CN115617933A - Multi-dimensional trajectory analysis and visualization method and device based on spatio-temporal data - Google Patents
Multi-dimensional trajectory analysis and visualization method and device based on spatio-temporal data Download PDFInfo
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Abstract
The invention discloses a multi-dimensional trajectory analysis and visualization method and a device based on spatio-temporal data, wherein the method comprises the steps of obtaining a spatio-temporal data source of a historical time period and a spatial position, responding to the fact that the spatio-temporal data source comprises at least one target characteristic value, filtering and sequencing the spatio-temporal data source to obtain an ordered sequence, wherein the ordered sequence is at least one piece of trajectory data, and the target characteristic value comprises characteristic values of different objects with corresponding time attributes and spatial attributes; processing the ordered sequence according to different track analysis dimensions to obtain first data matrixes under different dimensions; filtering the first data matrix based on a target time period to obtain a second data matrix; and combining the second data matrix with the map and web visual data to perform point location display, track editing, track playing and/or linkage interaction, and adopting a plurality of dimension operation track data such as time, data categories, multiple objects and the like to make the visual form more diversified and have strong expandability.
Description
Technical Field
The invention relates to the field of trajectory analysis, in particular to a multi-dimensional trajectory analysis and visualization method and device based on spatiotemporal data.
Background
In the big data intelligence era, along with the general use of wireless sensing positioning equipment and the progress of core collection technology, moving object space-time trajectory data presents explosive growth, and the big data of orbit includes human activity orbit, traffic vehicle orbit, natural phenomenon orbit etc. has that the type is abundant, the dimension is various, the sample is huge, the resolution ratio is meticulous, the characteristics that increase is fast, all has huge research application value to each trade. In an information system, a data mining technology, a data analysis technology and a data visualization technology are utilized to analyze the behavior rule of a target from massive target space-time trajectory data, so that intelligent perception situation information is realized.
The existing analysis and visualization interaction technology aiming at space-time trajectory data is as follows:
(1) According to the single item structured information collision analysis technology, information of other dimensions is collided by single item information of a known target object, but the technology depends on the input of structured data and cannot be applied to spatio-temporal data which is not subjected to structured processing, and the visualization technology is mainly used in a display form aiming at structured information types and lacks multi-dimensional display combining time attributes and space attributes.
(2) The method comprises the steps of performing spatio-temporal characteristic value association analysis technology and association track visualization, dividing a target track and each association track into track sections, and counting the association tracks, but in the way, the association tracks are searched by taking a single object as a reference, the dimensionality and visualization forms are not diverse enough, the association analysis efficiency can only be improved, and the application scene is single.
(3) The trajectory data clustering technology and the analysis result are visualized, the characteristics of the target trajectory such as direction characteristics, similarity and the like are subjected to operation analysis by adopting a related algorithm, the similarity and abnormal characteristics are extracted, the practical application value is mined, the pertinence is strong, such analysis modes have no specific visualization mode, but only the data characteristics of a certain dimensionality of the data are subjected to deep quantitative analysis, and the method is suitable for performing visualization display on certain data attributes or data statistical results and cannot express the complete attributes of the spatiotemporal data.
Disclosure of Invention
The technical problem mentioned above is addressed. An embodiment of the present application aims to provide a method and an apparatus for multidimensional trajectory analysis and visualization based on spatiotemporal data, so as to solve the technical problems mentioned in the background art section above.
In a first aspect, the invention provides a multi-dimensional trajectory analysis and visualization method based on spatiotemporal data, comprising the following steps:
s1, acquiring a spatiotemporal data source of a historical time period and a spatial position, filtering and sequencing the spatiotemporal data source to obtain an ordered sequence in response to the fact that the spatiotemporal data source comprises at least one target characteristic value, the target characteristic value comprises characteristic values of different objects with corresponding time attributes and spatial attributes, wherein the ordered sequence is at least one piece of track data, and the track data comprises the time attributes, the spatial attributes and the corresponding target characteristic values;
s2, processing the ordered sequence according to different track analysis dimensions to obtain a first data matrix under different dimensions, wherein the first data matrix comprises at least one track sequence which comprises at least one piece of track data;
s3, filtering the first data matrix based on a target time period to obtain a second data matrix;
and S4, combining the second data matrix with the map and the web visualization data to perform point location display, track editing, track playing and/or linkage interaction.
Preferably, in step S1, the filtering and sorting are performed on the time-space data sources to obtain an ordered sequence, which specifically includes:
filtering data with empty spatial attributes in the spatio-temporal data source to obtain a filtered spatio-temporal data source;
and sequencing the filtered spatio-temporal data sources according to a time sequence to obtain an ordered sequence.
Preferably, the trajectory analysis dimension in step S2 includes a time dimension, a data category dimension, and a multi-object dimension.
Preferably, in the time dimension, the step S2 specifically includes:
s21, the first data matrix is initially a null matrix;
s22, acquiring the time attribute of the nth track data in the ordered sequence and converting the time attribute into a time stamp;
s23, judging whether the difference value between the timestamp of the nth track data and the time mark is greater than a time period value, wherein the initial value of the time mark is 0, the rest value is the time value of the (n-1) th track data, and n is greater than or equal to 1;
s24, in response to the fact that the difference value between the timestamp and the timestamp is larger than the time period value, a new track number sequence is established in the first data matrix, and the nth track data is inserted into the tail end of the new track number sequence;
s25, in response to the fact that the difference value between the timestamp and the timestamp is smaller than or equal to the time period value, inserting the nth track data into the head of the last track sequence of the first data matrix;
and S26, repeating the steps S22-S25 until n is equal to the total number of the track data in the ordered sequence, and constructing to obtain a first data matrix.
Preferably, in the data category dimension, step S2 specifically includes:
identifying the data category of each track data in the ordered sequence;
grouping according to different data types, forming a track number sequence by using track data of the same data type in each group, and forming a first data matrix.
Preferably, in the multi-object dimension, the step S2 specifically includes:
structuring all objects in the ordered sequence, each object having a corresponding unique identifier;
grouping according to different unique identifiers, wherein the track data with the same unique identifier in each group form a track number sequence and form a first data matrix.
Preferably, step S3 specifically includes:
inputting the first data matrix into a filter function with a target time period as a condition to obtain a second data matrix, wherein the formula is as follows:
D 2 ＝F(D 1 ,t 1 <t<t 2 )；
wherein the first data matrix is D 1 The second data matrix is D 2 T is the time of each trace data of each trace sequence in the first data matrix, t 1 Is a target time period start time, t 2 Is the end time of the target time period, F is D 1 As input, take t 1 <t<t 2 Is a filter function of the condition.
Preferably, step S4 specifically includes:
initializing tile data, a center point and a level of a map;
adjusting the size and the level of a visual window of the map according to the spatial attribute of the track data in the second data matrix;
and displaying point positions and/or tracks according to the spatial attributes of the track data in the second data matrix, or editing the tracks, playing the tracks and performing linkage interaction with a map.
In a second aspect, the present invention provides a multi-dimensional trajectory analysis and visualization apparatus based on spatiotemporal data, comprising:
the data acquisition module is configured to acquire a spatiotemporal data source of a historical time period and a spatial position, filter and sort the spatiotemporal data source to obtain an ordered sequence in response to the fact that the spatiotemporal data source comprises at least one target characteristic value, the target characteristic value comprises characteristic values of different objects with corresponding time attributes and spatial attributes, and the ordered sequence is at least one piece of track data which comprises the time attributes, the spatial attributes and the corresponding target characteristic values;
the analysis module is configured to process the ordered sequence according to different track analysis dimensions to obtain a first data matrix under different dimensions, wherein the first data matrix comprises at least one track sequence, and the track sequence comprises at least one piece of track data;
the filtering module is configured to filter the first data matrix based on a target time period to obtain a second data matrix;
and the visualization module is configured to combine the second data matrix with the map and the web visualization data for point location display, track editing, track playing and/or linkage interaction.
In a third aspect, the invention provides an electronic device comprising one or more processors; storage means for storing one or more programs which, when executed by one or more processors, cause the one or more processors to carry out a method as described in any one of the implementations of the first aspect.
In a fourth aspect, the invention provides a computer-readable storage medium, on which a computer program is stored which, when executed by a processor, implements the method as described in any implementation form of the first aspect.
Compared with the prior art, the invention has the following beneficial effects:
(1) The invention can take different objects as targets, and performs the display and interactive optimization of the trajectory analysis facing the space-time data, but does not limit the data category, any data with space-time attribute can be used as input, and trajectory analysis is performed by adopting multiple dimensions of time, data category, multiple objects and the like to obtain trajectory data of different groups, so that the visualization form is more diversified, and the expandability is strong.
(2) According to the method, the result data obtained by trajectory analysis is combined with the Web map data visualization technology, the time attribute and the space attribute of the display data can be conveniently fused, the map hierarchy is automatically adjusted according to the result data, all trajectory data and point data are ensured to be displayed in a visual window, the phenomenon of spatial confusion in the display of the trajectory data with large data volume is solved to a certain extent, the operability is improved, and the visual effect is good.
(3) According to the invention, the track data of each dimension is calculated by using the user-defined target time period, so that the actual application value of the track data in the time dimension is greatly improved, the personalized requirements of a user are met, the user can track the target time period in a user-defined manner, the time period of the target track is analyzed in a user-defined manner, the activity rules of moving targets such as people or vehicles are quickly searched, or the target object is reversely locked according to the rule characteristics of the specific time period, or the next behavior of the target is predicted according to the rule characteristics of the specific time period, and the future behavior of the target is mastered.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is an exemplary device architecture diagram in which one embodiment of the present application may be applied;
FIG. 2 is a schematic flow chart of a method for multi-dimensional trajectory analysis and visualization based on spatiotemporal data according to an embodiment of the present application;
FIG. 3 is a block flow diagram of a method for multi-dimensional trajectory analysis and visualization based on spatiotemporal data in an embodiment of the present application;
FIG. 4 is a block diagram of a trajectory analysis flow in the time dimension of a spatiotemporal data-based multidimensional trajectory analysis and visualization method according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a spatiotemporal data-based multi-dimensional trajectory analysis and visualization apparatus according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a computer device suitable for implementing an electronic apparatus according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, not all of the 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 invention.
Fig. 1 illustrates an exemplary device architecture 100 to which the spatiotemporal data-based multi-dimensional trajectory analysis and visualization method or the spatiotemporal data-based multi-dimensional trajectory analysis and visualization device of the embodiments of the present application may be applied.
As shown in fig. 1, the apparatus architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. Network 104 is the medium used to provide communication links between terminal devices 101, 102, 103 and server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
A user may use terminal devices 101, 102, 103 to interact with a server 105 over a network 104 to receive or send messages or the like. Various applications, such as data processing type applications, file processing type applications, etc., may be installed on the terminal apparatuses 101, 102, 103.
The terminal apparatuses 101, 102, and 103 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices including, but not limited to, smart phones, tablet computers, laptop portable computers, desktop computers, and the like. When the terminal apparatuses 101, 102, 103 are software, they can be installed in the electronic apparatuses listed above. It may be implemented as multiple pieces of software or software modules (e.g., software or software modules used to provide distributed services) or as a single piece of software or software module. And is not particularly limited herein.
The server 105 may be a server that provides various services, such as a background data processing server that processes files or data uploaded by the terminal devices 101, 102, 103. The background data processing server can process the acquired file or data to generate a processing result.
The multidimensional trajectory analysis and visualization method based on spatiotemporal data provided by the embodiment of the present application may be executed by the server 105, or may be executed by the terminal devices 101, 102, and 103, and accordingly, the multidimensional trajectory analysis and visualization apparatus based on spatiotemporal data may be provided in the server 105, or may be provided in the terminal devices 101, 102, and 103.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation. In the case where the processed data does not need to be acquired from a remote location, the above device architecture may not include a network, but only a server or a terminal device.
FIG. 2 illustrates a multi-dimensional trajectory analysis and visualization method based on spatiotemporal data according to an embodiment of the present application, including the following steps:
s1, acquiring a spatio-temporal data source of a historical time period and a spatial position, responding to the fact that the spatio-temporal data source comprises at least one target characteristic value, wherein the target characteristic value comprises characteristic values of different objects with corresponding time attributes and spatial attributes, filtering and sequencing the spatio-temporal data source to obtain an ordered sequence, the ordered sequence is at least one piece of track data, and the track data comprises the time attributes, the spatial attributes and the corresponding target characteristic values.
In a specific embodiment, the filtering and sorting the time-space data sources in step S1 to obtain an ordered sequence, specifically includes:
filtering data with empty spatial attributes in the spatio-temporal data source to obtain a filtered spatio-temporal data source;
and sequencing the filtered spatio-temporal data sources according to a time sequence to obtain an ordered sequence.
Specifically, referring to fig. 3, the historical time period, the spatial position, and the target feature value of interest need to be selected first to form a piece of trajectory data composed of the temporal, spatial, and target feature values in the following. Specifically, a request for acquiring spatiotemporal data sources of a historical time period and a position space may be initiated to a server, so as to obtain the spatiotemporal data sources corresponding to the historical time period and the position space.
And further, judging whether the space-time data source is empty or not, and if the space-time data source is empty, directly ending. When at least one target characteristic value exists in the time-space data source and the time-space data source is not empty, the time-space data source can be filtered, the data with the empty space attribute is filtered, and the data are sorted into an ordered sequence according to the time sequence. Preferably, the spatial attribute may be set to longitude and latitude.
When the target is a vehicle, the target characteristic values include a license plate number, a license plate color, a vehicle type, a vehicle brand, a vehicle color, and the like. When the target is a person, the target feature value includes a face picture, a body picture, and the like. Different types of characteristic values can be obtained according to different targets to form target characteristic values.
And S2, processing the ordered sequence according to different track analysis dimensions to obtain a first data matrix under different dimensions, wherein the first data matrix comprises at least one track sequence, and the track sequence comprises at least one piece of track data.
In a particular embodiment, the trajectory analysis dimensions in step S2 include a time dimension, a data category dimension, and a multi-object dimension. Specifically, in the time dimension, the activity time rule is analyzed by using time period operation, and the track data in each time period form a track number sequence; under the dimension of the data category, analyzing the data category classification track; and under the multi-object dimension, grouping operation analysis is carried out through the unique identification of the object.
In a specific embodiment, in the time dimension, the step S2 specifically includes:
s21, the first data matrix is initially a null matrix;
s22, acquiring the time attribute of the nth track data in the sequence and converting the time attribute into a time stamp;
s23, judging whether the difference value between the timestamp of the nth track data and the time mark is greater than a time period value, wherein the initial value of the time mark is 0, the rest value is the time value of the (n-1) th track data, and n is greater than or equal to 1;
s24, in response to the fact that the difference value between the timestamp and the timestamp is larger than the time period value, a new track number sequence is established in the first data matrix, and the nth track data is inserted into the tail end of the new track number sequence;
s25, in response to the fact that the difference value between the timestamp and the timestamp is smaller than or equal to the time period value, inserting the nth track data into the head of the last track sequence of the first data matrix;
and S26, repeating the steps S22-S25 until n is equal to the total number of the track data in the sequence, and constructing to obtain a first data matrix.
Specifically, referring to fig. 4, for spatio-temporal data, the time attribute t of each piece of trajectory data in the ordered sequence may be a camera capture time, a time when the server records the piece of data, or a database entry time. The timestamp c is a value obtained by converting the time attribute t into a timestamp, so that the operation is facilitated. The initial value of the time mark I is equal to 0, and is equal to the timestamp of the previous piece of track data in the subsequent calculation, and the difference between the time mark I and the timestamp c is greater than the time period value, which indicates that the difference between the previous piece of track data and the next piece of track data is greater than the time period value, and then the piece of track data needs to be inserted into the track number sequence of the next period. Preferably, the time period may be set to 7 hours.
The above steps are exemplified as follows:
it is assumed that there are track data 1, track data 2, track data 3, track data 4, and track data 5, which are 5 track data of a target arranged in time sequence, and the corresponding time attributes are converted into timestamps 1663052643664, 1663052743664, 1663052843664, 1663053343664, and 1663053443664, and the time period value is 200000.
First data matrix D 1 Is empty;
calculating the difference value between the track data 1 and the time mark: 1663052643664-0=1663052643664, which is greater than the time period value (i.e. 200000), a new track sequence l is created at the end of the first data matrix 0 And in the track array l 0 At the end of which track data 1 is inserted, i.e.D 1 ＝[l 0 ]，l 0 = [ track data 1 ]](ii) a The time stamp is changed to the time stamp of track data 1, i.e., 1663052643664.
Calculating the difference between the trajectory data 2 and the time mark: 1663053343664-1663052643664=100000, which is smaller than the time period value (i.e. 200000), at the end of the first data matrix the number of rows (i.e. the number of tracks l) 0 ) Is inserted into the track data 2, i.e. D 1 ＝[l 0 ]，l 0 = track data 1, track data 2](ii) a The time stamp is changed to the time stamp of track data 2, i.e., 1663052743664.
Calculating the difference between the trajectory data 3 and the time mark: 1663052843664-1663052743664=100000, which is smaller than the time period value (i.e. 200000), at the end of the first data matrix (i.e. the track number sequence l) 0 ) Is inserted into the track data 3, i.e. D 1 ＝[l 0 ]，l 0 = track data 3, track data 2, track data 1](ii) a The time stamp is changed to the time stamp of the track data 3, i.e., 1663052843664.
Calculating the difference between the trajectory data 4 and the time mark: 1663052843664-1663052843664=500000, which is larger than the time period value (i.e. 200000), creating a new track sequence l at the end of the first data matrix 1 And in the track array l 1 Is inserted with the track data 4, i.e. D 1 ＝[l 0 ,l 1 ]，l 0 = track data 3, track data 2, track data 1]，l 1 = [ track data 4)](ii) a The time stamp is changed to the time stamp of the track data 4, i.e., 1663053343664.
Calculating the difference between the trajectory data 5 and the time mark: 1663053443664-1663053343664=100000, which is smaller than the time period value (i.e. 200000), at the last sequence of the first data matrix (i.e. the track sequence l) 1 ) Is inserted into the track data 5, i.e. D 1 ＝[l 0 ,l 1 ]，l 0 = track data 3, track data 2, track data 1]，l 1 = track data 5, track data 4]。l 0 And l 1 Two trace arrays, respectively, that satisfy the time period value.
In a specific embodiment, in the data category dimension, step S2 specifically includes:
identifying the data category of each piece of track data in the ordered sequence;
grouping according to different data types, wherein track data of the same data type in each group form a track number sequence and form a first data matrix.
Specifically, because the track data in the ordered sequence has different data types, including data types of human faces, human bodies, mobile phone numbers, motor vehicles, and the like, the track analysis can be performed through different data types, and the following steps are exemplified:
the data types in the space-time data source are assumed to be classified and identified as 5 types of human faces, human bodies, motor vehicles, non-motor vehicles and mobile phone numbers, the track data 1, the track data 2, the track data 3, the track data 4, the track data 5, the track data 6, the track data 7 and the track data 8 are assumed to exist, the track data have data type attributes, the track data are 8 track data which are arranged in time sequence, and the corresponding data types are the human faces, the human bodies, the mobile phone numbers, the human faces, the motor vehicles, the non-motor vehicles, the mobile phone numbers and the motor vehicles respectively.
First data matrix D 1 Is empty;
creating a new track sequence l at the end of the first data matrix 0 And in the track array l 0 Is inserted with track data 1, i.e. D 1 ＝[l 0 ]，l 0 = [ track data 1 ]]。
And traversing the 8 track data in sequence, if the data type of the track data in traversal is consistent with the data type of the track data in the last sequence of the first data matrix, inserting the track data at the head of the last sequence of the first data matrix, and if the data type of the track data in traversal is inconsistent with the data type of the track data in the last sequence of the first data matrix, creating a new track sequence at the tail of the first data matrix and inserting the track data at the tail of the new track sequence.
To obtain D 1 ＝[l 0 ,l 1 ,l 2 ,l 3 ,l 4 ]，l 0 = track data 4, track data 1]，l 1 = [ track data 2 ]]，l 2 = track data 7, track data 3]，l 3 = track data 8, track data 5]，l 4 = [ track data 6)]。l 0 ,l 1 ,l 2 ,l 3 ,l 4 Each is a series of five traces that satisfy the data category grouping.
In a specific embodiment, in the multi-object dimension, the step S2 specifically includes:
structuring all objects in the ordered sequence, each object having a corresponding unique identifier;
grouping according to different unique identifiers, wherein the track data with the same unique identifier in each group form a track number sequence and form a first data matrix.
Specifically, because the track data in the ordered sequence corresponds to different objects, and the object corresponding to each piece of track data and the unique identifier ID thereof need to be obtained after data structuring, the track analysis can be performed according to multiple objects and the unique identifiers corresponding thereto, and the following steps are exemplified:
it is assumed that track data 1, track data 2, track data 3, track data 4, track data 5, track data 6, track data 7, and track data 8 are 8 pieces of track data arranged in time sequence and having unique identifiers after data structuring, and the corresponding unique identifiers after data structuring are id1, id2, id3, id1, id4, id5, id3, and id4, respectively.
First data matrix D 1 Is empty;
creating a new track sequence l at the end of the first data matrix 0 And in the track array l 0 Is inserted with track data 1, i.e. D 1 ＝[l 0 ]，l 0 = [ track data 1 ]]。
And traversing the 8 track data in sequence, if the unique identifier of the track data in traversal is consistent with the unique identifier of the track data in the last sequence of the first data matrix, inserting the track data at the head of the last sequence of the first data matrix, and if the unique identifier of the track data in traversal is inconsistent with the unique identifier of the track data in the last sequence of the first data matrix, creating a new track sequence at the tail of the first data matrix and inserting the track data at the tail of the new track sequence.
To obtain D 1 ＝[l 0 ,l 1 ,l 2 ,l 3 ,l 4 ]，l 0 = track data 4, track data 1]，l 1 = [ track data 2 ]]，l 2 = track data 7, track data 3]，l 3 = track data 8, track data 5]，l 4 = [ track data 6)]。l 0 ,l 1 ,l 2 ,l 3 ,l 4 Respectively, five trajectory series satisfying the multi-object grouping.
And S3, filtering the first data matrix based on the target time period to obtain a second data matrix.
In a specific embodiment, step S3 specifically includes:
inputting the first data matrix into a filter function with a target time period as a condition to obtain a second data matrix, wherein the formula is as follows:
D 2 ＝F(D 1 ,t 1 <t<t 2 )；
wherein the first data matrix is D 1 The second data matrix is D 2 T is the time of each trace data of each trace sequence in the first data matrix, t 1 Is a target time period start time, t 2 Is the end time of the target time period, F is D 1 As input, take t 1 <t<t 2 Is a conditional filter function.
Specifically, the target time period may be set to weekend, workday, holiday, morning, afternoon, night.
Taking the first data matrix D1 obtained in the example under the time dimension in S2 as an example, assuming that the target time period is night, t 1 Is 20 points, t 2 Is 6 points, for the first data matrix D 1 ＝[l 0 ,l 1 ]Filtering is carried out，l 0 = track data 3, track data 2, track data 1]，l 1 = track data 5, track data 4]Obtaining a second data matrix D 2 ＝[]Due to D 1 Does not satisfy t at time t of each trace data of each trace number series 1 <t<t 2 。
And S4, combining the second data matrix with the map and the web visualization data to perform point location display, track editing, track playing and/or linkage interaction.
In a specific embodiment, step S4 specifically includes:
initializing tile data, a center point and a level of a map;
adjusting the size and the level of a visual window of the map according to the spatial attribute of the track data in the second data matrix;
and displaying point positions and/or tracks according to the spatial attributes of the track data in the second data matrix, or editing the tracks, playing the tracks and performing linkage interaction with a map.
Specifically, a WEB map data visualization technology is combined, track analysis results are carried out according to different track analysis dimensions, and point location display, track editing, track playing and linkage interaction of a map and a list are applied by combining space dimensions. The spatial positions and the time sequence of the multiple objects are clearly displayed, the activity laws of the multiple objects such as people, vehicles and things are quickly found, for example, people and vehicles who are out at night in the daytime and night, the falling places of the people and vehicles, frequent passing of the vehicles in a specific area range, wandering people in a target area, continuous illegal conditions of the vehicles and the like are analyzed, a basic platform is provided for various object analysis and behavior prediction scenes, and convenience is brought to various use scenes.
The following illustrates a specific visualization process:
and pressing the WEB page according to the following steps of 7: the scale of 3 is divided into a left structure and a right structure, the left side is a WEB map, a 'playing' button and an 'editing' button are placed on the map, and the right side is provided with a track array and a 'showing/hiding' button. Take the first data matrix D1 obtained in the example under the time dimension in S2 as an example, D 1 ＝[l 0 ,l 1 ]，l 0 = track data 3, track data 2, track data 1]，l 1 = track data 5, track data 4]Showing on the right side of the page 0 ,l 1 Two series of tracks, e.g. will l 0 Specifically shown as the spatial location name and time of the trajectory data 1, the spatial location name and time of the trajectory data 2, the spatial location name and time of the trajectory data 3, and the total number of trajectory data. Click on l 0 The left map draws a track and three point positions according to the longitude and latitude of the track data 3, the track data 2 and the track data 1, clicks an 'edit' button on the map, can newly add track points, delete track points and change track points, clicks a 'play' button on the map, and can play the path of the track from a starting point to a termination point in sequence. When any point location on the map is clicked, the track data corresponding to the point location is highlighted on the right side. Correspondingly, when any track data on the right side is clicked, the point position corresponding to the track data is highlighted on the map. When a plurality of tracks are arranged on the map, the tracks are displayed in different colors.
The embodiment of the application carries out track analysis on various track data, has a more flexible analysis mode, can organize clearly to obtain a visual result aiming at a specific application scene, is not limited to the data type, the object and the like of the track data, greatly improves the actual application value of the track data in time dimension, meets the personalized requirements of a user, enables the user to customize a target tracking time period, customizes the time period of analyzing a target track, quickly searches the activity rule of a moving target such as a person or a vehicle and the like, or reversely locks the target object according to the rule characteristic of a specific time period, or predicts the next behavior of the target according to the rule characteristic of the specific time period, and grasps the future behavior of the target.
With further reference to fig. 5, as an implementation of the method shown in the above figures, the present application provides an embodiment of a multidimensional trajectory analysis and visualization apparatus based on spatiotemporal data, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be applied to various electronic devices.
The embodiment of the application provides a multidimensional trajectory analysis and visualization device based on spatiotemporal data, includes:
the data acquisition module 1 is configured to acquire a spatiotemporal data source of a historical time period and a spatial position, and in response to the fact that the spatiotemporal data source comprises at least one target characteristic value, the target characteristic value comprises characteristic values of different objects with corresponding time attributes and spatial attributes, the spatiotemporal data source is filtered and sequenced to obtain an ordered sequence, the ordered sequence is at least one piece of trajectory data, and the trajectory data comprises the time attributes, the spatial attributes and the corresponding target characteristic values;
the analysis module 2 is configured to process the ordered sequence according to different track analysis dimensions to obtain a first data matrix under different dimensions, wherein the first data matrix comprises at least one track sequence, and the track sequence comprises at least one piece of track data;
the filtering module 3 is configured to filter the first data matrix based on the target time period to obtain a second data matrix;
and the visualization module 4 is configured to combine the second data matrix with the map and the web visualization data to perform point location display, track editing, track playing and/or linkage interaction.
Reference is now made to fig. 6, which is a schematic diagram illustrating a computer device 600 suitable for implementing an electronic device (e.g., the server or the terminal device shown in fig. 1) according to an embodiment of the present application. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 6, the computer apparatus 600 includes a Central Processing Unit (CPU) 601 and a Graphics Processing Unit (GPU) 602, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 603 or a program loaded from a storage section 609 into a Random Access Memory (RAM) 604. In the RAM604, various programs and data necessary for the operation of the apparatus 600 are also stored. The CPU 601, GPU602, ROM 603, and RAM604 are connected to each other via a bus 605. An input/output (I/O) interface 606 is also connected to bus 605.
The following components are connected to the I/O interface 606: an input portion 607 including a keyboard, a mouse, and the like; an output section 608 including a display such as a Liquid Crystal Display (LCD) and a speaker; a storage section 609 including a hard disk and the like; and a communication section 610 including a network interface card such as a LAN card, a modem, or the like. The communication section 610 performs communication processing via a network such as the internet. The driver 611 may also be connected to the I/O interface 606 as needed. A removable medium 612 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 611 as necessary, so that a computer program read out therefrom is mounted into the storage section 609 as necessary.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer-readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via the communication section 610, and/or installed from the removable media 612. The computer programs, when executed by a Central Processing Unit (CPU) 601 and a Graphics Processor (GPU) 602, perform the above-described functions defined in the methods of the present application.
It should be noted that the computer readable medium described herein can be a computer readable signal medium or a computer readable medium or any combination of the two. The computer readable medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor device, apparatus, or a combination of any of the foregoing. More specific examples of the computer readable 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 present application, a computer readable medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution apparatus, device, or apparatus. 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 many 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 medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution apparatus, device, or apparatus. 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.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures 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 devices that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present application may be implemented by software or hardware. The modules described may also be provided in a processor.
As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: the method comprises the steps of obtaining a spatio-temporal data source of a historical time period and a spatial position, filtering and sequencing the spatio-temporal data source in response to the fact that the spatio-temporal data source comprises at least one target characteristic value which comprises characteristic values of different objects with corresponding time attributes and spatial attributes to obtain an ordered sequence, wherein the ordered sequence is at least one piece of track data, and the track data comprises the time attributes, the spatial attributes and the corresponding target characteristic values; processing the ordered sequence according to different track analysis dimensions to obtain a first data matrix under different dimensions, wherein the first data matrix comprises at least one track sequence which comprises at least one piece of track data; filtering the first data matrix based on a target time period to obtain a second data matrix; and combining the second data matrix with the map and the web visualization data to perform point location display, track editing, track playing and/or linkage interaction.
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 invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (11)
1. A multi-dimensional trajectory analysis and visualization method based on spatiotemporal data is characterized by comprising the following steps:
s1, acquiring a spatio-temporal data source of a historical time period and a spatial position, and in response to the fact that the spatio-temporal data source comprises at least one target characteristic value which comprises characteristic values of different objects with corresponding time attributes and spatial attributes, filtering and sequencing the spatio-temporal data source to obtain an ordered sequence, wherein the ordered sequence is at least one piece of track data, and the track data comprises the time attributes, the spatial attributes and the corresponding target characteristic values;
s2, processing the ordered sequence according to different track analysis dimensions to obtain a first data matrix under different dimensions, wherein the first data matrix comprises at least one track sequence which comprises at least one piece of track data;
s3, filtering the first data matrix based on a target time period to obtain a second data matrix;
and S4, combining the second data matrix with the map and the web visualization data to perform point location display, track editing, track playing and/or linkage interaction.
