CN112613546A - Track generation method, device, equipment and storage medium - Google Patents
Track generation method, device, equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a track generation method, a device, equipment and a storage medium. Wherein, the method comprises the following steps: dividing the moving area of the target object according to a preset size to obtain a plurality of grids; determining the position point of a target object contained in the grid, and determining an effective grid according to the grid containing the position point; clustering all position points contained in each effective grid in the effective grids respectively through a clustering algorithm to obtain a clustering center point corresponding to each effective grid; and connecting the clustering central points to obtain the track of the target object. According to the technical scheme provided by the embodiment of the invention, the clustering central points corresponding to each effective grid are determined by dividing the grids, and the clustering central points are connected to obtain the track of the target object, so that the difficulty and the complexity of the track generation process are reduced, and the track can be generated quickly.
Description
Technical Field
The embodiment of the invention relates to the technical field of computers, in particular to a track generation method, a track generation device, track generation equipment and a storage medium.
Background
During the motion process of the object, a plurality of discrete spatial displacement points can be obtained according to different positions of the object, and the track of the object can be obtained after the spatial displacement points are connected. For example, the daily activity space track of the person, the daily route track of the vehicle, the daily location track of the location software, and the like.
The current spatial displacement points are usually obtained by changing one position per second or per minute, and the track is difficult to generate because the position changes rapidly and the spatial displacement points are more.
Disclosure of Invention
The embodiment of the invention provides a track generation method, a device, equipment and a storage medium, which reduce the difficulty and complexity of a track generation process and can quickly generate a track.
In a first aspect, an embodiment of the present invention provides a trajectory generation method, where the method includes:
dividing the moving area of the target object according to a preset size to obtain a plurality of grids;
determining the position point of the target object contained in the grid, and determining an effective grid according to the grid containing the position point;
clustering all position points contained in each effective grid in the effective grids respectively through a clustering algorithm to obtain a clustering center point corresponding to each effective grid;
and performing connection operation on the clustering central points to obtain the track of the target object.
In a second aspect, an embodiment of the present invention provides a trajectory generation apparatus, including:
the grid division module is used for dividing the active area of the target object according to a preset size to obtain a plurality of grids;
the effective grid determining module is used for determining the position points of the target object contained in the grid and determining an effective grid according to the grid containing the position points;
the central point determining module is used for clustering all position points contained in each effective grid in the effective grids respectively through a clustering algorithm to obtain a clustering central point corresponding to each effective grid;
and the track generation module is used for performing connection operation on the clustering central points to obtain the track of the target object.
In a third aspect, an embodiment of the present invention provides a computer device, where the computer device includes:
one or more processors;
storage means for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors implement the trajectory generation method according to any embodiment of the present invention.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the trajectory generation method according to any embodiment of the present invention.
The embodiment of the invention provides a track generation method, a device, equipment and a storage medium, firstly dividing the moving area of a target object according to a preset size to obtain a plurality of grids, then determining the position points of the target object contained in the grid, determining an effective grid according to the grid containing the position points, then all the position points contained in each effective grid in the effective grids are respectively clustered through a clustering algorithm to obtain a clustering center point corresponding to each effective grid, finally the clustering center points are connected to obtain the track effect of the target object, through grid division, the clustering center point corresponding to each effective grid is determined, and the clustering center points are connected to obtain the track of the target object, so that the difficulty and complexity of the track generation process are reduced, and the track can be generated quickly.
Drawings
Fig. 1 is a flowchart of a trajectory generation method according to an embodiment of the present invention;
fig. 2A is a flowchart of a trajectory generation method according to a second embodiment of the present invention;
fig. 2B is a flowchart of determining an effective grid in the method according to the second embodiment of the present invention;
fig. 2C is a schematic diagram of a grid after division in the method according to the second embodiment of the present invention;
fig. 2D is a schematic diagram of a grid including location points in the method according to the second embodiment of the present invention;
fig. 2E is a schematic track diagram of a target object in the method according to the second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a track generation apparatus according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of a trajectory generation method according to an embodiment of the present invention, which is applicable to a situation where a trajectory is generated according to a position point of a target object. The trajectory generation method provided by this embodiment may be executed by the trajectory generation apparatus provided by the embodiment of the present invention, and the apparatus may be implemented by software and/or hardware and integrated in a computer device executing the method.
