Time sequence data feature value extraction system and method and related equipment
Through the time series data feature value extraction system, using multi-layer database design and target determination tree, task module and other technical means, the problems of messy high-frequency time series data processing process and low reuse rate are solved, and efficient feature extraction and data matching are achieved. .
Patent Information
- Application Number
- CN202411325694.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-23
- Publication Date
- 2025-02-18
AI Technical Summary
The prior art is difficult to effectively extract the characteristic values of high-frequency timing data and match them with structured data, resulting in messy data processing and low reuse rate.
A time series data feature value extraction system is proposed, including a data gateway module, a data selection module and a data processing module. Through multi-layer database design and target determination tree, task module and other technical means, it realizes effective filtering and feature extraction of high-frequency time series data. .
Effective filtering and feature extraction of high-frequency timing data is realized, the problems of messy data processing process and low reuse rate in the prior art are solved, and the standardization and reusability of the system are improved.
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Figure CN119474796A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of information technology, and in particular to a system and method for extracting feature values from time series data and related equipment. Background Art
[0002] With the rise of big models, data in various formats, especially high-frequency time series data based on time alignment, have gradually entered the mainstream. Most time series data have the characteristic of low effective data density. For example, for monitoring images and log systems, they usually only focus on data in a specific period or under specific conditions. At the same time, most time series data are stored in unstructured data storage systems represented by time series databases. How to extract the feature values of these high-frequency data and match them with other structured data in the information system has become a key issue.
[0003] Therefore, it is necessary to propose a time series data feature value extraction system, method and related equipment to solve the above problems. Summary of the invention
[0004] A series of simplified concepts are introduced in the Summary of the Invention section, which will be further described in detail in the Detailed Description of the Invention section. The Summary of the Invention section of this application does not mean to attempt to define the key features and essential technical features of the claimed technical solution, nor does it mean to attempt to determine the scope of protection of the claimed technical solution.
[0005] This application specifically includes the following aspects:
[0006] In a first aspect, the present application proposes a time series data feature value extraction system, the system comprising a data gateway module, a data selection module and a data processing module, wherein:
[0007] The data gateway module is used to collect initial time series data from different external data sources and store the initial time series data in a layer of time series database;
[0008] The data selection module is used to screen the initial time series data based on a preset target to obtain target data, and store the target data in the second-layer time series database;
[0009] The data processing module is used to extract features from the target data to obtain target feature data, and store the target feature data in a three-layer feature database.
[0010] In a feasible implementation manner, the data selection module further includes a first data input module, a data selector and a first data output module, wherein:
[0011] The first data input module is used to input the initial time series data into the data selector;
[0012] The data selector is used to filter and obtain the target data that meets the preset determination conditions through the preset determination conditions in the target determination tree;
[0013] The first data output module is used to output the target data and store it in the second-layer time series database.
[0014] In a feasible implementation, the target decision tree includes multiple decision modules, and different types of decision conditions are configured through the decision modules, wherein the decision conditions include determining the rising edge of a pulse signal, determining the falling edge of a pulse signal, determining whether a preset threshold is met, determining the relationship between two signal points, determining the value of a signal point, and determining adjacent values of a signal point.
[0015] In a feasible implementation manner, the data processing module further includes a second data input module, a calculation module, and a second data output module, wherein:
[0016] The second data input module is used to input the target data into the calculation module;
[0017] The calculation module is used to perform target subtask calculation on the target data to obtain the target feature data;
[0018] The second data output module is used to output the target feature data and store it in the three-layer database.
[0019] In a feasible implementation, the calculation module includes multiple task modules, and different target subtasks are configured through the task modules, wherein each of the task modules includes a channel module, a range module and an algorithm module to complete the calculation of the target data through the functions of different modules.
[0020] In a feasible implementation manner, the channel module is used to determine the calculation amount of the target data; the range module is used to determine the data range of the target data; and the algorithm module is used to extract the characteristic value of the target data.
