An integrated management method and system for enterprise digital assets

Abstract

This application relates to the field of enterprise digital asset management, and particularly to a comprehensive management method and system for enterprise digital assets. The method includes: acquiring and analyzing enterprise digital asset information to determine a migration target; according to the migration target, performing data encoding on the enterprise digital asset information to obtain the enterprise digital asset information after data encoding; dividing the data migration priorities of the enterprise digital asset information after data encoding to obtain a migration data set corresponding to each migration priority; and sequentially migrating the corresponding migration data sets according to the data migration priorities. This application collects enterprise digital assets, determines a migration target, encodes the data and divides the migration priorities, and migrates the data according to the divided priorities, effectively solving the problem of system jamming caused by a huge amount of data.

Description

Technical Field

[0001] This application relates to the field of enterprise digital asset management, and particularly to a comprehensive management method and system for enterprise digital assets. Background Art

[0002] At present, digital management of enterprise assets has brought a lot of convenience to enterprises. By integrating and optimizing various digital assets, it enhances the value utilization of data, strengthens decision-making support, optimizes operation processes, improves resource efficiency, ensures data security, and promotes business innovation and growth, thus comprehensively enhancing the competitiveness and market response ability of enterprises.

[0003] However, since enterprises face the problem of huge amounts of asset data during the process of managing digital assets, this problem may cause system lag during management due to the huge amount of data, thus affecting asset management. Summary of the Invention

[0004] This application provides a comprehensive management method and system for enterprise digital assets to solve the above problems.

[0005] In a first aspect, this application provides a comprehensive management method for enterprise digital assets, including:

[0006] Obtaining and analyzing enterprise digital asset information to determine migration targets;

[0007] According to the migration targets, encoding the enterprise digital asset information to obtain the encoded enterprise digital asset information;

[0008] Dividing the encoded enterprise digital asset information into data migration priorities to obtain a migration data set corresponding to each migration priority; and migrating the corresponding migration data sets in sequence according to the data migration priorities.

[0009] Through the above technical solutions, determining migration targets can more accurately obtain the enterprise digital asset information that needs to be migrated, providing more accurate preliminary preparations for data migration. Encoding the enterprise digital asset information can make the asset information more standardized and normalized, facilitating quick retrieval, classification, and statistics in the digital management system, thus significantly improving management efficiency, reducing human errors, and ensuring data accuracy. Dividing the enterprise digital asset information into data migration priorities can make data migration more orderly and reasonable, ensuring the stability of data migration. Migrating the corresponding migration data sets in sequence can effectively solve the problem of system lag caused by huge amounts of data, ensuring the smooth operation of the system.

[0010] Optionally, the obtaining and analyzing enterprise digital asset information and determining the migration target include:

[0011] Obtain the usage requirements for the enterprise to perform data migration, and determine the information collection scope according to the usage requirements;

[0012] Obtain the enterprise digital asset information according to the information collection scope;

[0013] Extract information from the enterprise digital asset information to obtain corresponding information features; analyze the information features, and determine the migration target according to the feature analysis results.

[0014] Through the above technical solution, determining the information collection scope through usage requirements, analyzing the features of the collected information and determining the migration target according to the feature analysis results can ensure the practicality of the collected information, and ensure that there are no omissions and oversights in the process of collecting and organizing the information. When determining the migration target, it can also ensure the accuracy and efficiency of determining the migration target.

[0015] Optionally, the encoding the enterprise digital asset information according to the migration target to obtain the encoded enterprise digital asset information includes:

[0016] Classify the enterprise digital asset information according to the feature analysis results, and obtain several datasets to be encoded according to the classification results;

[0017] Analyze each dataset to be encoded according to the classification results, and determine the data usage of each dataset to be encoded;

[0018] Encode each dataset to be encoded according to the data usage to obtain the encoded enterprise digital asset information.

[0019] Through the above technical solution, through systematic classification, the huge digital asset information is organized in an orderly manner, and this classification makes the data more structured. Clear data usage analysis and targeted data encoding can ensure the consistency and standardization of the data format, reduce errors in data analysis, and achieve efficient organization and management of enterprise digital asset information.

[0020] Optionally, the obtaining several datasets to be encoded according to the classification results includes:

[0021] Convert the format of the enterprise digital asset information, and perform redundancy analysis on the converted enterprise digital asset information to determine whether there is redundant information;

[0022] Extract the information content and location of the redundant information;

[0023] Analyzing the information content based on the location, and determining the necessity of the redundant information according to the information content analysis result;

[0024] Determining whether to delete the redundant information according to the necessity;

[0025] If it is determined to delete, the redundant information is deleted, and a data set to be encoded is obtained according to the classification result.

[0026] Through the above technical solutions, data management efficiency and system performance have been significantly improved through format conversion, redundancy analysis, information content analysis and location positioning, necessity judgment and redundancy deletion. Its advantages are that it can accurately identify and remove unnecessary redundant information, reduce storage space usage, improve data processing speed, and ensure data integrity and accuracy, laying a solid foundation for subsequent data coding and further analysis.

[0027] Optionally, analyzing each to-be-encoded data set according to the classification result to determine the data usage of each to-be-encoded data set includes:

[0028] For each data set to be encoded, extract the data content of each data to be encoded, and determine the usage frequency and several usage locations of each data to be encoded according to the data content extraction result;

[0029] Analyze the plurality of usage locations and determine a location weight of each usage location;

[0030] Sort the position weight of each use position, and determine the use position with the highest position weight according to the sorting result;

[0031] Determine the data usage of each to-be-encoded data according to the usage position with the highest position weight and the usage frequency;

[0032] According to the data usage of each to-be-encoded data set, the data usage of the corresponding to-be-encoded data set is determined.

[0033] Through the above technical solution, by extracting the content of the data to be encoded, analyzing the usage frequency and position weight, the main purpose of the data is determined, and the purpose of the entire data set is inferred accordingly. It can provide in-depth insights into the usage patterns and value of data, making data encoding and classification more accurate and efficient. This analysis method based on actual usage helps to optimize the data storage structure, improve the efficiency of data retrieval and application, and also helps to tap the potential value of data, providing strong support for corporate decision-making and business development.

