Traffic data warehouse construction method and apparatus, storage medium, and terminal

1 . A traffic data warehouse construction method, comprising:

creating, when a monitoring task creation instruction is received, a target monitoring task based on the monitoring task creation instruction;

loading a monitoring object corresponding to a monitoring object type parameter set in the target monitoring task, and obtaining a sampling unit set of the monitoring object;

obtaining a target monitoring task spatial range corresponding to a monitoring task spatial range parameter set in the target monitoring task;

calculating a spatial attribution relationship between a spatial range of each sampling unit in the sampling unit set and the target monitoring task spatial range;

determining a spatial coupling relationship between the monitoring object and the target monitoring task according to the spatial attribution relationship;

setting a calculation label and task label of the monitoring object based on the spatial coupling relationship, and generating a labeled monitoring object;

inputting the labeled monitoring object to a preset monitoring calculation function, and outputting a monitoring index calculation result; and

configuring the monitoring index calculation result in the labeled monitoring object, and distributing the configured monitoring object to a task database corresponding to the target monitoring task.

2 . The method of claim 1 , wherein the calculating a spatial attribution relationship between a spatial range of each sampling unit in the sampling unit set and the target monitoring task spatial range comprises:

obtaining a first spatial attribute description of the target monitoring task spatial range;

obtaining a second spatial attribute description of each sampling unit in the sampling unit set; and

labeling each sampling unit as belonging to the target monitoring task when the second spatial attribute description belongs to the first spatial attribute description.

3 . The method of claim 1 , wherein the calculating a spatial attribution relationship between a spatial range of each sampling unit in the sampling unit set and the target monitoring task spatial range comprises:

obtaining a first spatial attribute description of the target monitoring task spatial range;

obtaining a second spatial attribute description of each sampling unit in the sampling unit set when the first spatial attribute description of the target monitoring task spatial range is a first geometric figure, the second spatial attribute description comprising a second geometric figure, and the geometric feature comprising points, lines, and planes;

inputting the first geometric figure and the second geometric figure to a preset correlation judgment function, and outputting a judgment result; and

labeling each sampling unit as belonging to the target monitoring task when the judgment result is true.

4 . The method of claim 1 , wherein the determining a spatial coupling relationship between the monitoring object and the target monitoring task according to the spatial attribution relationship comprises:

determining that the monitoring object and the target monitoring task are spatially uncoupled when the spatial range corresponding to each sampling unit does not belong to the target monitoring task spatial range; or,

determining that the monitoring object and the target monitoring task are spatially coupled when the spatial range corresponding to each sampling unit belongs to the target monitoring task spatial range; or,

determining that the monitoring object and the target monitoring task are partially spatially coupled when the spatial range corresponding to at least one sampling unit belongs to the target monitoring task spatial range.

5 . The method of claim 1 , wherein the setting a calculation label and task label of the monitoring object based on the spatial coupling relationship comprises:

when the monitoring object and the target monitoring task are spatially coupled, obtaining the task label of the monitoring object, and

adding the target monitoring task to the task label of the monitoring object;

or,

when the monitoring object and the target monitoring task are partially spatially coupled, obtaining a sampling unit set corresponding to a part coupled with the target monitoring task in the monitoring object, and generating a target monitoring object,

setting a task label of the target monitoring object as that of the target monitoring task, and updating a calculation label of a sampling unit corresponding to the target monitoring object.

6 . The method of claim 1 , further comprising:

obtaining a life cycle set in the target monitoring task, the life cycle comprising begin time of the task and end time of the task; and

clearing a task label in the task label in the configured monitoring object when the end time is consistent with current time.

7 . The method of claim 1 , wherein the creating, when a monitoring task creation instruction is received, a target monitoring task based on the monitoring task creation instruction comprises:

extracting a plurality of parameters contained in the monitoring task creation instruction when the monitoring task creation instruction is received;

obtaining a preset monitoring task defining template; and

associating the plurality of parameters with an Identifier (ID) in the monitoring task defining template to generate the target monitoring task, the monitoring task defining template being tsk= id, (t bgn , t end ), Ω mo.type , TSD , where id uniquely identifies a monitoring task tsk, (t bgn , t end ) represent begin time and end time of the task respectively, defining a life cycle of the task, Ω mo.type represents a type of a monitoring object defined by the task, and TSD represents a spatial range parameter.

