Intelligent intelligent supervision system of civil aviation VoIP communication equipment

Abstract

The invention provides an intelligent intelligent supervision system for civil aviation VoIP communication equipment, relates to the basic field of civil aviation air traffic control equipment, and solves the limitation problems of non-centralized supervision, imperfect supervision, non-autonomous supervision and the like existing in an existing supervision mode. The system comprises a system architecture module which adopts a layered modular design to construct a double-layer system comprising a top layer management architecture and a bottom layer operation architecture; the function service module comprises an equipment state monitoring unit, a risk assessment engine and an operation and maintenance management unit; the data processing architecture forms a multi-stage processing channel by a data acquisition layer, a data transmission layer, a data storage layer, a data calculation layer and a data application layer which are connected in sequence; the technical support system comprises a micro-service component group, a continuous integration platform and a security authentication module. The intelligent supervision network architecture of the VoIP communication equipment is subjected to brand-new autonomous design, transformation from passive response to active management is completed, and the supervision efficiency and the treatment efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the basic field of civil aviation air traffic control equipment and is applied to the supervision process of VoIP communication equipment. Specifically, it relates to an intelligent supervision system for civil aviation VoIP communication equipment. Background Art

[0002] The field of civil aviation air traffic control is undergoing a crucial period of digital transformation of communication technologies. The VoIP technology has the advantages of flexible network formation, remote resource invocation, and emergency takeover. This technology realizes the digital transmission of voice signals based on the IP network, uses the SIP protocol to establish sessions, the RTP protocol to carry media streams, and forms IP data packets through encoding and compression such as G.729 for transmission, and completes protocol stripping and signal restoration at the remote end. With the implementation of standards such as "MH / T 4027-2019", centralized monitoring based on the SNMP protocol has become a mandatory requirement for civil aviation VoIP equipment, but it still faces systematic technical challenges in practical applications.

[0003] The existing supervision system has significant structural defects. At the equipment interconnection level, although the industry standard has clearly defined the SNMP unified management requirements, most manufacturers still use private protocols and non-public MIB modules, resulting in interoperability barriers between monitoring systems. This protocol fragmentation phenomenon not only makes it difficult to monitor cross-vendor equipment, but also exacerbates network security risks such as flood attacks due to the security vulnerability of private protocols. In terms of supervision efficiency, the traditional monitoring system has double defects of coarse data collection granularity and rigid algorithm rules: the lack of redundant design at the hardware level and the lack of stability at the software level result in the inability to comprehensively obtain key information such as the operating state of equipment and network behavior; the monitoring mechanism based on preset rules lacks the ability to adapt to scenarios and is difficult to identify potential risks in complex operating environments.

[0004] Multiple dimensions of functional shortcomings are also exposed at the technical implementation level. In the hardware system, the supervision terminals generally have problems such as scattered interfaces and low fault location accuracy, making it difficult to meet the requirements for monitoring the status of core components; at the software architecture level, the security defects of the operating system and frequent system crashes seriously restrict the continuity of supervision. In the data processing dimension, the existing system still lacks intelligent analysis capabilities and is difficult to achieve risk assessment and security warning. These technical defects lead to operation and maintenance pressures such as frequent false alarms and difficult fault tracing for supervision personnel, and they are even more sluggish in dealing with emergencies.

[0005] The transformation of the safety supervision mode also faces deep-seated contradictions. The traditional "one-on-one" supervision method can no longer adapt to the scale of the modern air traffic control system, and the new intelligent supervision encounters technical adaptation obstacles during implementation. Although the industry advocates the transformation towards organized and systematic supervision, the existing technical system is difficult to support the implementation of new supervision models such as off-site supervision and precise supervision. The superposition effect of weak data security mechanisms and insufficient system integration makes the contradiction between the input of supervision resources and the growth of task requirements increasingly prominent, seriously restricting the improvement of the civil aviation safety supervision efficiency.

[0006] In summary, the current technical system has encountered many bottlenecks in the process of intelligent evolution. Most supervision systems still remain in the basic state monitoring stage and cannot well identify the hidden fault modes during the operation of equipment independently. In terms of data value mining, the system can neither build a multi-source data fusion analysis model nor establish a knowledge-driven decision support mechanism, making it difficult to convert the massive monitoring data into effective management insights. This lack of intelligence not only reduces the active defense ability against potential safety hazards but also limits the scientific decision-making level of the supervision department in complex operating environments. Summary of the Invention

[0007] Based on the current situation in the background technology, the purpose of the present invention is to solve the limitations such as lack of centralized supervision, imperfection, and lack of autonomy in the existing supervision mode of civil aviation VoIP communication equipment. Therefore, an intelligent and smart supervision system for civil aviation VoIP communication equipment is proposed. According to the requirements of civil aviation management departments and air traffic control user units for the smart supervision of VoIP communication equipment, a new independent design is carried out for its smart supervision network architecture, and corresponding functional contents such as centralized supervision, risk analysis, and security policies are added to the system, which can realize the integration and efficient utilization of supervision resources, complete the transformation from passive response to active management, and improve the supervision efficiency and governance effectiveness.

[0008] The present invention adopts the following technical solutions to achieve the purpose:

[0009] An intelligent and smart supervision system for civil aviation VoIP communication equipment, comprising:

[0010] A system architecture module, which is constructed by adopting a hierarchical modular design to form a two-layer system including a top-level management architecture and a bottom-level operation architecture. The top-level management architecture is based on a service-oriented architecture to achieve loose coupling connection of functional components. The bottom-level operation architecture integrates standardized interfaces that conform to the SNMP V2 and above protocols and is used to establish protocol-level communication connections with VoIP communication equipment;

[0011] A functional service module includes a device status monitoring unit, a risk assessment engine, and an operation and maintenance management unit. The device status monitoring unit collects operating status data of VoIP communication devices in real time through the standardized interface. The risk assessment engine has a built-in machine learning model and performs fault prediction analysis on the collected data. The operation and maintenance management unit generates maintenance instructions based on the analysis results and feeds them back to the underlying operation architecture.

