Dynamic signal event rule engine processing method, system, equipment and medium

Abstract

The invention relates to a dynamic signal event rule engine processing method, system and device and a medium, and the method comprises the steps: writing preprocessed ship and aircraft data into message middleware; and an event rule is constructed, the event rule comprises a data source node, a service node and an output node, the service node is used for carrying out conditional judgment and logic processing on the dynamic and static data of the ship and the airplane, and the output node is used for outputting alarm information. And converting the event rule into an SQL statement of the Flink, and sending the SQL statement as a task to the Flink for operation. And when the Flink runs, reading real-time dynamic and static data of the ship and the airplane in the message-oriented middleware, judging based on an event rule, and writing the data meeting the requirement into an alarm theme. The problem that heterogeneous signals and data of different data sources are inconsistent when the event rule engine is applied to ship and aircraft dynamic signals in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a dynamic signal event rule engine processing method, system, device and medium. Background Art

[0002] In the global modern transportation system, dynamic signal monitoring and analysis of ships and aircraft is essential to improve safety, optimize operational efficiency, and implement precise maintenance. These dynamic signals include position, speed, heading, engine status, fuel consumption, and environmental conditions, and the amount of real-time data generated is huge and has a high frequency. This data is not only used for real-time monitoring and accident prevention, but also for subsequent analysis and decision support.

[0003] However, existing technologies have problems such as insufficient real-time data processing capabilities, poor rule adaptability, and heterogeneous data sources when processing dynamic signals of ships and aircraft. As a distributed stream processing framework, Flink performs well in real-time data processing, but when applied to the event rule engine of dynamic signals of ships and aircraft, there are problems such as heterogeneous signals from different data sources and inconsistent data. Summary of the invention

[0004] The embodiments of the present application provide a dynamic signal event rule engine processing method, system, device and medium to at least solve the problem of heterogeneous signals from different data sources and inconsistent data when the event rule engine is applied to dynamic signals of ships and aircraft in the related art.

[0005] In a first aspect, an embodiment of the present application provides a dynamic signal event rule engine processing method, which is applied to dynamic signal event processing of ships and aircraft, and the method includes:

[0006] Write the pre-processed ship and aircraft data into the message middleware;

[0007] Constructing an event rule, wherein the event rule includes a data source node, a service node and an output node, wherein the service node is used to perform conditional judgment and logic processing on the dynamic and static data of the ship and the aircraft, and the output node is used to output alarm information;

[0008] Convert the event rule into a Flink SQL statement, and send the SQL statement as a task to Flink for execution;

[0009] When the Flink is running, the real-time dynamic and static data of the ship and the real-time dynamic and static data of the aircraft in the message middleware are read, judgment is made based on the event rules, and the data that meets the requirements is written into the alarm topic.

[0010] In one embodiment, the step of writing the pre-processed ship and aircraft data into the message middleware includes:

[0011] Obtain real-time dynamic and static data of ships and aircraft;

[0012] Preprocessing the real-time dynamic and static data of the ship and the real-time dynamic and static data of the aircraft to obtain preprocessed ship and aircraft data, wherein the preprocessing includes data cleaning, formatting and merging;

[0013] The ship and aircraft data are written into the message middleware.

[0014] In one embodiment, the event rule is constructed, wherein the event rule includes a data source node, a service node, and an output node, including:

[0015] The event rules including data source nodes, business nodes and output nodes are arranged to form a directed acyclic graph; wherein,

[0016] The data source node includes dynamic and static data of the ship and the aircraft;

[0017] The service node includes an attribute node and an area node, the attribute node is used for conditional judgment, and the area node is used to judge whether the aircraft or ship is within a preset area;

[0018] The output node is used to output alarm information.

[0019] In one embodiment, after arranging the event rules including the data source node, the service node and the output node to form a directed acyclic graph, the method further includes:

[0020] According to business requirements, the order of the data source node, the business node and the output node is adjusted, and the configuration parameters of the attribute node and the region node are adjusted.

[0021] In one embodiment, converting the event rule into a Flink SQL statement and sending the SQL statement as a task to Flink for execution includes:

[0022] Convert the data source node into the DDL statement of source;

[0023] Convert the business node into a transform DDL statement;

[0024] Convert the output node into the DDL statement of the sink and generate an INSERT statement, wherein the DDL statement of the sink is the output target;

[0025] The DDL statements of the source, the transform, and the sink are sent as tasks to Flink for execution.

