CN115630131B - Trajectory data space-time slicing method and system and electronic equipment - Google Patents
Trajectory data space-time slicing method and system and electronic equipment Download PDFInfo
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Abstract
The invention relates to a method, a system and electronic equipment for space-time slicing of trajectory data, wherein the method comprises the steps of obtaining trajectory data; sampling the track data according to a sampling rule, forming an initial data set by the extracted track data, and forming an undetermined data set by the residual track data after extraction; clustering the track data in the initial data set based on a hierarchical clustering rule, and obtaining a target data set after clustering; calculating the central point of each type in the target data set according to the central point calculation rule, wherein the central points form a comparison data set; optimizing the comparison data set and the target data set according to the optimization judgment rule, the comparison data set and the target data set; recording a target data set and a pending data set as a merged data set according to a data merging rule; and based on a space-time slicing rule, carrying out data slicing on the track data in the combined data set, and carrying out tile-overlapping processing on the sliced track data to obtain a final data set. The invention has the effect of rationalizing the slices and improving the calculation efficiency.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method and a system for space-time slicing of trajectory data, and an electronic device.
Background
Although a big data distributed computing method is utilized in a massive trajectory data mining process, when a massive data set is computed, such as space-time cross computation, the computed data amount is enlarged to an n magnitude on the magnitude of an input data set, and a large amount of memory is consumed while the efficiency is still low.
Generally, the method for dealing with the above inefficiency is space-time slicing, that is, slicing data according to geographical locations and then segmenting slices according to time, and then distributing the calculated amount in each slice to reduce the cross-calculated amount, but this method often causes larger deviation of the calculated result due to unreasonable slice division and other reasons.
Disclosure of Invention
The method for space-time slicing of the trajectory data has the advantages that slicing is more reasonable, and therefore calculation efficiency is improved.
The above object of the present application is achieved by the following technical solutions:
a method of spatiotemporal slicing of trajectory data, comprising:
acquiring track data;
sampling the trajectory data according to a sampling rule, forming an initial data set by the extracted trajectory data, and forming an undetermined data set by the rest trajectory data after extraction;
clustering the track data in the initial data set based on a hierarchical clustering rule, and obtaining a target data set after clustering;
calculating the central point of each type in the target data set according to a central point calculation rule, wherein the central points form a comparison data set;
optimizing the comparison data set and the target data set according to an optimization judgment rule, the comparison data set and the target data set;
merging the target data set and the data set to be determined according to a data merging rule, and recording the merged data set as a merged data set;
based on a space-time slicing rule, carrying out data slicing on the track data in the merged data set, and marking the track data after data slicing as a slice set;
and performing tile-folding processing on the slice set to obtain a final data set.
By adopting the technical scheme, the track data is sampled and processed, the processed track data is divided into an initial data set and an undetermined data set, the initial data set is clustered according to a hierarchical clustering rule, the central point of a target data set is calculated according to a central point calculation rule, the target data set is optimized according to an optimization judgment rule, and after the data set is optimized, the target data set and the undetermined data set are combined according to a data combination rule to obtain a combined data set. And performing data slicing on the merged data set to obtain a slice set, and performing tiling processing on slices in the slice set. The problem that the edge of each slice is weak in connectivity can be effectively solved by performing tile folding treatment, and the relevance among the slices is enhanced to a certain extent, so that the slices are more reasonable.
Optionally, the optimizing the comparison data set and the target data set according to the optimization judgment rule, the comparison data set, and the target data set includes:
calculating a distance value between the track data of each class in the target data set and the central point of the corresponding class in the comparison data set;
determining an optimized parameter according to a parameter calculation rule and the distance value;
and optimizing the comparison data set and the target data set according to the optimization parameters and the optimization rules.
By adopting the technical scheme, the clustering condition of the target data set and the comparison data set is judged according to the optimization judgment rule, and when the clustering of the target data set and the comparison data set is unreasonable, the target data set and the comparison data set are continuously optimized according to the optimization rule. By adding the optimization judgment rule, the clustering of the target data set and the comparison data set can be more reasonable, and the accuracy of data calculation is further improved.
Optionally, the determining an optimization parameter according to the parameter calculation rule and the distance value includes:
obtaining a maximum value of the distance value of each class;
and calculating the optimized parameters according to the maximum value, a preset distance preset value and a parameter calculation rule.
Optionally, the optimizing the comparison data set and the target data set according to the optimization parameters and the optimization rules includes:
when the optimized parameter is not equal to the optimized preset value,
clustering the comparison data set and the target data set again according to a hierarchical clustering rule to generate current optimization parameters;
comparing the current optimization parameters with the optimization parameters;
and when the optimization parameters are smaller than the current optimization parameters, taking the target data set and the comparison data set corresponding to the optimization parameters as final target data sets and comparison data sets.
Optionally, the merging the target data set and the to-be-determined data set according to a data merging rule, and recording the merged data set as a merged data set, includes:
calculating the distance between the trajectory data in the undetermined data set and the central point in the comparison data set;
acquiring the minimum value of the distance;
obtaining the class of the central point corresponding to the minimum value;
and adding the track data in the undetermined data set into the corresponding class in the target data set to obtain a merged data set.
By adopting the technical scheme, the central points in the comparison data sets are selected, the distance values between the track data and the central points in the data sets to be detected are respectively calculated, the minimum value of all the distance values is obtained, the track data is added to the class where the central point corresponding to the minimum value is located, namely the track data is added to the corresponding class in the target data set, the combination of the data sets to be detected and the target data set is completed in such a way, a plurality of calculation steps before the data sets to be detected are reduced, the data sets to be detected are directly added to the target data sets through the data combination rule, and the calculation efficiency is improved to a certain extent.
Optionally, the slicing trajectory data in the merged data set based on the spatio-temporal slicing rule, and marking the sliced trajectory data as a slice set, includes:
the trajectory data comprises time data;
sorting the track data of each class in the merged data set in an ascending order according to the time data;
carrying out data slicing on the track data in the classes according to a preset slicing time length;
forming a slice by the track data every other slice duration;
the class of finished slices is denoted as slice class, which constitutes a set of slices.
Optionally, performing shingling processing on the slice set to obtain a final data set, including:
acquiring any adjacent slice in the slice set;
according to preset tile-overlapping duration, copying head data with the duration being the tile-overlapping duration in the slice with larger time data in the adjacent slice to tail data of the slice with smaller time data in the adjacent slice;
the processed slices are shingled slices;
the shingled slices constitute the final data set.
By adopting the technical scheme, the adjacent slice data are subjected to tile-overlapping processing, the problem of data incoherence caused by simple slicing according to time is reduced, and the relevance among the slice data is ensured to a certain extent.
The second purpose of the application is to provide a track data space-time slicing system.
The second application object of the present application is achieved by the following technical scheme:
a trajectory data spatiotemporal slicing system comprising:
the data acquisition module is used for acquiring track data;
the data sampling module is used for sampling the track data according to a sampling rule, the extracted track data form an initial data set, and the residual track data after extraction form an undetermined data set;
the data clustering module is used for clustering the track data in the initial data set according to a hierarchical clustering rule and obtaining a target data set after clustering;
the central calculation module is used for calculating the central point of each type in the target data set and determining a comparison data set according to a central point calculation rule;
the data optimization module is used for optimizing the comparison data set and the target data set according to an optimization judgment rule, the comparison data set and the target data set;
the data merging module is used for merging the target data set and the data set to be determined according to a data merging rule and determining a merged data set;
the data slicing module is used for carrying out data slicing on the track data in the merged data set according to a space-time slicing rule to determine a slice set;
and the data tiling module is used for performing tiling processing on the slice set to obtain a final data set.
The third purpose of the present application is to provide an electronic device.
The third objective of the present application is achieved by the following technical solutions:
an electronic device comprising a memory and a processor, the memory having stored thereon a computer program that can be loaded by the processor and that implements the trajectory data spatiotemporal slicing method described above.
The fourth purpose of the present application is to provide a computer storage medium capable of storing a corresponding program.
The fourth application purpose of the present application is achieved by the following technical solutions:
a computer readable storage medium storing a computer program capable of being loaded by a processor and executing any of the trajectory data spatiotemporal slicing methods described above.
In summary, the present application includes at least one of the following beneficial technical effects:
1. firstly, sampling track data, dividing the sampled track data into an initial data set and an undetermined data set, clustering the initial data set according to a hierarchical clustering rule, determining a target data set, calculating the central point of the target data set according to a central point calculation rule, judging whether the clustering of the target data set is reasonable according to an optimization judgment rule, continuously optimizing the target data set if the clustering is not reasonable, and merging the target data set and the undetermined data set according to a data merging rule after the data set is optimized to obtain a merged data set. And performing data slicing on the merged data set to obtain a slice set, and performing tiling processing on slices in the slice set. The problem of weak connectivity at the edges of the slices can be effectively solved by performing tile folding treatment, the relevance among the slices is enhanced to a certain extent, the slices are more reasonable, the calculation amount is reduced, and the calculation efficiency is improved;
2. and judging the clustering condition of the target data set and the comparison data set according to the optimization judgment rule, and when the clustering of the target data set and the comparison data set is unreasonable, continuing to optimize the target data set and the comparison data set according to the optimization rule. By adding the optimization judgment rule, the clustering of the target data set and the comparison data set can be more reasonable, and the accuracy of data calculation is further improved.
Drawings
FIG. 1 is a flow chart diagram of a trajectory data spatiotemporal slicing method provided in the present application.
FIG. 2 is a schematic diagram of the structure of a trajectory data spatiotemporal slicing system provided in the present application.
Fig. 3 is a schematic structural diagram of an electronic device provided in the present application.
In the figure, 200, a trajectory data space-time slicing system; 201. a data acquisition module; 202. a data sampling module; 203. a data clustering module; 204. a central computing module; 205. a data optimization module; 206. a data merging module; 207. a data slicing module; 208. a data tiling module; 301. a CPU; 302. a ROM; 303. a RAM; 304. an I/O interface; 305. an input section; 306. an output section; 307. a storage section; 308. a communication section; 309. a driver; 310. a removable media.
Detailed Description
The specific embodiments are only for explaining the present application and are not limiting to the present application, and those skilled in the art can make modifications to the embodiments without inventive contribution as required after reading the present specification, but all the embodiments are protected by patent law within the scope of the claims of the present application.
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In addition, the term "and/or" herein is only one kind of association relationship describing the association object, and means that there may be three kinds of relationships, for example, a and/or B, and may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship, unless otherwise specified.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs unless specifically defined otherwise. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
The embodiments of the present application will be described in further detail with reference to the drawings attached hereto.
The embodiment of the application provides a track data space-time slicing method, and the main flow of the method is described as follows.
As shown in fig. 1:
step S101: trajectory data is acquired.
Specifically, trajectory data to be processed is obtained, the trajectory data includes, but is not limited to, time data, longitude data, latitude data, and object identifiers, and the trajectory data is sorted in ascending order according to the time data.
Step S102: and sampling the track data according to a sampling rule, forming an initial data set by the extracted track data, and forming an undetermined data set by the residual track data after extraction.
Specifically, firstly, acquiring the data volume of the track data, judging whether the data volume is greater than a preset data volume value, if the data volume is less than the preset data volume value, not sampling the track data, namely acquiring all the sequenced track data and marking the sequenced track data as an initial data set; if the number of the trajectory data is larger than the preset number value, the sorted trajectory data is sampled, so that the calculation workload during data processing is reduced, and the calculation efficiency is improved. The preset value of the data volume is set manually.
And sampling the sequenced track data, wherein the sampling rule is set manually, one track data is extracted every s track data intervals, and the value of s is set manually. And (3) composing the extracted data into an initial data set, and composing the data which is not extracted into a pending data set, for example, if the track data has 100 tracks and s is 40, extracting the 1 st track data, the 41 th track data and the 81 th track data. The 1 st track data, the 41 th track data and the 81 th track data are initial data sets, and the rest other data are undetermined data sets.
Whether sampling is carried out or not is selected by judging the data volume of the track data, if the data volume is small, sampling is not carried out, the accuracy of a final result can be improved to a certain extent by non-sampling, if the data volume is large, sampling processing is carried out on the track data, the calculation amount of data processing can be reduced by the sampling processing, and further the calculation efficiency is improved.
Step S103: and clustering the track data in the initial data set based on a hierarchical clustering rule to obtain a target data set after clustering.
Specifically, the hierarchical clustering rule is a hierarchical clustering method, the trajectory data in the initial data set is calculated according to the hierarchical clustering method, the trajectory data in the initial data set is clustered through the calculation, a corresponding clustering tree can be obtained after clustering, different classes are arranged on the clustering tree, each class comprises the corresponding trajectory data, and the clustered trajectory data form a target data set. The target data set includes a plurality of track data, each track data having corresponding category information. The hierarchical clustering method is a technique known to those skilled in the art and will not be described herein.
Step S104: and calculating the central point of each type in the target data set according to the central point calculation rule, wherein the central points form a comparison data set.
Specifically, the target data set includes a plurality of classes, a center point of each class is calculated, that is, longitude data and latitude data of each trajectory data are converted into coordinate values of x, y and z, and then a center point in a coordinate system is found according to the values of x, y and z. The above-mentioned center point calculation method, i.e. the center point calculation rule, is a technique known to those skilled in the art, and is not described herein. And forming a comparison data set by the central points corresponding to each class.
Step S105: and optimizing the comparison data set and the target data set according to the optimization judgment rule, the comparison data set and the target data set.
Specifically, each central point in the comparison data set is sequentially acquired, a class corresponding to the central point is determined through the central point, all track data of the corresponding class are called from the target data set according to the acquired class, the distance values between the central point of the class and other track data of the class are respectively calculated, then the maximum value is selected from the distance values, the maximum value of each class is acquired, all the maximum values form a sequence, and then the optimization parameters are calculated according to the distance preset value and the sequence, wherein the specific calculation formula is as follows:
wherein la is an optimization parameter, n is the number of classes, l i And m is the maximum value of the ith class and is a distance preset value. The preset distance value is set manually and can be directly called from a database.
Judging whether the optimization parameters are equal to an optimization preset value or not, if so, indicating that the clustering of the target data set and the comparison data set is optimal and the optimization is not needed; if the value is not equal to the optimized preset value, the clustering of the target data set and the comparison data set is not optimal, and clustering needs to be performed again according to the hierarchical clustering rule. And calculating an optimization parameter and recording each optimization parameter every time of optimization, and continuing to perform next optimization when the optimization parameter corresponding to the first clustering is larger than the optimization parameter corresponding to the second clustering. When the 1 st to the q-th sub-optimization is carried out, the corresponding optimization parameters can be in a continuously reduced state, and when the q + 1-th sub-optimization is carried out, the obtained optimization parameters are larger than the q-th optimization parameters, the target data set and the comparison data set corresponding to the q-th time are used as final data sets. At this time, it is indicated that the optimization parameter corresponding to the qth time is minimum, so the clustering result of the target data set and the comparison data set corresponding to the optimization parameter is also optimal, and the target data set and the comparison data set are not optimized continuously. In one example, the optimized preset value is 0.
Step S106: and merging the target data set and the undetermined data set according to the data merging rule, and recording the merged data set as a merged data set.
Specifically, track data of a to-be-determined data set are sequentially acquired, for certain track data, distance values between the track data and each central point in the comparison data set are calculated, a minimum value is selected from the distance values, a central point corresponding to the minimum value is acquired, then a class corresponding to the central point is determined according to the central point, and the track data are added into a corresponding class of a target data set. And adding the track data in the undetermined data set into the target data set through the data merging rule. And when the track data in the data set to be determined are all added into the target data set, the target data set is a merged data set.
And if the undetermined data set exists, merging the target data set and the undetermined data set according to the data merging rule, and if the undetermined data set does not exist, namely the data volume of the track data is small and the sampling operation is not needed, not performing the merging operation. Each class in the merged dataset is a spatial slice.
Step S107: and based on a space-time slicing rule, carrying out data slicing on the track data in the combined data set, and marking the track data after data slicing as a slice set.
Specifically, each class in the merged dataset is obtained, data slicing operation is performed on each class, the slicing operation on each class includes sorting track data in the classes in an ascending order according to time data, data slicing is performed on the track data according to preset slicing time, each slice is recorded as a time slice, the slicing time is set manually, the class which completes data slicing is marked as a slice class, the time slices form slice classes, and the slice classes form slice sets.
Step S108: and performing tile-folding processing on the slice set to obtain a final data set.
Specifically, slice classes and preset tile-folding duration in a slice set are obtained, tile-folding processing is performed on each class, for a certain class, time slices in the class, such as a first slice and a second slice, are sequentially obtained, the first slice and the second slice are two adjacent time slices, head data of the second slice is copied to tail data of the first slice, the time length of the head data is the tile-folding duration, all the time slices in the class are processed according to the method, namely the head data of the first time slice after tile-folding and the tail data of the last time slice are not changed, except the last time slice, the tail data of each time slice is copied with the head data of the next time slice, so that tile-folding slices are obtained, and the tile-folding slices form a tile-folding class. Then, all slice classes in the slice set are subjected to the above operation. The shingles constitute the final data set.
It should be noted that the above-mentioned tile-folding duration is manually defined, and the time interval required by the slice data of the downstream application needs to be considered by the staff in the definition, for example, when the downstream analysis is analysis of long-time aggregation, the tile-folding duration is set to be longer, and if the downstream analysis is analysis of the adjoint relationship, the tile-folding duration is set to be shorter.
Step S109: and deleting the final data set according to the cache release rule.
Specifically, when the p-th time slice is calculated, the track data in the p-1 time slice can be removed from the memory under the control of a program, namely, when the p-th time slice is calculated, the data in the p-1 time slice is not needed, so that the track data in the p-1 time slice is removed. Therefore, the problem of memory unreleased caused by the service can be not considered.
An embodiment of the present application provides a trajectory data spatiotemporal slicing system 200, and referring to fig. 2, the trajectory data spatiotemporal slicing system 200 includes:
a data acquisition module 201, configured to acquire trajectory data;
the data sampling module 202 is used for sampling the trajectory data according to a sampling rule, the extracted trajectory data form an initial data set, and the rest trajectory data form an undetermined data set after extraction;
the data clustering module 203 is used for clustering the track data in the initial data set according to a hierarchical clustering rule to obtain a target data set after clustering;
a central calculating module 204, configured to calculate a central point of each class in the target data set according to a central point calculating rule, and determine a comparison data set;
a data optimization module 205, configured to optimize the comparison data set and the target data set according to an optimization judgment rule, the comparison data set and the target data set;
a data merging module 206, configured to merge the target data set and the to-be-determined data set according to a data merging rule, and determine a merged data set;
a data slicing module 207, configured to perform data slicing on the trajectory data in the merged data set according to a spatio-temporal slicing rule, and determine a slice set;
and a data imbrication module 208, configured to perform imbrication processing on the slice set to obtain a final data set.
It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working process of the described module may refer to the corresponding process in the foregoing method embodiment, and details are not described herein again.
The embodiment of the application discloses an electronic device. Referring to fig. 3, the electronic device includes a Central Processing Unit (CPU) 301 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 302 or a program loaded from a storage section 307 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data necessary for system operation are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other via a bus. An input/output (I/O) interface 304 is also connected to the bus.
The following components are connected to the I/O interface 304: an input section 305 including a keyboard, a mouse, and the like; an output section 306 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 307 including a hard disk and the like; and a communication section 308 including a network interface card such as a LAN card, a modem, or the like. The communication section 308 performs communication processing via a network such as the internet. Drivers 309 are also connected to the I/O interface 304 as needed. A removable medium 310 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 309 as necessary, so that a computer program read out therefrom is mounted into the storage section 307 as necessary.
In particular, according to embodiments of the present application, the process described above with reference to the flowchart fig. 1 may be implemented as a computer software program. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a machine-readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via the communication section 308, and/or installed from the removable medium 310. The above-described functions defined in the apparatus of the present application are executed when the computer program is executed by the Central Processing Unit (CPU) 301.
It should be noted that the computer readable medium shown in the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the application referred to in the present application is not limited to the embodiments with a particular combination of the above-mentioned features, but also encompasses other embodiments with any combination of the above-mentioned features or their equivalents without departing from the spirit of the application. For example, the above features may be replaced with (but not limited to) features having similar functions as those described in this application.
Claims (10)
1. A method for spatiotemporal slicing of trajectory data, comprising:
acquiring track data;
sampling the trajectory data according to a sampling rule, forming an initial data set by the extracted trajectory data, and forming an undetermined data set by the rest trajectory data after extraction;
based on a hierarchical clustering rule, clustering the track data in the initial data set to obtain a target data set;
calculating the central point of each type in the target data set according to a central point calculation rule, wherein the central points form a comparison data set;
optimizing the comparison data set and the target data set according to an optimization judgment rule, the comparison data set and the target data set;
merging the target data set and the data set to be determined according to a data merging rule, and recording the merged data set as a merged data set;
based on a space-time slicing rule, carrying out data slicing on the track data in the merged data set, and marking the track data after data slicing as a slice set;
and performing tile-overlapping processing on the slice set to obtain a final data set.
