CN115223371B - Big data analysis system of electric bicycle and working method thereof - Google Patents
Big data analysis system of electric bicycle and working method thereof Download PDFInfo
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Abstract
An electric bicycle big data analysis system and a working method thereof belong to the technical field of intelligent traffic. In order to solve the comprehensive supervision of the electric bicycle. The invention comprises an electric bicycle flow analysis module, an electric bicycle violation analysis module and a real-time monitoring module; the electric bicycle flow analysis module is used for electric bicycle GPS data acquisition and electric bicycle flow analysis; the electric bicycle violation analysis module is used for collecting electric bicycle violation events and analyzing the electric bicycle violation; the real-time monitoring module is used for monitoring the flow and the violation of regulations of the electric bicycles in the whole area and monitoring the flow and the violation of regulations of the electric bicycles at the road intersection. The invention calculates the electric bicycle flow and the violation events at each road intersection based on the electric bicycle GPS data and the video monitoring data, and carries out multi-dimensional analysis on the flow and the violation events of the electric bicycles.
Description
Technical Field
The invention belongs to the technical field of intelligent traffic, and particularly relates to an electric bicycle big data analysis system and a working method thereof.
Background
Along with the rapid development of social economy and the rapid construction of road traffic infrastructure, the living standard of people is continuously improved, the travel conditions of people are also improved, and the electric bicycles are economical in price and convenient to operate, and are used as travel tools of citizens, meal delivery tools of takeout persons and delivery tools of couriers, the number of the electric bicycles is rapidly increased, so that the problems of traffic jam, frequent violation events of the electric bicycles and frequent traffic accidents caused by the electric bicycles are also caused, and the management difficulty of urban traffic is increased. There is therefore a need for comprehensive monitoring of electric bicycles, including traffic monitoring of electric bicycles and management of electric bicycle violation events.
The patent with the publication number of CN106503450B and the patent name of 'an electric vehicle big data analysis and optimization system and method' provides an electric vehicle big data analysis and optimization system and a method, wherein the system comprises an electric vehicle database, an algorithm module component and a control parameter database, the electric vehicle database receives and stores data transmitted by an electric vehicle controller through a mobile data module supporting 3G communication, the algorithm module component accesses the electric vehicle database according to a corresponding algorithm and generates new control parameters to be reserved in the control parameter database until the new control parameters are covered according to a logic strategy adopted by the algorithm, and the parameters are transmitted to the electric vehicle controller through the mobile data module to realize continuous optimization of the control parameters of the controller. However, the patent does not analyze the data of the electric bicycle and lacks monitoring the flow and the violation events of the electric bicycle.
Disclosure of Invention
The invention aims to solve the problem of realizing comprehensive supervision of an electric bicycle and provides a big data analysis system of the electric bicycle and a working method thereof.
A big data analysis system of an electric bicycle comprises an electric bicycle flow analysis module, an electric bicycle violation analysis module and a real-time monitoring module;
the electric bicycle flow analysis module is used for electric bicycle GPS data acquisition and electric bicycle flow analysis;
the electric bicycle violation analysis module is used for collecting electric bicycle violation events and analyzing the electric bicycle violation events;
the real-time monitoring module is used for monitoring the flow and the violation of regulations of the electric bicycles in the whole area and the flow and the violation of regulations of the electric bicycles at the road intersection;
the electric bicycle flow analysis module, the electric bicycle violation analysis module and the real-time monitoring module are connected with one another through data interfaces.
A working method of an electric bicycle big data analysis system is realized by the electric bicycle big data analysis system, and comprises the following steps:
s1, carrying out electric bicycle GPS data acquisition by an electric bicycle flow analysis module;
s2, the electric bicycle flow analysis module analyzes the flow of the electric bicycle;
s3, collecting violation events of the electric bicycle by the violation analysis module of the electric bicycle;
s4, carrying out violation analysis on the electric bicycle by the violation analysis module of the electric bicycle;
s5, the real-time monitoring module monitors the flow and the violation of regulations of the electric bicycle in the whole area;
and S6, the real-time monitoring module monitors the flow of the electric bicycle at the road intersection and the violation of regulations.
Further, in the step S1, the electric bicycle GPS data acquisition method uploads GPS data to the Kafka system in real time for the electric bicycle terminal device, and the electric bicycle GPS data field includes: the electric bicycle flow analysis module collects GPS data of the electric bicycle from a Kafka system.
