A time series data integration method, device, equipment and medium

Abstract

The present invention discloses a method for integrating time series data, including: monitoring a CEP rule base, and intercepting an access request of a current non-deterministic state machine NFA object when an update of the current CEP rule is detected; generating a new non-deterministic state machine NFA object, and replacing the current non-deterministic state machine NFA with the new non-deterministic state machine NFA object; parsing the updated CEP rule base to obtain a signal set for the CEP rule base; filtering out data corresponding to the signal set from one or more monitored data streams; filtering out rule description events from the data corresponding to the signal set; searching for context information associated with the description information from the time series library; and integrating the description information with the context information. The present invention can dynamically load new matching rules without restarting the real-time Flink (real-time computing) program, so that when processing data, it has a real-time response processing capability with low latency, high throughput, and high efficiency.

Description

Technical Field

[0001] The present invention belongs to the field of data processing technology, and specifically relates to a time series data integration method, device, equipment and medium. Background Art

[0002] Complex Event Processing (CEP) is event processing that combines data from multiple sources to infer events or patterns that indicate more complex situations. The goal of complex event processing is to identify meaningful events (such as opportunities, threats) and respond to them quickly. Data collected from IoT terminal devices usually has a certain time sequence, but is also prone to disorder and delay. Summary of the invention

[0003] In view of the above-mentioned shortcomings of the prior art, the present invention provides a time series data integration method, device, equipment and medium to solve the above-mentioned technical problems.

[0004] The present invention provides a time series data integration method, the method comprising:

[0005] Monitor a CEP rule base, and when monitoring that a current CEP rule is updated, intercept an access request of a current non-deterministic state machine NFA object, wherein the CEP rule base includes a plurality of CEP rules;

[0006] Generate a new non-deterministic state machine NFA object according to the updated CEP rule, and replace the current non-deterministic state machine NFA with the new non-deterministic state machine NFA object;

[0007] Parsing the updated CEP rule base to obtain a signal set of the CEP rule base, wherein each CEP rule corresponds to a signal;

[0008] Filtering data corresponding to the signal set from the monitored one or more data streams;

[0009] Using rule matching conditions, filter out rule description events from the data corresponding to the signal set;

[0010] According to the description information of the rule-described event, searching the time series library for context information associated with the description information;

[0011] The description information is integrated with the context information and written into downstream applications.

[0012] In one embodiment of the present invention, the CEP rules include basic event determination rules and data items that need to be integrated in the information elements of the event.

[0013] In one embodiment of the present invention, the monitoring CEP rule base includes:

[0014] Active notification by the program;

[0015] Or monitor the log of the CEP rule base and capture the change data from the log through the Dbus tool to monitor the CEP rules.

[0016] In one embodiment of the present invention, before intercepting the access request of the current non-deterministic state machine NFA object, the method further includes:

[0017] Save the current state of the data stream and generate a new savepoint.

[0018] In one embodiment of the present invention, before replacing the current non-deterministic state machine NFA with the new non-deterministic state machine NFA object, the method further includes:

[0019] Clear the intermediate state storage data related to the current non-deterministic state machine NFA object and initialize a new non-deterministic state machine NFA.

[0020] In one embodiment of the present invention, the description information includes the subject and time of the rule describing the event, the context information includes information associated with the subject, and the downstream application includes at least one of Kafka message middleware, database, and storage medium.

[0021] The present invention provides a time series data integration device, the device comprising:

[0022] A monitoring module, used for monitoring a CEP rule base and intercepting an access request of a current non-deterministic state machine NFA object when an update of the current CEP rule is detected, wherein the CEP rule base includes a plurality of CEP rules;

[0023] An updating module, configured to generate a new non-deterministic state machine NFA object according to the updated CEP rule, and replace the current non-deterministic state machine NFA with the new non-deterministic state machine NFA object;

[0024] A parsing module, used to parse the updated CEP rule base to obtain a signal set of the CEP rule base, wherein each CEP rule corresponds to a signal;

[0025] A first screening module, configured to screen out data corresponding to the signal set from one or more monitored data streams;

[0026] A second screening module, using a rule matching condition, screens out rule-described events from the data corresponding to the signal set;

[0027] A search module, used to search for context information associated with the description information from a time series library according to the description information of the event described by the rule;

[0028] The integration module is used to integrate the description information with the context information and write them into downstream applications.

