CN103020222A - Visual mining method for vehicle GPS (global positioning system) data analysis and abnormality monitoring - Google Patents

Visual mining method for vehicle GPS (global positioning system) data analysis and abnormality monitoring Download PDF

Info

Publication number
CN103020222A
CN103020222A CN2012105361187A CN201210536118A CN103020222A CN 103020222 A CN103020222 A CN 103020222A CN 2012105361187 A CN2012105361187 A CN 2012105361187A CN 201210536118 A CN201210536118 A CN 201210536118A CN 103020222 A CN103020222 A CN 103020222A
Authority
CN
China
Prior art keywords
data
analysis
fingerprint
visual
time
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN2012105361187A
Other languages
Chinese (zh)
Other versions
CN103020222B (en
Inventor
蒲剑苏
屈华民
倪明选
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangzhou HKUST Fok Ying Tung Research Institute
Original Assignee
Guangzhou HKUST Fok Ying Tung Research Institute
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangzhou HKUST Fok Ying Tung Research Institute filed Critical Guangzhou HKUST Fok Ying Tung Research Institute
Priority to CN201210536118.7A priority Critical patent/CN103020222B/en
Publication of CN103020222A publication Critical patent/CN103020222A/en
Application granted granted Critical
Publication of CN103020222B publication Critical patent/CN103020222B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Abstract

The invention relates to a visual mining method based on a visualization technology for vehicle GPS (global positioning system) data analysis and abnormality monitoring. Original vehicle GPS data are converted by a data conversion module into a unique visual ''fingerprint stamp'' data model, a data index for real-time response to user interaction is provided to help a user to analyze data; and a visual vehicle data model is combined with a display based on a heat distribution map and a trajectory through a visualization module, detection of urban hot spot areas and traffic trajectory abnormality monitoring based on historical data are performed, and certain abstract concepts in the data, such as frequent rules and periodic rules, are displayed in an easy-to-understand way for an analyzer. The analysis threshold can be lowered, the scope of application can be expanded, and the analysis efficiency can be improved. Rich interaction operations can be realized through a user interaction module, monitoring and analysis can be performed by the user, and analysis and support can be provided for decisions of the user.

