CN113763509A

CN113763509A - Trace graph drawing method and device

Info

Publication number: CN113763509A
Application number: CN202111050726.2A
Authority: CN
Inventors: 刘亮; 肖伟; 赵耀; 丁一珊
Original assignee: 3Clear Technology Co Ltd
Current assignee: 3Clear Technology Co Ltd
Priority date: 2021-09-08
Filing date: 2021-09-08
Publication date: 2021-12-07
Anticipated expiration: 2041-09-08
Also published as: CN113763509B

Abstract

The disclosure provides a track graph drawing method and device, and belongs to the field of environmental science. The method comprises the following steps: receiving a backward trajectory drawing request, the backward trajectory drawing request including an identification of at least one target pollutant; determining a plurality of backward trajectory point data according to the backward trajectory drawing request, wherein the backward trajectory point data comprises a pollutant concentration value of the at least one target pollutant; and drawing a backward trace graph according to the plurality of backward trace point data, wherein the backward trace graph is used for representing the transmission path and the concentration change condition of the at least one target pollutant in a past preset time period. By adopting the method and the device, the efficiency of checking the air quality pollution condition can be improved.

Description

Trace graph drawing method and device

Technical Field

The invention relates to the field of environmental science, in particular to a method and a device for drawing a track graph.

Background

The backward trajectory graph in air quality prediction may be used to represent the transmission path of an air mass over a period of time.

Currently, a Hybrid Single-Particle Lagrangian Integrated Trajectory model (Hybrid Single-Particle Lagrangian Integrated Trajectory model) of the National Oceanic and Atmospheric Administration (NOAA) is commonly used to generate a backward Trajectory map, and the model uses a Lagrangian method to calculate backward transmission Trajectory points of an air mass. Assuming that a particle in air flutters with the wind, its motion trajectory is the integral of its vector in time and space.

However, the backward trajectory graph drawn by the HYSPLIT model is only a connecting line of all trajectory points, and cannot represent the air quality pollution condition.

Disclosure of Invention

In order to solve the problems in the prior art, embodiments of the present invention provide a method and an apparatus for drawing a track map. The technical scheme is as follows:

according to an aspect of the present disclosure, there is provided a trajectory graph drawing method, the method including:

receiving a backward trajectory drawing request, the backward trajectory drawing request including an identification of at least one target pollutant;

determining a plurality of backward trajectory point data according to the backward trajectory drawing request, wherein the backward trajectory point data comprises a pollutant concentration value of the at least one target pollutant;

and drawing a backward trace graph according to the plurality of backward trace point data, wherein the backward trace graph is used for representing the transmission path and the concentration change condition of the at least one target pollutant in a past preset time period.

Optionally, the backward trajectory drawing request further includes a target trajectory point;

determining a plurality of backward trajectory point data according to the backward trajectory drawing request, including:

acquiring backward track point data corresponding to the target track point according to the backward track drawing request;

determining at least one backtracking backward track point data according to the backward track point data corresponding to the target track point;

the drawing of the backward track graph according to the plurality of backward track point data comprises the following steps:

and taking the target track point as an end position, and drawing a backward track graph according to backward track point data corresponding to the target track point and the at least one backtraced backward track point data.

Optionally, the backward trajectory point data further includes a wind speed vector;

the step of determining at least one backtracking backward track point data according to the backward track point data corresponding to the target track point comprises the following steps:

determining backtracking backward track points according to the target track points, the opposite vectors of the wind speed vectors of the target track points and a preset time interval, and acquiring backward track point data corresponding to the backtracking backward track points;

and repeating the steps for each backward track point in the preset time period, and determining at least one backtracking backward track point data.

Optionally, the method for acquiring backward trace point data includes:

determining a target grid of the backward track points to be acquired in an environmental air quality numerical prediction mode;

acquiring wind speed vectors of all vertexes of the target grid at the current moment and a pollutant concentration value of the at least one target pollutant;

determining the wind speed vector of the backward track point to be acquired according to the wind speed vector of each vertex of the target grid;

and determining the pollutant concentration value of the at least one target pollutant of the backward tracing point to be acquired according to the pollutant concentration value of the at least one target pollutant of each vertex of the target grid.

Optionally, the determining the wind speed vector of the backward track point to be acquired according to the wind speed vector of each vertex of the target mesh includes:

determining the wind speed vector of the backward track point to be acquired according to the wind speed vector of each vertex of the target grid based on a bilinear interpolation algorithm;

the determining the pollutant concentration value of the at least one target pollutant of the backward tracing point to be acquired according to the pollutant concentration value of the at least one target pollutant of each vertex of the target grid includes:

and determining the pollutant concentration value of the at least one target pollutant of the backward track point to be acquired according to the pollutant concentration value of the at least one target pollutant of each vertex of the target grid based on a bilinear interpolation algorithm.

Optionally, the method further includes:

determining at least one supplementary backward track point data between adjacent backward track points according to the adjacent backward track points;

and taking the target track point as an end position, and drawing a backward track graph according to backward track point data corresponding to the target track point, the at least one backtracking backward track point data and the at least one supplementary backward track point data.

Optionally, the determining, according to the adjacent backward trajectory points, at least one supplementary backward trajectory point data between the adjacent backward trajectory points includes:

constructing a corresponding third-order Bezier curve according to adjacent backward track points, and determining at least one complementary backward track point on the third-order Bezier curve;

determining a contaminant concentration value of the at least one target contaminant of the at least one supplemental rearward trajectory point based on the contaminant concentration value of the at least one target contaminant of the adjacent rearward trajectory point.