8 . The method of claim 1 , wherein the monitoring object is mo= id, type, name, SD, MI, TT , where id uniquely identifies the monitoring object; type represents a type of the monitoring object; name represents a name of the monitoring object;

SD=(admin, road, coord, . . . ) represents a spatial attribute description set of the monitoring object, where admin represents a description of an administrative region that the object belongs to, road represents a name of a road where the object is located, and coord represents a description of a Geographic Information System (GIS) attribute of the object;

MI=(δ 1 , δ 2 , . . . , δ k ) represents a monitoring index set of the monitoring object, there being a fixed index set for each type of monitoring objects; and

TT=(tsk 1 , tsk 2 , . . . , tsk m ) represents the task label corresponding to the monitoring object.

9 . The method of claim 1 , wherein the monitoring task creation instruction is an instruction input to a client, and the instruction contains a plurality of parameters for creation of the monitoring task.

10 . The method of claim 9 , wherein the plurality of parameters for creation of the monitoring task comprise an ID of the monitoring task, a life cycle parameter of the monitoring task, a type parameter of the monitoring object in the monitoring task, and a spatial range parameter of the monitoring task.

11 . The method of claim 10 , wherein all monitoring objects are stored in a monitoring object library.

12 . The method of claim 1 , wherein a type of the monitoring object comprises a congestion sensor or a flow sensor.

13 . The method of claim 7 , wherein a task database is configured for the monitoring task, which stores monitoring object index data corresponding to each monitoring task.

14 . The method of claim 8 , wherein the sampling unit set corresponding to the monitoring object is Ω mo , the spatial range of each sampling unit su in Ω mo is represented as su.SD, and the target monitoring task spatial range is represented as tsk.TSD.

15 . The method of claim 1 , wherein the monitoring calculation function is

mo

.

δ

k

=

F

m

⁢

o

.

type

,

su

.

type

δ

k

(

Ω

m

⁢

o

)

,

where mo.type represents a type of the monitoring object, su.type represents a type of the sampling unit, Ω mo represents the sampling unit set of the monitoring object mo, and δ k represents a monitoring index set of the monitoring object.

16 . A computer storage medium storing a plurality of instructions suitable for a processor to load and execute to implement the blocks of the method of claim 1 .

17 . A terminal comprising a processor and a memory, wherein the memory stores a computer program suitable for the processor to load and execute to implement the blocks of the following traffic data warehouse construction method:

creating, when a monitoring task creation instruction is received, a target monitoring task based on the monitoring task creation instruction;

loading a monitoring object corresponding to a monitoring object type parameter set in the target monitoring task, and obtaining a sampling unit set of the monitoring object;

obtaining a target monitoring task spatial range corresponding to a monitoring task spatial range parameter set in the target monitoring task;

calculating a spatial attribution relationship between a spatial range of each sampling unit in the sampling unit set and the target monitoring task spatial range;

determining a spatial coupling relationship between the monitoring object and the target monitoring task according to the spatial attribution relationship;

setting a calculation label and task label of the monitoring object based on the spatial coupling relationship, and generating a labeled monitoring object;

inputting the labeled monitoring object to a preset monitoring calculation function, and outputting a monitoring index calculation result; and

configuring the monitoring index calculation result in the labeled monitoring object, and distributing the configured monitoring object to a task database corresponding to the target monitoring task.

18 . The terminal of claim 17 , wherein the calculating a spatial attribution relationship between a spatial range of each sampling unit in the sampling unit set and the target monitoring task spatial range comprises:

obtaining a first spatial attribute description of the target monitoring task spatial range;

obtaining a second spatial attribute description of each sampling unit in the sampling unit set; and

labeling each sampling unit as belonging to the target monitoring task when the second spatial attribute description belongs to the first spatial attribute description.

19 . The terminal of claim 17 , wherein the calculating a spatial attribution relationship between a spatial range of each sampling unit in the sampling unit set and the target monitoring task spatial range comprises:

obtaining a first spatial attribute description of the target monitoring task spatial range;

obtaining a second spatial attribute description of each sampling unit in the sampling unit set when the first spatial attribute description of the target monitoring task spatial range is a first geometric figure, the second spatial attribute description comprising a second geometric figure, and the geometric feature comprising points, lines, and planes;

inputting the first geometric figure and the second geometric figure to a preset correlation judgment function, and outputting a judgment result; and

labeling each sampling unit as belonging to the target monitoring task when the judgment result is true.

20 . The terminal of claim 17 , wherein the determining a spatial coupling relationship between the monitoring object and the target monitoring task according to the spatial attribution relationship comprises:

determining that the monitoring object and the target monitoring task are spatially uncoupled when the spatial range corresponding to each sampling unit does not belong to the target monitoring task spatial range; or

determining that the monitoring object and the target monitoring task are spatially coupled when the spatial range corresponding to each sampling unit belongs to the target monitoring task spatial range; or

determining that the monitoring object and the target monitoring task are partially spatially coupled when the spatial range corresponding to at least one sampling unit belongs to the target monitoring task spatial range.