Description

The visual method for digging that is used for vehicle GPS data analysis and exception monitoring
Technical field
The present invention relates to a kind of visualization technique that utilizes and realize method to vehicle GPS data analysis and exception monitoring.The method can support flow data in real-time display monitoring, with viewdata model " fingerprint " with some abstract concepts that exist in the vehicle GPS data, such as frequent rule and periodic law, holding intelligible mode with a kind of analyst shows, reduce and analyze threshold, enlarge range of application, improve analysis efficiency.
Background technology
Through more than 20 years facts have proved, gps system was a high precision, round-the-clock and global radio navigation, location and multifunction system regularly.The GPS related system is widely used in public security, medical treatment, fire-fighting, traffic, the fields such as logistics.In recent years, GPS equipment, mobile communication equipment and various kinds of sensors equipment being widely used in the world, produced the track data that comprises in a large number spatial geographical locations information, temporal information and other relevant information, its data volume often can reach TB level even PB level.Because these class data is in large scale, surpassed the scope that the traditional data treatment technology can effectively be processed, therefore, the gps data take traffic data and track of vehicle as representative is carried out efficient analysis and the degree of depth is excavated, become one of study hotspot in the present IT field.Gps data is carried out data mining and Knowledge Discovery has important social benefit and economic benefit, is the direction of scientific rersearch that present national governments, enterprise and research institution very pay attention to.Through behind the mining analysis, the knowledge of obtaining from the vehicle GPS data has very wide application prospect, and for example, traffic data can be applied to a plurality of fields such as city management, roading, traffic control, trip planning.
But the data that GPS collects include time, space characteristics simultaneously, can classify as space-time data.And in recent years along with the continuous expansion of data scale, stern challenge has been proposed for the analysis of space-time data.At first, because the complicacy of geographical space, when relating to the feature of space correlation in the data, traditional method such as statistics, data mining and machine learning can not be carried out complete analysis by full automatic method, whole process need expert participates in the overall process, utilize the people to the relevant understanding in space and zone, to the intrinsic attribute of space correlation and the implicit knowledge of relation.Secondly, the time correlation feature also is the phenomenon of a complexity.The mode of time with linearity itself changes, but the generation rule of event As time goes on but can be periodically to repeat, and repeatedly is cycled to repeat; Whole formation hierarchical structure, even have overlapping and inter-related characteristic on the time attribute between event and the event.And temporal characteristics also has the characteristics of isomery, and therefore, we must distinguish daytime and evening, working day and weekend, vacation and normal work period.These knowledge professionals or the user that participate in to analyze are had very deep understanding, but this is need to sense and be difficult to convey to a kind of sensation of machine.Therefore, the data with temporal characteristics also need a large amount of participations of expert in analysis, by analyze with appropriate expression-form with mining data in relevant rule.
The mass data that produces of miscellaneous information source in recent years, head and shoulders above the ability of these data of human brain analysis interpretation, owing to lacking effective analysis means of mass data, a large amount of computational resources are wasted, this has seriously hindered the progress of scientific research, and visual (Visualization) technology proposes thus.Modern data visualization (Data Visualization) technology refers to uses computer graphics and image processing techniques, data is changed to figure or image shows at screen, and carries out theory, method and the technology of interaction process.It relates to a plurality of fields such as computer graphics, image processing, computer-aided design (CAD), computer vision and human-computer interaction technology.The data visualization concept is at first from visualization in scientific computing (Visualization in Scientific Computing), scientists not only needs to analyze the data of being calculated by computing machine by graph image, and needs to understand the variation of data in computation process.In recent years, along with the development of network technology and ecommerce, the requirement of information visualization (Information Visualization) has been proposed.We can pass through data visualization technique, find implicit rule in a large amount of finance, communication and the business data, thereby provide foundation for decision-making.This has become focus new in the data visualization technique.
The visualized data analytical technology has been widened traditional figure table function, makes the user clearer to the analysis of data.For example the multidimensional data in the database is become multiple figure, this situation to reminder-data, inward nature and regularity have played very strong effect.When show to find as a result the time, map is shown as a setting simultaneously.The regularity of distribution that can show on the one hand its knowledge feature; Also can carry out Visual Explanation to the result who excavates on the other hand, thereby reach best analytical effect.Visualization technique makes the user see overall process, monitoring and the control data analysis process of data processing.And be accordingly, the conventional process analytical approach can only be applicable to the small-scale data, some abstract concepts that can not well represent the existence in the data analysis, and be difficult to hold intelligible mode with a kind of people and show large data, the real-time demonstration of flow data can not be supported.
Find through retrieval, utilize at present system and the company of vehicle-mounted GPS equipment, the secondary development vehicle monitoring system holistic conformation scheme of various GPS/GIS/GSM/GPRS vehicle monitoring system softwares, GSM and GPRS intelligent movable car-mounted terminal, system does not all have the method for digging of analyzing in to the vehicle GPS data monitoring.In the situation that unusual the generation, the user expends more time and resource with needs, can make a policy.
The present invention has filled up this technological gap, with the display analysis problem that effectively solves extensive vehicle GPS data and bring.