Optionally, the determining a contaminant concentration value of the at least one target contaminant of the at least one complementary backward trajectory point according to the contaminant concentration value of the at least one target contaminant of the adjacent backward trajectory point includes:

and determining the pollutant concentration value of the at least one target pollutant of the at least one supplementary backward track point according to the pollutant concentration value of the at least one target pollutant of the adjacent backward track points based on a linear interpolation algorithm.

Optionally, the drawing a backward trajectory graph according to the plurality of backward trajectory point data includes:

determining the color of each backward trajectory point according to the pollutant concentration value of the at least one target pollutant in the plurality of backward trajectory point data, wherein the color of each backward trajectory point is used for expressing the pollutant concentration value of the at least one target pollutant;

and rendering according to the color of each backward track point, and drawing a backward track graph.

Optionally, the method further includes:

and drawing an arrow on each backward track point of the backward track graph, wherein the pointing direction of the arrow is used for representing the wind direction, and the size of the arrow is proportional to the size of the wind speed.

According to another aspect of the present disclosure, there is provided a trajectory graph drawing apparatus including:

a receiving module for receiving a backward trajectory drawing request, the backward trajectory drawing request including an identification of at least one target pollutant;

a determining module, configured to determine, according to the backward trajectory drawing request, a plurality of backward trajectory point data, where the backward trajectory point data includes a contaminant concentration value of the at least one target contaminant;

and the drawing module is used for drawing a backward track graph according to the plurality of backward track point data, and the backward track graph is used for representing the transmission path and the concentration change condition of the at least one target pollutant in a past preset time period.

the determination module is to: acquiring backward track point data corresponding to the target track point according to the backward track drawing request; determining at least one backtracking backward track point data according to the backward track point data corresponding to the target track point;

the rendering module is to: and taking the target track point as an end position, and drawing a backward track graph according to backward track point data corresponding to the target track point and the at least one backtraced backward track point data.

the determination module is to:

Optionally, the determining module is configured to:

Optionally, the determining module is further configured to: determining at least one supplementary backward track point data between adjacent backward track points according to the adjacent backward track points;

the rendering module is to: and taking the target track point as an end position, and drawing a backward track graph according to backward track point data corresponding to the target track point, the at least one backtracking backward track point data and the at least one supplementary backward track point data.

Optionally, the determining module is configured to:

Optionally, the rendering module is configured to:

Optionally, the rendering module is further configured to:

According to another aspect of the present disclosure, there is provided an electronic device including:

a processor; and

a memory for storing a program, wherein the program is stored in the memory,

wherein the program comprises instructions which, when executed by the processor, cause the processor to perform the above-described trajectory mapping method.

According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the above-described trajectory map drawing method.

In the embodiment of the disclosure, after the backward trajectory drawing request is received, a plurality of backward trajectory point data are determined according to the backward trajectory drawing request, and then a backward trajectory graph is drawn according to the plurality of backward trajectory point data. Since the backward trace point data includes the pollutant concentration value of the target pollutant, the backward trace graph can be used for representing the transmission path and the concentration change condition of the target pollutant in the past preset time period. Through the trace graph drawing method provided by the embodiment of the disclosure, the drawn backward trace graph can intuitively show the concentration change condition of the target pollutant in the transmission path, and is not limited to the connecting line of the trace points, so that the efficiency of checking the air quality pollution condition can be improved by the embodiment of the disclosure.

Drawings

Further details, features and advantages of the disclosure are disclosed in the following description of exemplary embodiments, taken in conjunction with the accompanying drawings, in which:

FIG. 1 shows a flowchart of a trajectory graph drawing method according to an exemplary embodiment of the present disclosure;

FIG. 2 illustrates a flow diagram for obtaining backward track point data according to an exemplary embodiment of the present disclosure;

FIG. 3 shows an ambient air quality numerical prediction mode grid schematic in accordance with an exemplary embodiment of the present disclosure;

FIG. 4 shows a target grid schematic in accordance with an exemplary embodiment of the present disclosure;

fig. 5 shows a third order bezier curve schematic according to an exemplary embodiment of the present disclosure;

FIG. 6 shows a backward trajectory diagram according to an exemplary embodiment of the present disclosure;

FIG. 7 shows a backward trajectory diagram according to an exemplary embodiment of the present disclosure;

FIG. 8 shows a graphical illustration of color versus contaminant concentration values according to an example embodiment of the present disclosure;

FIG. 9 shows a backward trajectory diagram according to an exemplary embodiment of the present disclosure;

FIG. 10 shows a schematic block diagram of a trajectory mapping device according to an exemplary embodiment of the present disclosure;

FIG. 11 illustrates a block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure.

Detailed Description

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.

It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.

The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description. It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.

It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.

The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.

In order to clearly describe the methods provided by the embodiments of the present disclosure, the following description refers to the terms used.

An ambient air quality numerical prediction mode: based on the basic physical and chemical principles in the process of forming the atmospheric pollutants, the system simulates the physical and chemical processes of emission, diffusion, transportation, chemical reaction, removal and the like of the atmospheric pollutants by adopting a numerical calculation method, thereby predicting the air quality condition.

Forecasting the environmental air quality numerical value: the method is characterized in that an environmental air quality numerical prediction mode is utilized to predict the concentration and the temporal-spatial variation of main pollutants in the atmosphere, predict the environmental air quality conditions and the potential pollution processes of cities, areas and the like, provide guidance and service for the daily life and production activities of the public and provide scientific basis for management departments to take countermeasures.

Third order bezier curve: the Bessel curve is invented by Pierre Bezier, a French mathematician, lays a foundation for computer vector graphics, and has the main significance that the Bessel curve can be described mathematically no matter whether the Bessel curve is a straight line or a curve. Wherein a third order bezier curve may be used to delineate the curve profile.

And (3) linear interpolation algorithm: the interpolation function is an interpolation mode of a first-order polynomial, and the interpolation error of the interpolation function on an interpolation node is zero.

