CN111966767A

CN111966767A - Track thermodynamic diagram generation method and device, electronic equipment and storage medium

Info

Publication number: CN111966767A
Application number: CN202010594681.4A
Authority: CN
Inventors: 万江燕
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2020-06-28
Filing date: 2020-06-28
Publication date: 2020-11-20
Anticipated expiration: 2040-06-28
Also published as: CN111966767B

Abstract

The application discloses a track thermodynamic diagram generation method and device, and relates to the field of intelligent transportation, the field of cloud computing and the field of big data. The specific implementation scheme is as follows: converting the geographical coordinates of the target track point to be processed currently to generate plane coordinates of the target track point; determining whether a coordinate difference value between a center point coordinate and a plane coordinate of each cluster in the currently established N clusters is larger than a preset distance threshold value; if the coordinate difference value between the center point coordinate and the plane coordinate of the Mth cluster in the N clusters is smaller than or equal to a preset distance threshold value, adding the target track point into the Mth cluster; if the coordinate difference value between the center point coordinate and the plane coordinate of each cluster in the N clusters is larger than a preset distance threshold value, establishing a new cluster taking the plane coordinate as the center point coordinate; and traversing all target track points to be processed, and generating a corresponding track thermodynamic diagram according to the track points contained in each cluster in all the established clusters.

Description

Track thermodynamic diagram generation method and device, electronic equipment and storage medium

Technical Field

Embodiments of the present application relate generally to the field of data processing technology, and more particularly, to the field of intelligent transportation, cloud computing, and big data.

Background

The space-time trajectory is a recorded sequence of the position and time of a moving object and comprises basic information such as time, position, speed and the like. With the rapid development of technologies such as mobile internet, positioning system and the like, in the application fields such as traffic, logistics and the like, a large amount of space-time Trajectory (Trajectory) data can be collected in time through an intelligent mobile terminal. As an important spatio-temporal object data type and information source, the spatio-temporal trajectory data contains rich knowledge, and the application range of the spatio-temporal trajectory data covers various aspects such as human behaviors, traffic and logistics, emergency evacuation management, animal habits, marketing and the like.

As for each vehicle terminal in the vehicle network, a ten-thousand-magnitude space-time track may be reported, and the space-time track may contain a large number of track points, so that a user can conveniently know the distribution condition of the track points reported by each vehicle terminal, and a track thermodynamic diagram can be provided for the user. Therefore, how to generate a trajectory thermodynamic diagram based on reported trace points becomes an urgent problem to be solved.

Disclosure of Invention

The application provides a track thermodynamic diagram generation method and device, an electronic device and a storage medium.

According to a first aspect, there is provided a trajectory thermodynamic diagram generation method, comprising:

converting the geographical coordinates of the current target track point to be processed to generate the plane coordinates of the target track point;

determining whether a coordinate difference value between a center point coordinate and the plane coordinate of each cluster in N currently established clusters is larger than a preset distance threshold, wherein N is larger than or equal to 1;

if the coordinate difference value between the center point coordinate of the Mth cluster in the N clusters and the plane coordinate is smaller than or equal to a preset distance threshold value, adding the target track point into the Mth cluster;

if the coordinate difference value between the center point coordinate of each cluster in the N clusters and the plane coordinate is larger than a preset distance threshold value, establishing a new cluster taking the plane coordinate as the center point coordinate;

and traversing all target track points to be processed, and generating a corresponding track thermodynamic diagram according to the track points contained in each cluster in all the established clusters.

According to a second aspect, there is provided a trajectory thermodynamic diagram generation apparatus comprising:

the plane coordinate generating module is used for converting the geographic coordinates of the target track point to be processed currently to generate the plane coordinates of the target track point;

the first determining module is used for determining whether a coordinate difference value between a center point coordinate and the plane coordinate of each cluster in the currently established N clusters is greater than a preset distance threshold, wherein N is greater than or equal to 1;

the adding module is used for adding the target track point to the Mth cluster when the coordinate difference value between the center point coordinate of the Mth cluster in the N clusters and the plane coordinate is smaller than or equal to a preset distance threshold value;

the establishing module is used for establishing a new cluster taking the plane coordinate as the center point coordinate when the coordinate difference value between the center point coordinate of each cluster in the N clusters and the plane coordinate is greater than a preset distance threshold;

and the track thermodynamic diagram generation module is used for traversing all target track points to be processed and generating a corresponding track thermodynamic diagram according to the track points contained in each cluster in all the established clusters.

According to a third aspect, there is provided an electronic device comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of generating a trajectory thermodynamic diagram as described in the embodiments of the first aspect above.

According to a fourth aspect, there is provided a non-transitory computer-readable storage medium having stored thereon computer instructions for causing a computer to execute the method for generating a trajectory thermodynamic diagram as described in the first aspect.