Further, the electric bicycle flow rate analyzing method in step S2 includes the steps of:
s2.1, removing abnormal data from the electric bicycle GPS data collected in the step S1, wherein the abnormal data comprises: the GPS time abnormal data of the electric bicycle and the GPS repeated data of the electric bicycle are obtained;
s2.2, reading the geographical layer data of the road intersection, wherein the geographical layer data field of the road intersection comprises: the intersection number, the intersection name, the intersection longitude and the intersection latitude;
s2.3, correlating the vehicle positioning longitude and the vehicle positioning latitude in the electric bicycle GPS data processed in the step S2.1 with the road intersection geographical map layer data obtained in the step S2.2 to calculate the electric bicycle flow at the road intersection,
s2.4, according to the electric bicycle flow of the road intersection obtained in the step S2.3, calculating the total electric bicycle flow of each intersection by using intersection number aggregation, and calculating the electric bicycle flow ranking of the intersections according to the descending order of the flow;
and S2.5, calculating the total flow of the electric bicycles for 24 hours by utilizing time aggregation according to the electric bicycle flow at the road intersection obtained in the step S2.3.
Further, the specific implementation method in step S2.3 is:
s2.3.1, converting the positioning longitude and the positioning latitude of the electric bicycle, the longitude and the latitude of an intersection and the latitude of the intersection, and converting the WGS84 coordinates into the projection coordinates of the mercator:
x=lng*20037508.34/180
y=(math.log(math.tan((90+ lat)*math.pi/360))/(math.pi/180))*20037508.34/180
wherein: long is longitude of WGS84 coordinate, lat is latitude of WGS84 coordinate, math.log is function of solving natural logarithm, math.tan is function of solving tangent value of radian, math.pi is circumferential rate pi constant, x is x coordinate of mercator projection coordinate, and y is y coordinate of mercator projection coordinate;
s2.3.2, setting a buffer area at each intersection, wherein the radius threshold of the buffer area is 25 meters, and associating the mercator projection coordinates of the position of the electric bicycle with the buffer area of the intersection.
Further, the electric bicycle violation event collection method in the step S3 includes the following steps: the camera carries out real-time image acquisition to electric bicycle, uses image recognition algorithm to discern the electric bicycle incident of violating the regulations, uploads the electric bicycle incident of violating the regulations to the postgreSQL database, and the electric bicycle incident of violating the regulations includes: run a red light, go against the wrong direction, do not wear the helmet, do not put on the license, the manned violation event field of electric bicycle includes: violation time, violation location longitude, violation location latitude, violation event type; the electric bicycle violation analysis module collects the electric bicycle violation event data from the PostgreSQL database.
Further, the electric bicycle violation analyzing method in the step S4 includes the steps of:
s4.1, reading the electric bicycle violation event data collected in the step S3;
s4.2, reading the geographical map layer data of the road intersection, wherein the geographical map layer field of the road intersection comprises the following steps: the intersection number, the intersection name, the intersection longitude and the intersection latitude;
s4.3, correlating the longitude of the violation location and the latitude of the violation location in the electric bicycle violation event data with the geographical map layer data of the road intersection to calculate the violation number of the electric bicycles at the road intersection;
specifically, the longitude of the violation place, the latitude of the violation place, the longitude of the intersection and the latitude of the intersection are subjected to coordinate conversion, and the WGS84 coordinates are converted into the Mercator projection coordinates; setting a buffer area at each intersection, wherein the radius threshold of the buffer area is 30 meters, and associating the ink card tray projection coordinates of the electric bicycle violation event data with the buffer area of the intersection;
and S4.4, according to the violation quantity of the electric bicycles at the road intersection obtained in the step S4.3, aggregating and calculating the violation quantity of the electric bicycles at each intersection by using the intersection number, and sequencing the violation quantity from large to small to calculate the violation ranking of the electric bicycles at the intersections.
Further, the method for monitoring the flow and the violation of regulations of the electric bicycle in the whole area in the step S5 comprises the following steps: the real-time monitoring module calls a data interface to obtain the flow data and the violation event data of the electric bicycles in the whole area, and calls the data interface once every 15 minutes to monitor the flow and the violation conditions of the electric bicycles in the whole area in real time; the monitoring content comprises the following steps: the method comprises the steps of displaying an electric bicycle flow map, displaying the whole area electric bicycle flow, ranking the intersection electric bicycle flow, changing the electric bicycle flow in 24 hours and ranking the intersection electric bicycles against regulations.
Further, the method for monitoring the flow and the violation of regulations of the electric bicycle at the road intersection in the step S6 comprises the following steps: the real-time monitoring module calls a data interface to obtain the traffic data and the violation event data of the electric bicycles at the road intersection, and calls the data interface every 15 minutes to monitor the traffic and the violation conditions of the electric bicycles at the road intersection in real time; the monitoring content comprises the following steps: the method comprises the following steps of intersection video monitoring, intersection electric bicycle flow, intersection electric bicycle 24-hour flow change and intersection electric bicycle violation detail.
The invention has the beneficial effects that:
the invention relates to a working method of an electric bicycle big data analysis system, in particular to a kafka system which is a high-throughput distributed publishing and subscribing message system and can process all action flow data of a consumer in a website.
A buffer area: the method is an influence range or a service range of a geographic space target, and is an information analysis method for realizing data expansion in a two-dimensional space by forming a certain buffer area polygonal entity around a selected group or class of map elements (points, lines or surfaces) according to a set distance condition.
