CN114221978B - Urban rail transit cloud platform system - Google Patents
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Abstract
The present disclosure provides an urban rail transit cloud platform system, comprising: the internet of things monitoring subsystem comprises a data monitoring layer functional module and a data sensing layer functional module, and is used for acquiring operation data of the whole life cycle of urban rail transit operation and transmitting the operation data to a data cloud center; the data cloud center is used for storing and managing the operation data transmitted by the Internet of things monitoring subsystem, realizing and managing service based on the operation data, and providing data and service for the data service application subsystem; the data service application subsystem is used for providing train operation information for passengers, providing operation management service support for urban rail transit operation and asset management service support for the urban rail transit operation according to the data and the service provided by the data cloud center. The technical scheme can meet the requirements of instantaneity and sharing in the data acquisition and application process of urban rail transit operation.
Description
Technical Field
The disclosure relates to the technical field of transportation, in particular to an urban rail transit cloud platform system.
Background
With the development of national economy and urban circles in China, urban rail transit plays a significant and more important role in a comprehensive traffic system, and networked urban rail transit lines are formed into a larger scale. Under the background, urban rail transit is faced with high requirements on aspects of intelligent comprehensive service of passengers, high-safety operation production and the like, so that high requirements are also put on data monitoring, processing and service of driving, equipment, passenger flow and assets of the urban rail transit.
Currently, in the traditional automatic monitoring scheme of urban rail transit, the data acquisition category is simple, the data transmission speed is low, the processing delay and delay of the traditional data center are low in value change degree, and the integration sharing capability is poor, so that the service requirement of the existing urban rail transit is difficult to meet.
For example, most of IT (Information Technology ) data centers in urban rail transit industry adopt centralized data processing architecture, and there are many bottlenecks, such as poor lateral expansion capability, poor hardware platform compatibility, poor data sharing and service innovation capability, failure to meet the requirements of high concurrency and data volume service growth, and high maintenance cost.
It can be seen that how to meet real-time requirements and sharing requirements in operation data monitoring, processing and service in urban rail transit operation is a technical problem that needs to be solved currently.
Disclosure of Invention
The disclosure provides an urban rail transit cloud platform system, which is used for solving the problem of low real-time performance and sharing performance in the process of collecting and applying urban rail transit operation data in the prior art so as to improve the real-time performance and sharing performance in the process of collecting and applying the data.
The present disclosure provides an urban rail transit cloud platform system, comprising: the internet of things monitoring subsystem comprises a data monitoring layer functional module and a data sensing layer functional module, and is used for acquiring operation data of the whole life cycle of urban rail transit operation and transmitting the operation data to a data cloud center; the data cloud center comprises a data storage layer functional module, a data center service layer functional module and a data application layer functional module, and is used for storing and managing the operation data transmitted by the Internet of things monitoring subsystem, realizing and managing service based on the operation data and providing data and service for the data business application subsystem; the data service application subsystem comprises a rail transit passenger service module, an operation management module and an asset management module, and is used for providing train operation information for passengers, providing operation management service support for urban rail transit operation and asset management service support for the urban rail transit operation according to data and services provided by the data cloud center.
According to the urban rail transit cloud platform system provided by the disclosure, the data monitoring layer functional module comprises an internet of things sensor which is connected to the internet of things by adopting any one of the following access modes: ethernet, wireless fidelity, low power wide area network, cellular telephone, satellite access system, and bluetooth; the Internet of things sensor comprises at least one of the following sensors: positioning sensors, radio frequency identification RFID devices, infrared sensors, and laser scanners.
According to the urban rail transit cloud platform system provided by the disclosure, the internet of things monitoring subsystem further comprises a data preprocessing module, wherein the data preprocessing module is used for performing front-end preprocessing on the operation data collected by the data monitoring layer functional module, and the front-end preprocessing comprises data aggregation and fusion operation of the operation data through at least one of the following operations: data aggregation, data fusion, data cleaning, data classification and data abnormality judgment.
According to the urban rail transit cloud platform system provided by the disclosure, the data sensing layer functional module is further used for dividing the operation data into two types of real-time data and non-real-time data, and transmitting the real-time data and the non-real-time data to the data cloud center according to the types.