According to the technical scheme of the embodiment of the application, before all track points are clustered, the geographical coordinates of the track points are uniformly converted into plane coordinates, and only one-time coordinate conversion is needed for each track point, so that the time complexity is greatly reduced; in addition, in the embodiment of the present application, the coordinate difference between two points is calculated first, and the size of the coordinate difference is compared with the distance threshold, so as to determine whether the two points belong to the same cluster or not according to the size comparison result. Therefore, the distance operation is optimized compared with the traditional distance operation because a large number of floating point operations exist in the distance operation, so that the calculation of a large number of floating point numbers can be greatly reduced, the use of calculation resources is reduced, and the calculation performance is improved.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:

fig. 1 is a flowchart of a trajectory thermodynamic diagram generation method provided according to an embodiment of the present application;

FIG. 2 is a flow chart of another trajectory thermodynamic diagram generation method provided in accordance with an embodiment of the present application;

FIG. 3 is a flow chart of yet another trajectory thermodynamic diagram generation method provided in accordance with an embodiment of the present application;

FIG. 4 is a flow chart of another trajectory thermodynamic diagram generation method provided in accordance with an embodiment of the present application;

FIG. 5 is a flow chart of yet another trajectory thermodynamic diagram generation method provided in accordance with an embodiment of the present application;

fig. 6 is a block diagram of a trajectory thermodynamic diagram generation apparatus according to an embodiment of the present application;

fig. 7 is a block diagram of another trajectory thermodynamic diagram generation apparatus according to an embodiment of the present application;

fig. 8 is a block diagram of an electronic device for implementing a trajectory thermodynamic diagram generation method according to an embodiment of the present application.

Detailed Description

The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

FIG. 1 is a flow chart of a trajectory thermodynamic diagram generation method according to one embodiment of the present application. It should be noted that the trajectory thermodynamic diagram generation method according to the embodiment of the present application is applicable to the trajectory thermodynamic diagram generation device according to the embodiment of the present application, and the trajectory thermodynamic diagram generation device may be configured in an electronic device. Wherein the electronic device may be a server. As an example, the steps included in the trajectory thermodynamic diagram generation method according to the embodiment of the present application may be executed at a cloud server, so that the cloud server generates the trajectory thermodynamic diagram through a cloud computing function.

As shown in fig. 1, the trajectory thermodynamic diagram generation method may include:

step 101, converting the geographical coordinates of the target track point to be processed currently to generate a plane coordinate of the target track point.

It should be noted that the target trace point to be currently processed may be a trace point reported by the terminal device. In some embodiments of the application, the geographical coordinates of a plurality of target track points reported by the terminal device are obtained, and the geographical coordinates of the target track points to be processed at present are converted according to the mercator coordinate transformation model to generate the plane coordinates of the target track points. As a possible implementation, the terminal device may be a vehicle terminal device in a vehicle network. The vehicle terminal equipment can report track points recorded by the navigation system to the cloud server, so that the cloud server processes all the track points reported by the vehicle terminal equipment by utilizing cloud computing and big data processing technologies to generate a track thermodynamic diagram.

In the embodiment of the application, after the geographical coordinates of the plurality of target track points reported by the terminal device are obtained, the geographical coordinates of the target track points to be processed currently can be obtained, wherein the geographical coordinates can be longitude and latitude coordinates. And then, coordinate conversion can be carried out on the longitude and latitude coordinates of the current target track point to be processed by using the mercator coordinate conversion model so as to obtain the plane coordinates of the target track point. In the embodiment of the present application, the mercator coordinate transformation model may be represented as follows:

wherein x is the abscissa in the plane coordinate, y is the ordinate in the plane coordinate, longitudec is the longitude coordinate of the target track point, and latitude is the latitude coordinate of the target track point.

That is, the above formula (1) can be used to perform coordinate transformation on the longitude and latitude coordinates of the current target track point to be processed, so as to obtain the plane coordinates (x, y) of the target track point.

Step 102, determining whether a coordinate difference value between a center point coordinate and a plane coordinate of each cluster in the currently established N clusters is greater than a preset distance threshold, wherein N is greater than or equal to 1.

It should be noted that, in the present application, after obtaining the geographic coordinates of the target track points reported by the terminal device, when clustering processing is performed on the track points to be processed currently, the geographic coordinates of the track points to be processed currently are converted into plane coordinates, so that the coordinates of the cluster center point in the cluster established currently are plane coordinates, not geographic coordinates. That is to say, the coordinates of the points in each cluster in the currently established N clusters are all plane coordinates, and the coordinates of the central point of each cluster are also plane coordinates, so that only one time of coordinate transformation needs to be performed on the geographical coordinates of the currently classified target track points, the times of coordinate transformation of the track points can be greatly reduced, and only one time of coordinate transformation needs to be performed on the coordinates of each point, so that the time complexity of the coordinate transformation stage is controlled within o (N), wherein N is the total number of the target track points. In the conventional track point distance clustering, the geographic coordinate of the current target track point to be processed is usually converted into a planar coordinate, the geographic coordinate of the central point of the current cluster is converted into a planar coordinate, and then the distance between the current target track point to be processed and the central point of the current cluster is calculated, so that the classification of the current target track point to be processed is realized. Therefore, the geographical coordinates of the track points are uniformly converted into plane coordinates before all the track points are clustered, and only one-time coordinate conversion is needed for each track point, so that the time complexity is greatly reduced.

In step S102, a coordinate difference between the center point coordinate of each cluster of the currently established N clusters and the plane coordinate of the current target track point to be processed is calculated, and then, the coordinate difference between the center point coordinate of each cluster and the plane coordinate of the current target track point to be processed may be compared with a preset distance threshold. For example, assuming that there are L currently established clusters, a coordinate difference between a center point coordinate of each of the L clusters and a plane coordinate of a current target trace point to be processed may be calculated, and based on the coordinate difference and a preset distance threshold, it is determined whether the current target trace point to be processed is added to one of the L clusters or a new cluster with the target trace point as a center point needs to be established.