WGS84 coordinate system: is a geocentric coordinate system adopted internationally. The origin of coordinates is the earth centroid, the Z axis of the rectangular coordinate system of the earth centroid space points to the protocol earth pole (CTP) direction defined by BIH (international time service organization) 1984.0, the X axis points to the intersection point of the meridian plane of BIH 1984.0 and the equator of CTP, and the Y axis, the Z axis and the X axis are perpendicular to form a right-hand coordinate system, which is called world geodetic coordinate system in 1984.
Mercator projection coordinate system: is a positive axis equiangular cylindrical projection. Created in 1569 by the netherlands maplogist mercator. A cylinder in the same direction with the earth axis is supposed to be cut or cut on the earth, the graticule is projected on the cylindrical surface according to the equiangular condition, and the cylindrical surface is spread into a plane, so that the projection is obtained. Among the tangential and secant cylindrical projections, the mercator projection is the most commonly used tangential projection at the earliest.
The working method of the big data analysis system of the electric bicycle calculates the flow and the violation events of the electric bicycle at each road intersection based on the GPS data and the video monitoring data of the electric bicycle, and analyzes the flow and the violation events of the electric bicycle from multiple dimensions, wherein the analysis dimensions comprise: the method comprises the following steps of intersection electric bicycle flow, whole-area electric bicycle flow, intersection electric bicycle flow ranking, intersection electric bicycle 24-hour flow change, whole-area electric bicycle 24-hour flow change, intersection electric bicycle violation ranking and intersection electric bicycle violation detail. The system monitors the flow of the electric bicycle and the violation events in a certain area in real time, and strengthens the supervision of the electric bicycle.
Drawings
Fig. 1 is a schematic structural diagram of an electric bicycle big data analysis system according to the present invention;
FIG. 2 is a flowchart illustrating the operation of the electric bicycle flow analysis module of the big data analysis system of the electric bicycle according to the present invention;
FIG. 3 is a flowchart of the operation of the electric bicycle violation analysis module of the electric bicycle big data analysis system according to the present invention;
fig. 4 is a flowchart illustrating a real-time monitoring module of the big data analysis system of the electric bicycle according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and the detailed description. It is to be understood that the embodiments described herein are illustrative only and are not limiting, i.e., that the embodiments described are only a few embodiments, rather than all, of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations, and the present invention may have other embodiments.
Thus, the following detailed description of specific embodiments of the present invention presented in the accompanying drawings is not intended to limit the scope of the invention as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the detailed description of the invention without inventive step, are within the scope of protection of the invention.
For a further understanding of the contents, features and effects of the present invention, the following embodiments will be illustrated in detail with reference to the accompanying drawings 1-4:
the first specific implementation way is as follows:
a big data analysis system of an electric bicycle comprises an electric bicycle flow analysis module, an electric bicycle violation analysis module and a real-time monitoring module;
the electric bicycle flow analysis module is used for electric bicycle GPS data acquisition and electric bicycle flow analysis;
the electric bicycle violation analysis module is used for collecting electric bicycle violation events and analyzing the electric bicycle violation events;
the real-time monitoring module is used for monitoring the flow and the violation of regulations of the electric bicycles in the whole area and the flow and the violation of regulations of the electric bicycles at the road intersection;
the electric bicycle flow analysis module, the electric bicycle violation analysis module and the real-time monitoring module are connected with one another through data interfaces.
