Locomotive micro-service intelligent operation and maintenance method and system fused with single Beidou navigation technology

Abstract

The invention discloses a locomotive micro-service intelligent operation and maintenance method and a locomotive micro-service intelligent operation and maintenance system fused with a single Beidou navigation technology, a ground server is built through KAFKA software so as to establish a plurality of Topic information categories, and a plurality of Topic information categories are established according to a real-time data queue formed by arranging locomotive vehicle-mounted data in sequence according to a publishing time sequence and a plurality of message types of a single Beidou positioning system. The method comprises the following steps of: classifying locomotive vehicle-mounted data to obtain Topic category data of a plurality of locomotive messages, storing the Topic category data of the locomotive messages into a Topic information category, and realizing operation maintenance of a locomotive by a ground user according to the Topic category data of the locomotive messages stored in the Topic information category. Through the built ground server, in the operation and maintenance process of the locomotive, the coupling degree of all the modules is low, and expansibility and flexibility are high.

Description

Technical Field

[0001] The present invention relates to the technical field of operation and maintenance management, and in particular to a locomotive microservice intelligent operation and maintenance method and system integrating single Beidou navigation technology. Background Art

[0002] The operation and maintenance management of locomotives can ensure railway safety: Through an intelligent locomotive operation and maintenance management system, functions such as remote monitoring, intelligent diagnosis, and automatic control of locomotives can be realized, timely discovering and solving locomotive faults, and ensuring the safety and stability of railway transportation. Improve management efficiency: The intelligent locomotive operation and maintenance management system can improve the maintenance efficiency of locomotives and reduce the time of locomotives in the repair state to ensure the normal operation of locomotives, thereby improving the efficiency of the entire railway transportation system. Reduce the labor intensity of operation and maintenance personnel: The intelligent system can undertake a large amount of data collection and analysis work, reducing the work burden of operation and maintenance personnel. With the development of technology, locomotive operation and maintenance management is also promoting the improvement of the railway operation management mode, making it more modern and scientific.

[0003] However, traditional locomotive operation and maintenance systems adopt relatively old IT operation and maintenance methods, and the operation and maintenance objects are mostly physical servers. They usually adopt a monolithic architecture, with high coupling degrees among various modules, limited scalability and flexibility. The degree of automation of operation and maintenance processes is low, such as manual monitoring and deployment are required. In terms of monitoring, basic metrics such as CPU and memory may be mainly concerned, and log analysis is also relatively basic. In terms of high availability, it may rely on hardware redundancy, and the disaster recovery time is relatively long. Data transmission mainly relies on the Internet or local area network, with high requirements for network stability. Summary of the Invention

[0004] The present invention discloses a locomotive microservice intelligent operation and maintenance method and system integrating single Beidou navigation technology to overcome the above technical problems.

[0005] To achieve the above object, the technical solution of the present invention is:

[0006] A locomotive microservice intelligent operation and maintenance method integrating single Beidou navigation technology includes the following steps:

[0007] S1: Obtain multiple message types based on the single Beidou positioning system, including BDGGA, BDRMC, BDVTG, BDZDA;

[0008] S2: Obtain locomotive on-vehicle data, and form a real-time data queue by arranging the locomotive on-vehicle data in sequence according to the release time;

[0009] The locomotive on-vehicle data includes locomotive microcomputer data information, locomotive monitoring information, and locomotive safety information;

[0010] S3: Set up a ground server through KAFKA software to establish multiple Topic information categories, including locomotive real-time information processing data categories, locomotive fault diagnosis data categories, and offline data categories;

[0011] S4: Classify the locomotive on-vehicle data according to multiple message types of the real-time data queue and the single Beidou positioning system to obtain data of Topic categories of multiple locomotive messages, including locomotive real-time information processing data, locomotive fault diagnosis data, and offline data;

[0012] S5: Store the data of the Topic categories of the locomotive messages into the Topic information categories, that is, store the locomotive real-time information processing data into the locomotive real-time information processing data category, store the locomotive fault diagnosis data into the locomotive fault diagnosis data category, and store the offline data into the offline data category;

[0013] S6: Ground users perform operation and maintenance of the locomotive according to the data of the Topic categories of the locomotive messages stored in the Topic information categories.

