Metadata processing method and device, computer equipment and storage medium

Abstract

The invention relates to a metadata processing method and device, computer equipment, a storage medium and a computer program product. The method comprises the steps that the interaction frequency of each piece of metadata is acquired; according to the interaction frequency of each piece of metadata, determining the category of each piece of metadata; wherein the category of the metadata comprises a first category and a second category; wherein the interaction frequency of the first category is higher than that of the second category; in response to an interaction operation of an account on a metadata database, adding the metadata of which the category is the first category into the metadata database; and under the condition that the interaction frequency of the metadata database is within a preset threshold range, adding the metadata of which the category is the second category into the metadata database. By adopting the method, the management of the metadata database can be optimized, and the interaction efficiency and accuracy of the metadata database can be improved.

Description

Technical Field

[0001] The present application relates to the field of big data technology, and in particular to a metadata processing method, apparatus, computer equipment, storage medium and computer program product. Background Art

[0002] With the continuous development of big data, efficient data instruction has become particularly important. Effective management of metadata can help users better track and trace data. In this context, many database management services have been derived. These services copy the metadata of the metadata center to the database management tool to achieve centralized management of metadata of different systems. In addition, database management services usually have higher performance and faster access speed.

[0003] However, most common database management tools still require users to manually collect metadata, or use automated scheduled tasks to collect metadata in full. This process easily leads to inconsistencies between the metadata in the database management tool and the data in the metadata center, and ultimately cannot guarantee data consistency, thus failing to meet actual needs. Summary of the invention

[0004] Based on this, it is necessary to provide a metadata processing method, apparatus, computer equipment, computer-readable storage medium and computer program product to address the above technical issues.

[0005] In a first aspect, the present application provides a metadata processing method. The method comprises:

[0006] Get the interaction frequency of each metadata;

[0007] Determine the category of each metadata according to the interaction frequency of each metadata; wherein the categories of the metadata include: a first category and a second category; wherein the interaction frequency of the first category is higher than that of the second category;

[0008] In response to an interactive operation of the account on the metadata database, adding the metadata of the first category to the metadata database;

[0009] When the metadata database interaction frequency is within a preset threshold range, the metadata of the second category is added to the metadata database.

[0010] In one embodiment, determining the category of the metadata according to the interaction frequency of each metadata includes:

[0011] Get the update time and access method of each metadata;

[0012] By using a preset binary strategy, the interaction frequency, update time, and access method of each metadata are analyzed to determine the category of each metadata.

[0013] In one embodiment, the use of a preset binary strategy to analyze the interaction frequency, update time, and access method of the metadata to determine the category of each metadata includes:

[0014] According to the preset initial metadata and the corresponding categories, determining the proportion of the first category and the proportion of the second category in the initial metadata;

[0015] According to the characteristics of the metadata, searching the initial metadata for target metadata matching the characteristics, and determining the category corresponding to the target metadata and the proportion of each category; wherein the characteristics include at least one of the following: interaction frequency, update time, and access mode;

[0016] Using a preset Bayesian classification strategy, according to the proportion of the first category and the second category in the initial metadata, and the proportion of each category in the target metadata, the category coefficient of the metadata is obtained;

[0017] The category of the metadata is determined according to the category coefficient.

[0018] In one of the embodiments, adding the metadata of the first category to a metadata database includes:

[0019] Locking the source address of the metadata;

[0020] The metadata of the first category is added to a metadata database from the source address of the metadata.

[0021] In one embodiment, the metadata category further includes a third category; the metadata of the third category includes metadata that has not been accessed; and the method further includes:

[0022] Every time a preset time interval passes, the metadata of the third category is added to the metadata database.

[0023] In one of the embodiments, the interaction frequency of the metadata corresponds to a unique user; and the metadata database corresponds to a unique user.