[0026] In one embodiment, before sending the DDL statement of the source, the DDL statement of the transform, and the DDL statement of the sink as tasks to Flink for execution, the method further includes:

[0027] Generate an INSERT statement based on the DDL statement of the sink;

[0028] The transform DDL statement is spliced ​​after the INSERT statement as the WHERE condition, the attributes of the ship and the aircraft are converted into the field output of the alarm, and the field output of the alarm is written into the message middleware.

[0029] In one embodiment, after writing the data that meets the requirements into the alarm topic, the method further includes:

[0030] Subscribe to relevant alarm topics based on business needs;

[0031] In response to receiving the subscribed alarm topic, the subscribed alarm topic is sent to the user terminal.

[0032] In a second aspect, an embodiment of the present application provides a dynamic signal event rule engine processing system, which is applied to dynamic signal event processing of ships and aircraft, and the system includes a message middleware module, an event rule building module, a conversion module and an alarm theme module, wherein:

[0033] The middleware module is used to write the pre-processed ship and aircraft data into the message middleware;

[0034] The event rule building module is used to build event rules, and the event rules include data source nodes, business nodes and output nodes, wherein the business nodes are used to perform conditional judgment and logic processing on the dynamic and static data of the ship and the aircraft, and the output nodes are used to output alarm information;

[0035] The conversion module is used to convert the event rule into a Flink SQL statement, and send the SQL statement as a task to Flink for execution;

[0036] The alarm topic module is used to read the real-time dynamic and static data of the ship and the real-time dynamic and static data of the aircraft in the message middleware when the Flink is running, make judgments based on the event rules, and write the data that meets the requirements into the alarm topic.

[0037] In a third aspect, an embodiment of the present application provides a computer 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, it implements a dynamic signal event rule engine processing method as described in the first aspect above.

[0038] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a dynamic signal event rule engine processing method as described in the first aspect above.

[0039] A dynamic signal event rule engine processing method, system, device and medium provided by the embodiments of the present application have at least the following technical effects.

[0040] Through Flink's stream processing framework, it provides low-latency and high-throughput data processing capabilities, realizes real-time processing of dynamic signals of ships and aircraft, and meets the processing requirements of high-frequency data streams. At the same time, it realizes the generation and adjustment of dynamic rules, enhances the system's response capabilities to complex and sudden events, integrates heterogeneous signals from different data sources, provides unified, comprehensive and accurate data analysis, eliminates information islands, and improves the comprehensiveness of data processing and analysis. It solves the problem of heterogeneous signals from different data sources and inconsistent data when the event rule engine is applied to dynamic signals of ships and aircraft in related technologies.

[0041] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0043] Figure 1 It is a flow chart of a dynamic signal event rule engine processing method;

[0044] Figure 2 is a flowchart of step S102 according to an exemplary embodiment;

[0045] Figure 3 is a schematic diagram of arranging rule events according to an exemplary embodiment;

[0046] Figure 4 is a flow chart of 103 according to an exemplary embodiment;

[0047] Figure 5 is a schematic diagram of node conversion according to an exemplary embodiment;

[0048] Figure 6 is a structural block diagram of a dynamic signal event rule engine processing system according to an exemplary embodiment;

[0049] Figure 7 It is a structural block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0051] Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. In addition, it can also be understood that although the efforts made in this development process may be complicated and lengthy, for ordinary technicians in this field related to the content disclosed in this application, some changes in design, manufacturing or production based on the technical content disclosed in this application are just conventional technical means, and should not be understood as insufficient content disclosed in this application.

[0052] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0053] Unless otherwise defined, the technical terms or scientific terms involved in this application should be understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the" and the like involved in this application do not indicate a quantitative limitation, and may represent the singular or plural. The terms "include", "comprise", "have" and any of their variations involved in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there may be three relationships, for example, "A and / or B" can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0054] In the existing technology, dynamic signal analysis of ships and aircraft usually relies on traditional batch processing systems or static rule engines. These methods have the following major technical defects and shortcomings:

[0055] Insufficient real-time data processing capabilities: Traditional batch processing systems and static rule engines have limited capabilities to process real-time data streams. Due to the extremely high frequency of data generation, traditional methods are often unable to process large amounts of dynamic signal data in a timely manner, resulting in decision delays and failure to meet the needs of real-time response. This is particularly prominent in highly dynamic and complex application scenarios such as ships and aircraft.

