CN114996373A - Public transportation big data system, method and storage medium based on geographic information system - Google Patents

Abstract

The invention relates to the technical field of public transport, in particular to a public transport big data system based on a geographic information system, a method and a storage medium, wherein the system comprises: the data acquisition module is used for acquiring bus basic data of different data sources and carrying out standardized processing on the bus basic data; the data aggregation module is used for overlaying and marking the bus basic data on the map basic geographic information to form a bus professional map; the main database is used for storing bus basic data, map basic geographic information and bus professional maps; and the function application module is used for analyzing the big data according to the basic data of the bus, setting a corresponding bus professional map according to the analysis result and applying the map. According to the scheme, one-dimensional to multi-dimensional conversion of the data is realized, the data acquisition, management, analysis and the like are facilitated, a professional bus professional map can be formed, and unified management and application of the data are realized.

Description

Public transportation big data system, method and storage medium based on geographic information system

Technical Field

The invention relates to the technical field of public transport, in particular to a public transport big data system based on a geographic information system, a method and a storage medium.

Background

The urban public transport system is an organic whole consisting of various urban public transport modes, and is called a public transport system for short. The public system can be divided into two subsystems, one is a public transportation means and facilities, and the other is public transportation planning and operation management.

Public traffic system provides convenience for people's trip, and it is as road transportation trade, and the core is that the service that provides is spatial displacement, and is closely relevant with geographic information system, even trade on the map, but current public traffic system still has many problems, for example: vehicles of different companies are on the same road, the speed limit standards are not uniform, and the driving of the vehicles is influenced; the station settings of the lines are not uniform, so that the stations are inconvenient to find; the existing map does not visually display the bus route distribution, so that passengers can not visually know the driving route of the bus, and more targeted selection is performed.

The main reasons for these problems are that various data of the public transportation system are not centralized, asynchronous and have different sources; however, the existing data has many data outlets, non-uniform calibers and preferential acquisition ways, so that the problem of incomplete and inaccurate data exists even if the data is acquired, and further, the subsequent analysis result deviates from the actual result, so that the flow for managing according to the data is dispersed, the working specification is non-uniform, and a professional public transportation map which is possibly dynamically updated cannot be formed.

Disclosure of Invention

One of the purposes of the invention is to provide a public transportation big data system based on a geographic information system, which can form a professional public transportation professional map and realize unified data management and application.

The invention provides a basic scheme I: public transit big data system based on geographic information system includes:

the data acquisition module is used for acquiring bus basic data of different data sources and carrying out standardized processing on the bus basic data;

the data aggregation module is used for overlaying and labeling the bus basic data on the map basic geographic information to form a bus professional map;

the main database is used for storing bus basic data, map basic geographic information and bus professional maps;

and the function application module is used for analyzing the big data according to the basic data of the bus, setting a corresponding bus professional map according to the analysis result and applying the map.

The beneficial effects of the first basic scheme are as follows: the data acquisition module acquires bus basic data of different data sources, performs standardized processing on the bus basic data, and stores the bus basic data into main data so as to be convenient for other modules or systems to call, and the standardized data have uniform format, so that a user can obtain the data of the same standard to the maximum extent, the problem that the data is not comprehensive and inaccurate is solved, and the subsequent analysis result has no deviation from the actual result;

the data aggregation module superposes and marks basic bus data on the basic map geographic information to form a special bus map, so that various data can be displayed in one map, the application in scheduling is facilitated, and the working standard is unified;

the function application module performs big data analysis according to the basic data of the bus, performs corresponding bus professional map setting according to the analysis result, and applies the map, thereby realizing accurate and standardized application of data from different sources.

The scheme realizes the one-dimensional to multi-dimensional conversion of the data, is more favorable for data acquisition, management, analysis and the like, can form a professional bus professional map, and realizes unified management and application of the data.

Further, the data acquisition module acquires public transportation basic data and comprises: collecting basic bus data on site, manually counting the basic bus data and calling one or more types of available basic bus data in ERP;

the bus basic data comprises: infrastructure data, operational data, security data, engineering data, and service data; wherein the infrastructure data comprises: site data, station yard data, charging pile data and maintenance factory data;

operational data, including: line data, passenger flow data and shift data;

secure data, comprising: accident data and speed limit data;

crew data comprising: vehicle data and maintenance vehicle data;

service data, comprising: complaint data.

Has the advantages that: various data acquisition modes are adopted to meet different acquisition requirements; the bus basic data comprises infrastructure data, operation data, safety data, crew data and service data so as to meet the data requirements of different applications.

Further, the data aggregation module is used for calling map basic geographic information and bus basic data in a main database;

on the basis of the map data, layers of infrastructure data, operation data, safety data, engineering data and service data are sequentially added to form a bus professional map.

Has the beneficial effects that: and multi-layer superposition realizes one-dimensional to multi-dimensional conversion of data.

Further, the function application module includes:

the operation submodule is used for carrying out big data analysis according to the passenger flow data and acquiring real-time passenger flow data and average passenger flow data of each station in the month and day;

overlaying and marking station data, station yard data, line data, real-time passenger flow data, monthly and daily passenger flow data and shift data on the map basic geographic information to form a professional map of the bus operation;

acquiring a stop selection signal, and displaying stop data, station yard data, line data, real-time passenger flow data, daily average passenger flow data and shift data of a selected stop on a professional map of the operating bus according to the stop selection signal;

the service submodule is used for carrying out big data analysis according to the complaint data in the preset time period and obtaining the complaint quantity data and the complaint reason data of each site;

overlaying and marking station data, station yard data, line data, complaint quantity data and complaint reason data on the map basic geographic information to form a service bus professional map;

acquiring a stop selection signal, and displaying stop data, station yard data, line data, a preset time period, complaint quantity data and complaint reason data of a selected stop on a service bus professional map according to the stop selection signal;

the safety submodule is used for carrying out big data analysis according to accident data in a preset time period and acquiring road safety accident data, personal injury accident data and other accident data of each station;

overlaying and marking station data, line data, road safety accident data, personal injury accident data and other accident data on the map basic geographic information to form a safety bus professional map;