According to the urban rail transit cloud platform system provided by the disclosure, the data storage layer functional module stores, schedules and manages the operation data through the server, the storage device and the network.
According to the urban rail transit cloud platform system provided by the disclosure, the data center platform service layer functional module performs construction, deployment, operation and calling of the urban rail transit data center platform service through at least one of the following operations: intelligent data mapping processing, business theme domain construction, data processing algorithm development and machine learning advanced model driving.
According to the urban rail transit cloud platform system provided by the disclosure, the data application layer functional module provides data and services for the data business application subsystem by creating a customized technical service module based on the functional module and the functional component.
According to the urban rail transit cloud platform system provided by the disclosure, the internet of things monitoring subsystem further comprises an alarm module, wherein the alarm module is used for generating an alarm signal to carry out alarm prompt when the data preprocessing module determines that abnormal data exists after carrying out data abnormal judgment.
According to the urban rail transit cloud platform system provided by the disclosure, the data center platform service layer function module comprises: the data processing layer unit is used for carrying out mapping processing and theme domain construction on the operation data; the data calculation layer unit is used for carrying out rail transit operation state early warning analysis and rail transit digital twin system construction according to the operation data after mapping processing; the data auditing unit is used for auditing the data output by the data calculation layer unit; and the data storage unit is used for storing the data which is checked by the data checking unit and is passed by the data checking unit so as to be called by the data application layer function module.
According to the urban rail transit cloud platform system provided by the disclosure, the rail transit passenger service module is used for providing at least one of the following services: guiding passengers, providing station navigation, planning intelligent travel paths of the passengers, intelligently monitoring passenger flows of the stations and publishing public information; the operation management module is configured to provide at least one of the following services: transport organization management, operation production management, intelligent station management, comprehensive intelligent operation maintenance management and emergency management; the asset management module is configured to provide at least one of the following services: enterprise asset management, equipment asset management, and full lifecycle closed loop management.
According to the urban rail transit cloud platform system, the data cloud center is adopted to store the operation data acquired by the data monitoring subsystem, the service is realized based on the operation data, the data and the service are provided for the data business application subsystem, the detection, the processing and the application of the operation data of urban rail transit are realized, and the instantaneity and the sharing performance in the data acquisition and the application process of urban rail transit operation are improved.
The utility model provides an urban rail transit cloud platform system relates to thing networking monitoring technology field and data cloud platform technical field. Specifically, the embodiment of the disclosure uses the technical means of the cloud platform to process the monitoring data of the urban rail transit Internet of things monitoring, so as to achieve the purposes of data service and value improvement. According to the technical scheme, the traditional urban rail transit data monitoring cost is saved through the Internet of things monitoring means, the multi-equipment, multi-system and multi-professional data fusion monitoring and efficient transmission capacity of urban rail transit are improved, the cloud platform-based data comprehensive service center is constructed, and the urban rail transit comprehensive business data application capacity is comprehensively improved.
The urban rail transit data cloud platform system based on the internet of things monitoring and the construction method of the urban rail transit data cloud platform system provided by the disclosure develop the urban rail transit monitoring data cloud platform system, and the system realizes the intellectualization, standardization and standardization of the internet of things monitoring and data processing of the rail transit, meets the current monitoring requirements and has far wider application prospects.
Drawings
In order to more clearly illustrate the present disclosure or the prior art solutions, a brief description will be given below of the drawings that are needed in the embodiments or prior art descriptions, it being apparent that the drawings in the following description are some embodiments of the present disclosure and that other drawings may be obtained from these drawings without inventive effort to a person of ordinary skill in the art.
Fig. 1 is a schematic structural diagram of an urban rail transit cloud platform system provided by the present disclosure;
FIG. 2 is a schematic diagram of the data monitoring subsystem provided by the present disclosure;
fig. 3 is a schematic structural diagram of a data cloud center provided by the present disclosure;
fig. 4 is a schematic structural diagram of a data service application subsystem provided by the present disclosure;
fig. 5 is a flow chart of a data processing procedure of operation data provided in the present disclosure.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the present disclosure more apparent, the technical solutions in the present disclosure will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are some, but not all, embodiments of the present disclosure. All other embodiments, which can be made by one of ordinary skill in the art without inventive effort, based on the embodiments in this disclosure are intended to be within the scope of this disclosure.
