CN103139287A - Map aggregation vehicle refreshing method based on distributed calculating - Google Patents
Map aggregation vehicle refreshing method based on distributed calculating Download PDFInfo
- Publication number
- CN103139287A CN103139287A CN2012105305350A CN201210530535A CN103139287A CN 103139287 A CN103139287 A CN 103139287A CN 2012105305350 A CN2012105305350 A CN 2012105305350A CN 201210530535 A CN201210530535 A CN 201210530535A CN 103139287 A CN103139287 A CN 103139287A
- Authority
- CN
- China
- Prior art keywords
- gps
- vehicle
- map
- data
- grid
- 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
Links
Landscapes
- Instructional Devices (AREA)
- Traffic Control Systems (AREA)
- Navigation (AREA)
Abstract
A map aggregation vehicle refreshing method based on distributed calculating comprises two parts, namely, global position system (GPS) data receiving and analyzing on a server-side and client-side map aggregating on a client-side, wherein the two parts are carried out without sequence. A server receives and analyzes the GPS data, specifically, a GPS server receives real-time GPS data sent from a vehicle terminal through an Internet, and the main server of a GPS calculating cluster carries out adjustment according to current load of each work station, and allocates GPS data packages to each work station to carry out data processing. The client-side map aggregating specifically comprises that the client-side requests to obtain the GPS data of all vehicles from the main server of the GPS calculating cluster, sends the longitude and latitude coordinates and the visual field size of a current map range to the main server of the GPS calculating cluster, and requests the main server to carry out statistical calculation of map aggregating. The map aggregation vehicle refreshing method based on the distributed calculating uses a distributed calculating technology to solve the bottleneck problem of vehicle display quantities, guarantees the improvement of system performance, and meanwhile enhances the expansibility of the system, and reduces the cost of maintenance and upgrade.
Description
Technical field
The invention belongs to GPS supervisory control system field, be specifically related to a kind of map polymerization vehicle method for refreshing based on Distributed Calculation.
Background technology
At present, in the GPS supervisory control system, the most frequently used function is on electronic chart, shows in real time and refresh real time position and other satellite informations of vehicle.Processor and the disposal ability of display card and the restriction of Internet Transmission bottleneck due to PC, the vehicle fleet size that shows on electronic chart has certain upper limit, after surpassing this limits value, the performance of system will significantly decrease, affect the normal use of other system function, therefore how to break through the bottleneck that vehicle shows quantity, allow the user can see real-time vehicle information as much as possible in system, just become a technical barrier.The scheme that generally adopts now is that the map polymerization refreshes to reduce unnecessary map, but this scheme has obvious limitation, it is exactly the burden that it can only reduce display card, but increased the burden of CPU and internal memory, and along with the continuous growth of vehicle fleet size, spend in CPU and also constantly growth thereupon of memory source that the map polymerization is calculated, finally still can arrive the treatable limiting value of system.
In view of this, the inventor furthers investigate for the defective of prior art, and has this case to produce.
Summary of the invention
Technical problem to be solved by this invention is to provide a kind of map polymerization vehicle method for refreshing based on Distributed Calculation, the method has been used distributed computing technology, solve the problem that vehicle shows the quantity bottleneck, in the performance that guarantees the raising system, the autgmentability of enhancing system, the cost of reduction maintenance upgrade.