[0014] Further, in S5, the method of storing the data of the Topic categories of the locomotive messages into the Topic information categories is as follows:

[0015] S51: Create producers for locomotive Topic information categories and locomotive Topic message categories through Kafka;

[0016] S52: According to the real-time data queue, based on the data protocol between the preset locomotive microcomputer data information, locomotive monitoring information, locomotive safety information and producers of data of multiple categories of the locomotive, parse the real-time data queue into different message groups through the Map method; obtain the data of the Topic categories of the locomotive messages;

[0017] S53: Through the producers of the locomotive Topic message categories, store the data of the Topic categories of the locomotive messages into the Topic information categories, that is, the locomotive message category Topic container.

[0018] Further, the producers of the locomotive Topic message categories store the data of the Topic categories of the locomotive messages through the Broker of the message cache server to realize the storage of the data of the Topic categories of the locomotive messages in the Topic information categories.

[0019] Further, in S6: When there is a ground user actively subscribing to the Topic information category, the data of the Topic categories of the locomotive messages stored in the Topic information category are sent to the ground user interface by means of background push;

[0020] When there is no ground user actively subscribing to the Topic information category, according to the Topic information category subscribed by the ground user, the data of the Topic category of the locomotive messages stored in the Topic information category is sent to the ground user interface, so that the ground user can realize the operation and maintenance of the locomotive based on the data of the Topic category of the locomotive messages obtained according to the subscribed Topic information category and the data in the pushed Topic information category.

[0021] An operation and maintenance system for a locomotive microservice intelligent operation and maintenance method integrating single Beidou navigation technology, including a Beidou wireless transmission module based on a Beidou third-generation navigation unit, a locomotive on-vehicle collection unit, and a ground integrated microservice unit;

[0022] The Beidou wireless transmission module based on the Beidou third-generation navigation unit is used to obtain multiple message types of the single Beidou positioning system, including BDGGA, BDRMC, BDVTG, and BDZDA;

[0023] The locomotive on-vehicle collection unit is used to obtain locomotive on-vehicle data to form a real-time data queue arranged in the order of the release time; wherein, the locomotive on-vehicle data includes locomotive microcomputer data information, locomotive monitoring information, and locomotive safety information;

[0024] The ground integrated microservice unit is used to build a ground server through KAFKA software to establish multiple Topic information categories; at the same time, according to the real-time data queue and multiple message types of the single Beidou positioning system, classify the locomotive on-vehicle data to obtain data of multiple Topic categories of locomotive messages; and store the data of the Topic categories of the locomotive messages into the Topic information categories; so that the ground user can realize the operation and maintenance of the locomotive based on the data of the Topic categories of the locomotive messages stored in the Topic information categories.

[0025] Furthermore, the operation and maintenance system is built through Git.

[0026] Beneficial effects: A locomotive microservice intelligent operation and maintenance method and system integrating single Beidou navigation technology build a ground server through KAFKA software to establish multiple Topic information categories. According to the real-time data queue formed by arranging locomotive on-vehicle data in sequence according to the release time and multiple message types of the single Beidou positioning system, the locomotive on-vehicle data is classified to obtain data of multiple Topic categories of locomotive messages, and the data of the Topic categories of the locomotive messages is stored in the Topic information categories. Ground users can realize the operation and maintenance of the locomotive according to the data of the Topic categories of the locomotive messages stored in the Topic information categories. The present invention uses the third-generation Beidou short message technology to transmit the data of the locomotive with power on, solving the problem that the traditional locomotive operation and maintenance system relies on the Internet or local area network for data transmission and has high requirements for network stability. Through the built ground server, the coupling degree of each module is low during the operation and maintenance of the locomotive, and the scalability and flexibility are high. Description of the Drawings

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0028] Figure 1 It is a schematic flowchart of the locomotive microservice intelligent operation and maintenance method of the present invention;

[0029] Figure 2 It is a schematic diagram of the model composition of the locomotive microservice intelligent operation and maintenance system in the embodiment of the present invention;

[0030] Figure 3 It is a schematic diagram of the single Beidou RNSS protocol in the embodiment of the present invention;

[0031] Figure 4 It is a schematic diagram of the locomotive on-vehicle message microservice queue model in the embodiment of the present invention;

[0032] Figure 5 It is a schematic diagram of the locomotive fault queue model in the embodiment of the present invention;

