CN103139287B - A kind of map aggregation vehicle method for refreshing based on Distributed Calculation - Google Patents
A kind of map aggregation vehicle method for refreshing based on Distributed Calculation Download PDFInfo
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Abstract
A kind of map aggregation vehicle method for refreshing based on Distributed Calculation, including two large divisions, are specifically that server end receives and connects analysis gps data, with client map aggregation two parts, the two parts are without sequencing；Server end receive and connect analysis gps data specifically include：The real-time GPS data that GPS server receives to send from car-mounted terminal by Internet, the master server of GPS computing clusters are scheduled according to the present load of each work station, and gps data bag is distributed to each work station carries out data processing；Client map aggregation specifically includes：Client and sends the latitude and longitude coordinates of current map scope and visual field size gives GPS computing cluster master servers, the statistics that request carries out map aggregation calculates to the gps data of all vehicles of GPS computing cluster master server acquisition requests.The present invention has used distributed computing technology, and to solve the problems, such as vehicle display number bottleneck, while the performance of raising system is ensured, the autgmentability of strengthening system, reduces the cost of maintenance upgrade.
Description
Technical field
The invention belongs to GPS monitoring systems field, and in particular to a kind of map aggregation vehicle brush based on Distributed Calculation
New method.
Background technology
At present, in GPS monitoring systems, most common function is that on the electronic map, real-time display simultaneously refreshes vehicle
Real time position and other satellite informations.Due to the processor and the disposal ability of display card and the limit of network transmission bottleneck of PC machine
System, the vehicle fleet size shown on the electronic map has certain upper limit, and after this limits value, the performance of system just has
It is obvious to decline, the normal use of other systems function is influenced, therefore how to break through the bottleneck of vehicle display number, allow user's energy
It is enough that real-time vehicle information as much as possible is seen in system, just become a technical barrier.The scheme generally used now
It is that map aggregation is refreshed with reducing unnecessary map, but this scheme has obvious limitation, is exactly that it can only reduce display
The burden of card, but adds the burden of CPU and memory, and as the continuous of vehicle fleet size increases, spends in map aggregation meter
The CPU counted in also will constantly increase therewith with memory source, still can finally reach the treatable limiting value of system.
In view of this, the present inventor is directed to the defects of prior art and furthers investigate, and has this case generation.
The content of the invention
The technical problems to be solved by the invention are that providing a kind of map aggregation vehicle based on Distributed Calculation refreshes
Method, this method have used distributed computing technology, to solve the problems, such as vehicle display number bottleneck, are ensureing raising system
While performance, the autgmentability of strengthening system, reduces the cost of maintenance upgrade.
The present invention solves above-mentioned technical problem using following technical scheme：
A kind of map aggregation vehicle method for refreshing based on Distributed Calculation, including two large divisions, first as described below
Divide with Part II without sequencing；
Part I：Server end receives and connects analysis gps data, specifically comprises the following steps：
Step 1.1：The real-time GPS data that GPS server receives to send from car-mounted terminal by Internet, and carry out
Simple parsing, is converted to the binary data format of internal system；
Step 1.2：GPS server at regular intervals, will receive parsed several gps datas form it is one big
Data packet, the master server of GPS computing clusters is sent the packet to by LAN or private network；
Step 1.3：Big data bag is first split as multiple individually gps data bags by the master server of GPS computing clusters, so
It is scheduled afterwards according to the present load of each work station, gps data bag is distributed to each work station carries out data processing；
Step 1.4：Work station handles gps data bag, preserves gps data into database, and carry out longitude and latitude to gps data
The processing calculated is spent, then the data after processing are returned to the master server of GPS computing clusters；
Step 1.5：The gps data returned in step 1.4 is cached to distributed caching by the master server of GPS computing clusters
In a dictionary data structure in, the key of the dictionary data structure is the identifier of vehicle, and the value of the key is exactly a memory
A structure of the structure as the gps data that step 1.4 returns, the gps data returned for preserving work station processing are right
Each vehicle identifiers are answered, a corresponding newest gps data of gps time are only preserved in dictionary data structure, if to protect