[0034] Optionally, converting the enterprise digital asset information into a format, performing redundancy analysis on the converted enterprise digital asset information, and determining whether there is redundant information includes:

[0035] Performing a security check on the enterprise digital asset information, and determining whether there is a security problem according to the check result;

[0036] If there is a security problem, determining the problem data according to the check result;

[0037] Determining the problem type of the security problem according to the problem data, and processing the problem according to the problem type to obtain security data;

[0038] Converting the format of the security data to obtain normalized security data;

[0039] Determining the data type of the normalized security data according to the preset format conversion logic;

[0040] Determining a redundancy analysis method according to the data type, and performing redundancy analysis on the normalized security data according to the redundancy analysis method to determine whether there is redundant information.

[0041] Through the above technical solutions, a comprehensive security check, accurate identification and classification processing of problem data, normalization processing of security data, and redundancy analysis based on data type are used to construct an efficient and secure data management system. Its advantage is that it can timely discover and solve potential security hazards, ensure the integrity and security of data; at the same time, through format normalization and redundancy analysis based on data type, the efficiency and accuracy of data processing are improved, data redundancy is reduced, and solid data support is provided for enterprise digital asset management.

[0042] Optionally, migrating the corresponding migration data sets in sequence according to the data migration priority includes:

[0043] Obtaining migration channel information and calculating the data length of each migration data set;

[0044] Analyzing the migration channel information to determine the unit migration amount of each migration channel;

[0045] Comparing the data length with the unit migration amount of each migration channel respectively to determine the migration proportion of the data length;

[0046] Allocating each migration data set according to the migration priority and the migration proportion to obtain an allocation result;

[0047] Migrating the corresponding migration data sets in sequence according to the allocation result.

[0048] Through the above technical solution, by precisely analyzing the migration channel information, calculating the matching degree between the data length and the unit migration amount, and combining the migration priority, the allocation and migration order of the data set are optimized. This method not only improves the efficiency and accuracy of data migration, but also ensures that high-priority data can be processed first, thereby effectively managing resources and reducing the waiting time and potential resource conflicts during the migration process.

[0049] Optionally, when comparing the data length with the unit migration amount of each migration channel to determine the migration proportion of the data length, refer to the following formula:

[0050] ;

[0051] where, represents the migration proportion of the th migration data set, represents the data length of the th migration data set, represents the priority factor of the th migration data set, represents the dependency factor between the th migration data set and the migration data set with a dependency relationship, represents the maximum dependency factor between the th migration data set and the migration data set with a dependency relationship, represents the maximum priority factor of the th migration data set.

[0052] Through the above technical solution, by precisely analyzing the migration channel information, calculating the matching degree between the data length and the unit migration amount, and combining the migration priority, the allocation and migration order of the data set are optimized. This method not only improves the efficiency and accuracy of data migration, but also ensures that high-priority data can be processed first, thereby effectively managing resources and reducing the waiting time and potential resource conflicts during the migration process.

[0053] Optionally, for the calculation of the priority factor, refer to the following formula:

[0054] ;

[0055] where, represents the priority factor, represents the weight of the data usage, represents the weight of the usage purpose, represents the evaluation score of the data usage of the th migration data set, represents the The evaluation score of the data usage frequency of a migration dataset;

[0056] The calculation of the dependency factor includes:

[0057] Analyze each migration dataset, and determine whether there is a dependency relationship between any two migration datasets according to the dataset analysis results;

[0058] If there is a dependency relationship between any two migration datasets, determine the dependency level according to the dependency relationship;

[0059] Calculate the dependency factor according to the dependency level, referring to the following formula:

[0060] ;

[0061] Wherein, represents the th dependency level of the migration dataset with a migration dataset dependency relationship, represents the th maximum dependency level of the migration dataset with a migration dataset dependency relationship.

[0062] Through the above technical solution, not only the basic factor of data length is considered, but also the priority factor and the dependency factor are incorporated, and the sum of the maximum values of these factors is used as the basis for normalization, thus ensuring the fairness and efficiency of the migration strategy. This calculation method helps to preferentially migrate those datasets that are both important and urgent and have the least impact on the dependencies of other datasets in the case of limited resources, thereby optimizing the efficiency and effect of the overall migration process.

[0063] In a second aspect, the present application provides an enterprise digital asset comprehensive management system, and the system includes:

[0064] An acquisition and analysis module, which acquires and analyzes enterprise digital asset information to determine a migration target;

[0065] A data encoding module, which encodes the enterprise digital asset information according to the migration target to obtain the encoded enterprise digital asset information;

[0066] A data migration module, which divides the data migration priorities of the encoded enterprise digital asset information to obtain migration datasets corresponding to each migration priority; and migrates the corresponding migration datasets in sequence according to the data migration priorities.

[0067] Optionally, the acquisition and analysis module is specifically configured to:

[0068] Obtain the usage requirements for the enterprise to perform data migration, and determine the information collection scope according to the usage requirements;

[0069] Obtain the enterprise digital asset information according to the information collection scope;

[0070] Extract information from the enterprise digital asset information to obtain corresponding information features; analyze the information features, and determine the migration target according to the feature analysis results.

[0071] Optionally, the data encoding module is specifically used for:

[0072] Classify the enterprise digital asset information according to the feature analysis results, and obtain a number of datasets to be encoded according to the classification results;

[0073] Analyze each dataset to be encoded according to the classification results, and determine the data usage of each dataset to be encoded;

[0074] Encode each dataset to be encoded according to the data usage to obtain the encoded enterprise digital asset information.

[0075] Optionally, the data encoding module is specifically used for:

[0076] Convert the format of the enterprise digital asset information, and perform redundancy analysis on the converted enterprise digital asset information to determine whether there is redundant information;

[0077] Extract the information content and location of the redundant information;

[0078] Analyze the information content based on the location, and determine the necessity of the existence of the redundant information according to the information content analysis results;

[0079] Determine whether to delete the redundant information according to the necessity of existence;

[0080] If it is determined to delete, delete the redundant information, and obtain the datasets to be encoded according to the classification results.