Referring to fig. 1, the method of the present embodiment includes, but is not limited to, the following steps:
and S110, dividing the moving area of the target object according to a preset size to obtain a plurality of grids.
The target object may be a person or an object that can be displaced or changed in position. The preset size can be designed in advance and can be determined according to specific situations.
Because the position change data of the target object is usually acquired once per second or every minute, correspondingly, the position change of the target object is faster, the acquired data is more, and the generation of the track is difficult. In order to generate the track of the target object, the active region of the target object needs to be divided according to a preset size (for example, 50cm × 50cm), and after the division, a plurality of grids can be obtained, each grid corresponding to a part of the active region, so as to determine the position points of the target object included in the grid in the following.
For example, the activity area may be represented by some location maps that can form a planar coordinate system, such as a map corresponding to the activity area, so that the subsequent division into a preset size and acquisition of the geographic location of the corresponding grid are possible.
Optionally, before dividing the active area of the target object according to a preset size to obtain a plurality of grids, the method may further include: and determining the target object and the active area corresponding to the target object.
Specifically, in order to generate the track of the target object, what the target object is needs to be determined before the moving area of the target object is divided according to the preset size, and after the target object is determined, the corresponding moving area can be determined through the target object, for example, if the target object is a person, the corresponding moving area can be a city where the target object is located, and if the target object is a vehicle, the corresponding moving area can be an area formed by a position where the vehicle runs, so that the moving area of the target object can be conveniently divided according to the preset size subsequently, and a plurality of grids can be obtained.
S120, determining the position point of the target object contained in the grid, and determining an effective grid according to the grid containing the position point.
The position points of the target object are distributed in the active area of the target object, and the grids are divided according to the active area of the target object, so that the position points of the target object contained in the grids can be determined, and then the effective grids and the ineffective grids can be determined according to the grids containing the position points. For example, a grid containing location points may be determined as the active grid; the validity of the position point can be judged first, and then whether the corresponding grid is the valid grid or not can be determined according to the validity of the position point.
And S130, clustering all position points contained in each effective grid in the effective grids respectively through a clustering algorithm to obtain a clustering center point corresponding to each effective grid.
Wherein clustering algorithms are algorithms based on similarity, with more similarity between patterns in one cluster than between patterns not in the same cluster. The clustering algorithm is commonly used in many ways, such as a partition method, a hierarchy method, a density algorithm, a model algorithm and the like. The division method comprises a K-Means algorithm, a K-Means + + algorithm, a CLARANS algorithm and the like.
After the effective grids are determined, all position points contained in the current effective grid are clustered through a clustering algorithm aiming at each effective grid in the effective grids, and a clustering center point corresponding to the current effective grid can be obtained. Because the position points of the target object are more, if each position point is analyzed, a large amount of manpower and material resources are consumed, and at the moment, all the position points of the target object in the effective grid are replaced by the cluster center point, so that the workload and the complexity of generating a track can be reduced.
Further, when the target object is in a moving state, due to the fact that the change situation of the moving state is complex, such as sudden acceleration or sudden deceleration, etc., at this time, a time period (for example, 9:00-10:00, 24 hours system, in the present invention, the time is 24 hours system) corresponding to the moving state may be divided according to a preset time length (for example, 10 minutes), that is, 9:00-9:10, 9:10-9:20, …, 9:50-10:00, and all position points included in an effective grid corresponding to a first sub-period (for example, the last minute) in the divided time period are clustered once through a clustering algorithm to obtain a corresponding clustering center point, so that the workload may be reduced, and the accuracy of the algorithm may be improved at the same time.
The present embodiment does not specifically limit the preset time length and the specific value of the first sub-period.
Alternatively, the clustering algorithm may include the K-Means + + algorithm.
Specifically, the main ideas of the K-Means + + algorithm are: assuming that n initial cluster centers have already been selected (0< n < K, the value of K is known), when the (n + 1) th cluster center is selected: the points farther away from the current n cluster centers have a higher probability to be selected as the n +1 th cluster center, and a random method is used when the first cluster center (n is 1) is selected.
The clustering center point determined by the K-Means + + algorithm is more accurate and closer to the actual situation, so that the accuracy of the finally obtained target object track is higher.