The present invention solves the problems of the technologies described above by the following technical solutions:
A kind of map polymerization vehicle method for refreshing based on Distributed Calculation comprises the two large divisions, and the following stated first and second portion are without sequencing;
First: server end is accepted and connects to analyse gps data, specifically comprises the steps:
Step 1.1:GPS server is accepted the real-time GPS data of sending from car-mounted terminal by Internet, and simply resolves, and is converted to the binary data format of internal system;
Step 1.2:GPS server will receive that several gps datas of resolving form a large packet at set intervals, this Packet Generation is calculated the master server of cluster to GPS by local area network (LAN) or private network;
The master server that step 1.3:GPS calculates cluster first is split as large packet a plurality of independent gps data bags, then dispatches according to the present load of each work station, gps data is responsible for assigning carries out the data processing to each work station;
Step 1.4: work station is processed the gps data bag, preserves gps data in database, and gps data is carried out the processing of calculation of longitude ﹠ latitude, and the data after then processing return to the master server that GPS calculates cluster;
the master server that step 1.5:GPS calculates cluster is cached to the gps data that returns in step 1.4 in a dictionary data structure in distributed caching, the key of this dictionary data structure is the identifier of vehicle, the value of this key the same structure of gps data that to be exactly an internal storage structure return with step 1.4, be used for preserving work station and process the gps data that returns, corresponding each vehicle identifiers, only preserve the up-to-date gps data of gps time of a correspondence in the dictionary data structure, if the gps time of the gps data of preserving is also wanted Zao words than the gps time of current data of having preserved, abandon this time preserving,
Second portion: the polymerization of client map specifically comprises the steps:
Step 2.1: the user starts client software, load electronic chart, and calculate the gps data of all vehicles of cluster master server acquisition request to GPS, and sending the latitude and longitude coordinates of current body of a map or chart and visual field size is calculated the cluster master server to GPS, the statistical computation of map polymerization is carried out in request;
Step 2.2:GPS calculates the cluster master server and receives gps data and the request of map aggregate statistics, at first all vehicles is sorted by vehicle identifiers, is divided into several little vehicle lists, and gives each numbering of table, and the result after sequence is placed in distributed caching; Then with this work station vehicle list to be processed sequence number, the latitude and longitude coordinates of the body of a map or chart of request polymerization sends to each work station, distributes each work station to carry out the concrete map aggregate statistics of each vehicle list;
Step 2.3: work station is according to the latitude and longitude coordinates of the body of a map or chart of the request polymerization of receiving, this part map is divided into again N*N rectangular grid of the capable N row of N, and calculate the length of each grid and wide, create simultaneously a dictionary data structure and store statistics, the key of dictionary is the row and column of grid, corresponding value is an object, and this object has two member variables, and first is the sum that a counter is deposited vehicle in grid corresponding to this key; Second is a List object, the vehicle identifiers in store this grid in the inside; Then work station according to the vehicle list sequence number own to be processed of receiving, obtains the gps data of pending vehicle list from distributed caching, and the List of these vehicle GPS data of searching loop;
Step 2.4: work station is inner in the circulation described in step 2.3, and the interface that uses engine map to provide, at first current according to each car longitude and latitude judge that this vehicle whether in the body of a map or chart of asking, if not, jumps to next car, restart this step; If so, judge the grid at this vehicle place, algorithm is:
Row=the longitude of the upper left corner (longitude of gps data-body of a map or chart)/grid width if row are not integer, is listed as=rounds (row)+1;
OK=(latitude of the latitude-gps data in the body of a map or chart upper left corner)/grid length, if capable be not integer, go=round (OK)+1;
After judgement, according to [OK, row] that obtain, find storage objects of statistics corresponding to this key from described dictionary data structure, with the vehicle fleet counter of corresponding grid, the value of counter is added 1; Identifier with this vehicle joins in the List that preserves vehicle identifiers simultaneously;
Step 2.5: after all vehicle GPS datacycle were finished dealing with, the dictionary that work station will be stored statistics carried out returning to GPS calculating cluster master server after Binary Serialization;
Step 2.6:GPS calculates the cluster master server and receives the statistics that in step 2.5, each work station returns, after all have distributed the work station of this subtask all to return results when confirmation, GPS calculates the cluster master server all statisticses is gathered: in each statistics, have identical [OK, row] corresponding vehicle fleet counter addition, obtain the vehicle fleet in the map subregion grid described in step 2.3;
Step 2.7:GPS calculates the data that the cluster master server needs to return to client simultaneously a List structure, each element of this List storing described in a step 2.3 map subregion grid in the statistics object, following member variable is arranged in this statistics object:
Vehicle fleet in each grid area of map described in step 2.3;
The central point latitude and longitude coordinates of each grid area of map described in step 2.3;
If the vehicle fleet in certain grid less than certain threshold values, should comprise all the vehicle identifiers list data structure in this grid; If vehicle fleet greater than this threshold values, does not need to comprise the vehicle identifiers list data structure in this grid;
Step 2.8: client is received the data of the statistics List structure that calculating cluster master server returns, this List of searching loop, statistics to the map subregion grid described in each step 2.3, judge whether to exist this regional vehicles identifications list, if there is no, use the central point latitude and longitude coordinates of the grid area described in step 2.7, draw the sign icon of vehicle polymerization on map, and show this regional vehicle number under this icon; If there is this regional vehicles identifications list in statistics, according to these vehicles identifications, obtains the latest GPS data of these vehicles from distributed caching, and show the icon of these vehicles on map;
Step 2.9: client is regularly calculated the cluster master server to GPS and is sent the request of map aggregate statistics, before refreshing map, first original vehicle icon is all removed at every turn.