[0012] The data processing architecture consists of a multi-level processing channel consisting of a data collection layer, a data transmission layer, a data storage layer, a data computing layer, and a data application layer connected in sequence. The collection layer is equipped with a distributed data collection node group. The transmission layer uses a message queue mechanism to achieve cross-layer data routing. The storage layer deploys a distributed database cluster and a cache acceleration layer. The computing layer uses a streaming computing engine to perform real-time data analysis. The application layer outputs device monitoring views through a visual interface.

[0013] The technical support system includes a microservice component group, a continuous integration platform and a security authentication module; among them, the microservice component group is deployed on a cloud computing platform based on containerization technology to achieve dynamic expansion and load balancing of functional service modules; the continuous integration platform builds an automated deployment pipeline to achieve version iteration of system components; the security authentication module uses a two-way encryption protocol to ensure data transmission security.

[0014] Specifically, in the system architecture module, the top-level management architecture includes a risk assessment component and an intelligent decision-making component. With the assistance of other components in the system, the risk assessment component establishes an equipment risk prediction model by analyzing historical operating data. The intelligent decision-making component generates a decision recommendation set including troubleshooting paths and performance optimization solutions based on the risk assessment results, and provides real-time feedback to the visual supervision interface.

[0015] Specifically, in the system architecture module, the underlying operating architecture includes a core service layer, a data interaction layer, and a technical infrastructure layer stacked in sequence; the core service layer integrates equipment status monitoring services and risk assessment service modules, the data interaction layer configures a multi-protocol adapter interface and deploys a streaming data processing engine, and the technical infrastructure layer includes various types of VoIP communication equipment clusters and distributed cloud computing platforms; service calls and data flows are realized between each layer through standardized data interfaces.

[0016] Preferably, the functional service module specifically includes:

[0017] The monitoring alarm unit is equipped with a multi-level alarm classification mechanism and a false alarm filtering module. The false alarm filtering module integrates a dual verification mechanism of intelligent algorithm filtering and manual marking feedback;

[0018] The device management unit implements tree-like topology display and dynamic status feedback for devices across vendors, and has a mechanism for automatically removing device abnormality flags.

[0019] The log report unit builds a dual-track recording system including alarm logs and operation logs. The alarm log records multi-dimensional data fields such as device manufacturer, IP address and alarm level, while the operation log records user behavior characteristics and operation time parameters.

[0020] Preferably, the functional service module further includes:

[0021] The data statistics unit uses timeline dynamic visualization technology to display alarm data in multi-dimensional charts, and is used for real-time rendering of bar charts and line charts and interactive data analysis;

[0022] The risk assessment unit integrates an equipment lifecycle prediction model and a failure mode analysis algorithm. The equipment lifecycle prediction model establishes a function for estimating the remaining life of the equipment based on historical operating data, and the failure mode analysis algorithm outputs a priority ranking scheme for troubleshooting paths.

[0023] The interface interaction unit is equipped with a customizable dashboard component and alarm response interface, and also has a multi-channel distribution strategy for alarm notifications and a visual feedback mechanism for device status marking.

[0024] Specifically, in the data processing architecture:

[0025] The data collection layer is configured with a multi-protocol adapter interface group, including an SNMP protocol collection module and an SDK encapsulation component, to implement real-time / batch hybrid collection modes for heterogeneous data sources through device manufacturer APIs.

[0026] The data transmission layer deploys a distributed message middleware cluster and streaming data pipeline. The message middleware cluster uses Kafka to build a data buffer queue, and the streaming data pipeline integrates the Flink framework to output the calculated data to the application or storage system.

[0027] The data storage layer adopts a hierarchical storage architecture: the HDFS distributed file system is used to persistently store raw data blocks, the HBase columnar database is used to organize structured monitoring indicators, and the Redis in-memory database is used to build a real-time status cache area, forming a complete multi-level data storage system.

[0028] The data computing layer includes a stream-batch integrated processing engine and a machine learning computing framework. The stream-batch integrated processing engine uses Flink to implement a hybrid computing mode of real-time alarm detection and offline trend analysis. The machine learning computing framework uses TensorFlow to build a device failure prediction model.

[0029] The data application layer is configured with dynamic visualization components, which use timeline sliding controls to display the real-time alarm heat map generated by streaming computing and the equipment life prediction curve output by the machine learning model.

[0030] The data processing architecture further includes a data interface layer for providing a multi-dimensional data service interface group, including SQL query interfaces, RESTful API interfaces, and streaming data subscription interfaces, to support the real-time monitoring dashboard in the data application layer to call real-time status cache data and the risk assessment engine to call offline analysis data sets.

[0031] Preferably, the technical support system specifically includes:

[0032] A security gateway cluster that integrates a JWT token verification module and a single sign-on authentication service, and has a three-layer security filtering mechanism including routing forwarding, load balancing, and access control;

[0033] A microservice governance component group that implements service registration and discovery functions based on the SpringCloud framework, and dynamically manages the communication strategy of the RabbitMQ message queue through the Nacos configuration center;

[0034] A distributed storage unit that constructs a real-time status cache layer using a Redis in-memory database, and combines a master-slave MySQL cluster to achieve disaster recovery synchronization of structured data.

[0035] Preferably, the technical support system further includes a containerized deployment unit that generates Docker image files through Jenkins to build an automated delivery pipeline, and uses the Kubernetes orchestration engine to achieve elastic scaling of the container cluster; the microservice governance component group also integrates a Zipkin distributed tracing system for establishing a real-time monitoring map of the service call link; the distributed storage unit also extends an object storage service module to save unstructured data by docking with a cloud object storage system through a standardized S3 protocol interface.

[0036] Preferably, the technical support system further includes a dual-active disaster recovery unit that configures a dual-active storage node group and an encrypted transmission channel, where:

[0037] The dual-active storage node group adopts a storage mechanism that combines full backup and incremental backup, generates a benchmark data mirror through a timed full backup operation, and captures incremental backup data packets based on log tracking technology; the encrypted transmission channel deploys a national cryptography algorithm encryption module to implement end-to-end encryption protection for the backup data transmission process;

[0038] The dual-active disaster recovery unit integrates an automatic failover module, which automatically activates the standby node and synchronizes incremental backup data packets when the primary storage node fails to ensure business continuity.

