Data processing method, electronic device, and program product

Abstract

The application provides a data processing method, an electronic device and a program product. The method comprises the following steps: in response to an export request sent by a client, determining at least one data type and an export parameter corresponding to the export request, and a current running parameter of a server; determining export data information according to the at least one data type and the export parameter; generating a task list according to the export data information and the current running parameter, the task list comprising a plurality of export tasks and a task index corresponding to each export task; and sending the task list to the client, so that the client downloads data corresponding to the plurality of export tasks according to the plurality of task indexes, thereby improving operation and maintenance efficiency.

Description

Technical Field

[0001] This application relates to the field of data processing, and more particularly to a data processing method, electronic device, and program product. Background Technology

[0002] In modern information system operation and maintenance management, various systems such as cloud computing platforms, distributed storage systems, and enterprise-level server clusters continuously generate massive amounts of operational data during operation. This includes business logs, system logs, alarm information, event logs, audit logs, performance statistics, and hardware device health status logs. This data is distributed and stored at different levels of the system, such as the operating system kernel, application service modules, and hardware management interfaces, and is in various formats, such as text logs, binary configuration files, and structured database records.

[0003] When operations and maintenance personnel need to troubleshoot, optimize performance, audit security, or back up configurations of a system, they often need to extract and integrate relevant information from each data source using specific tools or scripts. This process is cumbersome and can easily lead to the omission of critical data.

[0004] The above export process mainly relies on scattered tools or scripts. In addition, a large number of export operations will consume too much CPU, memory or disk I / O resources, which may affect system response delay or even crash, resulting in low operation and maintenance efficiency. Summary of the Invention

[0005] This application provides a data processing method, electronic device, and program product to solve the technical problem of low operation and maintenance efficiency in related technologies.

[0006] In a first aspect, this application provides a data processing method applied to a server, the method comprising:

[0007] In response to an export request sent by a client, determine at least one data type and export parameters corresponding to the export request, as well as the current operating parameters of the server;

[0008] Determine the exported data information based on at least one data type and export parameters;

[0009] Based on the exported data and current running parameters, a task list is generated. The task list includes multiple exported tasks and the task index corresponding to each exported task.

[0010] Send a task list to the client so that the client can download the data corresponding to multiple export tasks based on multiple task indices.

[0011] In this way, by accurately parsing export requests and combining them with real-time server operating parameters to generate a task list containing multiple export tasks and corresponding task indexes, it not only supports clients to flexibly download data on demand, but also dynamically adapts server resources to avoid overload, significantly improving the efficiency and accuracy of data export and the stability of server operation, thus improving operational efficiency.

[0012] Optionally, using the method described above, a task list is generated based on the exported data and current running parameters, including:

[0013] Determine the sharding parameters based on the exported data and current running parameters;

[0014] Based on the sharding parameters, the exported data information is sharded to obtain multiple data shards;

[0015] For any given data shard, generate the corresponding export task and task index for that data shard;

[0016] A task list is generated based on the export tasks and task indexes corresponding to the multiple data shards.

[0017] In this way, by combining the exported data information with the current server operating parameters to dynamically determine the sharding parameters and perform data sharding, and then generating a dedicated export task and index for each shard and integrating them into a task list, we can achieve precise matching between data splitting and server resource load, ensuring data integrity and independent parsing, while also supporting clients to download data accurately on demand, significantly improving the overall efficiency of data export and the stability of server operation.

[0018] Optionally, using the method described above, the sharding parameters are determined based on the exported data information and current running parameters, including:

[0019] Based on the exported data information, determine the total amount of data and its storage type;

[0020] The sharding size threshold is determined by a preset algorithm based on the total amount of data and storage type.

[0021] Determine the number of concurrent threads based on the current running parameters;

[0022] The sharding parameters include the sharding size threshold and the number of concurrent threads.

[0023] In this way, by combining the core attributes of the exported data information to determine the sharding size threshold, and combining the current server operating parameters to determine the number of concurrent threads, the sharding parameters are formulated to both fit the characteristics of the data itself to ensure the rationality of the splitting, and adapt to the server resource status to avoid resource overload. This lays a precise parameter foundation for subsequent efficient and stable sharding processing and task execution.

[0024] Optionally, the method described above determines the exported data information based on at least one data type and exported parameters, including:

[0025] For any given data type, determine the exported data based on the data type and the exported parameters.

[0026] Based on the data type, determine the target model from multiple preset models;

[0027] Based on the target model, the exported data is analyzed and processed to obtain the analytical data;

[0028] Determine the exported data information based on the exported data and analysis data corresponding to at least one data type.

[0029] In this way, data is precisely selected and exported for each data type and matched with a dedicated preset model to generate analysis data. Finally, the original exported data and value-added analysis data are integrated to form exported data information. This not only ensures the relevance and completeness of the data, but also adds value to the data through model analysis, significantly improving the practicality and accuracy of the exported data information.

[0030] Alternatively, the method described above may further include:

[0031] Within a preset time period, determine whether a heartbeat signal sent by the client has been received;

[0032] If not, mark the export task's status as paused and release the resources corresponding to the export task.

[0033] In this way, by detecting the client's heartbeat signal within a preset time, the exported task is marked as paused and the corresponding resources are released if no heartbeat is received. This not only avoids the waste caused by the server resources being occupied by unresponsive tasks for a long time and ensures the stability of server operation, but also retains the possibility of the task being resumed later, thus balancing resource utilization and the convenience of task resumption.

[0034] Alternatively, the method described above may further include:

[0035] Receive export task requests sent by the client. The export task requests include the task index.

[0036] Determine the data corresponding to the task index;

[0037] Based on the data corresponding to the task index and the export parameters, generate a response message corresponding to the export task request and send the response message to the client. The response message includes the data or data address corresponding to the export task.