Further, the vehicle identifiers in described step 1.5 is license plate number, or vehicle carried mobile phone number, or unique ID of generating of database.
the invention has the advantages that: the present invention is on traditional map polymerization technique scheme, carry out the improvement of novelty, it is mainly the map polymerization processing capacity that will originally all be born by client, principle by load balancing, distributing to distributed work station processes, and these work stations can be born by the common PC of cheapness, even can use existing machine in use, can take full advantage of like this CPU computing capability of idle machine, according to the description in technical solution of the present invention, suppose that whole map is divided into N zone, be 10 and the number of thresholds that shows vehicle GPS information is set in each local, need on map to show that the icon that refreshes mostly is N*10 most, minimum only have N, if the vehicle fleet size that needs to process increases, the client map need to show that the number of icons that refreshes does not increase, the load that is distributed work station strengthens, so just can be simply support ever-increasing vehicle number with the method for the quantity that increases work station, and do not need client is done any change, so just eliminated a large amount of gps datas of client dissection process fully and shown the bottleneck of the performance that a large amount of icons bring at map, improved greatly the extensibility of whole system.Simultaneously, because concrete polymerization calculating is all completed at server end, if when needing to improve aggregating algorithm later on, only need the program of update server end to get final product, a large amount of clients is also unaffected, and this has also reduced the cost of maintenance upgrade.
Description of drawings
The invention will be further described in conjunction with the embodiments with reference to the accompanying drawings.
Fig. 1 is system logic block diagram of the present invention.
Embodiment
As shown in Figure 1, the GPS supervisory control system that the present invention relates to comprises following module: the GPS server, GPS calculates cluster and client.The function and efficacy of each module is as follows:
GPS server: be connected with GPS terminal, possess the function of accepting and resolve the vehicle GPS data.Be responsible for forwarding real-time GPS data to the map calculation cluster.
GPS calculates cluster: a cluster that is comprised of one group of server, it is comprised of a master server and a plurality of work station, master server is connected by Internet or local area network (LAN) with the GPS server, main service is connected by local area network (LAN) with client with all working station, possesses the function of gps data being carried out distributed treatment.Be responsible for accepting and resolve the gps data that the GPS server transmits, according to the request of client, gps data carried out the map aggregate statistics, and statistics is sent it back to client.
Client: be connected by local area network (LAN) with the map calculation cluster, possess map and show and client end interface.Be responsible for obtaining required vehicle real-time GPS data and map aggregate statistics information from the map calculation cluster, and the vehicle real time after showing polymerization on map.
Concrete map polymerization vehicle method for refreshing comprises the two large divisions, and the following stated first and second portion are without sequencing;
First: server end is accepted and connects to analyse gps data, specifically comprises the steps:
Step 1.1:GPS server is accepted the real-time GPS data of sending from car-mounted terminal by Internet, and simply resolves, and is converted to the binary data format of internal system;
Step 1.2:GPS server will receive that several gps datas of resolving form a large packet at set intervals, this Packet Generation is calculated the master server of cluster to GPS by local area network (LAN) or private network;
The master server that step 1.3:GPS calculates cluster first is split as large packet a plurality of independent gps data bags, then dispatches according to the present load of each work station, gps data is responsible for assigning carries out the data processing to each work station;
Step 1.4: work station is processed the gps data bag, preserves gps data in database, and gps data is carried out the processing of calculation of longitude ﹠ latitude, and the data after then processing return to the master server that GPS calculates cluster;
the master server that step 1.5:GPS calculates cluster is cached to the gps data that returns in step 1.4 in a dictionary data structure in distributed caching, the key of this dictionary data structure is that the identifier of vehicle (can be license plate number, or vehicle carried mobile phone number, or unique ID of database generation), the value of this key the same structure of gps data that to be exactly an internal storage structure return with step 1.4, be used for preserving work station and process the gps data that returns, corresponding each vehicle identifiers, only preserve the up-to-date gps data of gps time of a correspondence in the dictionary data structure, if the gps time of the gps data of preserving is also wanted Zao words than the gps time of current data of having preserved, abandon this time preserving,
