium performance are matched and analyzed, the optimal storage medium is selected, data backup and temporary cache are created, and a data storage decision table is generated;

[0010] S4: Using the data storage decision table, verifying the integrity of the backup data and the cache response speed, optimizing the encoding format using the statistical characteristics of the optical and electromagnetic storage data, and dynamically adjusting the compression level according to real-time changes in the data to generate an encoding compression configuration;

[0011] S5: Detecting data validity according to the coding and compression configuration, adjusting the storage layout by periodically scanning the optical and electromagnetic stored data, automatically identifying and cleaning redundant data, and obtaining redundant data management records;

[0012] S6: Analyze the optical and electromagnetic storage distribution from the redundant data management records, track and locate the data storage location, optimize the data migration path through hierarchical management, and establish the dynamic adjustment result of the optical and electromagnetic storage.

[0013] As a further solution of the present invention, the media performance score is based on a weighted calculation of read and write speed, error rate, durability and cost; the data performance requirement table includes the size, access frequency and urgency classification of the data to be stored; the data storage decision table includes strategies for selecting the optimal storage medium, data backup and temporary cache; the encoding compression configuration includes the selection of encoding rules for each data stream, the ratio of original to compressed data and compression efficiency indicators; the redundant data management record includes the identified redundant data type, the amount of redundant data and the storage efficiency after processing; the dynamic adjustment results of the optical and electromagnetic storage include the data storage location before and after adjustment, data migration path optimization, migration efficiency evaluation and performance optimization indicators.

[0014] As a further embodiment of the present invention, data on read / write speed, error rate, durability, and cost of optical and electromagnetic storage media are collected, the data is sorted and graded according to read / write speed and error rate, and a score is calculated for each storage medium. The steps for obtaining the media performance score are as follows:

[0015] S101: Collect read / write speed, error rate, durability, and cost data for optical and electromagnetic storage media, input the data into a spreadsheet, perform initial classification on the data, organize basic performance parameters of differentiated media, and generate a storage media dataset;

[0016] S102: sorting the storage medium data set in descending order using a spreadsheet function, calculating the average read and write speed and average error rate of each medium, and dividing the performance level by numerical range to obtain sorted and graded data;

[0017] S103: Calculate the score of each storage medium based on the sorted and graded data, set weights for the read and write speed and the error rate, and output the performance score of the medium based on the weighted result to obtain a medium performance score.

[0018] As a further solution of the present invention, the size, access frequency, and urgency of the data to be stored are evaluated based on the medium performance score, and the data is divided into differentiated storage requirement categories by sorting and prioritizing the parameters. The steps of obtaining the data performance requirement table are specifically as follows:

[0019] S201: Based on the medium performance score, information on the size, access frequency, and urgency of the data to be stored is collected and entered into a data table. The data is initially sorted according to urgency to generate an initial data sorting table.

[0020] S202: Based on the initialized data sorting table, using the sorting function of a spreadsheet, iteratively sort the data size and access frequency, classify the data according to access frequency and data size, and obtain a data access classification table;

[0021] S203: Using the data access classification table, assigning differentiated storage medium priorities, classifying data into multiple storage requirement categories, assigning the optimal storage medium to each category of data, and obtaining a data performance requirement table.

[0022] As a further solution of the present invention, the steps of performing matching analysis between data categories and media performance using the data performance requirement table, selecting the optimal storage medium, performing data backup and creating a temporary cache, and generating a data storage decision table are as follows:

[0023] S301: Using the data performance requirement table, compare data categories with media performance, evaluate storage efficiency and cost, perform matching analysis, set data backup time and frequency, and generate a matching analysis table;

[0024] S302: Based on the matching analysis table, select the optimal storage medium, plan a data backup strategy, determine the data backup time and frequency, and generate a backup operation table;

[0025] S303: Extract the backup strategy from the backup operation table, create a temporary cache to optimize data access, set the cache time and update frequency, implement the temporary cache strategy, and obtain a data storage decision table.

[0026] As a further solution of the present invention, the data storage decision table is used to check the integrity of backup data and verify the cache response speed, the statistical characteristics of optical and electromagnetic storage data are utilized to optimize the encoding format, and the compression level is dynamically adjusted according to the real-time changes of the data. The steps of generating the encoding compression configuration are specifically as follows:

[0027] S401: Analyze statistical characteristics of optical and electromagnetic storage data based on the data storage decision table, identify normal patterns and fluctuations in data streams, adjust encoding parameters based on differentiated data types and frequencies, optimize processing efficiency of optical and electromagnetic storage, and generate data statistical analysis results;

[0028] S402: Based on the statistical analysis results of the data, monitor the changes in the optical and electromagnetic storage data stream in real time, dynamically adjust the encoding format and compression parameters to match the requirements of data storage efficiency and transmission speed, optimize the performance of data storage and transmission, and perform compression testing to obtain compression parameter adjustment records;

[0029] S403: Using the compression parameter adjustment record, identify and record the encoding and compression configuration of the optical and electromagnetic storage data stream, check the optimal settings of the encoding format and compression level, match the real-time network status and data characteristics, and establish the encoding compression configuration.