Bilinear interpolation algorithm: the linear interpolation extension of the interpolation function with two variables has the core idea that linear interpolation is respectively carried out in two directions.

The embodiment of the disclosure provides a track graph drawing method, which can be completed by a terminal, a server and/or other equipment with processing capacity. The method provided by the embodiment of the present disclosure may be completed by any one of the above devices, or may be completed by a plurality of devices together, which is not limited in the present disclosure.

Taking a server as an example, the track graph drawing method according to the embodiment of the present disclosure will be described below with reference to a flowchart of the track graph drawing method shown in fig. 1.

Step 101, the server receives a backward trajectory drawing request.

Wherein the backward trajectory drawing request may include at least one or more of: at least one target pollutant identification, target track point, backward track identification, and target time point.

The target pollutant may include fine Particulate Matter (PM)_2.5) Inhalable Particles (PM)₁₀) Sulfur dioxide (SO)₂) Nitrogen dioxide (NO)₂) Carbon monoxide (CO), ozone (O)₃). The embodiments of the present disclosure are not limited to specific target contaminants.

The target track point may be a geographical location coordinate of the target location for representing the termination location of the backward track graph.

The identification of the backward trace may be used to indicate the request type. When the server receives a request containing the identification of the backward track, the track graph drawing method provided by the embodiment of the disclosure can be executed.

The target time point may be used to represent the termination time of the backward trajectory graph.

In the embodiment of the present disclosure, the backward trace graph may be used to represent a transmission path and a concentration change condition of at least one target pollutant within a past preset time period.

The situation that the server receives the backward trajectory drawing request may include the following three situations:

firstly, a terminal sends a backward track drawing request triggered by a user to a server.

In one possible implementation, the user may set a target pollutant, a target location, and a target time point to be queried on the terminal, choose to draw a forward trace graph or a backward trace graph, and click on a confirmation query option on the terminal. And then, the terminal can integrate the parameters set by the user to generate a corresponding track drawing request, when the user inquires the backward track graph, the terminal generates the backward track drawing request, and sends the backward track drawing request to the server. In this case, the server may respond to the request of the terminal to satisfy the query requirement of the user.

Secondly, the terminal periodically sends a backward track drawing request to the server.

In a possible implementation manner, a user may preset a target pollutant and a target location to be queried on the terminal, and set a drawing backward trajectory diagram. And when the preset period is reached, the terminal can acquire the current time as a target time point, generate a backward trajectory drawing request according to the preset parameters and the current time, and send the backward trajectory drawing request to the server. In this case, the terminal may periodically update the backward trajectory diagram so that the user views the updated backward trajectory diagram in real time. The preset period may be set by a user, which is not limited in the embodiment of the present disclosure.

Thirdly, when the preset trigger condition is reached, the terminal sends a backward track drawing request to the server.

Wherein the trigger condition may be that a contaminant concentration value of the at least one target contaminant exceeds a preset threshold value. The preset threshold may be set by a user, which is not limited in the embodiments of the present disclosure.

In one possible embodiment, the target site may be provided with a contaminant monitoring device, which may monitor the contaminant concentration at the target site. When the pollutant monitoring device monitors that the pollutant concentration value of one or more target pollutants exceeds a preset threshold value, a monitoring message carrying the geographic coordinates of a target location, the identification of at least one target pollutant and monitoring time can be sent to the terminal. When the terminal receives the monitoring message, the monitoring time can be used as a target time point, a backward track drawing request is generated according to the parameters carried by the monitoring message and the mark of the backward track, and the backward track drawing request is sent to the server. In this case, since the backward trace graph is a tracing source of the pollutant transmission trace in the past period, when the pollutant concentration value exceeds the threshold value, the terminal can display the backward trace graph of the target pollutant and warn the user, so that the user can judge the pollutant transmission contribution of the transmission trace passing through the region to the target site.

When the inquired target pollutants are multiple, the backward track drawing request sent by the terminal to the server may carry the identifiers of the multiple target pollutants, or the terminal may also send multiple backward track drawing requests to the server, wherein one backward track drawing request corresponds to one target pollutant. The embodiments of the present disclosure are not limited thereto.

When the server receives the backward trajectory drawing request sent by the terminal, the server may obtain information carried in the backward trajectory drawing request, and then execute the processing of step 102.

And step 102, the server determines a plurality of backward track point data according to the backward track drawing request.

Wherein the backward trajectory point data may comprise at least one or more of: position information of the trace points, a pollutant concentration value of at least one target pollutant, a wind speed vector and an arrival time.

The direction of the wind speed vector may be used to represent the wind direction and the magnitude of the wind speed vector may be used to represent the magnitude of the wind speed.

The arrival time can be used to represent the time at which the bolus of air arrives at the trace point.

In a possible implementation manner, after receiving the backward trajectory drawing request, the server may obtain information carried therein, that is, an identifier of at least one target pollutant, a target trajectory point, an identifier of the backward trajectory, and a target time point. The server can determine the backward track point data corresponding to the target track point according to the information, and further determine the rest backward track points according to the target track point.

Optionally, the server may determine track points at other positions in the backward track graph, and the corresponding processing may be as follows: acquiring backward track point data corresponding to the target track point according to the backward track drawing request; and determining at least one backtracking backward track point data according to the backward track point data corresponding to the target track point.

The backtracking backward trace point can be predicted by the server, and the backtracking starts from the target trace point at a preset time interval and the position of the air mass. For example, the backward trajectory point may be the position where the air mass was located every 1 hour before the target trajectory point.