Second portion: the polymerization of client map specifically comprises the steps:
Step 2.1: the user starts client software, load electronic chart, and calculate the gps data of all vehicles of cluster master server acquisition request to GPS, and sending the latitude and longitude coordinates (upper left corner longitude and latitude and lower right corner longitude and latitude) of current body of a map or chart and visual field size is calculated the cluster master server to GPS, the statistical computation of map polymerization is carried out in request;
Step 2.2:GPS calculates the cluster master server and receives gps data and the request of map aggregate statistics, at first all vehicles are sorted by vehicle identifiers, be divided into several little vehicle lists, and to each numbering of table, for example 1-500 car is No. 1,501-1000 is No. 2 (sequence is only carried out when master server starts, and the upper limit of numbering is according to the quantity dynamic change of work station), and the result after sequence is placed in distributed caching; Then with this work station vehicle list to be processed sequence number, the latitude and longitude coordinates of the body of a map or chart of request polymerization sends to each work station, distributes each work station to carry out the concrete map aggregate statistics of each vehicle list;
Step 2.3: work station is according to the latitude and longitude coordinates of the body of a map or chart of the request polymerization of receiving, this part map is divided into again N*N rectangular grid of the capable N row of N, and calculate the length of each grid and wide, create simultaneously a dictionary data structure and store statistics, the key of dictionary is the row and column of grid, corresponding value is an object, and this object has two member variables, and first is the sum that a counter is deposited vehicle in grid corresponding to this key; Second is a List object, the vehicle identifiers in store this grid in the inside; Then work station according to the vehicle list sequence number own to be processed of receiving, obtains the gps data of pending vehicle list from distributed caching, and the List of these vehicle GPS data of searching loop;
Step 2.4: work station is inner in the circulation described in step 2.3, and the interface that uses engine map to provide, at first current according to each car longitude and latitude judge that this vehicle whether in the body of a map or chart of asking, if not, jumps to next car, restart this step; If so, judge the grid at this vehicle place, algorithm is:
Row=the longitude of the upper left corner (longitude of gps data-body of a map or chart)/grid width if row are not integer, is listed as=rounds (row)+1;
OK=(latitude of the latitude-gps data in the body of a map or chart upper left corner)/grid length, if capable be not integer, go=round (OK)+1;
After judgement, according to [OK, row] that obtain, find storage objects of statistics corresponding to this key from described dictionary data structure, with the vehicle fleet counter of corresponding grid, the value of counter is added 1; Identifier with this vehicle joins in the List that preserves vehicle identifiers simultaneously;
Step 2.5: after all vehicle GPS datacycle were finished dealing with, the dictionary that work station will be stored statistics carried out returning to GPS calculating cluster master server after Binary Serialization;
Step 2.6:GPS calculates the cluster master server and receives the statistics that in step 2.5, each work station returns, after all have distributed the work station of this subtask all to return results when confirmation, GPS calculates the cluster master server all statisticses is gathered: in each statistics, have identical [OK, row] corresponding vehicle fleet counter addition, obtain the vehicle fleet in the map subregion grid described in step 2.3;
Step 2.7:GPS calculates the data that the cluster master server needs to return to client simultaneously a List structure, each element of this List storing described in a step 2.3 map subregion grid in the statistics object, following member variable is arranged in this statistics object:
Vehicle fleet in each grid area of map described in step 2.3;
The central point latitude and longitude coordinates of each grid area of map described in step 2.3;
If the vehicle fleet in certain grid less than certain threshold value, should comprise all the vehicle identifiers list data structure in this grid; If vehicle fleet greater than this threshold value, does not need to comprise the vehicle identifiers list data structure in this grid;
Step 2.8: client is received the data of the statistics List structure that calculating cluster master server returns, this List of searching loop, statistics to the map subregion grid described in each step 2.3, judge whether to exist this regional vehicles identifications list, if there is no, use the central point latitude and longitude coordinates of the grid area described in step 2.7, draw the sign icon of vehicle polymerization on map, and show this regional vehicle number under this icon; If there is this regional vehicles identifications list in statistics, according to these vehicles identifications, obtains the latest GPS data of these vehicles from distributed caching, and show the icon of these vehicles on map;
Step 2.9: client is regularly calculated the cluster master server to GPS and is sent the request of map aggregate statistics, before refreshing map, first original vehicle icon is all removed at every turn.
