IT data asset update and maintenance methods, media and equipment based on smart tags

Abstract

The present invention discloses an IT data asset update and maintenance method, medium and device based on smart tags, the method comprising: collecting historical data of each IT data asset corresponding to each group of second IT data asset information, inputting the historical data into a trained second neural network model, outputting update adjustment factors of relevant parameters of each IT data asset in each group of second IT data asset information, calculating the second update cycle based on the first update cycle and the update adjustment factor, and obtaining an update mapping table corresponding to each group of second IT data asset information; dynamically updating and maintaining the update mapping table corresponding to each group of second IT data asset information, and updating relevant parameters of the IT data assets corresponding to each group of second IT data asset information based on the second update cycle recorded in the update mapping table. The above scheme can realize automatic adjustment of the update cycle of relevant parameters of IT data asset information, reduce computing power while ensuring the accuracy of updates.

Description

Technical Field

[0001] The present application relates to the field of computer network security technology, and in particular to an IT data asset update and maintenance method, medium and device based on smart tags. Background Art

[0002] IT resource data covers many aspects and is an indispensable element in the process of information technology management and operation. The current update method of IT resource data often adopts real-time monitoring and updating or updating according to a preset fixed sampling cycle. The former requires real-time monitoring of every data of IT resources interacting with users and updating in real time, which is computationally complex; the latter does not take into account the particularity of each IT resource, such as security level, access volume, etc., according to the fixed sampling cycle, and cannot provide a personalized update service. In addition, hardware resources and software resources rely on manual verification of update compliance after the update, which is time-consuming and labor-intensive, and is not convenient for the update and maintenance of IT data assets. Summary of the invention

[0003] In view of the above problems, the present application provides a technical solution for IT data asset update and maintenance based on smart tags to solve the problems of existing IT resource data update and maintenance methods that are complex to operate and inconvenient to update and maintain.

[0004] To achieve the above objectives, in a first aspect, the present application provides an IT data asset update and maintenance method based on smart tags, the method comprising:

[0005] Acquire first IT data asset information, perform cluster analysis on the first IT data asset information to obtain multiple IT data asset information categories, and classify the first IT data asset information based on the IT data asset information categories to obtain multiple groups of second IT data asset information, each group of the second IT data asset information contains relevant parameters of multiple IT data assets;

[0006] Inputting multiple sets of second IT data asset information into the trained first neural network model, outputting a first update cycle corresponding to each set of second IT data asset information, and updating relevant parameters of IT data assets in each set of second IT data asset information based on the first update cycle;

[0007] After a preset time period, historical data of each IT data asset corresponding to each group of second IT data asset information is collected, the historical data is input into a trained second neural network model, an update adjustment factor of a relevant parameter of each IT data asset in each group of the second IT data asset information is output, a second update cycle is calculated based on the first update cycle and the update adjustment factor, and an update mapping table corresponding to each group of the second IT data asset information is obtained, each group of the update mapping table contains a plurality of second update cycles, each of the second update cycle corresponds to a next update sampling cycle of relevant parameters of one or more IT data assets, and the historical data includes operation data, access data and security events of each IT data asset within a preset time period;

[0008] Dynamically update and maintain an update mapping table corresponding to each group of the second IT data asset information, and update relevant parameters of the IT data assets corresponding to each group of the second IT data asset information based on a second update period recorded in the update mapping table;

[0009] The method further comprises:

[0010] Building a procurement database for IT data assets, wherein the procurement database records a plurality of sets of first parameters, each of which includes a type of the purchased IT data asset, a procurement completion time, and procurement-related parameters;

[0011] After updating the relevant parameters of a certain second IT data asset, recording the type of the currently updated IT data asset, and obtaining the relevant parameters of the updated second IT data asset, which are recorded as second parameters;

[0012] If the currently updated IT data asset belongs to a hardware resource, determining whether there is a first parameter matching the second parameter in the procurement database, and if not, issuing a first prompt message;

[0013] If the currently updated IT data asset belongs to a software resource, then the resource manufacturer's website to which the current IT data asset belongs is searched to see whether there is a third parameter that matches the second parameter. If not, a second prompt message is issued.

[0014] Further, determining whether there is a first parameter matching the second parameter in the procurement database includes:

[0015] Obtain a first update timestamp after the relevant parameters of the current second IT data asset are updated, determine a first timestamp range according to the first update timestamp and the security level of the corresponding IT data asset, and search whether there is a first parameter that is compatible with the second parameter within the first timestamp range.

[0016] Further, searching whether the resource vendor website to which the current IT data asset belongs has a third parameter that matches the second parameter includes:

[0017] Obtain the version number and second update timestamp of the updated software resource, determine the second timestamp range according to the second update timestamp and the security level, and search the resource manufacturer website to which the current IT data asset belongs within the second timestamp range to see whether there is a version number that matches the version number of the updated software resource.

[0018] Furthermore, inputting the historical data into the trained second neural network model includes:

[0019] Performing data cleaning on the historical data, and inputting the cleaned historical data into the trained second neural network model;

[0020] The data cleaning includes:

[0021] The historical data is grouped and arranged according to the distance from the current timestamp to obtain a number of historical data segments, a score is calculated based on the historical data segments, the historical data segments with scores lower than the preset score are eliminated, and the score corresponding to the current historical data is calculated according to the scores and weights of the remaining historical data segments. The closer the distance to the current timestamp is, the greater the weight of the historical data segment;

[0022] The score corresponding to the historical data segment is calculated based on the security level of the IT data asset, the amount of accessed data, and the amount of secure data.

[0023] Furthermore, the second neural network model calculates the update adjustment factor according to the following method:

[0024] Set the initial value of the adjustment factor;

[0025] Assign a weight to each IT data asset's operation data, access data, and security events within a preset time period, and calculate an adjustment score based on each weight and data volume;

[0026] Calculate the updated adjustment factor according to the adjustment score and the initial value of the adjustment factor;

[0027] The calculating the second update period based on the first update period and the update adjustment factor comprises:

[0028] The first update period and the update adjustment factor are multiplied to obtain the second update period, and the update adjustment factor has a value range of [0.1, 2].

