Time sequence processing method and device, electronic equipment and storage medium

Abstract

The invention provides a time sequence processing method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a plurality of time sequences, and extracting a plurality of metadata features of the time sequences; extracting a first target metadata feature corresponding to a first preset feature dimension from the plurality of metadata features; according to the first target metadata features, grouping the plurality of time sequences to obtain a plurality of time sequence groups, the time sequences in the same time sequence group having the same first target metadata features; and grouping and storing the plurality of time sequences according to the different time sequence groups. The storage efficiency of the time sequence can be effectively improved, so that the subsequent index processing efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a time series processing method, device, electronic device and storage medium. Background Art

[0002] With the rapid development of the Internet of Things, time series data is increasingly being used in multiple fields. In scenarios such as edge-cloud collaboration and massive series, a large number of time series are stored in multiple fragmented files due to short data synchronization cycles and frequent data flushing.

[0003] It is known from related technologies that, in the current process of storing time series, decentralized processing is often used, which is not conducive to improving the storage efficiency of time series, thereby affecting the subsequent index processing efficiency.

[0004] Therefore, finding a time series processing method that can effectively improve the storage efficiency of time series has become a current research hotspot. Summary of the invention

[0005] The present invention provides a time series processing method, device, electronic device and storage medium, which can effectively improve the storage efficiency of the time series, thereby improving the subsequent index processing efficiency.

[0006] The present invention provides a time series processing method, which comprises: acquiring multiple time series and extracting multiple metadata features of the time series; extracting a first target metadata feature corresponding to a first preset feature dimension from the multiple metadata features; grouping the multiple time series according to the first target metadata feature to obtain multiple time series groups, wherein the time series in the same time series group have the same first target metadata feature; and grouping and storing the multiple time series according to different time series groups.

[0007] According to a time series processing method provided by the present invention, after grouping the multiple time series to obtain multiple time series groups, the method further includes: splicing the time series within the time series group to obtain a spliced ​​time series; grouping and storing the multiple time series according to different time series groups specifically includes: grouping and storing the spliced ​​time series based on the time series groups.

[0008] According to a time series processing method provided by the present invention, after grouping and storing the spliced ​​time series based on the time series group, the method further includes: establishing a multi-level index for the time series group; and based on the multi-level index, indexing and querying the time series in multiple time series groups.

[0009] According to a time series processing method provided by the present invention, the multi-level index includes at least a first index corresponding to the spliced ​​time series, and a second index corresponding to multiple time series in the spliced ​​time series; the indexing and querying the time series in multiple time series groups based on the multi-level index specifically includes: based on the first index, indexing and querying the spliced ​​time series corresponding to the first index in multiple time series groups; based on the second index, indexing and querying the time series corresponding to the second index in the indexed spliced ​​time series corresponding to the first index.

[0010] According to a time series processing method provided by the present invention, the first index is determined in the following manner: based on the sequence characteristics of the spliced ​​time series, a first index corresponding to the spliced ​​time series is constructed.

[0011] According to a time series processing method provided by the present invention, the second index is determined in the following manner: the starting position and the ending position of each time series in the spliced ​​time series are determined respectively; based on the starting position and the ending position, the second index is determined.

[0012] According to a time series processing method provided by the present invention, after the multiple time series are grouped and stored according to different time series groups, the method further includes: obtaining an integration instruction initiated by a user, wherein the integration instruction is used to represent an instruction to integrate the multiple time series according to a second preset feature dimension, and the second preset feature dimension is a feature dimension different from the first preset feature dimension; in different time series groups, indexing a plurality of time series that match the second preset feature dimension; and integrating the indexed plurality of time series that match the second preset feature dimension according to the second preset feature dimension to obtain an integrated time series.

[0013] According to a time series processing method provided by the present invention, after obtaining the integrated time series, the method further includes: storing the integrated time series.

[0014] According to a time series processing method provided by the present invention, the metadata features of the time series include at least the sequence name of the time series, the data type of the time series, the encoding method of the time series, the compression method of the time series, the sequence label of the time series, and the sequence generation time of the time series.

