CN211604175U - Data acquisition system based on urban mobile network - Google Patents

Abstract

The utility model discloses a data acquisition system based on an urban flow network, comprising: a bus real-time and prediction subsystem, a taxi temporal distribution subsystem, and a road pedestrian and non-motor vehicle monitoring subsystem, all of which are connected to a monitoring platform; The data obtained by the bus real-time and prediction subsystem, taxi temporal distribution subsystem, road pedestrian and non-motor vehicle monitoring subsystem are fitted in space and time, and the temporal and spatial distribution map of the city is established with fine granularity. The data of the three are fitted in space and time, and the temporal and spatial distribution map of the city is established with fine granularity, and then an urban flow network system is constructed to provide early warning of public conflict times.

Description

A Data Acquisition System Based on Urban Flow Network

technical field

The present disclosure relates to the field of computer technology, in particular to a data acquisition system based on an urban flow network.

Background technique

In the field of urban transportation in China, the contradiction between traditional taxis and special cars based on the Internet is becoming more and more serious. Emergencies in the taxi industry have become a thorny problem for local government management departments. Emergency early warning of urban transportation and related public events There is a lag in the mechanism. A city’s public passenger transport management service center has also become the first case in China’s taxi industry brought to court by a special car operator. The key is that it is very likely to induce greater conflicts in the whole society. Relying on traditional experience-based management, simple confiscation and post-event contingency plans has been unable to form an effective and timely response to related mass emergencies. Therefore, if we can build an early warning information model that triggers such emergencies, find out the key factors and emergencies. At the same time, if the potential emergencies that may exist can be mined from a large amount of time series data generated by travel, it will undoubtedly have high theoretical value and practical significance for conflict prevention.

At present, the hardware system architecture of urban data collection is not perfect, and it is impossible to effectively collect bus data, taxi data and pedestrian intersection data effectively. Therefore, it is necessary to build a complete urban data collection system.

Utility model content

In order to solve the deficiencies of the prior art, the present disclosure provides a data collection system based on an urban flow network, which can realize the relevant early warning of the public conflict time.

In order to achieve the above object, the application adopts the following technical solutions:

A data acquisition system based on an urban flow network, comprising: a monitoring platform, the monitoring platform communicates with a bus real-time and prediction subsystem, a taxi temporal distribution subsystem, and a road pedestrian and non-motor vehicle monitoring subsystem;

The bus real-time and prediction subsystem includes a bus server, and the bus server communicates with the location information collection module and the card swipe information collection module respectively;

The location information collection module on the bus is used to collect and monitor the longitude and latitude position information data of the bus, and the card swipe information collection module on the bus is used to collect and monitor the card swipe information data on the bus;

The taxi temporal distribution subsystem includes a taxi server, and the taxi server is in communication connection with the intelligent device of the taxi Internet of Vehicles;

The intelligent equipment for the Internet of Vehicles installed in the taxi includes a transfer box, a meter, a service evaluator, and a reversing camera connected to the on-board terminal. The on-board terminal is connected to the smart ceiling light screen and the dispatch information screen through the transfer box. The data collected by the smart dome light screen, dispatch information screen, meter, service evaluator, and reversing camera are transmitted to the taxi server. At the same time, the built-in GPS positioning device and speed measurement device of the vehicle terminal can transfer the real-time location information and speed data of the taxi. sent to the taxi server;

The non-motor vehicle monitoring subsystem includes a non-motor vehicle server, the non-motor vehicle server is connected in communication with the real-time monitoring module, and the road intersection real-time monitoring modules distributed at the urban road intersections send the monitored data to the non-motor vehicle through the data transmission module. server.

In a further technical solution, the monitoring platform includes a server, and the server is connected to the display device and the input device through a communication module.

Compared with the prior art, the beneficial effects of the present disclosure are:

In the technical solution of the present disclosure, the data collection and transmission of buses, taxis and pedestrian intersections are realized through the hardware architecture, and the display of the three subsystems is realized on the server.

