Adaptive lifecycle dynamic management method for attribute graph based on multimodal data knowledge

Abstract

The present application discloses a method for dynamic management of the adaptive lifecycle of a property graph based on multi-modal data knowledge, the method comprising: obtaining target graph data matched by the current update round; wherein the target graph data matches multiple data to be updated; based on the target graph data, obtaining a target storage unit matching the data to be updated from a target database, and determining metadata matching the target storage unit; wherein the metadata includes a timestamp and a target storage duration matching the corresponding data to be updated; obtaining update information matching the data to be updated, and updating the lifecycle matching the data to be updated in the target storage unit based on the update information and the metadata. In the above manner, the present application can improve the update efficiency of the lifecycle matching the data to be updated.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method for adaptive lifecycle dynamic management of attribute graphs based on multi-modal data knowledge. Background Art

[0002] Data lifecycle management in databases is a key link to ensure system reliability and performance, covering the entire process from data creation to deletion. Traditional methods mostly rely on manual operations, which are not only time-consuming and labor-intensive but also prone to errors, and lack automated tools and technical means to improve efficiency. With the surge in data volume and the diversification of data types, the limitations of existing management methods are becoming increasingly apparent, making it difficult to meet the needs of large-scale data processing.

[0003] In view of this, how to propose a more efficient data lifecycle management method has become an urgent problem to be solved. Summary of the invention

[0004] The main technical problem solved by this application is to provide a method for adaptive lifecycle dynamic management of attribute graphs based on multi-modal data knowledge, which can improve the update efficiency of the lifecycle matching the data to be updated.

[0005] To solve the above technical problems, a technical solution adopted in the present application is: to provide a method for dynamic management of the adaptive lifecycle of an attribute graph based on multimodal data knowledge, including: obtaining target graph data matching the current update round; wherein the target graph data matches multiple data to be updated; based on the target graph data, obtaining a target storage unit matching the data to be updated from a target database, and determining metadata matching the target storage unit; wherein the metadata includes a timestamp and a target storage duration matching the corresponding data to be updated; obtaining update information matching the data to be updated, and updating the lifecycle matching the data to be updated in the target storage unit based on the update information and the metadata.

[0006] To solve the above-mentioned technical problems, another technical solution adopted in the present application is: to provide an electronic device, comprising: a memory and a processor coupled to each other, the memory storing program instructions, and the processor being used to execute the program instructions to implement the attribute graph adaptive lifecycle dynamic management method based on multimodal data knowledge as mentioned in the above-mentioned technical solution.

[0007] In order to solve the above-mentioned technical problems, another technical solution adopted in the present application is: providing a computer-readable storage medium on which program instructions are stored, and when the program instructions are executed by a processor, the adaptive lifecycle dynamic management method of the attribute graph based on multimodal data knowledge mentioned in the above-mentioned technical solution is implemented.

[0008] The beneficial effects of the present application are as follows: Different from the prior art, the adaptive lifecycle dynamic management method of the attribute graph based on multi-mode data knowledge proposed in the present application determines the target storage unit from the target database according to the target graph data, and then after obtaining the update information, uses the information to be updated and multi-threaded methods and technologies to concurrently update the lifecycle of the data to be updated in the target storage unit, thereby improving the efficiency of data lifecycle update. In addition, matching the timestamp and target storage duration of the data to be updated with the corresponding target storage unit as metadata helps to simplify the format of data storage in the target database and improve the performance of the target database. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0010] Figure 1 It is a flowchart of an implementation method of a property graph adaptive lifecycle dynamic management method based on multi-modal data knowledge of the present application;

[0011] Figure 2 yes Figure 1 Step S102 corresponds to a flow chart of another implementation method;

[0012] Figure 3 yes Figure 1 A schematic diagram of a flow chart corresponding to an implementation method after step S102;

[0013] Figure 4 yes Figure 1 Step S103 corresponds to a flow chart of another implementation method;

[0014] Figure 5 It is a structural schematic diagram of an embodiment of the electronic device of the present application;

[0015] Figure 6 It is a structural schematic diagram of an implementation method of a computer-readable storage medium of the present application. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments, and different embodiments can be adaptively combined. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0017] See also Figure 1 , Figure 1 This is a flow chart of an implementation method of a method for adaptive lifecycle dynamic management of a property graph based on multi-modal data knowledge in this application, the method comprising:

[0018] S101: Obtain target graph data matched in the current update round; wherein the target graph data matches multiple data to be updated.