[0029] Furthermore, the method further comprises:

[0030] After each update of the relevant parameters of the IT data asset, adding an update mark for the second update period in the update mapping table;

[0031] When the proportion of the second update cycles with update marks exceeds the preset proportion, the average value of all the second update cycles recorded in the same update mapping table is counted;

[0032] Traversing and searching the current update mapping table to see whether there is a second update cycle that is smaller than the average value and whose difference from the average value is greater than a preset difference, if so, screening out the second update cycle that meets the condition, and obtaining the second update cycle set to be corrected;

[0033] Correcting all second update periods in the second update period set according to a preset correction factor to obtain a corrected second update period set;

[0034] Using the revised second update cycle set to replace the corresponding second update cycle in the current update mapping table to obtain a revised update mapping table;

[0035] The relevant parameters of the IT data assets are updated based on the revised update mapping table.

[0036] Furthermore, the method further comprises:

[0037] After receiving the global update instruction, relevant parameters of the IT data assets corresponding to each group of the second IT data asset information are updated, and the start timestamp information corresponding to the second update period in the update mapping table corresponding to each group of the second IT data asset information is reset.

[0038] Furthermore, when the IT data assets are hardware resources, the relevant parameters of the second IT data assets include server configuration information, network equipment details information, terminal equipment list information, printer and scanner information; when the IT data assets are software resources, the relevant parameters of the second IT data assets include operating system installation and licensing information, application software list information and database management system details information.

[0039] In a second aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the smart tag-based IT data asset update and maintenance method as described in the first aspect of the present application.

[0040] In a third aspect, the present application provides an electronic device having a computer program stored thereon, comprising a processor and a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by the processor, the smart tag-based IT data asset update and maintenance method as described in the first aspect of the present application is implemented.

[0041] Different from the prior art, the IT data asset update and maintenance method, medium and device based on smart tags in the above technical solution include: performing cluster analysis on the first IT data asset information to obtain multiple groups of second IT data asset information; inputting the multiple groups of second IT data asset information into the trained first neural network model, outputting the first update cycle corresponding to each group of second IT data asset information, and updating the relevant parameters of the IT data assets in each group of second IT data asset information based on the first update cycle; after a preset time period, collecting the historical data of each IT data asset corresponding to each group of second IT data asset information, inputting the historical data into the trained second neural network model, outputting the update adjustment factor of the relevant parameters of each IT data asset in each group of second IT data asset information, calculating the second update cycle based on the first update cycle and the update adjustment factor, and obtaining the update mapping table corresponding to each group of second IT data asset information; dynamically updating and maintaining the update mapping table corresponding to each group of second IT data asset information, and updating the relevant parameters of the IT data assets corresponding to each group of second IT data asset information based on the second update cycle recorded in the update mapping table. The above solution can realize automatic adjustment of the update cycle of parameters related to IT data asset information, reducing computing power while ensuring the accuracy of updates.

[0042] The above-mentioned records related to the invention content are only an overview of the technical solution of the present application. In order to enable ordinary technicians in the field to more clearly understand the technical solution of the present application, and then implement it according to the text of the specification and the contents recorded in the drawings, and to make the above-mentioned purpose and other purposes, features and advantages of the present application easier to understand, the following is an explanation in combination with the specific implementation mode and drawings of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The drawings are only used to illustrate the principles, implementation methods, applications, characteristics and effects of the specific embodiments of the present invention and other related contents, and shall not be considered as limiting the present application.

[0044] In the drawings of the specification:

[0045] Figure 1 This is a flow chart of the IT data asset update and maintenance method based on smart tags described in the first exemplary embodiment of the present application;

[0046] Figure 2 This is a flow chart of the IT data asset update and maintenance method based on smart tags described in the second exemplary embodiment of the present application;

[0047] Figure 3 This is a flow chart of the IT data asset update and maintenance method based on smart tags according to the third exemplary embodiment of the present application;

[0048] Figure 4 This is a flow chart of the IT data asset update and maintenance method based on smart tags according to the fourth exemplary embodiment of the present application;

[0049] Figure 5 A system architecture diagram for implementing the IT data asset update and maintenance method based on smart tags described in this application;

[0050] Figure 6 A schematic diagram of an electronic device according to a first exemplary embodiment of the present application;

[0051] The reference numerals in the above drawings are described as follows:

[0052] 10. Electronic equipment;

[0053] 101. Processor;

[0054] 102. Storage medium. DETAILED DESCRIPTION

[0055] In order to explain in detail the possible application scenarios, technical principles, specific schemes that can be implemented, and the purposes and effects that can be achieved, the following is a detailed description of the specific embodiments listed in conjunction with the accompanying drawings. The embodiments described herein are only used to more clearly illustrate the technical solutions of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0056] Reference to "embodiment" herein means that the specific features, structures or characteristics described in conjunction with the embodiment may be included in at least one embodiment of the present application. The term "embodiment" appearing in various places in the specification does not necessarily refer to the same embodiment, nor does it particularly limit its independence or association with other embodiments. In principle, in the present application, as long as there is no technical contradiction or conflict, the various technical features mentioned in the embodiments can be combined in any way to form a corresponding implementable technical solution.

[0057] Unless otherwise defined, the technical terms used in this document have the same meanings as those generally understood by those skilled in the art to which this application belongs; the use of relevant terms in this document is only for describing specific embodiments and is not intended to limit this application.

[0058] In the description of this application, the term "and / or" is an expression used to describe the logical relationship between objects, indicating that three relationships may exist, for example, A and / or B, which means: A exists, B exists, and A and B exist at the same time. In addition, the character " / " in this article generally indicates that the objects before and after are in an "or" logical relationship.

[0059] In the present application, terms such as “first” and “second” are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship of quantity, priority or sequence between these entities or operations.

[0060] Without further limitations, in this application, the words "include", "comprises", "has" or other similar open-ended expressions used in sentences are intended to cover non-exclusive inclusion. These expressions do not exclude the presence of additional elements in the process, method or product including the elements, so that the process, method or product including a series of elements may include not only those limited elements, but also other elements not explicitly listed, or also include elements inherent to such process, method or product.

[0061] In this application, expressions such as "greater than", "less than", "exceed" and the like are understood to exclude the number itself; expressions such as "above", "below", "within" and the like are understood to include the number itself. In addition, in the description of the embodiments of this application, "multiple" means more than two (including two), and similar expressions related to "multiple" are also understood in this way, such as "multiple groups", "multiple times", etc., unless otherwise clearly and specifically limited.

[0062] In the description of the embodiments of the present application, space-related expressions used, such as "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "vertical", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicate the orientation or position relationship based on the orientation or position relationship shown in the specific embodiments or drawings, and are only for the convenience of describing the specific embodiments of the present application or facilitating the reader's understanding, and do not indicate or imply that the referred device or component must have a specific position, a specific orientation, or be constructed or operated in a specific orientation, and therefore cannot be understood as a limitation on the embodiments of the present application.