[0024] In a second aspect, the present application also provides a metadata processing device. The device comprises:

[0025] A frequency acquisition module is used to obtain the interaction frequency of each metadata;

[0026] A category determination module, used to determine the category of each metadata according to the interaction frequency of each metadata; wherein the categories of the metadata include: a first category and a second category; wherein the interaction frequency of the first category is higher than that of the second category;

[0027] A data adding module, configured to add the metadata of the first category to the metadata database in response to an interactive operation of the account on the metadata database;

[0028] The data adding module is further configured to add the metadata of the second category to the metadata database when the metadata database interaction frequency is within a preset threshold range.

[0029] In one embodiment, the category determination module includes:

[0030] The feature acquisition submodule is used to obtain the update time and access method of each metadata;

[0031] The category determination submodule is used to analyze the interaction frequency, update time, and access method of each metadata using a preset binary strategy to determine the category of each metadata.

[0032] In one embodiment, the category determination submodule includes:

[0033] A data acquisition unit, configured to determine, according to the preset initial metadata and the corresponding categories, a proportion of the first category and a proportion of the second category in the initial metadata;

[0034] The data acquisition unit is further used to search the initial metadata for target metadata matching the feature according to the feature of the metadata, and determine the category corresponding to the target metadata and the proportion of each category; wherein the feature includes at least one of the following: interaction frequency, update time, and access mode;

[0035] A coefficient determination unit, configured to obtain a category coefficient of the metadata according to a proportion of the first category and a proportion of the second category in the initial metadata and a proportion of each category in the target metadata by using a preset Bayesian classification strategy;

[0036] The category determination unit is used to determine the category of the metadata according to the category coefficient.

[0037] In one embodiment, the data adding module includes:

[0038] An address locking submodule, used to lock the source address of the metadata;

[0039] The data adding submodule is used to add the metadata of the first category to the metadata database from the source address of the metadata.

[0040] In one embodiment, the metadata category further includes a third category; the metadata of the third category includes metadata that has not been accessed; and the data adding module is further used to:

[0041] Every time a preset time interval passes, the metadata of the third category is added to the metadata database.

[0042] In one of the embodiments, the interaction frequency of the metadata corresponds to a unique user; and the metadata database corresponds to a unique user.

[0043] In a third aspect, the present application further provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the metadata processing method as described in any one of the embodiments of the present disclosure is implemented.

[0044] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the metadata processing method as described in any one of the embodiments of the present disclosure is implemented.

[0045] In a fifth aspect, the present application further provides a computer program product, wherein the computer program product includes a computer program, and when the computer program is executed by a processor, the metadata processing method as described in any one of the embodiments of the present disclosure is implemented.

[0046] The metadata processing method, device, computer equipment, storage medium and computer program product determine the category of metadata according to the interaction frequency of metadata, and add the metadata of the first category to the metadata database when the account interacts with the metadata database, and add the metadata of the second category to the metadata database when the interaction frequency of the metadata database is within a preset threshold range. By analyzing the interaction frequency of metadata, metadata is divided into high-frequency categories (first categories) and low-frequency categories (second categories), thereby optimizing the management of the metadata database. Metadata of high-frequency categories can be added first because of their more frequent interactions, ensuring the system's rapid response to account needs; while metadata of low-frequency categories are added when the interaction frequency of the metadata database is within a preset range, avoiding the frequent interaction of metadata of the second category with the metadata database, which leads to a longer interaction response time of the account to the metadata database, thereby improving the interaction efficiency of the account to the metadata database. At the same time, this classification and addition strategy can also improve the flexibility and efficiency of data acquisition. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A schematic diagram of a metadata processing method in one embodiment;

[0048] Figure 2 A schematic diagram of a first process of metadata classification in one embodiment;

[0049] Figure 3 A second flow chart of metadata classification in one embodiment;

[0050] Figure 4 A schematic diagram of a process of adding metadata to a metadata database in one embodiment;

[0051] Figure 5 A schematic diagram of a flow chart of a metadata processing method in one embodiment;

[0052] Figure 6 is a structural block diagram of a metadata processing device in one embodiment;

[0053] Figure 7 A structural block diagram of a metadata processing device implemented in one embodiment;

[0054] Figure 8 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0056] In one embodiment, Figure 1 As shown, a metadata processing method is provided. This embodiment uses the method applied to a server as an example for illustration. It can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0057] Step S100, obtaining the interaction frequency of each metadata.