[0038] In this way, the server can accurately locate the corresponding data by receiving the export task request containing the task index, and generate a response message containing the data or data address by combining the export parameters and sending it back to the client. This not only achieves accurate data delivery, but also allows for flexible selection of transmission methods based on the data volume to reduce server load, significantly improving the targeting and efficiency of data download.

[0039] Secondly, this application provides a data processing method applied to a client, the method comprising:

[0040] Retrieve the task list sent by the server. The task list includes multiple export tasks and the task index corresponding to each export task.

[0041] For any given export task, generate an export task request based on the task index corresponding to the export task, and send the export task request to the server.

[0042] Get the response message corresponding to the export task request. The response message includes the data or data address corresponding to the export task.

[0043] The exported data is determined based on the response information corresponding to each of the multiple export tasks.

[0044] In this way, by obtaining the task list and generating export task requests on demand based on the task index, and integrating the exported data after receiving the response message, the system achieves accurate positioning and flexible download of each data segment, which not only ensures the integrity of the exported data, but also improves the data download efficiency and reduces the transmission load on the server.

[0045] Optionally, the method described above, before obtaining the list of tasks sent by the server, also includes:

[0046] The export interface is displayed, which includes type components corresponding to multiple data types, multiple export dimension components, and multiple encryption type components.

[0047] In response to a user's selection of at least one type component, determine at least one data type;

[0048] In response to the user's selection of any export dimension component, determine the export dimension type;

[0049] In response to the user's selection of any encryption type component, determine the encryption type;

[0050] Generate an export request based on at least one data type, export dimension type, and encryption type, and send the export request to the server.

[0051] In this way, by displaying a visual export interface that includes components for type, export dimension, and encryption type, and responding to user selections to generate and send export requests, the process of inputting user export requirements is simplified, the operational threshold is lowered, and personalized user needs are accurately captured. This ensures that the export request parameters are complete and match the actual needs, laying the foundation for efficient processing by the server.

[0052] Thirdly, this application provides a data processing apparatus, the apparatus comprising:

[0053] The first determining module is used to respond to an export request sent by the client, and determine at least one data type and export parameters corresponding to the export request, as well as the current operating parameters of the server.

[0054] The second determining module is used to determine the exported data information based on at least one data type and exported parameters;

[0055] The generation module is used to generate a task list based on the exported data information and the current running parameters. The task list includes multiple exported tasks and the task index corresponding to each exported task.

[0056] The sending module is used to send a task list to the client, so that the client can download the data corresponding to multiple export tasks based on multiple task indices.

[0057] Optionally, in the above-described apparatus, the generation module is specifically used for:

[0058] Determine the sharding parameters based on the exported data and current running parameters;

[0059] Based on the sharding parameters, the exported data information is sharded to obtain multiple data shards;

[0060] For any given data shard, generate the corresponding export task and task index for that data shard;

[0061] A task list is generated based on the export tasks and task indexes corresponding to the multiple data shards.

[0062] Optionally, in the above-described apparatus, the generation module is specifically used for:

[0063] Based on the exported data information, determine the total amount of data and its storage type;

[0064] The sharding size threshold is determined by a preset algorithm based on the total amount of data and storage type.

[0065] Determine the number of concurrent threads based on the current running parameters;

[0066] The sharding parameters include the sharding size threshold and the number of concurrent threads.

[0067] Optionally, in the above apparatus, the second determining module is specifically used for:

[0068] For any given data type, determine the exported data based on the data type and the exported parameters.

[0069] Based on the data type, determine the target model from multiple preset models;

[0070] Based on the target model, the exported data is analyzed and processed to obtain the analytical data;

[0071] Determine the exported data information based on the exported data and analysis data corresponding to at least one data type.

[0072] Optionally, the apparatus as described above further includes a determination module, which is used for:

[0073] Within a preset time period, determine whether a heartbeat signal sent by the client has been received;

[0074] If not, mark the export task's status as paused and release the resources corresponding to the export task.

[0075] Optionally, the apparatus as described above further includes a receiving module, which is used for:

[0076] Receive export task requests sent by the client. The export task requests include the task index.

[0077] Determine the data corresponding to the task index;

[0078] Based on the data corresponding to the task index and the export parameters, generate a response message corresponding to the export task request and send the response message to the client. The response message includes the data or data address corresponding to the export task.

[0079] Fourthly, this application provides a data processing apparatus, the apparatus comprising:

[0080] The acquisition module is used to acquire the task list sent by the server. The task list includes multiple export tasks and the task index corresponding to each export task.

[0081] The generation module is used to generate an export task request for any export task based on the task index corresponding to the export task, and send the export task request to the server.

[0082] The acquisition module is also used to acquire the response message corresponding to the export task request. The response message includes the data or data address corresponding to the export task.

[0083] The determination module is used to determine the exported data based on the response information corresponding to multiple export tasks.

[0084] Optionally, the device as described above further includes an interaction module, which is used for:

[0085] The export interface is displayed, which includes type components corresponding to multiple data types, multiple export dimension components, and multiple encryption type components.

[0086] In response to a user's selection of at least one type component, determine at least one data type;

[0087] In response to the user's selection of any export dimension component, determine the export dimension type;

[0088] In response to the user's selection of any encryption type component, determine the encryption type;

[0089] Generate an export request based on at least one data type, export dimension type, and encryption type, and send the export request to the server.

[0090] Fifthly, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0091] The memory stores the instructions that the computer executes;

[0092] The processor executes computer-executable instructions stored in memory to implement any of the methods of the first aspect.