acquiring a stop selection signal, and displaying stop data, station yard data, line data, running data, a preset time period, data of road safety accidents, data of personal injury accidents and other accident data of a selected stop on a professional map of the safe bus according to the stop selection signal;

the bus service sub-module is used for analyzing big data according to the bus basic data to obtain position data, radiation range data, radiation bus route data, bus data, main vehicle type data and bus service point basic condition information of each bus service point; wherein the locomotive service points are station yards, charging piles and maintenance plants;

superposing and marking position data, radiation range data, radiation bus line data, bus data, main bus type data, basic situation information of the engineering point, vehicle data and maintenance bus data on the map basic geographic information to form a professional map of the engineering bus;

acquiring a maintenance point selection signal, and displaying position data, radiation range data, radiation bus line data, bus data, main vehicle type data, maintenance point basic condition information, vehicle data and maintenance vehicle data of a selected maintenance point on a professional map of a maintenance bus according to the maintenance point selection signal;

the manpower submodule is used for carrying out big data analysis according to the station data and the line data and obtaining the employee address statistical data of each line departure point, and comprises: the system comprises departure line data, employee number data within a preset distance of a departure point and employee number data outside the preset distance of the departure point;

on the map basic geographic information, station data, station yard data, line data and employee address statistical data of departure points of all lines are superposed and labeled to form a manpower bus professional map;

and acquiring a departure point selection signal, and displaying station data, station yard data, line data and employee address statistical data of the selected departure point on the manual bus professional map according to the departure point selection signal.

Has the beneficial effects that: different functional modules realize different applications through different public transportation basic data so as to meet the comprehensive management of the public transportation.

Further, the system further comprises: the route planning module is used for acquiring each driving area of the new route;

inquiring station data in each driving area;

if the station data exist in each driving area, arranging, combining and connecting the stations of each driving area to form a plurality of planning lines, marking the planning lines on a bus professional map in a display mode different from the existing lines, and marking the existing line data of each station;

if the station data do not exist in the driving area, performing station area planning on the driving area without the station data; the stations of the driving area where the station data exist and the station areas of the driving area plan where the station data do not exist are arranged, combined and connected to form a plurality of planned routes, a display mode different from the existing routes is adopted to mark on a bus professional map, and meanwhile, the existing route data of each station are marked.

Has the advantages that: therefore, the bus route planning can be helped, and the workload of planners is greatly reduced.

The invention also aims to provide a public transportation big data method based on a geographic information system, which can form a professional public transportation map and realize unified management and application of data.

The invention provides a second basic scheme: the public transportation big data method based on the geographic information system comprises the following contents:

a data acquisition step: collecting bus basic data of different data sources, and carrying out standardized processing on the bus basic data;

a data aggregation step: superposing and marking the basic bus data on the basic map geographic information to form a bus professional map;

and function application steps: and analyzing the big data according to the basic data of the bus, and setting and applying a special map corresponding to the bus according to an analysis result.

The second basic scheme has the beneficial effects that: the method comprises the steps of collecting bus basic data of different data sources, carrying out standardization processing on the bus basic data so as to be convenient for other modules or systems to call, and enabling the data after standardization processing to be uniform in format, so that a user can obtain the data of the same standard to the maximum extent, the problem that the data is incomplete and inaccurate is solved, and the follow-up analysis result has no deviation from the actual result;

the basic bus data are overlapped and marked on the map basic geographic information, so that a bus professional map is formed, various data are displayed in one map, the application in scheduling is facilitated, and the working specifications are unified;

and (3) carrying out big data analysis according to the basic data of the bus, and carrying out corresponding bus professional map setting and application according to the analysis result, thereby realizing the accurate and standardized application of data from different sources.

The scheme realizes the one-dimensional to multi-dimensional conversion of the data, is more favorable for data acquisition, management, analysis and the like, can form a professional bus professional map, and realizes unified management and application of the data.

Further, the collecting the public transportation basic data comprises: collecting basic bus data on site, manually counting the basic bus data and calling one or more types of available basic bus data in ERP;

the bus basic data comprises: infrastructure data, operational data, security data, crew data, and service data; wherein the infrastructure data comprises: station data, station yard data, charging pile data and maintenance factory data;

operational data, including: line data, passenger flow data and shift data;

secure data, comprising: accident data and speed limit data;

crew data comprising: vehicle data and maintenance vehicle data;

service data, comprising: complaint data.

Has the advantages that: various data acquisition modes are adopted to meet different acquisition requirements; the bus basic data comprises infrastructure data, operation data, safety data, crew data and service data so as to meet the data requirements of different applications.

Further, the function applying step includes:

the operation substep is as follows: performing big data analysis according to the passenger flow data to obtain real-time passenger flow data and average passenger flow data of each station in the current month and day;

overlaying and marking station data, station yard data, line data, real-time passenger flow data, monthly and daily passenger flow data and shift data on the map basic geographic information to form a professional map of the bus operation;

acquiring a stop selection signal, and displaying stop data, station yard data, line data, real-time passenger flow data, daily average passenger flow data and shift data of a selected stop on a professional map of the operating bus according to the stop selection signal;

service substeps: performing big data analysis according to the complaint data in the preset time period to obtain complaint quantity data and complaint reason data of each station;

overlaying and labeling station data, line data, complaint quantity data and complaint reason data on the map basic geographic information to form a service bus professional map;

acquiring a stop selection signal, and displaying stop data, station yard data, line data, a preset time period, complaint quantity data and complaint reason data of a selected stop on a professional map of the service bus according to the stop selection signal;

a safety sub-step: big data analysis is carried out according to accident data in a preset time period, and road safety accident data, personal injury accident data and other accident data of all stations are obtained;

overlaying and marking station data, line data, road safety accident data, personal injury accident data and other accident data on the map basic geographic information to form a safety bus professional map;

acquiring a stop selection signal, and displaying stop data, station yard data, line data, running data, a preset time period, data of road safety accidents, data of personal injury accidents and other accident data of a selected stop on a professional map of the safe bus according to the stop selection signal;