[0058] In an exemplary embodiment, the server can use a metadata analysis table to record the information of metadata information accessed and used by users within a preset period of time, record the user information of daily metadata access, frequently accessed programs, metadata update time and access method (such as SQL execution access or querying metadata information through an interface, etc.), and directly obtain the interaction frequency of each metadata, that is, the access frequency of metadata, etc. at the back end of the server. In the actual use process, before using this method, the metadata center can be monitored, and specifically, the access frequency of each metadata can be obtained by monitoring the metadata center; in another exemplary embodiment, the metadata can be copied to the database of the server to observe the interaction frequency of each metadata over a period of time.

[0059] Step S200, determining the category of each metadata according to the interaction frequency of each metadata; wherein the categories of the metadata include: a first category and a second category; wherein the interaction frequency of the first category is higher than that of the second category.

[0060] In an exemplary embodiment, the first category and the second category may include hot data and cold data, etc.; wherein the hot data may include metadata with an interaction frequency greater than a preset threshold, and the cold data may include metadata with an interaction frequency less than a preset threshold, etc.

[0061] Step S300: in response to the interactive operation of the account on the metadata database, adding the metadata of the first category to the metadata database.

[0062] In an exemplary embodiment, the interactive operation of the account to the metadata database may include the account's search and use of the metadata database, etc. Specifically, it may include the server monitoring the account logging into the metadata database, etc.; that is, when monitoring the account logging into the metadata database, the metadata of the first category is added to the metadata database; wherein the metadata database may include DMS (Database Management Service), which is a platform that provides one-stop management services for multi-source databases, etc. It is understandable that copying the data of the metadata center to the DMS for the account to use is faster than using it directly from the metadata database, and the DMS can integrate the metadata centers of different systems, that is, the metadata corresponding to multiple systems can be managed through one DMS, etc.

[0063] In an exemplary embodiment, the source address of the metadata may include a metadata center, that is, metadata of the first category in the metadata center is added to a metadata database, and the like.

[0064] In an exemplary embodiment, the collection of metadata of the first category can be automatically triggered (i.e., metadata of the first category is added to the metadata database) by monitoring the time node of the DMS account logging into the database. In an exemplary embodiment, when collecting metadata of the first category, the collection task can be locked to prevent multiple collection triggering in a short period of time, ensuring that each collection task cannot be submitted repeatedly in a short period of time. It can be understood that metadata of the first category is a subset of the full amount of data, so it will not occupy too many network resources. Collection tasks can be frequently performed multiple times from the metadata center within a period of time, avoiding the problem of inconsistent metadata.

[0065] Step S400: when the metadata database interaction frequency is within a preset threshold range, adding the metadata of the second category to the metadata database.

[0066] In an exemplary embodiment, the threshold range may include a non-interaction peak period of the metadata database, and the second category of metadata may include cold data. It can be understood that the account's interaction frequency with cold data is lower than that with hot data; therefore, cold data may not be updated in real time. Therefore, in order not to affect the account's interaction with the metadata database, the second category of metadata is added to the metadata database, etc.

[0067] In an exemplary embodiment, DMS can be used to monitor the network request situation in each time period, and trigger the collection of metadata of the second category (adding the metadata of the second category to the metadata database) in the time period when the network request is lower than the preset threshold, avoiding peak time periods, etc.