The terminology used in the one or more embodiments of the disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of the disclosure. As used in one or more embodiments of the present disclosure and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used in one or more embodiments of the present disclosure refers to and encompasses any or all possible combinations of one or more of the associated listed items.
The word "if" as used herein may be interpreted as "at … …" or "at … …" or "in response to a determination" depending on the context.
In the related art, operation data of urban rail transit are mostly collected by adopting a centralized data processing architecture, and the centralized data processing architecture has the problems of poor data sharing and service innovation capability, incapability of meeting the requirements of high concurrency and data volume service growth, high maintenance cost and the like. In addition, the traditional automatic monitoring scheme of urban rail transit has the problems of simple data acquisition category and low data transmission speed, and the traditional data center has the problems of slow and late processing, low value change degree and poor integration sharing capability, and has difficulty in meeting the service requirements of the existing urban rail transit.
To address these issues, embodiments of the present disclosure provide an urban rail transit cloud platform system.
Specifically, in order to save the manpower cost of traditional automatic monitoring technology and the integrated processing of monitoring data, solve subway automatic monitoring difficult problem, the technical scheme of the embodiment of the disclosure utilizes the internet of things monitoring and cloud platform technology, provides a rail transit data cloud platform system based on internet of things monitoring.
For a long time, urban rail transit informatization monitoring has the problems of passive post management, redundant waste of perceived data, lack of effective monitoring and evaluation means and the like, and the real-time and mass monitoring requirements of informatization monitoring are continuously increased, so that the requirements can be fully met based on the data platform mode of front-end monitoring of the Internet of things and rear-end processing of the cloud platform center. Based on the background, the technical scheme of the embodiment of the disclosure develops the urban rail transit data cloud platform system based on the internet of things monitoring by integrating the internet of things monitoring means and the cloud platform center processing technology.
Exemplary embodiments of the present disclosure are described in detail below with reference to the attached drawing figures.
Shown in fig. 1 is a schematic diagram of an urban rail transit cloud platform system according to an embodiment of the present disclosure. As shown in fig. 1, the urban rail transit cloud platform system in the embodiment of the present disclosure includes:
The internet of things monitoring subsystem 110 comprises a data monitoring layer functional module 111 and a data sensing layer functional module 112, and is used for collecting operation data of the whole life cycle of urban rail transit operation and transmitting the operation data to a data cloud center.
Specifically, the full life cycle of urban rail transit operation refers to the whole process from purchasing to operating to scrapping of the assets and equipment of the urban rail transit.
The data cloud center 120 includes a data storage layer function module 121, a data center platform service layer function module 122, and a data application layer function module 123, which are configured to store and manage operation data transmitted by the monitoring subsystem of the internet of things, implement and manage services based on the operation data, and provide data and services to the data service application subsystem.
Specifically, the data cloud center is based on cloud computing and cloud service implementation. Cloud computing is a technical architecture, mainly comprising virtualization, automatic deployment, distributed computing and other technologies, and has the advantage of being capable of externally exhibiting excellent parallel computing performance, scale scalability and robustness. The cloud service is an on-demand distributed and quantifiable IT service provided by the outside under the support of the technical architecture of cloud computing, and can be used for replacing the IT service built locally by a user.
The data service application subsystem 130 comprises a rail transit passenger service module 131, an operation management module 132 and an asset management module 133, and is used for providing train operation information for passengers, operation management service support for urban rail transit operation and asset management service support for urban rail transit operation according to data and services provided by the data cloud center.
The technical scheme of the embodiment of the disclosure relates to the technical field of monitoring of the Internet of things and the technical field of data cloud platforms. Specifically, the embodiment of the disclosure uses the technical means of the cloud platform to process the monitoring data of the urban rail transit Internet of things monitoring, so as to achieve the purposes of data service and value improvement.
According to the technical scheme, the traditional urban rail transit data monitoring cost is saved through the Internet of things monitoring means, the multi-equipment, multi-system and multi-professional data fusion monitoring and efficient transmission capacity of urban rail transit are improved, the cloud platform-based data comprehensive service center is constructed, and the urban rail transit comprehensive business data application capacity is comprehensively improved.