It can be understood that the conventional clustering algorithm utilizes the euclidean DISTANCE between two points to perform clustering, for example, the euclidean DISTANCE between two points is calculated, and the euclidean DISTANCE between the two points is compared with a preset DISTANCE threshold, if the euclidean DISTANCE is less than or equal to the DISTANCE threshold, the two points are clustered into one class, and if the euclidean DISTANCE is greater than the DISTANCE threshold, the two points are considered to belong to different classes. However, in the present application, compared with the conventional clustering algorithm, the same DISTANCE threshold value DISTANCE is adopted for DISTANCE judgment, but the present application firstly calculates the coordinate difference between two points, compares the coordinate difference with the DISTANCE threshold value, and then judges whether the two points belong to the same cluster or not according to the size comparison result. Therefore, the distance operation is optimized compared with the traditional distance operation because a large number of floating point operations exist in the distance operation, so that the calculation of a large number of floating point numbers can be greatly reduced, the use of calculation resources is reduced, and the calculation performance is improved. For a specific implementation manner of the optimized distance operation, reference may be made to the description of the subsequent embodiments.

And 103, if the coordinate difference value between the center point coordinate and the plane coordinate of the Mth cluster in the N clusters is smaller than or equal to a preset distance threshold, adding the target track point into the Mth cluster.

And 104, if the coordinate difference value between the central point coordinate and the plane coordinate of each cluster in the N clusters is larger than a preset distance threshold value, establishing a new cluster taking the plane coordinate of the target track point as the central point coordinate.

And 105, traversing all target track points to be processed, and generating a corresponding track thermodynamic diagram according to the track points contained in each cluster in all the established clusters.

Optionally, after traversing all target trace points to be processed, all the target trace points to be processed are placed into the clusters, at this time, the number of trace points included in each cluster in all the current clusters can be counted, and a trace thermodynamic diagram is generated according to the number of trace points included in each cluster in all the current clusters. Therefore, the target track points reported by the vehicle terminals are processed to form a thermodynamic diagram with the track points reported by all the vehicle terminals, and the thermodynamic diagram is provided for the user, so that the user can know the distribution condition of the track points of all the vehicle terminals, the user can conveniently analyze the track of the vehicle terminals according to the thermodynamic diagram, and data analysis support is provided for intelligent traffic.

According to the track thermodynamic diagram generation method, before all track points are clustered, the geographical coordinates of the track points are uniformly converted into plane coordinates, and only one-time coordinate conversion is needed for each track point, so that the time complexity is greatly reduced; in addition, in the embodiment of the present application, the coordinate difference between two points is calculated first, and the size of the coordinate difference is compared with the distance threshold, so as to determine whether the two points belong to the same cluster or not according to the size comparison result. Therefore, the distance operation is optimized compared with the traditional distance operation because a large number of floating point operations exist in the distance operation, so that the calculation of a large number of floating point numbers can be greatly reduced, the use of calculation resources is reduced, and the calculation performance is improved.

It should be noted that, the distance operation in the embodiment of the present application may be optimized as follows:

the traditional clustering algorithm is to use the euclidean DISTANCE between two points to realize clustering, for example, calculate the euclidean DISTANCE between two points, and compare the euclidean DISTANCE between the two points with a preset DISTANCE threshold, if the euclidean DISTANCE is less than or equal to the DISTANCE threshold DISTANCE, the two points are clustered into one class, and if the euclidean DISTANCE is greater than the DISTANCE threshold DISTANCE, the two points are considered to belong to different classes. Wherein the Euclidean distance between two points is

Where Δ x is a difference between horizontal coordinates of two points, Δ y is a difference between vertical coordinates of two points, and the DISTANCE threshold of the cluster (i.e., the preset DISTANCE threshold) is DISTANCE. The application optimizes through algebraic operation, can greatly reduce distance calculation, and can avoid complex operation of floating point numbers under the following conditions:

case 1:

due to the existence of

If Δ x>DISTANCE, then derive

It follows that, as long as Δ x is present>DISTANCE, then

Also, since the distance calculation can be optimized from the euclidean distance calculation to the abscissa difference calculation, the distance calculation can be reduced;

case 2: ,

due to the existence of

If Δ y>DISTANCE, then derive

It follows that as long as Δ y is present>DISTANCE, then

Also, since the distance calculation can be optimized from the euclidean distance calculation to the ordinate difference calculation, the distance calculation can be reduced;

case 3:

due to the existence of

If Δ x + Δ y<DISTANCE, then can derive

It follows that as long as there is Δ x + Δ y<DISTANCE, then

The same holds true, so that the distance calculation can be optimized from Euclidean distance calculation to the sum of the differences of the horizontal coordinate difference and the vertical coordinate difference, and the distance calculation can be reduced;

case 4:

due to the existence of

If it is not

Then it can be deduced

It follows that, if any

Then it is inevitable that

This is true, so optimizing the distance calculation from the euclidean distance calculation to the sum of the differences between the abscissa and ordinate differences can reduce the distance calculation.