[0056] Poor rule adaptability: Existing static rule engines mostly rely on predefined rule sets and lack the ability to flexibly adapt to dynamic changes and complex events. This limitation makes the rule engine unable to effectively respond to various changing rules and reduces the system's customization capabilities.

[0057] As a distributed stream processing framework, Flink has demonstrated excellent performance in processing large-scale real-time data streams with its low latency, high throughput, and high fault tolerance. Flink's stream processing capabilities can process high-frequency data streams in real time, and it has powerful state management and window operation functions.

[0058] However, applying Flink to the event rule engine for dynamic signals of ships and aircraft faces the following technical challenges: first, how to efficiently implement complex event processing rules in Flink and dynamically adjust the rules to support real-time event alerts; second, how to design and optimize Flink stream processing jobs to process heterogeneous signals from different data sources and ensure data consistency.

[0059] Therefore, there is an urgent need for an event rule engine technology based on Flink that can give full play to Flink's advantages in real-time stream processing, dynamically generate and adjust event rules, and process the dynamic signals of ships and aircraft in real time. At the same time, it should have efficient data processing capabilities, flexible rule configuration capabilities, and reliable event alarm mechanisms to achieve the configuration and efficient processing capabilities of complex rules. It solves the problem of heterogeneous signals from different data sources and inconsistent data when event rule engines are applied to dynamic signals of ships and aircraft in related technologies.

[0060] In view of the above problems, embodiments of the present application provide a dynamic signal event rule engine processing method, system, device and medium.

[0061] In a first aspect, an embodiment of the present application provides a dynamic signal event rule engine processing method. Figure 1 It is a flow chart of a dynamic signal event rule engine processing method, such as Figure 1 As shown, a dynamic signal event rule engine processing method includes:

[0062] Step S101: write the pre-processed ship and aircraft data into the message middleware.

[0063] Step S102: construct event rules, which include data source nodes, business nodes and output nodes. The business nodes are used to perform conditional judgment and logic processing on dynamic and static data of ships and aircraft, and the output nodes are used to output alarm information.

[0064] Step S103: convert the event rule into a Flink SQL statement, and send the SQL statement as a task to Flink for execution.

[0065] Step S104, when Flink is running, the real-time dynamic and static data of the ship and the real-time dynamic and static data of the aircraft in the message middleware are read, judgment is made based on the event rules, and the data that meets the requirements is written into the alarm topic.

[0066] In summary, a dynamic signal event rule engine processing method provided in an embodiment of the present application provides low-latency and high-throughput data processing capabilities through Flink's stream processing framework, realizes real-time processing of ship and aircraft dynamic signals, and meets the processing requirements of high-frequency data streams. At the same time, it realizes the generation and adjustment of dynamic rules, enhances the system's response capabilities to complex and sudden events, integrates heterogeneous signals from different data sources, provides unified, comprehensive and accurate data analysis, eliminates information island phenomena, and improves the comprehensiveness of data processing and analysis. It solves the problem of heterogeneous signals from different data sources and inconsistent data when event rule engines are applied to dynamic signals of ships and aircraft in related technologies.

[0067] In one embodiment, step S101, writing the pre-processed ship and aircraft data into the message middleware, specifically includes:

[0068] Obtain real-time dynamic and static data of ships and aircraft;

[0069] Preprocessing the real-time dynamic and static data of the ship and the real-time dynamic and static data of the aircraft to obtain the preprocessed ship and aircraft data, wherein the preprocessing includes data cleaning, formatting and merging;

[0070] Write ship and aircraft data into the message middleware.

[0071] Optionally, obtain real-time dynamic data (such as position, speed, heading, etc.) and static data (such as ship ID, type, capacity, etc.) of ships and aircraft from data sources such as sensors, GPS devices, and databases. Remove invalid or erroneous data. Handle missing values, such as filling default values ​​or deleting missing data. Convert data to a unified format, such as formatting dates and times to ISO 8601 standards. Ensure that all fields are of consistent type, such as converting strings to numbers. Merge dynamic data and static data to form a complete data record. For example, merge the ship's location data with the ship's basic information (such as ship ID, type). Write the preprocessed data to a message middleware (such as Kafka) for subsequent real-time processing and analysis.

[0072] Step S101 ensures data accuracy and consistency by cleaning and formatting data. Dynamic data and static data are merged to ensure data consistency when processing heterogeneous signals from different data sources, forming a complete data record for subsequent analysis and processing. The pre-processed data is written to the message middleware in real time to ensure data timeliness and availability.