The monitoring mode of the Internet of things can be used for collecting various information such as sound, light, heat, electricity, force and the like in real time, and intelligent sensing, identification and management of equipment facilities and processes of rail transit operation are realized by various possible access modes such as Ethernet, wireless fidelity, low-power wide area network, cellular telephone, satellite, bluetooth and the like.
The cloud computing is a novel computing mode, has the characteristics of resource pooling, on-demand use, volume charging, ubiquitous access, remote delivery and the like, can reduce hardware and software cost, improve system performance, infinitely expand storage capacity, flexibly, safely and expansively support service functions such as urban rail transit passenger comprehensive service, intelligent operation production, intelligent safe operation and maintenance and the like, and has higher cost performance compared with the traditional application mode.
According to the technical scheme, the urban rail transit data cloud platform system based on the Internet of things monitoring is developed, and the intelligent, standardized and normalized monitoring and data processing of the Internet of things of the rail transit are realized, so that the current monitoring requirements are met, and the application prospect is far wider.
As shown in fig. 2, the data monitoring layer function module 111 of the internet of things monitoring subsystem 110 collects operation data of urban traffic tracks by using internet of things sensors such as a global positioning system, an RFID (Radio Frequency Identification ), an infrared sensor, and a laser scanner, and the operation data includes full life cycle data of a plurality of stages such as planning design, civil construction, operation production, operation maintenance, passenger service, asset management, and the like of urban rail transit.
Specifically, the internet of things sensor may be one or more of a positioning sensor, a radio frequency identification (Radio Frequency Identification, RFID for short) device, an infrared sensor, or a laser scanner, and is not limited thereto. The positioning sensor may be a global positioning system sensor, and is not limited thereto. The different kinds of internet of things sensors can be connected to the internet of things through various access modes. These access modes include: ethernet, wireless fidelity, low power wide area network, cellular telephone, satellite access system, and bluetooth, and is not limited thereto.
The global positioning system (Global Positioning System, abbreviated as GPS), also called as global satellite positioning system, is a medium-distance circular orbit satellite navigation system which combines the technology of satellite and communication development and uses navigation satellites to measure time and distance. The user only needs to have a GPS terminal or GPS chip to use the service. The global positioning system sensor is a device provided with a GPS chip, and can acquire positioning data of the device.
Radio frequency identification is one of automatic identification technologies, non-contact two-way data communication is performed in a wireless radio frequency mode, and a recording medium is read and written in the wireless radio frequency mode, so that the purposes of identification target and data exchange are achieved. The recording medium can be an electronic tag or a radio frequency card.
The infrared sensor is a sensor for processing data by utilizing infrared rays, has the advantages of high sensitivity and the like, and can control the operation of the driving device. The infrared sensor is commonly used for non-contact temperature measurement, gas component analysis and nondestructive inspection, and is widely applied in the fields of medicine, military, space technology, environmental engineering and the like.
The laser scanner is developed by utilizing the principle of laser scanning detection, and is mainly composed of a laser scanning transmitter consisting of an optical mechanical scanner and a scanning optical system, a laser scanning receiver consisting of a receiving optical system and a photoelectric conversion electronic system, a controller and a semiconductor laser power supply.
Ethernet is a computer local area network technology. The IEEE 802.3 standard of the IEEE organization sets up the technical standard for ethernet, which specifies the contents of the link, electronic signal and medium access layer protocols including the physical layer. Ethernet is the most commonly used local area network technology today.
Wireless fidelity (WIRELESS FIDELITY, abbreviated as WIFI) is the most widely used wireless fidelity transmission technology today. In practice, the wired network signal is converted into a wireless signal for being received by a mobile terminal such as a related computer or a mobile phone supporting the technology.
The low power wide area network (Low Power Wide Area Network, LPWAN) is a long-range low power wireless communication network. Most LPWA technologies can achieve network coverage of several kilometers or even tens of kilometers. The method is more suitable for large-scale application deployment of the Internet of things due to the characteristics of wide network coverage, low terminal power consumption and the like.
Cellular telephones, i.e. cellular mobile communications (Cellular Mobile Communication), are connected between terminals and network devices by wireless channels using a cellular wireless networking scheme, thereby enabling users to communicate with each other during their activities. The method is mainly characterized by mobility of the terminal and has the functions of handover and automatic roaming across a local network. The cellular mobile communication service refers to services such as voice, data, video and image provided through a cellular mobile communication network composed of a base station subsystem, a mobile switching subsystem and the like.