2. The method of claim 1, wherein optimizing the comparison dataset and the target dataset according to an optimization decision rule, the comparison dataset and the target dataset comprises:
calculating a distance value between the track data of each class in the target data set and the central point of the corresponding class in the comparison data set;
determining an optimized parameter according to a parameter calculation rule and the distance value;
and optimizing the comparison data set and the target data set according to the optimization parameters and the optimization rules.
3. The method of claim 2, wherein the determining optimization parameters according to the parameter calculation rules and the distance values comprises:
obtaining a maximum value of the distance value of each class;
and calculating the optimized parameters according to the maximum value, a preset distance preset value and a parameter calculation rule.
4. A trajectory data spatiotemporal slicing method as defined in claim 2 wherein said optimizing said contrast data set and said target data set according to said optimization parameters and optimization rules comprises:
when the optimized parameter is not equal to the optimized preset value,
clustering the comparison data set and the target data set again according to a hierarchical clustering rule to generate current optimization parameters;
comparing the current optimization parameters with the optimization parameters;
and when the optimization parameters are smaller than the current optimization parameters, taking the target data set and the comparison data set corresponding to the optimization parameters as final target data sets and comparison data sets.
5. The method for spatiotemporal slicing of trajectory data according to claim 1, wherein said merging the target data set and the pending data set according to a data merging rule, and recording the merged data set as a merged data set comprises:
calculating the distance between the trajectory data in the undetermined data set and the central point in the comparison data set;
acquiring the minimum value of the distance;
obtaining the class of the central point corresponding to the minimum value;
and adding the track data in the undetermined data set into the corresponding class in the target data set to obtain a merged data set.
6. The method for spatiotemporal slicing trajectory data as defined in claim 1, wherein the slicing trajectory data in the merged data set based on spatiotemporal slicing rules, and marking the sliced trajectory data as a set of slices comprises:
the trajectory data comprises time data;
sorting the track data of each class in the merged data set in an ascending order according to the time data;
carrying out data slicing on the track data in the classes according to preset slicing duration;
forming a slice by the track data every other slice duration;
the class of finished slices is denoted as slice class, which constitutes a set of slices.
7. The method as claimed in claim 6, wherein the step of performing a tiling process on the set of slices to obtain a final data set comprises:
acquiring any adjacent slice in the slice set;
according to preset tile-overlapping duration, copying head data with the duration being the tile-overlapping duration in the slice with larger time data in the adjacent slices to tail data of the slice with smaller time data in the adjacent slices;
the processed slices are shingled slices;
the shingled slices constitute the final data set.
8. A trajectory data spatiotemporal slicing system, comprising:
a data acquisition module (201) for acquiring trajectory data;
the data sampling module (202) is used for sampling the trajectory data according to a sampling rule, the extracted trajectory data form an initial data set, and the rest trajectory data after extraction form an undetermined data set;
the data clustering module (203) is used for clustering the track data in the initial data set according to a hierarchical clustering rule to obtain a target data set after clustering;
a center calculation module (204) for calculating a center point of each class in the target data set and determining a comparison data set according to a center point calculation rule;
a data optimization module (205) for optimizing the comparison data set and the target data set according to an optimization judgment rule, the comparison data set and the target data set;
a data merging module (206) for merging the target data set and the data set to be determined according to a data merging rule to determine a merged data set;
a data slicing module (207) for performing data slicing on the trajectory data in the merged data set according to a spatio-temporal slicing rule to determine a slice set;
and the data tiling module (208) is used for performing tiling processing on the slice set to obtain a final data set.
9. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program that can be loaded by the processor and that executes the method according to any of claims 1 to 7.
10. A computer-readable storage medium, in which a computer program is stored which can be loaded by a processor and which executes the method of any one of claims 1 to 7.
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| CN113962283A (en) * | 2021-09-01 | 2022-01-21 | 南京航空航天大学 | Aircraft trajectory clustering method based on local self-adaptive dynamic time warping |
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