Summary of the invention
The technical problem to be solved in the present invention is, in the situation that extensive real-time stream, to the Higher Dimensional Space Time data of collecting, for the user provides the detecting of density-based city thermal map and based on the unusual Real Time Monitoring of traffic track of historical data, and be aided with historical data and statistical information, effectively analyze frequent rule and periodic law in the data, find out hiding rule and mistake, hold intelligible mode with a kind of analyst and show, reduce and analyze threshold, enlarge range of application, improve analysis efficiency.
The present invention is intended to propose a kind of visual method for digging for vehicle GPS data analysis and exception monitoring; make the user can be in the situation that the monitoring of extensive spatio-temporal data stream and real-time analysis detect result's concrete condition; unusual or the error message that can not be detected by conventional statistics and data mining algorithm of find hiding; and by a kind of visual data model " fingerprint " these abstract concepts are held intelligible mode with the analyst and show; analyze threshold thereby reduce; enlarge range of application, improve analysis efficiency.
The technical scheme that adopts for achieving the above object comprises data conversion module, visualization model, user interactive module design, by data conversion module the processing of original vehicle gps data is converted to visual " fingerprint " data model, make numeric data become the visual elements (shape of readability, color, size etc.), and provide can the real-time response user interactions data directory, assisted user is analyzed data; By visualization model vehicle viewdata model is treated to based on thermal map with based on the demonstration of track, allows the user that data are had direct feel; Realize abundant interactive operation by user interactive module, the user can be monitored and analyze, thereby analyze and support for user's decision-making provides.
The present invention monitors and analyzes than fairly large vehicle GPS data by a kind of visual data model " fingerprint ", and the unusual formal intuition with people's readability that wherein exists is showed analyst or expert.This viewdata model is intended to utilize advanced visualization technique people's intelligence to be embedded in the process of large data analysis, distance between analyst and extensive vehicle GPS data furthers, reduction provides the scope of application of application by the analysis threshold that large-scale data brings.Visual " fingerprint " model provides the intelligible mode of a kind of appearance to show extensive gps data, and supports the real-time demonstration of flow data.Whole model is used for monitoring and analyzing than fairly large vehicle GPS data, therefore be designed to space (S), time (T), and attribute (A) is to mapping: a S * T * A → Fingerprint of fingerprint model (Fingerprint)." fingerprint " data model (Fingerprint) is different from traditional data models, have two data structures, numeric data after the corresponding original data processing of abstract data structure (Abstract Form), the geological information that viewdata structure (Visual Form) corresponding data shows at screen.According to definition, at first select certain space scope (S), coordinate information and the size of record selection area in fingerprint model (F), in this scope (S) according to according to the time (T) with the row with row relations organize original gps data, delegation in the table represents a complete time period, such as one day, the corresponding row of the burst of complete each regular length of time period, such as hour of corresponding one day of each row, each field in the table has represented the respective value of attribute (A) at last, such as the statistical value in a hour.The fingerprint model can add corresponding geological information territory to generate viewdata model (Visual Form) for every attribute according to the abstract data structure that defines afterwards, built-in placement algorithm can generate corresponding geological information, such as the size of visualized elements border rectangle, shape type, coordinate information etc.Fingerprint data model of the present invention has adopted the placement algorithm of the ring-type nested structure of map-based to realize the demonstration of S * T * A → Fingerprint, and the position of corresponding fingerprint has represented the spatial information (S) that this visual structure is analyzed on the map; Adopt the corresponding time attribute of the nested layout of many rings to show (T) in the structure, corresponding complete time period of each ring, such as one day, time slicing of each fan-shaped burst correspondence was such as one hour in one day on the ring; Fan-shaped burst comes corresponding attribute (A) to show with color.This kind realized based on the abstract data structure of table, can produce corresponding index, can greatly improve the query rate of data, supports the real time data query analysis thereby reach the optimization system performance.
The fingerprint visual structure is suitable for detection, the analysis and comparison of frequent rule (Frequent Pattern) and periodic law (Periodic Pattern) very much.At first, each ring represents a complete time period in the nested layout of many rings, each ring in the layout has identical start time and concluding time, each time slicing is corresponding with the fan-shaped burst on the ring, encircle nested so that the position of the fan-shaped burst of the representative same time burst on each ring can be presented on the adjacent position more, represent the data (7 rings are arranged) in a week such as a fingerprint, a ring represents one day, fan-shaped burst represents corresponding one hour (24 fan-shaped bursts are arranged on the ring), and all fanning strips that represents 8pm are put at corresponding Preordering and all turned over clockwise near 270 positions of spending so; The color of fan-shaped burst has represented again corresponding property value simultaneously.Therefore, whether have periodically as long as observe the variation of color on fingerprint, distribute similar such as the fan-shaped burst of similar color at ring; Whether have frequent rule, repeat or concentrate on the upper a certain section zone of ring such as some Similar color and occur if changing.Abstract concept changes the visual information that is easy to analyst's understanding into like this.
After data conversion module receives that the vehicle GPS track data is as input, can at first process correction to the GPS raw data of collecting, mainly come the GPS positioning error, numerical map error and the coordinate projection mapping fault that exist in the data collection are revised by map-matching algorithm, road net informational linkage in vehicle location track and the numerical map is got up, and determine that thus moving target produces to reduce uncertain factor in the analysis with respect to the position of map.Then will revise GPS numeric data later and be converted to visual " fingerprint " data model, generate simultaneously a series of data directories, be used for online (Online) real-time response user interactions.