[0093] Sixthly, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0094] The memory stores the instructions that the computer executes;

[0095] The processor executes computer-executable instructions stored in memory to implement any of the methods in the second aspect.

[0096] In a seventh aspect, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any of the first aspects.

[0097] Eighthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any of the second aspects.

[0098] Ninthly, this application provides a computer program product, including a computer program that, when executed by a computer, implements the method as described in any of the first aspects.

[0099] In a tenth aspect, this application provides a computer program product, including a computer program that, when executed by a computer, implements the method as described in any of the second aspects.

[0100] The data processing method, electronic device, and program product provided in this application, in response to an export request sent by a client, determine at least one data type and export parameters corresponding to the export request, as well as the current operating parameters of the server; determine the export data information based on the at least one data type and export parameters; generate a task list based on the export data information and the current operating parameters, the task list including multiple export tasks and a task index corresponding to each export task; and send the task list to the client so that the client can download the data corresponding to multiple export tasks according to the multiple task indexes. In this way, by accurately parsing the export request and combining it with the server's real-time operating parameters to generate a task list containing multiple export tasks and corresponding task indexes, it not only supports the client to flexibly download data on demand, but also dynamically adapts to server resources to avoid overload, significantly improving the efficiency and accuracy of data export and the stability of server operation, thus improving operational efficiency. Attached Figure Description

[0101] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0102] Figure 1 This is a structural diagram illustrating an application scenario provided in an embodiment of this application.

[0103] Figure 2 A flowchart illustrating a data processing method provided in an embodiment of this application;

[0104] Figure 3 A flowchart illustrating another data processing method provided in an embodiment of this application;

[0105] Figure 4 A flowchart illustrating another data processing method provided in an embodiment of this application;

[0106] Figure 5 A flowchart illustrating another data processing method provided in an embodiment of this application;

[0107] Figure 6 This is a schematic diagram of the structure of a data processing device provided in an embodiment of this application;

[0108] Figure 7 This is a schematic diagram of another data processing apparatus provided in an embodiment of this application;

[0109] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0110] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0111] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0112] It should be noted that although the terms "first," "second," etc., are used to describe various types of information in the embodiments of this application, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. Optionally, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information.

[0113] It should be understood that the terms "comprising" or "including" indicate the presence of the previously mentioned features, steps, or operations, but do not preclude the presence, occurrence, or addition of one or more other features, steps, or operations. The terms "and / or," etc., used in this application can be interpreted as inclusive, or mean any one or any combination thereof. Optionally, "A and / or B" means "any one of the following: A; B; A and B." Additionally, the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0114] 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, data stored, data displayed, etc.) involved in one or more embodiments of this specification are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of related data must comply with relevant laws, regulations and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0115] In modern information system operation and maintenance management, various systems such as cloud computing platforms, distributed storage systems, and enterprise-level server clusters continuously generate massive amounts of operational data during operation. This includes business logs, system logs, alarm information, event logs, audit logs, performance statistics, and hardware device health status logs. This data is distributed and stored at different levels of the system, such as the operating system kernel, application service modules, and hardware management interfaces, and is in various formats, such as text logs, binary configuration files, and structured database records.

[0116] When operations and maintenance personnel need to troubleshoot, optimize performance, audit security, or back up configurations of a system, they often need to extract and integrate relevant information from each data source using specific tools or scripts. This process is cumbersome and can easily lead to the omission of critical data.

[0117] The above export process mainly relies on scattered tools or scripts. In addition, a large number of export operations will consume too much CPU, memory or disk I / O resources, which may affect system response delay or even crash, resulting in low operation and maintenance efficiency.

[0118] To address the aforementioned technical issues, this application provides a data processing method. After responding to a client's export request, the server first integrates the data type, export parameters, and its current operating parameters to accurately locate the export data information. Then, through task splitting and indexing, it generates a task list containing multiple export tasks and corresponding task indexes, allowing the client to download data on demand based on the index. This achieves dynamic adaptation between export tasks and system resources, efficient and flexible data downloading, while ensuring stable server operation and precise controllability of data export, thus improving operational efficiency.

[0119] Below, in conjunction with Figure 1 Examples are given to illustrate the application scenarios in which the data processing methods are used.

[0120] Figure 1 This is a structural diagram illustrating an application scenario provided in an embodiment of this application. Please refer to [link / reference]. Figure 1 , Figure 1 It can include a server and multiple clients.

[0121] The server and each client establish a communication connection through the network, supporting data interaction and command transmission.

[0122] The server can be used to provide services for integrating, filtering, splitting tasks, and exporting various types of data.

[0123] The server can be used to receive and parse export requests sent by various clients in real time, and collect its own current operating parameters, such as CPU utilization, memory usage, and disk I / O load.

[0124] The server can also be used to query and integrate exported data information from the corresponding data source based on data type and export parameters. Furthermore, based on the amount of exported data and the current running parameters, it can dynamically split and generate a task list containing multiple export tasks and corresponding task indexes, and feed this task list back to the client. During the client download process, it can provide support such as data fragmentation and positioning, dynamic resource allocation and connection maintenance to ensure that the export process does not affect the stable operation of its own core business.

[0125] The client can be used to send data export requests to the server and receive exported data.

[0126] The client can be used to generate and send an export request to the server based on user operation and maintenance needs, such as troubleshooting, performance optimization, security auditing, and configuration backup, specifying the type of data to be exported, filtering conditions, target storage location, and other export parameters.

[0127] The client can also receive a task list returned by the server, and based on the task index in the list, retrieve the data of the corresponding export task from the server through concurrent download or on-demand download. It can also store the exported data to the local file system or a remote specified storage node according to the preset configuration, so that users can perform subsequent operations such as data analysis, problem location or data archiving.