a mechanical service substep: performing big data analysis according to the basic bus data to obtain position data, radiation range data, radiation bus route data, bus data, main bus type data and basic situation information of the bus service points; wherein the locomotive service points are station yards, charging piles and maintenance plants;

superposing and marking position data, radiation range data, radiation bus line data, bus data, main bus type data, basic situation information of the engineering point, vehicle data and maintenance bus data on the map basic geographic information to form a professional map of the engineering bus;

acquiring a maintenance point selection signal, and displaying position data, radiation range data, radiation bus line data, bus data, main vehicle type data, maintenance point basic condition information, vehicle data and maintenance vehicle data of a selected maintenance point on a professional map of a maintenance bus according to the maintenance point selection signal;

manual substep: performing big data analysis according to the station data and the line data to obtain statistical data of the addresses of the employees at the departure points of each line, wherein the statistical data comprises the following steps: the system comprises departure line data, employee number data within a preset distance of a departure point and employee number data outside the preset distance of the departure point;

on the map basic geographic information, station data, station yard data, line data and employee address statistical data of departure points of all lines are superposed and labeled to form a manpower bus professional map;

and acquiring a departure point selection signal, and displaying station data, station yard data, line data and employee address statistical data of the selected departure point on the manual bus professional map according to the departure point selection signal.

Has the advantages that: different functional steps realize different applications through different public transportation basic data so as to meet the comprehensive management of the public transportation.

Further, the method further comprises: a route planning step, namely acquiring each driving area of the new route;

inquiring station data in each driving area;

if the station data exist in each driving area, arranging, combining and connecting the stations of each driving area to form a plurality of planning lines, marking the planning lines on a bus professional map in a display mode different from the existing lines, and marking the existing line data of each station;

if the station data do not exist in the driving area, performing station area planning on the driving area without the station data; and arranging, combining and connecting the stations of the driving area with the existing station data and the station areas planned by the driving area without the station data to form a plurality of planned routes, marking on a bus professional map in a display mode different from the existing routes, and marking the existing route data of each station.

Has the advantages that: therefore, the bus route planning can be helped, and the workload of planners is greatly reduced.

The invention further aims to provide a large bus data storage medium based on a geographic information system, which can form a professional bus professional map and realize unified data management and application.

The invention provides a third basic scheme: the geographic information system-based public transportation big data storage medium is characterized in that a computer program is stored on the storage medium, and when the computer program is executed by a processor, the steps of any one of the geographic information system-based public transportation big data storage methods are realized.

The third basic scheme has the beneficial effects that: the bus big data storage medium based on the geographic information system is characterized in that a computer program is stored in the storage medium, and when the computer program is executed by a processor, the steps of any one of the bus big data methods based on the geographic information system are realized, one-dimensional to multi-dimensional conversion of data can be realized, data collection, management, analysis and the like are facilitated, a professional bus professional map can be formed, unified management and application of the data are realized, and the application of the bus big data method based on the geographic information system is facilitated.

Drawings

FIG. 1 is a logic block diagram of an embodiment of a geographic information system based bus big data system of the present invention;

FIG. 2 is a schematic diagram of a professional map of a safe bus in an embodiment of a geographic information system-based public transportation big data system of the invention.

Detailed Description

The following is further detailed by way of specific embodiments:

example one

This embodiment is substantially as shown in figure 1: public transit big data system based on geographic information system includes:

the data acquisition module is used for acquiring bus basic data; the data acquisition module is used for acquiring bus basic data of different data sources, standardizing the bus basic data, unifying formats of the bus basic data of the different data sources, and transmitting the standardized bus basic data to the main database for storage; in this embodiment, the normalization process uses min-max normalization to convert all raw data into non-dimensionalized index values, i.e., each index value is in the same number level, so as to perform comprehensive evaluation analysis.

The data acquisition module acquires the basic data of the bus, and comprises the following steps: collecting basic bus data on site, manually counting the basic bus data and calling one or more types of available basic bus data in ERP;

the bus basic data comprises: infrastructure data, operational data, security data, crew data, and service data; wherein the infrastructure data includes: site data, station yard data, charging pile data and maintenance factory data;

operational data, including: line data, passenger flow data and shift data;

secure data, comprising: accident data and speed limit data;

crew data comprising: vehicle data and maintenance vehicle data;

service data, comprising: complaint data.

The data aggregation module is used for overlaying and marking the bus basic data on the map basic geographic information to form a bus professional map;

specifically, the data aggregation module is used for calling map basic geographic information and bus basic data in a main database; wherein the map-based geographic information includes: map data; in the present embodiment, the map-based Geographic Information is Information in a Geographic Information System (Geographic Information System or Geo-Information System, GIS). The GIS is a specific very important spatial information system, and is a technical system for collecting, storing, managing, operating, analyzing, displaying and describing relevant geographic distribution data in a space under the support of a computer hardware system and a software system, so that a collecting module can directly call map basic geographic information in the GIS;

on the basis of map data, the basic data of the bus are overlapped and marked, and the method comprises the following steps: on the basis of the map data, adding layers of infrastructure data, operation data, safety data, engineering data and service data in sequence to form a bus professional map; wherein the map data includes: road data and POI data;

the main database is used for storing bus basic data, map basic geographic information and bus professional maps; the main database is provided with a plurality of data interfaces for being connected with other systems so that the other systems can call the data in the main database;

the function application module is used for analyzing big data according to the basic data of the bus, setting a special map corresponding to the bus according to the analysis result and applying the special map;

specifically, the functional application module includes:

the operation submodule is used for carrying out big data analysis according to the passenger flow data and acquiring real-time passenger flow data and average passenger flow data of each station in the month and day;

on the map basic geographic information, station data, station yard data, line data, real-time passenger flow data, monthly and daily average passenger flow data and shift data are superposed and labeled to form a professional map of the bus operation, and the professional map is sent to a main database for storage;

and acquiring a stop selection signal, and displaying stop data, station yard data, line data, real-time passenger flow data, daily average passenger flow data and shift data of the selected stop on a professional map of the operating bus according to the stop selection signal.