[0029] The present invention provides an electronic device, comprising:

[0030] one or more processors;

[0031] The storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the electronic device implements the steps of the above-mentioned time series data integration method.

[0032] The present invention provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor of a computer, the computer executes the steps of the above-mentioned time series data integration method.

[0033] Beneficial effects of the present invention: A time series data integration method in the present invention includes: monitoring a CEP rule base, and when monitoring that a current CEP rule is updated, intercepting an access request to a current non-deterministic state machine NFA object, wherein the CEP rule base includes multiple CEP rules; generating a new non-deterministic state machine NFA object according to the updated CEP rule, and replacing the current non-deterministic state machine NFA with the new non-deterministic state machine NFA object; parsing the updated CEP rule base to obtain a signal set for the CEP rule base, wherein each CEP rule corresponds to a signal; filtering out data corresponding to the signal set from one or more monitored data streams; filtering out rule description events from the data corresponding to the signal set using rule matching conditions; searching for context information associated with the description information from the time series base according to the description information of the rule description event; integrating the description information with the context information, and writing them into downstream applications. The present invention clears and updates the non-deterministic state machine NFA when the CEP rules are dynamically updated, and initializes the new non-deterministic state machine NFA, so that new matching rules can be dynamically loaded without restarting the real-time Flink (real-time computing) program, so that when processing data, it has low latency, high throughput, and high-efficiency real-time response processing capabilities; the present invention includes multiple CEP rules, which can comprehensively identify various event scenarios; in addition, the rule base and the time series data metadata to be fused are stored in the background database. When the rules change, only the configuration needs to be updated, and the logical elements of event recognition are stored in the database, which is convenient for analysis and management.

[0034] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0036] Figure 1 is a schematic diagram of an implementation environment of a time series data integration method shown in an exemplary embodiment of the present application;

[0037] Figure 2 is a flow chart of a time series data integration method shown in an exemplary embodiment of the present application;

[0038] Figure 3 is a block diagram of a time series data integration device shown in an exemplary embodiment of the present application;

[0039] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0040] The following will describe the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention, not for limiting the scope of protection of the present invention.

[0041] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0042] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0043] Figure 1 is a schematic diagram of an exemplary implementation environment of the time series data integration method of the present application. Figure 1 , the implementation environment includes a terminal device 101 and a server 102, and the terminal device 101 and the server 102 communicate with each other through a wired or wireless network. The server can monitor the CEP rule base, and when it detects that the current CEP rule is updated, intercept the access request of the current non-deterministic state machine NFA object, wherein the CEP rule base includes multiple CEP rules; according to the updated CEP rules, a new non-deterministic state machine NFA object is generated, and the current non-deterministic state machine NFA is replaced with the new non-deterministic state machine NFA object; the updated CEP rule base is parsed to obtain a signal set of the CEP rule base, wherein each CEP rule corresponds to a signal; data corresponding to the signal set is filtered out from one or more monitored data streams; rule description events are filtered out from the data corresponding to the signal set using rule matching conditions; according to the description information of the rule description event, context information associated with the description information is searched from the timing library; the description information is integrated with the context information, and written into downstream applications.

[0044] It should be understood that Figure 1 The number of terminal devices 101 and servers 102 in the embodiment is only for illustration. Any number of terminal devices 101 and servers 102 may be provided according to actual needs.

[0045] Among them, the terminal device 101 corresponds to the client, which can be any electronic device with a user input interface, including but not limited to smart phones, tablets, laptops, computers, car computers, etc., among which the user input interface includes but is not limited to touch screens, keyboards, physical buttons, audio pickup devices, etc.

[0046] Among them, server 102 corresponds to the service end, which can be a server that provides various services. It can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network) and basic cloud computing services such as big data and artificial intelligence platforms. This is not restricted here.

[0047] The terminal device 101 can communicate with the server 102 through wireless networks such as 3G (third generation mobile information technology), 4G (fourth generation mobile information technology), and 5G (fifth generation mobile information technology), and this is not restricted here.

[0048] The embodiments of the present application respectively propose a time series data integration method, a time series data integration device, an electronic device, and a computer-readable storage medium, and these embodiments will be described in detail below.

[0049] See also Figure 2 , Figure 2 is a flowchart of a time series data integration method shown in an exemplary embodiment of the present application. The method can be applied to Figure 1 The implementation environment shown is specifically executed by the terminal device 101 in the implementation environment. It should be understood that the method can also be applied to other exemplary implementation environments and be specifically executed by devices in other implementation environments. This embodiment does not limit the implementation environment to which the method is applicable.