[0128] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0129] Figure 2 This is a flowchart illustrating a data processing method provided in an embodiment of this application. The execution entity in this embodiment can be a server or a processor within a server. The processor can be implemented through software or a combination of software and hardware. Please refer to... Figure 2 The method includes:

[0130] S201. In response to the export request sent by the client, determine at least one data type and export parameters corresponding to the export request, as well as the current operating parameters of the server.

[0131] An export request can refer to an instruction sent by a client to a server containing information related to the export request, based on the user's data export needs.

[0132] An export request can be used to trigger the server's data export process. Its content includes key information such as the data type to be exported and export parameters.

[0133] Data type can refer to the specific type of the data to be exported.

[0134] Data types can include business logs, system alarm records, hardware health status data, structured configuration information, audit logs, etc.

[0135] Different data types can correspond to different data sources and query interfaces.

[0136] Export parameters can refer to the export conditions and requirements specified by the user.

[0137] Export parameters may include, but are not limited to, time range filtering conditions, data attribute filtering rules, target storage type, data encryption requirements, quantity range limits, etc.

[0138] Current operating parameters can refer to the real-time resource load status information of the server when receiving export requests.

[0139] Current operating parameters may include real-time CPU utilization, remaining memory capacity, disk I / O throughput, network bandwidth utilization, etc.

[0140] The current operating parameters can be used to reflect the current resource load status of the server, so as to avoid the core business being affected by resource overload during subsequent data processing.

[0141] The server can receive export requests initiated by clients through a preset communication protocol. The export request carries information related to the export requirements specified by the user. By parsing the export request, at least one data type to be exported and export parameters can be extracted. At the same time, the server can collect its own current operating parameters in real time through the built-in resource monitoring unit.

[0142] Optionally, the method also includes sensing the client download status through heartbeat detection. Heartbeat detection can be performed as follows: within a preset time period, determine whether a heartbeat signal sent by the client has been received; if not, mark the task status of the export task as paused and release the resources corresponding to the export task.

[0143] The server can pre-set a reasonable preset duration and, after sending the task list, continuously listen and determine whether it can receive the heartbeat signal sent by the client within the preset duration.

[0144] A heartbeat signal can be a confirmation signal sent by the client to the server. It can include basic information such as the client's current download progress, network status, and completed export task identifiers, to prove that the client is still participating in the data download process normally.

[0145] If the server does not receive a heartbeat signal from the client within a preset time, it indicates that the client is likely experiencing an anomaly, such as network interruption, client program crash, user-initiated termination of download, or device offline.

[0146] If no heartbeat signal is received from the client within a preset time period, the server can perform the following operations: mark the task status of all unfinished export tasks corresponding to the client as paused, retain core information such as task index and completion progress, and reserve the foundation for the client to resume connection and data transmission in the future.

[0147] Release the server resources occupied by the export task, including the CPU threads, memory space, disk I / O quota, and dedicated data source connection allocated to it, to prevent these resources from being wasted due to long-term idleness by unresponsive tasks, and to ensure that server resources can be quickly returned and allocated to other normal export tasks or core business, thus ensuring the overall stability and resource utilization of the server.

[0148] S202. Determine the exported data information based on at least one data type and exported parameters.

[0149] Exported data information can refer to the target data set obtained by the server from the corresponding data source by querying, filtering and integrating the data according to the data type and export parameters.

[0150] Exported data information can include data body, data format identifier, total data volume, and other information.

[0151] Standardized data query interfaces can be pre-configured for different data types. During the query process, the server converts the exported parameters into corresponding query conditions and unifies the format and integrates the content of the target data obtained from each data source, removing redundant data and duplicate records to form well-structured and complete exported data information.

[0152] For example, calling the log database query interface for business log data, calling the hardware management module interface for hardware health status data, and calling the configuration database access interface for structured configuration information, etc.

[0153] For example, based on the export parameters such as "time range is the last 7 days" and "alarm level is severe", the data source of system alarm records can be jointly filtered to obtain target data that meets the conditions.

[0154] Optionally, the exported data information can be determined based on at least one data type and exported parameters in the following ways: for any data type, determine the exported data of the data type based on the data type and exported parameters; determine the target model among multiple preset models based on the data type; analyze and process the exported data based on the target model to obtain the analyzed data; determine the exported data information based on the exported data and analyzed data corresponding to at least one data type respectively.

[0155] For example, assuming the data type is "hardware data" and the export parameter is "temperature ≥ 40℃ for the last 3 days", then the raw hardware data that meets the temperature and other parameter requirements within the last 3 days will be filtered from the data source of the hardware management module and used as the export data of this data type.

[0156] The server can be pre-configured with multiple preset models, each designed for the characteristics and processing requirements of a specific data type.

[0157] For example, multiple preset models may include text log parsing models, hardware health assessment models, structured data deduplication models, etc.

[0158] For example, text data such as business logs corresponds to the log structured parsing model, hardware data corresponds to the health status scoring model, and structured data corresponds to the deduplication data cleaning model.

[0159] The server can automatically match and call the corresponding model as the target model based on the data type being processed, ensuring the relevance and accuracy of the analysis and processing.

[0160] If the target model is a log structured parsing model, then structured fields such as timestamps, device identifiers, error codes, and key event descriptions are extracted from unstructured text logs to generate analysis data.

[0161] If the target model is a hardware health assessment model, then conclusive information such as hardware failure risk score and remaining service life estimate are calculated based on the raw data to generate analytical data.

[0162] If the target model is a deduplication model, then duplicate entries in the structured transaction records are removed to generate a deduplicated, simplified dataset and a deduplication statistics report to generate analytical data.

[0163] S203. Generate a task list based on the exported data and current running parameters.