[0073] Figure 2 is a flow chart of step S102 according to an exemplary embodiment. Figure 2As shown, step S102, constructing event rules, the event rules include data source nodes, business nodes and output nodes, specifically including the following steps:

[0074] Step S1021: Arrange event rules including data source nodes, service nodes, and output nodes to form a directed acyclic graph.

[0075] Step S1022: The data source node includes dynamic and static data of ships and aircraft.

[0076] Step S1023: The business node includes an attribute node and an area node. The attribute node is used for conditional judgment, and the area node is used to judge whether the aircraft or the ship is within a preset area.

[0077] Step S1024: The output node is used to output alarm information.

[0078] Optionally, Figure 3 is a schematic diagram of arranging rule events according to an exemplary embodiment. Figure 3 As shown in the figure, event rules are arranged in the system to form a directed acyclic graph (DAG). The DAG graph consists of data source nodes, business nodes, and output nodes. Each node is connected by directed edges to ensure the order and logical relationship of the data flow. Define data source nodes, including dynamic and static data of ships and aircraft. Configure data source nodes to specify the source and format of data. Define attribute nodes for conditional judgment. Define area nodes to determine whether an aircraft or ship is within a preset area. Configure attribute nodes and area nodes to specify judgment conditions and area ranges. For example: if the speed of the ship is greater than 50 knots, an alarm is triggered; if the speed of the aircraft is greater than 500 knots, an alarm is triggered; if the position of the ship is within the preset no-fly zone, an alarm is triggered; if the position of the aircraft is within the preset no-fly zone, an alarm is triggered.

[0079] Step S102: By configuring attribute nodes and area nodes, the system can monitor the status of ships and aircraft in real time to ensure timely detection of abnormal situations. Qualified data will be written into the alarm topic, and the business system can subscribe to the topic to obtain alarm information in real time and take corresponding measures.

[0080] In one embodiment, after arranging the event rules including the data source nodes, the business nodes and the output nodes to form a directed acyclic graph in step S1021, the dynamic signal event rule engine processing further includes: adjusting the order of the data source nodes, the business nodes and the output nodes according to business needs, and adjusting the configuration parameters of the attribute nodes and the area nodes.

[0081] Optionally, you can drag and connect each node arbitrarily to form a DAG graph. Clarify business requirements and determine which data source nodes, business nodes, and output nodes need to be adjusted in order. Adjust the order of nodes in the DAG graph to ensure the logical correctness and processing efficiency of the data flow. Test the adjusted DAG graph to ensure that the adjusted node order meets expectations. For example, the business requirement is to first determine whether the aircraft or ship is in a no-fly zone, then determine whether its speed exceeds the threshold, and finally output an alarm message. The adjusted order of the aircraft node is: area node (determine whether the aircraft is in a no-fly zone) > attribute node (determine whether the aircraft speed exceeds 500 knots) > alarm information output. The adjusted order of the ship node is: attribute node (determine whether the ship speed exceeds 50 knots) > area node (determine whether the ship is in a no-fly zone) > alarm information output. It is worth noting that parameters such as ship / aircraft speed can be adjusted separately.

[0082] By adjusting the order of data source nodes, business nodes, and output nodes, as well as adjusting the configuration parameters of attribute nodes and region nodes according to business needs, the judgment conditions and region ranges can be adjusted according to actual needs to improve the adaptability and flexibility of the system.

[0083] Figure 4 is a flow chart of 103 according to an exemplary embodiment, as shown in FIG. Figure 4 As shown, step S103, converting the event rule into Flink's SQL statement, and sending the SQL statement as a task to Flink for execution. Specifically, the following steps are included:

[0084] Step S1031, converting the data source node into a DDL statement of source;

[0085] Step S1032: convert the business node into a transform DDL statement;

[0086] Step S1033: Convert the output node into a DDL statement of the sink and generate an INSERT statement, wherein the DDL statement of the sink is the output target;

[0087] Step S1034: Send the source DDL statement, the transform DDL statement, and the sink DDL statement as tasks to Flink for execution.