[0021] In a feasible implementation, the functional classification of the channel module includes extracting single-channel information, extracting dual-channel information and extracting multi-channel information; the functional classification of the range module includes filtering head and tail data based on time and using complete data; the functional classification of the algorithm module includes calculating the mean, calculating the minimum, calculating the maximum, summing, calculating the hit rate, calculating the pulse duration, taking values based on the head offset, taking values based on the head and tail data, judging whether there is an intersection, taking differences after integration, batch calculating the mean, batch calculating the minimum and batch calculating the maximum.
[0022] In a second aspect, the present application proposes a method for extracting feature values of time series data, which is applied to the system for extracting feature values of time series data in any one of the above embodiments, comprising:
[0023] Collecting initial time series data from different external data sources, and storing the initial time series data in a layer of time series database;
[0024] Screening the initial time series data based on a preset target to obtain target data, and storing the target data in a second-layer time series database;
[0025] Feature extraction is performed on the target data to obtain target feature data, and the target feature data is stored in a three-layer feature database.
[0026] In a third aspect, an electronic device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to implement the steps of the method for extracting feature values of time series data of the second aspect when executing the computer program stored in the memory.
[0027] In a fourth aspect, the present application further proposes a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the method for extracting feature values of time series data in the second aspect are implemented.
[0028] In summary, the time series data feature value extraction system proposed in this application breaks down the operation process of effectively filtering high-frequency time series data into several independent modules. Each module only needs simple configuration to ensure the normal operation of the entire system, and each module is designed as a standard component and reused in various projects through configuration files. The time series data feature value extraction system provided by this application overcomes the shortcomings of the existing high-frequency time series data processing process being messy and having a low reuse rate.
[0029] The time series data feature value extraction system proposed in this application, and other advantages, objectives and features of this application will be reflected in part through the following description, and in part will also be understood by technical personnel in this field through research and practice of this application. BRIEF DESCRIPTION OF THE DRAWINGS
[0030] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present specification. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:
[0031] Figure 1 A schematic diagram of the structure of a time series data feature value extraction system provided in an embodiment of the present application;
[0032] Figure 2 A schematic diagram of the structure of a data selection module provided in an embodiment of the present application;
[0033] Figure 3 A schematic diagram of the start and end condition judgment in the data selection module provided in an embodiment of the present application;
[0034] Figure 4 A schematic diagram of the structure of the decision tree provided in the embodiment of the present application;
[0035] Figure 5 A schematic diagram of the structure of the judgment module provided in the embodiment of the present application;
[0036] Figure 6 A schematic diagram of the structure of a data processing module provided in an embodiment of the present application;
[0037] Figure 7 A schematic diagram of the structure of a computing module provided in an embodiment of the present application;
[0038] Figure 8 A schematic diagram of the structure of a channel module provided in an embodiment of the present application;
[0039] Fig. 9 A schematic diagram of the structure of a scope module provided in an embodiment of the present application;
[0040] Fig.10 A schematic diagram of the structure of the algorithm module provided in the embodiment of the present application;
[0041] Fig.11 A flowchart of a method for extracting feature values from time series data provided in an embodiment of the present application. DETAILED DESCRIPTION
[0042] In order to better understand the technical solutions provided by the embodiments of this specification, the technical solutions of the embodiments of this specification are described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of this specification and the specific features in the embodiments are detailed descriptions of the technical solutions of the embodiments of this specification, rather than limitations on the technical solutions of this specification. In the absence of conflict, the embodiments of this specification and the technical features in the embodiments can be combined with each other.
[0043] In this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or equipment. In the absence of more restrictions, the elements limited by the statement "comprise one..." do not exclude the existence of other identical elements in the process, method, article or equipment including the elements. The term "more than two" includes two or more than two situations.
[0044] At present, the high-frequency time series data processing process is messy and the reuse rate is low. In order to solve the problems existing in the prior art, the present application provides a complete time series data feature extraction system to standardize the time series data processing process and realize system reuse.
[0045] See also Figure 1 , is a structural diagram of a time series data feature value extraction system provided in an embodiment of the present application, which may specifically include a data gateway module 11, a data selection module 12 and a data processing module 13, wherein:
[0046] The data gateway module 11 is used to collect initial time series data from different external data sources and store the initial time series data in a layer of time series database;
[0047] The data selection module 12 is used to screen the initial time series data based on a preset target to obtain target data, and store the target data in the second-layer time series database;
[0048] The data processing module 13 is used to extract features from the target data to obtain target feature data, and store the target feature data in a three-layer feature database.