[0063] Unless otherwise expressly specified or limited, in the description of the embodiments of the present application, the terms such as "install", "connect", "connect", "fix", "set", etc. used should be understood in a broad sense. For example, the "connection" can be a fixed connection, a detachable connection, or an integrated setting; it can be a mechanical connection, an electrical connection, or a communication connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium; it can be the internal connection of two elements or the interaction relationship between two elements. For technicians in the technical field to which the present application belongs, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0064] like Figure 1As shown, in a first aspect, the present application provides an IT data asset update and maintenance method based on smart tags, the method comprising:

[0065] Step S101: acquiring first IT data asset information, performing cluster analysis on the first IT data asset information to obtain multiple IT data asset information categories, and classifying the first IT data asset information based on the IT data asset information categories to obtain multiple groups of second IT data asset information, each group of the second IT data asset information containing relevant parameters of multiple IT data assets;

[0066] Step S102: inputting multiple groups of second IT data asset information into the trained first neural network model, outputting a first update cycle corresponding to each group of second IT data asset information, and updating relevant parameters of IT data assets in each group of second IT data asset information based on the first update cycle;

[0067] Step S103: after a preset time period, historical data of each IT data asset corresponding to each group of second IT data asset information is collected, the historical data is input into the trained second neural network model, an update adjustment factor of a relevant parameter of each IT data asset in each group of the second IT data asset information is output, a second update cycle is calculated based on the first update cycle and the update adjustment factor, and an update mapping table corresponding to each group of the second IT data asset information is obtained;

[0068] Each group of the update mapping tables includes a plurality of second update cycles, each of which corresponds to a next update sampling cycle of relevant parameters of one or more IT data assets, and the historical data includes operation data, access data and security events of each IT data asset within a preset time period;

[0069] Step S104: dynamically update and maintain an update mapping table corresponding to each group of the second IT data asset information, and update relevant parameters of the IT data assets corresponding to each group of the second IT data asset information based on the second update period recorded in the update mapping table.

[0070] In step S101, the first IT data asset information refers to the original data asset information related parameters, which are relatively messy data that has not been classified. After classification, the second IT data asset information obtained can mainly include the following categories: hardware resource data, software resource data, network resource data, and data resources. IT data assets refer to the resources themselves, such as servers, databases, application software, printers, etc., and the related parameters of IT data assets refer to the configuration information related to the resources, such as model, usage status, etc.

[0071] When the IT data asset is a hardware resource, the relevant parameters of the second IT data asset include server configuration information, network device details information, terminal device list information, printer and scanner information, the server configuration information includes processor model, memory size, hard disk capacity, etc., the network device details include router and switch models, port number, data transmission rate, etc., the terminal device list information includes the model, configuration and usage status of desktop computers, laptops, tablets, etc. The printer and scanner information includes the device model, location, usage frequency and maintenance records, etc.

[0072] When the IT data asset is a software resource, the relevant parameters of the second IT data asset include operating system installation and licensing information, application software inventory information, and database management system details information. The operating system installation and licensing information includes version, installation date, activation status, etc. The application software inventory information includes software name, version, license quantity and usage. The database management system details information includes database type, capacity, performance parameters, and access permission settings.

[0073] When the IT data asset is a network resource, the relevant parameters of the second IT data asset include a network topology diagram, IP address and domain name allocation records, and network traffic and bandwidth usage. The network topology diagram is used to display network devices and connection relationships to facilitate troubleshooting and planning. The IP address and domain name allocation records are used to ensure the reasonable allocation and effective management of network resources. The network traffic and bandwidth usage are used to monitor network performance and optimize resource allocation.

[0074] When the IT data asset is a data resource, the relevant parameters of the second IT data asset include database structure and content, data warehouse information, log files and data. The database structure and content include table structure, field definition, index settings and stored data content. The data warehouse is used to store data types, capacity, access rights and backup strategies. The log files and data are used to record system operations, user behaviors, etc. for security analysis and troubleshooting.

[0075] In step S102, the first neural network model can obtain the category and security level corresponding to each group of second IT data asset information for calculation, and then output the first update cycle corresponding to each group of second IT data asset information. Generally, the higher the security level of the IT data asset, the shorter the first update cycle corresponding to the second IT data asset information output by the first neural network model, that is, a higher update frequency is required for IT data assets with a high security level. In this embodiment, the first update cycle refers to the initial update cycle of each group of IT data asset information. For example, if the category is hardware resource data, the result output by the first neural network model is usually about 1 day.

[0076] In step S103, each group of the update mapping table contains multiple second update cycles, each of which corresponds to the next update sampling cycle of the relevant parameters of one or more IT data assets, and the historical data includes the operation data, access data and security events of each IT data asset within a preset time period. If the access volume value is larger, the security events are more, the number of abnormal operation data is more, or the operation resource occupancy rate is larger, the update adjustment factor can be adjusted to a smaller value in the negative or positive direction, so that the relevant parameters of the current IT data asset are updated more frequently. Conversely, the update cycle of the relevant parameters of the IT data asset can be adjusted to a larger value in the positive direction.

[0077] In step S104, each group of second IT data asset information includes multiple IT data asset related parameters, for example, the hardware resource category includes multiple server related parameters. For each server related parameter update cycle, an initial update cycle can be set first, and then the data of each server within a period of time is collected. Based on the calculation results of these data, the next update cycle of each server related parameter is adjusted, and the updated second update cycle is recorded in the update mapping table. The update mapping table is dynamically refreshed each time at least one server related parameter is updated.

[0078] Since the update of relevant parameters of IT data assets fully considers factors such as the current security level, access data, security incidents, etc. of IT data assets, and the corresponding update cycle changes accordingly with the dynamic changes of historical data, it effectively saves computing power compared to the real-time monitoring of relevant parameters of each group of IT data assets. Compared with the method of setting a fixed preset period to update the relevant parameters of each IT data asset, it can achieve differentiated updating of relevant parameters of IT data assets and improve monitoring efficiency.

[0079] like Figure 2 As shown, the method also includes:

[0080] First, enter step S201 to build a procurement database for IT data assets, wherein the procurement database records multiple sets of first parameters, each of which includes the type of the purchased IT data assets, the purchase completion time, and procurement-related parameters;

[0081] Then, step S202 is performed to record the type of the currently updated IT data asset after updating the relevant parameters of the second IT data asset, and obtain the relevant parameters of the updated second IT data asset, which are recorded as second parameters;

[0082] After step S202, step S203 may be entered: if the currently updated IT data asset belongs to a hardware resource, determining whether there is a first parameter matching the second parameter in the procurement database, and if not, issuing a first prompt message;

[0083] After step S202, step S204 may be entered: if the currently updated IT data asset belongs to a software resource, then searching the resource manufacturer website to which the current IT data asset belongs to see whether there is a third parameter that matches the second parameter, and if not, issuing a second prompt message.