the present invention is on traditional map polymerization technique scheme, carry out the improvement of novelty, it is mainly the map polymerization processing capacity that will originally all be born by client, principle by load balancing, distributing to distributed work station processes, and these work stations can be born by the common PC of cheapness, even can use existing machine in use, can take full advantage of like this CPU computing capability of idle machine, according to the description in technical solution of the present invention, suppose that whole map is divided into N zone, be 10 and the number of thresholds that shows vehicle GPS information is set in each local, need on map to show that the icon that refreshes mostly is N*10 most, minimum only have N, if the vehicle fleet size that needs to process increases, the client map need to show that the number of icons that refreshes does not increase, the load that is distributed work station strengthens, so just can be simply support ever-increasing vehicle number with the method for the quantity that increases work station, and do not need client is done any change, so just eliminated a large amount of gps datas of client dissection process fully and shown the bottleneck of the performance that a large amount of icons bring at map, improved greatly the extensibility of whole system.Simultaneously, because concrete polymerization calculating is all completed at server end, if when needing to improve aggregating algorithm later on, only need the program of update server end to get final product, a large amount of clients is also unaffected, and this has also reduced the cost of maintenance upgrade.
The above is only better enforcement use-case of the present invention, is not for limiting protection scope of the present invention.Within the spirit and principles in the present invention all, any modification of doing, be equal to and replace and improvement etc., within all should being included in protection scope of the present invention.
Claims (2)
1. map polymerization vehicle method for refreshing based on Distributed Calculation, it is characterized in that: comprise the two large divisions, the following stated first and second portion are without sequencing;
First: server end is accepted and connects to analyse gps data, specifically comprises the steps:
Step 1.1:GPS server is accepted the real-time GPS data of sending from car-mounted terminal by Internet, and simply resolves, and is converted to the binary data format of internal system;
Step 1.2:GPS server will receive that several gps datas of resolving form a large packet at set intervals, this Packet Generation is calculated the master server of cluster to GPS by local area network (LAN) or private network;
The master server that step 1.3:GPS calculates cluster first is split as large packet a plurality of independent gps data bags, then dispatches according to the present load of each work station, gps data is responsible for assigning carries out the data processing to each work station;
Step 1.4: work station is processed the gps data bag, preserves gps data in database, and gps data is carried out the processing of calculation of longitude ﹠ latitude, and the data after then processing return to the master server that GPS calculates cluster;
the master server that step 1.5:GPS calculates cluster is cached to the gps data that returns in step 1.4 in a dictionary data structure in distributed caching, the key of this dictionary data structure is the identifier of vehicle, the value of this key the same structure of gps data that to be exactly an internal storage structure return with step 1.4, be used for preserving work station and process the gps data that returns, corresponding each vehicle identifiers, only preserve the up-to-date gps data of gps time of a correspondence in the dictionary data structure, if the gps time of the gps data of preserving is also wanted Zao words than the gps time of current data of having preserved, abandon this time preserving,
Second portion: the polymerization of client map specifically comprises the steps:
Step 2.1: the user starts client software, load electronic chart, and calculate the gps data of all vehicles of cluster master server acquisition request to GPS, and sending the latitude and longitude coordinates of current body of a map or chart and visual field size is calculated the cluster master server to GPS, the statistical computation of map polymerization is carried out in request;