In one possible implementation, the server may obtain the pollutant concentration value of the target pollutant at the position and the wind speed vector at the position according to the position information of the target track point in the request. And integrating the obtained pollutant concentration value and the obtained wind speed vector of at least one target pollutant, and the target track point and the target time point in the request to obtain backward track point data corresponding to the corresponding target track point. Then, the server can predict the rest backward track points according to the backward track point data corresponding to the target track point.

Optionally, the server may determine a backward track point of the backtracking according to the wind speed vector, and the corresponding processing may be as follows: determining backtracking backward track points according to the target track points, the opposite vectors of the wind speed vectors of the target track points and a preset time interval, and acquiring backward track point data corresponding to the backtracking backward track points; and repeating the steps for each backward track point within a preset time period, and determining at least one backtracking backward track point data.

In a possible embodiment, after acquiring the wind speed vector on the target track point, the server may determine the wind power magnitude and the wind speed direction according to the wind speed vector, that is, the moving speed and the moving direction of the air mass at the position. Furthermore, the moving distance of the air mass after the preset time interval can be determined according to the wind power, and then the position of the previous backward track point can be determined according to the opposite direction of the wind speed direction and the position of the target track point.

Illustratively, let the wind velocity vector of the target trajectory point be (u)₀,v₀) The wind speed vector has a direction, and the direction is the wind speed direction; by passing

The wind speed is calculated and obtained, the unit is m/s, and after 1 hour, the movement distance of the air mass is the wind speed multiplied by 3600, and the unit is m. And determining the position of the track point in the front and back direction according to the position information of the target track point, the opposite direction of the wind speed direction and the movement distance.

And then, the server determines the backward track point data corresponding to the backward track point and the corresponding previous backward track point for the determined backward track point based on the same method as the target track point. By analogy, the server can determine backward track point data of each backward track point in the next preset time period. For example, the preset time period may be 24 hours after the target time point, the preset time interval is 1 hour, and the set of backward trace points is P ═ P₀,P₁,...,P₂₂,P₂₃}; the corresponding backward trajectory point data is: the component set U ═ U of the wind speed vector in the x-axis direction₀,u₁,...,u₂₂,u₂₃Y-axis direction component set V ═ V₀,v₁,...,v₂₂,v₂₃D, pollutant concentration value set D ═ D₀,d₁,...,d₂₂,d₂₃}。

In the above process, the backward trajectory point data acquired by the server may be data stored in the server in advance, or may be data requested to be acquired from another device.

Optionally, the server may obtain the backward trajectory point data based on the ambient air quality value prediction mode, as shown in the flowchart of fig. 2 for obtaining the backward trajectory point data, the corresponding processing may be as follows:

step 201, the server determines a target grid where the backward trajectory point to be acquired is located in an environmental air quality numerical prediction mode.

The environmental air quality numerical prediction mode periodically performs environmental air quality numerical prediction and outputs prediction results of 10 days in the future, wherein the prediction results can be the simulated wind speed of each city and area at each time point, the prediction of concentration values of various pollutants and the like.

As shown in fig. 3, the environmental air quality numerical prediction mode mesh diagram divides each city and area into regular meshes according to geographic information, and determines a corresponding environmental air quality numerical prediction result for a vertex of each mesh. Each mesh is fixed in size (e.g., each square is 5 km long), and the index number of the vertex is (x, y), where x and y are integers.

In a possible embodiment, the backward trajectory point P to be acquired is ordered_kHas a geographic position coordinate of (x)_k,y_k) And k is an integer of 0 or more. For any backward tracing point P_kThe server can determine the index number (i, j) of the vertex of the lower left corner of the target grid where the server is located according to the geographical position coordinates of the backward track point by the following formula:

i＝floor(i_k) (3)

j＝floor(j_k) (4)

wherein x is_kIs a backward tracing point P_kCoordinate in the x-axis direction, y_kIs a backward tracing point P_kCoordinate in the direction of the y-axis, x_minIs the coordinate of the grid point with index number (0,0) in the x-axis direction, y_minIs the coordinate of the grid point whose index number is (0,0) in the y-axis direction. l_xUnit length of the grid in the x-axis direction, l_yIs the unit length of the grid in the y-axis direction. i.e. i_kIs a backward tracing point P_kIndex number with decimal point, j, in the x-axis direction_kIs a backward tracing point P_kIndex number with decimal point in y-axis direction.

floor is a down rounding function, i is the index number of the vertex at the lower left corner of the target grid in the x-axis direction, and j is the index number of the vertex at the lower left corner of the target grid in the y-axis direction.

As shown in the target mesh diagram of fig. 4, the server may construct a corresponding rectangular mesh according to the determined vertices, where four vertices of the mesh are the lower left corner R_(i,j)Lower right corner point R_(i+1,j)Top left corner point R_(i,j+1)The upper right corner point R_(i+1,j+1)The corner marks of each point are corresponding index numbers. And determining the grid as a target grid of the backward track point to be acquired in an environmental air quality numerical prediction mode.

Step 202, the server obtains wind speed vectors of all vertexes of the target grid at the current moment and pollutant concentration values of at least one target pollutant.

In a possible implementation manner, after determining the target mesh, the server may use an arrival time corresponding to the backward trajectory point as a current time, and obtain, in a prediction result output by the environmental air quality numerical prediction mode, a wind speed vector of each vertex of the target mesh and a pollutant concentration value of at least one target pollutant at the current time. For example, if the server calculates the rear track of the last 24 hours of the day 0 point, the day 0 point data, the previous day 23 point data, the previous day 22 point data, … …, the previous day 2 point data and the previous day 1 point data which are predicted by the environmental air quality numerical prediction mode are required.