[0084] In short, if the IT data assets whose relevant parameters are currently updated belong to hardware resources, their compliance needs to be checked. The current inspection method often relies on manual post-verification, which is time-consuming and labor-intensive. However, this application builds a procurement database. When changes are detected in the relevant parameters of hardware resources, such as the addition of a certain type of server, or the memory of a server is replaced from 128G to 256G, the information in the procurement database before the current update time point will be traced back to see whether the content of this update matches the relevant parameters recorded in the procurement database, thereby realizing automatic verification of the compliance of hardware resource updates and improving efficiency. At the same time, if it is found that the updated parameters do not match the information recorded in the procurement database, a first prompt message will be issued to facilitate verification by the operation and maintenance personnel. Preferably, the first prompt message is also used to report the current unmatched relevant parameters and the location of the hardware resources.

[0085] If the IT data assets that are currently updating the relevant parameters belong to software resources, their compliance also needs to be checked. For example, when a version of an application software is found to be updated, this application will automatically search the website of the resource manufacturer to which the application software belongs to see whether there is a matching version number. If so, it means that the current application software is updated based on official channels. If there is a mismatch, an abnormal prompt message will also be issued to report the location of the electronic device where the application software is installed and the mismatched content to facilitate further verification by the operation and maintenance personnel.

[0086] Preferably, the first prompt information and the second prompt information may be sound prompts, light prompts, vibration prompts, or text or graphic prompts on a display interface of the display device.

[0087] In some embodiments, determining whether there is a first parameter that is compatible with the second parameter in the procurement database includes: obtaining a first update timestamp after the relevant parameters of the current second IT data asset are updated, determining a first timestamp range based on the first update timestamp and the security level of the corresponding IT data asset, and searching whether there is a first parameter that is compatible with the second parameter within the first timestamp range.

[0088] In short, for the procurement of hardware resources with different security levels, the corresponding process time is also different. Therefore, in order to improve the search efficiency, for hardware resources with higher security levels, you can start with the current timestamp and trace back to a longer range of procurement information. For example, the first timestamp range can be set to half a year later to determine whether there is a first parameter that matches the second parameter within half a year. For parameter updates of hardware resources with ordinary security levels, the retrieval time range can be adaptively shortened to improve search efficiency. For example, the first timestamp range can be set to 1 month. Through the above scheme, when matching the first parameter and the second parameter, adaptive adjustment of the search range of the second parameter can be achieved to improve search efficiency.

[0089] In some embodiments, searching whether the website of the resource manufacturer to which the current IT data asset belongs has a third parameter that matches the second parameter includes: obtaining the version number of the updated software resource and a second update timestamp, determining a second timestamp range based on the second update timestamp and the security level, and searching whether the website of the resource manufacturer to which the current IT data asset belongs has a version number that matches the version number of the updated software resource within the second timestamp range.

[0090] In short, the same applies to the update of software resource version numbers. Different search time ranges will be determined on the resource manufacturer's website according to the different security levels of the software resources, thereby improving search efficiency.

[0091] In some embodiments, Figure 3 As shown, the second neural network model calculates the update adjustment factor according to the following method:

[0092] First, enter step S301 to set the initial value of the adjustment factor;

[0093] Then, step S302 is performed to configure a weight for each IT data asset's operation data, access data, and security events within a preset time period, and to calculate an adjustment score based on each weight and data volume;

[0094] Then, the process proceeds to step S303 to calculate the updated adjustment factor according to the adjustment score and the initial value of the adjustment factor;

[0095] In this embodiment, the calculating of the second update period based on the first update period and the update adjustment factor includes: multiplying the first update period and the update adjustment factor to obtain the second update period, and the update adjustment factor has a value range of [0.1, 2].

[0096] In this embodiment, more factors are considered for the calculation of the update adjustment factor. For example, for the same security event, if the time point of occurrence is closer to the current timestamp, then the weight of the security event is higher than the security event farther from the current timestamp when allocating the weight ratio, so that the calculated update adjustment factor is more accurate. For example, if device A has 10 security events in the past month, and device B has 15 security events 6 months ago, then when the update adjustment factor is calculated using the above algorithm, the adjustment range of the update adjustment factor of device A will be greater than that of device B. If the initial update cycle determined by the two devices is 1 day / 1 time, then device A can be adjusted to 2 hours / time, and device B can be adjusted to 6 hours / time.

[0097] In some embodiments, Figure 4 As shown, the method also includes:

[0098] First, proceed to step S401: after each update of the relevant parameters of the IT data asset, add an update mark for the second update period in the update mapping table;

[0099] Then, the process proceeds to step S402: when the proportion of the second update cycles with update marks exceeds the preset proportion, the average values ​​of all the second update cycles recorded in the same update mapping table are counted;

[0100] Then, step S403 is entered: traversing and searching the current update mapping table to see whether there is a second update cycle that is smaller than the average value and whose difference from the average value is greater than a preset difference. If so, the second update cycle that meets the condition is screened out to obtain a set of second update cycles to be corrected;

[0101] Then proceed to step S404: correct all second update cycles in the second update cycle set according to a preset correction factor to obtain a corrected second update cycle set;

[0102] Then, the process proceeds to step S405: replacing the corresponding second update period in the current update mapping table with the revised second update period set to obtain a revised update mapping table;

[0103] Then proceed to step S406: update the relevant parameters of the IT data assets based on the revised update mapping table.

[0104] In this embodiment, the preset proportion can be set according to actual needs, such as being set to 50%, that is, after more than 50% of the relevant parameters of the IT data assets in the update mapping table have completed this update, the average value of all second update cycles recorded in the same update mapping table will be counted. If it is detected that there are IT data assets with a long update cycle, and most of the relevant parameters of the IT data assets have been updated many times but have not been updated, then the relevant parameters of this part of the IT data assets will be corrected according to the preset correction factor for their corresponding second update cycle to obtain a corrected second update cycle set, and then the relevant parameters of the IT data assets will be updated based on the corrected update mapping table.

[0105] In this way, the relevant parameters of IT data assets in the same group can be updated within a controllable time range, avoiding the situation where the relevant parameters of a certain IT data asset have not been updated after other relevant parameters have been updated, thereby facilitating the subsequent collection and organization of the second IT data asset information of the group.