Receive the data directory and vehicle viewdata model of generation when visualization model after, to remove the abstract data that noise changes in the raw data to these, change into the visual pattern of data by built-in placement algorithm, again it is played up on screen at last.Placement algorithm provides two types view for user selection and switching, is respectively the thermal map detecting of density-based city and based on the unusual Real Time Monitoring of traffic track of historical data.This module strengthens the readability that data visualization shows by the demonstration with abstract data model and relationship map map on the analytic system, is beneficial to the user and compares the combining cartographic information analysis.Therefore the data after processing are carried out visually, the user can carry out Real-Time Monitoring to the variation of the data collected.By offer the user based on thermal map and based on two kinds of track dissimilar demonstrations, and a kind of visualized data of novelty " fingerprint " shows historical data, when guaranteeing monitoring effect, the assisted user analysis.
The thermal map detecting of density-based city is shown as background with geographical map, then is aided with " fingerprint " model corresponding to thermal map and regional and comes corresponding demonstration.First the city map grid is turned to pixel, then we calculate vehicle fleet and the record that leaves or arrive to each pixel.Then, we use thermal map to show the zone that the pixel of those focuses very high (being total vehicle number) forms at the 2D map.Again according to the situation of thermal map, select the fingerprint in those very high zones of corresponding focus and carry out analysis and comparison afterwards.
Based on the unusual Real Time Monitoring of traffic track of historical data with real-time demonstration vehicle GPS track to map, divide according to the zone that defines simultaneously and become corresponding " fingerprint " next life, historical data is converted to easy visual elements, make things convenient for analyst's fast understanding and find rule, thereby analyze unusually, improve analysis efficiency.
When providing user data to show, realize abundant alternately by user interactive module, and with data-switching and visualization model that these operational feedback arrive, allow the user in time the data after processing be carried out space attribute analysis and time series analysis.In analytic process, the user can be to interested or feel to be undertaken alternately by the visualization structure to selection area in valuable zone, thereby further understand the space-time characteristic of this area's data.After finishing, the user can check or carries out correlation inquiry according to existing analysis result raw data, thereby result of study is compared and puts in order, finally analyzes and supports for user's decision-making provides.
Utilization of the present invention is monitored visual method for digging based on the space-time data of visualization technique, and the beneficial effect that can reach is as follows:
1) based on the abnormality detection directly perceived of visualization technique;
2) dynamic Higher Dimensional Space Time data characteristics is analyzed, and has good ductility;
3) abundant expert is interactive.It can provide more statistical information, historical data is converted to the visual cues of readability from numerical value knowledge, such as shape, and color, size etc.The purpose of this way is to keep the analyst to participate in all the time in the process of whole analysis, and utilizes their analysis ability to adjust parameter and sum up research.
4) user can aware the correlativity between the different attribute, and can filter and the eliminating of incoherent track by noise data, and then to the analysis for further study of interested situation.This structural support interactive mode simultaneously, thus allow the user progressively refinement revise the parameter of analyzing, finally obtain better analysis result.
Description of drawings
The present invention is further described below in conjunction with drawings and Examples.
Fig. 1 is the process flow diagram that the vehicle GPS data are processed and monitored.
Fig. 2 is visualized data structure " fingerprint " schematic diagram.
Fig. 3 is data visualization analysis process schematic diagram.
Fig. 4 shows the generative process schematic diagram based on thermal map.
Fig. 5 is based on track display generative process schematic diagram.
Embodiment
As shown in Figure 1, data conversion module has defined visualized data structure " fingerprint " and the corresponding interface that is applied in vehicle GPS data analysis and the exception monitoring, necessary I/O operation-interface also is provided simultaneously, the user can read original gps data easily in the middle of file, database and network flow, and is converted into the abstract structures such as table, figure, tree.Comprised two parts content in the visualization model, visualized data is processed and layout processing.Visualized data is processed the abstract data element that defines from data conversion module is added corresponding geological information territory, to safeguard the size of visualized elements, the information such as position; Layout processing is used placement algorithm, generates geological information, and it is set to the geological information territory of visualized data.The invention provides as shown in Figure 1 the detecting of density-based city thermal map and based on two kinds of dissimilar placement algorithms of the unusual Real Time Monitoring of traffic track of historical data to the user.Also comprise two parts in the interactive module, play up and process and interaction process.Play up the geological information generation graphic element that processing and utilizing obtains from visualization model, and it is presented in face of the user the most at last; Various alternative events are collected and processed to interaction process among modules, and on the data with result retroaction and modules.
As shown in Figure 1, the visualized data structure " fingerprint " of the assistant analysis that we define, at data conversion module the most basic data structures is defined, organize original gps data according to row and the relation of row, each bar raw data is as the delegation in the table, the row in corresponding this corresponding line of each attribute in the data.This kind realized based on the abstract data structure of table, can produce corresponding index, can greatly improve the query rate of data, supports the real time data query analysis thereby reach the optimization system performance.