[0050] See also Figure 2 , Figure 2 This is a flowchart of an exemplary time series data integration method of the present application. The time series data integration method at least includes steps S210 to S270, which are described in detail as follows:

[0051] Step S210, monitoring a CEP rule base, and intercepting an access request of a current non-deterministic state machine NFA object when it is detected that a current CEP rule is updated, wherein the CEP rule base includes a plurality of CEP rules;

[0052] Listening to the CEP rule base means monitoring the CEP rules, which can be understood as monitoring the data results of the CEP rules to determine whether the CEP rules are updated, that is, determining whether the data results of the CEP rules are updated.

[0053] It should be noted that a plurality of CEP rules are stored in the CEP rule base, and the CEP rules include basic event determination rules and data items that need to be integrated in the information elements of the event.

[0054] In one embodiment, the monitoring of the CEP rule base is actively implemented through a program to notify.

[0055] In another embodiment, the monitoring of the CEP rule base is to monitor the log of the CEP rule base, and capture the change data from the log through the Dbus tool, so as to realize the monitoring of the CEP rules. Please enter the title DBus focuses on data collection and real-time data flow calculation. Through simple and flexible configuration, it collects source data in a non-invasive way, and adopts a highly available stream computing framework to aggregate the data generated in the business process.

[0056] It should be noted that there are three types of CEP rules, namely, strict nearest neighbor, loose nearest neighbor, and non-deterministic loose nearest neighbor, that is, the above three rules can be identified.

[0057] Strict neighbor: means that all events appear in a strict order, without any unmatched events in between, and are determined by the next state. For example, for the pattern "a strict neighbor b", the event sequence [a, c, b1, b2] has no match.

[0058] Loose neighbor: indicates that mismatched events are allowed to occur in the middle, which is determined by the subsequent data. For example, for b1 and b2 that satisfy state b, for the pattern "a loose neighbor b", the event sequence [a,c,b1,b2] is matched to {a,b1).

[0059] Non-deterministic loose neighbor: indicates that the conditions are further relaxed, and previously matched events can be used again, determined by the subsequent data. For example, for b1 and b2 that satisfy state b, for the pattern "a non-deterministic loose neighbor b", the event sequence [a,c,b1,b2] is matched as {a,b1), {a,b2).

[0060] The present invention can fully identify various event scenarios by supporting three CEP rule modes: strict nearest neighbor, loose nearest neighbor, and non-deterministic loose. Moreover, different types of event recognition scenarios can be met by configuration changes, without the need to redevelop applications and deployment, which is simple and efficient.

[0061] When the current CEP rules are monitored to be updated, the access request of the current non-deterministic state machine NFA (Non-Determinate Finite Automat, NFA) object is intercepted. The current NFA object corresponds to the current CEP rules. Among them, NFA is a non-deterministic state machine that defines a pattern for matching complex events. Monitoring the current CEP rules is a response to the user's update request, which is a request to update the current CEP rules to new CEP rules. The request carries the updated CEP rules.

[0062] It is important to understand that Flink's working mechanism is to translate CEP rules into NFA objects, and the subsequent pattern matching process is an operation on NFA objects. When the current CEP rules are updated, it is necessary to intercept the access request of the current NFA object and then change the NFA object accordingly according to the updated CEP rules.

[0063] It should be noted that when the current CEP rule is not detected to be updated, monitoring continues.

[0064] It should also be noted that before intercepting the access request of the current non-deterministic state machine NFA object, the state of the current data flow should be saved to generate a new savepoint, and state information such as the watermark of the upstream data flow should be saved.

[0065] Step S220, generating a new non-deterministic state machine NFA object according to the updated CEP rule, and replacing the current non-deterministic state machine NFA with the new non-deterministic state machine NFA object;

[0066] It should be noted that different rule engines have different ways of generating NFA objects according to their execution differences. The Groovy rule engine has the JIT (Just In Time) feature and needs to generate new execution bytecodes online. The Drools rule engine can generate new NFA objects by generating rule description files in Okinawa.

[0067] Based on rule engines such as Groovy and Drools, CEO rules are stored in the database (rule base). Configuration updates can dynamically change Flink CEP rules and dynamically update the CEP rule base.