The service submodule is used for carrying out big data analysis according to the complaint data in the preset time period and obtaining the complaint quantity data and the complaint reason data of each site;

overlaying and marking station data, line data, complaint quantity data and complaint reason data on the map basic geographic information to form a service bus professional map, and sending the service bus professional map to a main database for storage;

acquiring a stop selection signal, and displaying stop data, station yard data, line data, a preset time period, complaint quantity data and complaint reason data of a selected stop on a service bus professional map according to the stop selection signal;

through the service submodule, line customer service parameter information can be clearly displayed, a service blind area can be monitored in real time, and passenger requirements can be visually known.

The safety submodule is used for carrying out big data analysis according to accident data in a preset time period and acquiring road safety accident data, personal injury accident data and other accident data of each station;

on the map basic geographic information, station data, line data, data of road safety accidents, data of personal injury accidents and other accident data are superposed and marked to form a safety bus professional map which is sent to a main database for storage;

acquiring a station selection signal, and displaying station data, station yard data, line data, operation data, a preset time period, road safety accident data, personal injury accident data and other accident data of a selected station on a safety bus professional map according to the station selection signal;

according to the data of the road safety accidents, obtaining safety wind control points in the line, and marking the safety wind control points on a special map of the safety bus; specifically, data of road safety accidents occurring at each stop are sorted from big to small, the first N stops are selected as safety wind control points, the selected safety wind control points are simultaneously used as safety wind control points on a line where the stop is located, the stop is marked on a professional map of the safe bus, and stop data, station yard data, line data, operation data, a preset time period, data of road safety accidents occurring, data of personal injury accidents and other accident data of the stop are displayed, as shown in fig. 2;

through the safety submodule, the safety wind control points which are automatically generated visually know the safety situation of the region, are convenient for unifying and standardizing the speed limit of each region and help managers to find the safety blind areas.

The bus service sub-module is used for analyzing big data according to the bus basic data to obtain position data, radiation range data, radiation bus route data, bus data, main vehicle type data and bus service point basic condition information of each bus service point; wherein the engineering points are station yards, charging piles and maintenance plants;

superposing and marking position data, radiation range data, radiation bus line data, bus data, main bus type data, basic condition information of the engineering point, vehicle data and maintenance bus data on the map basic geographic information to form a professional map of the engineering bus, and uploading the map to a main database for storage;

acquiring a maintenance point selection signal, and displaying position data, radiation range data, radiation bus line data, bus data, main vehicle type data, maintenance point basic condition information, vehicle data and maintenance vehicle data of a selected maintenance point on a professional map of a maintenance bus according to the maintenance point selection signal; (ii) a

The crew sub-module can clearly obtain the basic information, service lines and vehicle conditions of the crew points, know the blind spot area covered by the crew points in time and realize the possibility of wall map battle.

The manpower submodule is used for carrying out big data analysis according to the station data and the line data and obtaining the statistical data of the employee addresses of the departure points of each line, and comprises the following steps: the system comprises departure line data, employee number data within a preset distance of a departure point and employee number data outside the preset distance of the departure point;

on the map basic geographic information, station data, station yard data, line data and employee address statistical data of departure points of all lines are superposed and labeled to form a manpower bus professional map, and the map is sent to a main database for storage;

acquiring a departure point selection signal, and displaying station data, station yard data, line data and employee address statistical data of the selected departure point on a professional map of the manpower bus according to the departure point selection signal;

the human sub-module clearly acquires the basic information of the staff, so that the staff address and the line condition can be conveniently mastered, and the staff working line can be reasonably distributed.

The scheme realizes the one-dimensional to multi-dimensional conversion of the data, is more favorable for data acquisition, management, analysis and the like, can form a professional bus professional map, and realizes unified management and application of the data.

Example two

This embodiment is substantially the same as the above embodiment except that: the system also comprises a route planning module used for acquiring each driving area of the new route; wherein the driving area includes: a departure point area, a destination area, and a route area;

inquiring station data in each driving area;

if the station data exist in each driving area, arranging, combining and connecting the stations of each driving area to form a plurality of planning lines, marking the planning lines on a bus professional map in a display mode different from the existing lines, and marking the existing line data of each station;

if the station data do not exist in the driving area, performing station area planning on the driving area without the station data; specifically, if the driving area without the station data is a departure point area, acquiring an overlapping area of the departure point area and the radiation range data, and selecting a road area with the shortest average distance of employees within a preset distance of the departure point area in the overlapping area as a planned station area according to employee address statistical data within the preset distance of the departure point area, wherein the road area can be a specific road point; if the driving area without the station data is not the departure point area, acquiring an overlapping area of the driving area and the engineering point radiation range data, and taking a road area in the overlapping area as a planned station area;

the stations of the driving area where the station data exist and the station areas of the driving area plan where the station data do not exist are arranged, combined and connected to form a plurality of planned routes, a display mode different from the existing routes is adopted to mark on a bus professional map, and meanwhile, the existing route data of each station are marked.

Therefore, the bus route planning system can help plan the bus route, greatly reduce the workload of planners, only need to input each driving area into the route planning module, the route planning module can output all the planned routes and the optimal planned route for the driving area, and the planners can select which route is actually developed according to the station saturation condition of the planned routes.

Each bus running can shoot the video of the road through the vehicle-mounted camera and upload the video to the system, so that a more real-time urban panoramic map is developed.

The function application module further comprises: and the analysis submodule is used for analyzing the region, time interval, passenger flow, POI data and the like in the bus professional map and analyzing the interest rule of the consumer group in the bus professional map, so that the development of other industries is promoted.

EXAMPLE III

This embodiment is substantially the same as the above embodiment except that: the data acquisition module is also used for acquiring user portrait data; the data acquisition module can acquire user image data by accessing some existing riding software, a user of the system and a camera arranged on the vehicle and used for shooting the condition in the vehicle;

the system further comprises: the system comprises a user image analysis module and an advertisement matching module;

the user portrait analysis module is used for carrying out user portrait analysis according to the passenger flow data and the user portrait data in a preset time period;

and the advertisement matching module is used for matching corresponding advertisements according to the user portrait analysis result, the line data of the current running vehicle, the position data of the current running vehicle and the personnel data in the vehicle, setting corresponding playing frequency and times and playing in the current running vehicle.