[0049] In summary, the time series data feature value extraction system proposed in the embodiment of the present application is composed of a data gateway module 11, a data selection module 12 and a data processing module 13. The entire system adopts a three-layer database design. The first and second layers are time series databases for storing process data. The third layer is a feature database that supports relational and non-relational databases. Both the second and third layer databases can provide support for external applications.
[0050] Furthermore, the data gateway module 11 stores the collected time series data from various sources such as iba (Interface Bus Adapter), PLC (Programmable Logic Controller) and TCP / UDP (Transmission Control Protocol / User Datagram Protocol) messages into a first-layer time series database; the data selection module 12 is responsible for segmenting and screening the data and storing the results into a second-layer database; the data processing module 13 extracts features from the data in the second-layer database and stores them in a third-layer database.
[0051] In some examples, the data selection module 12 further includes a first data input module, a data selector, and a first data output module, wherein:
[0052] The first data input module is used to input the initial time series data into the data selector;
[0053] The data selector is used to filter and obtain the target data that meets the preset determination conditions through the preset determination conditions in the target determination tree;
[0054] The first data output module is used to output the target data and store it in the second-layer time series database.
[0055] For example, Figure 2 As shown, the data selection module 12 is composed of a first data input module, a series of data selectors and a first data output module. Figure 3 As shown, the data selection module 12 determines the range of valid high-frequency data through a data selector; the data selector in the data selection module 12 determines the start or end of the valid data amount through a decision tree (see Figure 4 ). The decision tree is composed of decision modules and decision module sets, which pass the logical operation results of leaf nodes to the root node step by step. The decision module set is composed of decision modules combined through logical operations (AND or OR).
[0056] In some examples, the target decision tree includes multiple decision modules, and different types of decision conditions are configured through the decision modules, wherein the decision conditions include determining the rising edge of a pulse signal, determining the falling edge of a pulse signal, determining whether a preset threshold is met, determining the relationship between two signal points, determining the value of a signal point, and determining adjacent values of a signal point.
[0057] For example, Figure 5As shown, there are 6 types of determination modules in the data selection module 12: determining the rising edge of a pulse signal, determining the falling edge of a pulse signal, determining whether a preset threshold is met, determining the relationship between two signal points, determining the value of a signal point, and determining adjacent values of a signal point.
[0058] In some examples, the data processing module 13 further includes a second data input module, a calculation module, and a second data output module, wherein:
[0059] The second data input module is used to input the target data into the calculation module;
[0060] The calculation module is used to perform target subtask calculation on the target data to obtain the target feature data;
[0061] The second data output module is used to output the target feature data and store it in the three-layer database.
[0062] For example, Figure 6 As shown, the data processing module 13 is composed of a second data input module, a series of calculation modules and a second data output module.
[0063] In some examples, the calculation module includes multiple task modules, and different target subtasks are configured through the task modules; wherein each of the task modules includes a channel module, a range module and an algorithm module to complete the calculation of the target data through the functions of different modules.
[0064] For example, Figure 7 As shown, the calculation module in the data processing module 13 is composed of a series of task modules. Specifically, each task module in the data processing module includes a channel module, a range module and an algorithm module.
[0065] Furthermore, the channel module, range module and algorithm module in the data processing module 13 can be reused in various task modules of the same computing module.
[0066] In some examples, the channel module is used to determine the computational amount of the target data; the range module is used to determine the data range of the target data; and the algorithm module is used to extract feature values of the target data.
[0067] Exemplarily, the present application determines the source of high-frequency data that needs to be calculated by configuring a channel module, determines the data range of the high-frequency data source by configuring a range module, and extracts characteristic values of the high-frequency data by configuring an algorithm module.