[0019] In one embodiment, a target database stores multiple data, and some of the data form corresponding graph data according to association relationships. In order to update the life cycle of multiple data in the target database that have association relationships, the target graph data matching the current update round is obtained, and the target graph data is composed of edge data and node data, and each edge data and each node data included in the target graph data is used as data to be updated.

[0020] S102: Based on the target graph data, a target storage unit matching the data to be updated is obtained from the target database, and metadata matching the target storage unit is determined; wherein the metadata includes a timestamp matching the corresponding data to be updated and a target storage duration.

[0021] In one embodiment, according to the acquired target graph data, the target storage unit matching the data to be updated is acquired from the target database, and metadata matching each target storage unit is determined. The metadata is used to describe the attribute information of the data to be updated in the target storage unit, including the timestamp matched by the data to be updated and the target storage duration. The timestamp is used to represent the time when the corresponding data to be updated is generated or stored in the target database, and the target storage duration is used to represent the storage duration of the corresponding data to be updated in the target database.

[0022] Specifically, the target database includes at least one storage server. After the data is stored in the corresponding storage server in the target database, the storage server allocates the corresponding initial storage unit according to the amount of data to store the data in the initial storage unit. In addition, the timestamp and target storage duration of the data matching are used as metadata of the corresponding initial storage unit. After the target graph data is acquired, all the data to be updated included in the target graph data are determined, and the target storage unit matching the data to be updated is located in the target database.

[0023] In one implementation scenario, the timestamp and target storage duration that the data to be updated matches are specified by relevant technical personnel, and to ensure the security of the data to be updated, the timestamp is obtained by encrypting it using a preset encryption method. For example, when the generation time of the data to be updated is "January 1, 2024", the timestamp obtained by encrypting the time using a preset encryption method is "1704038400". In addition, to ensure that the unit of the target storage duration is unified, the duration that the data to be updated is stored in the target database is converted into a unified unit to obtain the target storage duration. For example, when the set storage duration of the data in the target database is "one day", the target storage duration is determined to be "86400 seconds".

[0024] In another implementation scenario, the data matching timestamp and target storage duration may also be default. For example, the time when the data is stored in the target database is encrypted using a preset encryption method to obtain a matching timestamp; and the target storage duration is set to "86400 seconds" or other by default.

[0025] In a specific application scenario, the target database is an HBase database, and each data to be updated is written to the corresponding cell in the HBase database. The timestamp and target storage duration matched by the data to be updated are used as the metadata corresponding to the cell to facilitate the subsequent update of the life cycle of the data to be updated and the call of data in the target database during use.

[0026] S103: Acquire update information matching the data to be updated, and update the life cycle matching the data to be updated in the target storage unit based on the update information and metadata.

[0027] In one implementation, update information matching the data to be updated is obtained, and the life cycle matching the data to be updated in the target storage unit is updated in combination with the update information and metadata.

[0028] In one implementation scenario, the life cycle of data includes processes such as data creation, storage, update, deletion, and backup. The life cycle matching the data to be updated in the target storage unit is updated to adjust the storage duration of the data to be updated stored in the target database, thereby improving the flexibility of the target database in data storage.

[0029] The adaptive lifecycle dynamic management method of the attribute graph based on multi-modal data knowledge proposed in this application determines the target storage unit from the target database according to the target graph data, and then uses the update information to update the lifecycle of the data to be updated in the target storage unit after obtaining the update information, thereby improving the efficiency of data lifecycle update. In addition, matching the timestamp and target storage duration of the data to be updated with the corresponding target storage unit as metadata helps to simplify the data storage format in the target database and improve the performance of the target database.

[0030] See also Figure 2 , Figure 2 yes Figure 1 Step S102 in the flowchart corresponds to another embodiment. Specifically, the implementation process of step S102 includes:

[0031] S201: Acquire all the data to be updated in the target graph data, and use the initial storage unit where each data to be updated is located as a candidate storage unit.