[0033] Figure 6 It is a schematic diagram of the core components of the locomotive microservice container in the embodiment of the present invention;

[0034] Figure 7 It is a schematic flowchart of the continuous development and deployment work of the locomotive microservice in the embodiment of the present invention. Detailed Embodiments

[0035] 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. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0036] This embodiment introduces a locomotive microservice intelligent operation and maintenance method and system integrating single Beidou navigation technology, as Figure 1 shown, including the following steps:

[0037] S1: Based on the Beidou wireless transmission module, obtain multiple message types based on the single Beidou positioning system, that is, the Beidou-3 navigation unit, including BDGGA, BDRMC, BDVTG, BDZDA;

[0038] S2: Obtain locomotive on-vehicle data and form a real-time data queue by arranging the locomotive on-vehicle data in sequence according to the release time; the locomotive on-vehicle data includes locomotive microcomputer data information, locomotive monitoring information, and locomotive safety information;

[0039] S3: Build a ground server through KAFKA software to establish multiple Topic information categories, including locomotive real-time information processing data categories, locomotive fault diagnosis data categories, offline data categories, etc.;

[0040] S4: According to the real-time data queue and multiple message types of the single Beidou positioning system (i.e., BDGGA, BDRMC, BDVTG, BDZDA), classify the locomotive microcomputer data information, locomotive monitoring information, and locomotive safety information to obtain data of multiple locomotive message Topic categories, including locomotive real-time information processing data, locomotive fault diagnosis data, offline data, etc., and publish the data under these categories to the Broker of the message cache server for storage;

[0041] S5: Store the data of the locomotive message Topic category into the Topic information category, that is, store the locomotive real-time information processing data into the locomotive real-time information processing data category, and store the locomotive fault diagnosis data into the locomotive fault diagnosis data category; store the offline data into the offline data category;

[0042] Preferably, the method of storing the data of the locomotive message Topic category into the Topic information category in S5 is as follows:

[0043] S51: Create producers for the locomotive Topic information category and the locomotive Topic message category through Kafka;

[0044] Specifically, in this embodiment, a locomotive message category Topic and a producer of the locomotive Topic message category are created through the Kafka built-in command line.

[0045] S52: According to the real-time data queue, based on a preset data protocol between the locomotive microcomputer data information, locomotive monitoring information, locomotive safety information, and producers of data in multiple categories of the locomotive obtained by the locomotive on-vehicle acquisition unit, the real-time data queue is parsed into different message groups through the Map method; data of the Topic category of the locomotive message is obtained.

[0046] Specifically, the producers of data in multiple categories of the locomotive call the Produce interface to produce the message groups generated by the real-time data queue, that is, the data of the Topic category of the locomotive message, into the corresponding locomotive Topic information categories.

[0047] Specifically, the Map method is a key-value pair data structure. Each data information category is used as the Key, and the specific value of the data information is used as the Value to obtain multiple message groupings. It is a prior art in the field and will not be described in detail here.

[0048] S53: Through the producer of the locomotive Topic message category, the data of the Topic category of the locomotive message is stored in the Topic information category, that is, the locomotive message category Topic container.

[0049] Preferably, through the producer of the locomotive Topic message category, the data of the Topic category of the locomotive message is stored in the Topic information category through the Broker of the message cache server.

[0050] S6: The ground user realizes the operation and maintenance of the locomotive according to the data of the Topic category of the locomotive message stored in the Topic information category.

[0051] Preferably, S6 includes: when there is a ground user actively subscribing to the Topic information category, the data of the Topic category of the locomotive message stored in the Topic information category is sent to the ground user interface through background push.

[0052] When there is no ground user actively subscribing to the Topic information category, according to the Topic information category subscribed by the ground user, the data of the Topic category of the locomotive messages stored in the Topic information category is sent to the ground user interface, so that the ground user can realize the operation and maintenance of the locomotive based on the data of the Topic category of the locomotive messages obtained according to the subscribed Topic information category and the data in the pushed Topic information category.