Therefore, through the optimization, the complex floating-point number operation is converted into simple operation, and the complex floating-point number operation can be reduced by 99% in practice. According to the method and the device, distance calculation is optimized from traditional Euclidean distance calculation to coordinate difference calculation, so that the adopted distance comparison mode is also optimized when whether the current target track point to be processed can be added into the current existing cluster is judged. The following will be described with respect to the above-described case 1, case 2, case 3, and case 4, respectively.

In some embodiments of the present application, as shown in fig. 2, the specific implementation process of determining whether a coordinate difference between a center point coordinate and a plane coordinate of each of the currently established N clusters is greater than a preset distance threshold may include:

step 201, acquiring a horizontal coordinate difference value between a center point coordinate and a plane coordinate of a current cluster to be processed;

for example, after obtaining the plane coordinates of the current target track point to be processed, the distance between the target track point and the center point of the mth cluster among the current N clusters may be calculated, that is, the difference Δ x between the plane coordinates of the target track point and the center point coordinates of the mth cluster may be calculated.

Step 202, comparing the horizontal coordinate difference value with a preset first distance threshold value;

optionally, a magnitude of a horizontal coordinate difference Δ x between the plane coordinate of the target trace point and the center point coordinate of the mth cluster is compared with a preset first DISTANCE threshold value DISTANCE.

In step 203, if the horizontal coordinate difference is greater than the first distance threshold, it is determined that the coordinate difference is greater than a preset distance threshold.

That is, when the abscissa difference Δ x between the plane coordinate of the target track point and the center point coordinate of the mth cluster is greater than the first DISTANCE threshold value DISTANCE, it may be determined that the coordinate difference between the plane coordinate of the target track point and the center point coordinate of the mth cluster is greater than the preset DISTANCE threshold value DISTANCE, that is, the preset DISTANCE threshold value DISTANCE and the first DISTANCE threshold value DISTANCE are the same value, at this time, it may be considered that the target track point cannot be added to the mth cluster, at this time, the center point coordinate of the M +1 cluster among the current N clusters may be obtained, the abscissa difference Δ x between the plane coordinate of the target track point and the center point coordinate of the M +1 cluster may be calculated, the abscissa difference Δ x may be compared with the preset first DISTANCE threshold value, if the abscissa difference Δ x is greater than the preset DISTANCE threshold value DISTANCE, it may be determined that the plane coordinate of the target track point and the coordinate of the center point of the M +1 cluster are greater than the preset DISTANCE threshold value DISTANCE, and the abscissa difference Δ x may be determined that the plane coordinate of the target track point is greater than the And if the standard deviation value is larger than a preset distance threshold value, continuously acquiring the coordinates of the central points of the M +2 th cluster from the current N clusters until the target track point and the central points of all the current clusters are subjected to distance calculation, and the difference value delta x of the horizontal coordinates between the plane coordinates of the target track point and the coordinates of the central points of all the current clusters is larger than a first distance threshold value, establishing a new cluster taking the plane coordinates of the target track point as the coordinates of the central points.

It can be seen that, through the optimization of the above-mentioned case 1, the distance calculation is optimized from the euclidean distance calculation to the abscissa difference calculation, and through the fact that the abscissa differences between the plane coordinates of the target trace point and the coordinates of the central points of all current clusters are all greater than the first distance threshold, the target trace point is considered to be an isolated point with respect to all current clusters, and at this time, a new cluster can be established with the target trace point as the central point.

In some embodiments of the present application, as shown in fig. 3, the specific implementation process of determining that a coordinate difference between a center point coordinate and a plane coordinate of each of N currently established clusters is greater than a preset distance threshold may include:

301, acquiring a vertical coordinate difference value between a center point coordinate and a plane coordinate of a current cluster to be processed;

for example, after obtaining the plane coordinates of the current target track point to be processed, the distance between the target track point and the center point of the mth cluster among the current N clusters may be calculated, that is, the difference Δ y between the plane coordinates of the target track point and the center point coordinates of the mth cluster may be calculated.

Step 302, comparing the difference value of the longitudinal coordinates with a preset first distance threshold value;

optionally, a magnitude of a vertical coordinate difference Δ y between the plane coordinate of the target trace point and the center point coordinate of the mth cluster is compared with a preset first DISTANCE threshold value DISTANCE.

Step 303, if the vertical coordinate difference is greater than the first distance threshold, determining that the coordinate difference is greater than a preset distance threshold.

That is, when a difference Δ y between the plane coordinate of the target track point and the center point coordinate of the mth cluster is greater than a first DISTANCE threshold value DISTANCE, it may be determined that a difference between the plane coordinate of the target track point and the center point coordinate of the mth cluster is greater than a preset DISTANCE threshold value DISTANCE, that is, the preset DISTANCE threshold value DISTANCE is the same as the first DISTANCE threshold value DISTANCE, it may be considered that the target track point cannot be added to the mth cluster, at this time, the center point coordinate of the M +1 cluster among the current N clusters may be obtained, a difference Δ y between the plane coordinate of the target track point and the center point coordinate of the M +1 cluster may be calculated, the difference Δ y may be compared with the preset first DISTANCE threshold value, if the difference Δ y is greater than the preset DISTANCE threshold value DISTANCE, it may be determined that the plane coordinate of the target track point and the center point coordinate of the M +1 cluster are located, the difference Δ y may be determined that the difference Δ y is greater than the preset DISTANCE threshold value DISTANCE between the plane coordinate of the target track point and the M +1 cluster And if the standard deviation value is larger than a preset distance threshold value, continuously acquiring the coordinates of the central points of the M +2 th cluster from the current N clusters until the target track point and the central points of all the current clusters are subjected to distance calculation, and the difference value delta y of the vertical coordinates between the plane coordinates of the target track point and the coordinates of the central points of all the current clusters is larger than a first distance threshold value, establishing a new cluster taking the plane coordinates of the target track point as the coordinates of the central points.