[0068] In one embodiment, before replacing the current non-deterministic state machine NFA with the new non-deterministic state machine NFA object, the method further includes:

[0069] Clear the intermediate state storage data related to the current non-deterministic state machine NFA object and initialize a new non-deterministic state machine NFA.

[0070] Specifically, after replacing the new non-deterministic state machine NFA object, call the application program interface of the Flink state storage object to clear the intermediate state storage data in the state storage object, initialize the NFA object itself through the application program interface, and restore the data of the intermediate state storage object to the initial value.

[0071] The present invention clears and updates the non-deterministic state machine NFA when the CEP rules are dynamically updated, and initializes a new non-deterministic state machine NFA, so that new matching rules can be dynamically loaded without restarting the real-time Flink (real-time computing) program, thereby having low latency, high throughput, and high-efficiency real-time response processing capabilities when processing data.

[0072] Step S230, parsing the updated CEP rule base to obtain a signal set of the CEP rule base, wherein each CEP rule corresponds to a signal;

[0073] It should be noted that the CEP rules and the data elements to be subsequently integrated are stored in the CEP rule base in the form of multiple signals, and all event-related signals constitute a signal set.

[0074] The rule base and the metadata of the time series data to be fused are stored in the background database. When the rules change, only the configuration needs to be updated. The logical elements of event recognition are stored in the database for easy analysis and management.

[0075] Step S240, filtering out data corresponding to the signal set from the monitored one or more data streams;

[0076] In one or more data streams, in addition to the data corresponding to the signal set related to the event, other data is also included. Therefore, the data needs to be filtered to obtain the target data. In this way, the amount of data processed by Flink stream can be reduced by filtering the data corresponding to the signal set from the monitored one or more data streams.

[0077] Step S250, using rule matching conditions, filtering out rule description events from the data corresponding to the signal set;

[0078] Specifically, rule matching conditions are applied to discover rule-described events from the data stream. The Flink churn processing engine calls the rule engine to match events and identify the occurrence of events.

[0079] Step S260, searching the time series library for context information associated with the description information according to the description information of the rule description event;

[0080] The description information includes the subject and time of the rule describing the event, the context information includes information associated with the subject, and the downstream application includes at least one of Kafka message middleware, database, and storage medium. The subject can be an actor, a participant, a terminal device where the event occurs, etc.

[0081] Step S270: Integrate the description information with the context information and write them into downstream applications.

[0082] In one embodiment, the downstream application includes at least one of Kafka message middleware, a database, and a storage medium.

[0083] Figure 3 is a block diagram of a time series data integration device shown in an exemplary embodiment of the present application. The device can be applied to Figure 1 The device can also be applied to other exemplary implementation environments and specifically configured in other devices. This embodiment does not limit the implementation environment to which the device is applied.

[0084] like Figure 3 As shown, the present application provides a time series data integration device, the device comprising:

[0085] A monitoring module 310 is used to monitor a CEP rule base and intercept access requests to a current non-deterministic state machine NFA object when it is detected that a current CEP rule is updated. The CEP rule base includes a plurality of CEP rules.

[0086] An updating module 320, configured to generate a new non-deterministic state machine NFA object according to the updated CEP rule, and replace the current non-deterministic state machine NFA with the new non-deterministic state machine NFA object;

[0087] The parsing module 330 is used to parse the updated CEP rule base to obtain a signal set of the CEP rule base, wherein each CEP rule corresponds to a signal;

[0088] A first screening module 340, configured to screen out data corresponding to the signal set from the monitored one or more data streams;

[0089] A second screening module 350, using a rule matching condition, screens out rule-described events from the data corresponding to the signal set;

[0090] A search module 360, configured to search for context information associated with the description information from a time series library according to the description information of the event described in the rule;

[0091] The integration module 370 is used to integrate the description information with the context information and write them into downstream applications.

[0092] It should be noted that the time series data integration device provided in the above embodiment and the time series data integration method provided in the above embodiment belong to the same concept, wherein the specific manner in which each module and unit performs the operation has been described in detail in the method embodiment and will not be repeated here. In practical applications, the time series data integration device provided in the above embodiment can distribute the above functions to different functional modules as needed, that is, divide the internal structure of the device into different functional modules to complete all or part of the functions described above, and this is not limited here.

[0093] An embodiment of the present application also provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the electronic device implements the time series data integration method provided in the above-mentioned embodiments.