The second embodiment is as follows:
according to a specific embodiment, the working method of the big data analysis system of the electric bicycle comprises the following steps:
s1, carrying out electric bicycle GPS data acquisition by an electric bicycle flow analysis module;
further, the electric bicycle GPS data acquisition method in step S1 uploads the GPS data to the Kafka system in real time for the electric bicycle terminal device, and the electric bicycle GPS data field includes: the electric bicycle flow analysis module collects GPS data of the electric bicycle from a Kafka system;
s2, the electric bicycle flow analysis module analyzes the flow of the electric bicycle;
further, the method for analyzing the flow rate of the electric bicycle in the step S2 includes the steps of:
s2.1, removing abnormal data from the electric bicycle GPS data collected in the step S1, wherein the abnormal data comprises: the GPS time abnormal data of the electric bicycle and the GPS repeated data of the electric bicycle are obtained;
s2.2, reading the geographical map layer data of the road intersection, wherein the geographical map layer data field of the road intersection comprises the following steps: the intersection number, the intersection name, the intersection longitude and the intersection latitude;
s2.3, correlating the vehicle positioning longitude and the vehicle positioning latitude in the electric bicycle GPS data processed in the step S2.1 with the road intersection geographical map layer data obtained in the step S2.2, and calculating the electric bicycle flow of the road intersection;
further, the specific implementation method in step S2.3 is:
s2.3.1, converting the positioning longitude and the positioning latitude of the electric bicycle, the longitude and the latitude of an intersection and the latitude of the intersection, and converting the WGS84 coordinates into the projection coordinates of the mercator:
x=lng*20037508.34/180
y=(math.log(math.tan((90+ lat)*math.pi/360))/(math.pi/180))*20037508.34/180
wherein: long is longitude of WGS84 coordinate, lat is latitude of WGS84 coordinate, math.log is function of solving natural logarithm, math.tan is function of solving tangent value of radian, math.pi is circumferential rate pi constant, x is x coordinate of mercator projection coordinate, and y is y coordinate of mercator projection coordinate;
s2.3.2, setting a buffer area at each intersection, wherein the radius threshold of the buffer area is 25 meters, and associating the mercator projection coordinates of the position of the electric bicycle with the buffer areas of the intersections;
s2.4, according to the electric bicycle flow of the road intersection obtained in the step S2.3, calculating the total electric bicycle flow of each intersection by using intersection number aggregation, and calculating the electric bicycle flow ranking of the intersections according to the descending order of the flow;
s2.5, calculating the total flow of the electric bicycles for 24 hours by utilizing time aggregation according to the flow of the electric bicycles at the road intersection obtained in the step S2.3;
s3, collecting violation events of the electric bicycle by the violation analysis module of the electric bicycle;
further, the electric bicycle violation event collection method in the step S3 includes the following steps: the camera carries out real-time image acquisition to electric bicycle, uses image recognition algorithm to discern the electric bicycle incident of violating the regulations, uploads the electric bicycle incident of violating the regulations to the postgreSQL database, and the electric bicycle incident of violating the regulations includes: run a red light, go against the wrong direction, do not wear the helmet, do not put on the license, the violation of rules and regulations incident field of electric bicycle includes: violation time, violation location longitude, violation location latitude and violation event type; the electric bicycle violation analysis module acquires the electric bicycle violation event data from the PostgreSQL database;
s4, carrying out violation analysis on the electric bicycle by the violation analysis module of the electric bicycle;
further, the electric bicycle violation analyzing method in the step S4 includes the steps of:
s4.1, reading the electric bicycle violation event data collected in the step S3;
s4.2, reading the geographical layer data of the road intersection, wherein the geographical layer field of the road intersection comprises: the intersection number, the intersection name, the intersection longitude and the intersection latitude;
s4.3, correlating the longitude of the violation location and the latitude of the violation location in the electric bicycle violation event data with the geographical map layer data of the road intersection, and calculating the violation number of the electric bicycles at the road intersection;
specifically, the longitude of the violation place, the latitude of the violation place, the longitude of the intersection and the latitude of the intersection are subjected to coordinate conversion, and the WGS84 coordinates are converted into the Mercator projection coordinates; setting a buffer area at each intersection, wherein the radius threshold of the buffer area is 30 meters, and associating the ink card tray projection coordinates of the electric bicycle violation event data with the buffer area of the intersection;
s4.4, according to the violation quantity of the electric bicycles at the road intersection obtained in the step S4.3, the violation quantity of the electric bicycles at each intersection is calculated by using intersection number aggregation, and the violation ranks of the electric bicycles at the intersections are calculated according to the descending order of the violation quantity;
s5, the real-time monitoring module monitors the flow and the violation of regulations of the electric bicycle in the whole area;
further, the method for monitoring the flow and the violation of regulations of the electric bicycle in the whole area in the step S5 comprises the following steps: the real-time monitoring module calls a data interface to obtain the flow data and the violation event data of the electric bicycles in the whole area, and calls the data interface once every 15 minutes to monitor the flow and the violation conditions of the electric bicycles in the whole area in real time; the monitoring content comprises the following steps: displaying an electric bicycle flow map, displaying the flow of electric bicycles in the whole area, ranking the flow of electric bicycles at the intersection, changing the flow of the electric bicycles for 24 hours, and ranking the electric bicycles at the intersection against regulations;
further, the electric bicycle flow map display content comprises: the front end of the system takes a two-dimensional map of a certain area as a base map, the flow conditions of the electric bicycles are displayed at each road intersection on the map, and the flow of the electric bicycles is represented by different colors. The flow content of the full-area electric bicycle comprises the following steps: the total flow of the electric bicycle and the average flow of the electric bicycle per hour; the intersection electric bicycle flow ranking content comprises the following steps: the flow ranking sequence number, the intersection name and the flow of the electric bicycle are obtained; the 24-hour flow change content of the electric bicycle comprises the following contents: displaying the hourly flow change of the electric bicycle in a graph form; the intersection electric bicycle violation ranking content comprises the following steps: violation ranking sequence number, intersection name and violation quantity.