[0068] In some examples, the functional classification of the channel module includes extracting single-channel information, extracting dual-channel information, and extracting multi-channel information; the functional classification of the range module includes filtering head and tail data based on time and using complete data; the functional classification of the algorithm module includes calculating the mean, calculating the minimum, calculating the maximum, summing, calculating the hit rate, calculating the pulse duration, taking values based on the head offset, taking values based on the head and tail data, judging whether there is an intersection, taking differences after integration, batch calculating the mean, batch calculating the minimum, and batch calculating the maximum.
[0069] For example, Figure 8 As shown, there are three types of channel modules in the data processing module 13: extracting single-channel information, extracting dual-channel information, and extracting multi-channel information. Fig. 9 As shown, there are two types of range modules in the data processing module 13: filtering head and tail data based on time, and using complete data. Fig.10 As shown, the algorithm modules in the data processing module 13 must match the channel module, and the algorithm modules adapted to ChannelSingle are: calculating the mean, calculating the minimum, calculating the maximum, summing, calculating the hit rate, calculating the pulse duration, taking values based on the head offset, and taking values based on the head and tail data; the algorithm modules adapted to ChannelDual are: judging whether there is an intersection, and taking the difference after integration; the algorithm modules adapted to ChannelList are: batch calculating the mean, batch calculating the minimum, and batch calculating the maximum.
[0070] In summary, the time series data feature value extraction system provided in this application breaks down the operation process of high-frequency time series data effective value filtering into several independent modules. Each module only needs simple configuration to ensure the normal operation of the entire system, and each module is designed as a standard component that can be reused in various projects through configuration files.
[0071] The technical solution of the present application is further described in detail below through specific embodiments.
[0072] The time series data feature value extraction system provided in the present application includes a data gateway module 11, a data selection module 12 and a data processing module 13.
[0073] Step 1: In the data selection module 12, set the data start condition (dephosphorization signal start) and the data end condition (dephosphorization signal end) to filter the data.
[0074] Step 2: In the data selection module 12, the data filtering condition is implemented through a decision tree (ie, the dephosphorization signal is considered to be started only when the current pass number of R1 is 1 and the dephosphorization signal is On).
[0075] Step 3: In the data selection module 12, select the appropriate decision module and the corresponding configuration to implement the decision logic of each node of the decision tree to achieve the purpose of filtering the effective value of high-frequency data. Use the PointCheckerCurrentVal module to determine whether the current pass number of R1 is 1; use the PointCheckerCompareLastVal module to determine whether the dephosphorization signal changes from 1 to 0.
[0076] Step 4: In the data processing module 13, the computing task is completed by the computing module. Different task modules are set in the computing module to execute some subtasks in parallel. Each subtask includes a channel module, a range module and an algorithm module, and these three modules can be reused by different subtasks.
[0077] Step 5: In the data processing module 13, a channel module is configured to determine the source of high-frequency data that needs to be calculated.
[0078] Step 6: In the data processing module 13, a range module is configured to determine the data range of the high-frequency data source.
[0079] Step 7: In the data processing module 13, an algorithm module is configured to extract the characteristic values of high-frequency data, and the algorithm module only accepts the data source provided by the channel module adapted thereto.
[0080] Furthermore, the present application also proposes a method for extracting feature values of time series data, which is applied to any of the above embodiments of the system for extracting feature values of time series data, as follows: Fig.11 As shown, this is a flowchart of a method for extracting feature values of time series data proposed in this application, including:
[0081] S110, collecting initial time series data from different external data sources, and storing the initial time series data in a layer of time series database;
[0082] S120, screening the initial time series data based on a preset target to obtain target data, and storing the target data in a second-layer time series database;
[0083] S130, extracting features from the target data to obtain target feature data, and storing the target feature data in a three-layer feature database.
[0084] An embodiment of the present application also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of any of the above-mentioned methods for extracting feature values of time series data are implemented.
[0085] Since the electronic device introduced in this embodiment is a device used to implement a method for extracting feature values of time series data in the embodiment of the present application, based on the method introduced in the embodiment of the present application, technical personnel in this field can understand the specific implementation mode of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present application is not introduced in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of the present application is within the scope of protection of this application.
[0086] During the specific implementation process, when the computer program is executed by a processor, any implementation method of the embodiment corresponding to a method for extracting feature values of time series data in an embodiment of the present application can be implemented.