[0032] In one embodiment, the target database includes at least one storage server, and the storage server includes at least one initial storage unit. All data to be updated in the target graph data are obtained, and each initial storage unit where the data to be updated is located is used as a candidate storage unit.

[0033] S202: Obtain the split status and update status of each candidate storage unit match.

[0034] In one embodiment, after determining the candidate storage unit, the split state and update state of each candidate storage unit are obtained, wherein the split state is used to indicate whether the corresponding data is split when stored in the candidate storage unit, and the update state is used to indicate whether the data in the corresponding candidate storage unit is updated in the current update round.

[0035] In a specific application scenario, each candidate storage unit is equipped with a corresponding reference threshold. When storing data in a candidate storage unit, if the amount of data is determined to exceed the reference threshold, the candidate storage unit is split into at least two storage sub-units, and the data is divided and evenly stored in multiple storage sub-units.

[0036] S203: Determine a target storage unit from all candidate storage units based on the split state and the update state.

[0037] In one embodiment, according to the split state and the update state, a target storage unit that meets the update condition is determined from all candidate storage units, and the life cycle of the data to be updated in the target storage unit is updated through subsequent steps.

[0038] In one implementation scenario, in response to a candidate storage unit being split into multiple storage sub-units, and the life cycle of the data to be updated in the storage sub-unit is not updated in the current update round, the storage sub-unit is determined to meet the update conditions and the storage sub-unit is used as the target storage unit.

[0039] Alternatively, in response to the candidate storage unit not being split and the life cycle of the data to be updated in the candidate storage unit not being updated in the current update round, it is determined that the candidate storage unit meets the update condition and the candidate storage unit is used as the target storage unit.

[0040] In another embodiment, a persistent file matching the current update round is created in advance. According to the split state and update state of each candidate storage unit, it is determined whether the candidate storage unit or the storage subunit corresponding to the candidate storage unit has completed the update in the current update round. If so, the identity information of the candidate storage unit or the storage subunit corresponding to the candidate storage unit is recorded in the persistent file, so that the candidate storage unit or the storage subunit recorded in the persistent file can be avoided from being repeatedly updated in the subsequent update process, thereby avoiding the waste of update resources. Among them, each candidate storage unit and storage subunit is matched with unique identity information.

[0041] In a specific application scenario, the target database is an HBase database. A candidate storage unit in is split into storage sub-units and storage subunit , and the storage subunit in the current update round and storage subunit The data in the storage subunit has completed the life cycle update. and storage subunit The matched identity information is recorded in the persistent file so that the current update round no longer affects the storage subunit. and storage subunit The data in the life cycle is updated.

[0042] See also Figure 3 , Figure 3 yes Figure 1 The process diagram after step S102 corresponds to an implementation method. Specifically, the current update round is also matched with a thread parameter, which is determined based on the number of processing threads. Different processing threads are used to synchronously execute the update task of the life cycle of the data to be updated in at least part of the target storage unit. Based on this, the process after step S102 includes:

[0043] S301: Obtain distribution information of a target storage unit.

[0044] In one embodiment, distribution information of the target storage units is determined based on the number of storage servers in the target database and the number of target storage units in each storage server. By determining the distribution information, it is helpful to evenly distribute all target storage units to each processing thread in the subsequent process, thereby improving the efficiency of data life cycle update.

[0045] S302: Determine a matching relationship between a processing thread and a target storage unit based on thread parameters and distribution information.

[0046] In one embodiment, the matching relationship between the processing thread and the target storage unit is determined based on the thread parameters and the distribution information of the target storage unit, so that the target storage unit is allocated to the corresponding processing thread, so that each processing thread updates the life cycle of the data to be updated in the corresponding target storage unit, thereby improving the update efficiency of the data life cycle.

[0047] In one implementation scenario, based on the thread parameter and the number of target storage units, all target storage units are divided into a plurality of storage unit sets, and a storage unit set matching each processing thread is determined, wherein the number of storage unit sets is consistent with the thread parameter.