[0053] Preferably, this embodiment also discloses an operation and maintenance system for a locomotive microservice intelligent operation and maintenance method integrating single Beidou navigation technology, including a Beidou wireless transmission module based on a Beidou-3 navigation unit, a locomotive on-vehicle acquisition unit, and a ground integrated microservice unit;

[0054] The Beidou wireless transmission module based on the Beidou-3 navigation unit is used to obtain a single Beidou positioning system, that is, multiple message types of the Beidou-3 navigation unit, including BDGGA, BDRMC, BDVTG, and BDZDA;

[0055] The locomotive on-vehicle acquisition unit is used to obtain locomotive on-vehicle data to form a real-time data queue arranged in the order of the release time; wherein, the locomotive on-vehicle data includes locomotive microcomputer data information, locomotive monitoring information, and locomotive safety information;

[0056] The ground integrated microservice unit is used to KAFKA software build a ground server to establish multiple Topic information categories; at the same time, classify the locomotive on-vehicle data according to the real-time data queue and multiple message types of the single Beidou positioning system to obtain data of multiple Topic categories of locomotive messages; and store the data of the Topic categories of the locomotive messages into the Topic information categories; so that the ground user can realize the operation and maintenance of the locomotive based on the data of the Topic categories of the locomotive messages stored in the Topic information categories.

[0057] Specifically, the Beidou-3 receiver only supports a single NMEA data protocol format, that is, an information form starting with the BD transmission identifier, mainly including various information categories such as BDGGA, BDGLL, BDGSA, BDGSV, BDRMC, BDVTG, BDZDA, BDTXT, and BDANT. In this embodiment, taking the BDRMC message type as an example, a schematic diagram of the characteristic protocol of the Beidou-3 receiver in this embodiment is shown, as Figure 3The figure shows a schematic diagram of the single Beidou RNSS protocol. Pseudo-range measurement is carried out through the broadcast ephemeris of satellites, and various error terms are calibrated using precise satellite orbits and clock biases. According to the geometric principle of RNSS navigation and positioning and various observation error sources, the site coordinates are obtained, and key information such as BDRMC and BDGGA is acquired. Then, high-precision locomotive positioning data such as UTC time, longitude and latitude spatio-temporal information, positioning mode, altitude, and magnetic declination direction are obtained through the position.

[0058] As Figure 2 The figure shows the architecture diagram of the locomotive microservice operation and maintenance model of this embodiment. The locomotive microservice intelligent operation and maintenance model mainly consists of three parts: the locomotive on-vehicle acquisition unit, the Beidou wireless transmission module, and the ground integrated microservice unit.

[0059] As Figure 2 The figure shows ， The locomotive Beidou generation-three acquisition device (i.e., the Beidou wireless transmission module) uses the train bus WTB and the real-time Ethernet bus RTEB as the overall network topology structure. It consists of an AD sensor for collecting the voltage and current analog quantities of each locomotive component and a data acquisition and calculation integrated computing power unit, obtaining locomotive microcomputer data information, locomotive monitoring information, and locomotive safety information. Optionally, it includes the input voltage and current, output three-phase voltage and current of each key device on the locomotive (such as the four-quadrant rectifier and auxiliary inverter of the network control system), and the real-time status data of monitoring points such as the drive circuit voltage and safety system sub-components, and extracts characteristic variables for edge detection calculation.

[0060] Optionally, the extracted characteristic variables include event information related to faults generated during the operation of the locomotive. Among them, each type of event has its own characteristic variables. By combining the corresponding fault equations and locomotive variable parameters for edge calculation, the specific values of the characteristic variables are obtained. The locomotive fault queue model mentioned later will store these characteristic variables in the form of a container and extract them through the consumer strategy of kafka;

[0061] Specifically, the Beidou wireless transmission module in this embodiment integrates the Beidou generation-III navigation unit, periodically obtains wireless positioning RNSS information (positioning information), and performs safe backhaul of short messages RDSS for key locomotive information with or without power according to different service frequencies. The ground operation and maintenance system, namely the ground integrated microservice unit, adopts containerized management and front-end and back-end separation technologies, and is developed based on the SPRINGCLOUD microservice architecture, enabling the front-end page to call the back-end interface through asynchronous communication technology. The front-end technology architecture is implemented using NODEJS+VUE technology, and the back-end receives and subscribes to data streams through the NIFI+KAFKA architecture. Through the K8S+DOCKER container technology, cluster management and control of containers (Topic information categories) related to locomotive business services and general service container classes are realized. The locomotive microservice system adopts the GITLAB+CI / CD model to achieve version control of code and automated deployment of services.