[0015] The present invention also provides a time series processing device, which includes: an acquisition module, used to acquire multiple time series and extract multiple metadata features of the time series; an extraction module, used to extract a first target metadata feature corresponding to a first preset feature dimension from the multiple metadata features; a grouping module, used to group the multiple time series according to the first target metadata feature to obtain multiple time series groups, wherein the time series in the same time series group have the same first target metadata feature; and a storage module, used to group and store the multiple time series according to different time series groups.

[0016] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, any of the time series processing methods described above is implemented.

[0017] The present invention also provides a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the time series processing method described in any one of the above is implemented.

[0018] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the time series processing method described above is implemented.

[0019] The time series processing method, device, electronic device and storage medium provided by the present invention obtain multiple time series and extract multiple metadata features of the time series; extract the first target metadata feature corresponding to the first preset feature dimension from the multiple metadata features; group the multiple time series according to the first target metadata feature to obtain multiple time series groups, wherein the time series in the same time series group have the same first target metadata feature; and group and store the multiple time series according to different time series groups. The present invention groups and stores multiple time series according to different time series groups through the first target metadata feature, which can effectively improve the storage efficiency of the time series, thereby improving the efficiency of subsequent index processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 This is one of the flow charts of the time series processing method provided by the present invention.

[0022] Figure 2 This is the second flow chart of the time series processing method provided by the present invention.

[0023] Figure 3 This is the third flow chart of the time series processing method provided by the present invention.

[0024] Figure 4 It is a schematic diagram of a process of indexing and querying a time series in multiple time series groups based on a multi-level index provided by the present invention.

[0025] Figure 5 This is the fourth flow chart of the time series processing method provided by the present invention.

[0026] Figure 6 It is a structural schematic diagram of the time series processing device provided by the present invention.

[0027] Figure 7 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] Figure 1 This is one of the flow charts of the time series processing method provided by the present invention.

[0030] The following will be combined Figure 1 The process of the time series processing method provided by the present invention is described.

[0031] In an exemplary embodiment of the present invention, Figure 1 It can be seen that the time series processing method may include steps 110 to 140, and each step will be introduced below.

[0032] In step 110, a plurality of time series are acquired, and a plurality of metadata features of the time series are extracted.

[0033] In one embodiment, the time series may be a time series with less than 100 data points. In one example, multiple time series may be acquired, and multiple metadata features of the time series may be extracted. The metadata features may be considered as sequence features that characterize the time series.

[0034] In another embodiment, the metadata features of the time series may include at least the sequence name of the time series, the data type of the time series, the encoding method of the time series, the compression method of the time series, the sequence label of the time series, and the sequence generation time of the time series.

[0035] In step 120, a first target metadata feature corresponding to a first preset feature dimension is extracted from a plurality of metadata features.

[0036] In one embodiment, a first target metadata feature corresponding to a first preset feature dimension may be extracted for a time series from a plurality of metadata features. The first preset feature dimension may be determined by a user-initiated instruction. In one scenario, a factory includes multiple workshops. The production volume of each workshop over several consecutive days may be used as a time series. During the application process, the first preset feature dimension may be the production volume under the time dimension, and the production volume of each workshop in each day may be extracted.

[0037] In step 130 , multiple time series are grouped according to the first target metadata feature to obtain multiple time series groups, wherein the time series in the same time series group have the same first target metadata feature.

[0038] In step 140 , multiple time series are grouped and stored according to different time series groups.

[0039] In one embodiment, multiple time series can be grouped according to the first target metadata feature to obtain multiple time series groups, and the multiple time series can be grouped and stored according to different time series groups. This can solve the problem that the time series are stored in a dispersed and isolated manner, wasting a lot of storage space. The storage efficiency of the time series can be effectively improved, thereby improving the efficiency of subsequent index processing.

[0040] Continuing to use the embodiment described above as an example, the production volume of the workshop can be grouped according to the time dimension, so that multiple time series groups can be obtained. For example, the production volume of the workshop on the first day can be grouped to obtain time series group 1, the production volume of the workshop on the second day can be grouped to obtain time series group 2... The production volume of the workshop on the Nth day can be grouped to obtain time series group N. Through this embodiment, the problem that the time series is stored in a dispersed and isolated manner, wasting a lot of storage space, can be solved. It can effectively improve the storage efficiency of the time series, thereby improving the efficiency of subsequent index processing.