[0106] In some embodiments, the method also includes: after receiving a global update instruction, updating relevant parameters of the IT data assets corresponding to each group of the second IT data asset information, and resetting the start timestamp information corresponding to the second update period in the update mapping table corresponding to each group of the second IT data asset information.

[0107] In short, the global update instruction can be initiated by the upper system service. Through the global update instruction, the relevant parameters of the IT data assets corresponding to each group of the second IT data asset information can be updated, which is convenient for the subsequent collection and arrangement of relevant parameters.

[0108] like Figure 5 As shown, the system architecture diagram for implementing the IT data asset update and maintenance method based on smart tags described in this application includes a collection layer, a data layer, a processing layer, and an application layer. Specifically, the system may also include the following functional modules to implement different functions:

[0109] Smart label module: Automatically classify and label IT resource data through models and algorithms to achieve efficient organization and indexing of data. This process can be based on multiple dimensions such as data content, format, and source. These labels can accurately reflect the characteristics and attributes of resource assets, such as type, purpose, security risk level, resource type, configuration information, security status, etc.

[0110] Security risk module: Based on the data attributes of smart tags, accurate security risk assessment is carried out to identify potential security threats and vulnerabilities.

[0111] Automatic operation and maintenance module: Combined with smart tags, it realizes the automatic operation and maintenance and response of IT resources and improves operational efficiency.

[0112] Decision support module: Use data with intelligent tags to provide data support and insights for the company's strategic decision-making and business optimization.

[0113] Continuous optimization and iteration module: Based on the actual results and feedback of security operations, continuously optimize the algorithm and intelligent label system to improve the efficiency and security of data management.

[0114] In some embodiments, the system further includes a security risk module, an automatic operation and maintenance module, and a decision support module.

[0115] The security risk module includes the following:

[0116] Real-time monitoring and dynamic analysis module: Based on data tags, it realizes real-time monitoring and dynamic analysis of IT resources to timely discover and solve security problems.

[0117] Risk assessment module: Focus on monitoring data with sensitive labels. Once abnormal access or operation behavior is found, risk assessment will be conducted immediately and corresponding security response measures will be taken.

[0118] Automated vulnerability scanning module: Regularly scan IT resources for vulnerabilities to identify potential security risks.

[0119] Intelligent repair suggestion module: Provides targeted repair suggestions or automatic repair solutions based on the scan results and the attributes of smart tags to reduce the risk of security vulnerabilities being exploited.

[0120] Data access control module: Set different data access permissions according to the level and attributes of the smart label to ensure that sensitive data is not accessed by unauthorized personnel.

[0121] Security policy formulation module: Based on the data classification results of smart tags, formulate targeted security policies, such as encrypted storage, regular backup, access audit, etc., to improve data security.

[0122] The automatic operation and maintenance module includes the following:

[0123] Data classification and indexing module: Classify and index IT resource data based on the generated smart tags. By building an efficient data retrieval mechanism, data can be quickly located and queried, providing convenient data support for automatic operation and maintenance.

[0124] Intelligent operation and maintenance platform module: Build an operation and maintenance platform based on intelligent tags to realize the automated management and monitoring of IT resources. Through resource tags, potential problems can be intervened in advance to reduce the occurrence of failures and operation and maintenance costs.

[0125] Process optimization module: Use smart tags to optimize and simplify the operation and maintenance process to improve operation and maintenance efficiency and response speed. For example, smart tags can be used to quickly locate problematic resources and trigger corresponding processing processes.

[0126] The decision support module includes the following:

[0127] Data association analysis module: Based on the data classification results of intelligent tags, cross-domain and cross-system data association analysis is carried out to explore the potential connections and value points between data.

[0128] Optimize resource scheduling: Resource scheduling and optimization strategies based on smart tags can ensure the stable operation and data security of key business systems.

[0129] Data-driven decision support: Use IT resource data with smart tags to provide data support for the company's strategic decision-making and business optimization. Help companies discover new business opportunities and growth points through data correlation analysis and trend forecasting.

[0130] Personalized security services: Based on smart tags and user behavior analysis technology, we provide personalized security services for different user groups. For example, we provide administrators of key business systems with a higher level of security monitoring and protection services; and provide ordinary employees with convenient security training and guidance services.

[0131] The system of this application can realize multi-platform data integration and integrated operation, business modeling and association rule analysis, as well as automated response and intelligent operation.

[0132] Among them, the multi-platform data integration and integrated operation realize the data integration between multiple products such as host CWPP, asset management system, vulnerability scanning equipment, third-party threat intelligence platform, etc. through customized development and API interface docking, and build a comprehensive and real-time security situation view. This multi-platform data integration capability significantly improves the efficiency and accuracy of security operations.

[0133] Business modeling and association rule analysis Based on the association rule analysis capability of business scenarios, through in-depth analysis of business security requirements, the deep binding of business indicators and IT indicators is achieved, and a large number of association analysis and baseline analysis rules are formulated. This business modeling method makes security operations closer to business reality and improves the pertinence and effectiveness of security response.

[0134] Automated response and intelligent operation achieve rapid response and intelligent handling of security incidents by building key functional modules such as intelligent label generation, security risk assessment, and automatic operation and maintenance. This combination of automation and intelligence not only improves security operation efficiency, but also reduces the cost and risk of manual intervention.

[0135] Compared with the prior art, the system of the present application also has the following characteristics:

[0136] Improved intelligence and automation: Introducing advanced technologies such as machine learning and big data to achieve automated and intelligent analysis of resource data, significantly reducing manual intervention and improving analysis efficiency and accuracy. This intelligent processing method can identify security threats more quickly and accurately, and reduce false alarm rates.

[0137] Real-time response and rapid disposal: Build an efficient automated response mechanism. Once a security threat is detected by monitoring, the preset response process will be triggered immediately to achieve rapid disposal. This instant response capability can effectively curb the spread of security incidents and reduce losses.

[0138] Data-driven and decision-making support: Based on the collection and analysis of IT resource data element tags, a comprehensive security situation view is constructed. Through the visualization display platform, the security situation and operation effect are displayed in real time, providing intuitive and accurate data support for decision makers. This helps to improve the scientificity and accuracy of security decisions.

[0139] Continuous optimization and iteration capabilities: This proposal emphasizes continuous optimization and iteration of intelligent security operation methods through continuous data collection and analysis. This self-optimization capability enables the system to continuously adapt to new security threats and changing environments, and maintain an efficient and stable operating state.