Step 2.2:GPS calculates the cluster master server and receives gps data and the request of map aggregate statistics, at first all vehicles is sorted by vehicle identifiers, is divided into several little vehicle lists, and gives each numbering of table, and the result after sequence is placed in distributed caching; Then with this work station vehicle list to be processed sequence number, the latitude and longitude coordinates of the body of a map or chart of request polymerization sends to each work station, distributes each work station to carry out the concrete map aggregate statistics of each vehicle list;
Step 2.3: work station is according to the latitude and longitude coordinates of the body of a map or chart of the request polymerization of receiving, this part map is divided into again N*N rectangular grid of the capable N row of N, and calculate the length of each grid and wide, create simultaneously a dictionary data structure and store statistics, the key of dictionary is the row and column of grid, corresponding value is an object, and this object has two member variables, and first is the sum that a counter is deposited vehicle in grid corresponding to this key; Second is a List object, the vehicle identifiers in store this grid in the inside; Then work station according to the vehicle list sequence number own to be processed of receiving, obtains the gps data of pending vehicle list from distributed caching, and the List of these vehicle GPS data of searching loop;
Step 2.4: work station is inner in the circulation described in step 2.3, and the interface that uses engine map to provide, at first current according to each car longitude and latitude judge that this vehicle whether in the body of a map or chart of asking, if not, jumps to next car, restart this step; If so, judge the grid at this vehicle place, algorithm is:
Row=the longitude of the upper left corner (longitude of gps data-body of a map or chart)/grid width if row are not integer, is listed as=rounds (row)+1;
OK=(latitude of the latitude-gps data in the body of a map or chart upper left corner)/grid length, if capable be not integer, go=round (OK)+1;
After judgement, according to [OK, row] that obtain, find storage objects of statistics corresponding to this key from described dictionary data structure, with the vehicle fleet counter of corresponding grid, the value of counter is added 1; Identifier with this vehicle joins in the List that preserves vehicle identifiers simultaneously;
Step 2.5: after all vehicle GPS datacycle were finished dealing with, the dictionary that work station will be stored statistics carried out returning to GPS calculating cluster master server after Binary Serialization;
Step 2.6:GPS calculates the cluster master server and receives the statistics that in step 2.5, each work station returns, after all have distributed the work station of this subtask all to return results when confirmation, GPS calculates the cluster master server all statisticses is gathered: in each statistics, have identical [OK, row] corresponding vehicle fleet counter addition, obtain the vehicle fleet in the map subregion grid described in step 2.3;
Step 2.7:GPS calculates the data that the cluster master server needs to return to client simultaneously a List structure, each element of this List storing described in a step 2.3 map subregion grid in the statistics object, following member variable is arranged in this statistics object:
Vehicle fleet in each grid area of map described in step 2.3;
The central point latitude and longitude coordinates of each grid area of map described in step 2.3;
If the vehicle fleet in certain grid less than certain threshold values, should comprise all the vehicle identifiers list data structure in this grid; If vehicle fleet greater than this threshold values, does not need to comprise the vehicle identifiers list data structure in this grid;
Step 2.8: client is received the data of the statistics List structure that calculating cluster master server returns, this List of searching loop, statistics to the map subregion grid described in each step 2.3, judge whether to exist this regional vehicles identifications list, if there is no, use the central point latitude and longitude coordinates of the grid area described in step 2.7, draw the sign icon of vehicle polymerization on map, and show this regional vehicle number under this icon; If there is this regional vehicles identifications list in statistics, according to these vehicles identifications, obtains the latest GPS data of these vehicles from distributed caching, and show the icon of these vehicles on map;
Step 2.9: client is regularly calculated the cluster master server to GPS and is sent the request of map aggregate statistics, before refreshing map, first original vehicle icon is all removed at every turn.