As shown in the schematic diagram of the target mesh in fig. 4, the wind speed vector is divided into components in the directions of the x-axis and the y-axis, and then the data at each vertex of the target mesh may be: lower left corner R_(i,j)Component u of the wind velocity vector of (a) in the x-axis direction_(i,j)Component v in the y-axis direction_(i,j)Concentration value d of contaminants_(i,j)(ii) a Lower right corner R_(i+1,j)Component u of the wind velocity vector of (a) in the x-axis direction_(i+1,j)Component v in the y-axis direction_(i+1,j)Concentration value d of contaminants_(i+1,j)(ii) a Upper left corner R_(i,j+1)Component u of the wind velocity vector of (a) in the x-axis direction_(i,j+1)Component v in the y-axis direction_(i,j+1)Concentration value d of contaminants_(i,j+1)(ii) a Upper right corner R_(i+1,j+1)Component u of the wind velocity vector of (a) in the x-axis direction_(i+1，j+1)Component v in the y-axis direction_(i+1，j+1)Concentration value d of contaminants_(i+1，j+1)。

The pollutant concentration value in the data on the grid vertex is taken as an example of a target pollutant, and the rest pollutants are similar.

And step 203, the server determines the wind speed vector of the backward track point to be acquired according to the wind speed vector of each vertex of the target grid.

In a possible implementation manner, after acquiring the wind speed vectors at the vertices, the server may determine the distances between the backward trajectory point and the vertices, and then determine the wind speed vector on the backward trajectory point based on the wind speed vectors at the vertices and the determined distances.

Optionally, the server may determine the wind speed vector of the backward track point to be acquired according to the wind speed vector of each vertex of the target mesh based on a bilinear interpolation algorithm.

In one possible implementation, for the component in the x-axis direction, the server may calculate by the following formula:

u_k1＝u_(i，j)+(u_(i+1，j)-u_(i，j))×(i_k-i) (5)

u_k2＝u_(i，j+1)+(u_(i+1，j+1)-u_(i，j+1))×(i_k-i) (6)

u_k＝u_k1+(u_k2-u_k1)×(j_k-j) (7)

wherein u is_k1The index numbers shown for FIG. 4 are (i)_kComponent of the wind velocity vector of the point of j) in the x-axis direction, u_k2Is shown in FIG. 4 as index numbers (i)_kComponent of the wind velocity vector at the point of j +1) in the x-axis direction, u_kIs a backward tracing point P_kThe component of the wind velocity vector in the x-axis direction.

For the y-axis component, the server may calculate by the following formula:

v_k1＝v_(i，j)+(v_(i+1，j)-v_(i，J))×(i_k-i) (8)

v_k2＝v_(i，j+1)+(v_(i+1，j+1)-v_(i，j+1))×(i_k-i) (9)

v_k＝v_k1+(v_k2-v_k1)×(j_k-j) (10)

wherein v is_k1The index numbers shown for FIG. 4 are (i)_kThe component of the wind velocity vector of the point of j) in the y-axis direction, v_k2The index numbers shown for FIG. 4 are (i)_kComponent of the wind velocity vector at the point of j +1) in the y-axis direction, v_kIs a backward tracing point P_kThe component of the wind velocity vector in the y-axis direction.

Further, the server can determine the backward track point P to be acquired_kHas a wind velocity vector of (u)_k，v_k)。

And 204, the server determines the pollutant concentration value of at least one target pollutant of the backward tracing points to be acquired according to the pollutant concentration value of at least one target pollutant of each vertex of the target grid.

In a possible implementation manner, the server may determine the pollutant concentration value of the target pollutant on the backward trajectory point to be acquired according to the pollutant concentration value of any target pollutant at each vertex and the distance between the backward trajectory point to be acquired and each vertex, and the remaining pollutants are treated similarly.

Optionally, the server may determine, based on a bilinear interpolation algorithm, a pollutant concentration value of at least one target pollutant of the backward trajectory points to be acquired according to a pollutant concentration value of at least one target pollutant of each vertex of the target mesh.

In one possible embodiment, the server may calculate the contaminant concentration value for any target contaminant by the following formula:

d_k1＝d_(i，j)+(d_(i+1，j)-d_(i，j))×(i_k-i) (11)

d_k2＝d_(i，j+1)+(d_(i+1，j+1)-d_(i，j+1))×(i_k-i) (12)

d_k＝d_k1+(d_k2-d_k1)×(j_k-j) (13)

wherein d is_k1The index numbers shown for FIG. 4 are (i)_kJ) point of the pollutant concentration value, d_k2The index numbers shown for FIG. 4 are (i)_kJ +1) of the point, d_kIs a backward tracing point P_kThe contaminant concentration value of (a).

For the target track point and other backward track points determined by the server, corresponding backward track point data can be obtained by the method of the step 201 and the step 204. The method provided by the step 201 and the step 204 is linear operation, and compared with a lagrangian method of a HYSPLIT model, the method is simple in calculation, can improve the operation efficiency of the server, and realizes business operation.

Optionally, the server may supplement the backward trajectory points, so that the drawn backward trajectory graph is smoother, and the corresponding processing may be as follows: at least one complementary backward trajectory point data is determined between adjacent backward trajectory points according to the adjacent backward trajectory points.

In a possible implementation manner, after the server determines each backward track point in step 102, the server may supplement the backward track point between adjacent backward track points through a preset supplement rule based on the corresponding backward track point data, and determine the corresponding backward track point data. The supplemental backward trace points may be used to increase the density of the backward trace points, which are referred to herein as encrypted trace points.

Optionally, the server may determine a complementary backward track point according to a third-order bezier curve, and the corresponding processing may be as follows: constructing a corresponding third-order Bezier curve according to the adjacent backward track points, and determining at least one complementary backward track point on the third-order Bezier curve; and determining the pollutant concentration value of at least one target pollutant of at least one complementary backward tracing point according to the pollutant concentration value of at least one target pollutant of the adjacent backward tracing point.