[0140] Reduce operation and maintenance costs: Reduce manual operations and labor costs through automation and intelligence. At the same time, accurate analysis and rapid response can reduce business interruptions and losses caused by security incidents, thereby indirectly reducing overall operation and maintenance costs.

[0141] Enhanced security and compliance: Fine-grained labeling management of IT resources helps improve the precision and accuracy of security management. At the same time, this management method also helps meet relevant security regulations and compliance requirements and reduce compliance risks.

[0142] Multi-platform integration and fusion: By connecting data between multiple products, a comprehensive, real-time security situation view is constructed, significantly improving the efficiency and accuracy of security operations.

[0143] Business-oriented and targeted: Through business modeling and association rule analysis, security operations are closer to business reality, improving the targetedness and effectiveness of security response.

[0144] Automation and Intelligence: We have built the capabilities of automated response and intelligent operation, reducing the cost and risk of manual intervention and improving the efficiency of security operations.

[0145] In some embodiments, before automatically classifying resource data, a feature extraction process is required. The goal of feature extraction is to extract meaningful and quantifiable features from the accessed resource data for intelligent label generation. These features can characterize the characteristics of resources and provide key information for subsequent analysis. The Transformer model is used to extract features from preprocessed log data. Transformer can effectively capture long-term dependencies in sequence data.

[0146] Specifically, the feature extraction process includes the following steps:

[0147] a. Input embedding: Convert input data into a numerical format that the model can process and convert words into word embedding vectors.

[0148] b. Position encoding: Since the Transformer model does not contain any information about the order of elements, position encoding is used to inject the position information of elements in the sequence. Position encoding can be fixed or learned and is usually added to the input embedding.

[0149] c. Self-attention mechanism: This is the core part of the Transformer model. The self-attention mechanism enables the model to pay attention to different parts of the input sequence, making it possible to understand global context information. The self-attention mechanism calculates a weight for each element in the sequence to decide which elements should be paid attention to during the encoding process. In this way, the model can better capture the relationship and dependency between elements.

[0150] In this embodiment, the multi-head attention mechanism allows the model to focus on different aspects of the input sequence at the same time. The input is mapped to multiple different heads through linear transformation, each head independently calculates self-attention, and the results are concatenated or averaged as the final output.

[0151] In Transformer, residual connections and layer normalization are used to deepen the network and enhance the stability of training. Residual connections add input directly to output, which helps alleviate the gradient vanishing problem. Layer normalization normalizes the output of each layer, accelerating convergence and improving the generalization ability of the model.

[0152] d. Feature extraction: Through the multi-layer structure of Transformer, each layer performs self-attention calculation and feedforward neural network calculation, so that features of different abstract levels of the input data can be extracted.

[0153] e. Output processing: Depending on the task, you may need to add some additional processing steps after feature extraction. For example, in a classification task, you may need to add a fully connected layer and a softmax function on top of the features to generate the final prediction.

[0154] In some embodiments, the goal of resource tag generation is to automatically tag resources with relevant tags, which can accurately reflect the purpose, security level and other information of the resources, and provide key basis for subsequent analysis. The intelligent tag generation process includes the following steps:

[0155] a. Label encoding: Provide a label for each log data. Encode these labels using one-hot encoding to facilitate model training.

[0156] b. Training based on Mini-Batch SGD: Use the Mini-Batch SGD algorithm to train the label classifier. The specific steps are as follows:

[0157] i. Randomly select a small batch of data from the training set as input for each iteration;

[0158] ii. Pass the selected mini-batch data to the label classifier and calculate the output of the network;

[0159] iii. Calculate the loss function based on the network output and the actual label. Commonly used loss functions include cross entropy loss, mean square error, etc.;

[0160] iv. Use the Mini-Batch SGD algorithm to update the parameters of the classifier. According to the gradient of the loss function, make small adjustments to the parameters in the classifier;

[0161] v. Repeat the above steps until the training set is completely traversed and one epoch of training is completed.

[0162] c. Intelligent label generation: During the training process, the label classifier is used to predict the label probability of each data instance. According to actual needs, a probability threshold can be selected to determine the final label. For each data instance, the label with the highest probability is used as the result of intelligent label generation.

[0163] In this embodiment, the Mini-Batch SGD (Stochastic Gradient Descent) algorithm is a commonly used optimization algorithm in deep learning. Compared with the standard SGD, it uses a small batch of data instead of a data point or the entire data set each time it updates the model parameters. This can reduce the variance of the gradient update and utilize the parallelism of matrix operations to accelerate the training of the model.

[0164] In order to generate smart labels, it is necessary to define a suitable loss function that can measure the gap between the labels generated by the model and the true labels. Depending on the specific task, different loss functions can be selected, such as cross entropy loss, mean square error loss, etc.

[0165] In this embodiment, the smart label generation module is designed as follows, and specifically includes the following modules:

[0166] Tag library construction module: Design feature tags for resource data, build a vocabulary library with security features, and improve the accuracy of subsequent analysis.

[0167] Label encoding module: including label preprocessing, selecting appropriate encoding methods, building label encoding tables, implementing encoding conversion functions, and introducing encoding cache mechanisms. Through the design and implementation of this module, the original labels can be converted into numerical forms that can be processed by the model, providing effective input features for subsequent intelligent label generation.

[0168] Training and Optimization Module: This module trains and optimizes the model based on the Mini-Batch SGD algorithm. According to the algorithm principle, the gradient calculation and parameter update logic of Mini-Batch SGD are implemented. According to the selected loss function, the loss value between the label generated by the model and the true label is calculated. The training process is organized, including the number of iterations, batch data extraction, gradient update and other operations. After each iteration or cycle, the performance of the model on the validation set is evaluated and the optimal model parameters are saved.

[0169] Label generation module: This module is responsible for using the trained model to generate smart labels. It receives the raw data for which labels are to be generated. It performs the same preprocessing and feature extraction operations on the data for which labels are to be generated as on the training data. It inputs the processed data into the trained model to generate the corresponding smart labels. It performs necessary post-processing operations on the generated labels, such as label mapping, format conversion, etc.

[0170] In some embodiments, the present application also provides retrieval enhancement generation annotation, and the specific process includes the following steps:

[0171] Data collection and preprocessing: Collect a large amount of data elements from IT resources, including but not limited to log files, configuration files, network traffic data, etc. Preprocess the collected data, including data cleaning, format conversion and normalization, to ensure data quality and consistency.