The viewdata processing section of while in visualization model, by in data conversion module to the definition of visual structure " fingerprint ", extract corresponding geological information record, comprise size, shape type, coordinate information of visualized elements border rectangle etc.All there are the visual copy of this correspondence in any data element and data structure in the data conversion module.Visualization model has been safeguarded abstract data element and the direct two-way mapping of visualized data element by this mechanism, for the data modification in the reciprocal process provides convenient, so that the change of abstract data element or visualized data element can be reflected on the opposing party's data element rapidly.
As shown in Figure 2, visual structure " fingerprint " specific design has adopted take the radial topological design of annular map as the basis, helps customer analysis historical data or statistical information.This design other attributes such as representing density, speed that can distribute with different color-coded scheme.Structural each ring one time of representing, can select as required is the design that shows 7 ring designs in a week or show 31 rings of one month.Each sector represents one hour in the ring, and the time, growing direction was along clockwise direction.Whole layout is just as a clock, and 12 of midnights, the whole time, lowest position represented 12 noon, gets back at last the point at midnights 12 of top along increasing clockwise in top.The growth on date is according to the radius growing direction of structure, and the ring of innermost circle is representing the date the earliest, and outmost turns is the nearest date.Such as the record that shows January 18 to January 24,18 days record is positioned at the position of innermost circle at last so, and 24 then is outset part.
Every attribute such as density, the speed etc. in the zone of visual structure " fingerprint " representative are showed intuitively by color coding, analysis design such as on-board and off-board behavior focus can be the brighter secter pat of color, density is lower, and the darker secter pat of color represents that this area's on-board and off-board activity is very frequent.The size of " fingerprint " structure is directly proportional with the data sum of selected areas, and the more sizes of data recording are larger, otherwise structure is little at least for data.
As shown in Figure 3, be in order to study the clearly mutual relationship between the data dimension to the purpose of vehicle GPS data analysis, particularly in the space (S), between time (T) and the attribute (A), thereby.Distinguishing on the dependence basis of independently dimension and attribute, the vehicle GPS data analysis can be counted as the behavior that is similar to a mathematical function, and namely the value of subordinate variable is with respect to the variation of independent variable.For the vehicle GPS data, fundamental purpose is to understand functional dependence S * T → A, namely with respect to the behavior property of room and time.
Therefore first with regard to usage data module processing vehicle data later, all data of collecting are generated based on the hot spot region, city of thermal map with based on two kinds of dissimilar general views of the traffic track of historical data, and show further that in conjunction with viewdata " fingerprint " the overall data in city distribute, and comprise statistical information or the historical data of each department.This visual can combining geographic information and statistics show the behavior property with respect to room and time in selected area, as the traffic conditions of analyzing identical area is over time, the variation spatially of same alike result variable.Then data are carried out respectively space attribute analysis and time series analysis, mainly pay close attention to situation and development that (1) time dependent space distribution (situation) and (2) local time correlated variables spatially distributes.We call spatial analysis (Spatial Analysis) to (1), the local time series analysis (Local Temporal Analysis) of (2) called after.In analytic process, the user can freely explore any interesting place, and checks the details of the visual structure " fingerprint " of any generation.
After this, the user can be to interested or feel valuable zone, and the fragment of intercepting random time length is done further investigation.Can select the regional study of constant size such as the user, or discrete but each block size is all consistent; Or one section regular time the interval, or have in time gradual change or catastrophe characteristics; The trend in analysis time or space, perhaps the rule of the repetition of data on room and time such as the periodicity of time, detects part or global abnormal value, etc.Example is that the speed of a motor vehicle along highway surpasses limit value, such as 60 kilometers per hour, all vehicles in one day behavioural characteristic.After finishing, the user can check or carries out correlation inquiry according to existing analysis result raw data, thereby result of study is compared and puts in order.
As shown in Figure 4, we need to identify the higher hot zones of vehicle dealing ratio in the city.Thermal map among the figure on the background map represents the high-density region of vehicle with darker regions, and the zone that white portion represents relatively low density with.The user can select some interesting places with thermal map, does further to analyze.Fan-shaped burst in the fingerprint structure among the figure then is that color is more shallow brighter, illustrates that the vehicle dealing number in the corresponding time is higher, and color is more dark to illustrate that more secretly number is fewer.As: residential quarter, hospital, school, market, cinema, subway exit, recreation ground, square etc., the flow that the different vehicle dealing is arranged in different time sections, can be the setting of traffic route and platform, other Related service facilities such as the length of red street lamp provide Data support.
As shown in Figure 5, trace information is by connecting the position of every car departure place and destination on map, and plays up GPS sampled point track between any two with Bezier husband curve.Length of a curve is directly proportional with the track distance of vehicle registration.The shade of curve represents the number of this path vehicle, more deeply feels the vehicle that shows by the path more frequent.The multi-section vehicle can stay similar track through identical path, and we can come a part of subset in the track is merged according to the quantity of same paths.Fan-shaped burst in the fingerprint structure among the figure then is that color is more shallow brighter, illustrates that the vehicle average velocity in the corresponding time is faster, and color is more dark to illustrate that more secretly average velocity is slower.