S6, the real-time monitoring module monitors the flow and the violation of the traffic of the electric bicycle at the road intersection;
further, the method for monitoring the flow and the violation of regulations of the electric bicycle at the road intersection in the step S6 comprises the following steps: the real-time monitoring module calls a data interface to obtain the traffic data of the electric bicycles at the road intersections and the violation event data of the electric bicycles at the road intersections, and calls the data interface once every 15 minutes to monitor the traffic and the violation conditions of the electric bicycles at the road intersections in real time; the monitoring content comprises the following steps: the method comprises the following steps of intersection video monitoring, intersection electric bicycle flow, intersection electric bicycle 24-hour flow change and intersection electric bicycle violation detail.
Further, the intersection video monitoring content comprises: clicking each intersection on a system map, displaying a real-time monitoring video of the intersection, and selecting different cameras to check the real-time traffic condition of the intersection if the intersection has a plurality of cameras; the flow content of the electric bicycle at the intersection comprises the following steps: the total flow of the electric bicycles at the intersection and the average flow of the electric bicycles at the intersection per hour are calculated; the 24-hour flow change content of the electric bicycle at the intersection comprises the following contents: displaying the hourly flow change of the electric bicycle at the intersection in a curve graph mode; the detail contents of the violation of the electric bicycles at the intersection comprise: the number of running red lights, the number of going backwards, the number of helmets not worn, the number of people not on the card and the number of people carrying the rule violation.
The working method of the big data analysis system of the electric bicycle in the embodiment provides a calculation method and an analysis method of electric bicycle traffic and electric bicycle violation events, and realizes real-time monitoring of the electric bicycle traffic and the electric bicycle violation events in multiple dimensions.
The key points and points to be protected of the technology of the invention are as follows:
1. a method for calculating the traffic of electric bicycles and the violation events of electric bicycles and an analysis method.
2. A design method of a real-time monitoring module of an electric bicycle.
It is noted that relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element.
While the application has been described above with reference to specific embodiments, various modifications may be made and equivalents may be substituted for elements thereof without departing from the scope of the application. In particular, the various features of the embodiments disclosed herein may be used in any combination that is not inconsistent with the structure, and the failure to exhaustively describe such combinations in this specification is merely for brevity and resource conservation. Therefore, it is intended that the application not be limited to the particular embodiments disclosed, but that the application will include all embodiments falling within the scope of the appended claims.
Claims (1)
1. A working method of an electric bicycle big data analysis system is realized by relying on the electric bicycle big data analysis system, and the electric bicycle big data analysis system comprises an electric bicycle flow analysis module, an electric bicycle violation analysis module and a real-time monitoring module;
the electric bicycle flow analysis module is used for electric bicycle GPS data acquisition and electric bicycle flow analysis;
the electric bicycle violation analysis module is used for collecting electric bicycle violation events and analyzing the electric bicycle violation events;
the real-time monitoring module is used for monitoring the flow and the violation of regulations of the electric bicycles in the whole area and the flow and the violation of regulations of the electric bicycles at the road intersection;
the electric bicycle flow analysis module, the electric bicycle violation analysis module and the real-time monitoring module are connected with one another through data interfaces;
the method is characterized in that: the method comprises the following steps:
s1, an electric bicycle flow analysis module acquires electric bicycle GPS data;
the electric bicycle GPS data acquisition method in the step S1 is that the electric bicycle terminal equipment uploads GPS data to a Kafka system in real time, and the electric bicycle GPS data field comprises: the electric bicycle flow analysis module collects GPS data of the electric bicycle from a Kafka system;
s2, the electric bicycle flow analysis module analyzes the flow of the electric bicycle;
the electric bicycle flow analysis method in the step S2 comprises the following steps:
s2.1, removing abnormal data from the electric bicycle GPS data collected in the step S1, wherein the abnormal data comprises: the GPS time abnormal data of the electric bicycle and the GPS repeated data of the electric bicycle are obtained;