[0172] Information retrieval and extraction: Use the information retrieval function of the retrieval enhancement generation technology to retrieve information related to the current security operation scenario from the pre-processed data. Through an efficient indexing mechanism, key data elements can be quickly located and extracted. Through an efficient indexing mechanism, key data elements can be quickly located and extracted. Support the rapid retrieval of large-scale data sets and improve the efficiency of data processing.

[0173] Data element annotation: Annotate the extracted data elements, including classification, named entity recognition, relationship extraction, etc. The annotation process is based on the generation capability of retrieval enhancement generation technology, and improves the accuracy and completeness of annotation by introducing relevant information from external knowledge bases.

[0174] Generate annotation results: Associate the annotated data elements with the original data to generate a structured data set containing rich annotation information. Use the text generation capability of the search-enhanced generation technology to generate a readable and informative annotation result report that is easy for security operators to understand and use.

[0175] In this embodiment, generating the annotation result mainly includes the following steps:

[0176] Building a generative model: The generative model based on RAG technology can introduce relevant information from external knowledge bases to improve the accuracy and completeness of annotations. The powerful text generation capability can generate readable and informative annotation result reports.

[0177] Knowledge base construction and update: Build a knowledge base containing IT resource data elements and related knowledge to provide rich background information for the annotation process. Regularly update the knowledge base to ensure the accuracy and timeliness of the annotation information.

[0178] Optimize labeling strategies: Optimize labeling strategies according to different security operation scenarios and requirements to improve the pertinence and accuracy of labeling. Use machine learning algorithms to iteratively optimize the labeling process to improve labeling efficiency and quality.

[0179] In some embodiments, the present application also introduces large model technology topology automatic data annotation, specifically including:

[0180] Large model selection and training: Select suitable large models, such as pre-trained models based on the Transformer structure, which have strong contextual semantic understanding and complex reasoning capabilities. Use the collected IT resource data to train the large model so that it can identify and understand the key elements and patterns in the data.

[0181] Automated topology construction: Use the output of the big model to build the topology of IT resources. This includes identifying nodes, connection relationships, and data flows in the network. Through automated algorithms, the topology is optimized and adjusted to ensure its accuracy and completeness.

[0182] Data element labeling: Based on the topological structure, the semantic understanding ability of the big model is used to label key elements in IT resource data. This includes sensitive data, abnormal behavior, potential threats, etc. The labeling process can be semi-automatic, that is, the big model provides labeling suggestions, which are then confirmed and adjusted manually. It can also be fully automated, that is, the big model directly labels and ensures the accuracy of the labeling through subsequent verification.

[0183] The big model has powerful contextual semantic understanding and complex reasoning capabilities, and can accurately identify and understand key elements and patterns in IT resource data. Through training and optimization, the big model can adapt to different IT environments and business needs, providing accurate annotation and intelligent analysis.

[0184] Topology automation construction mainly uses the output of large models to build the topology of IT resources, which can automatically identify nodes, connection relationships and data flows in the network. Automated algorithms can optimize and adjust the topology structure to ensure its accuracy and completeness, providing a solid foundation for data annotation.

[0185] Data annotation technology uses the semantic understanding ability of large models to annotate data, which can accurately identify key elements such as sensitive data, abnormal behavior and potential threats. The annotation process can be semi-automated or fully automated, depending on actual needs.

[0186] In some embodiments, the present application also uses table enhancement generation for annotation utilization, specifically including:

[0187] Table enhancement generation model construction: Based on deep learning technology, a table enhancement generation model is constructed, which can understand natural language queries and generate SQL queries consistent with the underlying database structure.

[0188] Table enhancement generation training: Use a large amount of labeled IT resource data to train the table enhancement generation model so that it can accurately identify data elements and generate effective SQL queries.

[0189] Relevance identification: The table enhancement generation model receives natural language queries from users or systems and identifies data elements that are relevant to the query.

[0190] Annotation generation: Based on the identified data elements, the table enhancement generation model automatically generates annotations, which may include information such as the type, source, and importance of the data. The generated annotations are optimized to ensure that they are consistent with the underlying database structure, and the accuracy of the annotations is ensured through manual or automatic verification.

[0191] SQL query generation: The table enhancement generation model generates optimized SQL query statements based on the annotation results. The generated SQL query statements are submitted to the database for execution to retrieve data related to the query.

[0192] The table enhancement generation technology combines natural language processing and database query technology to achieve the automatic conversion from natural language query to SQL query. The table enhancement generation model can accurately identify data elements and generate SQL queries consistent with the underlying database structure, thereby improving the accuracy and efficiency of data retrieval.

[0193] Deep learning technology is the basis for building table enhancement generation models. By training deep learning models, they can be equipped with powerful natural language understanding and processing capabilities. Deep learning technology can also achieve self-optimization and iteration of the model, continuously improving the accuracy and efficiency of annotation.

[0194] Database query optimization technology is the key to improving data retrieval efficiency. By optimizing SQL query statements, database query time and resource consumption can be reduced. Database query optimization technology can also select appropriate query index strategies based on the distribution and characteristics of data to further improve query efficiency.

[0195] In a second aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the smart tag-based IT data asset update and maintenance method as described in the first aspect of the present invention is implemented.

[0196] The computer-readable storage medium may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories.

[0197] The non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a ferromagnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD ROM); the magnetic surface memory can be a disk memory or a tape memory.

[0198] The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), direct memory bus random access memory (DRRAM). The computer-readable storage medium described in the embodiments of the present invention is intended to include these and any other suitable types of memory.

[0199] like Figure 6 As shown, in the third aspect, the present invention provides an electronic device 10, including a processor 101 and a storage medium 102, on which a computer program is stored, and when the computer program is executed by the processor, the smart tag-based IT data asset update and maintenance method as described in the first aspect of the present invention is implemented.

[0200] In some embodiments, the processor can be implemented through software, hardware, firmware or a combination thereof, and can use circuits, single or multiple application specific integrated circuits (Application Specific Integrated Circuit, ASIC), digital signal processors (Digital Signal Processor, DSP), digital signal processing devices (Digital Signal Processing Device, DSPD), programmable logic devices (Programmable Logic Device, PLD), field programmable gate arrays (Field Programmable Gate Array, FPGA), central processing units (Central Processing Unit, CPU), controllers, microcontrollers, microprocessors, at least one of which, so that the processor can execute some or all of the steps or any combination of the steps in the smart tag-based IT data asset update and maintenance method described in the various embodiments of the present application.