[0041] Figure 2 This is the second flow chart of the time series processing method provided by the present invention.

[0042] The following will be combined Figure 2The process of another time series processing method is explained.

[0043] In an exemplary embodiment of the present invention, Figure 2 It can be seen that the time series processing method may include steps 210 to 250, wherein steps 210 to 230 are respectively the same or similar to steps 110 to 130. For specific implementations and beneficial effects thereof, please refer to the foregoing description, which will not be repeated in this embodiment. Steps 240 and 250 will be introduced below respectively.

[0044] In step 240, the time series in the time series group are spliced ​​to obtain a spliced ​​time series.

[0045] In step 250, the spliced ​​time series are grouped and stored based on the time series groups.

[0046] In one embodiment, in order to further save the storage cost of the time series group, the time series in each time series group can also be spliced ​​to obtain a spliced ​​time series. In one example, one time series group can correspond to one spliced ​​time series, and one time series group can also store multiple spliced ​​time series as needed.

[0047] Furthermore, the spliced ​​time series can be compressed and stored in groups based on the time series groups. Through this embodiment, the storage efficiency of the time series groups can be effectively improved.

[0048] Figure 3 This is the third flow chart of the time series processing method provided by the present invention.

[0049] The following will be combined Figure 3 The process of another time series processing method is introduced.

[0050] In an exemplary embodiment of the present invention, Figure 3 It can be seen that the time series processing method may include steps 310 to 370, wherein steps 310 to 350 are respectively the same as or similar to steps 210 to 250. For specific implementation methods and beneficial effects, please refer to the previous description, which will not be repeated in this embodiment. Steps 360 and 370 will be introduced below respectively.

[0051] In step 360 , a multi-level index is created for the time series group.

[0052] In step 370 , time series are indexed and queried in multiple time series groups based on the multi-level index.

[0053] In one embodiment, a multi-level index can be established for the time series group. Further, based on the multi-level index, the corresponding time series can be searched and queried in multiple time series groups. In this embodiment, the corresponding time series can be quickly and conveniently searched through the multi-level index.

[0054] Figure 4 It is a schematic diagram of a process of indexing and querying a time series in multiple time series groups based on a multi-level index provided by the present invention.

[0055] The following will be combined Figure 4 This section describes the process of indexing and querying time series in multiple time series groups based on multi-level indexes.

[0056] In an exemplary embodiment of the present invention, the multi-level index may include at least a first index corresponding to the spliced ​​time series and a second index corresponding to multiple time series in the spliced ​​time series. Figure 4 It can be seen that, based on the multi-level index, searching for time series in multiple time series groups by index may include step 410 and step 420 , and each step will be described below.

[0057] In step 410, based on the first index, a concatenated time series corresponding to the first index is searched in multiple time series groups.

[0058] In step 420, based on the second index, the time series corresponding to the second index is searched from the indexed concatenated time series corresponding to the first index.

[0059] In one embodiment, since the spliced ​​time series corresponds to a first index, and each time series in each spliced ​​time series corresponds to a second index, in the application process, the spliced ​​time series corresponding to the first index can be firstly searched in multiple time series groups based on the first index. Furthermore, based on the second index, the time series corresponding to the second index can be searched in the indexed spliced ​​time series corresponding to the first index.

[0060] In another embodiment, the first index and the second index may be stored at the end of the file storing the time series group. When a query request needs to be processed, the two-layer index may be obtained from the end of the file, and a mapping table may be constructed in the memory according to the first index and the second index. The mapping table may be used to locate the storage group to which the query belongs, and the location of the corresponding time series may be found. In this embodiment, by constructing a secondary index, the time series to be queried may be quickly and conveniently indexed.

[0061] In yet another exemplary embodiment of the present invention, the first index may be determined in the following manner: Based on the sequence features of the spliced ​​time series, a first index corresponding to the spliced ​​time series is constructed.