2. The spatio-temporal data-based multidimensional trajectory analysis and visualization method according to claim 1, wherein the spatio-temporal data sources are filtered and sorted in the step S1 to obtain an ordered sequence, specifically comprising:
filtering the data with empty space attribute in the space-time data source to obtain a filtered space-time data source;
and sequencing the filtered spatio-temporal data sources according to a time sequence to obtain the ordered sequence.
3. The method for multi-dimensional trajectory analysis and visualization based on spatiotemporal data according to claim 1, wherein the trajectory analysis dimensions in step S2 include a time dimension, a data category dimension and a multi-object dimension.
4. The spatiotemporal data-based multidimensional trajectory analysis and visualization method according to claim 3, wherein in the time dimension, the step S2 specifically comprises:
s21, the first data matrix is initially a null matrix;
s22, acquiring the time attribute of the nth track data in the ordered sequence and converting the time attribute into a time stamp;
s23, judging whether the difference value between the timestamp of the nth track data and the time mark is greater than a time period value, wherein the initial value of the time mark is 0, the rest values are the time values of the (n-1) th track data, and n is greater than or equal to 1;
s24, in response to the fact that the difference value between the timestamp and the timestamp is larger than the time period value, creating a new track number array in the first data matrix, and inserting the nth track data at the end of the new track number array;
s25, in response to the fact that the difference value between the timestamp and the timestamp is smaller than or equal to the time period value, inserting the nth track data into the first position in the last track number sequence of the first data matrix;
and S26, repeating the steps S22-S25 until n is equal to the total number of the track data in the sequence array, and constructing to obtain the first data matrix.
5. The spatiotemporal data-based multidimensional trajectory analysis and visualization method according to claim 3, wherein in the data category dimension, the step S2 specifically comprises:
identifying the data category of each track data in the ordered sequence;
grouping according to different data types, wherein the track data of the same data type in each group form a track sequence and form the first data matrix.
6. The spatiotemporal data-based multidimensional trajectory analysis and visualization method according to claim 3, wherein in the multi-object dimension, the step S2 specifically comprises:
structuring all objects in the ordered sequence, each object having a corresponding unique identifier;
grouping according to different unique identifiers, wherein the track data with the same unique identifier in each group form a track sequence and form the first data matrix.
7. The spatiotemporal data-based multidimensional trajectory analysis and visualization method according to claim 1, wherein the step S3 specifically comprises:
inputting the first data matrix into a filter function with the target time period as a condition to obtain a second data matrix, wherein the formula is as follows:
D 2 ＝F(D 1 ,t 1 <t<t 2 )；
wherein the first data matrix is D 1 The second data matrix is D 2 T is the time of each trace data of each trace sequence in the first data matrix, t 1 Is the target time period start time, t 2 Is the end time of the target time period, F is D 1 As input, take t 1 <t<t 2 Is a filter function of the condition.
8. The spatiotemporal data-based multidimensional trajectory analysis and visualization method according to claim 1, wherein the step S4 specifically comprises:
initializing tile data, a center point and a level of a map;
adjusting the size and the level of a visualization window of the map according to the spatial attribute of the track data in the second data matrix;
and displaying point positions and/or tracks according to the spatial attributes of the track data in the second data matrix, or editing the tracks, playing the tracks and performing linkage interaction with a map.
9. A multidimensional trajectory analysis and visualization device based on spatiotemporal data is characterized by comprising:
a data acquisition module configured to acquire a spatiotemporal data source of a historical time period and a spatial position, filter and sort the spatiotemporal data source in response to determining that the spatiotemporal data source includes at least one target feature value, the target feature value including feature values of different objects having corresponding time attributes and spatial attributes, to obtain an ordered sequence, the ordered sequence being at least one piece of trajectory data, the trajectory data including the time attributes, the spatial attributes, and corresponding target feature values thereof;
the analysis module is configured to process the ordered sequence according to different track analysis dimensions to obtain a first data matrix under different dimensions, wherein the first data matrix comprises at least one track sequence, and the track sequence comprises at least one piece of track data;
the filtering module is configured to filter the first data matrix based on a target time period to obtain a second data matrix;
and the visualization module is configured to combine the second data matrix with the map and the web visualization data to perform point location display, track editing, track playing and/or linkage interaction.
10. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-8.
11. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 8.
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| CN116776014B (en) * | 2023-07-10 | 2024-01-16 | 和智信(山东)大数据科技有限公司 | Multi-source track data representation method and device |
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