The satellite access system refers to an access system for connecting a user to a fixed wired communication network by using a satellite, which is also called a broadband satellite access system. In the case that the terrestrial communication network already forms a broadband multimedia communication network, the broadband satellite access system can use satellites operating at high altitude to cooperate with terrestrial communication, especially in areas with less dense population, users are scattered in a wider range, and the satellite access system is a practical, economical and reliable access mode.
Bluetooth is a radio technology supporting short-range communication of devices, and can exchange wireless information between a plurality of devices including mobile phones, palm computers, wireless headphones, notebook computers, related peripherals and the like. The communication distance supported by the communication device is generally within 10 meters. By using the Bluetooth technology, the communication between mobile communication terminal devices can be effectively simplified, and the communication between the devices and the Internet can be successfully simplified, so that the data transmission becomes quicker and more efficient, and the road is widened for wireless communication. Bluetooth is a global standard for wireless data and voice communications, which is a special short-range wireless technology connection that establishes a communication environment for fixed and mobile devices based on low-cost short-range wireless connections.
The operation data from different networks and different sensors of the Internet of things are often huge in quantity and different in data format, and in order to meet the requirements of real-time performance, high reliability, high stability, expandability and the like of monitoring scenes of the Internet of things of urban rail transit, the massive, multi-network, multi-source and heterogeneous operation data can be preprocessed.
In order to implement preprocessing of operation data, the monitoring subsystem of the internet of things may further include a data preprocessing module, configured to perform front-end preprocessing on the operation data collected by the functional module of the data monitoring layer, where the front-end preprocessing performs data aggregation and fusion operations on the operation data, and specifically, the front-end preprocessing includes one or more of the following operations: data aggregation, data fusion, data cleaning, data classification and data abnormality judgment.
The data aggregation is a process of collecting relevant data according to a determined data analysis framework, and provides materials and basis for data analysis. The data fusion is performed by using a computer to automatically analyze and synthesize a plurality of observation information obtained according to the time sequence under a certain criterion so as to complete the required decision and evaluation tasks. Data cleansing refers to the last procedure to find and correct identifiable errors in a data file, including checking for data consistency, processing invalid and missing values, etc.
In the embodiment of the disclosure, the monitoring subsystem of the internet of things may further include an alarm module, and after the data preprocessing module performs the data anomaly judgment, if abnormal data exists, the alarm module generates an alarm signal to alarm. The visible alarm module is used for generating an alarm signal to carry out alarm prompt when the data preprocessing module determines that abnormal data exists after carrying out data abnormality judgment.
The monitoring subsystem of the Internet of things can further comprise a display module, and after the display module receives the alarm signal, the display module can display alarm information and carry out sound alarm on a display screen according to the alarm signal. The alarm module is a cloud edge alarm module.
In one embodiment of the present disclosure, the data monitoring layer functional module may include a sensing sensor, a wireless/wired communication/communication unit, an edge calculation micro control unit, a cloud edge alarm unit, and a display unit. The sensing sensor unit can acquire real-time data, the real-time data is transmitted to the edge computing micro-control unit by the wireless/wired communication/communication unit for data processing, and the urban rail transit data monitoring state is generated by the cloud edge alarm unit and comprises an abnormal state and a non-abnormal state. And early warning information can be generated according to the urban rail transit data monitoring state to carry out early warning prompt, and the early warning information can be sent to a display unit for display.
Specifically, the edge computing micro-control unit can compare the urban rail transit data states acquired by various sensors with corresponding reference thresholds, judge whether the data states are abnormal states and enter a detection early warning mode, and the corresponding reference thresholds can be remotely set by the cloud platform center through the edge computing micro-control unit.
In the embodiment of the disclosure, after the front-end preprocessing is performed on the operation data by the data preprocessing module, the operation data can be further divided into two types of real-time data and non-real-time data by the data sensing layer functional module 112, and the real-time data and the non-real-time data are transmitted to the data cloud center by using the data transmission channel according to the types, so that support is provided for the subsequent processing of the operation data and cloud edge cooperative service. The data transmission channel may be a wired network or a wireless network.