[0062] During the application process, the sequence features of the spliced ​​time series can be extracted, and a first index corresponding to the spliced ​​time series can be constructed based on the sequence features. It can be understood that the first index can uniquely identify a spliced ​​time series.

[0063] In yet another exemplary embodiment of the present invention, the second index may be determined in the following manner: Respectively determine the starting position and ending position of each time series in the spliced ​​time series; Based on the starting position and the ending position, a second index is determined.

[0064] In one embodiment, the starting position and ending position of each time series in the spliced ​​time series can be determined respectively. Since a time series can be uniquely located based on the starting position and the ending position, a second index can be determined based on the starting position and the ending position. Furthermore, based on the second index, the corresponding time series can be uniquely located quickly and accurately at the end of the spliced ​​time series.

[0065] Figure 5 This is the fourth flow chart of the time series processing method provided by the present invention.

[0066] The following will be combined Figure 5 The process of yet another time series processing method is described.

[0067] In an exemplary embodiment of the present invention, Figure 5 It can be seen that the time series processing method may include steps 510 to 570, wherein steps 510 to 540 are respectively the same or similar to steps 110 to 140. For specific implementation methods and beneficial effects, please refer to the previous description, which will not be repeated in this embodiment. Steps 550 to 570 will be introduced below respectively.

[0068] In step 550, an integration instruction initiated by a user is obtained, wherein the integration instruction is used to represent an instruction to integrate multiple time series according to a second preset feature dimension, and the second preset feature dimension is a feature dimension different from the first preset feature dimension.

[0069] In step 560 , multiple time series matching the second preset feature dimension are indexed in different time series groups.

[0070] In one embodiment, an integration instruction initiated by a user may be obtained, wherein the integration instruction may be used to represent an instruction for integrating multiple time series according to a second preset feature dimension, wherein the second preset feature dimension is a feature dimension different from the first preset feature dimension. Continuing to use the embodiment described above as an example, the production volume of the workshop needs to be integrated according to each workshop dimension (corresponding to the second preset feature dimension).

[0071] Furthermore, multiple time series matching the second preset feature dimension can be indexed in different time series groups, wherein the indexing method can be used to index to the corresponding time series in the multi-level indexing method described above.

[0072] In step 570, the indexed multiple time series that match the second preset feature dimension are integrated according to the second preset feature dimension to obtain an integrated time series.

[0073] In one embodiment, the indexed multiple time series that match the second preset feature dimension may be integrated according to the second preset feature dimension, so as to obtain an integrated time series that matches the user's integration instruction.

[0074] In another embodiment, during the integration process, it may also be necessary to process the user's query request. The grouping method and the non-grouping method can be used together to provide queries and seamlessly connect. The query request may occur on the cloud server, and the purpose of the query request may be to obtain the value of a time series within a specified time range. The cloud server may set a scheduled integration task, which can regularly organize the grouped stored time series into non-grouped stored time series, but the task may not have been completed when the query request arrives, so it is necessary to process the grouped stored time series and the non-grouped stored time series at the same time. At this time, the server can query the grouped stored time series and the non-grouped stored time series respectively, wherein the first index and the second index method are used to query the time series data; finally, the server integrates the query results obtained separately and makes a query response.

[0075] In yet another exemplary embodiment of the present invention, Figure 5 The above embodiment is used as an example for explanation. After obtaining the integrated time series, the time series processing method further includes the following steps: Store the integrated time series.

[0076] In one embodiment, the integrated time series may be stored so as to be convenient for users to access.

[0077] According to the foregoing description, the time series processing method provided by the present invention obtains multiple time series and extracts multiple metadata features of the time series; extracts the first target metadata feature corresponding to the first preset feature dimension from the multiple metadata features; groups the multiple time series according to the first target metadata feature to obtain multiple time series groups, wherein the time series in the same time series group have the same first target metadata feature; and groups and stores the multiple time series according to different time series groups. The present invention can effectively improve the storage efficiency of the time series by grouping and storing the multiple time series according to different time series groups based on the first target metadata feature, thereby improving the efficiency of subsequent index processing.

[0078] The time series processing device provided by the present invention is described below. The time series processing device described below and the time series processing method described above can be referred to each other.