2. a kind of map polymerization vehicle method for refreshing based on Distributed Calculation as claimed in claim 1, it is characterized in that: the vehicle identifiers in described step 1.5 is license plate number, or vehicle carried mobile phone number, or unique ID of generating of database.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210530535.0A CN103139287B (en) | 2012-12-11 | 2012-12-11 | A kind of map aggregation vehicle method for refreshing based on Distributed Calculation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210530535.0A CN103139287B (en) | 2012-12-11 | 2012-12-11 | A kind of map aggregation vehicle method for refreshing based on Distributed Calculation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103139287A true CN103139287A (en) | 2013-06-05 |
| CN103139287B CN103139287B (en) | 2018-05-11 |
Family
ID=48498584
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201210530535.0A Active CN103139287B (en) | 2012-12-11 | 2012-12-11 | A kind of map aggregation vehicle method for refreshing based on Distributed Calculation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103139287B (en) |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104615648A (en) * | 2014-12-26 | 2015-05-13 | 北京中交兴路车联网科技有限公司 | Statistical system and method for vehicle data |
| CN105141981A (en) * | 2015-09-07 | 2015-12-09 | 天脉聚源(北京)传媒科技有限公司 | Method and device for displaying STB (Set Top Box) distribution diagram |
| CN105491124A (en) * | 2015-12-03 | 2016-04-13 | 北京航空航天大学 | Distributed aggregation method for moving vehicles |
| CN106017483A (en) * | 2016-05-06 | 2016-10-12 | 厦门蓝斯通信股份有限公司 | Map vehicle icon drawing method and system and navigation terminal |
| CN107291731A (en) * | 2016-03-31 | 2017-10-24 | 阿里巴巴集团控股有限公司 | The processing method and processing device of calculating business |
| CN108510778A (en) * | 2018-04-11 | 2018-09-07 | 航天科技控股集团股份有限公司 | A kind of implementation method of the vehicle polymerization display based on recorder management platform |
| CN108592913A (en) * | 2018-04-02 | 2018-09-28 | 深圳找哪科技有限公司 | A kind of computational methods determining location point in specific region |
| CN109255953A (en) * | 2018-09-19 | 2019-01-22 | 江苏本能科技有限公司 | Movable vehicle distribution methods of exhibiting and system based on region |
| CN110232066A (en) * | 2019-06-06 | 2019-09-13 | 南威互联网科技集团有限公司 | A kind of target cache method and system obtaining table data request |
| CN111026987A (en) * | 2018-10-10 | 2020-04-17 | 千寻位置网络有限公司 | Multi-layer polymerization method and system for displaying mass vehicle position distribution information |
| CN111090713A (en) * | 2019-12-17 | 2020-05-01 | 青岛海信移动通信技术股份有限公司 | Map processing method, server, intelligent terminal and computer readable storage medium |
| CN112801552A (en) * | 2021-03-25 | 2021-05-14 | 苏州智能交通信息科技股份有限公司 | Traffic big data mining and intelligent analysis-based network appointment and cruise supervision method |
| CN115223371A (en) * | 2022-09-20 | 2022-10-21 | 深圳市城市交通规划设计研究中心股份有限公司 | Big data analysis system of electric bicycle and working method thereof |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070032946A1 (en) * | 2005-08-03 | 2007-02-08 | Denso Corporation | Road map management system |
| CN101192215A (en) * | 2006-11-24 | 2008-06-04 | 中国科学院声学研究所 | Information aggregation and enquiry method based on geographic coordinates |
| CN101799990A (en) * | 2010-02-08 | 2010-08-11 | 深圳市同洲电子股份有限公司 | Warning method and system for unusual aggregation of vehicles |
| CN102735248A (en) * | 2011-04-14 | 2012-10-17 | 爱信艾达株式会社 | Map image display system, map image display device, map image display method, and computer program |
| CN102779420A (en) * | 2012-07-31 | 2012-11-14 | 哈尔滨工业大学 | Road traffic event automatic detection method based on real-time vehicle-mounted GPS (global position system) data |
-
2012
- 2012-12-11 CN CN201210530535.0A patent/CN103139287B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070032946A1 (en) * | 2005-08-03 | 2007-02-08 | Denso Corporation | Road map management system |
| CN101192215A (en) * | 2006-11-24 | 2008-06-04 | 中国科学院声学研究所 | Information aggregation and enquiry method based on geographic coordinates |
| CN101799990A (en) * | 2010-02-08 | 2010-08-11 | 深圳市同洲电子股份有限公司 | Warning method and system for unusual aggregation of vehicles |
| CN102735248A (en) * | 2011-04-14 | 2012-10-17 | 爱信艾达株式会社 | Map image display system, map image display device, map image display method, and computer program |
| CN102779420A (en) * | 2012-07-31 | 2012-11-14 | 哈尔滨工业大学 | Road traffic event automatic detection method based on real-time vehicle-mounted GPS (global position system) data |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104615648A (en) * | 2014-12-26 | 2015-05-13 | 北京中交兴路车联网科技有限公司 | Statistical system and method for vehicle data |
| CN105141981B (en) * | 2015-09-07 | 2018-08-24 | 天脉聚源(北京)传媒科技有限公司 | A kind of display methods and device of set-top box distribution map |
| CN105141981A (en) * | 2015-09-07 | 2015-12-09 | 天脉聚源(北京)传媒科技有限公司 | Method and device for displaying STB (Set Top Box) distribution diagram |