[0030] As a further solution of the present invention, according to the coding compression configuration, the steps of detecting the validity of data, adjusting the storage layout by periodically scanning the optical and electromagnetic stored data, automatically identifying and cleaning redundant data, and obtaining redundant data management records are specifically as follows:

[0031] S501: Using the coding compression configuration, detecting the validity of optical and electromagnetic data storage, performing classification scanning on the data stored at differentiated levels, evaluating the frequency of use and update time of the data, identifying outdated and unaccessed data, and generating a data validity detection result;

[0032] S502: Based on the data validity test result, an entropy weight method is used to perform weighted calculations on data access frequency and criticality, and the storage structure is adjusted to optimize access efficiency and response speed, thereby obtaining a storage layout adjustment record.

[0033] S503: Using the storage layout adjustment record, clean up redundant data in the optical and electromagnetic storage, automatically detect and delete duplicate data entries, check the integrity and validity of the data, and establish redundant data management records.

[0034] As a further solution of the present invention, the formula of the entropy weight method is as follows:

[0035] ;

[0036] in, For the The weight of the data indicator, For the The information entropy of the data indicators, For custom adjustment coefficient, is the data update frequency indicator, The total number of data indicators.

[0037] As a further solution of the present invention, the steps of analyzing the optical and electromagnetic storage distribution from the redundant data management records, tracking and locating the data storage location, optimizing the data migration path through hierarchical management, and establishing the dynamic adjustment results of the optical and electromagnetic storage are specifically as follows:

[0038] S601: Analyze the distribution of the overall optical and electromagnetic storage data using the redundant data management records, identify data storage areas and space utilization areas, track the optical and electromagnetic storage locations, identify key data nodes, and generate data distribution analysis results;

[0039] S602: Based on the data distribution analysis results, optimize the inefficient storage areas, adjust the optical and electromagnetic storage data to differentiated storage tiers, check the balance of data flow and optimal use of resources, and plan the migration path to obtain a data migration strategy;

[0040] S603: Use the data migration strategy to execute data migration of optical and electromagnetic storage, monitor data migration, avoid factors that affect data integrity and access speed, dynamically adjust data location, integrate hierarchical management information, verify migration effect, and generate dynamic adjustment results of optical and electromagnetic storage.

[0041] An optical and electromagnetic storage system based on hierarchical management, wherein the optical and electromagnetic storage system based on hierarchical management is used to execute the optical and electromagnetic storage method based on hierarchical management, and the system comprises:

[0042] The data analysis and scoring module collects data on the read and write speed, error rate, durability, and cost of optical and electromagnetic storage media, analyzes the parameters, ranks and ranks them according to read and write speed and error rate, and calculates a score for each storage medium to obtain a media performance score.

[0043] The data demand classification module analyzes the size, access frequency, and urgency of the data to be stored based on the media performance score, sorts and prioritizes the data attributes, divides the data into categories according to differentiated storage requirements, and establishes a data performance requirement table;

[0044] The media selection and matching module uses the data performance requirement table to perform matching analysis between data categories and storage medium performance, selects the optimal storage medium for data backup and temporary cache creation, and forms a data storage decision table;

[0045] The encoding adjustment module uses the data storage decision table to perform data backup integrity verification and cache response speed verification, optimize the encoding format, dynamically adjust the compression level according to the changes in the data flow, and generate the encoding compression configuration;

[0046] The data management module regularly scans the optical and electromagnetic storage data according to the coding compression configuration, adjusts the storage layout, identifies the optical and electromagnetic storage distribution density, optimizes the data migration path, and obtains the dynamic adjustment result of the optical and electromagnetic storage.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] This invention achieves in-depth scoring and classification of storage media by collecting and analyzing detailed performance data (such as read and write speeds, error rates, durability, and cost) for various optical and electromagnetic storage media, optimizing storage resource allocation. By accurately scoring storage media performance and integrating it with the specific requirements of the data to be stored (such as data size, access frequency, and urgency), data can be effectively allocated to the most suitable storage media. This not only improves data access speed and efficiency but also reduces overall storage costs, especially when processing large amounts of data. By dynamically adjusting data compression levels and encoding formats, data storage efficiency is further optimized and data redundancy is reduced. These optimization measures significantly improve the overall performance of data storage and access while ensuring the long-term security and integrity of data. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0055] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0056] Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0059] Example 1

[0060] See also Figure 1 The present invention provides a technical solution, an optical electromagnetic storage method based on hierarchical management, comprising the following steps:

[0061] S1: Collect data on the read / write speed, error rate, durability, and cost of optical and electromagnetic storage media. Sorting and grading the data based on read / write speed and error rate, and calculating a score for each storage medium to obtain a media performance score.

[0062] S2: Based on the media performance score, the size, access frequency, and urgency of the data to be stored are evaluated. By sorting and prioritizing the parameters, the data is divided into differentiated storage requirement categories to obtain a data performance requirement table.