[0079] Figure 6 It is a structural schematic diagram of the time series processing device provided by the present invention.

[0080] The following will be combined Figure 6 The structure of the time series processing device will be described.

[0081] In an exemplary embodiment of the present invention, Figure 6 It can be seen that the time series processing device may include an acquisition module 610, an extraction module 620, a grouping module 630, and a storage module 640. Each module will be introduced below.

[0082] The acquisition module 610 may be configured to acquire a plurality of time series and extract a plurality of metadata features of the time series; The extraction module 620 may be configured to extract a first target metadata feature corresponding to a first preset feature dimension from the plurality of metadata features; The grouping module 630 may be configured to group the plurality of time series according to the first target metadata feature to obtain a plurality of time series groups, wherein the time series in the same time series group have the same first target metadata feature; The storage module 640 may be configured to group and store the multiple time series according to different time series groups.

[0083] In an exemplary embodiment of the present invention, the storage module 640 may also be configured to: Splicing the time series in the time series group to obtain a spliced ​​time series; The storage module 640 may implement grouping and storing the multiple time series according to different time series groups in the following manner: The spliced ​​time series are grouped and stored based on the time series groups.

[0084] In an exemplary embodiment of the present invention, the storage module 640 may also be configured to: Establishing a multi-level index for the time series group; Based on the multi-level index, the time series is searched through the indexes in the multiple time series groups.

[0085] In an exemplary embodiment of the present invention, the multi-level index includes at least a first index corresponding to the spliced ​​time series, and a second index corresponding to a plurality of time series in the spliced ​​time series; The storage module 640 may implement indexing and searching the time series in the multiple time series groups based on the multi-level index in the following manner: Based on the first index, search the multiple time series groups for a concatenated time series corresponding to the first index; Based on the second index, a time series corresponding to the second index is searched in the indexed concatenated time series corresponding to the first index.

[0086] In an exemplary embodiment of the present invention, the storage module 640 may determine the first index in the following manner: Based on the sequence features of the spliced ​​time series, a first index corresponding to the spliced ​​time series is constructed.

[0087] In an exemplary embodiment of the present invention, the storage module 640 may determine the second index in the following manner: Respectively determine the starting position and the ending position of each of the time series in the spliced ​​time series; Based on the starting position and the ending position, the second index is determined.

[0088] In an exemplary embodiment of the present invention, the storage module 640 may also be configured to: Acquire an integration instruction initiated by a user, wherein the integration instruction is used to represent an instruction to integrate multiple time series according to a second preset feature dimension, and the second preset feature dimension is a feature dimension different from the first preset feature dimension; In different time series groups, index a plurality of time series that match the second preset feature dimension; The indexed multiple time series that match the second preset feature dimension are integrated according to the second preset feature dimension to obtain an integrated time series.

[0089] In an exemplary embodiment of the present invention, the storage module 640 may also be configured to: The integrated time series is stored.

[0090] In an exemplary embodiment of the present invention, the metadata features of the time series include at least the sequence name of the time series, the data type of the time series, the encoding method of the time series, the compression method of the time series, the sequence label of the time series, and the sequence generation time of the time series.

[0091] Figure 7 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 7 As shown, the electronic device may include: a processor 710, a communication interface 720, a memory 730 and a communication bus 740, wherein the processor 710, the communication interface 720 and the memory 730 communicate with each other through the communication bus 740. The processor 710 may call the logic instructions in the memory 730 to execute the time series processing method, which includes: obtaining multiple time series and extracting multiple metadata features of the time series; extracting a first target metadata feature corresponding to a first preset feature dimension from the multiple metadata features; grouping the multiple time series according to the first target metadata feature to obtain multiple time series groups, wherein the time series in the same time series group have the same first target metadata feature; and grouping and storing the multiple time series according to different time series groups.

[0092] In addition, the logic instructions in the above-mentioned memory 730 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. 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, etc. Various media that can store program codes.