[0081] Optionally, the data encoding module is specifically used for:

[0082] For each dataset to be encoded, extract the data content of each data to be encoded, and determine the usage frequency and several usage locations of each data to be encoded according to the data content extraction results;

[0083] Analyze the several usage locations to determine the location weight of each usage location;

[0084] Sort the position weights of each usage location, and determine the usage location with the highest position weight according to the sorting result.

[0085] Determine the data usage of each data to be encoded according to the usage location with the highest position weight and the usage frequency.

[0086] Determine the data usage of the corresponding dataset to be encoded according to the data usage of each data to be encoded.

[0087] Optionally, obtain an analysis module, which is specifically used for:

[0088] Perform a security check on the enterprise digital asset information, and determine whether there are security issues according to the check result.

[0089] If there are security issues, determine the problematic data according to the check result.

[0090] Determine the problem type of the security issue according to the problematic data, and perform problem handling according to the problem type to obtain secure data.

[0091] Convert the format of the secure data to obtain normalized secure data.

[0092] Determine the data type of the normalized secure data according to the preset format conversion logic.

[0093] Determine the redundancy analysis method according to the data type, and perform redundancy analysis on the normalized secure data according to the redundancy analysis method to determine whether there is redundant information.

[0094] Optionally, a data migration module, which is specifically used for:

[0095] Obtain migration channel information and calculate the data length of each migration dataset.

[0096] Analyze the migration channel information to determine the unit migration amount of each migration channel.

[0097] Compare the data lengths with the unit migration amounts of each migration channel respectively to determine the migration proportion of the data lengths.

[0098] Allocate each migration dataset according to the migration priority and the migration proportion to obtain an allocation result.

[0099] Migrate the corresponding migration datasets sequentially according to the allocation result.

[0100] Optionally, a data migration module, which is specifically used for:

[0101] ;

[0102] Among them, represents the migration proportion of the th migration dataset, represents the data length of the th migration dataset, represents the priority factor of the th migration dataset, represents the dependency factor between the th migration dataset and the migration dataset with a dependency relationship, represents the maximum dependency factor between the th migration dataset and the migration dataset with a dependency relationship, represents the maximum priority factor of the th migration dataset.

[0103] Optionally, the data migration module is specifically used for:

[0104] ;

[0105] Among them, represents the priority factor, represents the weight of the data usage, represents the weight of the usage purpose, represents the evaluation score of the data usage of the th migration dataset, represents the evaluation score of the data usage frequency of the th migration dataset;

[0106] The enterprise digital asset comprehensive management system further includes a factor calculation module, which is specifically used for:

[0107] Analyze each migration dataset, and determine whether there is a dependency relationship between any two migration datasets according to the dataset analysis result;

[0108] If there is a dependency relationship between any two migration datasets, determine the dependency level according to the dependency relationship;

[0109] Calculate the dependency factor according to the dependency level, referring to the following formula:

[0110] ;

[0111] Among them, represents the th dependency level of the migration dataset with a migration dataset dependency relationship, represents the th maximum dependency level of the migration dataset with a migration dataset dependency relationship. Brief Description of the Drawings

[0112] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0113] Figure 1 It is a schematic diagram of an application scenario provided by an embodiment of the present application;

[0114] Figure 2 It is a flowchart of a comprehensive enterprise digital asset management method provided by an embodiment of the present application;

[0115] Figure 3 It is a schematic structural diagram of a comprehensive enterprise digital asset management system provided by an embodiment of the present application. Detailed Description of the Embodiments

[0116] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts fall within the scope of protection of the present application.

[0117] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after, unless otherwise specified.

[0118] The following will further describe the embodiments of the present application in detail with reference to the drawings of the specification.

[0119] In the process of managing enterprise digital assets, due to the huge amount of data in enterprise digital asset information, the management system will be stuck during the management process, affecting the system operation and thus the enterprise management efficiency.

[0120] Based on this, the present application provides a comprehensive management method for enterprise digital assets, which acquires and analyzes enterprise digital asset information and determines migration targets, enabling more accurate acquisition of the enterprise digital asset information that needs to be migrated and providing more precise preliminary preparations for data migration. Data encoding of enterprise digital asset information can make the asset information more standardized and normalized, facilitating rapid retrieval, classification, and statistics in a digital management system, thereby significantly improving management efficiency, reducing human errors, and ensuring data accuracy. Division of the data migration priority for enterprise digital asset information can make data migration more orderly and reasonable, ensuring the stability of data migration. Sequentially migrating the corresponding migration data sets can effectively solve the problem of system lag caused by a large amount of data and ensure the smooth operation of the system.

[0121] Figure 1 FIG. is a schematic diagram of an application scenario provided by the present application. When an enterprise conducts digital asset management, the method provided by the present application is applied.

[0122] Specifically, the method provided by the present application is applied to any server. The server collects enterprise digital asset information that needs to be migrated, performs data encoding on the collected data, divides the priority of the data after data encoding, and migrates the data according to the divided priority, making the server more efficient and smooth during the data migration process and improving asset management efficiency.

[0123] The specific implementation manner can refer to the following embodiments.

[0124] Figure 2 FIG. is a flowchart of a comprehensive management method for enterprise digital assets provided by an embodiment of the present application. The method of this embodiment can be applied to the server in the above scenario. As Figure 2 shown, the method includes:

[0125] S201. Acquire and analyze enterprise digital asset information and determine migration targets.

[0126] The migration target can be the corresponding target when migrating enterprise digital asset information.

[0127] Enterprise digital asset information can be all the data generated by an enterprise during its operation.

[0128] Specifically, formulate a template for collecting enterprise digital asset information, analyze the usage types and characteristics of the collected enterprise digital asset information. The enterprise digital asset information can be divided into three usage types: internal, external, and public according to the usage scope. The characteristics of the enterprise digital asset information can be practicality, technicality, and confidentiality. Based on the analysis results of the usage types and characteristics, the corresponding migration targets can be determined.