[0039] In summary, due to the adoption of this technical solution, the beneficial effects of the present invention are as follows:

[0040] The present invention constructs an intelligent supervision system for air traffic control VoIP communication equipment, and comprehensively improves the supervision efficiency through multi-dimensional technology integration and architecture innovation. The system will adopt a unified data integration framework to achieve centralized monitoring of all network components such as voice communication switching system (VCS), very high frequency (VHF) air-ground communication radio, data recorder (DR), core switch, and edge devices, eliminating the information island phenomenon in traditional supervision and significantly improving the comprehensiveness and timeliness of fault diagnosis. The predictive maintenance model constructed by the system can deeply mine the operation trend data of VoIP communication equipment, identify potential fault modes in advance and trigger the early warning mechanism, effectively reducing the risk of unplanned downtime and essentially improving the system reliability.

[0041] In terms of system architecture design, the present invention constructs a loosely coupled top-level management architecture by introducing the modular concept, and realizes the dynamic reorganization and seamless expansion of functional components in combination with the flexible expansion characteristics of the SWIM architecture. The underlying operation architecture supports the access of multi-source devices through standardized interface protocols, and cooperates with customizable configuration strategies to ensure that the system can not only adapt to the personalized needs of different air traffic control units, but also meet the continuous compliance requirements of industry standards. The built-in complete operation audit and maintenance record module in the system provides full life cycle data support for VoIP communication equipment, strengthening the quality traceability and process optimization capabilities.

[0042] At the technical implementation level, the present invention deeply integrates Internet of Things perception and big data analysis technologies to construct an intelligent supervision center platform, realizing real-time status perception of supervision objects, multi-dimensional data fusion analysis, and automated emergency response. Through the dual mechanisms of computer-aided diagnosis and artificial intelligence decision support, the present invention greatly improves the fault location accuracy and processing efficiency. The unique risk assessment and trend prediction function of the system transforms traditional passive supervision into an active defense mode, significantly enhancing the adaptability to complex operating environments.

[0043] In terms of comprehensive effectiveness, the system of the present invention integrates core functional modules such as device management, intelligent alarm, and log analysis, and realizes precise supervision through a data-driven decision-making mechanism. The unique intelligent algorithm engine supports the application of automated supervision scenarios, reducing the intensity of manual intervention while improving the supervision response speed. For communication, navigation, and surveillance technology support units, the system provides an intuitive visual monitoring interface and decision-making assistance tools, effectively improving the operation and maintenance efficiency; for civil aviation management departments, a full-process intelligent platform covering equipment approval, safety supervision, and effectiveness evaluation is constructed to promote the transformation and upgrading of the industry supervision mode towards standardization and systematization. Description of the Drawings

[0044] Figure 1 It is a schematic diagram of the overall composition of the intelligent supervision system of the present invention;

[0045] Figure 2 It is a detailed schematic diagram of the top - level management architecture in the system of the present invention;

[0046] Figure 3 It is a schematic diagram for the system of the present invention to realize cross - vendor device supervision;

[0047] Figure 4 It is a detailed schematic diagram of the relevant content of the functional service module in the system of the present invention;

[0048] Figure 5 It is a detailed schematic diagram of the relevant content of the data - processing architecture in the system of the present invention;

[0049] Figure 6 It is a detailed schematic diagram of the relevant content of the technical support system in the system of the present invention. Specific embodiments

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and illustrated in the drawings here can be arranged and designed in various different configurations.

[0051] Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed present invention, but merely represents the selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0052] Embodiment 1

[0053] An intelligent and smart supervision system for civil aviation VoIP communication devices, Figure 1 shows 4 key components of the system, which can be viewed synchronously. The introduction of them in this embodiment is as follows:

[0054] The system architecture module adopts a hierarchical and modular design to construct a two - layer system including a top - level management architecture and a bottom - level operation architecture. The top - level management architecture is based on a service - oriented architecture to achieve loose - coupling connection of functional components. The bottom - level operation architecture integrates standardized interfaces that comply with the SNMP V2 and above protocols and is used to establish protocol - level communication connections with VoIP communication devices;

[0055] Functional service module, including a device status monitoring unit, a risk assessment engine and an operation and maintenance management unit. The device status monitoring unit collects the operation status data of VoIP communication devices in real time through a standardized interface. The risk assessment engine has a built-in machine learning model and performs fault prediction analysis on the collected data. The operation and maintenance management unit generates maintenance instructions according to the analysis results and feeds them back to the underlying operation architecture;

[0056] Data processing architecture, which consists of a data acquisition layer, a data transmission layer, a data storage layer, a data calculation layer and a data application layer connected in sequence to form a multi-level processing channel. The acquisition layer is configured with a distributed data acquisition node group. The transmission layer uses a message queue mechanism to achieve cross-level data routing. The storage layer deploys a distributed database cluster and a cache acceleration layer. The calculation layer sets up a streaming calculation engine to perform real-time data analysis. The application layer outputs a device supervision view through a visualization interface;

[0057] Technical support system, including a microservice component group, a continuous integration platform and a security authentication module; among them, the microservice component group is deployed on a cloud computing platform based on containerization technology to achieve dynamic expansion and load balancing of the functional service module; the continuous integration platform builds an automated deployment pipeline to achieve version iteration of system components; the security authentication module uses a two-way encryption protocol to ensure data transmission security.

[0058] Based on the above key components, this embodiment has made a brand-new independent design of the intelligent supervision network architecture for VoIP communication devices, thus solving the problems of non-centralized, imperfect and non-independent supervision in the existing supervision mode. Civil aviation management departments can rely on the intelligent supervision service platform under this system to achieve intelligent supervision of VoIP communication devices, complete the transformation from passive response to active management, improve supervision efficiency and governance effectiveness, cope with the growing air traffic communication devices, and enhance civil aviation safety.