[0062] Specifically, the ground integrated microservice system calls the data provided by the back-end container through the front-end VUE front-end interface technology, including real-time data of various locomotive systems, non-real-time fault data of the locomotive, and Beidou positioning data of the locomotive, etc., so as to display the locomotive data provided by the back-end service through the front-end.

[0063] As Figure 4 Shown is a schematic diagram of the locomotive message queue model in this embodiment, which includes a message queue producer Beidou generation-III satellite data receiving unit, a message cache server cluster, and a message subscription server cluster. Among them, the message subscription server cluster uses KAFKA software as a message intermediate processing module to generate different TOPIC information sources from locomotive network control system data, locomotive safety equipment data, monitoring system data, locomotive fault data, etc., for different locomotive microservices to subscribe and call. The message cache server cluster divides locomotive real-time data, fault data, and offline data into different cache models BROKER, routes messages to different message queues, and realizes the establishment of the locomotive message queue model. Its working process is as follows: The Beidou generation-III satellite data receiving unit, as the producer of the locomotive message queue, publishes various locomotive business information to the Broker of the message cache server for persistent operation for different service consumer groups to subscribe. The key system establishes a Topic according to the message category, and the key sub-components form their own Partition message queues. Optionally, the locomotive fault data cache service unit realizes the physical association of key variables according to different fault category Topics, so as to establish a diagnostic algorithm model.

[0064] Specifically, the key systems of the locomotive in this embodiment include: the locomotive microcomputer network control system (abbreviated as the TCMS system), the locomotive safety control system (LKJ system), the locomotive monitoring system (6A system), and the locomotive health diagnosis system (PHM system), etc. The key sub-components of the locomotive include: the locomotive driver display unit DDU, the locomotive traction control unit TCU, the locomotive auxiliary control unit ACU, the locomotive main control unit CCU, and the locomotive brake unit BCU, etc.

[0065] As Figure 5 shown in the schematic diagram of the locomotive fault queue model, where each fault entry is associated with different variables. The Partition partition is used to assign an ordered ID (offset) to each message, and fuzzy matching is performed using the English name of the fault. When the fault variable queue reaches the conditional threshold, the fault entry will be pushed to the foreground as real-time information for early warning display. A reasonable consumption strategy is set to evenly distribute all messages to different partitions, thereby achieving system load balancing. Optionally, in this embodiment, the KAFKA software is used to create a fault TOPIC with a unique ID identifier, and physical associations are made for information such as the number of fault occurrences and the fault name. At the same time, different Partition partitions are created to store fault-associated variables. Optionally, for fault identification, this embodiment solves it through the fault queue. Locomotive operation and maintenance include the maintenance of the locomotive operation status, including real-time monitoring, as well as fault resolution and fault prediction processing. In this embodiment, a model for locomotive operation and maintenance is built, and through this model, the locomotive can be well monitored and managed.

[0066]

[0067] Table 1 Comparison of Model Parameters of Beidou-2 and Beidou-3 Navigation Receivers

[0068] As shown in Table 1, the parameter comparison between the Beidou-3 and Beidou-2 modules has the same service frequency points. The Beidou-3 navigation system has higher positioning accuracy, speed measurement accuracy, and acquisition sensitivity parameters in RNSS, and the RDSS short message capacity is also as high as 1700 bytes (for civilian use). Taking the B2 frequency point of Beidou-3 as an example, according to the application requirements of the target receiver for the B2 frequency point, the acquisition sensitivity when there is interference is -155 dBm. According to the receiver sensitivity formula:

[0069] Ps = 10 * Log(KT) + 10 * Log(BW) + Eb / No - Gp + NFsys

[0070] Where: Ps is the receiver sensitivity; K is the Boltzmann constant; T is the absolute temperature of the ambient temperature, taking T = 302K, BW is the signal bandwidth of the system, Eb / No is the output signal-to-noise ratio of the system, Gp is the processing gain of the system, and NFsys is the noise figure of the receiver system. It can be calculated that when the noise figure of the receiving system is below 10 dB, the system meets the requirement that the sensitivity is better than -155 dBm.