In this embodiment, the user portrait data includes: user gender, age, and occupation type; if the preset time period is 7 in the morning: 00-9: 00, a user portrait analysis module carries out user portrait analysis according to the passenger flow data and the user portrait data in the time period to obtain that the passenger flow volume in the time period is large, and the user is mainly a female white collar; the advertisement matching module may match the corresponding advertisement according to the user portrait analysis result, the route data of the currently driving vehicle, the position data of the currently driving vehicle, and the personnel data in the vehicle, for example: in the driving process, advertisements of articles with high female white collar requirements, such as cosmetic stores, examination counseling institutions and the like, which exist near the next station are played, and according to the personnel data in the vehicle, the playing frequency is high when the number of personnel is large, the playing frequency is low when the number of personnel is small, and the playing times of different advertisements are shown, for example: the played advertisements comprise male products and female products, at the moment, more females and fewer males exist in the personnel data in the vehicle, the advertisement playing times of the male products are fewer, and the advertisement playing times of the female products are more. Therefore, the advertisement in the vehicle is more targeted, the played advertisement can generate better propaganda effect, and the user in the vehicle can acquire the advertisement information related to the demand of the user, so that the success probability of advertisement propaganda is increased.

The system, still include: the system comprises a panoramic map construction module, a duplicate removal optimization module, a privacy processing module, a splicing framework module and a transition construction module;

the panoramic map construction module is used for acquiring image data of each vehicle; in the embodiment, each running vehicle is provided with a vehicle-mounted camera for shooting vehicle influence data, namely shooting a video of a road;

the duplication removing optimization module is used for optimizing images contained in vehicle image data acquired by a plurality of vehicles to the same place in a plurality of vehicle numbers; in the embodiment, the same image is analyzed through an evaluation algorithm, the time, the view field range, the number of people in the image, the number of vehicles, the landmark buildings and the like are included, the optimal image is selected, namely the time is latest, the view field range is widest, the landmark buildings are included, and the maximum number or the minimum number of people and the minimum number of vehicles are set according to the requirement;

the privacy processing module is used for carrying out privacy processing; in the embodiment, mosaic processing is carried out on the human face and the vehicle license plate contained in the image;

the splicing framework module is used for constructing a panoramic map according to the optimal image; and then through covering the public transit system in city, realize real-time panorama map and update.

And the transition construction module is used for constructing environment transition when people move in the panoramic image according to the vehicle image data, and solving the problem caused by transition in the conventional photo deformation mode.

The system, further comprising: the road maintenance management system comprises a road analysis module and a road maintenance management module;

the road analysis module is used for analyzing road damage according to the vehicle image data to obtain road damage data; specifically, the road analysis module includes: the point location detection submodule, the vibration detection identification module and the road analysis submodule;

the point location detection submodule is used for carrying out point location detection on the basis of images contained in the vehicle image data;

the vibration detection and identification module is used for carrying out image vibration detection based on the vehicle image data and images contained in the vehicle image data and is also used for carrying out vehicle vibration data detection;

the road analysis submodule is used for judging the road damage position according to the point location detection result, the image vibration data and the vehicle vibration data detection result based on the vehicle image data and the image contained in the vehicle image data, and recording the road damage data, wherein the road damage data comprise: a road damage position, vehicle image data of the position and a panoramic map; specifically, when the image vibration data and the vehicle vibration data are detected to be higher than the preset image vibration data and the preset vehicle vibration data, judging that the road of the corresponding point location is damaged;

the road maintenance management module is used for generating a road maintenance order according to the road damage data; specifically, a road maintenance order containing a road damage position, vehicle image data of the position and a panoramic map is generated and pushed to a road maintenance functional department; therefore, the road damage condition can be automatically detected, and the road damage condition can be found and maintained in time, so that damage diffusion or accidents can be prevented.

Example four

The embodiment provides a public transportation big data method based on a geographic information system, which comprises the following contents:

a data acquisition step: collecting bus basic data of different data sources, and carrying out standardized processing on the bus basic data; wherein gather public transit basic data and include: collecting basic bus data on site, manually counting the basic bus data and calling one or more types of available basic bus data in ERP; the bus basic data comprises: infrastructure data, operational data, security data, crew data, and service data; wherein the infrastructure data includes: site data, station yard data, charging pile data and maintenance factory data; operational data, including: line data, passenger flow data and shift data; secure data, comprising: accident data and speed limit data; -engineering data comprising: vehicle data and maintenance vehicle data; service data, including: complaint data.

A data aggregation step: superposing and marking the basic bus data on the basic map geographic information to form a bus professional map; specifically, on the basis of map data, the basic data of the bus is overlapped and marked, and the method comprises the following steps: on the basis of the map data, adding layers of infrastructure data, operation data, safety data, engineering data and service data in sequence to form a bus professional map; wherein the map data includes: road data and POI data;

the function application step: and analyzing the big data according to the basic data of the bus, and setting and applying a special map corresponding to the bus according to an analysis result.

Specifically, the function applying step includes:

the operation substep is as follows: performing big data analysis according to the passenger flow data to obtain real-time passenger flow data and average passenger flow data of each station in the current month and day;

overlaying and labeling station data, station yard data, line data, real-time passenger flow data, monthly average passenger flow data and shift data on the map basic geographic information to form a professional map of the operating bus;

acquiring a stop selection signal, and displaying stop data, station yard data, line data, real-time passenger flow data, daily average passenger flow data and shift data of a selected stop on a professional map of the operating bus according to the stop selection signal;

service substeps: performing big data analysis according to the complaint data in the preset time period to obtain complaint quantity data and complaint reason data of each site;

overlaying and labeling station data, line data, complaint quantity data and complaint reason data on the map basic geographic information to form a service bus professional map;

acquiring a stop selection signal, and displaying stop data, station yard data, line data, a preset time period, complaint quantity data and complaint reason data of a selected stop on a service bus professional map according to the stop selection signal;

a safety sub-step: big data analysis is carried out according to accident data in a preset time period, and road safety accident data, personal injury accident data and other accident data of all stations are obtained;