[0201] Finally, it should be noted that although the above embodiments have been described in the specification and drawings of this application, this does not limit the scope of patent protection of this application. All technical solutions generated by replacing or modifying equivalent structures or equivalent processes based on the essential concept of this application using the contents recorded in the specification and drawings of this application, as well as directly or indirectly implementing the technical solutions of the above embodiments in other related technical fields, are included in the scope of patent protection of this application.

Claims

1. A method for updating and maintaining IT data assets, characterized in that: The method comprises: Acquire first IT data asset information, perform cluster analysis on the first IT data asset information to obtain multiple IT data asset information categories, and classify the first IT data asset information based on the IT data asset information categories to obtain multiple groups of second IT data asset information, each group of the second IT data asset information contains relevant parameters of multiple IT data assets; Inputting multiple groups of second IT data asset information into the trained first neural network model, outputting the first update cycle corresponding to each group of second IT data asset information, and updating the relevant parameters of the IT data assets in each group of second IT data asset information based on the first update cycle, specifically including: the first neural network model obtains the category and security level corresponding to each group of second IT data asset information for calculation, and outputs the first update cycle corresponding to each group of second IT data asset information, the higher the security level of the second IT data asset information, the shorter the first update cycle corresponding to the second IT data asset information output by the first neural network model; After a preset time period, historical data of each IT data asset corresponding to each group of second IT data asset information is collected, the historical data is input into a trained second neural network model, an update adjustment factor of a relevant parameter of each IT data asset in each group of the second IT data asset information is output, a second update cycle is calculated based on the first update cycle and the update adjustment factor, and an update mapping table corresponding to each group of the second IT data asset information is obtained, each group of the update mapping table contains a plurality of second update cycles, each of the second update cycle corresponds to a next update sampling cycle of relevant parameters of one or more IT data assets, and the historical data includes operation data, access data and security events of each IT data asset within a preset time period; Dynamically update and maintain an update mapping table corresponding to each group of the second IT data asset information, and update relevant parameters of the IT data assets corresponding to each group of the second IT data asset information based on a second update period recorded in the update mapping table; The method further comprises: Building a procurement database for IT data assets, wherein the procurement database records a plurality of sets of first parameters, each of which includes a type of the purchased IT data asset, a procurement completion time, and procurement-related parameters; After updating the relevant parameters of a certain second IT data asset, recording the type of the currently updated IT data asset, and obtaining the relevant parameters of the updated second IT data asset, which are recorded as second parameters; If the currently updated IT data asset belongs to a hardware resource, determining whether there is a first parameter matching the second parameter in the procurement database, and if not, issuing a first prompt message; If the currently updated IT data asset belongs to a software resource, then the resource manufacturer's website to which the current IT data asset belongs is searched to see whether there is a third parameter that matches the second parameter. If not, a second prompt message is issued.

2. The IT data asset update and maintenance method according to claim 1, characterized in that: Determining whether there is a first parameter matching the second parameter in the procurement database includes: Obtain a first update timestamp after the relevant parameters of the current second IT data asset are updated, determine a first timestamp range according to the first update timestamp and the security level of the corresponding IT data asset, and search whether there is a first parameter that is compatible with the second parameter within the first timestamp range.

3. The IT data asset update and maintenance method according to claim 1, characterized in that: Searching whether the resource vendor website to which the current IT data asset belongs has a third parameter that matches the second parameter includes: Obtain the version number and second update timestamp of the updated software resource, determine the second timestamp range according to the second update timestamp and the security level corresponding to the software resource, and search the resource manufacturer website to which the current IT data asset belongs within the second timestamp range to see whether there is a version number that matches the version number of the updated software resource.

4. The IT data asset update and maintenance method according to claim 1, characterized in that: Inputting the historical data into the trained second neural network model comprises: Performing data cleaning on the historical data, and inputting the cleaned historical data into the trained second neural network model; The data cleaning includes: The historical data is grouped and arranged according to the distance from the current timestamp to obtain a number of historical data segments, a score is calculated based on the historical data segments, the historical data segments with scores lower than the preset score are eliminated, and the score corresponding to the current historical data is calculated according to the scores and weights of the remaining historical data segments. The closer the distance to the current timestamp is, the greater the weight of the historical data segment; The score corresponding to the historical data segment is calculated based on the security level of the IT data asset, the amount of accessed data, and the amount of secure data.

5. The IT data asset update and maintenance method according to claim 1, characterized in that: The second neural network model calculates the update adjustment factor according to the following method: Set the initial value of the adjustment factor; Assign a weight to each IT data asset's operation data, access data, and security events within a preset time period, and calculate an adjustment score based on each weight and data volume; Calculate the updated adjustment factor according to the adjustment score and the initial value of the adjustment factor; The calculating the second update period based on the first update period and the update adjustment factor comprises: The first update period and the update adjustment factor are multiplied to obtain the second update period, and the update adjustment factor has a value range of [0.1, 2].

6. The IT data asset update and maintenance method according to claim 1, characterized in that: The method further comprises: After each update of the relevant parameters of the IT data asset, adding an update mark for the second update period in the update mapping table; When the proportion of the second update cycles with update marks exceeds the preset proportion, the average value of all the second update cycles recorded in the same update mapping table is counted; Traversing and searching the current update mapping table to see whether there is a second update cycle that is smaller than the average value and whose difference from the average value is greater than a preset difference, if so, screening out the second update cycle that meets the condition, and obtaining the second update cycle set to be corrected; Correcting all second update periods in the second update period set according to a preset correction factor to obtain a corrected second update period set; Using the revised second update cycle set to replace the corresponding second update cycle in the current update mapping table to obtain a revised update mapping table; The relevant parameters of the IT data assets are updated based on the revised update mapping table.

7. The IT data asset update and maintenance method according to claim 1, characterized in that: The method further comprises: After receiving the global update instruction, relevant parameters of the IT data assets corresponding to each group of the second IT data asset information are updated, and the start timestamp information corresponding to the second update period in the update mapping table corresponding to each group of the second IT data asset information is reset.

8. The IT data asset update and maintenance method according to claim 1, characterized in that: When the IT data assets are hardware resources, the relevant parameters of the second IT data assets include server configuration information, network equipment details information, terminal equipment list information, printer and scanner information; when the IT data assets are software resources, the relevant parameters of the second IT data assets include operating system installation and licensing information, application software list information and database management system details information.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the IT data asset updating and maintenance method according to any one of claims 1 to 8 is implemented.

10. An electronic device having a computer program stored thereon, characterized in that: It comprises a processor and a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by the processor, the IT data asset update and maintenance method according to any one of claims 1 to 8 is implemented.

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