It can be seen that, through the optimization of the above case 2, the distance calculation is optimized from the euclidean distance calculation to the vertical coordinate difference calculation, and the vertical coordinate differences between the plane coordinates of the target track point and the coordinates of the central points of all current clusters are all greater than the first distance threshold, so that the target track point is considered to be an isolated point with respect to all current clusters, and at this time, a new cluster can be established with the target track point as the central point.

In some embodiments of the present application, as shown in fig. 4, the specific implementation process of determining that a coordinate difference between a center point coordinate and a plane coordinate of each of N currently established clusters is less than or equal to a preset distance threshold may include:

step 401, acquiring a horizontal coordinate difference value and a vertical coordinate difference value between a center point coordinate and a plane coordinate of a current cluster to be processed;

for example, after obtaining the plane coordinates of the current target track point to be processed, the distance between the target track point and the center point of the mth cluster among the current N clusters may be calculated, that is, the horizontal coordinate difference Δ x and the vertical coordinate difference Δ y between the plane coordinates of the target track point and the center point coordinates of the mth cluster may be calculated.

Step 402, adding the horizontal coordinate difference value and the vertical coordinate difference value to obtain a difference sum, and comparing the difference sum with a preset first distance threshold;

optionally, summing up a horizontal coordinate difference Δ x and a vertical coordinate difference Δ y between the plane coordinate of the target track point and the center point coordinate of the mth cluster to obtain a difference sum Δ x + Δ y, and comparing the difference sum Δ x + Δ y with the first DISTANCE threshold value DISTANCE.

In step 403, if the sum of the differences is less than or equal to the first distance threshold, it is determined that the coordinate difference is less than or equal to a preset distance threshold.

That is, when the sum Δ x + Δ y of the differences between the plane coordinates of the target track point and the center point coordinates of the mth cluster is less than or equal to the first DISTANCE threshold DISTANCE, it may be determined that the coordinate difference between the plane coordinates of the target track point and the center point coordinates of the mth cluster is less than or equal to the preset DISTANCE threshold DISTANCE, at this time, the target track point may be considered to satisfy the clustering condition, the target track point and the point in the mth cluster belong to the same class, and at this time, the target track point may be added to the mth cluster.

It can be understood that, if the sum Δ x + Δ y of the differences between the plane coordinates of the target track point and the center point coordinates of the mth cluster is greater than the first DISTANCE threshold value DISTANCE, the center point coordinates of the M +1 th cluster of the N clusters may be obtained, the sum of the differences between the plane coordinates of the target track point and the center point coordinates of the M +1 th cluster may be calculated, the sum of the differences is compared with the first DISTANCE threshold value, if the sum of the differences is less than or equal to the first DISTANCE threshold value DISTANCE, the target track point may be added to the M +1 th cluster, otherwise, the center point coordinates of the next cluster may be obtained from the N clusters, and the DISTANCE calculation between the target track point and the center point of the next cluster may be performed.

It can be seen that, through the optimization of the above case 3, the distance calculation is optimized from the euclidean distance calculation to the difference sum calculation, and through that the difference sum between the plane coordinate of the target track point and the center point coordinate of the current cluster is smaller than the first distance threshold, it can be considered that the target track point and the point in the current cluster belong to the same class, and at this time, the target track point is added to the current cluster to complete the classification of the target track point. Therefore, the distance calculation is optimized from the Euclidean distance calculation to the difference sum calculation, and most of the distance calculation between two points is filtered by judging the difference sum between the two points in advance, so that the floating point number calculation is greatly reduced.

In some embodiments of the present application, as shown in fig. 5, the specific implementation process of determining whether a coordinate difference between a center point coordinate and a plane coordinate of each of the currently established N clusters is greater than a preset distance threshold may include:

step 501, acquiring a horizontal coordinate difference value and a vertical coordinate difference value between a center point coordinate and a plane coordinate of a current cluster to be processed;

Step 502, adding the horizontal coordinate difference value and the vertical coordinate difference value to obtain a difference sum, and comparing the difference sum with a preset second distance threshold; wherein the second distance threshold is less than the first distance threshold;

optionally, summing up a horizontal coordinate difference value Δ x and a vertical coordinate difference value Δ y between the plane coordinate of the target track point and the center point coordinate of the mth cluster to obtain a difference sum Δ x + Δ y, and comparing the difference sum Δ x + Δ y with a second distance threshold. Wherein the second distance threshold may be

In step 503, if the sum of the differences is greater than the second distance threshold, it is determined that the coordinate difference is greater than the preset distance threshold.