[0088] Optionally, Figure 5 is a schematic diagram of node conversion according to an exemplary embodiment. Figure 5As shown, the backend reads the rules arranged in step S102, converts them into Flink's SQL statements, and converts the data source node into the source DDL statement (defines the source and format of the data.). If there are multiple data sources, multiple source DDL statements are generated. The arrangement content of the business node is converted into a transform DDL statement (defines the processing logic of the data, including conditional judgment and regional judgment). The data source used in each statement is determined according to the connected preceding data source node. If there are multiple business node branches, multiple transform statements are generated. If each business branch line has multiple business nodes, they are spliced ​​into multiple WHERE conditions and connected with an AND operator. The output node is converted into a sink DDL statement, a sink DDL statement of the message middleware kafka is created, and an INSERT statement is generated (defining the output target and format of the data, and inserting the processing results into the sink). The sink node is used as the target table to be inserted, and the generated transform DDL statement is spliced ​​after the INSERT statement as the WHERE condition, and the attributes required by the ship and the aircraft are converted into the alarm field output, and the alarm data can be written into kafka.

[0089] Step S103 automatically converts complex event rules into Flink's SQL statements, reducing the workload of manually writing SQL statements and improving development efficiency. The generated SQL statements are automatically submitted to Flink for execution, realizing the automated management of tasks. Flink's real-time processing capability ensures that data can be processed in real time and alarm information is generated in a timely manner. Flink's low latency feature ensures the high efficiency of data processing, so that alarm information can be output in the shortest time. Event rules can be adjusted at any time according to business needs, SQL statements can be regenerated and submitted to Flink for execution, which improves the flexibility of the system. Supports multiple data sources (such as Kafka, files, databases, etc.), enabling the system to process heterogeneous data from different data sources.

[0090] In one embodiment, before step S1033, sending the DDL statement of the source, the DDL statement of the transform, and the DDL statement of the sink as tasks to Flink for execution, the dynamic signal event rule engine processing method further includes:

[0091] Generate INSERT statements based on the sink's DDL statements;

[0092] The transform DDL statement is spliced ​​after the INSERT statement as the WHERE condition, converting the attributes of the ship and the aircraft into the field output of the alarm, and writing the field output of the alarm into the message middleware.

[0093] Optionally, convert the output node into a sink DDL statement, create a sink DDL statement for the message middleware kafka, and generate an INSERT statement. The sink node is used as the target table to be inserted. In the generated INSERT statement, add a WHERE clause containing the conditions extracted from the transform DDL statement. These conditions are used to ensure that only data that meets specific conditions will be inserted into the target table. Extract the required attributes (such as ship ID, speed, position, etc.) from the message middleware and convert these attributes into fields for alarm information. These fields will be included in the final alarm data for subsequent processing. Write the generated alarm data to the message queue of Kafka so that the business system can subscribe to and consume these alarm information. Ensure that the alarm information can be delivered to the relevant system or personnel in real time.

[0094] In one embodiment, after writing the data that meets the requirements into the alarm topic in step S104, the dynamic signal event rule engine processing method further includes:

[0095] Subscribe to relevant alarm topics based on business needs;

[0096] In response to receiving the subscribed alarm topic, the subscribed alarm topic is sent to the user terminal.

[0097] Optionally, subscribe to relevant alarm topics according to business needs. When receiving alarm information in the subscribed alarm topic, process the alarm information, such as recording logs, generating reports, etc. Send the processed alarm information to the user terminal, such as by email, SMS, push notification, etc. The user terminal can receive the alarm information in real time, ensuring that the abnormal situation is understood at the first time and countermeasures are taken in time. The user terminal can receive the alarm information in real time, ensuring that the abnormal situation is understood at the first time and countermeasures are taken in time. The subscribed topics and notification methods can be adjusted at any time according to business needs to improve the flexibility and adaptability of the system.

[0098] In summary, a dynamic signal event rule engine processing method provided in an embodiment of the present application provides, through Flink's stream processing framework, a dynamic signal event rule engine processing method provides low-latency and high-throughput data processing capabilities, realizes real-time processing of ship and aircraft dynamic signals, and meets the processing requirements of high-frequency data streams. At the same time, it realizes the generation and adjustment of dynamic rules, enhances the system's response capabilities to complex and sudden events, integrates heterogeneous signals from different data sources, provides unified, comprehensive and accurate data analysis, eliminates information island phenomena, and improves the comprehensiveness of data processing and analysis. It solves the problem of heterogeneous signals from different data sources and inconsistent data when event rule engines are applied to dynamic signals of ships and aircraft in related technologies.