[0164] The task list includes multiple export tasks, as well as a task index for each export task.

[0165] A task list can refer to a collection of tasks formed by the server after splitting the exported data information.

[0166] An export task can refer to an independent data processing unit formed by splitting the exported data information. Each export task corresponds to a continuous and complete data segment, which can be independently transmitted and stored.

[0167] A task index can refer to a set of unique identifiers and location information assigned to each exported task.

[0168] The task index can include the exported task identifier, data source location information, data size, resource allocation quota, etc.

[0169] The task index can be used by the client to accurately locate and download the data of the corresponding exported task.

[0170] Optionally, the task list may also include the execution priority of each exported task, so that the client can select the download order according to the priority.

[0171] Based on the total amount of exported data, the storage characteristics of data types, and the resource load status reflected by the current running parameters, task splitting rules can be determined. According to the task splitting rules, the exported data is split into multiple independent export tasks. Each export task corresponds to a continuous and complete data segment, ensuring that a single export task can be executed and transmitted independently. At the same time, a unique task index is generated for each export task, and all export tasks and their corresponding task indexes are organized to form a task list.

[0172] For example, when the total amount of data is large and the server's CPU utilization is low, the task splitting rules can appropriately increase the amount of data for a single task; when the server's memory utilization is high, the amount of data for a single task needs to be reduced to decrease resource consumption.

[0173] S204. Send a task list to the client so that the client can download the data corresponding to multiple export tasks based on multiple task indices.

[0174] The task list can be sent to the client that initiated the export request through a preset communication channel. After receiving the task list, the client sends a data download request to the server based on the multiple task indices in the list, thereby obtaining the data streams corresponding to multiple export tasks.

[0175] The client can choose to download data concurrently or sequentially based on its processing capabilities and network conditions.

[0176] For example, data for the corresponding export task can be requested from the server simultaneously based on multiple task indexes. The server can then quickly locate the data segment based on the task index and stream it.

[0177] During the download process, the client can display the download status in real time based on information such as data size and transmission progress in the task index. If an abnormal situation such as network interruption occurs, the download can be resumed from the interrupted position based on the task index without having to reacquire all the data.

[0178] Once the download is complete, the client can store the data to the local file system or a remote storage node according to the target storage type in the export request, thus completing the entire data export process.

[0179] The data processing method provided in this embodiment, in response to an export request sent by a client, determines at least one data type and export parameters corresponding to the export request, as well as the current operating parameters of the server; determines the export data information based on the at least one data type and export parameters; generates a task list based on the export data information and the current operating parameters, the task list including multiple export tasks and a task index corresponding to each export task; and sends the task list to the client so that the client can download the data corresponding to multiple export tasks according to the multiple task indexes. In this way, by accurately parsing the export request and combining it with the server's real-time operating parameters to generate a task list containing multiple export tasks and corresponding task indexes, it not only supports the client to flexibly download data on demand, but also dynamically adapts to server resources to avoid overload, significantly improving the efficiency and accuracy of data export and the stability of server operation, thus improving operational efficiency.

[0180] Below, in conjunction with Figure 3 The process of generating a task list based on exported data and current running parameters is explained.

[0181] Figure 3 This is a flowchart illustrating another data processing method provided in an embodiment of this application. Based on the above embodiments, see [link to relevant documentation]. Figure 3 The method includes:

[0182] S301. Determine the sharding parameters based on the exported data information and the current running parameters.

[0183] Sharding parameters can include shard size thresholds and the number of concurrent threads.

[0184] The data parsing module can be invoked to extract key attributes of the exported data, including but not limited to total data volume, data type, data storage density, and data continuity requirements. Real-time values ​​of current operating parameters, including CPU utilization, remaining memory capacity, disk I / O load, and network bandwidth utilization, can be obtained synchronously through the resource monitoring unit. Based on the key attributes of the exported data and the real-time values ​​of the current operating parameters, the sharding parameters can be determined through a preset parameter calculation model.

[0185] Optionally, the sharding parameters can be determined based on the exported data information and the current running parameters in the following ways: determine the total amount of data and storage type based on the exported data information; determine the sharding size threshold based on the total amount of data and storage type using a preset algorithm; and determine the number of concurrent threads based on the current running parameters; wherein the sharding parameters include the sharding size threshold and the number of concurrent threads.

[0186] For example, if the exported data is a text log, and the server's CPU utilization is less than 40% and the remaining memory capacity is greater than 8GB, then the shard size threshold should be set to 1.5GB-2GB, and the number of concurrent threads should be set to 8-10 to improve the splitting and downloading efficiency.

[0187] If the exported data is structured data, and the server's CPU utilization is higher than 70% and the remaining memory capacity is less than 2GB, then set the fragment size threshold to 500MB-800MB and the number of concurrent threads to 2-3 to avoid excessive resource consumption by a single task that could cause server lag.

[0188] For binary configuration data, regardless of resource status, the maximum shard size threshold shall not exceed 1GB, and the number of concurrent threads shall not exceed 5, to ensure that the data can be parsed independently after splitting.

[0189] S302. Based on the fragmentation parameters, the exported data information is fragmented to obtain multiple data fragments.

[0190] Based on the sharding parameters, the corresponding splitting rule template can be called to shard the exported data information, resulting in multiple candidate shards. The single-thread resource quota is calculated based on the number of concurrent threads. Based on the single-thread resource quota, the resource consumption of each candidate shard is verified. After the verification is passed, the multiple candidate shards are determined as multiple data shards.

[0191] For example, for text log data, splitting by line boundaries and using newline characters as the splitting identifier ensures that a single data shard contains complete log entries and avoids the situation where a log is split into two shards.

