CN104850657B - A kind of rate addition method of holographic situational map - Google Patents
A kind of rate addition method of holographic situational map Download PDFInfo
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- CN104850657B CN104850657B CN201510303764.2A CN201510303764A CN104850657B CN 104850657 B CN104850657 B CN 104850657B CN 201510303764 A CN201510303764 A CN 201510303764A CN 104850657 B CN104850657 B CN 104850657B
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
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Abstract
The invention discloses a kind of rate addition method of holographic situational map, including holographic situational map superposition flow and rule, and according to defined in these rules superposition parameter and superposition result concrete form.Can multi-source heterogeneous data be provided with the service of Internet map data superposition based on the rule.For multi-source heterogeneous multidate information by unified, flexible superposition rule so that the holographic situational map data after superposition can provide data result towards knowledge level, consistent with user's request.
Description
Technical field
The invention belongs to location-based service technology, technical field of geographic information, it is related to a kind of superposition side of holographic situational map
Method.
Background technology
Holographic situational map be based on position, comprehensively reflection position in itself and its various features related to position,
The numerical map of event or things, is contemporary location-based service industry growth requirement to be adapted in map family and one kind for growing up is new
Type map products.Holographic situational map content is included but is not limited to：Two-dimension vector map data, three-dimensional map data, panorama
Dynamic data such as data, navigation data, tile map data, indoor map data and multi-medium data, weather flight etc..With one
As situational map compare, holographic situational map has following both sides essential characteristic：
(1) holographic situational map is the set of the consistent four-dimensional spacetime positional information of semantic relation.Holographic situational map institute
More fully, multi-level, many granularities, comprehensive reflection locus are in itself and various passes for the position of reflection and its relevant information
Connection relation, covers the direct correlation of the person to person based on position, people and thing, thing and thing and contains information, each correlation
Semantic locations relation between information it is more clear and definite with it is consistent.
(2) holographic situational map is made up of series digit situational map.Holographic situational map can meet various applications
Demand, can form several scenes, it is possible to which various ways are presented to user.
Holographic situational map contains the various depth content information being described with different semantic locations, and these information exist
Geometric position, expression yardstick, semanteme, time-space relationship, there is inconsistency on logical AND attribute, but in specific space-time bar
Under part, specific scene, there is certain Semantic logical relation or time-space relationship, by these data are carried out space-time,
Superposition semantically can produce the data of the new value for having larger difference with single data.With the big number of influenza spread trend
As a example by according to the study, Sadelik A etc. are uncomfortable micro- by being related on 4,400,000 microbloggings being sent out 630,000, New York user
The data such as position, issuing time, social activities track in rich are analyzed, and obtain uncomfortable " the position focus of a user
Figure ", and the superposition on space-time is carried out to data based on this：Set up certain sequential relationship and to influenza development trend
Pattern-recognition such that it is able to the prediction to just making up to 90% accuracy rate before some individuality infection influenzas for 8 hours, it is aobvious and easy
See, the superposition of positional information causes that originally single, discrete information generates tool value high.Therefore, holographic position ground
The superposition of figure seek to by separate sources, different accuracy, different pieces of information model map datum by various information processings, synthesis
Rate addition method, it is final to obtain one from the cognitive new data set of knowledge level.
The data not yet having at present for holographic situational map carry out the Patents and document of the method for superposition.
The content of the invention
In order to solve above-mentioned technical problem, the invention provides a kind of rate addition method of holographic situational map.
The technical solution adopted in the present invention is：A kind of rate addition method of holographic situational map, it is characterised in that including with
Lower step：
Step 1：Superposition scene is determined according to user's request, the data content of superposition is filtered out, i.e., according to residing for user
Specific situation is established in position, scenario models is set up, so that it is determined that the map datum of which type of superposition needed under different scenes；
Step 2：Semantic locations to participating in the map datum of superposition judge；
If semantic locations are identical, order performs following step 3；
If semantic locations are different, firstly the need of the semantic conversion for carrying out position, make the description of position in same benchmark
Under carry out, then perform following step 3；
Step 3：Superposition granularity to participating in the map datum of superposition judges；
If superposition granularity is identical, order performs following step 4；
If superposition granularity is different, first by Map Generalization means, granularity map high is converted into relatively low granularity
Party B carries out superposition, then performs following step 4；
Step 4：Participating in the map datum of superposition carries out the computing of space-time superposition, the computing of geometry superposition and semantic superposition computing；
Step 5：Data set to being obtained after superposition optimizes screening, and superposition terminates.