Claims (9)

1. visual method for digging that is used for vehicle GPS data analysis and exception monitoring, it is characterized in that: the data visualization based on visualization technique excavates, in the situation that extensive real-time stream, by data conversion module the original vehicle gps data is converted to visual " fingerprint " data model, namely the GPS raw data is processed correction, road net informational linkage in vehicle location track and the numerical map is got up, and determine that thus moving target produces to reduce uncertain factor in the analysis with respect to the position of map, then will revise GPS numeric data later and be converted to visual " fingerprint " data model, generate simultaneously a series of data directories, be used for online real-time response user interactions; Receive the data directory and vehicle viewdata model of generation by visualization model after, to remove the abstract data that noise changes in the raw data to these, change into the visual pattern of data by built-in placement algorithm, again it is played up on screen at last; Realize abundant interactive operation by user interactive module, allow the user in time the data after processing be carried out space attribute analysis and time series analysis, thereby for the user provides the detecting of density-based city thermal map and based on the unusual Real Time Monitoring of traffic track of historical data, and be aided with historical data and statistical information, effectively analyze frequent rule and periodic law in the data, find out hiding rule and mistake, thereby the visual monitoring method for digging of analysis and support is provided for user's decision-making.
2. the visual method for digging for vehicle GPS data analysis and exception monitoring according to claim 1, it is characterized in that: based on visualization technique, be applicable to the Higher Dimensional Space Time Data Detection analysis that visualization technique is applied to include simultaneously time, space characteristics.
3. the visual method for digging for vehicle GPS data analysis and exception monitoring according to claim 1, it is characterized in that: data conversion module comes GPS positioning error, numerical map error and coordinate projection mapping fault are revised by map-matching algorithm.
4. the visual method for digging for vehicle GPS data analysis and exception monitoring according to claim 1, it is characterized in that: by a kind of visual data model " fingerprint " abstract concept is held intelligible mode with the analyst and show, even numeric data becomes the visual elements of readability, such as shape, color, size etc.
5. the visual method for digging for vehicle GPS data analysis and exception monitoring according to claim 4, it is characterized in that: " fingerprint " model is used for monitoring and analyzing than fairly large vehicle GPS data, therefore be designed to space (S), time (T), and attribute (A) is to mapping: a S * T * A → Fingerprint of fingerprint model (Fingerprint); And " fingerprint " data model is different from traditional data models, have two data structures, numeric data after the corresponding original data processing of abstract data structure (Abstract Form), the geological information that viewdata structure (Visual Form) corresponding data shows at screen.
6. the visual method for digging for vehicle GPS data analysis and exception monitoring according to claim 4, it is characterized in that: " fingerprint " model is at first selected certain space scope (S), coordinate information and the size of record selection area in fingerprint model (F), according to organizing original gps data according to the time (T) with row and the relation of row, the delegation in the table represents a complete time period in this scope (S); The fingerprint model can add corresponding geological information territory to generate viewdata model (Visual Form) for every attribute according to the abstract data structure that defines afterwards, built-in placement algorithm can generate corresponding geological information, such as the size of visualized elements border rectangle, shape type, coordinate information etc.
7. the visual method for digging for vehicle GPS data analysis and exception monitoring according to claim 4, it is characterized in that: the fingerprint data model has adopted the placement algorithm of the ring-type nested structure of map-based to realize the demonstration of S * T * A → Fingerprint, and the corresponding fingerprint position on the map has represented the space (S) that this visual structure is analyzed; Show (T) with the corresponding time attribute of the nested layout of many rings in the structure, corresponding complete time period of each ring, many rings have identical start time and concluding time, each time slicing is corresponding with fan-shaped burst, many rings are nested so that the fan-shaped burst on each ring can be presented on the adjacent position, and the color of fan-shaped burst has represented again corresponding property value.
8. the visual method for digging for vehicle GPS data analysis and exception monitoring according to claim 1, it is characterized in that: the placement algorithm of visualization model provides two types view for user selection and switching, be respectively the detecting of density-based city thermal map and based on the unusual Real Time Monitoring of traffic track of historical data, wherein thermal map detecting in density-based city is shown as background with geographical map, then is aided with " fingerprint " model corresponding to thermal map and regional and comes corresponding demonstration; Based on the unusual Real Time Monitoring of traffic track of historical data with real-time demonstration vehicle GPS track to map, divide according to the zone that defines simultaneously and become corresponding " fingerprint " next life, historical data is converted to easy visual elements.
9. the visual method for digging for vehicle GPS data analysis and exception monitoring according to claim 1, it is characterized in that: visualization model is by the demonstration with abstract data model and relationship map map on the analytic system, strengthen the readability that data visualization shows, be beneficial to user's comparison and combining cartographic information analysis.
CN201210536118.7A 2012-12-13 2012-12-13 For the visual method for digging of vehicle GPS data analysis and exception monitoring Active CN103020222B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201210536118.7A CN103020222B (en) 2012-12-13 2012-12-13 For the visual method for digging of vehicle GPS data analysis and exception monitoring