[0071] As Figure 6 shown in the core component diagram of the locomotive microservice container, the locomotive microservice intelligent operation and maintenance model adopts a master-slave architecture in service management, and rationally controls the microservice architecture through the KUBERNETES software, which consists of the master node Master of the China Railway Corporation and the slave nodes Node composed of each locomotive depot. The locomotive network data element node model is established through the DOCKER software; the core component API Server running on the master node is responsible for authorization functions such as microservice management and access control. Each locomotive microservice runs in an independent Pod (container). The master and slave nodes uniformly schedule and manage all containers through the Kubelet instruction. Optionally, the locomotive service nodes are deployed in the cluster in the form of Node through Kubelet, and virtual containers such as the locomotive service registration center POD, circuit breaker service POD, and algorithm calculation service POD are created through kubelet create-f*.yaml. The control loop is controlled by the Controller_Manager component to compare the current container state with the state expected by the user and modify it to the user-expected state to achieve automatic restart of the microservice. Then, a replica instruction set is created through Kubelet and Proxy proxy, and the old Pod is replaced with the new Pod template to achieve the "zero-downtime" rolling update of the locomotive microservice.

[0072] As Figure 7The following is the workflow diagram of the continuous development and deployment of locomotive microservices in this embodiment. The intelligent operation and maintenance model of locomotive microservices in this embodiment uses Git as the code management tool and provides a series of management functions including user permission management, software version control, automated code submission testing and deployment, etc. By reasonably configuring CICD and using the executor Gitlab-runner to execute the scripts defined in.gitlab-ci.yml in the project, the automated construction, compilation, and unit testing of locomotive operation and maintenance microservices are realized. When each stage is successfully executed, the continuous integration CI step of the project is completed (the full name of CI is Continuous integration, which specifically means that the development team frequently integrates the code into the shared repository and conducts automated construction and testing. Its core purpose is to ensure that no errors are introduced after the new code is integrated with the existing code, thereby improving the software quality and delivery speed), and then enter the next continuous deployment CD module. Optionally, in this embodiment, locomotive business developers dynamically create different system branches Branch on the Git server according to different service modules, and use the service executor Gitlab-runner to periodically poll whether the submitted code has changed. When there is a Push operation, it triggers the Pipeline (software pipeline) to realize the automated construction, compilation, and unit testing of locomotive operation and maintenance microservices. The operation and maintenance person in charge merges the code of the main branch to ensure that the currently submitted business module does not conflict with the main architecture, and continuously conducts automated deployment of Deployment.

[0073] The intelligent operation and maintenance model of locomotive microservices integrating single Beidou navigation technology in this embodiment relies on the large-scale application system of Beidou, a national heavy weapon, to conduct in-depth research on microservice and container processing technologies, so as to realize the intelligent operation and maintenance model with separated front and back ends for locomotives. Combining with the full life cycle service of locomotive component-level products, it improves the information service architecture model, which has positive significance for expanding the application of Beidou in railway equipment. The intelligent operation and maintenance model can start from the analysis of the intelligent operation and maintenance requirements of rail transit products, optimize and sort out the huge data set of locomotives, and build an operation and maintenance system with higher computing efficiency by splitting it into multiple loosely coupled microservice components and architecture models at a fine granularity. By establishing a more efficient fault diagnosis model with the accumulated real-time big data, and at the same time feeding back to the product design to improve the reliability management of product components, it greatly ensures the stability of product operation and further realizes the transformation of key product components from failure repair and preventive repair to condition-based repair and predictive repair.

[0074] In summary, the locomotive microservice intelligent operation and maintenance model of this embodiment integrates the single Beidou satellite navigation technology, solves the problems of the traditional technology in data transmission relying on the Internet or local area network and having high requirements for network stability. At the same time, the single Beidou satellite navigation only receives Beidou signals and processes information such as Beidou signal output, so it shields the positioning of the locomotive by other navigation systems such as GPS, can ensure that the operation of the locomotive is not affected by foreign navigation systems, and thus realizes railway traffic safety in various scenarios. At the same time, it can also avoid the impact of other satellite navigation systems on railway traffic safety, and ensure railway transportation safety and national defense safety. At the same time, the combination of the advanced microservice architecture system and locomotive services improves the intelligent and information level of railway operation.