Preferably, the space-time superposition computing described in step 4 includes statistics superposition, screening superposition, precision superposition, sequential
Superposition, state change superposition and movement locus superposition；Described geometry superposition computing includes that geometry is stacked, geometry cluster changes
Plus, wherein geometry is stacked the intersecting and merging including geometric figure, difference operation；Described semantic superposition computing is associated including Spatial Semantics
The association of superposition, Attribute Association superposition, semantic association superposition, wherein Spatial Semantics includes topological correlation, orientation and measurement association.
It is based on user data preferably, optimizing screening to the data set that is obtained after superposition described in step 5
And other screening conditions optimize screening to data set, geographical position and moving characteristic in particular according to semantic locations pass through
Position dynamic change stops superposition so as to enter Mobile state superposition untill position no longer changes.
Compared with the conventional method, the present invention has the advantage that as follows with effect：
1st, the present invention is when superposition rule is formulated, it is proposed that including granularity superposition rule, space-time superposition rule, geometry superposition
Rule, semantic superposition rule.Not only done from space geometry aspect, more from the multiple dimension such as granularity, time-space relationship, semantic relation
Consider, can be suitable for the various map datums of the feature such as many granularities of holographic situational map, multi-time Scales, multi-source heterogeneous；
2nd, propose before the map to different semantic locations carries out superposition, changed by granularity transform, semantic locations and ensured
The uniformity of map superposition result, the result that superposition is obtained in this way more meets the cognitive law of people, can be in system
Fusion superposition is carried out to map dataset under one location expression framework.
Brief description of the drawings
Fig. 1：The flow chart of the embodiment of the present invention；
Fig. 2：The position of the embodiment of the present invention chronologically superposition schematic diagram；
Fig. 3：Press state change superposition schematic diagram in the position of the embodiment of the present invention；
Fig. 4：The semantic locations transition diagram of the map superposition of the embodiment of the present invention.
Specific embodiment
Understand for the ease of those of ordinary skill in the art and implement the present invention, below in conjunction with the accompanying drawings and embodiment is to this hair
It is bright to be described in further detail, it will be appreciated that implementation example described herein is merely to illustrate and explain the present invention, not
For limiting the present invention.
The rate addition method of a kind of holographic situational map provided see Fig. 1, the present invention, comprises the following steps：
Step 1：Superposition scene is determined according to user's request, the data content of superposition is filtered out, i.e., according to residing for user
Specific situation is established in position, scenario models is set up, so that it is determined that the map datum of which type of superposition needed under different scenes；
Step 2：Semantic locations to participating in the map datum of superposition judge；
If semantic locations are identical, order performs following step 3；
If semantic locations are different, firstly the need of the semantic conversion for carrying out position, make the description of position in same benchmark
Under carry out, then perform following step 3；
Step 3：Superposition granularity to participating in the map datum of superposition judges；
If superposition granularity is identical, order performs following step 4；
If superposition granularity is different, first by Map Generalization means, granularity map high is converted into relatively low granularity
Party B carries out superposition, then performs following step 4；
For the granularity superposition of vertical level, what different holographic its position datas of situational map data type can be expressed
Granularity is different, and some map datums can only describe single granularity, and what is had is then not limited in a certain granularity.Therefore map datum
Superposition is likely to occur under same granularity, or under different grain size.Therefore, following principle is followed for the superposition of map granularity：
Holographic situational map is preferential to carry out superposition in the same granularity of respective expression, and the space superposition of correspondence granularity is also respectively adopted respectively
From the geometric figure of grain size category, if the map of superposition is beyond expression in thinner spatial granularity, need by map
The means such as comprehensive, superposition is carried out by the Party B that granularity map high is converted to relatively low granularity；But low granularity then can not be to granularity high
Carry out superposition.
Step 4：Participating in the map datum of superposition carries out the computing of space-time superposition, the computing of geometry superposition and semantic superposition computing；
The computing of space-time superposition include statistics superposition, screening superposition, precision superposition, sequential superposition, state change superposition and
Movement locus superposition；
(1) addition of statistics superposition, i.e. time, merging；
Merging to the time period of superposition information is to carry out overall fusion with the starting point of time period, and the single time is not considered
The length at end, calculates the end time of all time periods minimum start time and maximum, is summarised as the starting of time to termination
Whole time end.Such superposition operation is applied to description entity or event in the ad-hoc location duration.
(2) subtracting each other for superposition, i.e. time is screened；
The data such as issued before the specific time, or the data issued after a certain time.Such superposition is fitted
For screening the map datum in certain time period.
(3) precision superposition, i.e. time it is intersecting；
To multiple time periods of superposition information, most accurate part is taken.Being summarised as map datum has multiple time attributes,
Intercept the time period for overlapping.It is applicable to the corresponding time precision of raising information.
(4) sequence of sequential superposition, i.e. time；
See Fig. 2, to the time period of superposition information, be ranked up according to initial time or termination time, formed according to
One set of time period of the order that time order and function occurs." history sequentially " of such superposition operation suitable for event category information
Description, description change over time, the Changing Pattern of position.The time that sequential restructuring can be included according to positional information,
Can also be recombinated according to the issuing time of information.
(5) state change superposition；
See Fig. 3, the information of the different time collection (or issue) based on uniform location carries out attribute detection, obtains some
Value changes of particular community etc..Such superposition operation is applied to the information change monitoring to fixed position.
(6) movement locus superposition；
Multiple map data content is entered Mobile state superposition by time-based sequence and continuous linear space position.
The computing of geometry superposition includes that geometry is stacked, geometry cluster superposition, wherein geometry be stacked the friendship including geometric figure,
And, difference operation；
(1) geometry is stacked (intersecting and merging of geometric figure, difference)；
Geometry Overlap Analysis carry out intersecting and merging, difference operation to space geometry scope, and the mutual of multiple geometric figures is obtained respectively
The geometric ranges of overlap, merge after geometric ranges, the geometric ranges that do not overlap each other.
(2) geometry cluster superposition；
Space clustering superposition operation, be the space geometry entity included to position according to certain attributive classification, enter
The converging operation of row geometry, the operation is based on attribute coding's information, and point, line, surface are carried out into map geometric generalization, new so as to obtain
Point, line, surface be used to describe the distribution characteristics of position.
Semantic superposition computing includes Spatial Semantics association superposition, Attribute Association superposition, wherein semantic association superposition, space language
Justice association includes topological correlation, orientation and measurement association.
(1) Spatial Semantics association superposition；
Topological correlation：By position and the topological relationship calculation of position, the branch topology relationship between position is obtained, by this
Plant result of the incidence relation as space superposition.
Orientation and measurement association：Calculated by the distance of position and position, position relation, set up mutual orientation with measurement
Relation.Orientation and measurement association typically set up superposition relation with a kind of relative position relation.
(2) Attribute Association superposition；
Association is set up by the value of attribute information.Event information for example in attribute, sets up event correlation, superposition knot
Fruit is the location sets to particular event information description.
(3) semantic association superposition；
Semantic association refers to set up association by the semantic relation between map attribute.The relation of foundation is closed including example
Various semantic relations such as system, classification relation, subordinate relation, approximation relation, clustering relationships.
Step 5：Data set to being obtained after superposition optimizes screening, and superposition terminates.
Screening is optimized to data set, is that sieve is optimized to data set based on user data and other screening conditions
Choosing, geographical position and moving characteristic in particular according to semantic locations, by position dynamic change so as to enter Mobile state superposition, until
Position stops superposition untill no longer changing.
Below by way of specific embodiment, the present invention is further elaborated, for example, go for being cured to certain from certain residential building
The most quick traffic route of institute.
Step 1：According to user's request, it is known that the superposition scene is dynamic navigation scenarios, according to the model of scene, arrow is extracted
Measure base map data, traffic event data (traffic accident and road construction), navigation data, the Real-Time Traffic Volume number of electronic map
According to.
Step:2：Judge the location type of map datum：Vector Electronic Map, navigation data use longitude and latitude description
Semantic locations, and the semantic locations type of highway/railway that traffic event data and Real-Time Traffic Volume data are used is (accurate
To certain section).Therefore first have to carry out the semantic locations conversion of map datum, see Fig. 4, such as by the space bit of traffic events
Confidence breath is converted to the position coordinates string (BC sections) of the road having influence on, and the menace level attribute information of event is converted to influence
Drive time value, such as " major traffic accidents " be converted to " congestion 60 minutes "；And by traffic flow information " BF sections from west to
East jogging " is converted to " road-section average current speed in F → B directions is 15Km/h to 30Km/h, a length of 15 minutes when passing through ".
Step 3：Setting map superposition rule：
(1) granularity superposition rule：It is determined that under outdoor pattern, the maximum particle size that can be superimposed to is block level, bigger grain
Degree cannot superposition if architectural grade；
(2) the space-time rule of superposition is chosen to be " state change superposition ", with reference to traffic flow forecasting data, calculates traffic
The possible increased transit time of event, and the incremental time is associated with the road having influence on, obtain the different time periods not
Same road consuming value, by taking BC sections as an example：
(3) geometric figure rule is chosen to be the specific scope that " Spatial Overlap Analysis " calculate the road of influence, such as
Influence current length, the direction of road；For example in Fig. 4 traffic accident may cause the 50% current speed in the whole section in B → C sections
Degree is affected.
(4) semantic superposition rule：Average transit time this attribute based on road carries out time-consuming statistical computation,
So as to obtain a plurality of preferred pass.
Step 4：Positional information change according to the actual selection of user, is constantly screened again to superposition result, so that
Dynamic obtains optimal current circuit.
The content of the invention of this patent includes the flow of holographic situational map superposition and rule, and is determined according to these rules
The superposition parameter of justice and the concrete form of superposition result.Can provide mutual to multi-source heterogeneous data based on the rule
The superposition service of networking map datum.For multi-source heterogeneous multidate information by unified, flexible superposition rule so that superposition
Holographic situational map data afterwards can provide data result towards knowledge level, consistent with user's request.
It should be appreciated that the part that this specification is not elaborated belongs to prior art.
It should be understood that the above-mentioned description for preferred embodiment is more detailed, therefore can not be considered to this hair
The limitation of bright scope of patent protection, one of ordinary skill in the art is not departing from right of the present invention under enlightenment of the invention
It is required that under the ambit protected, replacement can also be made or deformed, each fall within protection scope of the present invention, the present invention
Scope is claimed should be determined by the appended claims.
Claims (3)
1. a kind of rate addition method of holographic situational map, it is characterised in that comprise the following steps：
Step 1：Superposition scene is determined according to user's request, the data content of superposition is filtered out, i.e., according to the location of user
Specific situation is established, scenario models is set up, so that it is determined that the map datum of which type of superposition needed under different scenes；
Step 2：Semantic locations to participating in the map datum of superposition judge；
If semantic locations are identical, order performs following step 3；
If semantic locations are different, firstly the need of the semantic conversion for carrying out position, the description of position is set to enter under same benchmark
OK, following step 3 is then performed；
The semantic conversion of the position, is the position coordinates that the spatial positional information of traffic events is converted to the road having influence on
String；
Step 3：Superposition granularity to participating in the map datum of superposition judges；
If superposition granularity is identical, order performs following step 4；
If superposition granularity is different, first by Map Generalization means, granularity map high is converted to the Party B of relatively low granularity
Superposition is carried out, following step 4 is then performed；
Step 4：Participating in the map datum of superposition carries out the computing of space-time superposition, the computing of geometry superposition and semantic superposition computing；
Step 5：Data set to being obtained after superposition optimizes screening, and superposition terminates.
2. the rate addition method of holographic situational map according to claim 1, it is characterised in that：Space-time described in step 4
Superposition computing includes statistics superposition, screening superposition, precision superposition, sequential superposition, state change superposition and movement locus superposition；
Described geometry superposition computing includes that geometry is stacked, geometry cluster superposition, wherein geometry be stacked the intersecting and merging including geometric figure,
Difference operation；Described semantic superposition computing includes Spatial Semantics association superposition, Attribute Association superposition, semantic association superposition, wherein
Spatial Semantics association includes topological correlation, orientation and measurement association.
3. the rate addition method of holographic situational map according to claim 1, it is characterised in that：Described in step 5 to repeatedly
Plus after the data set that obtains optimize screening, be based on user data, the geographical position of semantic locations and moving characteristic logarithm
Screening is optimized according to collection, by position dynamic change so as to enter Mobile state superposition, is stopped untill position no longer changes
Only superposition.
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| CN106980455A (en) * | 2016-01-15 | 2017-07-25 | 游军 | Electronic equipment and its spatial dimension choosing method and system of application |
| CN106844461B (en) * | 2016-12-21 | 2019-10-29 | 北京世纪高通科技有限公司 | Road conditions determine method and device in a kind of tile figure |
| CN109492065B (en) * | 2018-10-26 | 2021-07-20 | 桂林电子科技大学 | Extraction method of indoor semantic map space-time relationship |
| CN113176845B (en) * | 2021-04-23 | 2024-01-12 | 北京完美知识科技有限公司 | Method and device for displaying history information in history map |
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| CN102938229A (en) * | 2012-09-18 | 2013-02-20 | 中国人民解放军装甲兵工程学院 | Three-dimensional digital holography photon map |
| US8456954B1 (en) * | 2009-05-18 | 2013-06-04 | The United States Of America As Represented By The Secretary Of The Navy | Holographic navigation |
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| US8456954B1 (en) * | 2009-05-18 | 2013-06-04 | The United States Of America As Represented By The Secretary Of The Navy | Holographic navigation |
| CN102938229A (en) * | 2012-09-18 | 2013-02-20 | 中国人民解放军装甲兵工程学院 | Three-dimensional digital holography photon map |
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