[0192] For structured data, it is split according to the database record identifier and the record end identifier is used as the boundary to ensure that each shard contains complete field information and supports independent import into other systems.

[0193] For binary configuration data, it is split according to data block identifier. Each data block corresponds to a complete configuration unit. The split identifier can be a preset 16-bit binary feature code.

[0194] Based on the shard size threshold, the corresponding splitting rule template can be called to shard the exported data information, resulting in multiple candidate shards. The single-thread resource quota is calculated based on the number of concurrent threads. Based on the single-thread resource quota, the resource consumption of each candidate shard is verified. After the verification is passed, the multiple candidate shards are determined as multiple data shards.

[0195] For example, if the shard size threshold is 1GB, when processing text logs, the server will accumulate the size of log entries. When it approaches 1GB and the next log entry will cause the threshold to be exceeded, the current shard will be stopped, the shard will end with the last complete log entry as the boundary, and the next shard will be started.

[0196] Optionally, if the memory usage of a candidate partition exceeds the single-threaded quota, the partition will be split a second time.

[0197] S303. For any data shard, generate the corresponding export task and task index for the data shard.

[0198] For any data shard, feature extraction is performed on the data shard to obtain its features. Based on the features of the data shard, an export task corresponding to the data shard is generated, and a task index is generated for that data shard.

[0199] The index content includes at least the data source location identifier, resource allocation information, sharding characteristic information, and associated task identifier.

[0200] The data source location identifier can indicate the specific path of the data shard in the server storage system and the database shard identifier.

[0201] Resource allocation information can indicate the CPU thread identifier, memory address range, and disk I / O channel number occupied by the task, which facilitates the server to quickly allocate resources to respond to download requests.

[0202] Fragmentation feature information can represent the size, data type, and checksum of data fragments, which are used by the client to verify data integrity after downloading.

[0203] The associated task identifier can indicate if the fragment has a dependency relationship with other fragments. If so, a unique identifier of the associated task is recorded to guide the client download order.

[0204] S304. Generate a task list based on the export tasks and task indexes corresponding to the multiple data shards.

[0205] The list header can contain metadata information.

[0206] Metadata information may include the total number of tasks, the unique identifier of the export request, the server response time, and the total data size.

[0207] The main body of the list can contain complete information for each associated record, and each record corresponds to a download guide for a data segment.

[0208] The associated records can be sorted according to the execution priority of the exported tasks. If the priorities are the same, they can be sorted according to the generation time of the data shards. The sorted associated records are then encapsulated into a task list.

[0209] The implementation details of each step in this application embodiment can be found in the description of the corresponding steps or operations in the above method embodiments; repeated content will not be repeated.

[0210] The data processing method provided in this embodiment determines sharding parameters based on the exported data information and current operating parameters; shards the exported data information according to the sharding parameters to obtain multiple data shards; for any given data shard, an export task and task index corresponding to the data shard are generated; and a task list is generated based on the export tasks and task indexes corresponding to each of the multiple data shards. In this way, by dynamically determining sharding parameters and performing data sharding processing by combining the exported data information and the server's current operating parameters, and then generating a dedicated export task and index for each shard and integrating them into a task list, this method achieves precise adaptation between data splitting and server resource load, ensuring data integrity and independent parsing capability, while also supporting on-demand precise downloading by clients, significantly improving the overall efficiency of data export and the stability of server operation.

[0211] Below, in conjunction with Figure 4 This section explains the interaction process between the server and the client.

[0212] Figure 4 This is a flowchart illustrating another data processing method provided in an embodiment of this application. Based on the above embodiments, see also... Figure 4 The method includes:

[0213] S401. The client generates an export request and sends it to the server.

[0214] To enhance user convenience, the client comes pre-configured with a visual export interface.

[0215] It can respond to user actions on the export interface, generate an export request, and send the export request to the server.

[0216] Optionally, an export request can be generated as follows: display an export interface, which includes type components corresponding to multiple data types, multiple export dimension components, and multiple encryption type components; in response to the user's selection of at least one type component, determine at least one data type; in response to the user's selection of any export dimension component, determine the export dimension type; in response to the user's selection of any encryption type component, determine the encryption type; generate an export request based on at least one data type, export dimension type, and encryption type, and send the export request to the server.

[0217] Type components can be presented as checkboxes or drop-down lists. Each component corresponds to a preset data type, and a brief description of the data type is added next to the component.

[0218] The export dimension component can be presented as tabs or radio buttons, covering the core export needs of users, including time range, data volume, storage format, and transmission method.

[0219] The encryption type component can be presented as a radio button, providing data security options, including no encryption, AES encryption, RSA encryption, and SM4 national cryptographic encryption.

[0220] After selecting the encryption type, the interface will automatically pop up a key input box.

[0221] S402. In response to the export request sent by the client, the server determines at least one data type and export parameters corresponding to the export request, as well as the server's current operating parameters.

[0222] The execution process of S402 can be found in the execution process of S201, and will not be repeated here.

[0223] S403. The server determines the exported data information based on at least one data type and export parameters.

[0224] The execution process of S403 can be found in the execution process of S202, and will not be repeated here.

[0225] S404. The server generates a task list based on the exported data and current operating parameters, and sends the task list to the client.

[0226] The execution process of S404 can be found in the execution processes of S203 and S204, and will not be repeated here.

[0227] S405. For any export task, the client generates an export task request based on the task index corresponding to the export task and sends the export task request to the server.

[0228] The export task request includes the task index.

[0229] For any given export task, the client can extract the task index corresponding to the export task, generate an export task request, and send the export task request to the server.

[0230] S406. The server determines the data corresponding to the task index.