[0129] S202. According to the migration target, encode the enterprise digital asset information to obtain the encoded enterprise digital asset information.

[0130] Data encoding can be used to encode the obtained data to achieve data differentiation or data marking, and can be implemented using numerical data encoding techniques.

[0131] Specifically, according to the migration target, design an encoding scheme and corresponding encoding principles, and encode the data according to the encoding scheme to obtain the encoded enterprise digital asset information. The already obtained enterprise digital asset information can be classified to determine which data needs to be encoded and which data does not need to be encoded. After obtaining the data that needs to be encoded, encode the data according to the data encoding rules. For example, add the corresponding byte length at the end of the data to obtain the encoded enterprise digital asset information.

[0132] S203. Divide the encoded enterprise digital asset information into data migration priorities to obtain a migration data set corresponding to each migration priority; migrate the corresponding migration data sets in sequence according to the data migration priorities.

[0133] The data migration priority can be the migration level when the data is migrated, which can be used as the basis for judging the migration order.

[0134] The migration data set can be a set of data with the same data migration priority obtained after dividing the data into priorities.

[0135] Specifically, when dividing the encoded enterprise digital asset information into priorities, it can be divided from high priority to low priority according to the urgency of data use. For example, data that needs to be used immediately is divided into high priority, and data with relatively less urgent use needs is divided into low priority. After dividing the data migration priorities, determine the corresponding migration data sets according to the completed priorities. When dividing the migration data sets, first arrange the migration data sets according to the divided priorities, and then migrate the migration data sets in sequence according to the arranged order to ensure the smooth progress of the operation.

[0136] Through the method provided in this embodiment, by analyzing the obtained enterprise digital asset information to determine the migration target, it is possible to more accurately obtain the enterprise digital asset information that needs to be migrated, providing more accurate preliminary preparations for data migration. Encoding the enterprise digital asset information can make the asset information more standardized and normalized, facilitating quick retrieval, classification, and statistics in the digital management system, thereby significantly improving management efficiency, reducing human errors, and ensuring data accuracy. Dividing the priority of data migration for enterprise digital asset information can make data migration more orderly and reasonable, ensuring the stability of data migration. Sequentially migrating the corresponding migration data sets can effectively solve the problem of system jamming caused by a huge amount of data, ensuring the smooth operation of the system.

[0137] In some embodiments, obtain the usage requirements for the enterprise to perform data migration. According to the usage requirements, determine the information collection scope; according to the information collection scope, obtain the enterprise digital asset information; extract information from the enterprise digital asset information to obtain the corresponding information features; analyze the information features, and according to the feature analysis results, determine the migration target.

[0138] The usage requirements can be the requirements for enterprise digital asset information required by the enterprise according to the development of enterprise projects or other reasons.

[0139] The information collection scope can be the scope of information that meets the usage requirements, such as enterprise logos, enterprise equipment, enterprise websites, and enterprise personnel attendance information.

[0140] The information features can be the type and structure of the data.

[0141] Specifically, when obtaining the usage requirements for the data, it is possible to analyze the current relevant development of the enterprise that has been collected to obtain the usage requirements. Based on the usage requirements, determine which enterprise digital asset information needs to be collected, and use data mining technology and machine learning technology to extract key features from the collected information. Analyze the extracted key features to understand the data distribution, correlation, outliers, etc. According to the feature analysis results, determine the migration target.

[0142] Through the method provided in this embodiment, by determining the information collection scope through usage requirements, analyzing the features of the collected information, and determining the migration target according to the feature analysis results, it is possible to ensure the practicality of the collected information, and ensure that there are no omissions and oversights in the process of collecting and organizing the information. When determining the migration target, it is also possible to ensure the accuracy and efficiency of determining the migration target.

[0143] In some embodiments, according to the feature analysis results, the enterprise digital asset information is classified, and several datasets to be encoded are obtained according to the classification results; according to the classification results, each dataset to be encoded is analyzed to determine the data usage of each dataset to be encoded; according to the data usage, each dataset to be encoded is encoded to obtain the enterprise digital asset information after data encoding.

[0144] The dataset to be encoded can be each type of dataset that needs to be encoded according to the result of classification based on information.

[0145] The data usage can be the support of the data for enterprise decision-making, the prediction and evaluation of the market environment, and the management of customer relationships during enterprise operation.

[0146] Specifically, the analysis of data features includes but is not limited to identifying key attributes, structures, formats, contents, etc. in the data. Based on the results of feature analysis, these digital asset information are classified according to certain criteria. The classification criteria can be based on the type, source, and business domain of the data. After classification, the data in each category or subcategory will form an independent dataset to be encoded. For each dataset to be encoded, its specific usage in enterprise operation, decision support, compliance, etc., that is, the data usage, can be clarified.

[0147] Analyze the encoding scheme established in the above embodiments to determine the encoding rules. According to the encoding rules, encode the data in each dataset. After encoding, perform encoding verification to ensure that the encoded data conforms to the encoding rules and the data quality is not affected. After the above operations, the enterprise digital asset information after data encoding is obtained.

[0148] Through the method provided in this embodiment, through systematic classification, the huge digital asset information is organized in an orderly manner, and this classification makes the data more structured. Clear data usage analysis and targeted data encoding can ensure the consistency and standardization of data formats, reduce errors in data analysis, and realize the efficient organization and management of enterprise digital asset information.

[0149] In some embodiments, convert the format of the enterprise digital asset information, perform redundancy analysis on the converted enterprise digital asset information to determine whether there is redundant information; extract the information content and location of the redundant information; based on the location, analyze the information content, and determine the necessity of the existence of the redundant information according to the information content analysis results; determine whether to delete the redundant information according to the necessity of existence; if it is determined to delete, delete the redundant information, and obtain the dataset to be encoded according to the classification results.

[0150] Format conversion can be the conversion of document formats, audio and video formats, image formats, and data.

[0151] Redundancy analysis can be the redundancy analysis of data and the redundancy analysis of information.