s2.2, reading the geographical layer data of the road intersection, wherein the geographical layer data field of the road intersection comprises: the intersection number, the intersection name, the intersection longitude and the intersection latitude;
s2.3, correlating the vehicle positioning longitude and the vehicle positioning latitude in the electric bicycle GPS data processed in the step S2.1 with the road intersection geographical map layer data obtained in the step S2.2, and calculating the electric bicycle flow of the road intersection;
the specific implementation method in step S2.3 is:
s2.3.1, converting the positioning longitude and the positioning latitude of the electric bicycle, the longitude and the latitude of an intersection and the latitude of the intersection, and converting the WGS84 coordinates into the projection coordinates of the mercator:
x＝lng*20037508.34/180
y＝(math.log(math.tan((90+lat)*math.pi/360))/(math.pi/180))*20037508.34/180
wherein: long is longitude of WGS84 coordinate, lat is latitude of WGS84 coordinate, math.log is function of solving natural logarithm, math.tan is function of solving tangent value of radian, math.pi is circumferential rate pi constant, x is x coordinate of mercator projection coordinate, and y is y coordinate of mercator projection coordinate;
s2.3.2 setting a buffer area at each intersection, wherein the radius threshold of the buffer area is 25 m, and associating the mercator projection coordinate of the position of the electric bicycle with the buffer areas of the intersections;
s2.4, according to the electric bicycle flow of the road intersection obtained in the step S2.3, calculating the total electric bicycle flow of each intersection by using intersection number aggregation, and calculating the electric bicycle flow ranking of the intersections according to the descending order of the flow;
s2.5, calculating the total flow of the electric bicycles for 24 hours by utilizing time aggregation according to the electric bicycle flow of the road intersection obtained in the step S2.3;
s3, collecting violation events of the electric bicycle by the violation analysis module of the electric bicycle;
the electric bicycle violation event collection method in the step S3 comprises the following steps: the camera carries out real-time image acquisition to electric bicycle, uses image recognition algorithm to discern the electric bicycle incident of violating the regulations, uploads the electric bicycle incident of violating the regulations to the postgreSQL database, and the electric bicycle incident of violating the regulations includes: run a red light, go against the wrong direction, do not wear the helmet, do not put on the license, the violation of rules and regulations incident field of electric bicycle includes: violation time, violation location longitude, violation location latitude, violation event type; the electric bicycle violation analysis module acquires the electric bicycle violation event data from the PostgreSQL database;
s4, carrying out violation analysis on the electric bicycle by the violation analysis module of the electric bicycle;
the electric bicycle violation analysis method in the step S4 comprises the following steps:
s4.1, reading the electric bicycle violation event data collected in the step S3;
s4.2, reading the geographical map layer data of the road intersection, wherein the geographical map layer field of the road intersection comprises the following steps: the intersection number, the intersection name, the intersection longitude and the intersection latitude;
s4.3, correlating the longitude of the violation location and the latitude of the violation location in the electric bicycle violation event data with the geographical map layer data of the road intersection to calculate the violation number of the electric bicycles at the road intersection;
specifically, the longitude of the violation place, the latitude of the violation place, the longitude of the intersection and the latitude of the intersection are subjected to coordinate conversion, and the WGS84 coordinates are converted into the Mercator projection coordinates; setting a buffer area at each intersection, wherein the radius threshold of the buffer area is 30 meters, and associating the ink card tray projection coordinates of the electric bicycle violation event data with the buffer area of the intersection;
s4.4, according to the violation quantity of the electric bicycles at the road intersection obtained in the step S4.3, the violation quantity of the electric bicycles at each intersection is calculated by using intersection number aggregation, and the violation ranks of the electric bicycles at the intersections are calculated according to the descending order of the violation quantity;
s5, the real-time monitoring module monitors the flow and the violation of regulations of the electric bicycle in the whole area;
the method for monitoring the flow and the violation of regulations of the electric bicycle in the whole area in the step S5 comprises the following steps: the real-time monitoring module calls a data interface to obtain the flow data and the violation event data of the electric bicycles in the whole area, and calls the data interface once every 15 minutes to monitor the flow and the violation conditions of the electric bicycles in the whole area in real time; the monitoring content comprises the following steps: displaying an electric bicycle flow map, displaying the flow of electric bicycles in the whole area, ranking the flow of electric bicycles at the intersection, changing the flow of the electric bicycles for 24 hours, and ranking the electric bicycles at the intersection against regulations;