[0093] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the time series processing method provided by the above methods, and the method includes: obtaining multiple time series and extracting multiple metadata features of the time series; extracting a first target metadata feature corresponding to a first preset feature dimension from the multiple metadata features; grouping the multiple time series according to the first target metadata feature to obtain multiple time series groups, wherein the time series in the same time series group have the same first target metadata feature; and grouping and storing the multiple time series according to different time series groups.

[0094] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the time series processing method provided by the above-mentioned methods, the method comprising: acquiring multiple time series and extracting multiple metadata features of the time series; extracting a first target metadata feature corresponding to a first preset feature dimension from the multiple metadata features; grouping the multiple time series according to the first target metadata feature to obtain multiple time series groups, wherein the time series in the same time series group have the same first target metadata feature; and grouping and storing the multiple time series according to different time series groups.

[0095] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0096] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A time series processing method, characterized in that: The method comprises: Acquire multiple time series and extract multiple metadata features of the time series; Extracting a first target metadata feature corresponding to a first preset feature dimension from the plurality of metadata features; According to the first target metadata feature, the plurality of time series are grouped to obtain a plurality of time series groups, wherein the time series in the same time series group have the same first target metadata feature; The multiple time series are grouped and stored according to different time series groups.

2. The time series processing method according to claim 1, characterized in that: After grouping the multiple time series to obtain multiple time series groups, the method further includes: Splicing the time series in the time series group to obtain a spliced ​​time series; The storing the plurality of time series in groups according to different time series groups specifically includes: The spliced ​​time series are grouped and stored based on the time series groups.

3. The time series processing method according to claim 2, characterized in that: After storing the spliced ​​time series in groups based on the time series groups, the method further includes: Establishing a multi-level index for the time series group; Based on the multi-level index, the time series is searched through the indexes in the multiple time series groups.

4. The time series processing method according to claim 3, characterized in that: The multi-level index at least includes a first index corresponding to the spliced ​​time series and a second index corresponding to a plurality of time series in the spliced ​​time series; The indexing and searching for the time series in the plurality of time series groups based on the multi-level index specifically includes: Based on the first index, search the multiple time series groups for a concatenated time series corresponding to the first index; Based on the second index, a time series corresponding to the second index is searched in the indexed concatenated time series corresponding to the first index.

5. The time series processing method according to claim 4, characterized in that: The first index is determined in the following manner: Based on the sequence features of the spliced ​​time series, a first index corresponding to the spliced ​​time series is constructed.

6. The time series processing method according to claim 4, characterized in that: The second index is determined in the following manner: Respectively determine the starting position and the ending position of each of the time series in the spliced ​​time series; Based on the starting position and the ending position, the second index is determined.

7. The time series processing method according to claim 1 or 3, characterized in that: After storing the plurality of time series in groups according to different time series groups, the method further includes: Acquire an integration instruction initiated by a user, wherein the integration instruction is used to represent an instruction to integrate multiple time series according to a second preset feature dimension, and the second preset feature dimension is a feature dimension different from the first preset feature dimension; In different time series groups, index a plurality of time series that match the second preset feature dimension; The indexed multiple time series that match the second preset feature dimension are integrated according to the second preset feature dimension to obtain an integrated time series.

8. The time series processing method according to claim 7, characterized in that: After obtaining the integrated time series, the method further includes: The integrated time series is stored.

9. The time series processing method according to claim 1, characterized in that: The metadata features of the time series include at least the sequence name of the time series, the data type of the time series, the encoding method of the time series, the compression method of the time series, the sequence label of the time series, and the sequence generation time of the time series.

10. A time series processing device, characterized in that: The device comprises: An acquisition module, used to acquire multiple time series and extract multiple metadata features of the time series; An extraction module, used to extract a first target metadata feature corresponding to a first preset feature dimension from the plurality of metadata features; a grouping module, configured to group the plurality of time series according to the first target metadata feature to obtain a plurality of time series groups, wherein the time series in the same time series group have the same first target metadata feature; The storage module is used to group and store the multiple time series according to different time series groups.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the time series processing method according to any one of claims 1 to 9 is implemented.

12. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the time series processing method according to any one of claims 1 to 9 is implemented.

13. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the time series processing method according to any one of claims 1 to 9 is implemented.

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