Among the three subsystems disclosed in the present disclosure, public transportation embodies the most stable vehicle operation mode in the city, and is also the "aorta" of urban traffic; while the taxi system is the dynamic tracking of urban vehicles, which can reflect the relevant vehicle dynamics of the entire city, Including regional distribution, average speed, hot spots, etc.; road pedestrians and non-motor vehicles reflect the relevant traffic flow at the intersection. The hardware structure of the three subsystems is constructed to become an urban mobility network for early warning. The three subsystems are integrated with each other, and their functions complement each other.

Description of drawings

The accompanying drawings that form a part of the present application are used to provide further understanding of the present application, and the schematic embodiments and descriptions of the present application are used to explain the present application and do not constitute improper limitations on the present application.

Fig. 1 is the system schematic diagram of the embodiment of the present application;

Fig. 2 is the architecture diagram of the bus real-time and prediction subsystem of the embodiment of the present application;

Fig. 3 is the architecture diagram of the taxi temporal distribution subsystem of the embodiment of the present application;

FIG. 4 is a structural diagram of a road pedestrian and a non-motor vehicle subsystem according to an embodiment of the present application.

Detailed ways

It should be noted that the following detailed description is exemplary and intended to provide further explanation of the application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

It should be noted that the terminology used herein is for the purpose of describing specific embodiments only, and is not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural as well, furthermore, it is to be understood that when the terms "comprising" and/or "including" are used in this specification, it indicates that There are features, steps, operations, devices, components and/or combinations thereof.

In a typical implementation of the present application, as shown in FIG. 1 , a data acquisition system based on an urban mobile network is provided, including a bus real-time and prediction subsystem 4, a taxi time and a bus that are all connected to the monitoring platform 1. State distribution subsystem 5, road pedestrian and non-motor vehicle monitoring subsystem 6.

The urban mobility network is composed of three systems: bus real-time and prediction subsystem, taxi temporal distribution subsystem, road pedestrian and non-motor vehicle subsystem, which are embedded and complementary in geography. To a certain extent, complex road conditions can be restored and modeled. The three subsystems are embedded and complementary. Buses provide urban passenger flow, taxis can reflect real-time traffic information of private cars, and road pedestrians and non-motor vehicles can reflect the situation at intersections. The three subsystems form an urban mobility network to complement each other and improve the mobility of urban roads.

Among them, the bus real-time and prediction subsystem 4 includes a location information collection module 7, a card swipe information collection module 8, and an arrival information collection module. The information collection module is used to realize the collection and monitoring of the card swiping information data on the vehicle. The arrival information collection module is used to collect the time information of vehicles arriving at the station, which is convenient for real-time arrival prediction.

As shown in Figure 2, the GPS positioning device 7-1 in the location information acquisition module collects the latitude and longitude information in real time when the bus is running, and sends the data information to the bus server for data processing through the data transmission device 7-2, When the bus arrives at the station, the data sending device 7-2 sends information such as arrival time and departure time to the bus server. When the bus arrives at the station, passengers can get on the bus by swiping the RFID card 8-1 or scanning the QR code 8-2, and this information is also sent to the bus server through the data sending device 7-2 for data processing.

The bus server will train all the processed structured historical data through the LSTM neural network, and then make multi-stop, multi-line and multi-variable arrival predictions for the bus vehicles running during working hours. In order to obtain the accurate arrival time, the problem of the precise time value of traffic flow is relieved first, and the scheduling of bus vehicles and the predicted value of arrival at the station play an auxiliary role in the early warning of public conflicts.

In this way, the bus arrival time is accurate to the minute. Once a bus has an abnormal signal or fails to arrive on time, and exceeds its corresponding delay threshold time point, it can be considered that a public conflict or emergency has occurred.

Through the bus real-time and prediction subsystem, the reverse scheduling of bus lines can also be reversed to relieve traffic pressure and reduce the occurrence of public conflict events from the aspect of alleviating the flow of people.