[0059] Embodiment 2

[0060] Based on Embodiment 1, this embodiment introduces the system architecture module in the system in detail. When designing, an intelligent supervision architecture is constructed based on standardization, normalization and modularization, following the principles of scalability, security, openness and hierarchy. Since the technical specifications of VoIP communication devices issued by the Civil Aviation Administration stipulate that all VoIP communication devices must provide interfaces of SNMP V2 or above, so that the working status information and system operation information of the overall system and each component within the system can be uniformly supervised by the SNMP protocol. Therefore, the system in this embodiment should also support interfaces above SNMP V2.

[0061] Meanwhile, the modular design method is adopted to provide an open design, allowing the system to connect more devices and add more advanced functions. In this embodiment, the service-oriented architecture (SOA) design concept of SWIM is adopted to design the underlying operation architecture and the top-level management architecture of the air traffic control department.

[0062] In this embodiment, the top-level management architecture is the intelligent supervision business layer of air traffic control, and the detailed content can be referred to Figure 2 for illustration; it faces the air traffic control department and aims to unify the intelligent monitoring and intelligent supervision of VoIP communication devices, mainly carrying the services of communication, recording, monitoring, evaluation, and decision-making. Specifically, it will optimize the unified supervision of devices from different manufacturers on the same interface. As Figure 3 shown, it realizes the cross-vendor device supervision, enables the system to establish a connection with the VoIP communication device and achieve communication, and then transmits and records the status and abnormal information of each communication device. In addition, the system monitors the device status in real time, and once a change occurs, the relevant information will be immediately updated and displayed on the terminal interface.

[0063] In this embodiment, a risk assessment component is newly added to the top-level management architecture, enabling the system to use historical data for big data analysis and artificial intelligence calculation, evaluate the risk points and potential problems of the VoIP communication device, and predict the remaining life of the device, that is, the time from the current moment to the next possible risk occurrence of the device. At the same time, an intelligent decision-making component is also newly added, enabling the system to provide decision support information such as actual fault troubleshooting suggestions and device performance optimization measures based on the analysis results. The above-mentioned services together form the core interface display of the intelligent supervision system for VoIP communication devices.

[0064] For the underlying operation architecture, in this embodiment, it is divided into a core service layer, a data interaction layer, and a technical infrastructure layer that are stacked in sequence; among them, the core service layer provides services through the decomposition of business activities, and divides the frequently used and recurring modules into basic services. Other services are further divided into different business component groups. Therefore, the core service layer not only includes monitoring and alarming, device management, log reports, user management, and system configuration services, but also newly adds data statistics, risk assessment, and intelligent decision-making services, as well as basic query, SMS, authentication, security, backup, report, and management services, etc. These services together realize the business of the intelligent supervision system and provide support for the functions to be realized.

[0065] The data interaction layer is located at the next lower level and provides the data foundation for the core service layer. This layer provides data support for air traffic control equipment to the VoIP communication equipment intelligent supervision system, mainly consisting of the data of VoIP voice communication systems from various manufacturers. In addition, this layer also provides interfaces for data communication between core services to support service registration, acquisition, and communication. The data interaction layer also assumes the role of monitoring services, covering service monitoring, system monitoring, and network monitoring. This layer also provides various data acquisition methods, including but not limited to communication protocols, third-party toolkits, and short-range communication technologies. In terms of data processing, it integrates capabilities such as data mining, batch processing, high-speed query, data analysis, real-time data processing, and machine learning algorithms to facilitate the implementation of upper-layer services.

[0066] The bottom layer is the technical infrastructure layer, which provides physical network connections and necessary hardware devices. This layer adds VoIP communication equipment from different manufacturers, such as civil aviation communication equipment like high-frequency air-ground communication radios, very high frequency air-ground communication radios, voice communication switching systems, recorders, automatic message switching machines for aeronautical fixed telecommunication networks, very high frequency air-ground communication common systems, very high frequency data communication ground stations, and aeronautical airport surface broadband mobile communication ground stations. In addition, the hardware devices in this layer also cover the database, operating system, distributed file service, data processing platform and gateway, cache of the supervision system, as well as ground-to-ground and ground-to-air networks. These constitute the infrastructure platform for the operation of the entire system.

[0067] Each level in this embodiment is compatible with each other and works collaboratively to successfully achieve tasks such as real-time monitoring, device management, early warning, risk assessment, and intelligent decision-making of VoIP communication equipment.

[0068] Embodiment 3

[0069] Based on any of the above embodiments, this embodiment details the functional service modules in the system. The intelligent supervision system should be divided into unit modules such as monitoring and alarming, device management, log reports, data statistics, risk assessment, system configuration, and basic services, as can be seen in the Figure 4 schematic; there are corresponding sub-functions under different units, and there are also specific business processes between them. This embodiment introduces each unit in turn.

[0070] The monitoring and alarm unit is optimized to centrally and uniformly monitor the ATC communication equipment of various manufacturers, covering sub-services such as alarm classification, alarm notification, false alarm filtering, real-time monitoring, and feedback mechanism. There is a certain business process between each service. Specifically, real-time monitoring will be carried out first. When an alarm occurs, the alarm time, content, and quantity will be displayed. At the same time, a new false alarm filtering mechanism is added, which can be filtered manually or intelligently. When the alarm classification is determined, including the alarm status, level, and type, for example, if the alarm classification is not a false alarm but a fault or warning, alarm notification will be carried out, and the users in the ATC department will be notified through ringing, interface display, or email and SMS. At the same time, a feedback mechanism for the alarm will appear on the interface. When it comes, it will be marked for processing. After the alarm is resolved or classified as a false alarm, the mark will disappear.

[0071] The equipment management unit covers sub-services such as equipment classification, equipment management, equipment addition, real-time management, and feedback mechanism. There is a certain business process for each service. Specifically, equipment addition will be carried out first, and manual addition or automatic addition can be selected. Then, the newly connected equipment will be classified according to the equipment manufacturer, type, and name, etc., and equipment management is supported, including functions such as sorting, adding and deleting, and retrieving. And the equipment can be managed in real time on the interface. The optimization shows a group of equipment from different manufacturers in the form of a tree. Clicking on different equipment in the tree can intuitively display the equipment status of the current equipment, including the status and anomalies of the equipment. At the same time, the upgraded feedback mechanism can gray out the temporarily deactivated equipment or mark the abnormal equipment until the fault is resolved or marked as a false alarm, and the mark will automatically disappear.