[0094] Figure 4 The structure diagram of the computer system suitable for implementing the electronic device of the embodiment of the present application is shown. It should be noted that: Figure 4 The computer system 400 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0095] like Figure 4 As shown, the computer system 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage part 408 to the random access memory (RAM) 403, such as executing the method described in the above embodiment. In the RAM 403, various programs and data required for system operation are also stored. The CPU 401, ROM 402 and RAM 403 are connected to each other through a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0096] The following components are connected to the I / O interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed so that a computer program read therefrom is installed into the storage section 408 as needed.

[0097] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer readable medium, the computer program including a computer program for executing the process. Figure 2 In such an embodiment, the computer program may be downloaded and installed from a network via the communication section 409, and / or installed from a removable medium 411. When the computer program is executed by the central processing unit (CPU) 401, various functions defined in the system of the present application are executed.

[0098] It should be noted that the computer-readable medium shown in the embodiment of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable computer program is carried. This propagated data signal can take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. A computer program contained on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0099] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0100] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0101] Another aspect of the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor of a computer, causes the computer to execute the time series data integration method as described above. The computer-readable storage medium may be included in the electronic device described in the above embodiment, or may exist independently without being assembled into the electronic device.

[0102] Another aspect of the present application also provides a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the time series data integration method provided in each of the above embodiments.

[0103] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present invention. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A time series data integration method, characterized in that: The method comprises: Monitor a CEP rule base, and when monitoring that a current CEP rule is updated, intercept an access request of a current non-deterministic state machine NFA object, wherein the CEP rule base includes a plurality of CEP rules; Generate a new non-deterministic state machine NFA object according to the updated CEP rule, and replace the current non-deterministic state machine NFA with the new non-deterministic state machine NFA object; Parsing the updated CEP rule base to obtain a signal set of the CEP rule base, wherein each CEP rule corresponds to a signal; Filtering data corresponding to the signal set from the monitored one or more data streams; Using rule matching conditions, filter out rule description events from the data corresponding to the signal set; According to the description information of the rule-described event, searching the time series library for context information associated with the description information; Integrate the description information with the context information and write them into downstream applications; Before replacing the current non-deterministic state machine NFA with the new non-deterministic state machine NFA object, the method further includes: Clear the intermediate state storage data related to the current non-deterministic state machine NFA object and initialize a new non-deterministic state machine NFA.

2. The time series data integration method according to claim 1, characterized in that: The CEP rules include basic event determination rules and data items that need to be integrated in the information elements of the event.

3. The time series data integration method according to claim 1, characterized in that: The monitoring CEP rule base includes: Active notification by the program; Or monitor the log of the CEP rule base and capture the change data from the log through the Dbus tool to monitor the CEP rules.

4. The time series data integration method according to claim 1, characterized in that: Before intercepting the access request of the current non-deterministic state machine NFA object, the method further includes: Save the current state of the data stream and generate a new savepoint.

5. The time series data integration method according to claim 1, characterized in that: The description information includes the subject and time of the rule describing the event, the context information includes information associated with the subject, and the downstream application includes at least one of Kafka message middleware, a database, and a storage medium.

6. A time series data integration device, characterized in that: The device comprises: A monitoring module, used for monitoring a CEP rule base and intercepting an access request of a current non-deterministic state machine NFA object when it is detected that a current CEP rule is updated, wherein the CEP rule base includes a plurality of CEP rules; An updating module, configured to generate a new non-deterministic state machine NFA object according to the updated CEP rule, and replace the current non-deterministic state machine NFA with the new non-deterministic state machine NFA object; A parsing module, used to parse the updated CEP rule base to obtain a signal set of the CEP rule base, wherein each CEP rule corresponds to a signal; A first screening module, used to screen out data corresponding to the signal set from one or more monitored data streams; A second screening module, using a rule matching condition, screens out rule-described events from the data corresponding to the signal set; A search module, used to search for context information associated with the description information from a time series library according to the description information of the event described by the rule; An integration module, used to integrate the description information with the context information and write them into downstream applications; Before replacing the current non-deterministic state machine NFA with the new non-deterministic state machine NFA object, the intermediate state storage data related to the current non-deterministic state machine NFA object is cleared, and the new non-deterministic state machine NFA is initialized.

7. An electronic device, characterized in that: The electronic device comprises: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the electronic device to implement the steps of the time series data integration method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor of a computer, the computer is caused to execute the steps of the time series data integration method according to any one of claims 1 to 5.

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