That is, when the sum Δ x + Δ y of the differences between the plane coordinates of the target trace point and the coordinates of the center point of the mth cluster is greater than the second distance threshold

Determining that the coordinate difference between the plane coordinate of the target track point and the center point coordinate of the mth cluster is greater than a preset DISTANCE threshold value DISTANCE, at this time, it may be considered that the target track point cannot be added to the mth cluster, at this time, the center point coordinate of the M +1 th cluster of the current N clusters may be obtained, and a sum Δ x + Δ y of differences between the plane coordinate of the target track point and the center point coordinate of the M +1 th cluster is calculated, and the sum Δ x + Δ y of differences is compared with a preset second DISTANCE threshold value, if the sum Δ x + Δ y of differences is greater than the second DISTANCE threshold value, it may be determined that the coordinate difference between the plane coordinate of the target track point and the center point coordinate of the M +1 th cluster is greater than the preset DISTANCE threshold value, at this time, the center point coordinate of the M +2 th cluster is continuously obtained from the current N clusters, and establishing a new cluster taking the plane coordinate of the target track point as the coordinate of the central point until the distance between the target track point and the central points of all the current clusters is calculated, and the sum delta x + delta y of the difference values between the plane coordinate of the target track point and the coordinates of the central points of all the current clusters is greater than a second distance threshold.

It can be seen that, through the optimization of the above-mentioned case 4, the distance calculation is optimized from the euclidean distance calculation to the difference sum calculation, and the sum of the differences between the plane coordinates of the target track point and the coordinates of the central points of all current clusters is greater than the second distance threshold, it can be considered that the target track point is an isolated point with respect to all current clusters, and at this time, a new cluster can be established with the target track point as the central point.

In some embodiments of the present application, if the abscissa difference is less than or equal to the first distance threshold, the ordinate difference is less than or equal to the first distance threshold, the difference sum is greater than the first distance threshold, and the difference sum is less than or equal to the second distance threshold, then calculating a euclidean distance value between the center point coordinate of the cluster currently to be processed and the plane coordinate according to the abscissa difference and the ordinate difference; and determining the Euclidean distance value as a coordinate difference value between the coordinate of the central point of the cluster to be processed currently and the plane coordinate.

For example, if the difference between the plane coordinate of the target track point and the coordinates of the center point of one of the current N clusters is smaller than or equal to the first distance threshold, the distance determination screening may be performed using the difference between the vertical coordinates, for example, if the difference between the plane coordinate of the target track point and the coordinates of the center point of one of the current N clusters is smaller than or equal to the first distance threshold, the distance determination screening may be continued using the sum of the differences, for example, if the sum of the differences between the plane coordinate of the target track point and the coordinates of the center points of all of the current N clusters is greater than the first distance threshold, the distance determination screening may be continued using the sum of the differences and the second distance threshold, for example, if the sum of the differences between the plane coordinate of the target track point and the coordinates of the center point of one of the current N clusters is smaller than or equal to the second distance threshold, then, at this time, it may be considered that the target trace point cannot be classified through the distance operation optimization, and at this time, the target trace point needs to be classified by using the euclidean distance determination condition, that is, the euclidean distance value between the target trace point and the center point of the current cluster to be processed needs to be calculated

If the Euclidean distance value is used

Determining the coordinate difference value of the coordinate of the central point of the cluster to be processed currently and the plane coordinate of the target track point, if the Euclidean distance value

Adding the target track point into the current cluster to be processed if the DISTANCE is less than or equal to the DISTANCE threshold DISTANCE, and if the Euclidean DISTANCE value is less than or equal to the Euclidean DISTANCE value

And if the DISTANCE is greater than the DISTANCE threshold DISTANCE, establishing a new cluster taking the plane coordinate of the target track point as the coordinate of the central point. Therefore, the present application filters most of the inter-two-point distance calculations by determining the abscissa distance, the ordinate distance, and the like between two points in advance, and if the inter-two-point distance calculation does not meet the filtering condition, the calculation of the euclidean distance value between two points is still performed. Therefore, by adding the filtering and screening conditions, the distance operation is optimized, the complex floating-point number operation is converted into simple operation, a large amount of floating-point number calculation is greatly reduced, the use of calculation resources is reduced, and the calculation performance is improved.

In order to realize the above embodiments, the present application also provides a trajectory thermodynamic diagram generation apparatus.

Fig. 6 is a block diagram of a trajectory thermodynamic diagram generation apparatus according to an embodiment of the present application. As shown in fig. 6, the trajectory thermodynamic diagram generating apparatus 600 may include: a plane coordinate generating module 601, a first determining module 602, an adding module 603, a building module 604, and a trajectory thermodynamic diagram generating module 605.

Specifically, the plane coordinate generating module 601 is configured to perform conversion processing on the geographic coordinates of the current target track point to be processed, and generate a plane coordinate of the target track point. In some embodiments of the present application, the plane coordinate generating module 601 obtains geographic coordinates of a plurality of target track points reported by the terminal device, and performs conversion processing on the geographic coordinates of the target track points to be processed currently according to the mercator coordinate transformation model to generate plane coordinates of the target track points.

The first determining module 602 is configured to determine whether a coordinate difference between a center point coordinate and a plane coordinate of each of N currently established clusters is greater than a preset distance threshold, where N is greater than or equal to 1.

The adding module 603 is configured to add the target track point to an mth cluster when a coordinate difference between a center point coordinate of the mth cluster among the N clusters and the plane coordinate is less than or equal to a preset distance threshold.

The establishing module 604 is configured to establish a new cluster using the plane coordinate as the center point coordinate when the coordinate difference between the center point coordinate and the plane coordinate of each of the N clusters is greater than a preset distance threshold.

The track thermodynamic diagram generating module 605 is configured to traverse all target track points to be processed, and generate a corresponding track thermodynamic diagram according to the track points included in each cluster in all the established clusters.

In some embodiments of the present application, the first determining module 602 is specifically configured to: acquiring a horizontal coordinate difference value between a center point coordinate and a plane coordinate of a current cluster to be processed; comparing the horizontal coordinate difference value with a preset first distance threshold value; and if the horizontal coordinate difference value is larger than the first distance threshold value, determining that the coordinate difference value is larger than a preset distance threshold value.

In some embodiments of the present application, the first determining module 602 is specifically configured to: acquiring a vertical coordinate difference value between a center point coordinate and a plane coordinate of a current cluster to be processed; comparing the difference value of the longitudinal coordinates with a preset first distance threshold value; and if the vertical coordinate difference value is larger than the first distance threshold value, determining that the coordinate difference value is larger than a preset distance threshold value.

In some embodiments of the present application, the first determining module 602 is specifically configured to: acquiring a horizontal coordinate difference value and a vertical coordinate difference value between a center point coordinate and a plane coordinate of a current cluster to be processed; adding the horizontal coordinate difference value and the vertical coordinate difference value to obtain a difference sum, and comparing the difference sum with a preset first distance threshold; and if the sum of the differences is smaller than or equal to the first distance threshold, determining that the coordinate difference is smaller than or equal to a preset distance threshold.

In some embodiments of the present application, the first determining module 602 is specifically configured to: acquiring a horizontal coordinate difference value and a vertical coordinate difference value between a center point coordinate and a plane coordinate of a current cluster to be processed; adding the horizontal coordinate difference value and the vertical coordinate difference value to obtain a difference sum, and comparing the difference sum with a preset second distance threshold; wherein the second distance threshold is less than the first distance threshold; and if the sum of the differences is greater than a second distance threshold, determining that the coordinate difference is greater than a preset distance threshold.

In some embodiments of the present application, as shown in fig. 7, the trajectory thermodynamic diagram generating apparatus 600 may further include: a calculation module 606 and a second determination module 607. The calculating module 606 is configured to calculate a euclidean distance value between the center point coordinate of the current cluster to be processed and the plane coordinate according to the horizontal coordinate difference value and the vertical coordinate difference value when the horizontal coordinate difference value is less than or equal to a first distance threshold value, the vertical coordinate difference value is less than or equal to a first distance threshold value, the sum of the difference values is greater than the first distance threshold value, and the sum of the difference values is less than or equal to a second distance threshold value; the second determining module 607 is configured to determine the euclidean distance value as a coordinate difference between the coordinates of the center point of the current cluster to be processed and the plane coordinates.

According to the track thermodynamic diagram generation device, before all track points are clustered, the geographical coordinates of the track points are uniformly converted into plane coordinates, and only one-time coordinate conversion is needed for each track point, so that the time complexity is greatly reduced; in addition, in the embodiment of the present application, the coordinate difference between two points is calculated first, and the size of the coordinate difference is compared with the distance threshold, so as to determine whether the two points belong to the same cluster or not according to the size comparison result. Therefore, the distance operation is optimized compared with the traditional distance operation because a large number of floating point operations exist in the distance operation, so that the calculation of a large number of floating point numbers can be greatly reduced, the use of calculation resources is reduced, and the calculation performance is improved.

According to an embodiment of the present application, an electronic device and a readable storage medium are also provided.

Fig. 8 is a block diagram of an electronic device to implement a trajectory thermodynamic diagram generation method according to an embodiment of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.

As shown in fig. 8, the electronic apparatus includes: one or more processors 801, memory 802, and interfaces for connecting the various components, including a high speed interface and a low speed interface. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). Fig. 8 illustrates an example of a processor 801.

The memory 802 is a non-transitory computer readable storage medium as provided herein. Wherein the memory stores instructions executable by at least one processor to cause the at least one processor to perform the trajectory thermodynamic diagram generation method provided herein. The non-transitory computer-readable storage medium of the present application stores computer instructions for causing a computer to perform the trajectory thermodynamic diagram generation method provided by the present application.

The memory 802 is a non-transitory computer readable storage medium, and can be used for storing non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the trajectory thermodynamic diagram generation method in the embodiment of the present application (for example, the plane coordinate generation module 601, the first determination module 602, the adding module 603, the establishing module 604, and the trajectory thermodynamic diagram generation module 605 shown in fig. 6). The processor 801 executes various functional applications of the server and data processing by running non-transitory software programs, instructions, and modules stored in the memory 802, that is, implements the trajectory thermodynamic diagram generation method in the above-described method embodiment.

The memory 802 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of an electronic device to implement the trajectory thermodynamic diagram generation method, and the like. Further, the memory 802 may include high speed random access memory and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 802 may optionally include memory located remotely from the processor 801, which may be connected via a network to an electronic device to implement the trajectory thermodynamic diagram generation method. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The electronic device to implement the trajectory thermodynamic diagram generation method may further include: an input device 803 and an output device 804. The processor 801, the memory 802, the input device 803, and the output device 804 may be connected by a bus or other means, and are exemplified by a bus in fig. 8.