[0152] Redundant information can be the repetition or unnecessary existence of information.

[0153] Specifically, for data format conversion, for example, uniformly converting data into data represented by strings, and normalizing the data in this way. The method of hash value verification can be used to scan the data after format conversion, identify duplicate, similar or irrelevant data, and understand the reasons for the generation of redundant data, which can be data entry errors, repeated imports, system errors, etc. Analyze the impact of redundant data on the overall quality and usage efficiency of enterprise digital asset information, analyze by integrating redundant information and its location, judge whether redundant information needs to be deleted, and classify and organize the data of the information after redundancy processing to obtain the dataset to be encoded.

[0154] Since the analysis of redundant information includes but is not limited to simple redundant information and information that is redundant but must exist, when judging redundant information, it is necessary to judge the necessity of the existence of redundant information.

[0155] For example, if the company name and company logo appear simultaneously in multiple contracts, it is necessary to judge the location and redundant information of the repeatedly appearing company name and company logo. Since in the above situation, the company name and company logo cannot be deleted from the contract, this situation does not belong to the category of redundant information, that is, it is considered that the redundant information in this situation has the necessity to exist, and the redundant information that appears cannot be deleted.

[0156] Through the method provided in this embodiment, through steps such as format conversion, redundancy analysis, information content analysis and location positioning, necessity of existence judgment, and redundancy deletion, the data management efficiency and system performance are significantly improved. Its advantage lies in being able to accurately identify and remove unnecessary redundant information, reduce storage space occupation, improve data processing speed, while ensuring the integrity and accuracy of data, laying a solid foundation for subsequent data encoding and further analysis.

[0157] In some embodiments, for each data set to be encoded, the data content of each data to be encoded is extracted, and based on the data content extraction result, the usage frequency and several usage positions of each data to be encoded are determined; the several usage positions are analyzed to determine the position weight of each usage position; the position weight of each usage position is sorted, and based on the sorting result, the usage position with the highest position weight is determined; based on the usage position with the highest position weight and the usage frequency, the data purpose of each data to be encoded is determined; based on the data purpose of each data to be encoded, the data purpose of the corresponding data set to be encoded is determined.

[0158] Usage frequency can be the frequency with which an enterprise’s digital asset information is used after being accessed.

[0159] The usage location can be the specific environment in which the data is used.

[0160] The location weight can be the importance of the data function in the location used.

[0161] Specifically, each data set to be coded is traversed, and the data entries are checked one by one. For each data entry, its key data content is extracted, and the characteristics of each data entry are recorded, including data type, length, format, etc. For each data entry, the number of times it is used in different scenarios is counted to obtain the frequency of use. When the data is used, the specific location where it is used is recorded, such as in a certain system, module or function. The recorded usage locations are classified, and the importance of each usage location is evaluated and assigned corresponding weights. All usage locations are sorted from high to low according to their weight values, and the usage location with the highest weight is selected from the sorted list. The specific purpose of the data is determined based on the business nature of the highest weight location and the way the data is used. The purpose of each data entry in the data set is summarized, and the main application direction of the entire data set in the business is analyzed based on the purpose of a single data entry. After comprehensive analysis, the main data purpose of the entire data set to be coded is determined.

[0162] Through the method provided in this embodiment, by extracting the content of the data to be encoded, analyzing the frequency of use and position weight, the main purpose of the data is determined, and the purpose of the entire data set is inferred accordingly. It can provide in-depth insights into the usage patterns and value of data, making data encoding and classification more accurate and efficient. This analysis method based on actual usage helps to optimize the data storage structure, improve the efficiency of data retrieval and application, and also helps to tap the potential value of data, providing strong support for corporate decision-making and business development.

[0163] In some embodiments, security checks are performed on enterprise digital asset information. According to the inspection results, it is determined whether there are security issues. If there are security issues, problem data is determined based on the inspection results. Based on the problem data, the problem type of the security issue is determined, and problem handling is performed according to the problem type to obtain security data. The security data is format-converted to obtain normalized security data. According to the preset format conversion logic, the data type of the normalized security data is determined. According to the data type, the redundancy analysis method is determined, and the normalized security data is redundantly analyzed according to the redundancy analysis method to determine whether there is redundant information.

[0164] The problem data can be data with viruses, Trojans, garbled characters, and errors.

[0165] The preset format conversion logic can be to uniformly convert the data into the form of a string.

[0166] Specifically, inspections for viruses, Trojans, garbled characters, and errors are performed on enterprise digital asset information. For example, if the data has viruses and Trojans, the problem data needs to be isolated and scanned for viruses. If there is a problem with garbled data, the garbled data needs to be judged. If only part of the data is garbled, the garbled part is cropped and the entire data segment is compared with the backup data. The data at the corresponding garbled position in the backup data is cropped, and the cropped data is pasted into the originally garbled data to ensure the correct modification of the garbled data. If there is an error in the data, the content and format of the error data are checked to determine whether it is a content problem or a format problem. If there is a content problem, the error data is compared with the backup data, and the error data is corrected according to the content of the backup data. If there is a format problem, the backup data is extracted, the format of the backup data is converted, and the converted data is used to replace the previously format-incorrect data. When format-converting the security data, the data can be uniformly converted into the string format to obtain normalized security data.

[0167] The converted normalized security data is verified to ensure the accuracy of the data type. A redundancy analysis strategy is formulated to determine the threshold and standard for redundancy analysis. The normalized security data is scanned to explore the reasons for the generation of redundant information. According to the results of the redundancy analysis, the proportion and severity of the redundant information in the data are evaluated, and it is judged whether these redundant information will affect subsequent data analysis or business operations.

[0168] Through the method provided in this embodiment, an efficient and secure data management system is constructed through comprehensive security checks, precise identification and classification of problem data, normalization of security data, and redundancy analysis based on data types. Its advantages lie in being able to detect and solve potential security hazards in a timely manner, ensuring the integrity and security of data; at the same time, through format normalization and redundancy analysis based on data types, the efficiency and accuracy of data processing are improved, data redundancy is reduced, providing solid data support for enterprise digital asset management.