[0072] Log Report Unit, covering function items such as log classification, included elements, log query, log management, report export, etc. Specifically, there are two types of logs. One is alarm logs, and the other is system operation logs. The log content needs to include content such as time range, alarm information or operation information. Specifically, optimizing alarm logs should at least include equipment manufacturer, equipment type, equipment name, equipment IP, alarm level, alarm type, alarm content, alarm status, and new addition time. For example, Equipment Company, Seat, Seat 1, 10.0.56.4, Fault, Connection Abnormal, No Handheld Radio on Interface Box, Headphone, Telephone Handset Connection, Unread, 2024-11-26 11:40:40. And operation logs record events such as user login, access, operation, data change, etc., which are used for auditing and security monitoring. Its content should at least include client IP, user name, operation type, operation description, request elapsed time, and operation time. For example, 10.0.59.202, admin, Modify, Modify Alarm Rule, 153ms, 2024-11-26 11:20:50. In addition, log query can be in the way of fuzzy matching or exact matching, and the current query options can also be reset. A log management module is also required, including functions such as sorting logs, generating templates, applying templates, and exporting. Optimize the log export function to support user-defined styles and scheduled export, and preview and download are supported before export.

[0073] Data Statistics Unit, covering statistical forms, chart statistics, table statistics, periodic statistics, and additional services. Specifically, the system supports presenting the results of data analysis in chart form and table form. Upgrade the chart statistics component. Different types of charts are used for statistics on data in different aspects. For example, a bar chart can be used to understand which equipment type is more likely to have problems, a bar graph can be used to identify which manufacturer's equipment has better quality or needs more attention, a line chart can be used to understand the dynamic changes in the alarm situation, etc. At the same time, new data update methods are added, such as real-time update, on-demand update, or scheduled update. Add a table statistics scenario. Through table statistics, users can understand the alarm quantity of equipment from each manufacturer, the alarm quantity of each equipment type, etc., support the sorting and query functions, and also support periodic statistics at intervals such as daily, monthly, quarterly, and annual basis. This business section advocates presenting charts and tables intuitively, including using scroll bars to view data, presenting data intuitively in an aggregated or sampled manner, or zooming in or out the time axis to view data.

[0074] The risk assessment unit adds sub - services such as assessment scope, assessment management, risk assessment, fault prediction, and advanced functions. There are business logic relationships among each function. Specifically, the assessment scope is determined by setting options for the device scope and time range. The assessment management function is added, including sub - modules for risk assessment and fault prediction. Among them, the risk assessment sub - module assesses the risk level, probability, and risk content of the supervised objects, and the fault prediction sub - module should support predicting the fault type and the fault occurrence time. And advanced functions are provided, such as marking high - risk items, setting risk priorities, adding an intelligent decision - making function, providing decision - making support information according to the analysis results, including fault troubleshooting suggestions, equipment optimization plans, etc. Through visual display and intelligent recommendation, it helps the supervisors make decisions quickly.

[0075] The interface interaction unit is related to system configuration and covers settings in different aspects such as user management, system, alarm, report, interface interaction, data synchronization, security, and backup, and supports parallel settings. Specifically, user management needs to support sub - functions such as role definition, user registration and management, permission allocation for roles, and online user statistics. At the same time, the system settings page should include sub - pages such as time settings, multi - voice support module, feedback entry, and support service. In addition, the alarm settings should support adding, deleting, querying, and modifying supervised devices, support modifying the alarm rules and alarm notification methods for supervised objects, and the time period for receiving notifications. In terms of report settings, a timed report component is added, providing a timed report template for quickly generating common reports, and should include global report settings such as the default storage location and export format. A component for adjusting the theme and interface layout is added, such as functions for adjusting icon size, text size, arrangement order, etc.

[0076] In this embodiment, some repetitive services can be separately divided into SMS services, authentication services, security services, backup services, storage services, and user login services, including supporting user login options, redirect pages after successful or failed login, supporting password modification, and interface prompts for password complexity requirements after entering the password, as well as basic services such as query services, report services, and management services.

[0077] By implementing strategies such as integrating many functional modules, optimizing the data statistics module, and adding risk assessment and intelligent decision - making modules, this embodiment can achieve intelligent monitoring and intelligent supervision of civil aviation air traffic control VoIP devices, that is, centrally and uniformly supervise the air traffic control communication devices of each manufacturer, such as frequentis, Schmid, Tianao, and equipment companies. When a communication device fails, the system can quickly prompt the fault status of the communication device, and use mature risk assessment and intelligent decision - making models to achieve real - time monitoring of the device, providing decision - making support for fault handling and predictive maintenance.

[0078] Embodiment 4

[0079] Based on any of the above embodiments, this embodiment details the data processing architecture in the system. This part mainly focuses on the collection, storage, processing, and analysis of VoIP communication device data in the network. As Figure 5 shown, the complete data architecture consists of a data application layer, a data interface layer, a data computing layer, a data storage layer, a data transmission layer, and a data acquisition layer. This design is because through hierarchical division, each layer is responsible for its own functions, and at the same time, it conducts conflict-free and more transparent data interaction with other layers. Each layer provides interfaces for interaction with the upper and lower layers, thus realizing the complete process of data from collection, storage, calculation to application. This embodiment details each layer in turn.

[0080] The data acquisition layer is the hardware facility layer, responsible for collecting real-time data of VoIP communication devices, including operating status, etc. It supports real-time acquisition or batch acquisition, and new external interfaces are added. For communication devices of different manufacturers, data can be obtained through the SNMP protocol, or through public and private APIs, or by accessing the SDK of the device manufacturer, the third-party packaged SDK, or the short-distance wired interface method. It also supports batch acquisition of data from databases, systems, or application log databases. For the processing of a large amount of data, the method of batch processing and paging query is adopted to avoid memory overflow caused by loading too much data at one time.