The input device 803 may receive input numeric or character information and generate key signal inputs related to user settings and function control of an electronic apparatus to implement the trajectory thermodynamic diagram generation method, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointing stick, one or more mouse buttons, a track ball, a joystick, or the like. The output devices 804 may include a display device, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibrating motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.

Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and the present invention is not limited thereto as long as the desired results of the technical solutions disclosed in the present application can be achieved.

The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A trajectory thermodynamic diagram generation method, comprising:

2. The trajectory thermodynamic diagram generation method according to claim 1, wherein the converting the geographic coordinates of the target trajectory point to be processed currently to generate the plane coordinates of the target trajectory point includes:

acquiring geographic coordinates of a plurality of target track points reported by the terminal equipment;

and converting the geographical coordinates of the current target track point to be processed according to the mercator coordinate transformation model to generate the plane coordinates of the target track point.

3. The trajectory thermodynamic diagram generation method of claim 1, wherein determining that a coordinate difference between the center point coordinates and the plane coordinates of each of the currently established N clusters is greater than a preset distance threshold comprises:

acquiring a horizontal coordinate difference value between the coordinate of the central point of the cluster to be processed currently and the plane coordinate;

comparing the horizontal coordinate difference value with a preset first distance threshold value;

and if the horizontal coordinate difference value is larger than the first distance threshold value, determining that the coordinate difference value is larger than a preset distance threshold value.

4. The trajectory thermodynamic diagram generation method of claim 1, wherein determining that a coordinate difference between the center point coordinates and the plane coordinates of each of the currently established N clusters is greater than a preset distance threshold comprises:

acquiring a vertical coordinate difference value between the coordinate of the center point of the cluster to be processed currently and the plane coordinate;

comparing the difference value of the longitudinal coordinates with a preset first distance threshold value;

and if the vertical coordinate difference value is larger than the first distance threshold value, determining that the coordinate difference value is larger than a preset distance threshold value.

5. The trajectory thermodynamic diagram generation method of claim 1, wherein determining that a coordinate difference between the center point coordinates and the plane coordinates of each of the currently established N clusters is less than or equal to a preset distance threshold comprises:

acquiring a horizontal coordinate difference value and a vertical coordinate difference value between the coordinate of the center point of the cluster to be processed and the plane coordinate;

adding the horizontal coordinate difference value and the vertical coordinate difference value to obtain a difference sum, and comparing the difference sum with a preset first distance threshold;

and if the sum of the differences is smaller than or equal to the first distance threshold, determining that the coordinate difference is smaller than or equal to a preset distance threshold.

6. The trajectory thermodynamic diagram generation method of claim 1, wherein determining that a coordinate difference between the center point coordinates and the plane coordinates of each of the currently established N clusters is greater than a preset distance threshold comprises:

adding the horizontal coordinate difference value and the vertical coordinate difference value to obtain a difference sum, and comparing the difference sum with a preset second distance threshold; wherein the second distance threshold is less than the first distance threshold;

and if the sum of the differences is greater than the second distance threshold, determining that the coordinate difference is greater than a preset distance threshold.

7. The trajectory thermodynamic diagram generation method of any one of claims 3 to 6 further comprising:

if the horizontal coordinate difference is smaller than or equal to the first distance threshold, the vertical coordinate difference is smaller than or equal to the first distance threshold, the difference sum is larger than the first distance threshold, and the difference sum is smaller than or equal to the second distance threshold, calculating a Euclidean distance value between the center point coordinate of the cluster to be processed currently and the plane coordinate according to the horizontal coordinate difference and the vertical coordinate difference;

and determining the Euclidean distance value as a coordinate difference value between the coordinate of the central point of the cluster to be processed currently and the plane coordinate.

8. A trajectory thermodynamic diagram generation apparatus comprising:

9. The trajectory thermodynamic diagram generation device of claim 8, wherein the planar coordinate generation module is specifically configured to:

10. The trajectory thermodynamic diagram generation apparatus of claim 8, wherein the first determination module is specifically configured to:

11. The trajectory thermodynamic diagram generation apparatus of claim 8, wherein the first determination module is specifically configured to:

12. The trajectory thermodynamic diagram generation apparatus of claim 8, wherein the first determination module is specifically configured to:

13. The trajectory thermodynamic diagram generation apparatus of claim 8, wherein the first determination module is specifically configured to:

14. The trajectory thermodynamic diagram generating device as claimed in any one of claims 10 to 13, further comprising:

a calculating module, configured to calculate, when the abscissa difference is smaller than or equal to the first distance threshold, the ordinate difference is smaller than or equal to the first distance threshold, the difference sum is greater than the first distance threshold, and the difference sum is smaller than or equal to the second distance threshold, a euclidean distance value between the center point coordinate of the cluster to be currently processed and the plane coordinate according to the abscissa difference and the ordinate difference value;

and the second determining module is used for determining the Euclidean distance value as a coordinate difference value between the coordinate of the central point of the cluster to be processed currently and the plane coordinate.

15. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the trajectory thermodynamic diagram generation method of any one of claims 1 to 7.

16. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the trajectory thermodynamic diagram generation method of any one of claims 1 to 7.