If the gps time for the gps data deposited is also early than the gps time of data that has currently preserved, then abandon this time preserving；
Part II：Client map aggregation, specifically comprises the following steps：
Step 2.1：User starts client software, loads electronic map, and obtain to the request of GPS computing clusters master server
The gps data of all vehicles is taken, and sends the latitude and longitude coordinates of current map scope and visual field size gives GPS computing clusters main clothes
Business device, the statistics that request carries out map aggregation calculate；
Step 2.2：GPS computing cluster master servers receive gps data and map aggregation statistics request, first by all cars
Sort by vehicle identifiers, be divided into several small vehicle lists, and give each numbering of table, the result after sequence is placed on distribution
In formula caching；Then by this work station vehicle list sequence number to be processed, the latitude and longitude coordinates of the body of a map or chart of polymerization is asked, are sent out
Each work station is given, distributes the specific map aggregation statistics that each work station carries out each vehicle list；
Step 2.3：The latitude and longitude coordinates for the body of a map or chart that work station polymerize according to received request, by this part map again
N*N rectangular grid of N rows N row is divided into, and calculates the length and width of each grid, while creates a dictionary data knot
Structure stores statistical result, and the key of dictionary is the row and column of grid, and corresponding value is an object, this object has two members
Variable, first is a counter to store the sum of vehicle in the corresponding grid of the key；Second is one List pairs
As the vehicle identifiers in the in store grid in the inside；Then work station is according to oneself received vehicle list sequence to be processed
Number, obtain the gps data of pending vehicle list from distributed caching, and the List of searching loop this vehicle GPS data；
Step 2.4：Work station uses the interface of engine map offer, first root inside the circulation described in step 2.3
According to the current longitude and latitude of each car judge the vehicle whether request body of a map or chart, if it is not, then jumping to next car, again
Start this step；If it is, judging the grid where the vehicle, algorithm is：
Row=（The longitude in the longitude of gps data-body of a map or chart upper left corner）/ grid width, if row are not integer, arranges
=rounding（Row）+1；
Row=（The latitude of the latitude-gps data in the body of a map or chart upper left corner）/ grid length, if capable is not integer,
Row=rounding（OK）+1；
After judgement, according to what is obtained【OK, arrange】, the corresponding storage statistics of this key is found from the dictionary data structure
Object, by the vehicle fleet counter of corresponding grid, adds 1 by the value of counter；The identifier of this vehicle is added to guarantor at the same time
In the List for depositing vehicle identifiers；
Step 2.5：After the completion of all vehicle GPS datacycle processing, work station carries out the dictionary for storing statistical result
GPS computing cluster master servers are returned to after Binary Serialization；
Step 2.6：GPS computing cluster master servers receive the statistical result that each work station returns in step 2.5, when true
Recognize after all work stations for being assigned with this subtask all return the result, GPS computing clusters master server is to all statistical results
Collected：By in each statistical result, have identical【OK, arrange】Corresponding vehicle fleet counter is added, and is obtained in step 2.3
Vehicle fleet in the map subregion grid；
Step 2.7：GPS computing clusters master server needs to return to the data of a List structure to client at the same time, should
Each element of List stores the statistics object in the map subregion grid described in a step 2.3, the statistics
There is following member variable in data object：
The 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 is less than some threshold values, all vehicle identifiers row in the grid should be included
List data structure；If vehicle fleet is more than the threshold values, the vehicle identifiers table data knot in the grid need not be included
Structure；
Step 2.8：Client receives the data of the statistical result List structures of computing cluster master server return, circulation time
The List is gone through, to the statistical result of the map subregion grid described in each step 2.3, judges whether the car in the region
Identification list, if it does not exist, then using the central point latitude and longitude coordinates of the grid area described in step 2.7, in map
The upper vehicle number drawn the mark icon of vehicle polymerization, and show under the icon region；Should if existed in statistical result
The vehicles identifications list in region, then according to these vehicles identifications, obtain the latest GPS number of these vehicles from distributed caching
According to, and show on map the icon of these vehicles；
Step 2.9：Client timing sends map aggregation statistics request to GPS computing clusters master server, is brushing every time
Before new map, first original vehicle icon is all removed.