In the embodiment of the disclosure, both the abnormal data and the non-abnormal data need to be uploaded to a data cloud center for further analysis and processing.
Text, image or video material in the real-time data can be displayed in real-time in a subsequent service, and the non-real-time data can be used in subsequent processing and calculation processes.
Non-anomalous data is stored in the data cloud center to a storage area in the data storage layer function module to further perform anomaly alerting based on the non-anomalous data when needed.
As shown in fig. 3, the data storage layer function module 121 of the data cloud center 120 performs storage, scheduling and management of operation data through a logic resource server, a storage device and a network, so as to implement management of monitoring data resources, that is, unified storage, unified scheduling and unified management of operation data.
As shown in fig. 3, when the middle data platform service layer function module 122 of the data cloud center 120 performs construction, deployment, operation and call of middle data platform service of urban rail transit data, any one of the following operations may be performed: intelligent data mapping processing, business theme domain construction, data processing algorithm development and machine learning advanced model driving.
The data may be generated in different information systems and various data sources, and the physical positions of the data also vary with the different system structures, for example, the data may be centralized, distributed or mixed, so that the data sharing among the systems is not facilitated, the comprehensive utilization of the data is not facilitated, and the availability of the data is reduced. The intelligent data mapping can realize data sharing, exchange and basic support in the system so as to integrate information resources and maximize data value.
A topic domain is typically a collection of data topics that are closely related, and business topic domain construction refers to the construction of business data topics that are related to a business.
Machine learning is a multi-domain interdisciplinary, involving multiple disciplines such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory, and the like. It is specially studied how a computer simulates or implements learning behavior of a human to acquire new knowledge or skills, and reorganizes existing knowledge structures to continuously improve own performance. It is an artificial intelligence core, which is the fundamental way to make computers intelligent. Machine learning models are classified into two main categories, supervised learning and unsupervised learning, according to the types of data that can be used. Supervised learning mainly includes models for classification and for regression. The model for classification includes: linear classifier, support vector machine, naive bayes, K-nearest neighbor, decision tree, integrated model, etc., the models for regression include: linear regression, support vector machines, K-nearest neighbors, regression trees, integrated models, etc. Unsupervised learning mainly includes: data clustering, data dimension reduction and the like.
In addition, the data center service layer module can also realize complex business extension analysis, data value extraction and rendering. Specifically, the data center service layer module realizes the functions of complex service extension analysis, data value extraction and rendering by the following units:
and the data processing layer unit is used for carrying out mapping processing and theme domain construction on the operation data.
And the data calculation layer unit is used for carrying out rail transit operation state early warning analysis and rail transit digital twin system construction according to the operation data after mapping processing.
And the data auditing unit is used for auditing the data output by the data calculation layer unit.
The data storage unit is used for storing the data which passes the auditing by the data auditing unit so as to be called by the data application layer function module.
As shown in fig. 3, the data processing layer unit is configured to perform intelligent data mapping processing and theme domain construction on data transmitted by the data monitoring layer functional module and the data sensing layer functional module, and the data computing layer unit is driven by a data processing algorithm development and a machine learning advanced model to perform rail transit operation state early warning analysis and rail transit digital twin system construction, and perform complex service extension analysis, data value refinement and rendering by using the algorithm and the advanced model.
In addition, the data application layer function module 123 provides data and services to the data business application subsystem by creating a customized technical service module based on the function module and the function component. The data application layer functional module can support the realization of business functions such as intelligent passenger service, intelligent operation production management, intelligent operation maintenance safety and the like by providing data application service for the data business application subsystem.
In the embodiment of the present disclosure, the data storage layer function module may be a function module of an IaaS (Infrastructure AS A SERVICE) layer of cloud service, the data Platform service layer function module may be a function module of a PaaS (Platform AS A SERVICE) layer of cloud service, and the data application layer function module may be a function module of a SaaS (Software AS A SERVICE) layer of cloud service. Under the support of the functional modules, the data service application subsystem can provide intelligent application services of multiple scenes of rail transit.