[0099] In a second aspect, an embodiment of the present application provides a dynamic signal event rule engine processing system. Figure 6 FIG. 1 is a block diagram of a dynamic signal event rule engine processing system according to an exemplary embodiment. Figure 6 As shown, the system is applied to dynamic signal event processing of ships and aircraft, and the system includes a message middleware module 610, an event rule building module 620, a conversion module 630 and an alarm theme module 640, wherein:

[0100] The middleware module 610 is used to write the pre-processed ship and aircraft data into the message middleware;

[0101] The event rule building module 620 is used to build event rules. The event rules include data source nodes, business nodes and output nodes. The business nodes are used to perform conditional judgment and logic processing on dynamic and static data of ships and aircrafts, and the output nodes are used to output alarm information.

[0102] The conversion module 630 is used to convert the event rules into Flink's SQL statements, and send the SQL statements as tasks to Flink for execution;

[0103] The alarm topic module 640 is used to read the real-time dynamic and static data of ships and aircraft in the message middleware when Flink is running, make judgments based on event rules, and write the data that meets the requirements into the alarm topic.

[0104] In summary, the present application provides a dynamic signal event rule engine processing system. Through the message middleware module 610, the event rule module 620, the conversion module 630 and the alarm topic module 640. Specifically, through the stream processing framework of Flink, low-latency and high-throughput data processing capabilities are provided to achieve real-time processing of dynamic signals of ships and aircraft, and meet the processing requirements of high-frequency data streams. At the same time, the generation and adjustment of dynamic rules are realized, the system's response capability to complex and sudden events is enhanced, heterogeneous signals from different data sources are integrated, unified, comprehensive and accurate data analysis is provided, information islands are eliminated, and the comprehensiveness of data processing and analysis is improved. Solve the problem of heterogeneous signals from different data sources and inconsistent data when the event rule engine is applied to dynamic signals of ships and aircraft in the related technology.

[0105] It should be noted that the dynamic signal event rule engine processing system provided in this embodiment is used to implement the above-mentioned implementation mode, and the descriptions that have been made will not be repeated. As used above, the terms "module", "unit", "subunit", etc. can implement a combination of software and / or hardware for a predetermined function. Although the device described in the above embodiment is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0106] In a third aspect, an embodiment of the present application provides an electronic device, Figure 7 FIG. 1 is a block diagram of an electronic device according to an exemplary embodiment. Figure 7 As shown, the electronic device may include a processor 71 and a memory 72 storing computer program instructions.

[0107] Specifically, the processor 71 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0108] Among them, the memory 72 may include a large-capacity memory for data or instructions. By way of example and not limitation, the memory 72 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 72 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 72 may be inside or outside the data processing device. In a specific embodiment, the memory 72 is a non-volatile memory. In a specific embodiment, the memory 72 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (Programmable Read-Only Memory, PROM for short), an erasable PROM (Erasable ProgrammableRead-Only Memory, EPROM for short), an electrically erasable PROM (Electrically Erasable ProgrammableRead-Only Memory, EEPROM for short), an electrically alterable ROM (Electrically Alterable Read-Only Memory, EAROM for short) or a flash memory (FLASH) or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM can be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0109] The memory 72 may be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 71 .

[0110] The processor 71 implements any one of the dynamic signal event rule engine processing methods in the above embodiments by reading and executing computer program instructions stored in the memory 72 .

[0111] In one embodiment, a dynamic signal event rule engine processing device may further include a communication interface 73 and a bus 70. Figure 7 As shown, the processor 71, the memory 72, and the communication interface 73 are connected via a bus 70 and communicate with each other.

[0112] The communication interface 73 is used to implement communication between the modules, devices, units and / or equipment in the embodiment of the present application. The communication port 73 can also implement data communication with other components such as: external devices, image / data acquisition equipment, databases, external storage, and image / data processing workstations.

[0113] The bus 70 includes hardware, software or both, coupling the components of a dynamic signal event rule engine processing device to each other. The bus 70 includes but is not limited to at least one of the following: a data bus, an address bus, a control bus, an expansion bus, and a local bus. By way of example and not limitation, bus 70 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses or a combination of two or more of the above. Where appropriate, bus 70 may include one or more buses. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.

[0114] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a program stored thereon, and when the program is executed by a processor, a dynamic signal event rule engine processing method provided in the first aspect is implemented.

[0115] The readable storage medium may include but is not limited to: a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device or any suitable combination of the above.

[0116] In a possible implementation, the present invention can also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to enable the terminal device to execute the steps of a dynamic signal event rule engine processing method provided in the first aspect.