Further, the vehicle identifiers in the step 1.5 are license plate numbers, or vehicle carried mobile phone number, or database generation
Unique ID.
The advantage of the invention is that：The present invention carries out the improvement of novelty on traditional map aggregation technical solution,
Mainly by the original map aggregation processing function all undertaken by client, by the principle of load balancing, distribution is distributed to
Work station handled, and these work stations can be undertaken by cheap common PC, it might even be possible to used using existing
In machine, can so make full use of the CPU computing capabilitys of idle machine；It is false according to the description in technical solution of the present invention
If whole map is divided into N number of region, and setting shows that the number of thresholds of vehicle GPS information is 10 in each local, then
The icon that display refreshes is needed to be up to N*10 on map, vehicle fleet size that is at least only N number of, and handling if desired
Increase, client map need show refresh number of icons do not increase, simply distributed work station load increase and
, thus ever-increasing vehicle number simply can be supported with the method for the quantity of increase work station, without to visitor
Any change is done at family end, thus completely eliminates a large amount of gps datas of client dissection process and in the big spirogram of map denotation
The bottleneck for the performance that marker tape comes, greatly improves the scalability of whole system.Simultaneously as specific polymerization is calculated and all existed
Server end is completed, when improving aggregating algorithm if desired later, it is only necessary to updates the program of server end, substantial amounts of visitor
Family end is simultaneously unaffected, and this reduces the cost of maintenance upgrade.
Brief description of the drawings
The invention will be further described in conjunction with the embodiments with reference to the accompanying drawings.
Fig. 1 is present system logic diagram.
Embodiment
As shown in Figure 1, GPS monitoring systems of the present invention include following modules：GPS server, GPS computing clusters and
Client.The function of each module and effect are as follows：
GPS server：It is connected with GPS terminal, possesses receiving and parse the function of vehicle GPS data.It is responsible for ground
Figure computing cluster forwards real-time GPS data.
GPS computing clusters：A cluster being made of one group of server, it is by a master server and multiple work stations
Composition, master server are led to GPS server by Internet or LAN connection, main service with all working station and client
LAN connection is crossed, possesses the function that distributed treatment is carried out to gps data.It is responsible for receiving and parses what GPS server transmitted
Gps data, map aggregation statistics is carried out according to the request of client to gps data, and statistical result is sent back to client.
Client：With map calculation cluster by LAN connection, possess map denotation and client end interface.It is responsible for from ground
Figure computing cluster obtain needed for vehicle real-time GPS data and map aggregation statistical information, and after show on map and polymerizeing
Vehicle real time.
Specific map aggregation vehicle method for refreshing, including two large divisions, Part I as described below is with Part II without elder generation
Order afterwards；
Part I：Server end receives and connects analysis gps data, specifically comprises the following steps：
Step 1.1：The real-time GPS data that GPS server receives to send from car-mounted terminal by Internet, and carry out
Simple parsing, is converted to the binary data format of internal system；
Step 1.2：GPS server at regular intervals, will receive parsed several gps datas form it is one big
Data packet, the master server of GPS computing clusters is sent the packet to by LAN or private network；
Step 1.3：Big data bag is first split as multiple individually gps data bags by the master server of GPS computing clusters, so
It is scheduled afterwards according to the present load of each work station, gps data bag is distributed to each work station carries out data processing；
Step 1.4：Work station handles gps data bag, preserves gps data into database, and carry out longitude and latitude to gps data
The processing calculated is spent, then the data after processing are returned to the master server of GPS computing clusters；
Step 1.5：The gps data returned in step 1.4 is cached to distributed caching by the master server of GPS computing clusters
In a dictionary data structure in, the key of the dictionary data structure is the identifier of vehicle（Can be license plate number, or vehicle-mounted hand
Machine number, or unique ID of database generation）, the value of the key is exactly an internal storage structure as the gps data that step 1.4 returns
A structure, the gps data returned for preserving work station processing, corresponding each vehicle identifiers, dictionary data structure
In only preserve a corresponding newest gps data of gps time, if the gps time for the gps data to be preserved than has currently been protected
If the gps time for the data deposited also wants morning, then abandon this time preserving；
Part II：Client map aggregation, specifically comprises the following steps：