[0087] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.
[0088] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-readable program code.
[0089] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0090] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.
[0091] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.
[0092] An embodiment of the present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device executes the process of the method for extracting feature values of time series data.
[0093] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integration. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state drive (SSD)), etc.
[0094] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.
[0095] In the several embodiments provided in the present application, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and 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 an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.
[0096] 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 solution of this embodiment.
[0097] 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.
[0098] 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, and the computer software product is stored in a storage medium, including a number of 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 various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.
[0099] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application 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 application.
[0100] Although the preferred embodiments of this specification have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of this specification.
[0101] Obviously, those skilled in the art can make various changes and modifications to this specification without departing from the spirit and scope of this specification. Thus, if these modifications and variations of this specification fall within the scope of the claims of this specification and their equivalents, this specification is also intended to include these modifications and variations.
Claims
1. A time series data feature value extraction system, characterized in that: It includes a data gateway module, a data selection module and a data processing module, wherein: The data gateway module is used to collect initial time series data from different external data sources and store the initial time series data in a layer of time series database; The data selection module is used to screen the initial time series data based on a preset target to obtain target data, and store the target data in the second-layer time series database; The data processing module is used to extract features from the target data to obtain target feature data, and store the target feature data in a three-layer feature database.
2. The time series data feature value extraction system according to claim 1, characterized in that: The data selection module further includes a first data input module, a data selector and a first data output module; wherein: The first data input module is used to input the initial time series data into the data selector; The data selector is used to filter and obtain the target data that meets the preset determination conditions through the preset determination conditions in the target determination tree; The first data output module is used to output the target data and store it in the second-layer time series database.
3. The time series data feature value extraction system according to claim 2, characterized in that: The target decision tree includes multiple decision modules, and different types of decision conditions are configured through the decision modules; wherein the decision conditions include judging the rising edge of a pulse signal, judging the falling edge of a pulse signal, judging whether a preset threshold is met, judging the relationship between two signal points, judging the value of a signal point, and judging the adjacent values of a signal point.
4. The system for extracting feature values from time series data according to claim 1, characterized in that: The data processing module further includes a second data input module, a calculation module and a second data output module, wherein: The second data input module is used to input the target data into the calculation module; The calculation module is used to perform target subtask calculation on the target data to obtain the target feature data; The second data output module is used to output the target feature data and store it in the three-layer database.
5. The system for extracting feature values from time series data according to claim 4, characterized in that: The calculation module includes multiple task modules, and different target subtasks are configured through the task modules, wherein each of the task modules includes a channel module, a range module and an algorithm module to complete the calculation of the target data through the functions of different modules.
6. The time series data feature value extraction system according to claim 5, characterized in that: The channel module is used to determine the calculation amount of the target data; the range module is used to determine the data range of the target data; and the algorithm module is used to extract the characteristic value of the target data.
7. The time series data feature value extraction system according to claim 5, characterized in that: The functional classification of the channel module includes extracting single-channel information, extracting dual-channel information and extracting multi-channel information; the functional classification of the range module includes filtering head and tail data based on time and using complete data; The functional classification of the algorithm module includes calculating the mean, calculating the minimum, calculating the maximum, summing, calculating the hit rate, calculating the pulse duration, taking values according to the head offset, taking values according to the head and tail data, judging whether there is an intersection, taking differences after integration, batch calculating the mean, batch calculating the minimum and batch calculating the maximum.
8. A method for extracting feature values of time series data, applied to the time series data feature value extraction system according to any one of claims 1 to 7, characterized in that: include: Collecting initial time series data from different external data sources, and storing the initial time series data in a layer of time series database; Screening the initial time series data based on a preset target to obtain target data, and storing the target data in a second-layer time series database; Feature extraction is performed on the target data to obtain target feature data, and the target feature data is stored in a three-layer feature database.
9. An electronic device, comprising: A memory and a processor, wherein the processor is used to implement the steps of the method for extracting feature values of time series data as described in claim 8 when executing a computer program stored in the memory.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for extracting feature values of time series data as claimed in claim 8 are implemented.