[0063] S3: Based on the data performance requirement table, the system analyzes the matching between data category and media performance, selects the optimal storage medium, performs data backup and creates a temporary cache, and generates a data storage decision table.

[0064] S4: Uses a data storage decision table to verify backup data integrity and cache response speed. Leveraging the statistical characteristics of optical and electromagnetic storage data, it performs information entropy analysis and optimizes encoding formats. It also dynamically adjusts compression levels based on real-time data changes, matching network and storage conditions, and generating encoding and compression configurations.

[0065] S5: Based on the coding and compression configuration, data validity and redundancy detection is implemented. By regularly scanning the optical and electromagnetic storage data, the storage layout is adjusted, redundant data is automatically identified and cleaned, storage resource utilization is optimized, and redundant data management records are obtained.

[0066] S6: Analyze the storage distribution of optical and electromagnetic storage data from redundant data management records, track and locate data storage locations, optimize data migration paths through hierarchical management, avoid access delays, verify data access efficiency, and establish dynamic adjustment results for optical and electromagnetic storage.

[0067] The media performance score is based on a weighted calculation of read and write speed, error rate, durability and cost. The data performance requirement table includes the size, access frequency and urgency classification of the data to be stored. The data storage decision table includes strategies for selecting the optimal storage medium, data backup and temporary cache. The encoding and compression configuration includes the selection of encoding rules for each data stream, the ratio of original to compressed data and compression efficiency indicators. The redundant data management record includes the identified redundant data type, the amount of redundant data and the storage efficiency after processing. The dynamic adjustment results of optical and electromagnetic storage include the data storage location before and after adjustment, data migration path optimization, migration efficiency evaluation and performance optimization indicators.

[0068] See also Figure 2 , collects data on the read / write speed, error rate, durability, and cost of optical and electromagnetic storage media, sorts and ranks the data based on read / write speed and error rate, and calculates a score for each storage medium. The specific steps for obtaining the media performance score are as follows:

[0069] S101: Collect read / write speed, error rate, durability, and cost data for optical and electromagnetic storage media, input the data into a spreadsheet, perform initial classification on the data, organize the basic performance parameters of the differentiated media, and generate a storage media dataset. The execution process is as follows:

[0070] Collect data on the read and write speeds, error rates, durability, and costs of optical and electromagnetic storage media. This data collection requires detailed planning to ensure data accuracy and completeness. Samples of various types of storage media, such as optical discs and hard disks, are identified. Multiple tests are performed using standard test software to obtain read and write speed and error rate data. During data collection, repeated measurements are performed to reduce errors. The storage media's operating environment and test conditions are recorded to ensure data consistency. After data organization, the data is entered into a spreadsheet. Through data cleaning and processing, outliers are removed and the data is smoothed for subsequent data analysis and comparison. Data classification in the spreadsheet will be based on the type of storage media, such as optical or electromagnetic, and its performance parameters, such as read and write speeds and error rates, to allow for preliminary performance evaluation and generate a storage media dataset.

[0071] S102: Using a spreadsheet function to sort the read / write speed and error rate data in descending order for the storage medium dataset, calculate the average read / write speed and average error rate for each medium, and divide the performance levels by numerical ranges to obtain the sorted and graded data. The execution process is as follows;

[0072] Sort the read and write speed and error rate data in descending order according to the formula , calculate the average read and write speed and average error rate. Where, Represents the read and write speed of a single storage medium. represents the error rate, and is the corresponding weight, Represents the total number of storage media.

[0073] Detailed explanation of the formula and the process of formula calculation and derivation:

[0074] Consider the read and write speed of the storage medium and error rate , using weight and , weighted to reflect that read and write speeds are more important than error rates in performance scoring.

[0075] There are five types of storage media, the specific data are:

[0076] Storage medium A: MB / s,

[0077] Storage medium B: MB / s,

[0078] Storage medium C: MB / s,

[0079] Storage medium D: MB / s,

[0080] Storage medium E: MB / s,

[0081] The calculation formula is as follows:

[0082]

[0083] The calculation results are simplified and averaged to obtain a score, which represents the storage medium performance evaluation after comprehensively considering the read and write speed and error rate.

[0084] S103: Calculate the score of each storage medium based on the sorted and graded data, set weights for read / write speed and error rate, and output the performance score of the medium based on the weighted result. The execution process for obtaining the medium performance score is as follows:

[0085] Based on the sorted and graded data, a score is calculated for each storage medium. The calculation process includes setting appropriate weights for each medium to ensure that the impact of read and write speeds and error rates in the overall score is properly reflected. When setting weights, the actual usage scenarios and performance requirements of the storage medium must be considered. For example, high-speed storage media focus more on read and write speeds, while high-reliability storage media focus more on low error rates. The read and write speeds and error rates of each medium are comprehensively considered, and a weighted average method is used to calculate the comprehensive score for each medium. The score helps users make decisions based on specific needs when selecting storage media. The score is calculated not only based on the original data, but also requires adjusting the weights according to industry standards and performance data to ensure the fairness and accuracy of the score and obtain a medium performance score.

[0086] See also Figure 3 Based on the media performance score, the size, access frequency, and urgency of the data to be stored are evaluated. By sorting and prioritizing the parameters, the data is divided into differentiated storage requirement categories. The specific steps to obtain the data performance requirement table are as follows:

[0087] S201: Based on the media performance score, information on the size, access frequency, and urgency of the data to be stored is collected and entered into a data table. The data is then initially sorted by urgency. The execution process for generating the initial data sorting table is as follows;

[0088] Based on the media performance score, information on the size, access frequency and urgency of the data to be stored is collected. During the entire data collection process, the specific type of data, such as documents, pictures or videos, is determined. The type directly affects the size of the data. The access frequency information of the data is obtained by querying the system log or user feedback. The urgency is assessed based on business needs and the time sensitivity of the data. After collection, the data is entered into the data table, and automated tools are used to verify the integrity and accuracy of the data to prevent entry errors. Initial sorting is based on urgency to ensure that urgent data can be processed first. Sorting uses the automatic sorting function of the spreadsheet to make data management more efficient and facilitate subsequent processing and analysis. It reflects the priority of different data items and provides a basis for subsequent data processing and storage allocation, generating an initialization data sorting table.

[0089] S202: Based on the initialized data sorting table, the spreadsheet sorting function is used to iteratively sort the data size and access frequency, and the data is classified according to access frequency and data size to obtain the data access classification table. The execution process is as follows;

[0090] Based on the initialized data sorting table, the spreadsheet sorting function is used to sort the data size, putting large data items in the front row to give priority to the configuration of storage space in subsequent processing, and iteratively sort the access frequency. The system will sort the data from high to low according to the access frequency to ensure that high-frequency data can be quickly reached. The iterative sorting process ensures that the data is optimized not only according to the urgency, but also according to the access requirements and storage space occupied. The classification of each data item is clearly displayed, such as frequently accessed small files or infrequently accessed large files, which helps to more accurately plan data storage and allocate resources, and obtain a data access classification table.

[0091] S203: Using the data access classification table, assign differentiated storage media priorities, classify data into multiple storage requirement categories, and assign the optimal storage medium to each category of data to obtain the data performance requirement table. The execution process is as follows:

[0092] Using the data access classification table, differentiated storage media priorities are assigned. During the allocation process, data is classified into multiple storage requirement categories, such as temporary data, long-term storage data, high-frequency access data, etc. The optimal storage medium is assigned to each type of data. Considering the performance score of the medium, high-performance media is assigned first for data with high access frequency, while less frequently accessed data can consider lower-cost storage solutions. Data with high urgency should be stored on media with high fault tolerance to prevent data loss. The entire data allocation process is supported by an automated system to ensure the timeliness and accuracy of the allocation. The storage media, performance requirements and expected costs of each type of data are listed in detail to provide guidance for actual storage operations and obtain a data performance requirement table.

[0093] See also Figure 4 ,Through the data performance requirement table, we conduct matching analysis on data category and media performance, select the optimal storage medium, perform data backup and create temporary cache, and generate the data storage decision table in the following steps:

[0094] S301: Using the data performance requirement table, compare data categories with media performance, evaluate storage efficiency and cost, perform matching analysis, set data backup time and frequency, and generate a matching analysis table. The execution process is as follows:

[0095] Using the data performance requirements table, we begin to compare the performance of data categories and storage media. The comparative analysis is performed through automated tools to ensure that the data classification matches the performance characteristics of the media. Storage efficiency and cost evaluation are calculated through an algorithm model. The model considers factors such as the read and write speed, error rate, durability and cost of the storage media. The evaluation results will guide the setting of data backup time and frequency, and set appropriate backup strategies for each data category to maximize the use of storage resources and reduce costs. The matching degree between each data and the recommended storage media, the backup time and frequency, and the expected storage cost are listed in detail to ensure the economy and efficiency of the storage solution and generate a matching analysis table.

[0096] S302: Based on the matching analysis table, the optimal storage medium is selected, a data backup strategy is planned, the data backup time and frequency are determined, and the execution process of generating a backup operation table is as follows;

[0097] Based on the matching analysis table, select the optimal storage medium. During the selection process, pay special attention to matching the access frequency and size of the data with the performance of the medium to ensure efficient use of storage resources. When planning data backup strategies, refer to the access frequency and modification frequency of the data, and set a higher backup frequency for data that is frequently modified or accessed. At the same time, consider the security and recovery needs of the data, determine the backup time and frequency of the data, include detailed backup time plans and frequency settings, provide clear guidance for actual backup operations, ensure data continuity and recoverability, and generate a backup operation table.

[0098] S303: Extract the backup strategy from the backup operation table, create a temporary cache to optimize data access, set the cache time and update frequency, implement the temporary cache strategy, and obtain the data storage decision table. The execution process is as follows;

[0099] Extract the backup strategy from the backup operation table, create a temporary cache to optimize data access, set the cache time and update frequency according to the data access pattern and frequency. For example, set a shorter cache update cycle for frequently accessed data to provide fast access. For infrequently accessed data, you can set a longer cache time to reduce the system load. The implemented temporary cache strategy is managed by an automated system to ensure the real-time and effectiveness of the cache, including the cache strategy, update frequency and corresponding data category, to provide an optimized solution for data access, improve the system's response speed and efficiency, and obtain a data storage decision table.

[0100] See also Figure 5 ,Using data storage decision table, we can check the integrity of backup data and verify the cache response speed. We ,use the statistical characteristics of optical and electromagnetic storage data to optimize ,the encoding format, and dynamically adjust the compression level according to the real-time ,changes of data. The steps for generating the encoding compression ,configuration are as follows:

[0101] S401: Based on the data storage decision table, the statistical characteristics of the optical and electromagnetic storage data are analyzed to identify the normal patterns and fluctuations of the data stream. The encoding parameters are adjusted according to the differentiated data types and frequencies to optimize the processing efficiency of the optical and electromagnetic storage. The execution process of generating data statistical analysis results is as follows;

[0102] Substep S401, based on the data storage decision table, performs a crucial statistical analysis of the optical and electromagnetic storage data. By analyzing and evaluating the results, we can identify the normal patterns and fluctuations in the data stream, helping us understand how different data types behave in the optical and electromagnetic storage system. Based on these statistical characteristics, the system adjusts encoding parameters for differentiated data types and frequencies to optimize the processing efficiency of the optical and electromagnetic storage. This adjustment process involves optimizing the data storage structure and access algorithms to ensure efficient and accurate data processing, generating statistical analysis results using the following formula:

[0103] ;

[0104] in, represents the variance of the data, is the total number of data points, It is The value of the data point, is the mean of the data.

[0105] S402: Based on the statistical analysis results, the changes in the optical and electromagnetic storage data stream are monitored in real time, and the encoding format and compression parameters are dynamically adjusted to match the requirements of data storage efficiency and transmission speed, optimize the performance of data storage and transmission, and perform compression testing. The execution process of obtaining the compression parameter adjustment record is as follows;

[0106] Based on the statistical analysis results, substep S402 monitors changes in the optical and electromagnetic storage data stream in real time and dynamically adjusts the encoding format and compression parameters to match data storage efficiency and transmission speed requirements. By analyzing real-time changes in the data stream, it identifies encoding and compression settings that require optimization to improve data storage density and transmission efficiency. Dynamic adjustments include algorithm selection and parameter configuration to adapt to data stream characteristics and network conditions. Compression testing is then performed to verify the effectiveness of these adjustments, ensuring data integrity and access speed during storage and transmission, and generating a record of compression parameter adjustments.

[0107] S403: Using compression parameter adjustment records, identify and record the encoding and compression configuration of the optical and electromagnetic storage data stream, check the optimal settings of the encoding format and compression level, match the real-time network status and data characteristics, and establish the encoding and compression configuration. The execution process is as follows;

[0108] In sub-step S403, the system uses compression parameter adjustment records to identify and record the encoding and compression configuration of the optical and electromagnetic storage data stream. Through detailed recording and analysis, it verifies whether the encoding format and compression level settings are optimal, ensuring that the configuration can match the real-time network conditions and data characteristics. It provides a clear reference framework for managing and optimizing the storage and transmission of data streams, and establishes an encoding and compression configuration table using the formula:

[0109] ;

[0110] in, Indicates the adaptability of the configuration, is the total number of configurations, It is The performance response of each configuration, It is the strictness of the network and data conditions that the configuration needs to meet.

[0111] See also Figure 6 ,According to the encoding compression configuration, the validity of the data is detected, the storage layout is adjusted by regularly scanning the optical and electromagnetic storage data, and redundant data is automatically identified and cleaned. The specific steps for obtaining redundant data management records are as follows:

[0112] S501: Using the coding compression configuration, detect the validity of optical and electromagnetic data storage, perform classification scanning on the data stored in the differentiated layers, evaluate the frequency of use and update time of the data, identify outdated and unaccessed data, and generate the data validity test results. The execution process is as follows;

[0113] Substep S501 utilizes a coding compression configuration to detect the validity of optical and electromagnetic data storage, a critical process to ensure optimal data storage and management. This involves categorizing and scanning data stored at differentiated levels, including evaluating the frequency of data use and update time. The system's ability to identify outdated and unaccessed data is crucial for maintaining storage efficiency and data quality. The data validity test results are generated using the formula:

[0114] ;

[0115] in, Indicates the validity index of the data. is the total number of data entries, is the current time, It is The last access time of each data entry, is the frequency of use of the data entry.

[0116] S502: Based on the data validity test results, the entropy weight method is used to perform weighted calculations on data access frequency and criticality, and the storage structure is adjusted to optimize access efficiency and response speed. The execution process for obtaining the storage layout adjustment record is as follows;

[0117] Substep S502, based on the data validity test results, uses the entropy weighting method to weight data access frequency and criticality. This is a crucial step in optimizing the storage structure. The entropy weighting method analyzes the data's distribution characteristics to determine the weight of each data attribute, helping to identify data characteristics that are most critical to the decision-making process. This allows the system to adjust the storage structure to optimize access efficiency and response speed, and generates a storage layout adjustment record.

[0118] The formula of the entropy weight method is as follows:

[0119] ;

[0120] in, For the The weight of the data indicator, For the The information entropy of the data indicators, For custom adjustment coefficient, is the data update frequency indicator, The total number of data indicators.

[0121] The execution process is as follows:

[0122] Based on the data validity test results, calculate the information entropy of each data indicator and update frequency , set a custom adjustment coefficient for each indicator The coefficient is set by the data administrator based on the importance and sensitivity of the data to calculate the weight of each indicator It not only considers the frequency of data use and criticality, but also integrates the frequency of data updates and the administrator's subjective assessment of data importance to perform more accurate storage structure optimization.

[0123] S503: Use the storage layout adjustment record to clean up redundant data in the optical and electromagnetic storage, automatically detect and delete duplicate data entries, check the integrity and validity of the data, and establish a redundant data management record. The execution process is as follows;

[0124] Substep S503 uses the storage layout adjustment record to clean up redundant data in the optical and electromagnetic storage, a critical operation for ensuring data storage efficiency and quality. By automatically detecting and removing duplicate data entries, the system maintains data cleanliness and accuracy. This involves a thorough review of the optical and electromagnetic storage system to ensure that all data is up-to-date and relevant. The system verifies data integrity and validity, and generates redundant data management records.

[0125] See also Figure 7 ,Analyze the distribution of optical and electromagnetic storage from the redundant data ,management records, track and locate the data storage location, optimize the data ,migration path through hierarchical management, and establish the ,dynamic adjustment results of optical and electromagnetic storage as follows:

[0126] S601: Analyze the distribution of the overall optical and electromagnetic storage data using redundant data management records, identify data storage areas and space utilization areas, track the optical and electromagnetic storage locations, identify key data nodes, and generate data distribution analysis results. The execution process is as follows;

[0127] Sub-step S601 uses redundant data management records to analyze the distribution of the overall optical and electromagnetic storage data, which can optimize the storage structure, help identify data storage areas and space utilization, and provide basic data for further optimization. By tracking the optical and electromagnetic storage locations in detail, the system can identify key data nodes that contain frequently accessed or critical business data, and describe the usage of each storage area in detail, including storage density, access frequency, and space efficiency, to generate data distribution analysis results using the formula:

[0128] ;

[0129] in, Indicates the average space utilization of the storage area, is the total number of storage areas, It is The space used by the storage area, It is The total space of the storage area.

[0130] S602: Based on the data distribution analysis results, optimize inefficient storage areas, adjust optical and electromagnetic storage data to differentiated storage tiers, check data flow balance and optimal resource utilization, and plan migration paths. The execution process of the data migration strategy is as follows:

[0131] Based on the data distribution analysis results, substep S602 optimizes inefficient storage areas and relocates optical and electromagnetic storage data to differentiated storage tiers. This process involves reassessing the balance of data flows and optimal resource utilization, ensuring that data is distributed to appropriate storage tiers based on demand and importance. Migration path planning is crucial, involving calculating the optimal data migration path and method to minimize the impact on existing operations. The resulting data migration strategy is formulated as follows:

[0132] ;

[0133] in, Indicates the overall data migration efficiency, is the total amount of data being migrated, It is The access frequency of data, is the importance score of the data, It is the migration The cost of data.

[0134] S603: Using the data migration policy, executing data migration for the optical and electromagnetic storage, monitoring data migration, avoiding factors that affect data integrity and access speed, dynamically adjusting data location, integrating tiered management information, verifying migration results, and generating dynamic adjustment results for the optical and electromagnetic storage. The execution process is as follows:

[0135] Substep S603 uses the data migration strategy to execute data migration for optical and electromagnetic storage and monitors the entire migration process. This is a key step in ensuring data integrity and access speed are not compromised. By dynamically adjusting data locations and integrating information from tiered management, the system can continuously adjust and optimize data storage strategies. Monitoring and verifying the migration results are crucial to ensure that data performs as expected in the new storage environment. The dynamic adjustment results for optical and electromagnetic storage are generated using the following formula:

[0136] ;

[0137] in, represents the ratio of post-migration to pre-migration performance, is the total number of data entries, and Respectively represent The access speed of each data entry before and after migration.

[0138] See also Figure 8 An optical and electromagnetic storage system based on hierarchical management is used to execute the above-mentioned optical and electromagnetic storage method based on hierarchical management. The system includes:

[0139] The parameter adjustment module adjusts the wavelength selection parameters according to the type and priority of the data stream, assigns target wavelengths to differentiated data streams, and generates an optical wavelength configuration table;

[0140] The real-time monitoring module uses the optical wavelength configuration table to monitor the network status of the optical and electromagnetic storage data stream, adjust the frequency and phase for differentiated wavelengths, and generate frequency and phase optimization records;

[0141] The wavelength filtering module filters light waves of different wavelengths based on frequency and phase optimization records, monitors and verifies the isolation of data streams in real time, and creates light wave isolation effect evaluation results;

[0142] The coding adjustment module uses the results of optical wave isolation effect evaluation to optimize the coding format, dynamically adjust the compression level according to the changes in data flow, and generate coding compression configuration;

[0143] The data management module regularly scans the optical and electromagnetic storage data according to the coding compression configuration, adjusts the storage layout, identifies the optical and electromagnetic storage distribution density, optimizes the data migration path, and obtains the dynamic adjustment results of the optical and electromagnetic storage.

[0144] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. An optical and electromagnetic storage method based on hierarchical management, characterized in that: The following steps are involved: Collect data on the read and write speed, error rate, durability, and cost of optical and electromagnetic storage media, sort and grade the data based on read and write speed and error rate, and calculate a score for each storage medium to obtain a media performance score. Based on the media performance score, the size, access frequency, and urgency of the data to be stored are evaluated, and the data is divided into differentiated storage requirement categories by sorting and prioritizing the parameters to obtain a data performance requirement table; Through the data performance requirement table, the data category and medium performance are matched and analyzed, the optimal storage medium is selected, data backup and temporary cache are created, and a data storage decision table is generated; The data storage decision table is used to check the integrity of backup data and verify the cache response speed. The statistical characteristics of optical and electromagnetic storage data are used to optimize the encoding format and dynamically adjust the compression level according to the real-time changes of the data to generate an encoding compression configuration. Based on the data storage decision table, the statistical characteristics of the optical and electromagnetic storage data are analyzed to identify the normal pattern and volatility of the data stream, the encoding parameters are adjusted according to the differentiated data types and frequencies, the processing efficiency of the optical and electromagnetic storage is optimized, and the data statistical analysis results are generated; The formula used is: ； in, represents the variance of the data, is the total number of data points, It is The value of the data point, is the mean of the data; Based on the statistical analysis results of the data, real-time monitoring of changes in the optical and electromagnetic storage data stream is performed, and the encoding format and compression parameters are dynamically adjusted to match the requirements of data storage efficiency and transmission speed, thereby optimizing the performance of data storage and transmission. Compression testing is also performed to obtain compression parameter adjustment records; Using the compression parameter adjustment record, identifying and recording the encoding and compression configuration of the optical and electromagnetic storage data stream, checking the optimal settings of the encoding format and compression level, matching the real-time network status and data characteristics, and establishing the encoding and compression configuration; According to the coding and compression configuration, the validity of the data is detected, the storage layout is adjusted by regularly scanning the optical and electromagnetic stored data, and redundant data is automatically identified and cleared to obtain redundant data management records; The optical and electromagnetic storage distribution is analyzed from the redundant data management records, the data storage location is tracked and located, and the data migration path is optimized through hierarchical management to establish the dynamic adjustment result of the optical and electromagnetic storage.

2. The optical electromagnetic storage method based on hierarchical management according to claim 1, characterized in that: The media performance score is based on a weighted calculation of read and write speed, error rate, durability and cost. The data performance requirement table includes the size, access frequency and urgency classification of the data to be stored. The data storage decision table includes strategies for selecting the optimal storage medium, data backup and temporary cache. The encoding and compression configuration includes the encoding rule selection for each data stream, the ratio of original to compressed data and compression efficiency index. The redundant data management record includes the identified redundant data type, the amount of redundant data and the storage efficiency after processing. The dynamic adjustment results of the optical and electromagnetic storage include the data storage location before and after adjustment, data migration path optimization, migration efficiency evaluation and performance optimization index.

3. The optical electromagnetic storage method based on hierarchical management according to claim 1, characterized in that: Collect data on read / write speed, error rate, durability, and cost of optical and electromagnetic storage media. Sorting and grading the data based on read / write speed and error rate, and calculating a score for each storage medium, the steps for obtaining a media performance score are as follows: Collect data on read and write speeds, error rates, durability, and costs for optical and electromagnetic storage media, enter the data into a spreadsheet, perform initial classification on the data, organize the basic performance parameters of differentiated media, and generate a storage media dataset; Based on the storage medium data set, using a spreadsheet function to sort the read and write speed and error rate data in descending order, respectively calculating the average read and write speed and average error rate of each medium, dividing the performance level by numerical intervals, and obtaining sorted and graded data; Based on the sorted and graded data, a score for each storage medium is calculated, weights for the read and write speed and the error rate are set, and a performance score for the medium is output based on the weighted result to obtain a medium performance score.

4. The optical electromagnetic storage method based on hierarchical management according to claim 1, characterized in that: Based on the media performance score, the size, access frequency, and urgency of the data to be stored are evaluated. By sorting and prioritizing the parameters, the data is divided into differentiated storage requirement categories. The steps for obtaining the data performance requirement table are as follows: According to the medium performance score, information on the size, access frequency and urgency of the data to be stored is collected and entered into a data table, and the data is initially sorted according to the urgency to generate an initialization data sorting table; Based on the initialized data sorting table, using the sorting function of the spreadsheet, iteratively sorting the data size and access frequency, classifying the data according to access frequency and data size, and obtaining a data access classification table; By utilizing the data access classification table, differentiated storage medium priorities are assigned, data is classified into multiple storage requirement categories, and the optimal storage medium is assigned to each category of data, thereby obtaining a data performance requirement table.

5. The optical electromagnetic storage method based on hierarchical management according to claim 1, characterized in that: The steps of matching and analyzing data categories and media performance using the data performance requirement table, selecting the optimal storage medium, performing data backup and creating a temporary cache, and generating a data storage decision table are as follows: Using the data performance requirement table, compare data categories with media performance, evaluate storage efficiency and cost, perform matching analysis, set data backup time and frequency, and generate a matching analysis table; Based on the matching analysis table, the optimal storage medium is selected, a data backup strategy is planned, the data backup time and frequency are determined, and a backup operation table is generated; A backup strategy is extracted from the backup operation table, a temporary cache is created to optimize data access, the cache time and update frequency are set, the temporary cache strategy is implemented, and a data storage decision table is obtained.

6. The optical electromagnetic storage method based on hierarchical management according to claim 1, characterized in that: The steps of detecting data validity according to the coding compression configuration, adjusting the storage layout by regularly scanning the optical and electromagnetic stored data, automatically identifying and cleaning redundant data, and obtaining redundant data management records are as follows: Using the coding compression configuration, detecting the validity of optical and electromagnetic data storage, performing classification scanning on the data stored in differentiated levels, evaluating the frequency of use and update time of the data, identifying obsolete and unaccessed data, and generating data validity detection results; Based on the data validity test results, an entropy weight method is used to perform weighted calculations on data access frequency and criticality, and the storage structure is adjusted to optimize access efficiency and response speed, thereby obtaining a storage layout adjustment record. The storage layout adjustment record is used to clean up redundant data in optical and electromagnetic storage, automatically detect and delete duplicate data entries, check the integrity and validity of data, and establish redundant data management records.

7. The optical electromagnetic storage method based on hierarchical management according to claim 6, characterized in that: The formula of the entropy weight method is as follows: ； in, For the The weight of the data indicator, For the The information entropy of the data indicators, For custom adjustment coefficient, is the data update frequency indicator, The total number of data indicators.

8. The optical electromagnetic storage method based on hierarchical management according to claim 1, characterized in that: The steps of analyzing the optical and electromagnetic storage distribution from the redundant data management records, tracking and locating the data storage location, optimizing the data migration path through hierarchical management, and establishing the optical and electromagnetic storage dynamic adjustment result are as follows: Utilizing the redundant data management records, analyzing the distribution of the overall optical and electromagnetic storage data, identifying data storage areas and space utilization areas, tracking the optical and electromagnetic storage locations, identifying key data nodes, and generating data distribution analysis results; Based on the data distribution analysis results, optimize inefficient storage areas, adjust optical and electromagnetic storage data to differentiated storage tiers, check data flow balance and optimal resource usage, and plan migration paths to obtain data migration strategies. The data migration strategy is used to execute data migration of optical and electromagnetic storage, monitor data migration, avoid factors that affect data integrity and access speed, dynamically adjust data location, integrate hierarchical management information, verify migration effects, and generate dynamic adjustment results of optical and electromagnetic storage.

9. An optical and electromagnetic storage system based on hierarchical management, characterized in that: The optical and electromagnetic storage method based on hierarchical management according to any one of claims 1 to 8, wherein the system comprises: The data analysis and scoring module collects data on the read and write speed, error rate, durability, and cost of optical and electromagnetic storage media, analyzes the parameters, ranks and ranks them according to read and write speed and error rate, and calculates a score for each storage medium to obtain a media performance score. The data demand classification module analyzes the size, access frequency, and urgency of the data to be stored based on the media performance score, sorts and prioritizes the data attributes, divides the data into categories according to differentiated storage requirements, and establishes a data performance requirement table; The media selection and matching module uses the data performance requirement table to perform matching analysis between data categories and storage medium performance, selects the optimal storage medium for data backup and temporary cache creation, and forms a data storage decision table; The encoding adjustment module uses the data storage decision table to perform data backup integrity verification and cache response speed verification, optimize the encoding format, dynamically adjust the compression level according to the changes in the data flow, and generate the encoding compression configuration; The data management module regularly scans the optical and electromagnetic storage data according to the coding compression configuration, adjusts the storage layout, identifies the optical and electromagnetic storage distribution density, optimizes the data migration path, and obtains the dynamic adjustment result of the optical and electromagnetic storage.

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