And S140, performing connection operation on the clustering central points to obtain the track of the target object.
After the clustering center points corresponding to each effective grid are obtained, the clustering center points are connected according to a preset rule, and then the track of the target object can be obtained. Specifically, the preset rule may be a time sequence of the clustering center points, or a distance between the geographical positions corresponding to the clustering center points, and the like, which is not specifically limited in this embodiment.
According to the technical scheme provided by the embodiment, firstly, an active area of a target object is divided according to a preset size to obtain a plurality of grids, then, position points of the target object contained in the grids are determined, effective grids are determined according to the grids containing the position points, then, all the position points contained in each effective grid in the effective grids are clustered through a clustering algorithm respectively to obtain a clustering center point corresponding to each effective grid, finally, the clustering center points are connected to obtain a track of the target object, the clustering center points corresponding to each effective grid are determined through the grids, the clustering center points are connected to obtain the track of the target object, so that the difficulty and the complexity of the track generation process are reduced, and the track can be generated quickly.
Example two
Fig. 2A is a flowchart of a trajectory generation method according to a second embodiment of the present invention. The embodiment of the invention is optimized on the basis of the embodiment. Optionally, the present embodiment explains the process of obtaining the target object trajectory in detail.
Referring to fig. 2A, the method of the present embodiment includes, but is not limited to, the following steps:
and S210, dividing the moving area of the target object according to a preset size to obtain a plurality of grids.
S220, determining the position points of the target object contained in the grids, and determining the effective grids according to the grids containing the position points.
Wherein, the position point corresponds to the time within the preset time period. The preset time period may be preset, for example, a day or a week, etc. In general, the current position of the positioning device carried by the target object can be obtained by using various types of positioning technologies, the current position can be represented by position points, and each position point corresponds to a time within a corresponding preset time period.
Optionally, fig. 2B is a flowchart of determining an effective grid in the method provided by the second embodiment of the present invention, and as shown in fig. 2B, the process of determining an effective grid includes, but is not limited to, the following steps:
s2201, determining the grid including the position point as the grid to be confirmed.
If the position point is abnormal or has other abnormal conditions, the position point may have a position error, and then to ensure the accuracy of the valid grids, the grid including the position point is firstly determined as the grid to be confirmed, so as to verify the validity of the position point in the grid to be confirmed subsequently, and determine which of the grids to be confirmed are valid grids and which are invalid grids.
S2202, if it is detected that a position point in a first preset time period in a preset time period is located in different grids to be confirmed, acquiring geographic positions corresponding to a current position point, N continuous position points before a time corresponding to the current position point, and M continuous position points after the time corresponding to the current position point.
And the time corresponding to the current position point, the times corresponding to the continuous N position points respectively and the times corresponding to the continuous M position points respectively are all contained in the first preset time period. The first preset time period may be pre-designed and it is within the preset time period, for example, assuming that the preset time period is 7: 00-23: 00, the first preset time period may be any time period from 7 points to 23 points, and is not limited. The specific values of N and M may be set according to actual situations, and embodiments of the present invention are not particularly limited.
If the position points in the first preset time period (for example, 9:30-10:00) in the preset time period (for example, 7: 00-23: 00) are detected to be located in different grids to be confirmed, and if the position points in the 9:30-10:00 are located in two grids to be confirmed, the current position point, N continuous position points before the time corresponding to the current position point and M continuous position points after the time corresponding to the current position point are respectively obtained, so that the effectiveness of the current position point is determined according to the geographic positions.
S2203, respectively determining a first distance between the current location point and each of the N location points according to each geographic location, respectively determining a second distance between the current location point and each of the M location points according to each geographic location, and adding the total number of the first distances and the total number of the second distances to obtain a first total number.
According to the geographic position corresponding to the current position point and the geographic position corresponding to each of the N position points, the first distance between the current position point and each of the N position points can be determined, and N first distances can be obtained in total. Similarly, according to the geographic position corresponding to the current position point and the geographic position corresponding to each of the M position points, the second distance between the current position point and each of the M position points can be determined, and the total M second distances can be obtained. The total number of the first distances and the total number of the second distances are added to obtain a first total number, namely N + M.
S2204, determining a first number exceeding the distance threshold in the first distance and a second number exceeding the distance threshold in the second distance, and adding the first number and the second number to obtain a second total number.
The distance threshold may be obtained by a duration corresponding to the first preset time period and a movement speed of the target object, which may also be determined according to a specific situation, and this embodiment is not limited in particular.
After obtaining the N first distances, by comparing each first distance with the distance threshold, a first number (assuming b, and b ≦ N) of the first distances exceeding the distance threshold may be obtained, and in the case where the first distance exceeds the distance threshold, the first distance is described as an abnormal distance. Likewise, after obtaining the M second distances, by comparing the second distances with the distance threshold, a second number (assuming c, and c ≦ M) of the second distances exceeding the distance threshold may be obtained, and in the case where the second distance exceeds the distance threshold, the second distance is described as an abnormal distance. The first number and the second number are added to obtain a second total number, namely the total number of the abnormal distances is b + c.
S2205, dividing the second total number by the first total number to obtain a corresponding ratio, and determining the validity of the current position point according to the ratio.
Dividing the second total number by the first total number to obtain a ratio
The ratio represents the proportion of the total number of the abnormal distances in the first total number, the ratio is compared with a preset first threshold value, and if the ratio is larger than the preset first threshold value, the current position point is invalid; if the ratio is smaller than or equal to the preset first threshold, the current location point is valid, so as to obtain the validity of the current location point, where the preset first threshold may be pre-designed, or may be determined according to a specific situation, and this embodiment is not particularly limited.
S2206, determining the grids to be confirmed that the included position points are all valid as valid grids.
After the validity of the current position point is determined according to the method, the grids to be confirmed, which contain all the effective position points, are determined as effective grids, so that all the position points contained in each effective grid in the effective grids can be clustered respectively through a clustering algorithm in the following process, and a clustering center point corresponding to each effective grid is obtained.
In the embodiment of the invention, the effectiveness of the position points is determined firstly, and then the effectiveness of the grids containing the position points is determined, so that the adverse effect of position drift or abnormal conditions on the track obtaining process can be effectively avoided, and the accuracy of the finally obtained track is improved.
And S230, clustering all position points contained in each effective grid in the effective grids respectively through a clustering algorithm to obtain a clustering center point corresponding to each effective grid.
S240, determining effective time corresponding to the clustering center point of each effective grid, and performing connection operation on the clustering center points according to the time sequence of the effective time to obtain the track of the target object.
After the clustering center point corresponding to each effective grid is obtained, because the position point corresponds to the time within the preset time period, the clustering center point also corresponds to the time within the preset time period, and therefore, the effective time corresponding to the clustering center point of each effective grid can be obtained. After the effective time corresponding to the clustering center point of each effective grid is determined, the clustering center points are connected according to the time sequence of the effective time, and then the track of the target object can be obtained.
In the embodiment of the invention, the clustering central points are connected according to the time sequence of the effective time to obtain the track of the target object, the mode is simpler, the obtained track corresponds to the time, and the query is convenient.
Optionally, before performing a connection operation on the clustering center points according to the time sequence of the effective time to obtain the trajectory of the target object, the method may further include: when the target object is in a preset situation, if the position points corresponding to a plurality of moments in the preset time period are detected to be missing, acquiring historical tracks corresponding to the plurality of moments of the target object, and determining the historical tracks as effective tracks corresponding to the target object at the plurality of moments; correspondingly, the connecting operation is performed on the clustering central points according to the time sequence of the effective time to obtain the track of the target object, and the method specifically includes: and connecting the clustering central point and the effective track according to the effective time and the time sequence of the plurality of times to obtain the track of the target object.
The historical track may be a track of the target object in the active area in the last month, or may be a track of the target object in the active area in the last week, and the specific time is not limited in this embodiment. The historical track may be obtained through big data, or may be obtained through other positioning manners, or may be a record of a previously generated track, and the obtaining manner of the historical track is not particularly limited in this embodiment.
Specifically, when the missing of the position points corresponding to the multiple moments in the preset time period is detected, the history tracks corresponding to the multiple moments of the target object are obtained, the history tracks are determined to be the effective tracks corresponding to the target object at the multiple moments, and then the clustering center points and the effective tracks are connected according to the time sequence of the effective moments corresponding to the clustering center points of each effective grid and the multiple moments, so that the tracks of the target object are obtained.
In the embodiment of the invention, the readability of the finally obtained track is improved by replacing the missing track with the corresponding historical track under the condition that the position point is missing.
Further, the preset situation may include at least one of that the movement speed of the target object reaches a preset threshold, that the position locating signal where the target object is located is not covered, and that the locating function in the device used by the target object is in a closed state.
The preset situation is a situation corresponding to the position point missing.
For example, the trajectory generation method in the foregoing embodiment is described below by using a specific example, fig. 2C is a schematic diagram of a grid divided in the method provided by the second embodiment of the present invention, fig. 2D is a schematic diagram of a grid including a location point in the method provided by the second embodiment of the present invention, and fig. 2E is a schematic diagram of a trajectory of a target object in the method provided by the second embodiment of the present invention.
As shown in fig. 2C, the active region of the target object is divided according to a preset size, so as to obtain a plurality of grids. As shown in fig. 2D, the location points of the target object contained within the mesh are determined. Then, determining effective grids according to the grids containing the position points, determining the 4 th grid and the 6 th grid in the first row in fig. 2D and the 6 th grid in the first row as invalid grids, finally, clustering all the position points contained in each effective grid in the effective grids respectively through a clustering algorithm to obtain a clustering center point corresponding to each effective grid, performing connection operation on the clustering center points, and performing smoothing processing to obtain a track schematic diagram of the target object, which is specifically shown in fig. 2E.
The technical solution provided in this embodiment is to divide an active area of a target object according to a preset size to obtain a plurality of grids, determine location points of the target object included in the grids, determine effective grids according to the grids including the location points, then cluster all the location points included in each effective grid in the effective grids respectively by a clustering algorithm to obtain a cluster center point corresponding to each effective grid, finally determine an effective time corresponding to the cluster center point of each effective grid, perform a connection operation on the cluster center points according to the time sequence of the effective time to obtain a track of the target object, partition the grids first, then determine the validity of the location points, and determine the validity of the grids including the location points, so as to effectively avoid adverse effects of location drift or abnormal conditions on the process of obtaining the track, meanwhile, the clustering central points corresponding to each effective grid are determined, and the clustering central points are connected according to the time sequence of the effective time to obtain the track of the target object, so that the difficulty and the complexity of the track generation process are reduced, the track can be generated quickly, the obtained track is related to the time, and the readability is enhanced.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a trajectory generation device according to a third embodiment of the present invention, and as shown in fig. 3, the trajectory generation device may include:
the mesh dividing module 310 is configured to divide an active area of the target object according to a preset size to obtain a plurality of meshes;
an effective grid determining module 320, configured to determine a location point of the target object included in the grid, and determine an effective grid according to the grid including the location point;
a central point determining module 330, configured to cluster all the position points included in each effective grid in the effective grids respectively through a clustering algorithm, so as to obtain a clustering central point corresponding to each effective grid;
and the trajectory generation module 340 is configured to perform a connection operation on the clustering center points to obtain a trajectory of the target object.
According to the technical scheme provided by the embodiment, firstly, an active area of a target object is divided according to a preset size to obtain a plurality of grids, then, position points of the target object contained in the grids are determined, effective grids are determined according to the grids containing the position points, then, all the position points contained in each effective grid in the effective grids are clustered through a clustering algorithm respectively to obtain a clustering center point corresponding to each effective grid, finally, the clustering center points are connected to obtain a track effect of the target object, the clustering center points corresponding to each effective grid are determined through grid division, and the clustering center points are connected to obtain a track of the target object, so that the difficulty and the complexity of a track generation process are reduced, and the track can be generated quickly.
Further, the trajectory generation device may further include: and the activity area determining module is used for determining the target object and the activity area corresponding to the target object.
Further, the position point corresponds to a moment in a preset time period; accordingly, the trajectory generation module 340 may be specifically configured to: and determining effective moments corresponding to the clustering central points of each effective grid, and performing connection operation on the clustering central points according to the time sequence of the effective moments to obtain the track of the target object.