[0048] Specifically, according to the above distribution information, the total number of target storage units is determined, and all target storage units are divided into multiple storage unit sets, and the number of storage unit sets is consistent with the thread parameter. The processing threads are matched with the storage unit sets one by one, and the matching relationship between each processing thread and each target storage unit is determined according to the matched processing threads and storage unit sets. For example, after matching the processing thread A with the storage unit set a, the processing thread A will subsequently perform the data life cycle update task on each target storage unit in the storage unit a.

[0049] In another embodiment, in response to the thread parameter being less than the number of storage servers, each processing thread is matched with a storage server, and the target storage units in the unmatched storage servers are divided into a plurality of storage unit sets, the number of which is consistent with the thread parameter. A storage unit set matched with each processing thread is determined, so that each processing thread updates the data life cycle of the matched storage server and the target storage unit in the matched storage unit set.

[0050] In a specific application scenario, the target database includes a storage server , Storage Server and storage servers , and set two processing threads for the current update round. Connect one of the processing threads to the storage server Matching, so that the processing thread to the storage server The target storage unit in the data life cycle is updated; and another processing thread is connected to the storage server Matching, so that the processing thread to the storage server The target storage unit in the data life cycle is updated. In addition, the storage server The target storage units in the storage unit set are divided into a storage unit set m and a storage unit set n, one of the processing threads is matched with the storage unit set m, and the other processing thread is matched with the storage unit set n, so that the processing threads update the data life cycle of the target storage units in the matching storage unit set respectively.

[0051] In another embodiment, in response to the thread parameter being greater than the number of storage servers, the target storage unit in each storage server is divided into multiple storage unit sets, and the number of storage unit sets corresponding to each storage server is consistent with the thread parameter. For all storage servers, all storage unit sets obtained after the division are evenly distributed to each processing thread, so that each processing thread updates the data life cycle of the target storage unit in the matching storage unit set.

[0052] See also Figure 4 , Figure 4 yes Figure 1 Step S103 in the flowchart corresponds to another embodiment. Specifically, the implementation process of step S103 includes:

[0053] S401: Acquire update information matching the data to be updated; wherein the update information is used to adjust the target storage duration corresponding to the data to be updated.

[0054] In one implementation, update information matching each to-be-updated data is obtained, and the update information is used to adjust a target storage duration matching the corresponding to-be-updated data.

[0055] Specifically, when the target storage duration of the data to be updated needs to be changed, the corresponding update information is obtained. The update information is the storage duration that is expected to be changed, for example, it can be "one month", "one year" or "two years", etc.

[0056] S402: For the target storage unit, based on the timestamp in the metadata and the target storage duration, obtain the remaining storage duration of the data to be updated.

[0057] In one implementation, for each target storage unit, the remaining storage duration of the corresponding data to be updated is obtained according to the timestamp and the target storage duration in the metadata matched by the target storage unit.

[0058] Specifically, for each target storage unit, the storage deadline of the data to be updated in the target storage unit is determined according to the timestamp and the target storage duration. In response to the storage deadline being after the current time, it indicates that the corresponding data to be updated has not expired, and the time difference between the storage deadline and the current time is used as the remaining storage duration of the corresponding data to be updated. Alternatively, in response to the above storage deadline being the current time, or the storage deadline being before the current time, it indicates that the corresponding data to be updated has expired, and the expired data to be updated is deleted from the target database.

[0059] S403: In response to the remaining storage duration satisfying a preset update condition, the target storage duration corresponding to the data to be updated is updated using the update information.

[0060] In one implementation, in response to the data to be updated in the target storage unit not being expired, the target storage duration of the data to be updated is updated using corresponding update information.

[0061] Specifically, when the data to be updated has not expired, the target storage duration of the data to be updated is updated using the corresponding update data. For example, if the target storage duration of the data to be updated is "one month", when it is desired to extend the target storage duration of the data to be updated, and the corresponding update data obtained is "one year", the target storage duration of the data to be updated is updated to "one year".