[0081] The data transmission layer transmits the data collected by the acquisition layer to the supervision system. It integrates a variety of technologies to achieve efficient data transmission capabilities. Among them, Sqoop is used for data transmission between relational databases (such as MySQL, Oracle, etc.) and Hadoop (such as HDFS, Hive, etc.), Flume is responsible for the collection and transmission of log data, Logstash collects, converts, and transmits log data, Canal focuses on the capture and transmission of database incremental logs, Kafka, as a distributed message middleware, provides efficient message transmission and processing capabilities, MyBatis supports customized SQL and plays a role in data conversion and mapping, converting the original data into a standard format for analysis, and the API defines the interfaces and rules for data transmission, enabling different systems to achieve data transmission and sharing by calling the API.

[0082] The data storage layer stores and manages the massive amounts of data transmitted to the system. To ensure the efficient and reliable storage of big data, this layer also relies on the collaborative work of multiple technologies. Among them, for the distributed storage system, HDFS, Ceph, and Kudu are optional, providing data storage solutions with high scalability and fault tolerance to meet the needs of massive data storage; for the distributed database, HBase and MySQL are optional, providing efficient storage and query capabilities for structured data. Although MySQL is mainly used as a relational database management system, it can also be deployed distributively through technologies such as clustering to handle large-scale data. In addition, a new Alluxio distributed cache layer is added to accelerate data access speed. Redis and Ignite can be selected to take advantage of in-memory computing to further improve the data processing performance of the data transmission layer, and the construction based on the cloud computing platform is upgraded to implement the digital foundation of smart civil aviation and build a secure, reliable, green, and intensive industry-level cloud platform.

[0083] The data calculation layer provides technical support for the data interface layer and integrates the collaborative work of multiple technologies. Among them, Hive provides the query and analysis capabilities for large-scale data sets based on Hadoop, while Apache Spark is a more general and high-performance big data processing framework that supports various scenarios such as batch processing and stream processing, complementing Hive. A hybrid architecture of Apache Storm and Flink can be adopted to process real-time data, with Flink being preferred as it performs better in state management and low latency. TensorFlow can be selected for machine learning calculations to introduce intelligent algorithm support to the data calculation layer. The Presto distributed SQL query engine is upgraded to further enhance the query performance of the data calculation layer. A new Kubernetes container orchestration platform is added to provide an efficient resource management and deployment environment for the above calculation frameworks, ensuring the stability and scalability of the data calculation layer.

[0084] The data interface layer is a core hierarchical structure additionally included in the data processing architecture, which provides comprehensive data unification services, integrating various data interaction methods such as SQL queries, FTP file transfers, Web Services (WS), MDX (Multidimensional Expressions), and API interfaces to ensure the flexibility and accessibility of data. This layer utilizes the data processing and analysis capabilities of the data calculation layer to provide a series of services for the data application layer, optimizing basic analysis to obtain an overview of the data, multidimensional analysis to explore the deep associations of the data, discover hidden patterns and rules in the data, real-time analysis to quickly respond to data changes, and data sharing to promote data circulation and cooperation among different systems and roles.

[0085] As the uppermost layer, the data application layer is closely connected to the underlying data interface layer, computing layer, storage layer, transmission layer, and acquisition layer, jointly building a complete data processing ecosystem. New applications such as risk assessment, intelligent decision-making, and data statistics are added, making full use of the large amount of diverse data resources provided by the data storage layer to achieve comprehensive monitoring and management of in-use communication devices. At the same time, through tight integration with the data storage layer, the data application layer can access and call data stored in distributed databases, file systems, etc. in real time, providing data support for functions such as device monitoring and alarming, and logging. In addition, various modes such as batch processing, stream processing, and machine learning provided by the data computing layer strongly support the implementation of the data application layer. These technologies complement each other, jointly promoting the improvement of the data architecture and providing efficient data processing capabilities.

[0086] In the process of applying technologies related to the system data level, an unreasonable data architecture can lead to data overflow, redundancy, or abuse. Therefore, it is crucial to design a reasonable and high-performance data architecture to correctly process the status or alarm data of VoIP communication devices, etc. In this embodiment, the system also adds a data exchange and sharing mechanism to ensure that data between systems can be transmitted and synchronized in real time and accurately. It is also necessary to consider data security and privacy protection to prevent data leakage and abuse.

[0087] Embodiment 5

[0088] Based on any of the above embodiments, this embodiment details the technical support system in the system. Technical support needs to optimize continuous deployment technology, data storage technology, monitoring service technology, and front-end and back-end technology, etc. according to business requirements and data architecture requirements. At the same time, the scalability, reliability, and stability of the technology are also considered to ensure that the intelligent supervision network can operate continuously and stably. As Figure 6 shown, the technology stack described in this embodiment can be divided into a client layer, a gateway layer, a microservice layer, a storage service layer, and a continuous integration layer from high to low.

[0089] The client layer provides a user interface. Adopting the Vue core front-end framework, it offers responsive data binding and component-based development capabilities. Upgrading to TypeScript enhances the type safety of the code, improving development efficiency and maintainability. The new Vite front-end build tool, with its extremely fast cold start and instant hot module update features, accelerates the development iteration speed. To build a beautiful and consistent user interface, ant-design-vue is introduced, providing a rich set of UI components. In terms of state management, the official pinia state management library is integrated to simplify the state management of complex applications. For data visualization, echarts is combined to create various interactive charts. To improve the flexibility and efficiency of writing styles, the unocss atomic CSS tool is adopted. For table processing, the vxe-table table component library can be used to support complex table scenarios and rich editing functions. To implement the micro-frontend architecture, qiankun is introduced to integrate multiple sub-applications into the main application, achieving modularization and independent deployment of the application. In addition, ES6+ syntax features are supported to enhance the modernity and readability of the code, and data communication with the backend is carried out through the HTTP protocol and RESTful APIs to ensure the simplicity and efficiency of front-end and back-end interactions.