In one possible embodiment, the adjacent backward track points are respectively P_kAnd P_k+1The server may construct a third order bezier curve by the following formula:

f(t)＝P_k×(1-t)³+3×P_t1×t×(1-t)²+3×P_t2×t²×(1-t)+P_k+1×t³ (16)

wherein, P_t1Is the first control point of the third-order Bezier curve, P_t2Is the second control point of the third-order Bezier curve, P_kIs the starting point of the third-order Bessel curve, P_k+1The end point of the third order bezier curve. P_k-1Is P_kFront and rear track points of (P)_k+2Is P_k+1The latter backward trace point.

In addition, since the backward track point of the starting position has no forward backward track point, when P is_kWhen it is a backward tracing point of the starting position, P_t1Determined by the following equation:

since the backward track point of the termination position has no backward track point, when P is_k+1At the backward track point of the end position, P_t2Determined by the following equation:

as shown in the schematic diagram of the third-order bezier curve in fig. 5, the above f (t) is a function corresponding to the third-order bezier curve, where t is a value variable, and a value is between 0 and 1. And each determined value t can determine a point on the third-order Bezier curve, and each determined point is used as an encryption track point except the starting point and the end point of the third-order Bezier curve. For example, the value interval of t is set to 0.1, the values are sequentially set to 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 and 1, the values are respectively substituted into the function f (t), and the encrypted trace point P is calculated_k，0、P_k，1、P_k，2、P_k，3、P_k，4、P_k，5、P_k，6、P_k，7、P_k，8、P_k，9、P_k，10In which P is_k，0And P_kSame, P_k，10And P_k+1The same, there are 9 encryption track points in the middle of every adjacent backward track point of group like this, and every track point of group has 11 track points altogether. The trace points of each set are shown in table 1 below:

TABLE 1 track point combination

Serial number	Starting point	Encrypted tracing point	Terminal point
				Group 0	P₀(P_0，0)	P_0，1 P_0，2 P_0，3 P_0，4 P_0，5 P_0，6 P_0，7 P_0，8 P_0，9	P₁(P_0，10)
Group 1	P₁(P_1，0)	P_1，1 P_1，2 P_1，3 P_1，4 P_1，5 P_1，6 P_1，7 P_1，8 P_1，9	P₂(P_1，10)
				Group 2	P₂(P_2，0)	P_2，1 P_2，2 P_2，3 P_2，4 P_2，5 P_2，6 P_2，7 P_2.8 P_2，9	P₃(P_2，10)
…	…	…	…
				Group 21	P₂₁(P_21，0)	P_21，1 P_21，2 P_21，3 P_21，4 P_21，5 P_21，6 P_21，7 P_21，8 P_21，9	P₂₂(P_21，10)
Group 22	P₂₂(P_22，0)	P_22，1 P_22，2 P_22，3 P_22，4 P_22，5 P_22，6 P_22，7 P_22，8 P_22，9	P₂₃(P_22，10)

After each group of track points is determined, the server can determine the distance between the encryption track point and the starting point of the group for any encryption track point, and further determine the pollutant concentration value of at least one target pollutant corresponding to the encryption track point based on the pollutant concentration values of the starting point and the end point and the determined distance.

In addition, the wind speed vector on the encrypted track point can also be determined based on the same method as the method for determining the pollutant concentration value, and the details are not repeated here.

Optionally, the server may determine the pollutant concentration value of the at least one target pollutant of the at least one complementary backward trajectory point according to the pollutant concentration value of the at least one target pollutant of the adjacent backward trajectory point based on a linear interpolation algorithm.

In one possible embodiment, for any one of the above-mentioned sets of track points, the server may determine the sum of the distances between each two adjacent track points, i.e. the length of the third-order bezier curve between the start point and the end point of the set, by the following formula:

wherein s is the sum of the distances of the kth group of track points, N +2 is the sum of the number of the group of track points, the value range of N is a positive integer larger than 0, and N is any integer from 0 to N. x is the number of_k，nIs a track point P_k，nCoordinate in the x-axis direction, y_k，nIs a track point P_k，nCoordinates in the y-axis direction.

Then, the server may determine the distance between each encrypted trace point and the starting point in the set of trace points by the following formula:

wherein s is_mFor encrypting trace points P in the kth set of trace points_k,mAnd the starting point P_k,0M is a positive integer from 1 to N.

The server may then determine a contaminant concentration value on each encrypted trace point in the set of trace points by:

wherein d is_k,mFor encrypting trace points P in the kth set of trace points_k,mConcentration value of contaminants of d_kIs the starting point P of the k-th group_k,0(i.e. P)_k) Concentration value of contaminants of d_k+1Is the end point P of the k-th group_k,N+1(i.e. P)_k+1) The contaminant concentration value of (a).

Supplement to the track point after through three-order Bessel curve, can improve the smoothness degree of curve when drawing the back trajectory graph, conveniently demonstrate on the map, do not influence the bandwagon effect when the map is zoomed.

And 103, drawing a backward track graph by the server according to the plurality of backward track point data.

In a possible implementation manner, after the server determines the plurality of backward track points by the method described above, each backward track point may be connected according to the sequence of the arrival time of each backward track point, and the connection line is a corresponding backward track. Meanwhile, the server can also display the target pollutant concentration value on each backward track point in a preset display mode, for example, as shown in the backward track schematic diagram of fig. 6, the corresponding target pollutant concentration value can be displayed in a text mode on each backward track point. That is, the backward trajectory graph drawn by the server can simultaneously represent the backward trajectory of the target pollutant and the concentration value of the target pollutant on each backward trajectory point. Further, the server may transmit the corresponding backward trajectory graph to the terminal corresponding to the backward trajectory drawing request in step 101.