[0169] In some embodiments, migration channel information is obtained, and the data length of each migration data set is calculated; the migration channel information is analyzed to determine the unit migration amount of each migration channel; the data length is compared with the unit migration amount of each migration channel respectively to determine the migration proportion of the data length; according to the migration priority and the migration proportion, each migration data set is allocated to obtain an allocation result; according to the allocation result, the corresponding migration data sets are migrated in sequence.

[0170] The migration channel information may be detailed information and configurations related to the channels involved in the data migration process.

[0171] The unit migration amount may be the amount of data that the migration channel can migrate per unit time.

[0172] The migration proportion may be the ratio of the data length to the unit migration amount of each migration channel.

[0173] The migration priority may be the sorting of the migration data sets during the migration process.

[0174] Specifically, collect information on all available migration channels, including key parameters such as the bandwidth, stability, and latency of the channels, calculate the data length of each migration data set, determine the amount of data that each channel can migrate per unit time, that is, the unit migration amount, according to the migration channel information (such as bandwidth, transmission speed, etc.), compare the data length with the unit migration amount of each migration channel respectively, calculate the migration proportion of each data set on a specific channel according to the comparison result, allocate each migration data set according to the migration priority and the migration proportion, and migrate the corresponding migration data sets in sequence according to the allocation result.

[0175] Through the method provided in this embodiment, by precisely analyzing the migration channel information, calculating the matching degree between the data length and the unit migration amount, and combining the migration priority to optimize the allocation and migration order of the data sets. This method not only improves the efficiency and accuracy of data migration, but also ensures that high-priority data can be processed first, thereby effectively managing resources and reducing the waiting time and potential resource conflicts during the migration process.

[0176] In some embodiments, the data length is compared with the unit migration amount of each migration channel to determine the migration proportion of the data length, referring to the following formula.

[0177] The priority factor can be a numerical value assigned to a relative importance or urgency of the migration data set.

[0178] The dependency factor can be a measure of the degree of dependence between the migration data set and other migration data sets with which it has a dependency relationship.

[0179] ;

[0180] Wherein, represents the migration proportion of the th migration data set, represents the data length of the th migration data set, represents the priority factor of the th migration data set, represents the dependency factor between the th migration data set and the migration data sets with which it has a dependency relationship, represents the maximum dependency factor between the th migration data set and the migration data sets with which it has a dependency relationship, represents the maximum priority factor of the th migration data set.

[0181] Specifically, during the data migration process, by multiplying the data length, dependency factor, and priority factor of the th migration data set, the numerator is obtained. Multiply the sum of the data lengths by the maximum value of the priority factor, the maximum value of the dependency factor, and the data length of the th migration data set to obtain the denominator. Divide the numerator by the denominator to obtain the migration proportion.

[0182] Through the method provided in this embodiment, not only the basic factor of data length is considered, but also the priority factor and dependency factor are incorporated, and the sum of the maximum values of these factors is used as the normalization basis, thus ensuring the fairness and efficiency of the migration strategy. This calculation method helps to preferentially migrate those data sets that are both important and urgent and have the least impact on the dependence of other data sets in the case of limited resources, thereby optimizing the efficiency and effect of the overall migration process.

[0183] In some embodiments, the calculation of the priority factor refers to the following formula:

[0184] ;

[0185] Wherein, Represents a priority factor, Represents the weight of data usage, Represents the weight of usage, Represents the evaluation score of the data usage of the Represents the evaluation score of the data usage frequency of the

[0186] Calculation of the dependency factor, including:

[0187] Analyze each migration dataset, and determine whether there is a dependency relationship between any two migration datasets according to the dataset analysis results;

[0188] If there is a dependency relationship between any two migration datasets, determine the dependency level according to the dependency relationship;

[0189] Calculate the dependency factor according to the dependency level, referring to the following formula:

[0190] ;

[0191] Among them, Represents the dependency level of the Represents the maximum dependency level of the

[0192] Specifically, multiply the weight of the th data usage by the evaluation score of the data usage of the th migration dataset, and add the product of the weight of the th data usage by the evaluation score of the data usage frequency of the th migration dataset, to obtain the calculation result of the priority factor of the th migration dataset.

[0193] Divide the th migration dataset's dependency level with the migration dataset dependency by the th migration dataset's maximum dependency level with the migration dataset dependency, to obtain the dependency factor of the

[0194] Through the method provided in this embodiment, by combining the weights of data usage and the weights of usage frequencies to calculate the priority factor, it is possible to ensure that data that is more important or more frequently used for the business obtains a higher migration priority. At the same time, considering the dependency relationships between the migration data sets and calculating the dependency factor accordingly can ensure that, during the migration process, data sets that highly depend on other data sets can be migrated in the correct order and priority, thereby avoiding the risks of data inconsistency and migration failure and improving the overall efficiency and success rate of data migration.

[0195] Figure 3 The following is a schematic structural diagram of an enterprise digital asset comprehensive management system provided by an embodiment of the present application. As Figure 3 shown, the enterprise digital asset comprehensive management system 300 of this embodiment includes: an acquisition and analysis module 301, a data encoding module 302, and a data migration module 303.

[0196] The acquisition and analysis module 301 is configured to acquire and analyze enterprise digital asset information and determine the migration target.

[0197] The data encoding module 302 encodes the enterprise digital asset information according to the migration target to obtain the encoded enterprise digital asset information.

[0198] The data migration module 303 divides the priority of data migration for the encoded enterprise digital asset information to obtain migration data sets corresponding to each migration priority; and migrates the corresponding migration data sets in sequence according to the data migration priority.

[0199] Optionally, the acquisition and analysis module 301 is specifically configured to:

[0200] Acquire the usage requirements of the enterprise for data migration, and determine the information collection scope according to the usage requirements;

[0201] Acquire the enterprise digital asset information according to the information collection scope;

[0202] Extract information from the enterprise digital asset information to obtain corresponding information features;

[0203] Analyze the information features, and determine the migration target according to the feature analysis result.