overlaying and marking station data, line data, road safety accident data, personal injury accident data and other accident data on the map basic geographic information to form a safety bus professional map;

acquiring a station selection signal, and displaying station data, station yard data, line data, operation data, a preset time period, road safety accident data, personal injury accident data and other accident data of a selected station on a safety bus professional map according to the station selection signal;

a mechanical service substep: performing big data analysis according to the basic bus data to obtain position data, radiation range data, radiation bus route data, bus data, main bus type data and basic situation information of the bus service points; wherein the engineering points are station yards, charging piles and maintenance plants;

superposing and marking position data, radiation range data, radiation bus line data, bus data, main bus type data, basic situation information of the engineering point, vehicle data and maintenance bus data on the map basic geographic information to form a professional map of the engineering bus;

acquiring a maintenance point selection signal, and displaying position data, radiation range data, radiation bus line data, bus data, main vehicle type data, maintenance point basic condition information, vehicle data and maintenance vehicle data of a selected maintenance point on a professional map of a maintenance bus according to the maintenance point selection signal;

manual substep: big data analysis is carried out according to the station data and the line data, and the statistical data of the employee address of each line departure point is obtained, which comprises the following steps: the system comprises departure line data, employee number data within a preset distance of a departure point and employee number data outside the preset distance of the departure point;

overlaying and marking station data, station yard data, line data and employee address statistical data of departure points of all lines on the map basic geographic information to form a manpower bus professional map;

and acquiring a departure point selection signal, and displaying station data, station yard data, line data and employee address statistical data of the selected departure point on the manual bus professional map according to the departure point selection signal.

The embodiment can be carried out through the system in the embodiment I, so that one-dimensional to multi-dimensional conversion of data can be realized, the data acquisition, management, analysis and the like are facilitated, a professional bus professional map can be formed, and unified management and application of the data are realized.

The embodiment also provides a geographic information system-based bus big data storage medium, wherein a computer program is stored on the storage medium, and when the computer program is executed by a processor, the steps of any one of the geographic information system-based bus big data storage methods are realized.

The public transportation big data method based on the geographic information system can be stored in a storage medium if the public transportation big data method is realized in the form of a software functional unit and is sold or used as an independent product. Based on such understanding, all or part of the flow in the method of the above embodiments may be realized by a computer program, which may be stored in a readable storage medium and used for instructing relevant hardware, and when the computer program is executed by a processor, the steps of the above method embodiments may be realized. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like.

EXAMPLE five

This example is substantially the same as example four, except that: further comprising: a route planning step, namely acquiring each driving area of the new route; wherein the driving region includes: a departure point area, a destination area, and a route area;

inquiring station data in each driving area;

if the station data exist in each driving area, arranging, combining and connecting the stations of each driving area to form a plurality of planning lines, marking the planning lines on a bus professional map in a display mode different from the existing lines, and marking the existing line data of each station;

if the station data do not exist in the driving area, performing station area planning on the driving area without the station data; specifically, if the driving area without the station data is a departure point area, acquiring an overlapping area of the departure point area and the radiation range data, and selecting a road area with the shortest average distance of employees within a preset distance of the departure point area in the overlapping area as a planned station area according to employee address statistical data within the preset distance of the departure point area, wherein the road area can be a specific road point; if the driving area without the station data is not the departure point area, acquiring an overlapping area of the driving area and the engineering point radiation range data, and taking a road area in the overlapping area as a planned station area;

the stations of the driving area where the station data exist and the station areas of the driving area plan where the station data do not exist are arranged, combined and connected to form a plurality of planned routes, a display mode different from the existing routes is adopted to mark on a bus professional map, and meanwhile, the existing route data of each station are marked.

Therefore, the planning of the bus route can be facilitated, the workload of planners is greatly reduced, all the planned routes and the optimal planned route can be obtained only by inputting each driving area into the route planning module, and the planners can select which route is actually developed according to the station saturation condition of the planned routes.

Each bus running can shoot the video of the road through the vehicle-mounted camera and upload the video to the system, so that a more real-time urban panoramic map is developed.

The function application step further comprises: and the analysis substep is used for analyzing the region, time interval, passenger flow, POI data and the like in the bus professional map and analyzing the interest rule of the consumer group, so that the development of other industries is promoted.

EXAMPLE six

This embodiment is substantially the same as the above embodiment except that:

the data acquisition step further comprises the step of acquiring user portrait data; the method comprises the steps that a user of the system constructed by the method and a camera arranged on a vehicle are accessed to some existing riding software to acquire user image data;

the method further comprises the following steps: a user image analysis step and an advertisement matching step;

the user portrait analysis step: performing user portrait analysis according to passenger flow data and user portrait data in a preset time period;

the advertisement matching step comprises: and matching corresponding advertisements according to the user portrait analysis result, the line data of the current running vehicle, the position data of the current running vehicle and the personnel data in the vehicle, setting corresponding playing frequency and times, and playing in the current running vehicle.

In this embodiment, the user portrait data includes: user gender, age, and occupation type; if the preset time period is 7 in the morning: 00-9: 00, a user portrait analysis module carries out user portrait analysis according to the passenger flow data and the user portrait data in the time period to obtain that the passenger flow volume in the time period is large, and the user is mainly a female white collar; then the corresponding advertisement can be matched according to the user portrait analysis result, the route data of the current running vehicle, the position data of the current running vehicle and the personnel data in the vehicle, for example: in the driving process, advertisements of articles with high female white collar demands existing near the next station are played, such as cosmetic stores, examination counseling institutions and the like, and according to the personnel data in the vehicle, the playing frequency is high when the number of personnel is large, the playing frequency is low when the number of personnel is small, and the playing times of different advertisements are shown, for example: the played advertisements comprise male products and female products, at the moment, more females and fewer males exist in the personnel data in the vehicle, the advertisement playing frequency of the male products is low, and the advertisement playing frequency of the female products is high. Therefore, the advertisement playing in the vehicle is more targeted, the played advertisement can generate better propaganda effect, and users in the vehicle can acquire the advertisement information related to the demands of the users, so that the success probability of advertisement propaganda is increased.