Further, the trajectory generation device may further include: an effective track determining module, configured to, before a track of a target object is obtained by performing a connection operation on cluster center points according to a time sequence of the effective time, obtain, when the target object is in a preset situation, if it is detected that position points corresponding to multiple times within the preset time period are missing, a historical track corresponding to the multiple times of the target object is obtained, and determine the historical track as an effective track corresponding to the target object at the multiple times; accordingly, the trajectory generation module 340 may be specifically configured to: and connecting the clustering center point and the effective track according to the effective time and the time sequence of the plurality of times to obtain the track of the target object.
Further, the preset situation includes at least one of that the movement speed of the target object reaches a preset threshold, that the position and location signal of the target object is not covered, and that a location function in a device used by the target object is in an off state.
Further, the clustering algorithm comprises a K-Means + + algorithm.
Further, the effective grid determining module 320 may be specifically configured to: determining the grids containing the position points as grids to be confirmed; if the position points in the first preset time period in the preset time period are detected to be located in different grids to be confirmed, acquiring geographic positions corresponding to the current position point, N continuous position points before the time corresponding to the current position point and M continuous position points after the time corresponding to the current position point, wherein the time corresponding to the current position point, the time corresponding to the N continuous position points and the time corresponding to the M continuous position points are all contained in the first preset time period; respectively determining a first distance between the current position point and each of the N position points according to each geographic position, respectively determining a second distance between the current position point and each of the M position points according to each geographic position, and adding the total number of the first distances and the total number of the second distances to obtain a first total number; determining a first number exceeding a distance threshold in the first distance and a second number exceeding the distance threshold in the second distance, and adding the first number and the second number to obtain a second total number; dividing the second total number by the first total number to obtain a corresponding ratio, and determining the validity of the current position point according to the ratio; and determining the contained position points as valid grids to be confirmed.
The trajectory generation device provided by the embodiment can be applied to the trajectory generation method provided by any of the above embodiments, and has corresponding functions and beneficial effects.
Example four
Fig. 4 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention, as shown in fig. 4, the computer device includes a processor 410, a storage device 420, and a communication device 430; the number of the processors 410 in the computer device may be one or more, and one processor 410 is taken as an example in fig. 4; the processor 410, the storage 420 and the communication means 430 in the computer device may be connected by a bus or other means, and fig. 4 illustrates the connection by a bus as an example.
The storage device 420, which is a computer-readable storage medium, may be used to store software programs, computer-executable programs, and modules, such as the modules corresponding to the trajectory generation method in the embodiment of the present invention (for example, the meshing module 310, the effective mesh determination module 320, the center point determination module 330, and the trajectory generation module 340 used in the trajectory generation device). The processor 410 executes various functional applications and data processing of the computer device by executing software programs, instructions and modules stored in the storage 420, that is, implements the trajectory generation method described above.
The storage device 420 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the storage 420 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the storage 420 may further include memory located remotely from the processor 410, which may be connected to a computer device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
And a communication device 430 for implementing a network connection or a mobile data connection between the servers.
The computer device provided by the embodiment can be used for executing the trajectory generation method provided by any of the above embodiments, and has corresponding functions and beneficial effects.
EXAMPLE five
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a trajectory generation method in any embodiment of the present invention, where the method specifically includes:
dividing the moving area of the target object according to a preset size to obtain a plurality of grids;
determining the position point of the target object contained in the grid, and determining an effective grid according to the grid containing the position point;
clustering all position points contained in each effective grid in the effective grids respectively through a clustering algorithm to obtain a clustering center point corresponding to each effective grid;
and performing connection operation on the clustering central points to obtain the track of the target object.
Of course, the storage medium provided by the embodiment of the present invention contains computer-executable instructions, and the computer-executable instructions are not limited to the operations of the method described above, and may also perform related operations in the trajectory generation method provided by any embodiment of the present invention.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server or a network device) to execute the methods according to the embodiments of the present invention.
It should be noted that, in the embodiment of the trajectory generation apparatus, the included units and modules are merely divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A trajectory generation method, comprising:
dividing the moving area of the target object according to a preset size to obtain a plurality of grids;
determining the position point of the target object contained in the grid, and determining an effective grid according to the grid containing the position point;
clustering all position points contained in each effective grid in the effective grids respectively through a clustering algorithm to obtain a clustering center point corresponding to each effective grid;
and performing connection operation on the clustering central points to obtain the track of the target object.
2. The method according to claim 1, wherein before dividing the active area of the target object into a plurality of grids according to a preset size, the method further comprises:
and determining the target object and an activity area corresponding to the target object.
3. The method of claim 2, wherein the location point corresponds to a time within a preset time period;
correspondingly, the connecting the clustering center points to obtain the track of the target object includes:
and determining effective moments corresponding to the clustering central points of each effective grid, and performing connection operation on the clustering central points according to the time sequence of the effective moments to obtain the track of the target object.
4. The method according to claim 3, wherein before the connecting the clustering center points according to the time sequence of the effective time to obtain the trajectory of the target object, the method further comprises:
when the target object is in a preset situation, if the position points corresponding to a plurality of moments in the preset time period are detected to be missing, acquiring historical tracks corresponding to the plurality of moments of the target object, and determining the historical tracks as effective tracks corresponding to the target object at the plurality of moments;
correspondingly, the connecting the clustering central points according to the time sequence of the effective time to obtain the track of the target object includes:
and connecting the clustering center point and the effective track according to the effective time and the time sequence of the plurality of times to obtain the track of the target object.
5. The method of claim 4, wherein the preset context comprises at least one of a speed of movement of the target object reaching a preset threshold, a position locating signal being uncovered by the target object, and a position locating function in a device used by the target object being in an off state.
6. The method of claim 1, wherein the clustering algorithm comprises a K-Means + + algorithm.
7. The method of claim 3, wherein determining the effective grid from the grid containing the location points comprises:
determining the grids containing the position points as grids to be confirmed;
if the position points in the first preset time period in the preset time period are detected to be located in different grids to be confirmed, acquiring geographic positions corresponding to the current position point, N continuous position points before the time corresponding to the current position point and M continuous position points after the time corresponding to the current position point, wherein the time corresponding to the current position point, the time corresponding to the N continuous position points and the time corresponding to the M continuous position points are all contained in the first preset time period;
respectively determining a first distance between the current position point and each of the N position points according to each geographic position, respectively determining a second distance between the current position point and each of the M position points according to each geographic position, and adding the total number of the first distances and the total number of the second distances to obtain a first total number;
determining a first number exceeding a distance threshold in the first distance and a second number exceeding the distance threshold in the second distance, and adding the first number and the second number to obtain a second total number;
dividing the second total number by the first total number to obtain a corresponding ratio, and determining the validity of the current position point according to the ratio;
and determining the grids to be confirmed, of which the contained position points are all valid, as valid grids.
8. A trajectory generation device, comprising:
the grid division module is used for dividing the active area of the target object according to a preset size to obtain a plurality of grids;
the effective grid determining module is used for determining the position points of the target object contained in the grid and determining an effective grid according to the grid containing the position points;
the central point determining module is used for clustering all position points contained in each effective grid in the effective grids respectively through a clustering algorithm to obtain a clustering central point corresponding to each effective grid;
and the track generation module is used for performing connection operation on the clustering central points to obtain the track of the target object.
9. A computer device, characterized in that the computer device comprises:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the trajectory generation method of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the trajectory generation method according to any one of claims 1 to 7.
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| CN202011491954.9A CN112613546A (en) | 2020-12-16 | 2020-12-16 | Track generation method, device, equipment and storage medium |
| PCT/CN2021/120044 WO2022127260A1 (en) | 2020-12-16 | 2021-09-24 | Track generating method, apparatus and device, and storage medium |
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| CN202011491954.9A CN112613546A (en) | 2020-12-16 | 2020-12-16 | Track generation method, device, equipment and storage medium |
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| CN113641948A (en) * | 2021-10-18 | 2021-11-12 | 南京瀚海星宇信息技术有限公司 | Space trajectory conflict monitoring method, device, equipment and medium |
| CN114155391A (en) * | 2022-01-30 | 2022-03-08 | 浙江口碑网络技术有限公司 | Trajectory processing method and device and computer equipment |
| WO2022127260A1 (en) * | 2020-12-16 | 2022-06-23 | 北京锐安科技有限公司 | Track generating method, apparatus and device, and storage medium |
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| WO2022127260A1 (en) | 2022-06-23 |
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