As shown in fig. 4, in an embodiment of the present disclosure, the rail transit passenger service module of the data service application subsystem is configured to provide passenger integrated service support, and specifically includes at least one of the following services: guiding passengers, providing station navigation, planning intelligent travel paths of the passengers, intelligently monitoring passenger flows of the stations and publishing public information; the operation management module is used for providing operation, maintenance and management service support and specifically comprises at least one of the following services: transport organization management, operation production management, intelligent station management, comprehensive intelligent operation maintenance management and emergency management; the asset management module is used for providing asset closed-loop management business support and specifically comprises at least one of the following services: enterprise asset management, equipment asset management, and full lifecycle closed loop management.
The data service application subsystem 130 may provide intelligent application services for multiple scenarios of rail transit based on data and services provided by the PaaS data middle station service layer and the SaaS data application layer.
In the embodiment of the disclosure, monitoring data of the internet of things is transmitted to a rail transit data cloud center, and by utilizing cloud edge cooperative computing service in combination with a three-dimensional virtual reality technology, a real-time monitoring configuration technology and a video recognition intelligent analysis technology, the multi-dimensional operation state real-time visualization of stations, lines and networks can be constructed, so that rail transit intelligent application such as multi-scene business analysis, multi-mode simulation, multi-professional linkage fusion and the like based on the real-time monitoring of the internet of things and a cloud center data model is realized. The method solves the challenges of massive heterogeneous connection, service instantaneity, service intelligence, data interoperability and security and privacy protection of the Internet of things system in a high-distributed scene, and is particularly suitable for application scenes with special requirements such as low time delay, high bandwidth, high reliability, massive connection, heterogeneous convergence, local security privacy protection and the like.
According to the technical scheme, the urban rail transit data automation, the intellectualization and the high-efficiency collection and the convergence based on the Internet of things are realized, the urban rail transit data model construction and the cloud edge collaborative data service calculation based on the cloud computing center are realized, the urban rail transit multi-scene, multi-specialty and intelligent application based on the cloud platform system is realized, the accuracy and the instantaneity of data monitoring can be comprehensively realized through the cloud platform system, the high efficiency and the intelligence of the complex application of the data are realized, the data service and the value promotion are realized, and the rail transit data value change capability is realized.
As shown in fig. 5, in an embodiment of the disclosure, for a testing method of an urban rail transit data cloud platform system based on internet of things monitoring, the testing method includes the following steps:
The internet of things monitoring subsystem 110 performs two functions. Firstly, the detection function is realized through the data monitoring layer function module 311 of the internet of things a, after the real-time monitoring data acquired by the sensing sensor 312 a is cleaned, processed and classified, the real-time monitoring data is transmitted to the b-edge computing micro-control unit 313 through the wireless/wired communication/communication unit, the b-edge computing micro-control unit executes the step 341 to judge whether the state of the real-time monitoring data is abnormal, and when the state of the real-time monitoring data is abnormal, the state is displayed in the d display unit 316 through the c-cloud edge alarm unit 315. Secondly, a transmission function is realized through the B data sensing layer functional module 314, specifically, abnormal state data and non-abnormal state data are uploaded to the data cloud center 120 through the B data sensing layer functional module by utilizing a data transmission channel for further analysis, so that cloud-edge coordination is realized.
The data cloud center 120 realizes three functions after receiving the monitoring data uploaded from the internet of things monitoring subsystem 110. First, the C IaaS data storage layer function module 321 logically resources IT infrastructures such as a plurality of servers, a storage, a network, and the like, and uniformly stores, schedules and manages the monitored data resources. Secondly, the service layer function module 322 in the D PaaS data realizes the convergence processing, mapping processing, theme domain construction, rail traffic operation state early warning analysis, rail traffic digital twin system construction and the like of various monitoring data through the E data processing layer unit and the f data calculating layer unit 323, and then the data is submitted to the g data auditing unit 324 for auditing, the data auditing unit 324 executes the step 342 to judge whether the data passes the auditing, and the data after the auditing is stored through the h data storage unit 325 for being used by the E SaaS data application layer function module 326. Thirdly, the E SaaS data application layer functional module realizes data application service based on the PaaS data middle platform service layer and the SaaS customized, modularized and componentized technical service.
The data service application subsystem 130 provides service support based on the PaaS data middle platform service layer by means of the SaaS data application layer, and achieves functions of passenger comprehensive service support, intelligent operation and maintenance management service support, rail traffic asset management service support and the like through the F data service application support function module 331.