The method further comprises the following steps: the method comprises the steps of panoramic map construction, duplicate removal and optimization, privacy processing, splicing framework and transition construction;

and (3) panoramic map construction: acquiring image data of each vehicle; in the embodiment, each running vehicle is provided with a vehicle-mounted camera for shooting vehicle influence data, namely shooting a video of a road;

a duplication elimination optimization step: selecting images contained in vehicle image data acquired by multiple vehicles at the same place; in the embodiment, the same image is analyzed through an evaluation algorithm, the time, the view field range, the number of people in the image, the number of vehicles, the landmark buildings and the like are included, the optimal image is selected, namely the time is latest, the view field range is widest, the landmark buildings are included, and the maximum number or the minimum number of people and the minimum number of vehicles are set according to the requirement;

a privacy processing step: carrying out privacy processing on the image; in the embodiment, mosaic processing is carried out on the human face and the vehicle license plate contained in the image;

splicing the frameworks: constructing a panoramic map according to the optimal image; and then through covering the public transit system in city, realize real-time panorama map and update.

A transition construction step: according to the vehicle image data, environment transition when people move in the panoramic image is constructed, and the problem caused by transition in a photo deformation mode in the prior art is solved.

The method further comprises the following steps: a road analysis step and a road maintenance management step;

a road analysis step: analyzing road damage according to the vehicle image data to obtain road damage data; in particular, the amount of the solvent to be used,

performing point location detection based on images contained in the vehicle image data;

carrying out image vibration detection based on the vehicle image data and images contained in the vehicle image data, and also carrying out vehicle vibration data detection;

based on vehicle image data and the image that contains, according to position location testing result, image vibration data and vehicle vibration data testing result, judge the road and damage the position to record road damage data, wherein road damage data includes: a road damage position, vehicle image data of the position and a panoramic map; specifically, when the image vibration data and the vehicle vibration data are detected to be higher than the preset image vibration data and the preset vehicle vibration data, judging that the road of the corresponding point location is damaged;

road maintenance management: generating a road maintenance order according to the road damage data; specifically, a road maintenance order containing a road damage position, vehicle image data of the position and a panoramic map is generated and pushed to a road maintenance functional department; therefore, the road damage condition can be automatically detected, and the road damage condition can be found and maintained in time, so that damage diffusion or accidents can be prevented.

The foregoing are embodiments of the present invention and are not intended to limit the scope of the invention to the particular forms set forth in the specification, which are set forth in the claims below, but rather are to be construed as the full breadth and scope of the claims, as defined by the appended claims, as defined in the appended claims, in order to provide a thorough understanding of the present invention. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be defined by the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.

Claims (10)

1. Public transit big data system based on geographic information system, its characterized in that: the method comprises the following steps:

the data acquisition module is used for acquiring bus basic data of different data sources and carrying out standardized processing on the bus basic data;

the data aggregation module is used for overlaying and labeling the bus basic data on the map basic geographic information to form a bus professional map;

the main database is used for storing bus basic data, map basic geographic information and bus professional maps;

and the function application module is used for analyzing the big data according to the basic data of the bus, setting a corresponding bus professional map according to the analysis result and applying the map.

2. The geographic information system-based public transportation big data system as claimed in claim 1, wherein: the data acquisition module acquires bus basic data and comprises the following steps: collecting basic bus data on site, manually counting the basic bus data and calling one or more types of available basic bus data in ERP;

the bus basic data comprises: infrastructure data, operational data, security data, crew data, and service data; wherein the infrastructure data includes: station data, station yard data, charging pile data and maintenance factory data;

operational data, including: line data, passenger flow data and shift data;

secure data, comprising: accident data and speed limit data;

crew data comprising: vehicle data and maintenance vehicle data;

service data, comprising: complaint data.

3. The geographic information system-based public transportation big data system as claimed in claim 1, wherein: the data aggregation module is used for calling map basic geographic information and bus basic data in the main database;

on the basis of the map data, layers of infrastructure data, operation data, safety data, engineering data and service data are sequentially added to form a bus professional map.

4. The geographic information system-based public transportation big data system as claimed in claim 2, wherein: the function application module comprises:

the operation submodule is used for carrying out big data analysis according to the passenger flow data and acquiring real-time passenger flow data and average passenger flow data of each station in the month and day;

overlaying and marking station data, station yard data, line data, real-time passenger flow data, monthly and daily passenger flow data and shift data on the map basic geographic information to form a professional map of the bus operation;

acquiring a stop selection signal, and displaying stop data, station yard data, line data, real-time passenger flow data, daily average passenger flow data and shift data of a selected stop on a professional map of the operating bus according to the stop selection signal;

the service submodule is used for carrying out big data analysis according to the complaint data in the preset time period and obtaining the complaint quantity data and the complaint reason data of each site;

overlaying and marking station data, station yard data, line data, complaint quantity data and complaint reason data on the map basic geographic information to form a service bus professional map;

acquiring a stop selection signal, and displaying stop data, station yard data, line data, a preset time period, complaint quantity data and complaint reason data of a selected stop on a service bus professional map according to the stop selection signal;

the safety submodule is used for carrying out big data analysis according to accident data in a preset time period and acquiring road safety accident data, personal injury accident data and other accident data of each station;

overlaying and marking station data, line data, road safety accident data, personal injury accident data and other accident data on the map basic geographic information to form a safety bus professional map;

acquiring a station selection signal, and displaying station data, station yard data, line data, operation data, a preset time period, road safety accident data, personal injury accident data and other accident data of a selected station on a safety bus professional map according to the station selection signal;

the bus service sub-module is used for analyzing big data according to the bus basic data to obtain position data, radiation range data, radiation bus route data, bus data, main bus type data and basic condition information of bus service points of each bus service point; wherein the engineering points are station yards, charging piles and maintenance plants;

superposing and marking position data, radiation range data, radiation bus line data, bus data, main bus type data, basic situation information of the engineering point, vehicle data and maintenance bus data on the map basic geographic information to form a professional map of the engineering bus;

acquiring a maintenance point selection signal, and displaying position data, radiation range data, radiation bus line data, bus data, main vehicle type data, maintenance point basic condition information, vehicle data and maintenance vehicle data of a selected maintenance point on a professional map of a maintenance bus according to the maintenance point selection signal;

the manpower submodule is used for carrying out big data analysis according to the station data and the line data and obtaining the employee address statistical data of each line departure point, and comprises: the system comprises departure line data, employee number data within a preset distance of a departure point and employee number data outside the preset distance of the departure point;

overlaying and marking station data, station yard data, line data and employee address statistical data of departure points of all lines on the map basic geographic information to form a manpower bus professional map;

and acquiring a departure point selection signal, and displaying station data, station yard data, line data and employee address statistical data of the selected departure point on the manual bus professional map according to the departure point selection signal.