| CN105491124A (en) * | 2015-12-03 | 2016-04-13 | 北京航空航天大学 | Distributed aggregation method for moving vehicles |
| CN105491124B (en) * | 2015-12-03 | 2018-11-02 | 北京航空航天大学 | Mobile vehicle distribution polymerization |
| CN107291731A (en) * | 2016-03-31 | 2017-10-24 | 阿里巴巴集团控股有限公司 | The processing method and processing device of calculating business |
| CN107291731B (en) * | 2016-03-31 | 2020-11-24 | 阿里巴巴集团控股有限公司 | Processing method and device of computing service |
| CN106017483A (en) * | 2016-05-06 | 2016-10-12 | 厦门蓝斯通信股份有限公司 | Map vehicle icon drawing method and system and navigation terminal |
| CN106017483B (en) * | 2016-05-06 | 2019-10-11 | 厦门蓝斯通信股份有限公司 | A kind of method for drafting, drawing system and the navigation terminal of map vehicle icon |
| CN108592913A (en) * | 2018-04-02 | 2018-09-28 | 深圳找哪科技有限公司 | A kind of computational methods determining location point in specific region |
| CN108510778A (en) * | 2018-04-11 | 2018-09-07 | 航天科技控股集团股份有限公司 | A kind of implementation method of the vehicle polymerization display based on recorder management platform |
| CN109255953A (en) * | 2018-09-19 | 2019-01-22 | 江苏本能科技有限公司 | Movable vehicle distribution methods of exhibiting and system based on region |
| CN111026987A (en) * | 2018-10-10 | 2020-04-17 | 千寻位置网络有限公司 | Multi-layer polymerization method and system for displaying mass vehicle position distribution information |
| CN110232066A (en) * | 2019-06-06 | 2019-09-13 | 南威互联网科技集团有限公司 | A kind of target cache method and system obtaining table data request |
| CN111090713A (en) * | 2019-12-17 | 2020-05-01 | 青岛海信移动通信技术股份有限公司 | Map processing method, server, intelligent terminal and computer readable storage medium |
| CN112801552A (en) * | 2021-03-25 | 2021-05-14 | 苏州智能交通信息科技股份有限公司 | Traffic big data mining and intelligent analysis-based network appointment and cruise supervision method |
| CN115223371A (en) * | 2022-09-20 | 2022-10-21 | 深圳市城市交通规划设计研究中心股份有限公司 | Big data analysis system of electric bicycle and working method thereof |
| CN115223371B (en) * | 2022-09-20 | 2023-02-14 | 深圳市城市交通规划设计研究中心股份有限公司 | Big data analysis system of electric bicycle and working method thereof |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103139287B (en) | 2018-05-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103139287A (en) | Map aggregation vehicle refreshing method based on distributed calculating | |
| Wolfson et al. | Updating and querying databases that track mobile units | |
| EP2875653B1 (en) | Method for generating a dataset structure for location-based services | |
| Bao et al. | Efficient evaluation of k-range nearest neighbor queries in road networks | |
| CN105702017B (en) | A kind of vehicle dispatching method and device | |
| CN101370025A (en) | Storing method, scheduling method and management system for geographic information data | |
| CN103795793B (en) | Road vehicle monitoring platform system based on double server clusters | |
| CN108415048B (en) | Large-scale network RTK positioning method and system based on spatial clustering | |
| US9723045B2 (en) | Communicating tuples in a message | |
| CN109194746A (en) | Heterogeneous Information processing method based on Internet of Things | |
| CN109213792A (en) | Method, server-side, client, device and the readable storage medium storing program for executing of data processing | |
| CN103517405B (en) | A kind of method and system of network positions, mobile terminal and network side equipment | |
| CN104539681A (en) | Distributed GIS accelerating system and GIS service processing method | |
| CN116363854B (en) | Shared travel vehicle dispatching method and device and computer equipment | |
| CN108920552A (en) | A kind of distributed index method towards multi-source high amount of traffic | |
| CN111325428A (en) | Work order pushing method and device and storage medium | |
| CN110134738A (en) | Distributed memory system resource predictor method, device | |
| CN103593435B (en) | Approximate treatment system and method for uncertain data PT-TopK query | |
| CN105893605A (en) | Distributed calculating platform facing to spatio-temporal data k neighbor query and query method | |
| CN105335313A (en) | Basic data transmission method and apparatus | |
| CN114500428A (en) | Navigation sharing method and device, electronic equipment, storage medium and program product | |
| Bozdog et al. | RideMatcher: peer-to-peer matching of passengers for efficient ridesharing | |
| CN115801813A (en) | Memory cache based activity query method and device | |
| CN104331336B (en) | Be matched with the multilayer nest balancing method of loads of high-performance computer structure | |
| Xiang et al. | Impact of multi-query optimization in sensor networks |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |