CN111324823B - Track inference method and device and electronic equipment - Google Patents
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Abstract
The application provides a track inference method, a track inference device and electronic equipment, and relates to the field of data. The track inference method comprises the steps of comparing the difference value of each estimated passing time and the acquisition time interval by acquiring the acquisition time interval of the position node pair meeting the sparse condition and the estimated passing time corresponding to different passing strategies, so as to sort the passing strategies corresponding to the estimated passing time and provide a server to draw tracks. Therefore, the ordering of the passing strategy among the adjacent position nodes which is closer to the real situation can be obtained under the condition that the acquired space distance between the adjacent position nodes is overlarge, and the authenticity of drawing the server track is improved.
Description
Technical Field
The present application relates to the field of data, and in particular, to a track inference method, apparatus, and electronic device.
Background
In recent years, there has been an increasing demand for mining historical track data, such as searching for lost people, criminal suspects tracking, and searching by crutch children. Mining of historical track data is also gradually evolving towards face data. And (3) mining the track data based on the face data, determining the position and the time point of the target person through face snapshot and recognition of each collector, and reversely calculating one or more motion tracks possibly existing in the target person according to the positions and the time points.
However, such services all face various technical challenges. First, the face snapshot and recognition have higher requirements on environment complexity, picture definition and other external conditions, especially at traffic intersections with dense people flow, more faces may exist in one frame of image, and the recognition algorithm is inevitably omitted. Secondly, compared with vehicles, the moving modes of people are more flexible and changeable, the vehicles can only run along the road with established rules, which brings great convenience for the snapshot and identification of vehicle information, however, people can walk normally, and different vehicles can be possibly replaced, so that a plurality of scenes which cannot be snapped to the face are generated in the middle, the data space interval acquired in adjacent time is overlarge, and the tracing of a track which is more similar to the real situation becomes very difficult.
Disclosure of Invention
In view of the above, an object of an embodiment of the present application is to provide a track estimation method, a track estimation device, and an electronic device for improving the above-mentioned problems.
In a first aspect, an embodiment of the present application provides a track inference method, including: acquiring an acquisition time interval between position node pairs meeting the sparse condition; determining at least one effective path corresponding to the position node pairs by combining a road network database; evaluating estimated passing time corresponding to a passing strategy of passing through each effective path by adopting each type of preselected passing mode; and sorting according to the difference value between the estimated passing time and the acquisition time interval corresponding to each passing strategy and the order from small to large so as to obtain the sorting of a plurality of passing strategies between the position node pairs.
In a second aspect, an embodiment of the present application provides a trajectory inference apparatus, including: the acquisition module acquires an acquisition time interval between the position node pairs meeting the sparse condition; the matching module is used for determining at least one effective path corresponding to the position node pair by combining a road network database; the evaluation module evaluates estimated passing time corresponding to a passing strategy of passing through each effective path by adopting each type of preselected passing mode; and the sequencing module is used for sequencing according to the difference value between the estimated passing time and the acquisition time interval corresponding to each passing strategy and the sequence from small to large so as to obtain sequencing of a plurality of passing strategies among the position node pairs.
In a third aspect, an embodiment of the present application provides an electronic device, including: the acquisition module is used for acquiring the acquisition time interval between the position node pairs meeting the sparse condition; the matching module is used for determining at least one effective path corresponding to the position node pair by combining a road network database; the evaluation module is used for evaluating estimated passing time corresponding to the passing strategy of passing each effective path by adopting each type of preselected passing mode; and the sequencing module is used for sequencing according to the difference value between the estimated passing time and the acquisition time interval corresponding to each passing strategy and the sequence from small to large so as to obtain sequencing of a plurality of passing strategies among the position node pairs.
Compared with the prior art, the track inference method provided by the application comprises the steps of comparing the difference value of each estimated passing time and the acquisition time interval by acquiring the acquisition time interval of the position node pair meeting the sparse condition and the estimated passing time corresponding to different passing strategies, so as to sort the passing strategies corresponding to the estimated passing time and provide a server to draw tracks. Therefore, the ordering of the passing strategy among the adjacent position nodes which is closer to the real situation can be obtained under the condition that the acquired space distance between the adjacent position nodes is overlarge, and the authenticity of drawing the server track is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is an application scenario diagram provided in a preferred embodiment of the present application.
Fig. 2 is a block diagram of an electronic device according to a preferred embodiment of the application.
Fig. 3 is a flowchart illustrating steps of a trajectory inference method according to an embodiment of the present application.
Fig. 4 shows a flow chart of sub-steps of step S103 in fig. 3.
Fig. 5 shows a block schematic diagram of a trajectory inference device according to an embodiment of the present application.
Icon: 100-an electronic device; 300-collector; 400-a server; 101-memory; 102-a memory controller; 103-a processor; 104-a peripheral interface; 105-a communication unit; 200-trajectory inference means; 201-an acquisition module; 202-a matching module; 203-an evaluation module; 204-a ranking module.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present application.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
Fig. 1 shows an application scenario diagram provided in a preferred embodiment of the present application, including an electronic device 100, a server 400 and a collector 300, where the server 400 is respectively in communication connection with the collector 300 and the electronic device 100, and the collector 300 is configured to collect a location node and send the location node to the server 400, where the location node includes feature information, collection time information and location information. It should be noted that, in the embodiment of the present application, the collector 300 may be a face camera, a bayonet camera, a MAC collector, an RFID collector, and other devices. The server 400 is configured to sort the location nodes of the same feature information according to the order of the collection time, and then send the location nodes to the electronic device 100. The electronic device 100 is configured to receive the location node sent by the server 400, and further configured to send the obtained track scheme sequence to the server 400, so that the server 400 draws a track closer to the real situation.
Fig. 2 is a block diagram of an electronic device 100 according to a preferred embodiment of the application. The electronic device 100 comprises a trajectory inference means 200, a memory 101, a memory controller 102, a processor 103, a peripheral interface 104, a communication unit 105.
The memory 101, the memory controller 102, the processor 103, the peripheral interface 104 and the communication unit 105 are electrically connected directly or indirectly to each other, so as to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The trajectory inference means 200 comprise at least one software function module which may be stored in the memory 101 in the form of software or firmware (firmware) or cured in an Operating System (OS) of the electronic device 100. The processor 103 is configured to execute executable modules stored in the memory 101, such as software functional modules or computer programs included in the trajectory inference device 200.
The Memory 101 may be, but is not limited to, a random access Memory (Random Access Memory, RAM), a Read Only Memory (ROM), a programmable Read Only Memory (Programmable Read-Only Memory, PROM), an erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), an electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc. The memory 101 is configured to store a program, and the processor 103 executes the program after receiving an execution instruction, and a method executed by the server defined by the flow disclosed in any embodiment of the present application may be applied to the processor 103 or implemented by the processor 103.
The processor 103 may be an integrated circuit chip with signal processing capabilities. The processor 103 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), a voice processor, a video processor, and the like; but also digital signal processors, application specific integrated circuits, field programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. The general purpose processor may be a microprocessor or the processor 103 may be any conventional processor or the like.
The peripheral interface 104 is used to couple various input/output devices to the processor 103 and the memory 101. In some embodiments, the peripheral interface 104, the processor 103, and the memory controller 102 may be implemented in a single chip. In other examples, they may be implemented by separate chips.
The communication unit 105 is used for realizing data interaction with the outside. For example, communication with the server 400 may be performed to receive pairs of location nodes sent by the server 400, and ordering of traffic policies may also be sent to the server 400.
First embodiment
Referring to fig. 3, fig. 3 is a flowchart of a track inference method according to an embodiment of the application. The track inference method comprises the following steps:
step S101, acquiring an acquisition time interval between a pair of position nodes satisfying the sparse condition.
In the embodiment of the present application, the communication unit 105 receives a plurality of location nodes which are sent by the server 400 and have the same feature information according to the collection time sequence, and sequentially screens out the location node pairs meeting the sparse condition according to the distance between the location information of two adjacent location nodes and the preset distance threshold. For example, the distance threshold may be set to 5km, and if the spatial distance of a pair of location nodes is greater than 5km, the pair of location nodes satisfies the sparseness condition; if not, the sparse condition is not satisfied. And simultaneously acquiring two corresponding acquisition time information of the position node pair meeting the sparse condition, and subtracting the two acquisition time information to obtain an acquisition time interval of the position node pair.
Step S102, determining at least one effective path corresponding to the position node pair by combining a road network database.
In the embodiment of the application, the obtained position node pairs meeting the sparse condition are combined with the road network database to obtain the effective paths among the plurality of position node pairs, wherein the effective paths are the passable connection paths among the position node pairs. The road network database stores road network data of each region, and can analyze the passable connection paths between two different positions. It should be noted that the road network database is also an important part of each navigation software.
Wherein each effective path is composed of a plurality of real road segments. The real road section is the minimum unit of the road network database record path, and the real road section can be a traffic line between two adjacent intersections on the traffic network. It is to be understood that there may be one or more real road segments overlapping between different active paths. For example, an effective path a and an effective path B are obtained according to the position node pair and the road network database, wherein the effective path a comprises a real road section a, a real road section B and a real road section C, the effective path B comprises a real road section D, a real road section B, a real road section E and a real road section F, and the real road section B is the real road section repeatedly included by the effective path a and the effective path B.
Step S103, estimating estimated passing time corresponding to the passing strategy of passing each effective path by adopting each type of preselected passing mode.
Specifically, the preselected traffic mode can be a traffic mode such as walking, riding an electric motorcycle or driving, and each traffic strategy comprises a corresponding effective path and a class of traffic modes. It should be noted that, the obtained estimated route time is the estimated reasonable route time, and is also the route time close to the real situation, and the estimated route time may be obtained by performing an in-situ test or analyzing call history data.
It should be noted that, when the traffic mode of the present application is a public transportation vehicle having a fixed operation route pattern, such as a bus, an urban rail transit, etc. The data can directly inquire a route map database to draw an accurate track between the position node pairs, and the time spent by the public transportation motor vehicle according to the fixed operation route is listed as the estimated route time corresponding to the traffic strategy of the operation route of the public transportation motor vehicle for the public transportation motor vehicle route. For example, an a-way bus can be taken between the position node pairs, the time for the a-way bus to pass through the position node pairs is T1, the traffic policy is a fixed running route of the a-way bus between the position node pairs, and the estimated traffic time corresponding to the traffic policy is T1.
In the embodiment of the application, the estimated passing time corresponding to different passing strategies is obtained by combining a plurality of effective paths with different passing modes.
Specifically, referring to fig. 4, step S103 may include the following sub-steps:
and step S1031, obtaining a path acquisition node set corresponding to each effective path.
Each path collection node set may correspond to an effective path between the pair of location nodes. Further, each path collection node set includes at least one collector node disposed on a corresponding effective path, and it should be noted that the collector 300 may be a face camera, a bayonet camera, a MAC collector, an RFID collector, and other devices, and is configured to collect data such as corresponding face, passing vehicles, MAC addresses, and RFID radio frequency codes. And almost every intersection is provided with collectors 300 in the present case, and some longer real road sections are provided with even a plurality of collectors 300. Wherein the collector nodes are the corresponding location information of each collector 300.
In the embodiment of the present application, a plurality of collector nodes on the effective path or capable of representing the path are selected according to the effective path between the position node pairs, and a plurality of collector 300 numbers are combined into a set for representing the effective path. Optionally, the obtaining a path collection node set corresponding to each effective path may be:
a) Acquiring the collector node nearest to each real road section in the effective path,
in the embodiment of the present application, the collector node is the position information corresponding to the collector 300. Optionally, the manner of acquiring the collector node closest to each real road segment may be:
the screening area is obtained according to the position node pairs. The screening area is used for primarily screening collector nodes in the area irradiated by the effective path between the pair of position nodes. For example, the position node pairs may be connected, and the circular region may be obtained by using the connecting line as a diameter. The effective path between the pairs of location nodes can be substantially covered by a circular area of diameter, typically with the connection lines of the pairs of location nodes.
And obtaining the screening area according to the circumscribed rectangle of the circular area. It should be noted that, the screening area of the rectangle can be limited by only four vertices, so that the circumscribed rectangle is adopted as the screening area, and the circular area can be completely covered.
And then acquiring collector nodes corresponding to all collectors 300 in the screening area. In the embodiment of the application, the collector nodes obtained according to the screening area can cover the real road sections on all the effective paths between the position node pairs.
And finally, screening out the nearest collector node of each real road section according to the space distance. In the embodiment of the application, the screened collector nodes are in one-to-one correspondence with each real road section, so that each screened collector node can represent one real road section. As an embodiment, the screening the collector node closest to each real road segment may include:
(1) And acquiring the starting node of each real road section.
In the embodiment of the application, each real road section has a start node and a stop node, for example, two adjacent real road sections, and the stop node of the front real road section is also the start node of the rear real road section.
(2) And taking each starting node as a circle center, and taking the connecting line of the starting node and any adjacent starting node as a radius to obtain a corresponding real road section area.
In the embodiment of the application, the starting node on the two adjacent real road sections of the real road section corresponding to the starting node is an adjacent starting node, the two adjacent starting nodes are closer to the starting node and are farther from the starting node, and specifically, a small real road section area can be obtained by taking the connecting line between the starting node and the adjacent starting node closer to the starting node as the radius; the connecting line between the starting node and the starting node with a longer distance can be used as a radius to obtain a larger real road section area.
(3) And comparing the distance between the collector node and the corresponding starting node in each real road section area.
In the embodiment of the application, if the collector node falls in a real road section area obtained by taking the initial node as a circle center, the collector node is the collector node corresponding to the initial node, and the straight line distance between each collector node and the initial node is compared. It should be explained that the real road section area of each start node overlaps with the real road section area of the adjacent start node, and the collector nodes in the overlapping area have two or even three corresponding start nodes.
(4) And screening out the collector nodes closest to each real road section.
In the embodiment of the application, the collector node with the shortest linear distance from the initial node is screened out. It should be noted that, due to the density problem of the collector 300 in the special area, the closest two adjacent real road segments may be the same collector node, and the collector node may simultaneously represent the two adjacent real road segments.
Further, each effective path is composed of a plurality of real road segments, when the nearest collector node of each real road segment is acquired, the acquired collector nodes are used for replacing the corresponding real road segments, so that the collector node combination can represent the corresponding effective path. For example, the effective path a includes a real road segment a, a real road segment B, and a real road segment C, and if the collector node closest to the real road segment a is the collector node a, the collector node closest to the real road segment B is the collector node B, and the collector node closest to the real road segment C is the collector node C, the real road segment a may be characterized by the collector node a, the real road segment B may be characterized by the collector node B, and the real road segment C may be characterized by the collector node C, so that the combination of the collector node a, the collector node B, and the collector node C may be capable of characterizing the effective path a.
b) And sequencing the acquired collector nodes according to the arrangement sequence of each real road section in the effective path to obtain the path collection node set so as to represent one divided road section in the effective path by adopting each group of adjacent collector nodes.
In the embodiment of the application, according to the ordering condition of real road segments in an effective path, the collector nodes corresponding to each real road segment in the effective path are ordered, for example, the obtained effective path A consists of a real road segment A, a real road segment B and a real road segment C, the real road segments of the effective path A are ordered into a real road segment A-a real road segment B-a real road segment C, the real road segment A can be represented by the collector nodes A, the real road segment B can be represented by the collector nodes B, the real road segment C can be represented by the collector nodes C, the collector nodes A are numbered as a, the collector nodes B are numbered as B, the collector nodes C are numbered as C, and the obtained path collection nodes corresponding to the effective path A are A= { a, B, C }
It should be explained that each collector node set may characterize an effective path. Because the collector nodes corresponding to each real road section are the collector nodes closest to the real road section, the paths obtained by superposing the divided road sections represented by each group of adjacent collector nodes in the same collector node set are close to the effective paths corresponding to the collector node set.
Substep S1033, evaluating an evaluation time of passing through the corresponding divided road section by using each type of pre-selected passing mode according to the historical data collected by the collector 300 corresponding to each group of adjacent collector nodes.
In the embodiment of the present application, according to the historical data collected by the collector 300, the corresponding evaluation time of different traffic modes between each group of adjacent collector nodes is evaluated. It should be explained that, the historical data is the time required for the same target collected and recorded by the collector 300 to pass through the divided road sections corresponding to a group of adjacent collector nodes according to different traffic modes, and the collector 300 sends the corresponding historical data to the electronic device so as to evaluate the evaluation time of each divided road section. For example, two collectors 300 corresponding to a group of adjacent collector nodes, where the time average value of the divided road sections corresponding to the group of adjacent collector nodes through which the same object passes by the same object in a walking manner is T1, and the time average value of the divided road sections corresponding to the group of adjacent collector nodes through which the same object passes by the same object in a riding manner is T2, then the evaluation time for evaluating the road sections corresponding to the road sections by the walking manner is T1, and then the evaluation time for evaluating the road sections corresponding to the road sections by the riding manner is T2. It is understood that the acquisition evaluation time may be obtained not only according to an average value of the historical data, but also according to the actual situation, the historical data with the highest occurrence frequency may be acquired by adopting normal distribution and used as the evaluation time corresponding to the divided road section.
In the substep S1034, according to the multiple evaluation times corresponding to the path collection node set, calculating the estimated passing time of each passing policy corresponding to the effective path.
In the embodiment of the application, the estimated time corresponding to each same class of passing mode in the same path acquisition node set is overlapped to obtain the estimated passing time of different passing strategies. It should be noted that if N sets of path collection nodes corresponding to the obtained effective paths have M types of traffic modes at the same time, estimated passing time corresponding to n×m traffic strategies will be obtained. For example, the path collection node set of the effective path a sequentially includes a collector node a, a collector node B and a collector node C, where the collector node a to the collector node B analyze to obtain an estimated time of walking for T1, an estimated time of riding an electric motorcycle for T2, and the collector node B to the collector node C analyze to obtain an estimated time of walking for T3, and an estimated time of riding an electric motorcycle for T4, then the estimated transit time of passing through the effective path a in a walking manner for t1+t3, and the estimated transit time of passing through the effective path a in a passing manner for T2+t4. It will be appreciated that in other embodiments of the application, the traffic policy may also consist of a combination of different traffic patterns. For example, the estimated passing time corresponding to the effective path a is t1+t3, and the estimated passing time corresponding to the effective path a is t2+t4, and then the estimated passing time corresponding to the combined passing mode of riding the motorcycle and walking is t1+t4 or t2+t3.
Step S104, sorting according to the difference between the estimated passing time and the collection time interval corresponding to each passing policy, in order from small to large, so as to obtain a sorting of the passing policies between the position node pairs.
In the embodiment of the application, the obtained difference value obtained by subtracting the predicted passing time corresponding to each passing strategy from the acquisition time interval is sequenced according to the sequence from big to small, and the passing strategies corresponding to each difference value are sequenced according to the sequencing of the difference value, so that the sequencing of different passing strategies more similar to the real situation is obtained. It should be noted that, the sorting may be in a two-dimensional matrix form, and each path collection node set and each passing mode are respectively used as an abscissa and an ordinate, and the corresponding differences are listed in order for sorting, which may be a one-to-one sorting.
It should be noted that, the traffic policy with the highest ranking may be sent to the server 400, or the traffic policy with the first five or the first ten may be sent to the server 400 according to the actual requirement, so that the server 400 draws a track closer to the actual situation.
Second embodiment
Referring to fig. 5, fig. 5 is a schematic functional block diagram of a trajectory estimation device 200 according to a preferred embodiment of the application. The trajectory inference means 200 is used to obtain an ordering of the traffic strategies closest to the real situation in case of data sparseness.
The track inference device 200 provided in the embodiment of the application includes: an acquisition module 201, a matching module 202, an evaluation module 203 and a ranking module 204.
An acquisition module 201, configured to acquire an acquisition time interval between a pair of location nodes that satisfy a sparse condition.
In the embodiment of the present application, step S101 is performed by the acquisition module 201.
A matching module 202, configured to determine at least one valid path corresponding to the location node pair in combination with a road network database.
In an embodiment of the present application, step S102 is performed by the matching module 202.
And the evaluation module 203 is configured to evaluate the estimated transit time corresponding to the transit policy of passing through each of the effective paths by using each type of preselected transit mode.
In the embodiment of the present application, step S103 is performed by the evaluation module 203.
And the sorting module 204 is configured to sort the traffic strategies according to the difference between the estimated passing time and the collection time interval corresponding to each traffic strategy in order from small to large, so as to obtain a sorting of the plurality of traffic strategies between the position node pairs.
In an embodiment of the present application, step S104 is performed by the sorting module 204.
In summary, the application provides a track inference method, a track inference device and electronic equipment. The track inference method comprises the steps of comparing the difference value of each estimated passing time and the acquisition time interval by acquiring the acquisition time interval of the position node pair meeting the sparse condition and the estimated passing time corresponding to different passing strategies, sequencing the passing strategies corresponding to the estimated passing time, and uploading the sequencing of the passing strategies to a server for the server to draw tracks. Therefore, the ordering of the traffic strategy closer to the real situation can be obtained under the condition that the acquired adjacent data space distance is too large, and the authenticity of track drawing is improved.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The apparatus embodiments described above are merely illustrative, for example, of the flowcharts and block diagrams in the figures that illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application. It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. A method of trajectory inference, the method comprising:
acquiring an acquisition time interval between position node pairs meeting the sparse condition;
determining at least one effective path corresponding to the position node pairs by combining a road network database;
evaluating estimated passing time corresponding to a passing strategy of passing through each effective path by adopting each type of preselected passing mode;
sorting according to the difference value between the estimated passing time and the acquisition time interval corresponding to each passing strategy and the order from small to large so as to obtain the sorting of a plurality of passing strategies between the position node pairs;
the step of evaluating the estimated passing time corresponding to the passing strategy of passing each effective path by adopting each type of preselected passing mode comprises the following steps:
acquiring a path acquisition node set corresponding to each effective path, wherein the path acquisition node set comprises a plurality of collector nodes corresponding to the effective paths; each group of adjacent collector nodes corresponds to one division road section in the effective path;
according to the historical data collected by the collectors corresponding to each group of adjacent collector nodes, evaluating the evaluation time of passing through the corresponding divided road section by adopting each type of preselected passing mode;
and calculating the estimated passing time of each passing strategy corresponding to the effective path according to a plurality of estimated times corresponding to the path acquisition node set.
2. The trajectory inference method of claim 1 wherein the step of obtaining a set of path acquisition nodes corresponding to each of the active paths comprises:
acquiring a collector node nearest to each real road section in the effective path;
and sequencing the acquired collector nodes according to the arrangement sequence of each real road section in the effective path to obtain the path collection node set so as to represent one divided road section in the effective path by adopting each group of adjacent collector nodes.
3. The trajectory inference method of claim 2, wherein the step of acquiring the collector node nearest to each real road segment in the effective path includes:
obtaining a screening area according to the position node pairs;
acquiring all collector nodes in the screening area;
and screening out the nearest collector node of each real road section according to the space distance.
4. The trajectory inference method as claimed in claim 3, wherein said step of obtaining a screening area from said pair of location nodes comprises:
connecting the position node pairs, and obtaining a circular area by taking the connecting wire as the diameter;
and obtaining the screening area according to the circumscribed rectangle of the circular area.
5. The trajectory inference method of claim 3 wherein the step of screening out the nearest collector node to each of the road segments based on spatial distance comprises:
acquiring the initial node of each real road section;
taking each initial node as a circle center, and taking the initial node and any adjacent initial node as radiuses to obtain a corresponding real road section area;
comparing the distance between the collector node and the corresponding starting node in each real road section area;
and screening out the collector nodes nearest to each road section.
6. A trajectory inference device, the device comprising:
the acquisition module acquires an acquisition time interval between the position node pairs meeting the sparse condition;
the matching module is used for determining at least one effective path corresponding to the position node pair by combining a road network database;
the evaluation module evaluates estimated passing time corresponding to a passing strategy of passing through each effective path by adopting each type of preselected passing mode;
the sorting module sorts the estimated passing time and the acquisition time interval according to the difference value between the estimated passing time and the acquisition time interval corresponding to each passing strategy in order from small to large so as to obtain the sorting of a plurality of passing strategies between the position node pairs;
the method for evaluating the estimated passing time corresponding to the passing strategy of passing through each effective path by adopting each type of preselected passing mode by the evaluation module comprises the following steps:
acquiring a path acquisition node set corresponding to each effective path, wherein the path acquisition node set comprises a plurality of collector nodes corresponding to the effective paths; each group of adjacent collector nodes corresponds to one division road section in the effective path;
according to the historical data collected by the collectors corresponding to each group of adjacent collector nodes, evaluating the evaluation time of passing through the corresponding divided road section by adopting each type of preselected passing mode;
and calculating the estimated passing time of each passing strategy corresponding to the effective path according to a plurality of estimated times corresponding to the path acquisition node set.
7. The trajectory inference device of claim 6 wherein said evaluation module performs said method of obtaining a set of path acquisition nodes corresponding to each of said active paths, and comprises:
acquiring a collector node nearest to each real road section in the effective path;
and sequencing the acquired collector nodes according to the arrangement sequence of each real road section in the effective path to obtain the path collection node set so as to represent one divided road section in the effective path by adopting each group of adjacent collector nodes.
8. An electronic device, the device comprising:
a memory;
a processor; and
a trajectory inference device installed in the memory and including one or more software function modules executed by the processor, the trajectory inference device comprising:
the acquisition module acquires an acquisition time interval between the position node pairs meeting the sparse condition;
the matching module is used for determining at least one effective path corresponding to the position node pair by combining a road network database;
the evaluation module evaluates estimated passing time corresponding to a passing strategy of passing through each effective path by adopting each type of preselected passing mode;
the sorting module sorts the estimated passing time and the acquisition time interval according to the difference value between the estimated passing time and the acquisition time interval corresponding to each passing strategy in order from small to large so as to obtain the sorting of a plurality of passing strategies between the position node pairs;
the evaluation module is further used for acquiring a path acquisition node set corresponding to each effective path, wherein the path acquisition node set comprises a plurality of collector nodes corresponding to the effective paths; each group of adjacent collector nodes corresponds to one division road section in the effective path;
according to the historical data collected by the collectors corresponding to each group of adjacent collector nodes, evaluating the evaluation time of passing through the corresponding divided road section by adopting each type of preselected passing mode;
and calculating the estimated passing time of each passing strategy corresponding to the effective path according to a plurality of estimated times corresponding to the path acquisition node set.
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