According to the urban rail transit cloud platform system, the data cloud center is adopted to store the operation data acquired by the data monitoring subsystem, the service is realized based on the operation data, the data and the service are provided for the data service application subsystem, the detection, the processing and the application of the operation data of urban rail transit are realized, and the instantaneity and the sharing performance in the data acquisition and the application process of urban rail transit operation are improved.
Finally, it should be noted that: the above embodiments are merely for illustrating the technical solution of the present disclosure, and are not limiting thereof; although the present disclosure has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present disclosure.
Claims (8)
1. An urban rail transit cloud platform system, comprising:
the internet of things monitoring subsystem comprises a data monitoring layer functional module and a data sensing layer functional module, and is used for acquiring operation data of the whole life cycle of urban rail transit operation and transmitting the operation data to a data cloud center; the data monitoring layer functional module comprises a perception sensor, an edge calculation micro-control unit and a cloud edge alarm unit, wherein the perception sensor is used for acquiring real-time operation data of urban rail transit operation, the edge calculation micro-control unit is used for processing the real-time operation data and generating an urban rail transit data monitoring state through the cloud edge alarm unit, and the urban rail transit data monitoring state comprises an abnormal state and a non-abnormal state;
The data cloud center comprises a data storage layer functional module, a data center service layer functional module and a data application layer functional module, and is used for storing and managing the operation data transmitted by the Internet of things monitoring subsystem, realizing and managing service based on the operation data and providing data and service for the data business application subsystem;
the data service application subsystem comprises a rail transit passenger service module, an operation management module and an asset management module, and is used for providing train operation information for passengers, providing operation management service support for urban rail transit operation and asset management service support for the urban rail transit operation according to data and services provided by the data cloud center;
the data center service layer functional module is used for constructing, deploying, operating and calling center service of urban rail transit data by the following operations:
Intelligent data mapping processing, business theme domain construction, data processing algorithm development and machine learning advanced model driving;
the data center service layer function module comprises:
The data processing layer unit is used for carrying out mapping processing and theme domain construction on the operation data;
the data calculation layer unit is used for carrying out rail transit operation state early warning analysis and rail transit digital twin system construction according to the operation data after mapping processing;
The data auditing unit is used for auditing the data output by the data calculation layer unit;
And the data storage unit is used for storing the data which is checked by the data checking unit and is passed by the data checking unit so as to be called by the data application layer function module.
2. The system of claim 1, wherein the data monitoring layer functional module comprises an internet of things sensor that accesses the internet of things using any one of the following access methods:
Ethernet, wireless fidelity, low power wide area network, cellular telephone, satellite access system, and bluetooth;
The Internet of things sensor comprises at least one of the following sensors:
positioning sensors, radio frequency identification RFID devices, infrared sensors, and laser scanners.
3. The system of claim 1, wherein the internet of things monitoring subsystem further comprises a data preprocessing module configured to perform front-end preprocessing on the operation data collected by the data monitoring layer function module, the front-end preprocessing including performing data aggregation and fusion operations on the operation data by at least one of:
data aggregation, data fusion, data cleaning, data classification and data abnormality judgment.
4. The system of claim 1, wherein the data sensing layer function module is further configured to divide the operational data into two categories, real-time data and non-real-time data, and transmit the real-time data and the non-real-time data to the data cloud center by category.
5. The system of claim 1, wherein the data storage layer function module performs storage, scheduling, and management of the operational data through a server, a storage device, and a network.
6. The system of claim 1, wherein a data application layer function module provides data and services to the data business application subsystem by creating a customized technology service module based on the function module and the function component.
7. The system of claim 3, wherein the internet of things monitoring subsystem further comprises an alarm module for generating an alarm signal to alert when the data preprocessing module determines that abnormal data exists after the data abnormality determination.
8. The system of claim 1, wherein the rail transit passenger service module is configured to provide at least one of the following services: guiding passengers, providing station navigation, planning intelligent travel paths of the passengers, intelligently monitoring passenger flows of the stations and publishing public information;
the operation management module is configured to provide at least one of the following services: transport organization management, operation production management, intelligent station management, comprehensive intelligent operation maintenance management and emergency management;
The asset management module is configured to provide at least one of the following services: enterprise asset management, equipment asset management, and full lifecycle closed loop management.
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