Step 2.1：User starts client software, loads electronic map, and obtain to the request of GPS computing clusters master server
The gps data of all vehicles is taken, and sends the latitude and longitude coordinates of current map scope（Upper left corner longitude and latitude and lower right corner longitude and latitude
Degree）And visual field size gives GPS computing cluster master servers, the statistics that request carries out map aggregation calculates；
Step 2.2：GPS computing cluster master servers receive gps data and map aggregation statistics request, first by all cars
Sort by vehicle identifiers, be divided into several small vehicle lists, and give each numbering of table, such as the 1-500 car is 1
Number, the 501-1000 is No. 2（Sequence is only carried out when master server starts, and the upper limit of numbering is the quantity according to work station
Dynamic change）, the result after sequence is placed in distributed caching；Then by this work station vehicle list sequence number to be processed,
The latitude and longitude coordinates of the body of a map or chart of polymerization are asked, are sent to each work station, each work station is distributed and carries out each vehicle stock
The specific map aggregation statistics of table；
Step 2.3：The latitude and longitude coordinates for the body of a map or chart that work station polymerize according to received request, by this part map again
N*N rectangular grid of N rows N row is divided into, and calculates the length and width of each grid, while creates a dictionary data knot
Structure stores statistical result, and the key of dictionary is the row and column of grid, and corresponding value is an object, this object has two members
Variable, first is a counter to store the sum of vehicle in the corresponding grid of the key；Second is one List pairs
As the vehicle identifiers in the in store grid in the inside；Then work station is according to oneself received vehicle list sequence to be processed
Number, obtain the gps data of pending vehicle list from distributed caching, and the List of searching loop this vehicle GPS data；
Step 2.4：Work station uses the interface of engine map offer, first root inside the circulation described in step 2.3
According to the current longitude and latitude of each car judge the vehicle whether request body of a map or chart, if it is not, then jumping to next car, again
Start this step；If it is, judging the grid where the vehicle, algorithm is：
Row=（The longitude in the longitude of gps data-body of a map or chart upper left corner）/ grid width, if row are not integer, arranges
=rounding（Row）+1；
Row=（The latitude of the latitude-gps data in the body of a map or chart upper left corner）/ grid length, if capable is not integer,
Row=rounding（OK）+1；
After judgement, according to what is obtained【OK, arrange】, the corresponding storage statistics of this key is found from the dictionary data structure
Object, by the vehicle fleet counter of corresponding grid, adds 1 by the value of counter；The identifier of this vehicle is added to guarantor at the same time
In the List for depositing vehicle identifiers；
Step 2.5：After the completion of all vehicle GPS datacycle processing, work station carries out the dictionary for storing statistical result
GPS computing cluster master servers are returned to after Binary Serialization；
Step 2.6：GPS computing cluster master servers receive the statistical result that each work station returns in step 2.5, when true
Recognize after all work stations for being assigned with this subtask all return the result, GPS computing clusters master server is to all statistical results
Collected：By in each statistical result, have identical【OK, arrange】Corresponding vehicle fleet counter is added, and is obtained in step 2.3
Vehicle fleet in the map subregion grid；
Step 2.7：GPS computing clusters master server needs to return to the data of a List structure to client at the same time, should
Each element of List stores the statistics object in the map subregion grid described in a step 2.3, the statistics
There is following member variable in data object：
The 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 is less than some threshold value, all vehicle identifiers row in the grid should be included
List data structure；If vehicle fleet is more than the threshold value, the vehicle identifiers table data knot in the grid need not be included
Structure；
Step 2.8：Client receives the data of the statistical result List structures of computing cluster master server return, circulation time
The List is gone through, to the statistical result of the map subregion grid described in each step 2.3, judges whether the car in the region
Identification list, if it does not exist, then using the central point latitude and longitude coordinates of the grid area described in step 2.7, in map
The upper vehicle number drawn the mark icon of vehicle polymerization, and show under the icon region；Should if existed in statistical result
The vehicles identifications list in region, then according to these vehicles identifications, obtain the latest GPS number of these vehicles from distributed caching
According to, and show on map the icon of these vehicles；
Step 2.9：Client timing sends map aggregation statistics request to GPS computing clusters master server, is brushing every time
Before new map, first original vehicle icon is all removed.