Specifically, the bus location information collection module is a GPS real-time positioning device. The card swiping information acquisition module is an HMXF-A2-CZ type QR code and RFID card swiping integrated module. It has two power input terminals and two digital signal output terminals, and sends data to the bus company data settlement center through the wireless base station. Carry out relevant information processing. The arrival information collection module is a wireless 433 base station, which determines whether the bus arrives at the station by collecting the distance information of the bus. The information acquisition module has two power input terminals, four digital signal input terminals, and one digital signal output terminal.

The taxi temporal distribution subsystem 5 includes an intelligent device 9 for the Internet of Vehicles for taxis.

The intelligent equipment of the Internet of Taxi is used to collect information such as the location and running status of the taxis; the analysis module of the road traffic space-time situation mainly analyzes the distribution situation of the taxis in the city.

The intelligent equipment of the taxi Internet of Vehicles is the equipment loaded on the taxi, such as the TGR20 vehicle terminal and the YGL60 transfer box, the meter, the advertising screen, the intelligent dome light screen, the dispatching information screen, the service evaluator, and the reversing camera.

As shown in Figure 3, taxis are used to reflect the basic situation of the city's traffic flow. When the taxis take orders on the road, the TGR20 vehicle terminal 9-1 will display the smart dome light screen 9-3 and the dispatch information screen 9-4. , meter 9-5, service evaluator 9-6, reversing camera 9-7 and other equipment information is transmitted to the taxi server, YGL60 transfer box 9-2 is connected to the smart dome light screen 9-3 and the scheduling information screen 9-4 And transfer the data to the TGR20 vehicle terminal. The GPS positioning device and the speed measuring device built in the vehicle terminal 9-1 will send the real-time location information and speed of the taxi and other related data to the taxi temporal distribution subsystem 5 . After all the data is analyzed and processed, relevant calculations are performed through temporal analysis of road traffic, and a regional distribution map is generated, which will reflect the regional division of taxis, as well as the travel laws and travel paths of Jinan citizens.

The taxi server is composed of communication gateway, communication center, database, WEB application server, map server, monitoring and dispatching seats and network equipment (router, firewall, switch) and other equipment.

The intelligent vehicle terminal that collects taxi data on the taxi is also the integration of hardware equipment, including intelligent dome light screen, TGR20 vehicle terminal, GPS positioning equipment, speed measurement equipment, etc.

Taxi server: The specific functions include: traffic situation analysis based on big data: including building road network elements, building a real-time traffic network, collecting and publishing road conditions, etc.

Travel area division: According to the density of urban citizens' travel and the distribution of communities, the city is divided into 607 areas, and the Jinan area is gridded.

Analysis of travel laws in hotspot areas: Calculate the distribution map of vehicle dispatching areas within a time period, in order to determine which areas have more departures and which areas have fewer departures.

Regional distribution map: Contains five distribution maps:

Departure area distribution map: Study the distribution map of the number of departure areas at each time point. The distribution map of the departure area can fully indicate which area has more departures and which area has less departures. Areas with a large number of areas indicate that the area has more demand for cars in the area, and areas with a small number of areas indicate that the demand for vehicles in the area is less.

Destination area distribution map: Study the distribution map of the number of regional destination arrivals at each time point. The distribution map of the destination area can fully indicate which area has more people and which area has fewer people. By comparing the distribution map of the destination area in different time periods, you can study the areas where people go to the concentrated area, analyze the flow direction and concentration of the people, and provide data support for the setting of travel sites.

O-D map of hot-spot departure area (abbreviation for Origin and Destination): The area with the largest number of departures is counted, the top 20 are selected, and then the O-D map is drawn according to the destination. By studying the destination distribution of the hotspot departure area, it is possible to study the destination of the people from the hotspot departure area. According to the distribution of different time points, the travel destination characteristics of the people from the hotspot departure area can be studied in detail.

O-D map of hotspot destination area: count the areas with the largest number of destinations, select the top 20, and then draw an O-D map according to the departure point. By studying the distribution of departure places in the hotspot destination area, the characteristics of the travel destinations of the people from the hotspot departure area can be studied.

Travel O-D Trend Statistics Chart: An O-D chart that counts the number of taxis from one area to another. By analyzing the distribution of the number of trips from one area to another in different time periods, you can fully understand the O-D flow trend of trips.

Specifically, the spatiotemporal situation analysis of road traffic is to establish an urban road network and a real-time traffic network on GIS, and then analyze the data information of the taxi through the information collection of the intelligent equipment of the taxi network, including the division of the taxi area, To calculate the regional distribution of taxi travel; travel law analysis, to analyze the travel law and travel path of Jinan citizens. In view of the fact that emergencies in the taxi industry have become a thorny problem that plagues local government management departments, the urban mobility network system of the present disclosure can monitor and give early warning of relevant public conflicts through the taxi temporal distribution subsystem, which can prevent social conflicts. Early warning of factors and quick response.

The road pedestrian and non-motor vehicle monitoring subsystem 6 includes real-time monitoring equipment at road intersections. The real-time monitoring equipment at the road intersection is used to obtain the information of pedestrians and non-motor vehicles at the intersection; the target detection and recognition module is used to detect real-time targets such as pedestrians and non-motor vehicles at the intersection and calculate the traffic flow at the intersection.

As shown in FIG. 4 , the real-time monitoring modules 10 are distributed at road intersections in the city, wherein the high-definition camera 10-1 sends the monitored data to the non-motor vehicle server through the data sending module 10-2, and the target detection The identification module detects road pedestrians and non-motor vehicles in real-time monitoring through the YOLO neural network, and analyzes the target. Through the urban intersection flow map module, the detected target information is used to generate the urban intersection pedestrian and non-motor vehicle flow map.

The hardware of the target detection and recognition module can be a camera specifically, and the data is transmitted to the server of the non-motor vehicle, and the server is used for target detection and recognition.

The non-motor vehicle server generates the traffic map of the urban intersection, which means the detection of the number of road pedestrians and non-motor vehicles at the urban intersection after the server detects the target through the detection of the camera.

The real-time detection module at the intersection is used to obtain the information of pedestrians and non-motor vehicles at the intersection; the target detection and recognition module is used to detect real-time targets such as pedestrians and non-motor vehicles at the intersection and calculate the traffic flow at the intersection. Among them, the real-time monitoring module of the road crossing is a 5 million POE star-level high-definition camera and its related accessories. The pedestrian and non-motor vehicle analysis module uses the YOLO3-based end-to-end target detection algorithm to detect the number of pedestrian non-motor vehicle targets. Traffic information at the intersection.

The road traffic spatiotemporal situation analysis module is a data analysis platform built for road mobile vehicles, including road network element analysis, real-time traffic construction, hot spot map and other related sub-modules. The real-time monitoring module for road crossings is Hikvision's 5 million POE star-level high-definition camera and its related accessories. The pedestrian and non-motor vehicle analysis module is an end-to-end target detection algorithm based on YOLO3, which detects the number of pedestrians and non-motor vehicles.

When the bus real-time and prediction subsystem 4 monitors abnormal information, the taxi spatiotemporal distribution subsystem 5 monitors abnormal information, and the road pedestrian and non-motor vehicle subsystem 6 monitors abnormal information, the overall urban mobility network system 1 platform Analysis is carried out, the indicators of environmental impact factors are superimposed, and through the public conflict early warning execution mechanism, according to the prescribed process, relevant departments can take relevant measures on the spot in time after receiving the public conflict early warning signal.

The environmental impact factor indicators are mainly the time series generated by the temperature, whether there is rainfall (snowfall), the wind strength and the time in the urban flow network system.

Fit a nonlinear regression to the time period using these three influencing factors to match the data for each day.

The specific embodiment of the present disclosure adopts the combined modeling of public transportation, taxis, road pedestrians and non-motor vehicles to construct a dynamic model of the urban flow network, including the real-time and prediction subsystem of buses, and the temporal distribution subsystem of taxis. The system and road pedestrian and non-motor vehicle monitoring subsystem, and then superimpose the corresponding environmental impact factor indicators, so as to realize the conflict early warning based on the urban mobility network.

Combined modeling of public transportation, taxis, road pedestrians and non-motorized vehicles: Public transportation and taxi data are imported into the map model (AutoNavi or Baidu SDK) in parallel to reflect the driving status of vehicles on the road. The road pedestrian and non-motor vehicle data are imported into the map model. Pedestrian and non-motor vehicle data are reflected at the intersection.

About Dynamic Models: Using time series to visualize dynamic models of spatiotemporal urban flow networks. That is to say, the fitting sequence is constructed by using environmental factors and time series, and the dynamic model of the urban flow network, just like in the video, buses, taxis, pedestrians and non-motor vehicles will move dynamically on the map according to the particles. Show and move. Visualization uses kepler.gl technology, an open source geospatial toolkit.

Among the three subsystems, public transportation reflects the smoothest vehicle operation mode in the city and is also the "aorta" of urban traffic; while the taxi system is the dynamic tracking of urban vehicles, which can reflect the relevant vehicle dynamics of the entire city, including regional distribution. , average speed, hot spots, etc.; road pedestrians and non-motor vehicles reflect the relevant traffic flow at the intersection. The three subsystems are integrated with each other, their functions are complementary, and they complement each other. The data of the three are fitted in space and time, and the temporal and spatial distribution map of the city is established with fine granularity, and then an urban flow network system is constructed to provide relevant early warnings for public conflict times. .

Public transportation embodies the most stable vehicle operation mode in the city, while the taxi system is the dynamic tracking of urban vehicles, which can reveal the vehicle dynamics in the city, and the pedestrian status on the road can be obtained by monitoring the target pedestrian detection at the intersection. The three complement each other and complement each other. The data of the three are fitted in space and time, and the temporal and spatial distribution map of the city is established in a fine-grained manner, and an urban flow network system is constructed to provide early warning of public conflict times.

Through the fitting data sequence of time series and environmental influencing factors, the distribution status of the above three (buses, taxis, pedestrians at intersections and non-motor vehicles) on the three-dimensional map is constructed, that is, the urban flow network system is constructed.

In the technical solution of the present disclosure, when the bus real-time and prediction subsystem monitors abnormal information, the taxi spatiotemporal distribution subsystem monitors abnormal information, and the road pedestrian and non-motor vehicle monitoring subsystem monitors abnormal information, the three subsystems monitor the abnormal information. Modeling and analysis is carried out through the overall urban mobility network system to warn of public conflicts that occur, so that relevant departments can take timely measures on the spot after receiving the conflict early warning signals to prevent the people and public properties from being hurt and lost.

The above descriptions are only preferred embodiments of the present application, and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application shall be included within the protection scope of this application.

Claims (2)

1. A data acquisition system based on a city flow network is characterized by comprising: the monitoring platform is respectively communicated with the bus real-time and prediction subsystem, the taxi temporal distribution subsystem and the road pedestrian and non-motor vehicle monitoring subsystem;

the bus real-time and prediction subsystem comprises a bus server, and the bus server is respectively communicated with the position information acquisition module and the card swiping information acquisition module;

the system comprises a position information acquisition module arranged on a bus and used for acquiring and monitoring longitude and latitude position information data of the bus, and a card swiping information acquisition module arranged on the bus and used for acquiring and monitoring the card swiping information data of a bus;

the taxi temporal distribution subsystem comprises a taxi server, and the taxi server is in communication connection with the taxi networking intelligent equipment;

the taxi networking intelligent equipment arranged in the taxi comprises a switching box, a meter, a service evaluator and a reversing camera which are connected with a vehicle-mounted terminal, wherein the vehicle-mounted terminal is connected to an intelligent ceiling lamp screen and a scheduling information screen through the switching box;

the non-motor vehicle monitoring subsystem comprises a non-motor vehicle server, the non-motor vehicle server is in communication connection with the real-time monitoring module, and the real-time monitoring module is distributed at the road junction of the city road and sends monitored data to the non-motor vehicle server through the data sending module.

2. The data acquisition system based on the urban mobile network as claimed in claim 1, wherein the monitoring platform comprises a server, and the server is connected with the display device and the input device through a communication module.

Images