For the case that the queried target pollutants are multiple, such as the backward trajectory diagram shown in fig. 7, the server may draw a backward trajectory diagram, in which the concentration change conditions of multiple target pollutants are simultaneously characterized; the server may also plot a plurality of backward traces, wherein one backward trace represents a change in concentration of a target contaminant. This embodiment is not limited to this. When the terminal receives a plurality of backward trajectory diagrams, the plurality of backward trajectory diagrams can be displayed simultaneously, or at least one backward trajectory diagram can be displayed based on the selection of the user.

Optionally, the server may use the target track point as an end position, and draw the backward track graph according to the backward track point data corresponding to the target track point and the at least one backtraced backward track point data.

In a possible embodiment, when the backward trajectory drawing request includes the target trajectory point, since each backward trajectory point is predicted based on the next backward trajectory point, the server may draw the backward trajectory diagram with the target trajectory point as the end position of the backward trajectory in the prediction order of each backward trajectory point. The drawing manner is the same as the above, and is not described herein again.

Optionally, after determining the supplemented backward trajectory point data, the server may use the target trajectory point as an end position, and draw a backward trajectory graph according to the backward trajectory point data corresponding to the target trajectory point, the at least one backtraced backward trajectory point data, and the at least one supplemented backward trajectory point data.

In a possible implementation manner, when the server supplements the backward track points by the method described above, a backward track graph can be drawn according to the backward track point data of each track point based on the drawing manner similar to the above, and details are not repeated here.

Optionally, the server may characterize the contaminant concentration value by color, and the corresponding processing may be as follows: determining the color of each backward track point according to the pollutant concentration value of at least one target pollutant in the plurality of backward track point data; and rendering according to the color of each backward track point, and drawing a backward track graph.

Wherein the color of each backward trace point can be used to represent a contaminant concentration value of at least one target contaminant.

In one possible implementation, the technician may set a correspondence relationship between the color and the pollutant concentration value in advance, for example, the correspondence relationship may be a legend corresponding to the color and the pollutant concentration value as shown in fig. 8. The server may store a correspondence between the color and the pollutant concentration value, and when the backward trajectory diagram is drawn, the server may determine, according to the correspondence, a color corresponding to the pollutant concentration value of the target pollutant in each backward trajectory point, that is, determine a color corresponding to each backward trajectory point. Further, the server may render the backward trajectory based on the color corresponding to each backward trajectory point, resulting in a backward trajectory graph as shown in fig. 9.

Alternatively, the server may draw an arrow on each backward trajectory point of the backward trajectory graph.

The direction of the arrow may be used to indicate the wind direction, and the size of the arrow may be proportional to the wind speed.

When the backward locus diagram is drawn, the pointing direction of the arrow can be determined based on the direction of the wind speed vector, the size of the arrow can be determined based on the size of the wind speed vector, and corresponding drawing is performed in the backward locus diagram.

Through the drawing mode of the colors and/or the arrows, the backward trajectory graph drawn by the server can enable a user to know the transmission process and the air quality condition in the transmission process in one graph, and the information obtaining efficiency of the user is improved; moreover, the information is displayed in a concise mode by the drawing modes of the colors and/or the arrows, so that the user can conveniently check the information, and the display effect of the backward track graph is improved.

In the embodiment of the disclosure, after receiving the backward trajectory drawing request, the server determines a plurality of backward trajectory point data according to the backward trajectory drawing request, and then draws a backward trajectory graph according to the plurality of backward trajectory point data. Since the backward trace point data includes the pollutant concentration value of the target pollutant, the backward trace graph can be used for representing the transmission path and the concentration change condition of the target pollutant in the past preset time period. Through the trace graph drawing method provided by the embodiment of the disclosure, the backward trace graph drawn by the server can intuitively show the concentration change condition of the target pollutant in the transmission path, and is not limited to the connection line of the trace points, so that the efficiency of checking the air quality pollution condition can be improved by the embodiment of the disclosure.

The embodiment of the disclosure provides a track map drawing device, which is used for realizing the track map drawing method. A schematic block diagram of a trajectory mapping apparatus as shown in fig. 10, the apparatus comprising:

a receiving module 1001, configured to receive a backward trajectory drawing request, where the backward trajectory drawing request includes an identifier of at least one target pollutant;

a determining module 1002, configured to determine, according to the backward trajectory drawing request, a plurality of backward trajectory point data, where the backward trajectory point data includes a pollutant concentration value of at least one target pollutant;

and a drawing module 1003, configured to draw a backward trajectory graph according to the multiple backward trajectory point data, where the backward trajectory graph is used to represent a transmission path and a concentration change condition of the at least one target pollutant in a past preset time period.

the determining module 1002 is configured to: acquiring backward track point data corresponding to the target track point according to the backward track drawing request; determining at least one backtracking backward track point data according to the backward track point data corresponding to the target track point;

the rendering module 1003 is configured to: and taking the target track point as an end position, and drawing a backward track graph according to backward track point data corresponding to the target track point and at least one backtraced backward track point data.

Optionally, the backward track point data further includes a wind speed vector;

the determining module 1002 is configured to:

and repeating the steps for each backward track point within a preset time period, and determining at least one backtracking backward track point data.

Optionally, the determining module 1002 is configured to:

acquiring wind speed vectors of all vertexes of a target grid at the current moment and a pollutant concentration value of at least one target pollutant;

determining the wind speed vector of the backward track point to be acquired according to the wind speed vectors of all the vertexes of the target grid;

and determining the pollutant concentration value of at least one target pollutant of the backward tracing points to be acquired according to the pollutant concentration value of at least one target pollutant of each vertex of the target grid.