s6, the real-time monitoring module monitors the flow and the violation of the traffic of the electric bicycle at the road intersection;
the method for monitoring the flow and the violation of regulations of the electric bicycle at the road intersection in the step S6 comprises the following steps: the real-time monitoring module calls a data interface to obtain the traffic data of the electric bicycles at the road intersections and the violation event data of the electric bicycles at the road intersections, and calls the data interface once every 15 minutes to monitor the traffic and the violation conditions of the electric bicycles at the road intersections in real time; the monitoring content comprises the following steps: the method comprises the following steps of intersection video monitoring, intersection electric bicycle flow, intersection electric bicycle 24-hour flow change and intersection electric bicycle violation detail.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN202211140041.1A CN115223371B (en) | 2022-09-20 | 2022-09-20 | Big data analysis system of electric bicycle and working method thereof |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202211140041.1A CN115223371B (en) | 2022-09-20 | 2022-09-20 | Big data analysis system of electric bicycle and working method thereof |
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| Publication Number | Publication Date |
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| CN115223371A CN115223371A (en) | 2022-10-21 |
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Citations (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103139287A (en) * | 2012-12-11 | 2013-06-05 | 厦门雅迅网络股份有限公司 | Map aggregation vehicle refreshing method based on distributed calculating |
| CN104616501A (en) * | 2015-02-06 | 2015-05-13 | 河海大学常州校区 | Intelligent vision based vehicle law-violation parking detection system and method |
| CN105491124A (en) * | 2015-12-03 | 2016-04-13 | 北京航空航天大学 | Distributed aggregation method for moving vehicles |
| CN106503450A (en) * | 2016-10-25 | 2017-03-15 | 镇江恒驰科技有限公司 | A kind of electric motor car big data analysis optimization System and method for |
| CN106777811A (en) * | 2017-01-16 | 2017-05-31 | 昆明理工大学 | A kind of traffic channelling Map Design development approach |
| CN107036607A (en) * | 2015-12-15 | 2017-08-11 | 本田技研工业株式会社 | For the system and method for the map datum for examining vehicle |
| CN107146429A (en) * | 2017-07-03 | 2017-09-08 | 辽宁科技大学 | A kind of method for controlling traffic signal lights merged based on image and GPS information |
| CN107657010A (en) * | 2017-09-25 | 2018-02-02 | 南京市城市与交通规划设计研究院股份有限公司 | vehicle data analysis system and method |
| CN108898856A (en) * | 2018-09-13 | 2018-11-27 | 崔理哲 | Intelligent city's traffic optimization method and system |
| WO2019000770A1 (en) * | 2017-06-27 | 2019-01-03 | 苏州美天网络科技有限公司 | App-based electric vehicle anti-theft tracking system |
| CN109377762A (en) * | 2018-12-23 | 2019-02-22 | 郑州威科姆华大北斗导航科技有限公司 | Electric vehicle is driven against traffic regulations recording method and system |
| CN110223520A (en) * | 2019-07-16 | 2019-09-10 | 网链科技集团有限公司 | Electric bicycle hypervelocity recognition methods |
| CN110930707A (en) * | 2019-12-03 | 2020-03-27 | 浙江中核信息科技有限公司 | Non-motor vehicle traffic violation supervision system and supervision method |
| CN112365595A (en) * | 2020-11-24 | 2021-02-12 | 盐城工学院 | Analysis method for identifying traffic accident multi-point based on alarm data point element |
| CN112632399A (en) * | 2021-03-09 | 2021-04-09 | 四川万网鑫成信息科技有限公司 | Topological relation obtaining method and device based on spatial position and storage medium |
| CN112989223A (en) * | 2021-03-22 | 2021-06-18 | 杭州电子科技大学 | Electric bicycle track map matching method based on track correction and simplification |
| CN113538952A (en) * | 2021-06-04 | 2021-10-22 | 深圳市城市交通规划设计研究中心股份有限公司 | Bus interval abnormity identification and early warning system, method, equipment and storage medium |
| CN113554353A (en) * | 2021-08-25 | 2021-10-26 | 宁波工程学院 | Public bicycle space scheduling optimization method for avoiding space siltation |
| CN113570864A (en) * | 2021-09-22 | 2021-10-29 | 深圳市城市交通规划设计研究中心股份有限公司 | Method and device for matching running path of electric bicycle and storage medium |
| CN114537569A (en) * | 2022-01-25 | 2022-05-27 | 深圳飞亮智能科技有限公司 | Electric motor car intelligence center control system based on big data |
| CN114898573A (en) * | 2022-03-29 | 2022-08-12 | 深圳市综合交通与市政工程设计研究总院有限公司 | Monitoring system for traffic collinear section conventional non-motor vehicle flow |
Family Cites Families (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR2977122B1 (en) * | 2011-06-29 | 2013-07-19 | Geosys Sa | METHOD OF CORRECTING TIME SHIFT FOR AGRICULTURAL YIELD MEASUREMENT |
| CN102411872A (en) * | 2011-08-15 | 2012-04-11 | 彭认灿 | Method for generating line element buffer zone on earth elliposoid based on GIS (geographic information system) component |