[0062] In another embodiment, in response to allocating all target storage units to each processing thread, for each processing thread, based on the update information and metadata, the life cycle matching the data to be updated in the corresponding target storage unit is updated, that is, the target storage duration of the data to be updated that meets the preset update conditions is updated in sequence using the acquired update information. It should be noted that, in the actual processing process, different processing threads simultaneously update the data to be updated in the corresponding target storage unit to improve the update efficiency of the data to be updated.

[0063] The above scheme updates the target storage time of the data to be updated by using the update information, so as to help shorten the storage time of the data to be updated with lower value in the target database, or increase the storage time of the data to be updated with higher value in the target database, so as to improve the flexibility of data storage.

[0064] In another embodiment, the target database is also matched with a storage threshold. To ensure the security of data storage and update, the adaptive lifecycle dynamic management method of the attribute graph based on multi-modal data knowledge proposed in this application further includes: obtaining the current storage space information of the target database. In response to the current storage space information being greater than the storage threshold, the lifecycle update of the data to be updated is stopped.

[0065] Specifically, when the method mentioned in any of the above embodiments is used to update the life cycle of the data to be updated, the current storage space information of the target database is scanned in real time. If the current storage space information is greater than the storage threshold, the task of updating the life cycle of the data to be updated is stopped, and a prompt message is generated. The prompt message is used to prompt that the remaining storage space of the target database is too small.

[0066] In one implementation scenario, the storage threshold is determined according to the total storage amount of the target database. For example, the storage threshold may be 70% of the total storage amount of the target database, and may be set according to actual needs.

[0067] Optionally, in other implementations, to save computing resources, the current storage space information of the target database is detected at preset intervals, and when the current storage space information is greater than a storage threshold, the life cycle update of the data to be updated is stopped.

[0068] Alternatively, when updating the life cycle of the data to be updated, the remaining storage space information of the target database is scanned in real time. When the remaining storage space information is less than a preset threshold, the task of updating the life cycle of the data to be updated is stopped, and a prompt message is generated.

[0069] In another embodiment, the adaptive lifecycle dynamic management method of the attribute graph based on multi-modal data knowledge proposed in the present application further includes: obtaining the storage deadline corresponding to the data to be updated based on the timestamp and target storage duration in the metadata. In response to the storage deadline being before the current time, the data to be updated corresponding to the storage deadline is deleted from the target database.

[0070] Specifically, during the use of the target database, it is detected in real time whether the storage deadline corresponding to each data to be updated has been reached. When the storage deadline corresponding to any data to be updated has been reached, it is indicated that the corresponding data to be updated is expired, and it is deleted from the target database to avoid the expired data occupying the target data, thereby improving the space utilization of the target database.

[0071] See also Figure 5 , Figure 51 is a schematic diagram of the structure of an embodiment of an electronic device of the present application. The electronic device includes: a memory 10 and a processor 20 coupled to each other. The memory 10 stores program instructions, and the processor 20 is used to execute the program instructions to implement the adaptive life cycle dynamic management method of the attribute graph based on multi-mode data knowledge mentioned in any of the above embodiments. Specifically, the electronic device includes but is not limited to: a desktop computer, a laptop computer, a tablet computer, a server, etc., which are not limited here. In addition, the processor 20 can also be called a CPU (Center Processing Unit). The processor 20 may be an integrated circuit chip with signal processing capabilities. The processor 20 may also be a general-purpose processor, a digital signal processor (Digital Signal Processor, DSP), an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field-programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. In addition, the processor 20 may be implemented by an integrated circuit chip.

[0072] See also Figure 6 , Figure 6 The computer-readable storage medium of the present application is a schematic diagram of a structure of an embodiment of the computer-readable storage medium. The storage medium 30 stores program instructions 40 that can be executed by a processor, and when the program instructions 40 are executed by the processor, the adaptive lifecycle dynamic management method of the attribute graph based on multi-modal data knowledge mentioned in any of the above embodiments is implemented.

[0073] In the several embodiments provided in the present application, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device implementation described above is only schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0074] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0075] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0076] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially 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. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of each implementation method of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code.