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201210536118.7A CN103020222B (en) 2012-12-13 2012-12-13 For the visual method for digging of vehicle GPS data analysis and exception monitoring

Publications (2)

Publication Number Publication Date
CN103020222A true CN103020222A (en) 2013-04-03
CN103020222B CN103020222B (en) 2015-10-28

Family

ID=47968826

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201210536118.7A Active CN103020222B (en) 2012-12-13 2012-12-13 For the visual method for digging of vehicle GPS data analysis and exception monitoring

Country Status (1)

Country Link
CN (1) CN103020222B (en)

Cited By (19)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103309964A (en) * 2013-06-03 2013-09-18 广州市香港科大霍英东研究院 High-efficiency visible monitoring analysis system for large-scale traffic data
CN103745083A (en) * 2013-12-11 2014-04-23 深圳先进技术研究院 Trajectory data cleaning method and device
CN104899198A (en) * 2014-03-04 2015-09-09 苏州工业园区格网信息科技有限公司 Event spatio-temporal data analysis method based on GIS map
CN105489006A (en) * 2015-12-15 2016-04-13 浙江工业大学 Multi-scale road flow visual analysis method based on taxi GPS data
CN106230613A (en) * 2016-07-17 2016-12-14 合肥赑歌数据科技有限公司 A kind of fault pre-alarming algorithm based on Outlier mining
CN107463166A (en) * 2016-06-03 2017-12-12 苏州宝时得电动工具有限公司 Automatic running device and its control traveling method
US9880011B2 (en) 2015-07-31 2018-01-30 International Business Machines Corporation Simplification of trajectory representation
CN108171630A (en) * 2017-12-29 2018-06-15 三盟科技股份有限公司 Discovery method and system based on campus big data environment Students ' action trail
CN108475287A (en) * 2016-01-07 2018-08-31 亚马逊科技公司 Outlier for flow data detects
CN109270565A (en) * 2018-09-04 2019-01-25 广东翼卡车联网服务有限公司 A kind of processing unit of vehicle GPS big data
CN109975841A (en) * 2019-04-11 2019-07-05 吉旗(成都)科技有限公司 A kind of calculation method of GPS device reported data abnormal rate
CN110119482A (en) * 2019-05-13 2019-08-13 杭州电子科技大学 Based on the crowd of POI and multi-source mobile data collection trip mode visible analysis method
CN110197588A (en) * 2019-06-03 2019-09-03 长安大学 A kind of truck driving behavior appraisal procedure and device based on GPS track data
CN111241216A (en) * 2018-11-28 2020-06-05 顺丰科技有限公司 Visualization method, device, equipment and storage medium for server data optimization
CN111275962A (en) * 2019-12-30 2020-06-12 深圳市麦谷科技有限公司 Vehicle track data aggregation effect prediction method and device
CN111367906A (en) * 2019-07-23 2020-07-03 杭州海康威视系统技术有限公司 Abnormal vehicle identification method, device, equipment and computer readable storage medium
CN113327079A (en) * 2021-05-28 2021-08-31 东北师范大学 Path selection potential factor visual analysis method based on network car booking track
CN113779169A (en) * 2021-08-31 2021-12-10 西南电子技术研究所(中国电子科技集团公司第十研究所) Self-enhancement method of space-time data flow model
CN114665611A (en) * 2022-05-25 2022-06-24 融科能源系统(广东)有限公司 Power distribution cabinet operation intelligent monitoring management system based on data analysis

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5631970A (en) * 1993-05-21 1997-05-20 Hsu; Shin-Yi Process for identifying simple and complex objects from fused images and map data
CN1710553A (en) * 2005-04-22 2005-12-21 华东师范大学 Point-source comprehensive drafting and applied technical method
CN102750363A (en) * 2012-06-13 2012-10-24 天津市规划信息中心 Construction method of urban geographic information data warehouse

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5631970A (en) * 1993-05-21 1997-05-20 Hsu; Shin-Yi Process for identifying simple and complex objects from fused images and map data
CN1710553A (en) * 2005-04-22 2005-12-21 华东师范大学 Point-source comprehensive drafting and applied technical method
CN102750363A (en) * 2012-06-13 2012-10-24 天津市规划信息中心 Construction method of urban geographic information data warehouse