| US9283967B2 (en) * | 2014-07-16 | 2016-03-15 | GM Global Technology Operations LLC | Accurate curvature estimation algorithm for path planning of autonomous driving vehicle |
| CN104933869B (en) * | 2015-05-19 | 2017-02-01 | 交通运输部公路科学研究所 | Anti-noise type satellite positioning data-based method for identifying vehicle violation behaviors |
| US20170024369A1 (en) * | 2015-07-24 | 2017-01-26 | Ford Global Technologies, Llc | Event Tagging Systems And Methods |
| JP6634887B2 (en) * | 2016-03-02 | 2020-01-22 | 株式会社リコー | Information providing system, method, program and network system |
| CN106781620B (en) * | 2016-12-20 | 2022-02-15 | 斑马信息科技有限公司 | Violation information system and violation map construction method |
| CN108776727B (en) * | 2018-05-29 | 2021-10-29 | 福州大学 | Road geometric feature extraction method based on taxi track data |
| CN108847033B (en) * | 2018-08-22 | 2021-07-06 | 河南弋之众合信息科技有限公司 | Method and device for matching illegal road sections of vehicles and server |
| CN109191833A (en) * | 2018-08-29 | 2019-01-11 | 深圳市赛为智能股份有限公司 | A kind of intelligent transportation system and its management method |
| US11814133B2 (en) * | 2019-08-15 | 2023-11-14 | Lyft, Inc. | Systems and methods for configuring personal mobility vehicle brakes based on location |
| CN111127891B (en) * | 2019-12-27 | 2021-04-27 | 中国交通通信信息中心 | Road network checking method based on floating car track big data |
| US20210209803A1 (en) * | 2020-01-06 | 2021-07-08 | Quantela Inc | Computer-based method and system for geo-spatial analysis |
| CN112362067B (en) * | 2020-11-25 | 2022-09-30 | 江苏恒澄交科信息科技股份有限公司 | Autonomous route planning method for inland river intelligent ship |
| US20220242442A1 (en) * | 2021-02-03 | 2022-08-04 | John S. McNeely | Drive trajectory system and device |
-
2022
- 2022-09-20 CN CN202211140041.1A patent/CN115223371B/en active Active
Patent Citations (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103139287A (en) * | 2012-12-11 | 2013-06-05 | 厦门雅迅网络股份有限公司 | Map aggregation vehicle refreshing method based on distributed calculating |
| CN104616501A (en) * | 2015-02-06 | 2015-05-13 | 河海大学常州校区 | Intelligent vision based vehicle law-violation parking detection system and method |
| CN105491124A (en) * | 2015-12-03 | 2016-04-13 | 北京航空航天大学 | Distributed aggregation method for moving vehicles |
| CN107036607A (en) * | 2015-12-15 | 2017-08-11 | 本田技研工业株式会社 | For the system and method for the map datum for examining vehicle |
| CN106503450A (en) * | 2016-10-25 | 2017-03-15 | 镇江恒驰科技有限公司 | A kind of electric motor car big data analysis optimization System and method for |
| CN106777811A (en) * | 2017-01-16 | 2017-05-31 | 昆明理工大学 | A kind of traffic channelling Map Design development approach |
| WO2019000770A1 (en) * | 2017-06-27 | 2019-01-03 | 苏州美天网络科技有限公司 | App-based electric vehicle anti-theft tracking system |
| CN107146429A (en) * | 2017-07-03 | 2017-09-08 | 辽宁科技大学 | A kind of method for controlling traffic signal lights merged based on image and GPS information |
| CN107657010A (en) * | 2017-09-25 | 2018-02-02 | 南京市城市与交通规划设计研究院股份有限公司 | vehicle data analysis system and method |
| CN108898856A (en) * | 2018-09-13 | 2018-11-27 | 崔理哲 | Intelligent city's traffic optimization method and system |
| CN109377762A (en) * | 2018-12-23 | 2019-02-22 | 郑州威科姆华大北斗导航科技有限公司 | Electric vehicle is driven against traffic regulations recording method and system |
| CN110223520A (en) * | 2019-07-16 | 2019-09-10 | 网链科技集团有限公司 | Electric bicycle hypervelocity recognition methods |
| CN110930707A (en) * | 2019-12-03 | 2020-03-27 | 浙江中核信息科技有限公司 | Non-motor vehicle traffic violation supervision system and supervision method |
| CN112365595A (en) * | 2020-11-24 | 2021-02-12 | 盐城工学院 | Analysis method for identifying traffic accident multi-point based on alarm data point element |
| CN112632399A (en) * | 2021-03-09 | 2021-04-09 | 四川万网鑫成信息科技有限公司 | Topological relation obtaining method and device based on spatial position and storage medium |
| CN112989223A (en) * | 2021-03-22 | 2021-06-18 | 杭州电子科技大学 | Electric bicycle track map matching method based on track correction and simplification |
| CN113538952A (en) * | 2021-06-04 | 2021-10-22 | 深圳市城市交通规划设计研究中心股份有限公司 | Bus interval abnormity identification and early warning system, method, equipment and storage medium |
| CN113554353A (en) * | 2021-08-25 | 2021-10-26 | 宁波工程学院 | Public bicycle space scheduling optimization method for avoiding space siltation |
| CN113570864A (en) * | 2021-09-22 | 2021-10-29 | 深圳市城市交通规划设计研究中心股份有限公司 | Method and device for matching running path of electric bicycle and storage medium |
| CN114537569A (en) * | 2022-01-25 | 2022-05-27 | 深圳飞亮智能科技有限公司 | Electric motor car intelligence center control system based on big data |
| CN114898573A (en) * | 2022-03-29 | 2022-08-12 | 深圳市综合交通与市政工程设计研究总院有限公司 | Monitoring system for traffic collinear section conventional non-motor vehicle flow |
Non-Patent Citations (3)
| Title |
|---|
| Design and implementation of embedded GPS system;Shi Jun-yong;《2012 IEEE International Conference on Computer Science and Automation Engineering (CSAE)》;20120820;311-314 * |
| Robust and real-time multi-cue map verification for the road ahead;Oliver Hartmann;《17th International IEEE Conference on Intelligent Transportation Systems (ITSC)》;20141120;894-899 * |
| 电动自行车智能充电系统设计实现及关键技术研究;周雪梅;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20210215;C042-2051 * |
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