The present invention carries out the improvement of novelty on traditional map aggregation technical solution, mainly will originally all
The map aggregation processing function undertaken by client, by the principle of load balancing, distributes to distributed work station and is handled,
And these work stations can be undertaken by cheap common PC, it might even be possible to existing machine in use is used, so can be with
Make full use of the CPU computing capabilitys of idle machine；According to the description in technical solution of the present invention, it is assumed that whole map is divided into N
A region, and setting shows that the number of thresholds of vehicle GPS information is 10 in each local, then needs display to refresh on map
Icon be up to N*10, at least only N number of, and increase if necessary to the vehicle fleet size of processing, client map needs
The number of icons that display refreshes does not increase, and the simply load of distributed work station increases, and thus can simply use
The method for increasing the quantity of work station supports ever-increasing vehicle number, without doing any change to client, this
Sample just completely eliminates a large amount of gps datas of client dissection process and the bottleneck for the performance brought in a large amount of icons of map denotation,
Greatly improve the scalability of whole system.Simultaneously as specific polymerization is calculated and all completed in server end, Yi Houru
When fruit needs to improve aggregating algorithm, it is only necessary to the program of server end is updated, substantial amounts of client is simultaneously unaffected, this
Reduce the cost of maintenance upgrade.
Preferable the foregoing is merely the present invention implements use-case, is not intended to limit the scope of the present invention.It is all
Within the spirit and principles in the present invention, any modification, equivalent substitution and improvement for being made etc., should be included in the present invention's
Within protection domain.
Claims (2)
- A kind of 1. map aggregation vehicle method for refreshing based on Distributed Calculation, it is characterised in that：Including two large divisions, below A part is with Part II without sequencing；Part I：Server end receives and parses gps data, specifically comprises the following steps：Step 1.1：The real-time GPS data that GPS server receives to send from car-mounted terminal by Internet, and carry out simple Parsing, be converted to the binary data format of internal system；Step 1.2：GPS server at regular intervals, will receive several parsed gps datas and form a big data Bag, the master server of GPS computing clusters is sent the packet to by LAN or private network；Step 1.3：Big data bag is first split as multiple individually gps data bags, Ran Hougen by the master server of GPS computing clusters It is scheduled according to the present load of each work station, gps data bag is distributed to each work station carries out data processing；Step 1.4：Work station handles gps data bag, preserves gps data into database, and carry out longitude and latitude meter to gps data Data after processing, are then returned to the master server of GPS computing clusters by the processing of calculation；Step 1.5：The gps data returned in step 1.4 is cached in distributed caching by the master server of GPS computing clusters In one dictionary data structure, the key of the dictionary data structure is the identifier of vehicle, and the value of the key is exactly an internal storage structure The structure as gps data returned with step 1.4, the gps data returned for preserving work station processing are corresponding every A vehicle identifiers, a corresponding newest gps data of gps time is only preserved in dictionary data structure, if to be preserved If the gps time of gps data is also early than the gps time of data that has currently preserved, then abandon this time preserving；Part II：Client map aggregation, specifically comprises the following steps：Step 2.1：User starts client software, loads electronic map, and to GPS computing cluster master server acquisition requests institute There is the gps data of vehicle, and send the latitude and longitude coordinates of current map scope and visual field size gives GPS computing clusters main service Device, the statistics that request carries out map aggregation calculate；Step 2.2：GPS computing cluster master servers receive gps data and map aggregation statistics request, first press all vehicles Vehicle identifiers sort, and are divided into several small vehicle lists, and give each numbering of table, and the result after sequence is placed on distributed slow In depositing；Then by each work station vehicle list sequence number to be processed, the latitude and longitude coordinates of the body of a map or chart of polymerization is asked, are sent to Each work station, distributes the