[0090] When building the front-end entry system of the gateway layer, this embodiment adds a new design solution, which is a security gateway cluster. This solution adopts the Nginx and API Gateway technology stack, including JWT, CAS, Shiro, and Zuul, to build a core of a secure and efficient system architecture. Among them, Zuul acts as a proxy and reverse proxy, providing a unified entry point for the overall architecture of the system, and undertaking various functions such as routing forwarding, filter processing, load balancing, security control, and monitoring, optimizing the resource utilization rate and response speed of the system. Nginx, as a Web server / reverse proxy server and email (IMAP / POP3) proxy server, while providing high-performance HTTP services, also undertakes the task of load balancing to ensure the high availability and scalability of the system. And the APIGateway is the core hub of the architecture, integrating multiple security authentication mechanisms, including JWT (used for token-based authentication to ensure the security of requests), CAS (Central Authentication Service to achieve single sign-on function), and Shiro (Java security framework providing functions such as authentication, authorization, encryption, and session management). These technologies work together to ensure the stable operation of the system.

[0091] The microservice layer uses the Spring Boot and Spring Cloud technology stacks to build microservices. The service registration and discovery are implemented through Eureka in Nacos, enabling upper-layer applications to access dynamically. At the same time, Spring Cloud Config is used for service configuration management. To support communication between services, this embodiment introduces message middleware such as Kafka to provide an asynchronous communication mechanism, and integrates REST Feign to implement synchronous HTTP communication. The SPI design should be adopted for standardized interaction between the API service and the data storage layer to improve the flexibility and scalability of the system. In addition, Spring Boot Admin is integrated for service monitoring, and technologies such as Zipkin and Sleuth are used for distributed tracing and monitoring. The circuit breaker and rate-limiting strategy are implemented to protect the system stability. In terms of network monitoring, the Zabbix technology is introduced to monitor and collect data on remote servers and network status. Finally, for log management and report management, technologies such as ELK and Logstash are optional, providing functions of log collection, analysis, and report generation.

[0092] The storage service layer uses Redis cache technology to improve data access speed and ensures data reliability through the master-slave replication mechanism. To handle the large-scale file storage requirements, the Alibaba OSS distributed file service technology is introduced to provide a highly available, scalable, and high-performance file storage solution, forming a distributed storage unit. At the same time, the database is optimized to a distributed architecture, such as master-slave MySQL, to achieve the master-slave synchronization (hot standby) and disaster recovery switching functions of data. Such a design can not only enhance the read and write performance of data but also ensure the security and continuity of data in case of failures, enabling the data of the primary database to be replicated to the slave database in real time. When the primary database fails, the slave database can quickly take over to ensure the stable operation of the system.

[0093] In this embodiment, the continuous integration layer is the entire process of development and deployment. After the developer submits the code to the code repository, the Jenkins automation tool is added. It automatically obtains the latest code in the code repository, builds, tests, and packages it, and finally generates a deployable Docker image. This part can correspondingly form the containerized deployment unit of the technical support system. These deployable Docker images will then be orchestrated and deployed through the API of Kubernetes (k8s). K8s can automatically deploy Docker containers to appropriate nodes according to preset configurations and policies and manage their lifecycles. The Rancher container management platform is added to maintain and manage the code and containers, efficiently monitoring, deploying, and scaling containerized applications. Such a design makes full use of automation tools and containerization technologies, improving development efficiency and operation and maintenance convenience.

[0094] The technical support system further includes a dual-active disaster recovery unit. The dual-active disaster recovery unit is configured with a dual-active storage node group and an encrypted transmission channel. Through such fault recovery and fault tolerance mechanisms, such as redundant design, backup strategies, and recovery plans, it can cope with emergencies such as data loss and network interruption. For critical data, a full backup is performed once a day. After each full backup, an incremental backup is performed on the newly added or modified data. In addition, it is also necessary to ensure that communication data is not accessed or damaged by unauthorized parties.

[0095] Therefore, in this embodiment, the dual-active storage node group adopts a storage mechanism combining full backup and incremental backup. A reference data mirror is generated by triggering the full backup operation at regular intervals, and incremental backup data packets are captured based on the log tracking technology. The encrypted transmission channel deploys a national cryptography algorithm encryption module to implement end-to-end encryption protection for the backup data transmission process. The dual-active disaster recovery unit integrates an automatic fault switching module. When the primary storage node fails, the standby node is automatically activated and the incremental backup data packets are synchronized to ensure business continuity.

[0096] In summary, the intelligent and smart supervision system of the civil aviation VoIP communication equipment of the present invention conducts comprehensive consideration and design from four dimensions: architecture, business, data, and key technologies. New functions such as intelligent filtering of false alarms, risk assessment, and intelligent decision-making are added. The monitoring and alarming are optimized into a centralized supervision mode. The graphical data statistics module is optimized, and appropriate technology selection is determined. Technologies such as big data and artificial intelligence are explored and used to improve the accuracy and efficiency of data processing and analysis, keep up with the industry's cutting-edge technologies, and timely introduce new technologies and methods, thereby enhancing the competitiveness and practicality of the system. At the same time, aspects such as user experience, stability, fault recovery and fault tolerance mechanisms, security, and scalability are also strongly guaranteed during the construction of the intelligent supervision system.

Claims

1. An intelligent and smart supervision system for civil aviation VoIP communication equipment, characterized in that, include: The system architecture module adopts a layered modular design to build a two-tier system consisting of a top-level management architecture and a bottom-level operation architecture. The top-level management architecture is based on a service-oriented architecture to achieve loosely coupled connections between functional components. The bottom-level operation architecture integrates standardized interfaces compliant with SNMP V2 and above protocols for establishing protocol-level communication connections with VoIP communication equipment. A functional service module includes a device status monitoring unit, a risk assessment engine, and an operation and maintenance management unit. The device status monitoring unit collects operating status data of VoIP communication devices in real time through the standardized interface. The risk assessment engine has a built-in machine learning model and performs fault prediction analysis on the collected data. The operation and maintenance management unit generates maintenance instructions based on the analysis results and feeds them back to the underlying operation architecture. The data processing architecture consists of a multi-level processing channel consisting of a data collection layer, a data transmission layer, a data storage layer, a data computing layer, and a data application layer connected in sequence. The collection layer is equipped with a distributed data collection node group. The transmission layer uses a message queue mechanism to achieve cross-layer data routing. The storage layer deploys a distributed database cluster and a cache acceleration layer. The computing layer uses a streaming computing engine to perform real-time data analysis. The application layer outputs device monitoring views through a visual interface. The technical support system includes a microservice component group, a continuous integration platform and a security authentication module; among them, the microservice component group is deployed on a cloud computing platform based on containerization technology to achieve dynamic expansion and load balancing of functional service modules; the continuous integration platform builds an automated deployment pipeline to achieve version iteration of system components; the security authentication module uses a two-way encryption protocol to ensure data transmission security.