Cited By (27)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103309964A (en) * 2013-06-03 2013-09-18 广州市香港科大霍英东研究院 High-efficiency visible monitoring analysis system for large-scale traffic data
CN103745083A (en) * 2013-12-11 2014-04-23 深圳先进技术研究院 Trajectory data cleaning method and device
CN103745083B (en) * 2013-12-11 2017-01-25 深圳先进技术研究院 Trajectory data cleaning method and device
CN104899198A (en) * 2014-03-04 2015-09-09 苏州工业园区格网信息科技有限公司 Event spatio-temporal data analysis method based on GIS map
US9880011B2 (en) 2015-07-31 2018-01-30 International Business Machines Corporation Simplification of trajectory representation
CN105489006A (en) * 2015-12-15 2016-04-13 浙江工业大学 Multi-scale road flow visual analysis method based on taxi GPS data
CN108475287A (en) * 2016-01-07 2018-08-31 亚马逊科技公司 Outlier for flow data detects
CN107463166A (en) * 2016-06-03 2017-12-12 苏州宝时得电动工具有限公司 Automatic running device and its control traveling method
CN106230613A (en) * 2016-07-17 2016-12-14 合肥赑歌数据科技有限公司 A kind of fault pre-alarming algorithm based on Outlier mining
CN108171630A (en) * 2017-12-29 2018-06-15 三盟科技股份有限公司 Discovery method and system based on campus big data environment Students ' action trail
CN109270565A (en) * 2018-09-04 2019-01-25 广东翼卡车联网服务有限公司 A kind of processing unit of vehicle GPS big data
CN109270565B (en) * 2018-09-04 2023-03-07 广东翼卡车联网服务有限公司 Processing device for vehicle-mounted GPS big data
CN111241216A (en) * 2018-11-28 2020-06-05 顺丰科技有限公司 Visualization method, device, equipment and storage medium for server data optimization
CN109975841B (en) * 2019-04-11 2022-02-18 吉旗(成都)科技有限公司 Calculation method for reported data exception rate of GPS (Global positioning System) equipment
CN109975841A (en) * 2019-04-11 2019-07-05 吉旗(成都)科技有限公司 A kind of calculation method of GPS device reported data abnormal rate
CN110119482A (en) * 2019-05-13 2019-08-13 杭州电子科技大学 Based on the crowd of POI and multi-source mobile data collection trip mode visible analysis method
CN110119482B (en) * 2019-05-13 2021-04-06 杭州电子科技大学 Visual analysis method for crowd travel mode based on POI and multi-source mobile data set
CN110197588B (en) * 2019-06-03 2020-07-28 长安大学 Method and device for evaluating driving behavior of large truck based on GPS track data
CN110197588A (en) * 2019-06-03 2019-09-03 长安大学 A kind of truck driving behavior appraisal procedure and device based on GPS track data
CN111367906A (en) * 2019-07-23 2020-07-03 杭州海康威视系统技术有限公司 Abnormal vehicle identification method, device, equipment and computer readable storage medium
CN111367906B (en) * 2019-07-23 2023-09-05 杭州海康威视系统技术有限公司 Abnormal vehicle identification method, device, equipment and computer readable storage medium
CN111275962A (en) * 2019-12-30 2020-06-12 深圳市麦谷科技有限公司 Vehicle track data aggregation effect prediction method and device
CN113327079A (en) * 2021-05-28 2021-08-31 东北师范大学 Path selection potential factor visual analysis method based on network car booking track
CN113779169A (en) * 2021-08-31 2021-12-10 西南电子技术研究所(中国电子科技集团公司第十研究所) Self-enhancement method of space-time data flow model
CN113779169B (en) * 2021-08-31 2023-09-05 西南电子技术研究所(中国电子科技集团公司第十研究所) Space-time data stream model self-enhancement method
CN114665611A (en) * 2022-05-25 2022-06-24 融科能源系统(广东)有限公司 Power distribution cabinet operation intelligent monitoring management system based on data analysis
CN114665611B (en) * 2022-05-25 2022-07-29 融科能源系统(广东)有限公司 Power distribution cabinet operation intelligent monitoring management system based on data analysis

Also Published As

Publication number Publication date
CN103020222B (en) 2015-10-28

Similar Documents

Publication Publication Date Title
CN103020222B (en) For the visual method for digging of vehicle GPS data analysis and exception monitoring
CN103309964A (en) High-efficiency visible monitoring analysis system for large-scale traffic data
Andrienko et al. Spatio-temporal aggregation for visual analysis of movements
Twinam Danger zone: Land use and the geography of neighborhood crime
Zeng et al. Visualizing the relationship between human mobility and points of interest
Ratcliffe Crime mapping: spatial and temporal challenges
Lu et al. Visual analysis of multiple route choices based on general gps trajectories
Andrienko et al. A general framework for using aggregation in visual exploration of movement data
Jiang et al. Large-scale taxi O/D visual analytics for understanding metropolitan human movement patterns
CN110716935A (en) Track data analysis and visualization method and system based on online taxi appointment travel
Wu et al. Spatial–temporal visualization of city-wide crowd movement
CN106649651A (en) Transportation co-occurrence phenomenon visualized analysis method based on taxi trajectory data
CN115293570A (en) GIS-based territorial space planning system and method
Wang et al. Digital roadway interactive visualization and evaluation network applications to WSDOT operational data usage.
Chaolong et al. Research on visualization of multi-dimensional real-time traffic data stream based on cloud computing
Sun et al. TZVis: Visual analysis of bicycle data for traffic zone division
Russnák et al. Visualization and spatial analysis of police open data as a part of community policing in the city of Pardubice (Czech Republic)
Zhang et al. Detecting hotspots of urban residents’ behaviours based on spatio-temporal clustering techniques
Liu et al. Quantifying COVID-19 recovery process from a human mobility perspective: An intra-city study in Wuhan
Zheng et al. Visual analysis of bi-directional movement behavior
Kveladze et al. Visual analysis of speed bumps using floating car dataset
Kong et al. COOC: Visual exploration of co-occurrence mobility patterns in urban scenarios
Afonso et al. RoseTrajVis: visual analytics of trajectories with rose diagrams
Tao Big spatial flow data analytics
Ferreira et al. Visual Analytics of Trajectories with RoseTrajVis

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
C14 Grant of patent or utility model
GR01 Patent grant