[0117] Among them, the program code for executing the present invention can be written in any combination of one or more programming languages, and the program code can be executed completely on the user device, partially on the user device, as an independent software package, partially on the user device and partially on a remote device, or completely on the remote device.

[0118] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0119] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A dynamic signal event rule engine processing method, characterized in that: The method is applied to dynamic signal event processing of ships and aircraft, and the method comprises: Write the pre-processed ship and aircraft data into the message middleware; Constructing an event rule, wherein the event rule includes a data source node, a service node and an output node, wherein the service node is used to perform conditional judgment and logic processing on the dynamic and static data of the ship and the aircraft, and the output node is used to output alarm information; Convert the event rule into a Flink SQL statement, and send the SQL statement as a task to Flink for execution; When the Flink is running, the real-time dynamic and static data of the ship and the real-time dynamic and static data of the aircraft in the message middleware are read, judgment is made based on the event rules, and the data that meets the requirements is written into the alarm topic.

2. The method according to claim 1, characterized in that: The event rule is constructed, and the event rule includes a data source node, a service node, and an output node, including: The event rules including data source nodes, business nodes and output nodes are arranged to form a directed acyclic graph; wherein, The data source node includes dynamic and static data of the ship and the aircraft; The service node includes an attribute node and an area node, the attribute node is used for conditional judgment, and the area node is used to judge whether the aircraft or ship is within a preset area; The output node is used to output alarm information.

3. The method according to claim 2, characterized in that After arranging the event rules including the data source node, the service node and the output node to form a directed acyclic graph, the method further includes: According to business requirements, the order of the data source node, the business node and the output node is adjusted, and the configuration parameters of the attribute node and the region node are adjusted.

4. The method according to claim 1, characterized in that: The converting the event rule into a Flink SQL statement and sending the SQL statement as a task to Flink for execution includes: Convert the data source node into the DDL statement of source; Convert the business node into a transform DDL statement; Convert the output node into the DDL statement of the sink and generate an INSERT statement, wherein the DDL statement of the sink is the output target; The DDL statements of the source, the transform, and the sink are sent as tasks to Flink for execution.

5. The method according to claim 4, characterized in that: Before sending the DDL statement of the source, the DDL statement of the transform, and the DDL statement of the sink as tasks to Flink for execution, the method further includes: Generate an INSERT statement based on the DDL statement of the sink; The transform DDL statement is spliced ​​after the INSERT statement as the WHERE condition, the attributes of the ship and the aircraft are converted into the field output of the alarm, and the field output of the alarm is written into the message middleware.

6. The method according to claim 1, characterized in that: The step of writing the pre-processed ship and aircraft data into the message middleware includes: Obtain real-time dynamic and static data of ships and aircraft; Preprocessing the real-time dynamic and static data of the ship and the real-time dynamic and static data of the aircraft to obtain preprocessed ship and aircraft data, wherein the preprocessing includes data cleaning, formatting and merging; The ship and aircraft data are written into the message middleware.

7. The method according to claim 1, characterized in that: After writing the data that meets the requirements into the alarm topic, the method further includes: Subscribe to relevant alarm topics based on business needs; In response to receiving the subscribed alarm topic, the subscribed alarm topic is sent to the user terminal.

8. A dynamic signal event rule engine processing system, characterized in that: The system is applied to dynamic signal event processing of ships and aircrafts. The system includes a message middleware module, an event rule building module, a conversion module and an alarm theme module, wherein: The middleware module is used to write the pre-processed ship and aircraft data into the message middleware; The event rule building module is used to build event rules, and the event rules include data source nodes, business nodes and output nodes, wherein the business nodes are used to perform conditional judgment and logic processing on the dynamic and static data of the ship and the aircraft, and the output nodes are used to output alarm information; The conversion module is used to convert the event rule into a Flink SQL statement, and send the SQL statement as a task to Flink for execution; The alarm topic module is used to read the real-time dynamic and static data of the ship and the real-time dynamic and static data of the aircraft in the message middleware when the Flink is running, make judgments based on the event rules, and write the data that meets the requirements into the alarm topic.

9. An electronic device, characterized in that: The invention comprises a memory and a processor, a computer program stored in the memory and executable on the processor, and the processor implements a dynamic signal event rule engine processing method as claimed in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a dynamic signal event rule engine processing method according to any one of claims 1 to 7 is implemented.

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