specific map aggregation statistics that each work station carries out each vehicle list；Step 2.3：Work station splits this part map according to the latitude and longitude coordinates of the body of a map or chart of received request polymerization again The N*N rectangular grid arranged for N rows N, and the length and width of each grid are calculated, while create a dictionary data structure and come Statistical result is stored, the key of dictionary is the row and column of grid, and corresponding value is an object, this object there are two members to become Amount, first member variable is a vehicle fleet counter, is used to store the sum of vehicle in the corresponding grid of the key；The Two member variables are a List objects, the vehicle identifiers in the in store grid in the inside；Then work station is according to receiving Oneself vehicle list sequence number to be processed, the gps data of pending vehicle list is obtained from distributed caching, and circulate time Go through the List of this vehicle GPS data；Step 2.4：Work station is inside the circulation described in step 2.3, using the interface of engine map offer, first according to every The current longitude and latitude of car judge the vehicle whether request body of a map or chart, if it is not, then jumping to next car, restart This step；If it is, judging the grid where the vehicle, algorithm is：Row=（The longitude in the longitude of gps data-body of a map or chart upper left corner）/ grid width, if row are not integer, arrange=takes It is whole（Row）+1；Row=（The latitude of the latitude-gps data in the body of a map or chart upper left corner）/ grid length, if capable is not integer, go=takes It is whole（OK）+1；After judgement, according to what is obtained【OK, arrange】, the corresponding storage statistics pair of this key is found from the dictionary data structure As the vehicle fleet counter of corresponding grid is added 1；The identifier of this vehicle is added at the same time and preserves vehicle identifiers In List；Step 2.5：After the completion of all vehicle GPS datacycle processing, work station by the dictionary for storing statistical result carry out two into GPS computing cluster master servers are returned to after system serializing；Step 2.6：GPS computing cluster master servers receive the statistical result that each work station returns in step 2.5, when confirmation institute After having the work station for being assigned with this subtask all to return the result, GPS computing clusters master server carries out all statistical results Collect：By in each statistical result, have identical【OK, arrange】Corresponding vehicle fleet counter is added, and is obtained described in step 2.3 Map subregion grid in vehicle fleet；Step 2.7：GPS computing clusters master server needs to return to the data of a List structure, the List to client at the same time Each element store statistics object in the map subregion grid described in a step 2.3, the statistics There is following member variable in object：The 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 is less than some threshold values, all vehicle identifiers list numbers in the grid should be included According to structure；If vehicle fleet is more than the threshold values, the vehicle identifiers list data structure in the grid need not be included；Step 2.8：Client receives the data of the statistical result List structures of computing cluster master server return, and searching loop should List, to the statistical result of the map subregion grid described in each step 2.3, judges whether the vehicle mark in the region Know list, if it does not exist, then using the central point latitude and longitude coordinates of the grid area described in step 2.7, painted on map The mark icon of vehicle processed polymerization, and show under the icon vehicle number in the region；If there are the region in statistical result Vehicles identifications list, then according to these vehicles identifications, the latest GPS data of these vehicles are obtained from distributed caching, and The icon of these vehicles is shown on map；Step 2.9：Client timing sends map aggregation statistics request to GPS computing clusters master server, is refreshing ground every time Before figure, first original vehicle icon is all removed.
- A kind of 2. map aggregation vehicle method for refreshing based on Distributed Calculation as claimed in claim 1, it is characterised in that：Institute It is license plate number to state the vehicle identifiers in step 1.5, or vehicle carried mobile phone number, or unique ID of database generation.
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| JP4654823B2 (en) * | 2005-08-03 | 2011-03-23 | 株式会社デンソー | Road map data update system and road detection system |
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| 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 |
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