[0204] Optionally, the data encoding module 302 is specifically configured to:

[0205] Classify the enterprise digital asset information according to the feature analysis result, and obtain a number of data sets to be encoded according to the classification result;

[0206] According to the classification results, analyze each dataset to be encoded, and determine the data usage of each dataset to be encoded;

[0207] According to the data usage, perform data encoding on each dataset to be encoded, and obtain the enterprise digital asset information after data encoding;

[0208] Optionally, the data migration module 303 is specifically used for:

[0209] Obtain migration channel information and calculate the data length of each migration dataset;

[0210] Analyze the migration channel information to determine the unit migration volume of each migration channel;

[0211] Compare the data length with the unit migration volume of each migration channel respectively to determine the migration proportion of the data length;

[0212] Compare the data length with the unit migration volume of each migration channel respectively to determine the migration proportion of the data length, referring to the following formula:

[0213] ;

[0214] Where represents the migration proportion of the th migration dataset, represents the data length of the th migration dataset, represents the priority factor of the th migration dataset, represents the dependency factor between the th migration dataset and the migration dataset with a dependency relationship, represents the maximum dependency factor between the th migration dataset and the migration dataset with a dependency relationship, represents the maximum priority factor of the th migration dataset;

[0215] Allocate each migration dataset according to the migration priority and the migration proportion to obtain an allocation result;

[0216] Migrate the corresponding migration datasets in sequence according to the allocation result.

[0217] Optionally, the acquisition and analysis module 301 is specifically used for:

[0218] Obtain the usage requirements of the enterprise for data migration, and determine the information collection scope according to the usage requirements;

[0219] Obtain the enterprise digital asset information according to the information collection scope;

[0220] Extract information from the enterprise digital asset information to obtain corresponding information features; analyze the information features, and determine the migration target according to the feature analysis results.

[0221] Optionally, the data encoding module 302 is specifically used for:

[0222] Classify the enterprise digital asset information according to the feature analysis results, and obtain several data sets to be encoded according to the classification results;

[0223] Analyze each data set to be encoded according to the classification results, and determine the data usage of each data set to be encoded;

[0224] Encode each data set to be encoded according to the data usage to obtain the enterprise digital asset information after data encoding.

[0225] Optionally, the data encoding module 302 is specifically used for:

[0226] Convert the format of the enterprise digital asset information, and perform redundancy analysis on the converted enterprise digital asset information to determine whether there is redundant information;

[0227] Extract the information content and location of the redundant information;

[0228] Analyze the information content based on the location, and determine the necessity of the existence of the redundant information according to the information content analysis results;

[0229] Determine whether to delete the redundant information according to the necessity of existence;

[0230] If it is determined to delete, delete the redundant information, and obtain the data sets to be encoded according to the classification results.

[0231] Optionally, the data encoding module 302 is specifically used for:

[0232] For each data set to be encoded, extract the data content of each data to be encoded, and determine the usage frequency and several usage locations of each data to be encoded according to the data content extraction results;

[0233] Analyze the several usage locations to determine the location weight of each usage location;

[0234] Sort the location weights of each usage location, and determine the usage location with the highest location weight according to the sorting results;

[0235] Determine the data usage of each data to be encoded according to the usage location with the highest position weight and the usage frequency.

[0236] Determine the data usage of the corresponding dataset to be encoded according to the data usage of each data to be encoded.

[0237] Optionally, obtain the analysis module 301, which is specifically used for:

[0238] Conduct a security check on the enterprise digital asset information, and determine whether there are security issues according to the check results.

[0239] If there are security issues, determine the problematic data according to the check results.

[0240] Determine the problem type of the security issue according to the problematic data, and process the problem according to the problem type to obtain secure data.

[0241] Convert the format of the secure data to obtain normalized secure data.

[0242] Determine the data type of the normalized secure data according to the preset format conversion logic.

[0243] Determine the redundancy analysis method according to the data type, and perform redundancy analysis on the normalized secure data according to the redundancy analysis method to determine whether there is redundant information.

[0244] Optionally, the data migration module 303 is specifically used for:

[0245] Obtain the migration channel information and calculate the data length of each migration dataset.

[0246] Analyze the migration channel information to determine the unit migration amount of each migration channel.

[0247] Compare the data length with the unit migration amount of each migration channel respectively to determine the migration proportion of the data length.

[0248] Allocate each migration dataset according to the migration priority and the migration proportion to obtain an allocation result.

[0249] Migrate the corresponding migration datasets in sequence according to the allocation result.

[0250] Optionally, the data migration module is specifically used for:

[0251] ;

[0252] Wherein, represents the migration proportion of the th migration dataset, Indicates the data length of the th migration dataset, Indicates the priority factor of the th migration dataset, Indicates the th dependency factor between a migration dataset and a migration dataset with a dependency relationship, Indicates the th maximum dependency factor between a migration dataset and a migration dataset with a dependency relationship, Indicates the th maximum priority factor of the migration dataset.

[0253] Optionally, the enterprise digital asset comprehensive management system 300 further includes a factor calculation module 304, specifically used for:

[0254] ;

[0255] Among them, Indicates the priority factor, Indicates the weight of the data usage, Indicates the weight of the Indicates the th evaluation score of the data usage of the migration dataset, Indicates the th evaluation score of the data usage frequency of the migration dataset;

[0256] The calculation of the dependency factor includes:

[0257] Analyze each migration dataset, and determine whether there is a dependency relationship between any two migration datasets according to the dataset analysis result;

[0258] If there is a dependency relationship between any two migration datasets, determine the dependency level according to the dependency relationship;

[0259] Calculate the dependency factor according to the dependency level, referring to the following formula:

[0260] ;

[0261] Among them, Indicates the th dependency level between a migration dataset and a migration dataset with a migration dataset dependency relationship, Indicates the th maximum dependency level between a migration dataset and a migration dataset with a migration dataset dependency relationship.

[0262] The system of this embodiment can be used to execute the method of any of the above embodiments, and its implementation principle and technical effects are similar, which will not be elaborated here.