Optionally, the determining module 1002 is configured to:

determining a wind speed vector of a backward track point to be acquired according to the wind speed vectors of all vertexes of the target grid based on a bilinear interpolation algorithm;

and determining the pollutant concentration value of at least one target pollutant of the backward track points to be acquired according to the pollutant concentration value of at least one target pollutant of each vertex of the target grid based on a bilinear interpolation algorithm.

Optionally, the determining module 1002 is further configured to: determining at least one supplementary backward trajectory point data between adjacent backward trajectory points according to the adjacent backward trajectory points;

the rendering module 1003 is configured to: and taking the target track point as an end position, and drawing a backward track graph according to backward track point data corresponding to the target track point, at least one backtracking backward track point data and at least one supplementary backward track point data.

Optionally, the determining module 1002 is configured to:

constructing a corresponding third-order Bezier curve according to the adjacent backward track points, and determining at least one complementary backward track point on the third-order Bezier curve;

and determining the pollutant concentration value of at least one target pollutant of at least one complementary backward tracing point according to the pollutant concentration value of at least one target pollutant of the adjacent backward tracing point.

Optionally, the determining module 1002 is configured to:

and determining the pollutant concentration value of at least one target pollutant of at least one supplemented backward track point according to the pollutant concentration value of at least one target pollutant of the adjacent backward track points on the basis of a linear interpolation algorithm.

Optionally, the rendering module 1003 is configured to:

determining the color of each backward track point according to the pollutant concentration value of at least one target pollutant in the plurality of backward track point data, wherein the color of each backward track point is used for expressing the pollutant concentration value of at least one target pollutant;

Optionally, the rendering module 1003 is further configured to:

and drawing an arrow on each backward track point of the backward track graph, wherein the pointing direction of the arrow is used for indicating the wind direction, and the size of the arrow is proportional to the size of the wind speed.

An exemplary embodiment of the present disclosure also provides an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor. The memory stores a computer program executable by the at least one processor, the computer program, when executed by the at least one processor, is for causing the electronic device to perform a method according to an embodiment of the disclosure.

The disclosed exemplary embodiments also provide a non-transitory computer readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is adapted to cause the computer to perform a method according to an embodiment of the present disclosure.

The exemplary embodiments of the present disclosure also provide a computer program product comprising a computer program, wherein the computer program, when executed by a processor of a computer, is adapted to cause the computer to perform a method according to an embodiment of the present disclosure.

Referring to fig. 11, a block diagram of a structure of an electronic device 1100, which may be a server or a client of the present disclosure, which is an example of a hardware device that may be applied to aspects of the present disclosure, will now be described. Electronic device is intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable 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 disclosure described and/or claimed herein.

As shown in fig. 11, the electronic device 1100 includes a computing unit 1101, which can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)1102 or a computer program loaded from a storage unit 1108 into a Random Access Memory (RAM) 1103. In the RAM 1103, various programs and data necessary for the operation of the device 1100 may also be stored. The calculation unit 1101, the ROM 1102, and the RAM 1103 are connected to each other by a bus 1104. An input/output (I/O) interface 1105 is also connected to bus 1104.

A number of components in electronic device 1100 connect to I/O interface 1105, including: an input unit 1106, an output unit 1107, a storage unit 1108, and a communication unit 1109. The input unit 1106 may be any type of device capable of inputting information to the electronic device 1100, and the input unit 1106 may receive input numeric or character information and generate key signal inputs related to user settings and/or function controls of the electronic device. Output unit 1107 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, a video/audio output terminal, a vibrator, and/or a printer. The storage unit 1104 may include, but is not limited to, a magnetic disk, an optical disk. The communication unit 1109 allows the electronic device 1100 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and/or chipsets, such as bluetooth devices, WiFi devices, WiMax devices, cellular communication devices, and/or the like.

The computing unit 1101 can be a variety of general purpose and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 1101 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and the like. The calculation unit 1101 performs the respective methods and processes described above. For example, in some embodiments, the trajectory mapping method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 1108. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 1100 via the ROM 1102 and/or the communication unit 1109. In some embodiments, the computing unit 1101 may be configured to perform the trajectory graph drawing method by any other suitable means (e.g., by means of firmware).

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on 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.

As used in this disclosure, 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.

Claims

1. A method for tracing, the method comprising:

2. The trajectory diagram drawing method according to claim 1, wherein the backward trajectory drawing request further includes a target trajectory point;

3. The method of tracing according to claim 2, wherein said backward tracing point data further comprises a wind speed vector;

4. The method for drawing the track map according to any one of claims 2 to 3, wherein the method for acquiring backward track point data comprises:

5. The method for drawing the trajectory graph according to claim 4, wherein the determining the wind speed vector of the backward trajectory point to be acquired according to the wind speed vector of each vertex of the target mesh includes:

6. The method of tracing according to claim 2, said method further comprising:

7. The method of trajectory mapping according to claim 6, wherein said determining at least one complementary backward trajectory point data between adjacent backward trajectory points from said adjacent backward trajectory points comprises:

8. The trajectory mapping method of claim 7, wherein determining the contaminant concentration value of the at least one target contaminant of the at least one complementary rearward trajectory point from the contaminant concentration value of the at least one target contaminant of the adjacent rearward trajectory point comprises:

9. The method of claim 1, wherein said rendering a backward trajectory graph from said plurality of backward trajectory point data comprises:

10. The method of tracing according to claim 1, said method further comprising:

11. A trajectory graph drawing apparatus, characterized in that the apparatus comprises:

12. The apparatus according to claim 11, wherein the backward trajectory drawing request further includes a target trajectory point;

13. The apparatus of claim 11, wherein the rendering module is configured to:

14. An electronic device, comprising:

a processor; and

a memory for storing a program, wherein the program is stored in the memory,

wherein the program comprises instructions which, when executed by the processor, cause the processor to carry out the method according to any one of claims 1-10.

15. A non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any one of claims 1-10.