[0231] After receiving an export task request, the server can extract the storage path and task identifier from the task index, quickly locate the corresponding data shards through caching or direct access to the storage system, and verify the integrity of the data.

[0232] S407. The server generates a response message corresponding to the export task request based on the data and export parameters corresponding to the task index, and sends the response message to the client.

[0233] The response message includes the data or data address corresponding to the export task.

[0234] The server can process the located data according to the requirements in the export parameters. If the export parameters specify an encryption type, the data is first encrypted, then encapsulated according to the storage format requirements, and a response message is generated.

[0235] The process from S405 to S407 can be repeated multiple times until the client receives the response messages corresponding to all exported tasks in the task list.

[0236] S408: The client determines the exported data based on the response information corresponding to each of the multiple export tasks.

[0237] If the response message contains fragmented data, the client first verifies the data integrity using a checksum. After successful verification, it decrypts the data using a preset key and then marks the fragment sequence number according to the order in the task index. If the response message contains a data address, the client downloads the fragmented data through that address. After completion, it performs the same integrity verification and decryption operations. When all fragmented data corresponding to all export tasks has been downloaded, the client integrates all fragmented data in the order of the task list, removes temporary identifiers between fragments, and generates a complete data file that meets the export parameter requirements.

[0238] In this way, the export request generation process is simplified through a client-side visual interface. The server dynamically processes and splits tasks based on data requirements and its own operating parameters. The client accurately downloads each data segment as needed, which not only ensures the security, integrity, and server stability of the exported data, but also improves export efficiency and user convenience.

[0239] Below, in conjunction with Figure 5 This provides an explanation for the client.

[0240] Figure 5 This is a flowchart illustrating another data processing method provided in an embodiment of this application. Based on the above embodiments, see [link to relevant documentation]. Figure 5 The method includes:

[0241] S501, Get the list of tasks sent by the server.

[0242] The task list includes multiple export tasks, as well as a task index for each export task.

[0243] S502. For any export task, generate an export task request based on the task index corresponding to the export task, and send the export task request to the server.

[0244] S503. Obtain the response message corresponding to the export task request. The response message includes the data or data address corresponding to the export task.

[0245] S504. Determine the exported data based on the response information corresponding to each of the multiple export tasks.

[0246] The execution process of S501-S504 can be found in the above execution process, and will not be repeated here.

[0247] The data processing method provided in this embodiment obtains a task list sent by the server. The task list includes multiple export tasks and a task index corresponding to each export task. For any export task, an export task request is generated based on the task index and sent to the server. The response message corresponding to the export task request is obtained, and the response message includes the data or data address corresponding to the export task. Based on the response information corresponding to the multiple export tasks, the export data is determined. In this way, by obtaining the task list, generating export task requests on demand based on the task index, and integrating the export data after receiving the response messages, the method achieves accurate positioning and flexible downloading of each data segment. This ensures the integrity of the exported data, improves data download efficiency, and reduces the server's transmission load.

[0248] Figure 6 This is a schematic diagram of a data processing apparatus provided in an embodiment of this application. Please refer to... Figure 6 The data processing device 600 includes a first determining module 601, a second determining module 602, a generating module 603, and a sending module 604, wherein...

[0249] The first determining module 601 is used to respond to an export request sent by the client, and determine at least one data type and export parameters corresponding to the export request, as well as the current operating parameters of the server.

[0250] The second determining module 602 is used to determine the exported data information based on at least one data type and exported parameters;

[0251] The generation module 603 is used to generate a task list based on the exported data information and the current running parameters. The task list includes multiple exported tasks and the task index corresponding to each exported task.

[0252] The sending module 604 is used to send a task list to the client so that the client can download the data corresponding to multiple export tasks according to multiple task indices.

[0253] Optionally, in the above apparatus, the generation module 603 is specifically used for:

[0254] Determine the sharding parameters based on the exported data and current running parameters;

[0255] Based on the sharding parameters, the exported data information is sharded to obtain multiple data shards;

[0256] For any given data shard, generate the corresponding export task and task index for that data shard;

[0257] A task list is generated based on the export tasks and task indexes corresponding to the multiple data shards.

[0258] Optionally, in the above apparatus, the generation module 603 is specifically used for:

[0259] Based on the exported data information, determine the total amount of data and its storage type;

[0260] The sharding size threshold is determined by a preset algorithm based on the total amount of data and storage type.

[0261] Determine the number of concurrent threads based on the current running parameters;

[0262] The sharding parameters include the sharding size threshold and the number of concurrent threads.

[0263] Optionally, in the above apparatus, the second determining module 602 is specifically used for:

[0264] For any given data type, determine the exported data based on the data type and the exported parameters.

[0265] Based on the data type, determine the target model from multiple preset models;

[0266] Based on the target model, the exported data is analyzed and processed to obtain the analytical data;

[0267] Determine the exported data information based on the exported data and analysis data corresponding to at least one data type.

[0268] Optionally, the apparatus as described above further includes a determination module 605, which is used for:

[0269] Within a preset time period, determine whether a heartbeat signal sent by the client has been received;

[0270] If not, mark the export task's status as paused and release the resources corresponding to the export task.

[0271] Optionally, the apparatus as described above further includes a receiving module 606, which is used for:

[0272] Receive export task requests sent by the client. The export task requests include the task index.

[0273] Determine the data corresponding to the task index;

[0274] Based on the data corresponding to the task index and the export parameters, generate a response message corresponding to the export task request and send the response message to the client. The response message includes the data or data address corresponding to the export task.

[0275] Figure 7 This is a schematic diagram of a data processing apparatus provided in an embodiment of this application. Please refer to... Figure 7 The data processing device 700 includes an acquisition module 701, a generation module 702, and a determination module 703, wherein...