[0075] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A locomotive microservice intelligent operation and maintenance method integrating single Beidou navigation technology, characterized in that It includes the following steps: S1: Obtain multiple message types based on the single Beidou positioning system, including BDGGA, BDRMC, BDVTG, and BDZDA; S2: Obtain the on-board data of the locomotive and form a real-time data queue by arranging the on-board data of the locomotive in sequence according to the release time; The on-board data of the locomotive includes locomotive microcomputer data information, locomotive monitoring information, and locomotive safety information; S3: Build a ground server through KAFKA software to establish multiple Topic information categories, including locomotive real-time information processing data categories, locomotive fault diagnosis data categories, and offline data categories; S4: Classify the on-board data of the locomotive according to the real-time data queue and multiple message types of the single Beidou positioning system to obtain data of multiple locomotive message Topic categories, including locomotive real-time information processing data, locomotive fault diagnosis data, and offline data; S5: Store the data of the Topic categories of the locomotive messages into the Topic information categories, that is, store the locomotive real-time information processing data into the locomotive real-time information processing data category, store the locomotive fault diagnosis data into the locomotive fault diagnosis data category, and store the offline data into the offline data category; S6: Ground users perform operation and maintenance on the locomotive according to the data of the Topic categories of the locomotive messages stored in the Topic information categories.

2. The locomotive microservice intelligent operation and maintenance method integrating single Beidou navigation technology according to claim 1, wherein In S5, the method of storing the data of the Topic categories of the locomotive messages into the Topic information categories is as follows: S51: Create producers for the locomotive Topic information category and the locomotive Topic message category through Kafka; S52: According to the real-time data queue, based on the data protocol between the preset locomotive microcomputer data information, locomotive monitoring information, locomotive safety information and the producers of multiple categories of data of the locomotive, parse the real-time data queue into different message groups through the Map method; obtain the data of the Topic categories of the locomotive messages; S53: Through the producer of the locomotive Topic message category, store the data of the Topic category of the locomotive message into the Topic information category, that is, the locomotive message category Topic container.

3. The locomotive microservice intelligent operation and maintenance method integrating single Beidou navigation technology according to claim 2, characterized in that, The producer of the locomotive Topic message category stores the data of the Topic category of the locomotive message through the Broker of the message cache server to realize the storage of the data of the Topic category of the locomotive message in the Topic information category.

4. The locomotive microservice intelligent operation and maintenance method integrating single Beidou navigation technology according to claim 1, wherein, In S6: When there is a ground user actively subscribing to the Topic information category, the data of the Topic category of the locomotive message stored in the Topic information category is sent to the ground user interface by means of background push; When there is no ground user actively subscribing to the Topic information category, according to the reserved Topic information category of the ground user, the data of the Topic category of the locomotive messages stored in the Topic information category is sent to the ground user interface, so that the ground user can realize the operation and maintenance of the locomotive based on the data of the Topic category of the locomotive messages obtained according to the reserved Topic information category and the data in the pushed Topic information category.

5. An operation and maintenance system for a locomotive microservice intelligent operation and maintenance method integrating single Beidou navigation technology according to any one of claims 1-4, characterized in that, It includes a Beidou wireless transmission module based on a Beidou generation-three navigation unit, a locomotive on-vehicle acquisition unit, and a ground integrated microservice unit; The Beidou wireless transmission module based on the Beidou generation-three navigation unit is used to obtain multiple message types of the single Beidou positioning system, including BDGGA, BDRMC, BDVTG, and BDZDA; The locomotive on-vehicle acquisition unit is used to obtain locomotive on-vehicle data to form a real-time data queue arranged in sequence according to the release time; among them, the locomotive on-vehicle data includes locomotive microcomputer data information, locomotive monitoring information, and locomotive safety information; The ground integrated microservice unit is used to build a ground server through KAFKA software to establish multiple Topic information categories; at the same time, according to the real-time data queue and multiple message types of the single Beidou positioning system, classify the locomotive on-vehicle data to obtain data of multiple Topic categories of locomotive messages; and store the data of the Topic categories of the locomotive messages into the Topic information categories; so that the ground user can realize the operation and maintenance of the locomotive based on the data of the Topic categories of the locomotive messages stored in the Topic information categories.

6. The operation and maintenance system of a locomotive microservice intelligent operation and maintenance method integrating single Beidou navigation technology according to claim 5, characterized in that, The operation and maintenance system is built through Git.

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