[0077] The above description is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for adaptive lifecycle dynamic management of attribute graphs based on multi-modal data knowledge, characterized in that: include: Obtain target graph data matched in the current update round; wherein the target graph data matches multiple data to be updated; Based on the target graph data, a target storage unit matching the data to be updated is obtained from a target database, and metadata matching the target storage unit is determined; wherein the metadata includes a timestamp matching the corresponding data to be updated and a target storage duration; Acquire update information matching the data to be updated, and update the life cycle matching the data to be updated in the target storage unit based on the update information and the metadata; The target database includes at least one storage server, and the storage server includes at least one initial storage unit. The step of obtaining a target storage unit matching the data to be updated from the target database based on the target graph data includes: obtaining all the data to be updated in the target graph data, and taking the initial storage units where the data to be updated are located as candidate storage units; obtaining the split state and update state matched by each candidate storage unit; and determining the target storage unit from all the candidate storage units based on the split state and the update state. Among them, the current update round is also matched with a thread parameter, and the thread parameter is determined based on the number of processing threads. Based on the target graph data, a target storage unit matching the data to be updated is obtained from the target database, and the metadata matching the target storage unit is determined, including: obtaining distribution information of the target storage unit; based on the thread parameter and the distribution information, determining the matching relationship between the processing thread and the target storage unit; obtaining update information matching the data to be updated, and updating the life cycle matching the data to be updated in the target storage unit based on the update information and the metadata, including: for each of the processing threads, based on the update information and the metadata, updating the life cycle matching the data to be updated in the corresponding target storage unit.

2. The adaptive lifecycle dynamic management method of attribute graph based on multi-modal data knowledge according to claim 1 is characterized in that: The step of determining a target storage unit from all the candidate storage units based on the split state and the update state includes: In response to the candidate storage unit being split into a plurality of storage sub-units, and the life cycle of the data to be updated in the storage sub-unit not being updated in the current update round, taking the storage sub-unit as the target storage unit; In response to the candidate storage unit not being split and the life cycle of the data to be updated in the candidate storage unit not being updated in the current update round, the candidate storage unit is used as the target storage unit.

3. The adaptive lifecycle dynamic management method of attribute graph based on multi-modal data knowledge according to claim 1 is characterized in that: The acquiring update information matching the data to be updated, and updating the life cycle matching the data to be updated in the target storage unit based on the update information and the metadata, includes: Acquire the update information matching the data to be updated; wherein the update information is used to adjust the target storage duration corresponding to the data to be updated; For the target storage unit, based on the timestamp in the metadata and the target storage duration, obtaining the remaining storage duration of the data to be updated; In response to the remaining storage duration satisfying a preset update condition, the target storage duration corresponding to the to-be-updated data is updated using the update information.

4. The adaptive lifecycle dynamic management method of attribute graph based on multi-modal data knowledge according to claim 1 is characterized in that: The determining, based on the thread parameter and the distribution information, a matching relationship between the processing thread and the target storage unit includes: Based on the thread parameter and the number of all the target storage units, all the target storage units are divided into a plurality of storage unit sets; wherein the number of the storage unit sets is consistent with the thread parameter; A set of storage units matching each of the processing threads is determined.

5. The adaptive lifecycle dynamic management method of attribute graph based on multi-modal data knowledge according to claim 1 is characterized in that: The target database matches a storage threshold, and the method further comprises: Acquire current storage space information of the target database; In response to the current storage space information being greater than the storage threshold, stopping the life cycle update of the data to be updated.

6. The adaptive lifecycle dynamic management method of attribute graph based on multi-modal data knowledge according to claim 1 is characterized in that: The method further comprises: Based on the timestamp in the metadata and the target storage duration, obtaining a storage deadline corresponding to the data to be updated; In response to the storage deadline being before the current time, the to-be-updated data corresponding to the storage deadline is deleted from the target database.

7. An electronic device, characterized in that: include: A memory and a processor are coupled to each other, wherein the memory stores program instructions, and the processor is used to execute the program instructions to implement the attribute graph adaptive lifecycle dynamic management method based on multi-modal data knowledge as described in any one of claims 1-6.

8. A computer-readable storage medium having program instructions stored thereon, characterized in that: When the program instructions are executed by the processor, the method for adaptive lifecycle dynamic management of attribute graphs based on multi-modal data knowledge as described in any one of claims 1-6 is implemented.

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