5. The geographic information system-based public transportation big data system as claimed in claim 2, wherein: further comprising: the route planning module is used for acquiring each driving area of the new route;

inquiring station data in each driving area;

if the station data exist in each driving area, arranging, combining and connecting the stations of each driving area to form a plurality of planning lines, marking the planning lines on a bus professional map in a display mode different from the existing lines, and marking the existing line data of each station;

if the station data do not exist in the driving area, performing station area planning on the driving area without the station data; and arranging, combining and connecting the stations of the driving area with the existing station data and the station areas planned by the driving area without the station data to form a plurality of planned routes, marking on a bus professional map in a display mode different from the existing routes, and marking the existing route data of each station.

6. The public transportation big data method based on the geographic information system is characterized in that: the method comprises the following steps:

a data acquisition step: collecting bus basic data of different data sources, and carrying out standardized processing on the bus basic data;

a data aggregation step: overlaying and marking bus basic data on the map basic geographic information to form a bus professional map;

and function application steps: and analyzing big data according to the basic data of the bus, setting a special map corresponding to the bus according to an analysis result, and applying the special map.

7. The geographic information system-based bus big data method as recited in claim 6, wherein: the collecting of the bus basic data comprises the following steps: collecting basic bus data on site, manually counting the basic bus data and calling one or more types of available basic bus data in ERP;

the bus basic data comprises: infrastructure data, operational data, security data, engineering data, and service data; wherein the infrastructure data comprises: site data, station yard data, charging pile data and maintenance factory data;

operational data, including: line data, passenger flow data and shift data;

secure data, comprising: accident data and speed limit data;

crew data comprising: vehicle data and maintenance vehicle data;

service data, comprising: complaint data.

8. The geographic information system-based public transportation big data method according to claim 7, wherein: the function applying step includes:

the operation substep is as follows: performing big data analysis according to the passenger flow data to obtain real-time passenger flow data and average passenger flow data of each station in the month and day;

overlaying and marking station data, station yard data, line data, real-time passenger flow data, monthly and daily passenger flow data and shift data on the map basic geographic information to form a professional map of the bus operation;

acquiring a stop selection signal, and displaying stop data, station yard data, line data, real-time passenger flow data, daily average passenger flow data and shift data of a selected stop on a professional map of the operating bus according to the stop selection signal;

service substeps: performing big data analysis according to the complaint data in the preset time period to obtain complaint quantity data and complaint reason data of each site;

overlaying and marking station data, station yard data, line data, complaint quantity data and complaint reason data on the map basic geographic information to form a service bus professional map;

acquiring a stop selection signal, and displaying stop data, station yard data, line data, a preset time period, complaint quantity data and complaint reason data of a selected stop on a service bus professional map according to the stop selection signal;

a safety sub-step: big data analysis is carried out according to accident data in a preset time period, and road safety accident data, personal injury accident data and other accident data of all stations are obtained;

overlaying and marking station data, line data, road safety accident data, personal injury accident data and other accident data on the map basic geographic information to form a safety bus professional map;

acquiring a station selection signal, and displaying station data, station yard data, line data, operation data, a preset time period, road safety accident data, personal injury accident data and other accident data of a selected station on a safety bus professional map according to the station selection signal;

a mechanical work sub-step: performing big data analysis according to the basic data of the bus to obtain position data, radiation range data, radiation bus line data, bus data, main bus type data and basic condition information of the bus service points of each bus service point; wherein the engineering points are station yards, charging piles and maintenance plants;

superposing and marking position data, radiation range data, radiation bus line data, bus data, main bus type data, basic situation information of the engineering point, vehicle data and maintenance bus data on the map basic geographic information to form a professional map of the engineering bus;

acquiring a maintenance point selection signal, and displaying position data, radiation range data, radiation bus line data, bus data, main vehicle type data, maintenance point basic condition information, vehicle data and maintenance vehicle data of a selected maintenance point on a professional map of a maintenance bus according to the maintenance point selection signal;

manual substep: big data analysis is carried out according to the station data and the line data, and the statistical data of the employee address of each line departure point is obtained, which comprises the following steps: the system comprises departure line data, employee number data within a preset distance of a departure point and employee number data outside the preset distance of the departure point;

overlaying and marking station data, station yard data, line data and employee address statistical data of departure points of all lines on the map basic geographic information to form a manpower bus professional map;

and acquiring a departure point selection signal, and displaying station data, station yard data, line data and employee address statistical data of the selected departure point on the manual bus professional map according to the departure point selection signal.

9. The geographic information system-based public transportation big data method according to claim 7, wherein: further comprising: a route planning step, namely acquiring each driving area of the new route;

inquiring station data in each driving area;

if the station data exist in each driving area, arranging, combining and connecting the stations of each driving area to form a plurality of planning lines, marking the planning lines on a bus professional map in a display mode different from the existing lines, and marking the existing line data of each station;

if the station data do not exist in the driving area, performing station area planning on the driving area without the station data; the stations of the driving area where the station data exist and the station areas of the driving area plan where the station data do not exist are arranged, combined and connected to form a plurality of planned routes, a display mode different from the existing routes is adopted to mark on a bus professional map, and meanwhile, the existing route data of each station are marked.

10. Public transportation big data storage medium based on geographic information system, the storage medium has stored computer program, its characterized in that: the computer program realizing the steps of any one of the geographic information system based bus big data methods as claimed in claims 6-9 when being executed by a processor.

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