[0068] In the above metadata processing method, the category of metadata is determined according to the interaction frequency of metadata, and when the account interacts with the metadata database, the metadata of the first category is added to the metadata database, and when the interaction frequency of the metadata database is within the preset threshold range, the metadata of the second category is added to the metadata database. By analyzing the interaction frequency of metadata, metadata is divided into high-frequency categories (first category) and low-frequency categories (second category), thereby optimizing the management of the metadata database. The metadata of the high-frequency category can be added first because of its more frequent interaction, ensuring the system's rapid response to account needs; and the metadata of the low-frequency category is added when the interaction frequency of the metadata database is within the preset range, avoiding the frequent interaction of the metadata of the second category with the metadata database, which leads to a longer interaction response time of the account to the metadata database, thereby improving the interaction efficiency of the account to the metadata database. At the same time, this classification and addition strategy can also improve the flexibility and efficiency of data acquisition.

[0069] In one embodiment, Figure 2 As shown, determining the category of the metadata according to the interaction frequency of each metadata includes:

[0070] Step S201, obtaining the update time and access method of each metadata.

[0071] Step S202: using a preset binary strategy, analyzing the interaction frequency, update time, and access method of each metadata to determine the category of each metadata.

[0072] In an exemplary embodiment, metadata is divided into the first category and the second category by using the metadata interaction frequency, and the metadata classification problem can be solved by mapping the metadata classification problem into a binary classification recommendation problem, etc.; metadata interaction frequency, update time, and access method are used as features to analyze metadata with frequent account interactions, i.e., data of the first category, etc. Specifically, a Bayesian classification system filtering algorithm can be used to solve the classification problem, etc.; for example, the features are encoded, and the more frequent the access, the greater the recommendation probability; the longer the metadata update time is from the current time, the greater the recommendation probability; the SQL execution access method has a greater recommendation probability than other access methods, etc. The feature vector is constructed according to the above principles, and the Bayesian theory is used for classification, etc.

[0073] In this embodiment, a binary strategy is used to analyze the interaction frequency, update time, and access method of each metadata to determine the category of each metadata. By analyzing the interaction frequency, update time, and access method, the classification of metadata can be further refined, thereby improving the intelligent management of the database. By comprehensively considering the interaction frequency, update time, and access method, the real demand and usage habits of the account for metadata can be more accurately identified. This in-depth analysis not only helps to optimize the storage and retrieval strategy of metadata, but also ensures that high-value and high-timeliness metadata are processed first, thereby improving the response speed and overall efficiency of the system, and ultimately providing more accurate and timely data services for the account.

[0074] In one embodiment, Figure 3 As shown, the preset binary strategy is used to analyze the interaction frequency, update time, and access method of the metadata to determine the category of each metadata, including:

[0075] Step S211 : determining the proportion of the first category and the proportion of the second category in the initial metadata according to the preset initial metadata and the corresponding categories.

[0076] In an exemplary embodiment, some metadata, namely, initial metadata, may be acquired, and the metadata may be classified to determine the category of the initial metadata, the proportion of metadata of the first category, the proportion of metadata of the second category, and the like.

[0077] Step S212, according to the characteristics of the metadata, searching the initial metadata for target metadata matching the characteristics, and determining the category corresponding to the target metadata and the proportion of each category; wherein the characteristics include at least one of the following: interaction frequency, update time, and access method.

[0078] In an exemplary embodiment, the features of metadata to be classified, i.e., interaction frequency, update time, access method, etc., may be obtained; data having these features may be matched from the initial metadata, and the proportion of initial metadata having these features in metadata of the first category and the proportion of initial metadata having these features in metadata of the second category may be determined.

[0079] Step S213, using a preset Bayesian classification strategy, according to the proportion of the first category and the second category in the initial metadata, and the proportion of each category in the target metadata, the category coefficient of the metadata is obtained.