Claims

1. A comprehensive management method for enterprise digital assets, characterized in that: include: Obtain and analyze enterprise digital asset information and determine migration targets; According to the migration target, the enterprise digital asset information is data-encoded to obtain the data-encoded enterprise digital asset information; Dividing the enterprise digital asset information after the data encoding into data migration priorities to obtain a migration data set corresponding to each migration priority; Migrating corresponding migration data sets in sequence according to the data migration priority; The data migration priority is the migration level of data during migration, which serves as a basis for determining the migration order; The migration data set is a set of data with the same data migration priority obtained after the data is prioritized; The dividing of data migration priorities of the enterprise digital asset information after the data encoding includes: Classify data from high priority to low priority according to the urgency of its use; Prioritize data that needs to be used immediately; Assign low priority to data with low usage demand; The step of sequentially migrating corresponding migration data sets according to the data migration priority includes: Obtain migration channel information and calculate the data length of each migration data set; Analyzing the migration channel information to determine the unit migration amount of each migration channel; Analyze each migration data set, and determine whether there is a dependency relationship between any two migration data sets based on the data set analysis results; If there is a dependency relationship between any two migration data sets, determining the dependency level according to the dependency relationship; Determining a dependency factor according to the dependency level; Based on the dependency factor, the data length is compared with the unit migration amount of each migration channel to determine the migration proportion of the data length; Allocate each migration data set according to the migration priority and the migration proportion to obtain an allocation result; According to the allocation result, the corresponding migration data sets are migrated in sequence; Based on the dependency factor, the data length is compared with the unit migration amount of each migration channel to determine the migration proportion of the data length, referring to the following formula: ； in, Indicates Migration ratio of migration datasets, Indicates The data length of the migration data set, Indicates The priority factor of the migrated datasets, Indicates The dependency factor between a migration dataset and the migration dataset with a dependency relationship, represents the maximum dependency factor between the i-th migration dataset and the migration dataset with which there is a dependency relationship, Indicates The maximum priority factor for migrating datasets.

2. The method according to claim 1, characterized in that The acquisition and analysis of enterprise digital asset information and determination of migration targets include: Obtain the enterprise's usage requirements for data migration, and determine the scope of information collection based on the usage requirements; Acquiring the enterprise digital asset information according to the information collection scope; Extract the enterprise digital asset information to obtain corresponding information features; analyze the information features, and determine the migration target based on the feature analysis results.

3. The method according to claim 2, characterized in that The step of encoding the enterprise digital asset information according to the migration target to obtain the encoded enterprise digital asset information includes: Classifying the enterprise digital asset information according to the feature analysis results, and obtaining a number of data sets to be encoded according to the classification results; Analyze each data set to be encoded according to the classification result to determine the data usage of each data set to be encoded; According to the data usage, each data set to be encoded is encoded to obtain the enterprise digital asset information after the data encoding.

4. The method according to claim 3, characterized in that According to the classification results, several data sets to be encoded are obtained, including: Convert the digital asset information of the enterprise into a new format, and perform redundancy analysis on the converted digital asset information of the enterprise to determine whether there is redundant information; Extracting the information content and location of the redundant information; Analyzing the information content based on the location, and determining the necessity of the redundant information according to the information content analysis result; Determining whether to delete the redundant information according to the necessity; If it is determined to delete, the redundant information is deleted, and a data set to be encoded is obtained according to the classification result.

5. The method according to claim 3, characterized in that: The step of analyzing each data set to be encoded according to the classification result to determine the data usage of each data set to be encoded includes: For each data set to be encoded, extract the data content of each data to be encoded, and determine the usage frequency and several usage locations of each data to be encoded according to the data content extraction result; Analyze the plurality of usage locations and determine a location weight of each usage location; Sort the position weight of each use position, and determine the use position with the highest position weight according to the sorting result; Determine the data usage of each to-be-encoded data according to the usage position with the highest position weight and the usage frequency; According to the data usage of each to-be-encoded data set, the data usage of the corresponding to-be-encoded data set is determined.

6. The method according to claim 4, characterized in that The step of converting the format of the enterprise digital asset information and performing redundancy analysis on the converted enterprise digital asset information to determine whether redundant information exists includes: Conduct a security check on the digital asset information of the enterprise and determine whether there are security issues based on the check results; If there is a safety problem, determine the problematic data based on the inspection results; Determine the problem type of the security problem according to the problem data, and process the problem according to the problem type to obtain security data; Converting the security data into a normalized security data. Determining the data type of the normalized security data according to a preset format conversion logic; A redundancy analysis method is determined according to the data type, and redundancy analysis is performed on the normalized safety data according to the redundancy analysis method to determine whether redundant information exists.

7. The method according to claim 1, characterized in that The priority factor is calculated by referring to the following formula: ； in, represents the priority factor, Indicates the weight of data usage, Indicates the weight of the usage, Indicates The evaluation score of the data usage of the migrated dataset, Indicates an evaluation score of the data usage frequency of the migration data set; The calculation of the dependency factor is based on the following formula: ； in, Indicates The dependency level of the migration dataset and the existing migration dataset dependency, Indicates The maximum dependency level between a migration dataset and an existing migration dataset dependency.

8. An enterprise digital asset integrated management system, characterized in that: The method as claimed in any one of claims 1 to 7 comprises: Acquisition analysis module, to acquire and analyze enterprise digital asset information and determine migration targets; A data encoding module, which performs data encoding on the enterprise digital asset information according to the migration target to obtain the data-encoded enterprise digital asset information; The data migration module divides the enterprise digital asset information after the data encoding into data migration priorities to obtain a migration data set corresponding to each migration priority; the corresponding migration data sets are migrated in sequence according to the data migration priorities; the data migration priority is the migration level of the data when migrating, which serves as a basis for determining the migration order; the migration data set is a collection of data with the same data migration priority obtained after the data is prioritized; When the data migration module divides the data migration priority of the enterprise digital asset information after the data encoding, it is specifically used to: Classify data from high priority to low priority according to the urgency of its use; Prioritize data that needs to be used immediately; Assign low priority to data that is not in high demand.

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