[0276] The acquisition module 701 is used to acquire the task list sent by the server. The task list includes multiple export tasks and the task index corresponding to each export task.

[0277] The generation module 702 is used to generate an export task request for any export task based on the task index corresponding to the export task, and send the export task request to the server.

[0278] The acquisition module 701 is also used to acquire the response message corresponding to the export task request. The response message includes the data or data address corresponding to the export task.

[0279] The determination module 703 is used to determine the exported data based on the response information corresponding to multiple export tasks.

[0280] Optionally, the device described above further includes an interaction module 704, which is used for:

[0281] The export interface is displayed, which includes type components corresponding to multiple data types, multiple export dimension components, and multiple encryption type components.

[0282] In response to a user's selection of at least one type component, determine at least one data type;

[0283] In response to the user's selection of any export dimension component, determine the export dimension type;

[0284] In response to the user's selection of any encryption type component, determine the encryption type;

[0285] Generate an export request based on at least one data type, export dimension type, and encryption type, and send the export request to the server.

[0286] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Please refer to... Figure 8 The electronic device 800 may include: a memory 801, a processor 802, and a transceiver 803.

[0287] Memory 801 is used to store program instructions;

[0288] The processor 802 is used to execute the program instructions stored in the memory so that the electronic device 800 performs the above-described method.

[0289] Transceiver 803 may include a transmitter and / or a receiver. The transmitter may also be referred to as a transmitter, transmitter port, or transmitter interface, and the receiver may also be referred to as a receiver port, receiver interface, or similar descriptions. Exemplarily, memory 801, processor 802, and transceiver 803 are interconnected via bus 804.

[0290] This application also provides a computer program product that can be executed by a processor, and when the computer program product is executed, the above-described method can be implemented.

[0291] The data processing apparatus, electronic device, computer-readable storage medium, and computer program product of the embodiments of this application can execute the technical solutions shown in the above data processing method embodiments. Their implementation principles and beneficial effects are similar, and will not be described again here.

[0292] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0293] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0294] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0295] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0296] When integrated units / modules are implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor can be any suitable hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC, etc. Unless otherwise specified, the storage unit can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc.

[0297] If the integrated unit / module is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0298] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0299] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0300] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A data processing method, characterized in that, Applied to a server, the method includes: In response to an export request sent by a client, determine at least one data type and export parameters corresponding to the export request, as well as the current operating parameters of the server; Based on the at least one data type and the export parameters, the exported data information is determined, and the exported data information includes information on the data body, data format identifier, and total data volume. Based on the exported data information and the current running parameters, sharding parameters are determined. The sharding parameters include a sharding size threshold and a number of concurrent threads. The sharding size threshold is determined by a preset algorithm based on the total amount of data and storage type of the exported data information. The number of concurrent threads is determined based on the current running parameters. Based on the sharding parameters, the exported data information is sharded to obtain multiple data shards; For any given data shard, generate the corresponding export task and task index for that data shard; A task list is generated based on the export tasks and task indexes corresponding to the multiple data shards. The task list includes multiple export tasks and a task index corresponding to each export task. The task list is sent to the client so that the client can download data corresponding to multiple export tasks based on multiple task indices.

2. The method according to claim 1, characterized in that, Based on the exported data information and the current operating parameters, the sharding parameters are determined, including: Based on the exported data information, determine the total amount of data and the storage type.

3. The method according to any one of claims 1-2, characterized in that, Based on the at least one data type and the export parameters, the exported data information is determined, including: For any given data type, determine the exported data of that data type based on the data type and the exported parameters; Based on the data type, determine the target model from multiple preset models; Based on the target model, the exported data is analyzed and processed to obtain analytical data; Based on the exported data and analysis data corresponding to at least one of the data types, the exported data information is determined.

4. The method according to any one of claims 1-2, characterized in that, The method further includes: Within a preset time period, determine whether a heartbeat signal sent by the client has been received; If not, the task status of the exported task is marked as paused, and the resources corresponding to the exported task are released.

5. The method according to any one of claims 1-2, characterized in that, The method further includes: Receive an export task request sent by the client, the export task request including a task index; Determine the data corresponding to the task index; Based on the data corresponding to the task index and the export parameters, a response message corresponding to the export task request is generated and sent to the client. The response message includes the data or data address corresponding to the export task.

6. A data processing method, characterized in that, Applied to a client, the method includes: The server retrieves a task list sent by the server. This task list is generated by the server after determining sharding parameters based on the exported data information and current running parameters, and then sharding the exported data information according to these parameters. The sharding parameters include a shard size threshold and a number of concurrent threads. The shard size threshold is determined using a preset algorithm based on the total amount of exported data and its storage type. The number of concurrent threads is determined based on the current running parameters. The exported data information includes the data body, data format identifier, and total data amount. For any given export task, an export task request is generated based on the task index corresponding to the export task, and the export task request is sent to the server. Obtain the response message corresponding to the export task request, wherein the response message includes the data or data address corresponding to the export task; The exported data is determined based on the response information corresponding to each of the multiple export tasks.

7. The method according to claim 6, characterized in that, Before obtaining the list of tasks sent by the server, the method further includes: The export interface is displayed, which includes multiple type components corresponding to multiple data types, multiple export dimension components, and multiple encryption type components. In response to a user's selection of at least one type component, determine at least one data type; In response to the user's selection of any export dimension component, determine the export dimension type; In response to the user's selection of any encryption type component, the encryption type is determined; An export request is generated based on the at least one data type, the export dimension type, and the encryption type, and the export request is sent to the server.

8. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as claimed in any one of claims 1 to 5 or the method as claimed in any one of claims 6-7.

9. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1-5 or the method of any one of claims 6-7.

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