[0080] In an exemplary embodiment, the proportion of the first category in the initial metadata and the proportion of the target metadata in the metadata corresponding to the first category of the initial metadata may be multiplied to obtain the first category sub-coefficient; the proportion of the second category in the initial metadata and the proportion of the target metadata in the metadata corresponding to the second category of the initial metadata may be multiplied to obtain the second category sub-coefficient; the first category sub-coefficient and the second category sub-coefficient may be summed to obtain the third category sub-coefficient; the first category sub-coefficient and the third category sub-coefficient may be divided to obtain the category coefficient, etc. Specifically, the category coefficient may be obtained using the following formula (1):

[0081] (1)

[0082] Among them, p u (m i |A) indicates that the first category of metadata in the initial metadata of account u contains feature value m i The proportion of metadata; u (A) represents the proportion of metadata of the first category in the initial metadata; p u (m i |B) Indicates that the second category metadata in the initial metadata of account u contains feature value m i The proportion of metadata; u (B) represents the proportion of metadata of the second category in the initial metadata; P u (A|m i ) is the category coefficient, etc.

[0083] Step S214: determining the category of the metadata according to the category coefficient.

[0084] In an exemplary embodiment, a preset threshold may be set, and when the category coefficient exceeds the threshold, the category of the metadata is determined to be the first category, and when the category coefficient does not exceed the threshold, the category of the metadata is determined to be the second category, and so on.

[0085] In this embodiment, the accuracy and flexibility of classification can be significantly improved by classifying metadata through the Bayesian classification strategy. By utilizing the proportion of different categories in the initial metadata and the characteristics of the target metadata, the Bayesian classification can effectively infer the category coefficient of the new metadata. This probability-based analysis method takes into account a variety of features (such as interaction frequency, update time, and access method), making classification decisions more scientific and reasonable. In addition, as the system continues to accumulate new interaction data, the Bayesian model can be dynamically updated to adapt to changes in account requirements and further improve the system's ability to manage and respond to metadata.

[0086] In one embodiment, Figure 4 As shown, the step of adding the metadata of the first category to the metadata database includes:

[0087] Step S301, locking the source address of the metadata.

[0088] Step S302: adding the metadata of the first category from the source address of the metadata to a metadata database.

[0089] In an exemplary embodiment, before adding metadata of the first category to the metadata database, the source address of the metadata may be locked to prevent multiple triggering of additions in a short period of time, thereby ensuring that each addition task cannot be submitted repeatedly in a short period of time.

[0090] In this embodiment, locking the source address of the metadata can effectively prevent unauthorized modification or deletion, and enhance the security and integrity of the metadata. When adding metadata of the first category, the authenticity and accuracy of the data source are guaranteed, thereby maintaining data quality and reliability. In addition, such security measures can reduce potential data vulnerabilities, reduce classification errors caused by tampering of source data, and improve the overall trust and stability of the system.

[0091] In one embodiment, the metadata category further includes a third category; the metadata of the third category includes metadata that has not been accessed; and the method further includes:

[0092] Every time a preset time interval passes, the metadata of the third category is added to the metadata database.

[0093] In an exemplary embodiment, the third category may include useless metadata, that is, metadata that has not been used by the account, and this part of metadata is added to the metadata database after a preset time interval.

[0094] In this embodiment, metadata of the third category that has not been accessed is added to the metadata database at every preset time interval, which can effectively improve the comprehensiveness and potential value of the database. This method ensures that even data that is not frequently accessed can be taken into consideration, avoiding the loss or forgetting of important information caused by ignoring these data. Combined with the previous classification strategy, this regular update mechanism can not only enrich the content of the database, but also provide users with more exploration opportunities, promote data reuse and discovery, thereby improving the overall data asset value and user experience, especially for those metadata that may be of great significance in the future. At the same time, by extending the time interval, it is possible to avoid frequent interaction of useless metadata with the metadata database, which can effectively reduce the burden on the metadata database and further improve the efficiency of the metadata database.

[0095] In one embodiment, the interaction frequency of the metadata corresponds to a unique account; and the metadata database corresponds to a unique account.

[0096] In an exemplary embodiment, a metadata database corresponding to each account may be generated for each account, and the metadata may be classified using the interaction frequency of the account, and corresponding addition strategies may be implemented.