2. The intelligent and smart supervision system according to claim 1, wherein: In the system architecture module, the top-level management architecture includes a risk assessment component and an intelligent decision-making component. With the assistance of other components in the system, the risk assessment component establishes an equipment risk prediction model by analyzing historical operating data. The intelligent decision-making component generates a decision recommendation set including troubleshooting paths and performance optimization solutions based on the risk assessment results, and provides real-time feedback to the visual supervision interface.

3. The intelligent and smart supervision system according to claim 2, characterized in that: In the system architecture module, the underlying operating architecture includes a core service layer, a data interaction layer, and a technical infrastructure layer stacked in sequence; the core service layer integrates equipment status monitoring services and risk assessment service modules, the data interaction layer configures a multi-protocol adapter interface and deploys a streaming data processing engine, and the technical infrastructure layer includes various types of VoIP communication equipment clusters and distributed cloud computing platforms; service calls and data flows are realized between each layer through standardized data interfaces.

4. The intelligent and smart supervision system according to claim 1, characterized in that, Functional service modules include: The monitoring alarm unit is equipped with a multi-level alarm classification mechanism and a false alarm filtering module. The false alarm filtering module integrates a dual verification mechanism of intelligent algorithm filtering and manual marking feedback; The device management unit implements tree-like topology display and dynamic status feedback for devices across vendors, and has a mechanism for automatically removing device abnormality flags. The log report unit builds a dual-track recording system including alarm logs and operation logs. The alarm log records multi-dimensional data fields such as device manufacturer, IP address and alarm level, while the operation log records user behavior characteristics and operation time parameters.

5. The intelligent and smart supervision system according to claim 4, characterized in that The functional service module further includes: The data statistics unit uses timeline dynamic visualization technology to display alarm data in multi-dimensional charts, and is used for real-time rendering of bar charts and line charts and interactive data analysis; The risk assessment unit integrates an equipment lifecycle prediction model and a failure mode analysis algorithm. The equipment lifecycle prediction model establishes a function for estimating the remaining life of the equipment based on historical operating data, and the failure mode analysis algorithm outputs a priority ranking scheme for troubleshooting paths. The interface interaction unit is equipped with a customizable dashboard component and alarm response interface, and also has a multi-channel distribution strategy for alarm notifications and a visual feedback mechanism for device status marking.

6. The intelligent and smart supervision system according to claim 1, characterized in that In the data processing architecture: The data collection layer is configured with a multi-protocol adapter interface group, including an SNMP protocol collection module and an SDK encapsulation component, to implement real-time / batch hybrid collection modes for heterogeneous data sources through device manufacturer APIs. The data transmission layer deploys a distributed message middleware cluster and streaming data pipeline. The message middleware cluster uses Kafka to build a data buffer queue, and the streaming data pipeline integrates the Flink framework to output the calculated data to the application or storage system. The data storage layer adopts a hierarchical storage architecture: the HDFS distributed file system is used to persistently store raw data blocks, the HBase columnar database is used to organize structured monitoring indicators, and the Redis in-memory database is used to build a real-time status cache area, forming a complete multi-level data storage system.

7. The intelligent and smart supervision system according to claim 6, wherein In the data processing architecture: The data computing layer includes a stream-batch integrated processing engine and a machine learning computing framework. The stream-batch integrated processing engine uses Flink to implement a hybrid computing mode of real-time alarm detection and offline trend analysis. The machine learning computing framework uses TensorFlow to build a device failure prediction model. The data application layer is configured with dynamic visualization components, which use timeline sliding controls to display the real-time alarm heat map generated by streaming computing and the equipment life prediction curve output by the machine learning model. The data processing architecture also includes a data interface layer, which is used to provide a multidimensional data service interface group, including SQL query interface, RESTful API interface and streaming data subscription interface, to support the real-time monitoring dashboard of the data application layer to call real-time status cache data and the risk assessment engine to call offline analysis data sets.

8. The intelligent and smart supervision system according to claim 1, wherein, The technical support system specifically includes: Security gateway cluster, integrating JWT token verification module and single sign-on authentication service, with a three-layer security filtering mechanism including routing forwarding, load balancing and access control; Microservice governance component group, based on the SpringCloud framework to implement service registration and discovery functions, and dynamically manage the communication strategy of the RabbitMQ message queue through the Nacos configuration center; The distributed storage unit uses the Redis memory database to build a real-time status cache layer, combined with a hierarchical storage architecture or a master-slave MySQL cluster to achieve disaster recovery and synchronization of structured data.

9. The intelligent and smart supervision system according to claim 8, characterized in that, The technical support system further includes a containerized deployment unit, which generates Docker image files through Jenkins to build an automated delivery pipeline, and uses the Kubernetes orchestration engine to achieve elastic scaling of the container cluster; the microservice governance component group is also integrated with a Zipkin distributed tracing system for establishing a real-time monitoring map of the service call link; the distributed storage unit is also extended with an object storage service module, which realizes the preservation of unstructured data by docking with the cloud object storage system through a standardized S3 protocol interface.

10. The intelligent and smart supervision system according to claim 9, wherein, The technical support system further includes a dual-active disaster recovery unit, which configures a dual-active storage node group and an encrypted transmission channel, where: The dual-active storage node group adopts a storage mechanism that combines full backup and incremental backup, generates a benchmark data mirror through a timed full backup operation, and captures incremental backup data packets based on log tracking technology; the encrypted transmission channel deploys a national cryptography algorithm encryption module to implement end-to-end encryption protection for the backup data transmission process; The dual-active disaster recovery unit is integrated with an automatic fault switching module. When the primary storage node fails, the standby node is automatically activated and the incremental backup data packets are synchronized to ensure business continuity.

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