[0097] In this embodiment, the metadata interaction frequency and database are matched to a unique account, which can significantly improve the personalization and relevance of the data. This method ensures that the interaction behavior and needs of each account are accurately captured, so that the metadata classification and management are closer to the specific usage habits and preferences of the account. This personalized processing method can achieve higher quality recommendations and customized services, helping accounts find the required information more quickly and effectively. At the same time, this targeted metadata management can also optimize the use of system resources, reduce the storage of redundant data, and ultimately improve the efficiency and response speed of the overall system.

[0098] In an exemplary embodiment, the metadata processing method may be as follows: Figure 5 The flowchart shown is implemented, specifically including:

[0099] Step S901, monitoring an account access metadata event; specifically, the DMS (Database Management Service) can be used to monitor the account access metadata event;

[0100] Step S902, determine whether the account has access to metadata; if not, return to step S901 to continue monitoring; if yes, proceed to step S903;

[0101] Step S903, storing the account access metadata information into a preset table; that is, storing the account metadata access record into the preset table;

[0102] Step S904, regularly cleaning and maintaining the preset table;

[0103] Step S905, extracting the recent metadata access information of the account from the preset table;

[0104] Step S906, pre-processing the information to construct a hot metadata classification problem feature vector; that is, constructing a feature vector based on the interaction frequency of the account accessing the metadata, the access method, and the metadata update time;

[0105] Step S907, filtering hot spot metadata based on the collaborative filtering recommendation algorithm of Bayesian classification;

[0106] Step S908, marking the hot spot metadata and providing it to the metadata collection module for execution; that is, using a preset strategy, adding the metadata to the metadata database.

[0107] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0108] Based on the same inventive concept, the embodiment of the present application also provides a metadata processing device for implementing the metadata processing method involved above. The implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above method, so the specific limitations in the one or more metadata processing device embodiments provided below can refer to the limitations of the metadata processing method above, and will not be repeated here.

[0109] In one embodiment, Figure 6 As shown, a metadata processing device 100 is provided, comprising: a frequency acquisition module 101, a category determination module 102 and a data adding module 103, wherein:

[0110] A frequency acquisition module is used to obtain the interaction frequency of each metadata;

[0111] A category determination module, used to determine the category of each metadata according to the interaction frequency of each metadata; wherein the categories of the metadata include: a first category and a second category; wherein the interaction frequency of the first category is higher than that of the second category;

[0112] A data adding module, configured to add the metadata of the first category to the metadata database in response to an interactive operation of the account on the metadata database;

[0113] The data adding module is further configured to add the metadata of the second category to the metadata database when the metadata database interaction frequency is within a preset threshold range.

[0114] In one embodiment, the category determination module includes:

[0115] The feature acquisition submodule is used to obtain the update time and access method of each metadata;

[0116] The category determination submodule is used to analyze the interaction frequency, update time, and access method of each metadata using a preset binary strategy to determine the category of each metadata.

[0117] In one embodiment, the category determination submodule includes:

[0118] A data acquisition unit, configured to determine, according to the preset initial metadata and the corresponding categories, a proportion of the first category and a proportion of the second category in the initial metadata;

[0119] The data acquisition unit is further used to search the initial metadata for target metadata matching the feature according to the feature of the metadata, and determine the category corresponding to the target metadata and the proportion of each category; wherein the feature includes at least one of the following: interaction frequency, update time, and access mode;

[0120] A coefficient determination unit, configured to obtain a category coefficient of the metadata according to a proportion of the first category and a proportion of the second category in the initial metadata and a proportion of each category in the target metadata by using a preset Bayesian classification strategy;

[0121] The category determination unit is used to determine the category of the metadata according to the category coefficient.

[0122] In one embodiment, the data adding module includes:

[0123] An address locking submodule, used to lock the source address of the metadata;

[0124] The data adding submodule is used to add the metadata of the first category to the metadata database from the source address of the metadata.

[0125] In one embodiment, the metadata category further includes a third category; the metadata of the third category includes metadata that has not been accessed; and the data adding module is further used to:

[0126] Every time a preset time interval passes, the metadata of the third category is added to the metadata database.

[0127] In one of the embodiments, the interaction frequency of the metadata corresponds to a unique user; and the metadata database corresponds to a unique user.

[0128] In an exemplary embodiment, the metadata processing device may also be as follows: Figure 7 As shown, it includes a client 201, a server 202, a business library 203, and a target database 204; wherein:

[0129] The client connects to the account, provides the latest metadata to the account, and automatically triggers the metadata hot data collection task when the account logs into the metadata database; and connects to the server, and sends information to the server when monitoring the access to metadata;

[0130] The server receives information from the client, accesses metadata information from the business database, and the business database returns the latest metadata information. The server synchronizes the metadata information to the business database. The server customizes hotspot metadata for the account through the recommendation algorithm, classifies the hotspot metadata, initializes the task, initializes the collection task, adds it to the collection queue, and starts the metadata collection task.

[0131] According to the task, the corresponding data is retrieved from the target database, and the latest metadata information is synchronized to the target database.

[0132] Each module in the metadata processing device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0133] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 8As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store metadata. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a metadata processing method is implemented.

[0134] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0135] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0136] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0137] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0138] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the present application. It should be noted that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A metadata processing method, characterized in that: The method comprises: Get the interaction frequency of each metadata; Determine the category of each metadata according to the interaction frequency of each metadata; wherein the categories of the metadata include: a first category and a second category; wherein the interaction frequency of the first category is higher than that of the second category; In response to an interactive operation of the account on the metadata database, adding the metadata of the first category to the metadata database; When the metadata database interaction frequency is within a preset threshold range, the metadata of the second category is added to the metadata database.

2. The method according to claim 1, characterized in that Determining the category of the metadata according to the interaction frequency of each metadata includes: Get the update time and access method of each metadata; By using a preset binary strategy, the interaction frequency, update time, and access method of each metadata are analyzed to determine the category of each metadata.

3. The method according to claim 2, characterized in that The preset binary strategy is used to analyze the interaction frequency, update time, and access mode of the metadata to determine the category of each metadata, including: According to the preset initial metadata and the corresponding categories, determining the proportion of the first category and the proportion of the second category in the initial metadata; According to the characteristics of the metadata, searching the initial metadata for target metadata matching the characteristics, and determining the category corresponding to the target metadata and the proportion of each category; wherein the characteristics include at least one of the following: interaction frequency, update time, and access mode; Using a preset Bayesian classification strategy, according to the proportion of the first category and the second category in the initial metadata, and the proportion of each category in the target metadata, the category coefficient of the metadata is obtained; The category of the metadata is determined according to the category coefficient.

4. The method according to claim 1, characterized in that: The step of adding the metadata of the first category to a metadata database comprises: Locking the source address of the metadata; The metadata of the first category is added to a metadata database from the source address of the metadata.

5. The method according to claim 1, characterized in that The metadata category further includes a third category; the metadata of the third category includes metadata that has not been accessed; and the method further includes: Every time a preset time interval passes, the metadata of the third category is added to the metadata database.

6. The method according to any one of claims 1 to 5, characterized in that: The interaction frequency of the metadata corresponds to a unique user; and the metadata database corresponds to a unique user.

7. A metadata processing device, characterized in that: The device comprises: A frequency acquisition module is used to acquire the interaction frequency of each metadata; A category determination module, used to determine the category of each metadata according to the interaction frequency of each metadata; wherein the categories of the metadata include: a first category and a second category; wherein the interaction frequency of the first category is higher than that of the second category; A data adding module, configured to add the metadata of the first category to the metadata database in response to an interactive operation of the account on the metadata database; The data adding module is further configured to add the metadata of the second category to the metadata database when the metadata database interaction frequency is within a preset threshold range.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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